feat: remove Zep Cloud, add local SQLite graph store, English-first defaults & Ollama support
- Remove Zep Cloud dependency entirely (zep_entity_reader, zep_graph_memory_updater, zep_tools, zep_paging) - Add local SQLite-based graph store with LLM-powered entity extraction - Add local embeddings utility and graph tools service - Translate all backend docstrings, LLM prompts, and UI to English - Set English as default language, remove Chinese locale (zh.json) - Add Ollama setup guide and configure .env.example for Ollama - Add AGENTS.md for AI agent context - Fix pyright type errors in models/services - Add Nedbank Africa expansion preset - Clean up Docker configuration and README
This commit is contained in:
shayswrld
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.env.example
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.env.example
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@ -1,16 +1,31 @@
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# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API）
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# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/
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# 注意消耗较大，可先进行小于40轮的模拟尝试
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LLM_API_KEY=your_api_key_here
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LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
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LLM_MODEL_NAME=qwen-plus
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# ===== LLM Configuration =====
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# Supports any OpenAI-compatible API. Three options:
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# ===== ZEP记忆图谱配置 =====
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# 每月免费额度即可支撑简单使用：https://app.getzep.com/
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ZEP_API_KEY=your_zep_api_key_here
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# --- Option A: Ollama Cloud (recommended) ---
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# 1. Create an API key: https://ollama.com/settings/keys
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# 2. Pick a cloud model from: https://ollama.com/search?c=cloud
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LLM_API_KEY=your_ollama_api_key_here
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LLM_BASE_URL=https://ollama.com/v1
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LLM_MODEL_NAME=qwen3.5:397b
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# ===== 加速 LLM 配置（可选）=====
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# 注意如果不使用加速配置，env文件中就不要出现下面的配置项
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LLM_BOOST_API_KEY=your_api_key_here
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LLM_BOOST_BASE_URL=your_base_url_here
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LLM_BOOST_MODEL_NAME=your_model_name_here
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# --- Option B: Local Ollama ---
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# 1. Install Ollama: https://ollama.com
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# 2. Pull a model: ollama pull qwen2.5:7b
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# LLM_API_KEY=ollama
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# LLM_BASE_URL=http://localhost:11434/v1
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# LLM_MODEL_NAME=qwen2.5:7b
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# --- Option C: Other cloud providers (OpenAI, DeepSeek, Alibaba Bailian, etc.) ---
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# LLM_API_KEY=your_cloud_api_key
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# LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
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# LLM_MODEL_NAME=qwen-plus
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# ===== Optional: Boost LLM (faster/cheaper model for high-volume agent calls) =====
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# If you don't use this, DELETE these lines entirely — leaving placeholders breaks things.
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# LLM_BOOST_API_KEY=
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# LLM_BOOST_BASE_URL=
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# LLM_BOOST_MODEL_NAME=
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# ===== Optional: Flask / Simulation config =====
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# FLASK_DEBUG=True
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# OASIS_DEFAULT_MAX_ROUNDS=10
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@ -2,7 +2,7 @@
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.DS_Store
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Thumbs.db
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# 环境变量（保护敏感信息）
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# Environment variables (protect sensitive info)
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.env
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.env.local
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.env.*.local
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@ -0,0 +1,176 @@
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# AGENTS.md
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Guide for coding agents operating in this repository. MiroFish is a multi-agent
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AI prediction engine: Flask backend (`backend/`) + Vue 3 frontend (`frontend/`)
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+ shared i18n (`locales/`). Monorepo orchestrated by root `package.json` via
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`concurrently`. Code comments/docstrings are **English**; identifiers
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are English. License: AGPL-3.0.
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## Build / Run / Test Commands
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All commands run from repo root unless noted. Node >=18, Python >=3.11 <=3.12,
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`uv` for Python packages.
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```bash
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# Install everything (root + frontend npm + backend uv venv)
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npm run setup:all
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# Dev (both services, concurrent, --kill-others)
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npm run dev
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npm run backend # cd backend && uv run python run.py (Flask :5001)
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npm run frontend # cd frontend && npm run dev (Vite :3000)
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# Production build (frontend only)
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npm run build
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# Backend deps only
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npm run setup:backend # cd backend && uv sync
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# Docker
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docker compose up -d # reads root .env, ports 3000+5001, mounts backend/uploads
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```
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### Tests
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**There are no automated tests.** `pytest` + `pytest-asyncio` are declared in
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`backend/pyproject.toml` but unused — no `tests/` dir, no `conftest.py`, no
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`[tool.pytest.ini_options]`. Frontend has no vitest/jest.
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If you add tests, run them with:
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```bash
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cd backend && uv run pytest # all
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cd backend && uv run pytest path/to/test_file.py # single file
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cd backend && uv run pytest path/to/test_file.py::test_name # single test
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```
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`backend/scripts/test_profile_format.py` is a **manual print-based script**, not
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a pytest test — run it with `uv run python scripts/test_profile_format.py`.
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### Lint / Format
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**None configured.** No ruff/black/flake8/mypy/isort on the backend; no
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eslint/prettier on the frontend. No CI lint workflow (only Docker image build).
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Match the existing de-facto style described below; do not add a linter unless
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asked.
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## Backend Conventions (Python / Flask)
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**Package layout:** `backend/app/` with `api/` (blueprints), `models/`
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(dataclasses), `services/` (business logic), `utils/` (logger, retry, llm_client,
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locale). Entry point `backend/run.py`. Each `__init__.py` is a manifest: docstring
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+ relative imports + `__all__`.
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**Imports:** Relative for intra-package (`from .config import Config`,
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`from ..services.x import Y`). Absolute `from app...` only in `run.py` and
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`scripts/` (which shim `sys.path`). Group: stdlib → third-party → local, blank-line
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separated. Late imports inside functions are acceptable for optional/heavy deps.
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**Naming:** `snake_case` modules/functions, `PascalCase` classes, `UPPER_SNAKE`
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constants, `_`-prefixed private helpers. Blueprints: `snake_case_bp`
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(`graph_bp`, `simulation_bp`, `report_bp`). String IDs prefixed
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(`proj_`, `sim_`, `mirofish_`).
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**Models:** `@dataclass` + `(str, Enum)` for status enums. Hand-written
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`to_dict()` / `from_dict()` on every model. **No app-defined Pydantic models** —
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pydantic is only used in generated ontology code templates and the OASIS SDK.
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**Type hints:** Annotate function signatures (`typing.Dict/Any/List/Optional/
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Callable/Tuple`). Locals usually unannotated. Be consistent with neighbors.
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**Errors:** No custom exception classes; raise `ValueError` for bad state. Every
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Flask route wraps in `try/except Exception as e:` returning the uniform envelope:
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```python
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except Exception as e:
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return jsonify({"success": False, "error": str(e),
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"traceback": traceback.format_exc()}), 500
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```
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Success: `{"success": True, "data": {...}}` (often + `"count"`, `"message"`).
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`ValueError` → 400/404. Background threads catch exceptions and flip the
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`TaskManager`/`Project` status to `FAILED`.
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**Logging:** `from ..utils.logger import get_logger` then
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`logger = get_logger('mirofish.<area>')` at module top (areas: `api`,
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`api.simulation`, `build`, `request`, `retry`, `simulation`, etc.).
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f-string messages. Do **not** use `logging.getLogger(__name__)` —
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`ontology_generator.py` does this and is the one known inconsistency. Rotating
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file handler writes to `backend/logs/<date>.log`.
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**Async:** Flask is sync (`threaded=True`). Long work goes to
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`threading.Thread(target=..., daemon=True)`. **Background threads must re-call
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`set_locale()`** — locale is thread-local; capture `get_locale()` before spawning
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and restore it inside the thread. Retry helpers in `utils/retry.py`:
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`retry_with_backoff` (sync), `retry_with_backoff_async` (async),
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`RetryableAPIClient`.
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**Config:** `app/config.py` `Config` class loads root `.env` via
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`dotenv.load_dotenv('../../.env')` (relative to `config.py`). Required:
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`LLM_API_KEY`. Optional: `LLM_BASE_URL`, `LLM_MODEL_NAME`,
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`LLM_BOOST_*`, `FLASK_HOST/PORT/DEBUG`, `OASIS_DEFAULT_MAX_ROUNDS`,
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`REPORT_AGENT_*`, `SECRET_KEY`. `Config.validate()` is called in `run.py` before
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serving. Frontend env: `VITE_API_BASE_URL` (default `http://localhost:5001`).
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**Docstrings:** Module docstring (English, one line) at top of every `.py`.
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Functions/classes: English summary, Google-style `Args:`/`Returns:`. Flask routes
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embed request/response JSON shapes as literal blocks — treat these as the API docs.
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**Windows:** `run.py` applies a UTF-8 stdout fix before other imports. Keep that
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block first.
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## Frontend Conventions (Vue 3 + Vite, plain JS)
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**SFCs:** `<script setup>` Composition API only. No Options API, no
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`defineComponent({})`. `defineProps({...object syntax...})`,
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`defineEmits([...])`. Use `ref`, `reactive`, `computed`, `watch`, `useRouter()`,
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`useI18n()`.
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**Style:** No semicolons. 2-space indent. Single quotes. Trailing commas in
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multi-line objects/arrays. Plain JS (no TypeScript).
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**Imports:** Vue ecosystem first (`vue`, `vue-router`, `vue-i18n`), then local
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modules. Relative paths dominate (`../api/simulation`); `@` → `src` and
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`@locales` → root `locales/` aliases exist in `vite.config.js` and may be used.
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**Naming:** `PascalCase.vue` files (views suffixed `*View.vue`; step components
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`Step1..Step5`). `camelCase` JS vars/functions/refs. `kebab-case` template tags
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and CSS classes. Route names `PascalCase`.
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**State:** No Pinia/Vuex. `store/` holds `reactive()` singletons exporting
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setter/getter/clear functions. Component-local state via `ref`/`reactive`.
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**API:** Single `axios.create()` instance in `src/api/index.js` (`baseURL` from
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`VITE_API_BASE_URL || 'http://localhost:5001'`, 5min timeout). Request interceptor
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injects `Accept-Language` from `i18n.global.locale.value`; response interceptor
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checks the `success` envelope and rejects on failure. `requestWithRetry(fn,
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maxRetries=3, delay=1000)` with exponential backoff. Per-resource modules
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(`graph.js`, `simulation.js`, `report.js`) export named functions. JSDoc
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`@param`/`@returns` on each.
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**i18n:** `vue-i18n` `legacy: false`, `useI18n()`, `$t()`, `<i18n-t>` for rich
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inline text. Messages live in **root `locales/*.json`** (`en.json`, `zh.json`),
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shared with the backend's `utils/locale.py`. Persisted locale in `localStorage`
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(key `locale`, default `zh`).
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**CSS:** Plain CSS, no preprocessor. `<style scoped>` default on components;
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`App.vue` has unscoped global resets. Global font: `'JetBrains Mono', 'Space
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Grotesk', 'Noto Sans SC', monospace`. Palette: black/white + orange
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(`#FF4500`/`#FF5722`).
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## Ports / Proxy
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Backend `5001`, frontend dev `3000`. Vite proxies `/api` → `http://localhost:5001`
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(`changeOrigin`, `secure: false`). Note: `api/index.js` defaults to an absolute
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`baseURL`, so set `VITE_API_BASE_URL=''` to use the proxy.
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## Known Inconsistencies (fix when touching the file)
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- Frontend mixes `export function foo()` and `export const foo = () => {}`.
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- `traceback.format_exc()` is leaked in 500 responses (debug aid; not prod-safe).
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- `scripts/test_profile_format.py` is named like a pytest test but is a manual
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print-based script.
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## Agent Workflow Notes
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- Match the style of neighboring files; this repo has no linter to enforce.
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- Do not add dependencies (Python or JS) for what a few lines can do.
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- Keep the `{success, data}` / `{success, error}` envelope on every new route.
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- Re-set locale in any new background thread.
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- Don't commit `.env`; the root `.env` is gitignored and holds live keys.
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12
Dockerfile
12
Dockerfile
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@ -1,29 +1,29 @@
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FROM python:3.11
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# 安装 Node.js （满足 >=18）及必要工具
|
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# Install Node.js (>=18) and required tools
|
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RUN apt-get update \
|
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&& apt-get install -y --no-install-recommends nodejs npm \
|
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&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# 从 uv 官方镜像复制 uv
|
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# Copy uv from official uv image
|
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COPY --from=ghcr.io/astral-sh/uv:0.9.26 /uv /uvx /bin/
|
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|
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WORKDIR /app
|
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|
||||
# 先复制依赖描述文件以利用缓存
|
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# Copy dependency descriptor files first to leverage caching
|
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COPY package.json package-lock.json ./
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COPY frontend/package.json frontend/package-lock.json ./frontend/
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COPY backend/pyproject.toml backend/uv.lock ./backend/
|
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|
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# 安装依赖（Node + Python）
|
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# Install dependencies (Node + Python)
|
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RUN npm ci \
|
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&& npm ci --prefix frontend \
|
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&& cd backend && uv sync --frozen
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|
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# 复制项目源码
|
||||
# Copy project source code
|
||||
COPY . .
|
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|
||||
EXPOSE 3000 5001
|
||||
|
||||
# 同时启动前后端（开发模式）
|
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# Start both frontend and backend (dev mode)
|
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CMD ["npm", "run", "dev"]
|
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203
README-ZH.md
203
README-ZH.md
|
|
@ -1,203 +0,0 @@
|
|||
<div align="center">
|
||||
|
||||
<img src="./static/image/MiroFish_logo_compressed.jpeg" alt="MiroFish Logo" width="75%"/>
|
||||
|
||||
<a href="https://trendshift.io/repositories/16144" target="_blank"><img src="https://trendshift.io/api/badge/repositories/16144" alt="666ghj%2FMiroFish | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
|
||||
简洁通用的群体智能引擎，预测万物
|
||||
</br>
|
||||
<em>A Simple and Universal Swarm Intelligence Engine, Predicting Anything</em>
|
||||
|
||||
<a href="https://www.shanda.com/" target="_blank"><img src="./static/image/shanda_logo.png" alt="666ghj%2FMiroFish | Shanda" height="40"/></a>
|
||||
|
||||
[](https://github.com/666ghj/MiroFish/stargazers)
|
||||
[](https://github.com/666ghj/MiroFish/watchers)
|
||||
[](https://github.com/666ghj/MiroFish/network)
|
||||
[](https://hub.docker.com/)
|
||||
[](https://deepwiki.com/666ghj/MiroFish)
|
||||
|
||||
[](http://discord.gg/ePf5aPaHnA)
|
||||
[](https://x.com/mirofish_ai)
|
||||
[](https://www.instagram.com/mirofish_ai/)
|
||||
|
||||
[English](./README.md) | [中文文档](./README-ZH.md)
|
||||
|
||||
</div>
|
||||
|
||||
## ⚡ 项目概述
|
||||
|
||||
**MiroFish** 是一款基于多智能体技术的新一代 AI 预测引擎。通过提取现实世界的种子信息（如突发新闻、政策草案、金融信号），自动构建出高保真的平行数字世界。在此空间内，成千上万个具备独立人格、长期记忆与行为逻辑的智能体进行自由交互与社会演化。你可透过「上帝视角」动态注入变量，精准推演未来走向——**让未来在数字沙盘中预演，助决策在百战模拟后胜出**。
|
||||
|
||||
> 你只需：上传种子材料（数据分析报告或者有趣的小说故事），并用自然语言描述预测需求</br>
|
||||
> MiroFish 将返回：一份详尽的预测报告，以及一个可深度交互的高保真数字世界
|
||||
|
||||
### 我们的愿景
|
||||
|
||||
MiroFish 致力于打造映射现实的群体智能镜像，通过捕捉个体互动引发的群体涌现，突破传统预测的局限：
|
||||
|
||||
- **于宏观**：我们是决策者的预演实验室，让政策与公关在零风险中试错
|
||||
- **于微观**：我们是个人用户的创意沙盘，无论是推演小说结局还是探索脑洞，皆可有趣、好玩、触手可及
|
||||
|
||||
从严肃预测到趣味仿真，我们让每一个如果都能看见结果，让预测万物成为可能。
|
||||
|
||||
## 🌐 在线体验
|
||||
|
||||
欢迎访问在线 Demo 演示环境，体验我们为你准备的一次关于热点舆情事件的推演预测：[mirofish-live-demo](https://666ghj.github.io/mirofish-demo/)
|
||||
|
||||
## 📸 系统截图
|
||||
|
||||
<div align="center">
|
||||
<table>
|
||||
<tr>
|
||||
<td><img src="./static/image/Screenshot/运行截图1.png" alt="截图1" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/运行截图2.png" alt="截图2" width="100%"/></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><img src="./static/image/Screenshot/运行截图3.png" alt="截图3" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/运行截图4.png" alt="截图4" width="100%"/></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><img src="./static/image/Screenshot/运行截图5.png" alt="截图5" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/运行截图6.png" alt="截图6" width="100%"/></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
## 🎬 演示视频
|
||||
|
||||
### 1. 武汉大学舆情推演预测 + MiroFish项目讲解
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.bilibili.com/video/BV1VYBsBHEMY/" target="_blank"><img src="./static/image/武大模拟演示封面.png" alt="MiroFish Demo Video" width="75%"/></a>
|
||||
|
||||
点击图片查看使用微舆BettaFish生成的《武大舆情报告》进行预测的完整演示视频
|
||||
</div>
|
||||
|
||||
### 2. 《红楼梦》失传结局推演预测
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.bilibili.com/video/BV1cPk3BBExq" target="_blank"><img src="./static/image/红楼梦模拟推演封面.jpg" alt="MiroFish Demo Video" width="75%"/></a>
|
||||
|
||||
点击图片查看基于《红楼梦》前80回数十万字，MiroFish深度预测失传结局
|
||||
</div>
|
||||
|
||||
> **金融方向推演预测**、**时政要闻推演预测**等示例陆续更新中...
|
||||
|
||||
## 🔄 工作流程
|
||||
|
||||
1. **图谱构建**：现实种子提取 & 个体与群体记忆注入 & GraphRAG构建
|
||||
2. **环境搭建**：实体关系抽取 & 人设生成 & 环境配置Agent注入仿真参数
|
||||
3. **开始模拟**：双平台并行模拟 & 自动解析预测需求 & 动态更新时序记忆
|
||||
4. **报告生成**：ReportAgent拥有丰富的工具集与模拟后环境进行深度交互
|
||||
5. **深度互动**：与模拟世界中的任意一位进行对话 & 与ReportAgent进行对话
|
||||
|
||||
## 🚀 快速开始
|
||||
|
||||
### 一、源码部署（推荐）
|
||||
|
||||
#### 前置要求
|
||||
|
||||
| 工具 | 版本要求 | 说明 | 安装检查 |
|
||||
|------|---------|------|---------|
|
||||
| **Node.js** | 18+ | 前端运行环境，包含 npm | `node -v` |
|
||||
| **Python** | ≥3.11, ≤3.12 | 后端运行环境 | `python --version` |
|
||||
| **uv** | 最新版 | Python 包管理器 | `uv --version` |
|
||||
|
||||
#### 1. 配置环境变量
|
||||
|
||||
```bash
|
||||
# 复制示例配置文件
|
||||
cp .env.example .env
|
||||
|
||||
# 编辑 .env 文件，填入必要的 API 密钥
|
||||
```
|
||||
|
||||
**必需的环境变量：**
|
||||
|
||||
```env
|
||||
# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API）
|
||||
# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/
|
||||
# 注意消耗较大，可先进行小于40轮的模拟尝试
|
||||
LLM_API_KEY=your_api_key
|
||||
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
LLM_MODEL_NAME=qwen-plus
|
||||
|
||||
# Zep Cloud 配置
|
||||
# 每月免费额度即可支撑简单使用：https://app.getzep.com/
|
||||
ZEP_API_KEY=your_zep_api_key
|
||||
```
|
||||
|
||||
#### 2. 安装依赖
|
||||
|
||||
```bash
|
||||
# 一键安装所有依赖（根目录 + 前端 + 后端）
|
||||
npm run setup:all
|
||||
```
|
||||
|
||||
或者分步安装：
|
||||
|
||||
```bash
|
||||
# 安装 Node 依赖（根目录 + 前端）
|
||||
npm run setup
|
||||
|
||||
# 安装 Python 依赖（后端，自动创建虚拟环境）
|
||||
npm run setup:backend
|
||||
```
|
||||
|
||||
#### 3. 启动服务
|
||||
|
||||
```bash
|
||||
# 同时启动前后端（在项目根目录执行）
|
||||
npm run dev
|
||||
```
|
||||
|
||||
**服务地址：**
|
||||
- 前端：`http://localhost:3000`
|
||||
- 后端 API：`http://localhost:5001`
|
||||
|
||||
**单独启动：**
|
||||
|
||||
```bash
|
||||
npm run backend # 仅启动后端
|
||||
npm run frontend # 仅启动前端
|
||||
```
|
||||
|
||||
### 二、Docker 部署
|
||||
|
||||
```bash
|
||||
# 1. 配置环境变量（同源码部署）
|
||||
cp .env.example .env
|
||||
|
||||
# 2. 拉取镜像并启动
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
默认会读取根目录下的 `.env`，并映射端口 `3000（前端）/5001（后端）`
|
||||
|
||||
> 在 `docker-compose.yml` 中已通过注释提供加速镜像地址，可按需替换
|
||||
|
||||
## 📬 更多交流
|
||||
|
||||
<div align="center">
|
||||
<img src="./static/image/QQ群.png" alt="QQ交流群" width="60%"/>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
MiroFish团队长期招募全职/实习，如果你对多Agent应用感兴趣，欢迎投递简历至：**mirofish@shanda.com**
|
||||
|
||||
## 📄 致谢
|
||||
|
||||
**MiroFish 得到了盛大集团的战略支持和孵化！**
|
||||
|
||||
MiroFish 的仿真引擎由 **[OASIS](https://github.com/camel-ai/oasis)** 驱动，我们衷心感谢 CAMEL-AI 团队的开源贡献！
|
||||
|
||||
## 📈 项目统计
|
||||
|
||||
<a href="https://github.com/666ghj/MiroFish">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="static/image/star-history-dark.svg" />
|
||||
<source media="(prefers-color-scheme: light)" srcset="static/image/star-history-light.svg" />
|
||||
<img alt="666ghj/MiroFish Star History Chart" src="static/image/star-history-light.svg" />
|
||||
</picture>
|
||||
</a>
|
||||
59
README.md
59
README.md
|
|
@ -4,7 +4,7 @@
|
|||
|
||||
<a href="https://trendshift.io/repositories/16144" target="_blank"><img src="https://trendshift.io/api/badge/repositories/16144" alt="666ghj%2FMiroFish | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
|
||||
|
||||
简洁通用的群体智能引擎，预测万物
|
||||
A Simple and Universal Swarm Intelligence Engine, Predicting Anything
|
||||
</br>
|
||||
<em>A Simple and Universal Swarm Intelligence Engine, Predicting Anything</em>
|
||||
|
||||
|
|
@ -20,7 +20,7 @@
|
|||
[](https://x.com/mirofish_ai)
|
||||
[](https://www.instagram.com/mirofish_ai/)
|
||||
|
||||
[English](./README.md) | [中文文档](./README-ZH.md)
|
||||
[English](./README.md)
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -49,16 +49,16 @@ Welcome to visit our online demo environment and experience a prediction simulat
|
|||
<div align="center">
|
||||
<table>
|
||||
<tr>
|
||||
<td><img src="./static/image/Screenshot/运行截图1.png" alt="Screenshot 1" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/运行截图2.png" alt="Screenshot 2" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/screenshot1.png" alt="Screenshot 1" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/screenshot2.png" alt="Screenshot 2" width="100%"/></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><img src="./static/image/Screenshot/运行截图3.png" alt="Screenshot 3" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/运行截图4.png" alt="Screenshot 4" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/screenshot3.png" alt="Screenshot 3" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/screenshot4.png" alt="Screenshot 4" width="100%"/></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><img src="./static/image/Screenshot/运行截图5.png" alt="Screenshot 5" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/运行截图6.png" alt="Screenshot 6" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/screenshot5.png" alt="Screenshot 5" width="100%"/></td>
|
||||
<td><img src="./static/image/Screenshot/screenshot6.png" alt="Screenshot 6" width="100%"/></td>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
|
@ -68,7 +68,7 @@ Welcome to visit our online demo environment and experience a prediction simulat
|
|||
### 1. Wuhan University Public Opinion Simulation + MiroFish Project Introduction
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.bilibili.com/video/BV1VYBsBHEMY/" target="_blank"><img src="./static/image/武大模拟演示封面.png" alt="MiroFish Demo Video" width="75%"/></a>
|
||||
<a href="https://www.bilibili.com/video/BV1VYBsBHEMY/" target="_blank"><img src="./static/image/wuda_demo_cover.png" alt="MiroFish Demo Video" width="75%"/></a>
|
||||
|
||||
Click the image to watch the complete demo video for prediction using BettaFish-generated "Wuhan University Public Opinion Report"
|
||||
</div>
|
||||
|
|
@ -76,7 +76,7 @@ Click the image to watch the complete demo video for prediction using BettaFish-
|
|||
### 2. Dream of the Red Chamber Lost Ending Simulation
|
||||
|
||||
<div align="center">
|
||||
<a href="https://www.bilibili.com/video/BV1cPk3BBExq" target="_blank"><img src="./static/image/红楼梦模拟推演封面.jpg" alt="MiroFish Demo Video" width="75%"/></a>
|
||||
<a href="https://www.bilibili.com/video/BV1cPk3BBExq" target="_blank"><img src="./static/image/red_mansion_demo_cover.jpg" alt="MiroFish Demo Video" width="75%"/></a>
|
||||
|
||||
Click the image to watch MiroFish's deep prediction of the lost ending based on hundreds of thousands of words from the first 80 chapters of "Dream of the Red Chamber"
|
||||
</div>
|
||||
|
|
@ -114,19 +114,40 @@ cp .env.example .env
|
|||
|
||||
**Required Environment Variables:**
|
||||
|
||||
The only required key is `LLM_API_KEY`. The project supports any OpenAI-compatible LLM endpoint.
|
||||
|
||||
**Option A — Ollama Cloud (recommended):**
|
||||
|
||||
```env
|
||||
# LLM API Configuration (supports any LLM API with OpenAI SDK format)
|
||||
# Recommended: Alibaba Qwen-plus model via Bailian Platform: https://bailian.console.aliyun.com/
|
||||
# High consumption, try simulations with fewer than 40 rounds first
|
||||
LLM_API_KEY=your_api_key
|
||||
# 1. Create an API key: https://ollama.com/settings/keys
|
||||
# 2. Pick a cloud model from: https://ollama.com/search?c=cloud
|
||||
LLM_API_KEY=your_ollama_api_key
|
||||
LLM_BASE_URL=https://ollama.com/v1
|
||||
LLM_MODEL_NAME=qwen3.5:397b
|
||||
```
|
||||
|
||||
> **Model requirements**: The model must support `response_format={"type":"json_object"}` (JSON mode) and tool calling. Recommended cloud models: `qwen3.5:397b`, `glm-5.2`, `deepseek-v4-flash`, `gpt-oss:120b`.
|
||||
|
||||
**Option B — Local Ollama:**
|
||||
|
||||
```env
|
||||
# 1. Install Ollama: https://ollama.com
|
||||
# 2. Pull a model: ollama pull qwen2.5:7b
|
||||
LLM_API_KEY=ollama
|
||||
LLM_BASE_URL=http://localhost:11434/v1
|
||||
LLM_MODEL_NAME=qwen2.5:7b
|
||||
```
|
||||
|
||||
**Option C — Other cloud providers (OpenAI, DeepSeek, etc.):**
|
||||
|
||||
```env
|
||||
LLM_API_KEY=your_cloud_api_key
|
||||
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
LLM_MODEL_NAME=qwen-plus
|
||||
|
||||
# Zep Cloud Configuration
|
||||
# Free monthly quota is sufficient for simple usage: https://app.getzep.com/
|
||||
ZEP_API_KEY=your_zep_api_key
|
||||
```
|
||||
|
||||
> **No external SaaS required**: Knowledge graph storage, entity extraction, and semantic search are all handled locally via sqlite + LLM-based extraction. There is no Zep Cloud dependency.
|
||||
|
||||
#### 2. Install Dependencies
|
||||
|
||||
```bash
|
||||
|
|
@ -179,7 +200,7 @@ Reads `.env` from root directory by default, maps ports `3000 (frontend) / 5001
|
|||
## 📬 Join the Conversation
|
||||
|
||||
<div align="center">
|
||||
<img src="./static/image/QQ群.png" alt="QQ Group" width="60%"/>
|
||||
<img src="./static/image/qq_group.png" alt="QQ Group" width="60%"/>
|
||||
</div>
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
"""
|
||||
MiroFish Backend - Flask应用工厂
|
||||
MiroFish Backend - Flask application factory
|
||||
"""
|
||||
|
||||
import os
|
||||
import warnings
|
||||
|
||||
# 抑制 multiprocessing resource_tracker 的警告（来自第三方库如 transformers）
|
||||
# 需要在所有其他导入之前设置
|
||||
# Suppress multiprocessing resource_tracker warnings (from third-party libraries like transformers)
|
||||
# Must be set before all other imports
|
||||
warnings.filterwarnings("ignore", message=".*resource_tracker.*")
|
||||
|
||||
from flask import Flask, request
|
||||
|
|
@ -17,64 +17,65 @@ from .utils.logger import setup_logger, get_logger
|
|||
|
||||
|
||||
def create_app(config_class=Config):
|
||||
"""Flask应用工厂函数"""
|
||||
"""Flask application factory function"""
|
||||
app = Flask(__name__)
|
||||
app.config.from_object(config_class)
|
||||
|
||||
# 设置JSON编码：确保中文直接显示（而不是 \uXXXX 格式）
|
||||
# Flask >= 2.3 使用 app.json.ensure_ascii，旧版本使用 JSON_AS_ASCII 配置
|
||||
if hasattr(app, 'json') and hasattr(app.json, 'ensure_ascii'):
|
||||
|
||||
# Set JSON encoding: ensure Chinese characters display directly (instead of \uXXXX format)
|
||||
# Flask >= 2.3 uses app.json.ensure_ascii, older versions use JSON_AS_ASCII config
|
||||
if hasattr(app, "json") and hasattr(app.json, "ensure_ascii"):
|
||||
app.json.ensure_ascii = False
|
||||
|
||||
# 设置日志
|
||||
logger = setup_logger('mirofish')
|
||||
|
||||
# 只在 reloader 子进程中打印启动信息（避免 debug 模式下打印两次）
|
||||
is_reloader_process = os.environ.get('WERKZEUG_RUN_MAIN') == 'true'
|
||||
debug_mode = app.config.get('DEBUG', False)
|
||||
|
||||
# Set up logging
|
||||
logger = setup_logger("mirofish")
|
||||
|
||||
# Only log startup info in the reloader child process (avoid duplicate logs in debug mode)
|
||||
is_reloader_process = os.environ.get("WERKZEUG_RUN_MAIN") == "true"
|
||||
debug_mode = app.config.get("DEBUG", False)
|
||||
should_log_startup = not debug_mode or is_reloader_process
|
||||
|
||||
|
||||
if should_log_startup:
|
||||
logger.info("=" * 50)
|
||||
logger.info("MiroFish Backend 启动中...")
|
||||
logger.info("MiroFish Backend starting...")
|
||||
logger.info("=" * 50)
|
||||
|
||||
# 启用CORS
|
||||
|
||||
# Enable CORS
|
||||
CORS(app, resources={r"/api/*": {"origins": "*"}})
|
||||
|
||||
# 注册模拟进程清理函数（确保服务器关闭时终止所有模拟进程）
|
||||
|
||||
# Register simulation process cleanup function (ensure all simulation processes terminate on server shutdown)
|
||||
from .services.simulation_runner import SimulationRunner
|
||||
|
||||
SimulationRunner.register_cleanup()
|
||||
if should_log_startup:
|
||||
logger.info("已注册模拟进程清理函数")
|
||||
|
||||
# 请求日志中间件
|
||||
logger.info("Registered simulation process cleanup function")
|
||||
|
||||
# Request logging middleware
|
||||
@app.before_request
|
||||
def log_request():
|
||||
logger = get_logger('mirofish.request')
|
||||
logger.debug(f"请求: {request.method} {request.path}")
|
||||
if request.content_type and 'json' in request.content_type:
|
||||
logger.debug(f"请求体: {request.get_json(silent=True)}")
|
||||
|
||||
logger = get_logger("mirofish.request")
|
||||
logger.debug(f"Request: {request.method} {request.path}")
|
||||
if request.content_type and "json" in request.content_type:
|
||||
logger.debug(f"Request body: {request.get_json(silent=True)}")
|
||||
|
||||
@app.after_request
|
||||
def log_response(response):
|
||||
logger = get_logger('mirofish.request')
|
||||
logger.debug(f"响应: {response.status_code}")
|
||||
logger = get_logger("mirofish.request")
|
||||
logger.debug(f"Response: {response.status_code}")
|
||||
return response
|
||||
|
||||
# 注册蓝图
|
||||
from .api import graph_bp, simulation_bp, report_bp
|
||||
app.register_blueprint(graph_bp, url_prefix='/api/graph')
|
||||
app.register_blueprint(simulation_bp, url_prefix='/api/simulation')
|
||||
app.register_blueprint(report_bp, url_prefix='/api/report')
|
||||
|
||||
# 健康检查
|
||||
@app.route('/health')
|
||||
def health():
|
||||
return {'status': 'ok', 'service': 'MiroFish Backend'}
|
||||
|
||||
if should_log_startup:
|
||||
logger.info("MiroFish Backend 启动完成")
|
||||
|
||||
return app
|
||||
|
||||
# Register blueprints
|
||||
from .api import graph_bp, simulation_bp, report_bp
|
||||
|
||||
app.register_blueprint(graph_bp, url_prefix="/api/graph")
|
||||
app.register_blueprint(simulation_bp, url_prefix="/api/simulation")
|
||||
app.register_blueprint(report_bp, url_prefix="/api/report")
|
||||
|
||||
# Health check
|
||||
@app.route("/health")
|
||||
def health():
|
||||
return {"status": "ok", "service": "MiroFish Backend"}
|
||||
|
||||
if should_log_startup:
|
||||
logger.info("MiroFish Backend startup complete")
|
||||
|
||||
return app
|
||||
|
|
|
|||
|
|
@ -1,14 +1,13 @@
|
|||
"""
|
||||
API路由模块
|
||||
API routes module
|
||||
"""
|
||||
|
||||
from flask import Blueprint
|
||||
|
||||
graph_bp = Blueprint('graph', __name__)
|
||||
simulation_bp = Blueprint('simulation', __name__)
|
||||
report_bp = Blueprint('report', __name__)
|
||||
graph_bp = Blueprint("graph", __name__)
|
||||
simulation_bp = Blueprint("simulation", __name__)
|
||||
report_bp = Blueprint("report", __name__)
|
||||
|
||||
from . import graph # noqa: E402, F401
|
||||
from . import simulation # noqa: E402, F401
|
||||
from . import report # noqa: E402, F401
|
||||
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -1,76 +1,97 @@
|
|||
"""\n配置管理\n统一从项目根目录的 .env 文件加载配置\n"""
|
||||
"""
|
||||
Configuration management
|
||||
Loads settings from the .env file at the project root
|
||||
"""
|
||||
|
||||
import os
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# 加载项目根目录的 .env 文件
|
||||
# 路径: MiroFish/.env (相对于 backend/app/config.py)
|
||||
project_root_env = os.path.join(os.path.dirname(__file__), '../../.env')
|
||||
# Load the .env file from the project root
|
||||
# Path: MiroFish/.env (relative to backend/app/config.py)
|
||||
project_root_env = os.path.join(os.path.dirname(__file__), "../../.env")
|
||||
|
||||
if os.path.exists(project_root_env):
|
||||
load_dotenv(project_root_env, override=True)
|
||||
else:
|
||||
# 如果根目录没有 .env，尝试加载环境变量（用于生产环境）
|
||||
# If no .env in root, try loading from environment variables (for production)
|
||||
load_dotenv(override=True)
|
||||
|
||||
|
||||
class Config:
|
||||
"""Flask配置类"""
|
||||
|
||||
# Flask配置
|
||||
SECRET_KEY = os.environ.get('SECRET_KEY', 'mirofish-secret-key')
|
||||
DEBUG = os.environ.get('FLASK_DEBUG', 'False').lower() == 'true'
|
||||
|
||||
# JSON配置 - 禁用ASCII转义，让中文直接显示
|
||||
"""Flask configuration class"""
|
||||
|
||||
# Flask config
|
||||
SECRET_KEY = os.environ.get("SECRET_KEY", "mirofish-secret-key")
|
||||
DEBUG = os.environ.get("FLASK_DEBUG", "False").lower() == "true"
|
||||
|
||||
# JSON config - disable ASCII escaping so non-ASCII characters display directly
|
||||
JSON_AS_ASCII = False
|
||||
|
||||
# LLM配置（统一使用OpenAI格式）
|
||||
LLM_API_KEY = os.environ.get('LLM_API_KEY')
|
||||
LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1')
|
||||
LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini')
|
||||
|
||||
# Zep配置
|
||||
ZEP_API_KEY = os.environ.get('ZEP_API_KEY')
|
||||
|
||||
# 文件上传配置
|
||||
|
||||
# LLM config (unified OpenAI format)
|
||||
LLM_API_KEY = os.environ.get("LLM_API_KEY")
|
||||
LLM_BASE_URL = os.environ.get("LLM_BASE_URL", "https://api.openai.com/v1")
|
||||
LLM_MODEL_NAME = os.environ.get("LLM_MODEL_NAME", "gpt-4o-mini")
|
||||
|
||||
# File upload config
|
||||
MAX_CONTENT_LENGTH = 50 * 1024 * 1024 # 50MB
|
||||
UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), '../uploads')
|
||||
ALLOWED_EXTENSIONS = {'pdf', 'md', 'txt', 'markdown'}
|
||||
|
||||
# 文本处理配置
|
||||
DEFAULT_CHUNK_SIZE = 500 # 默认切块大小
|
||||
DEFAULT_CHUNK_OVERLAP = 50 # 默认重叠大小
|
||||
|
||||
# OASIS模拟配置
|
||||
OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '10'))
|
||||
OASIS_SIMULATION_DATA_DIR = os.path.join(os.path.dirname(__file__), '../uploads/simulations')
|
||||
|
||||
# OASIS平台可用动作配置
|
||||
UPLOAD_FOLDER = os.path.join(os.path.dirname(__file__), "../uploads")
|
||||
ALLOWED_EXTENSIONS = {"pdf", "md", "txt", "markdown"}
|
||||
|
||||
# Text processing config
|
||||
DEFAULT_CHUNK_SIZE = 500 # default chunk size
|
||||
DEFAULT_CHUNK_OVERLAP = 50 # default overlap size
|
||||
|
||||
# OASIS simulation config
|
||||
OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get("OASIS_DEFAULT_MAX_ROUNDS", "10"))
|
||||
OASIS_SIMULATION_DATA_DIR = os.path.join(
|
||||
os.path.dirname(__file__), "../uploads/simulations"
|
||||
)
|
||||
|
||||
# OASIS platform available actions config
|
||||
OASIS_TWITTER_ACTIONS = [
|
||||
'CREATE_POST', 'LIKE_POST', 'REPOST', 'FOLLOW', 'DO_NOTHING', 'QUOTE_POST'
|
||||
"CREATE_POST",
|
||||
"LIKE_POST",
|
||||
"REPOST",
|
||||
"FOLLOW",
|
||||
"DO_NOTHING",
|
||||
"QUOTE_POST",
|
||||
]
|
||||
OASIS_REDDIT_ACTIONS = [
|
||||
'LIKE_POST', 'DISLIKE_POST', 'CREATE_POST', 'CREATE_COMMENT',
|
||||
'LIKE_COMMENT', 'DISLIKE_COMMENT', 'SEARCH_POSTS', 'SEARCH_USER',
|
||||
'TREND', 'REFRESH', 'DO_NOTHING', 'FOLLOW', 'MUTE'
|
||||
"LIKE_POST",
|
||||
"DISLIKE_POST",
|
||||
"CREATE_POST",
|
||||
"CREATE_COMMENT",
|
||||
"LIKE_COMMENT",
|
||||
"DISLIKE_COMMENT",
|
||||
"SEARCH_POSTS",
|
||||
"SEARCH_USER",
|
||||
"TREND",
|
||||
"REFRESH",
|
||||
"DO_NOTHING",
|
||||
"FOLLOW",
|
||||
"MUTE",
|
||||
]
|
||||
|
||||
# Report Agent配置
|
||||
REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '5'))
|
||||
REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2'))
|
||||
REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.5'))
|
||||
|
||||
|
||||
# Report Agent config
|
||||
REPORT_AGENT_MAX_TOOL_CALLS = int(
|
||||
os.environ.get("REPORT_AGENT_MAX_TOOL_CALLS", "5")
|
||||
)
|
||||
REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(
|
||||
os.environ.get("REPORT_AGENT_MAX_REFLECTION_ROUNDS", "2")
|
||||
)
|
||||
REPORT_AGENT_TEMPERATURE = float(os.environ.get("REPORT_AGENT_TEMPERATURE", "0.5"))
|
||||
|
||||
@classmethod
|
||||
def validate(cls) -> list[str]:
|
||||
"""验证必要配置"""
|
||||
errors: list[str] = []
|
||||
def validate(cls):
|
||||
"""Validate required configuration"""
|
||||
errors = []
|
||||
if not cls.LLM_API_KEY:
|
||||
errors.append("LLM_API_KEY 未配置")
|
||||
if not cls.ZEP_API_KEY:
|
||||
errors.append("ZEP_API_KEY 未配置")
|
||||
if os.environ.get("ZEP_API_URL"):
|
||||
errors.append("ZEP_API_URL 不受支持；MiroFish 仅连接 Zep Cloud")
|
||||
errors.append("LLM_API_KEY not configured")
|
||||
if cls.DEBUG:
|
||||
import warnings
|
||||
warnings.warn("Flask DEBUG mode is enabled. Do not use in production.", RuntimeWarning)
|
||||
return errors
|
||||
|
||||
warnings.warn(
|
||||
"Flask DEBUG mode is enabled. Do not use in production.",
|
||||
RuntimeWarning,
|
||||
)
|
||||
return errors
|
||||
|
|
@ -1,9 +1,8 @@
|
|||
"""
|
||||
数据模型模块
|
||||
Data models module
|
||||
"""
|
||||
|
||||
from .task import TaskManager, TaskStatus
|
||||
from .project import Project, ProjectStatus, ProjectManager
|
||||
|
||||
__all__ = ['TaskManager', 'TaskStatus', 'Project', 'ProjectStatus', 'ProjectManager']
|
||||
|
||||
__all__ = ["TaskManager", "TaskStatus", "Project", "ProjectStatus", "ProjectManager"]
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
"""
|
||||
项目上下文管理
|
||||
用于在服务端持久化项目状态，避免前端在接口间传递大量数据
|
||||
Project context management
|
||||
Used to persist project state on the server side, avoiding the frontend passing large amounts of data between interfaces
|
||||
"""
|
||||
|
||||
import os
|
||||
|
|
@ -15,51 +15,55 @@ from ..config import Config
|
|||
|
||||
|
||||
class ProjectStatus(str, Enum):
|
||||
"""项目状态"""
|
||||
CREATED = "created" # 刚创建，文件已上传
|
||||
ONTOLOGY_GENERATED = "ontology_generated" # 本体已生成
|
||||
GRAPH_BUILDING = "graph_building" # 图谱构建中
|
||||
GRAPH_COMPLETED = "graph_completed" # 图谱构建完成
|
||||
FAILED = "failed" # 失败
|
||||
"""Project status"""
|
||||
|
||||
CREATED = "created" # just created, files uploaded
|
||||
ONTOLOGY_GENERATED = "ontology_generated" # ontology generated
|
||||
GRAPH_BUILDING = "graph_building" # graph building in progress
|
||||
GRAPH_COMPLETED = "graph_completed" # graph build completed
|
||||
FAILED = "failed" # failed
|
||||
|
||||
|
||||
@dataclass
|
||||
class Project:
|
||||
"""项目数据模型"""
|
||||
"""Project data model"""
|
||||
|
||||
project_id: str
|
||||
name: str
|
||||
status: ProjectStatus
|
||||
created_at: str
|
||||
updated_at: str
|
||||
|
||||
# 文件信息
|
||||
files: List[Dict[str, str]] = field(default_factory=list) # [{filename, path, size}]
|
||||
|
||||
# File info
|
||||
files: List[Dict[str, str]] = field(
|
||||
default_factory=list
|
||||
) # [{filename, path, size}]
|
||||
total_text_length: int = 0
|
||||
|
||||
# 本体信息（接口1生成后填充）
|
||||
|
||||
# Ontology info (populated after endpoint 1 generates it)
|
||||
ontology: Optional[Dict[str, Any]] = None
|
||||
analysis_summary: Optional[str] = None
|
||||
|
||||
# 图谱信息（接口2完成后填充）
|
||||
|
||||
# Graph info (populated after endpoint 2 completes)
|
||||
graph_id: Optional[str] = None
|
||||
graph_build_task_id: Optional[str] = None
|
||||
zep_batch_id: Optional[str] = None
|
||||
zep_batch_operation_id: Optional[str] = None
|
||||
|
||||
# 配置
|
||||
|
||||
# Config
|
||||
simulation_requirement: Optional[str] = None
|
||||
chunk_size: int = 500
|
||||
chunk_overlap: int = 50
|
||||
|
||||
# 错误信息
|
||||
|
||||
# Error info
|
||||
error: Optional[str] = None
|
||||
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""转换为字典"""
|
||||
"""Convert to dictionary"""
|
||||
return {
|
||||
"project_id": self.project_id,
|
||||
"name": self.name,
|
||||
"status": self.status.value if isinstance(self.status, ProjectStatus) else self.status,
|
||||
"status": self.status.value
|
||||
if isinstance(self.status, ProjectStatus)
|
||||
else self.status,
|
||||
"created_at": self.created_at,
|
||||
"updated_at": self.updated_at,
|
||||
"files": self.files,
|
||||
|
|
@ -68,253 +72,241 @@ class Project:
|
|||
"analysis_summary": self.analysis_summary,
|
||||
"graph_id": self.graph_id,
|
||||
"graph_build_task_id": self.graph_build_task_id,
|
||||
"zep_batch_id": self.zep_batch_id,
|
||||
"zep_batch_operation_id": self.zep_batch_operation_id,
|
||||
"simulation_requirement": self.simulation_requirement,
|
||||
"chunk_size": self.chunk_size,
|
||||
"chunk_overlap": self.chunk_overlap,
|
||||
"error": self.error
|
||||
"error": self.error,
|
||||
}
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> 'Project':
|
||||
"""从字典创建"""
|
||||
status = data.get('status', 'created')
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "Project":
|
||||
"""Create from dictionary"""
|
||||
status = data.get("status", "created")
|
||||
if isinstance(status, str):
|
||||
status = ProjectStatus(status)
|
||||
|
||||
|
||||
return cls(
|
||||
project_id=data['project_id'],
|
||||
name=data.get('name', 'Unnamed Project'),
|
||||
project_id=data["project_id"],
|
||||
name=data.get("name", "Unnamed Project"),
|
||||
status=status,
|
||||
created_at=data.get('created_at', ''),
|
||||
updated_at=data.get('updated_at', ''),
|
||||
files=data.get('files', []),
|
||||
total_text_length=data.get('total_text_length', 0),
|
||||
ontology=data.get('ontology'),
|
||||
analysis_summary=data.get('analysis_summary'),
|
||||
graph_id=data.get('graph_id'),
|
||||
graph_build_task_id=data.get('graph_build_task_id'),
|
||||
zep_batch_id=data.get('zep_batch_id'),
|
||||
zep_batch_operation_id=data.get('zep_batch_operation_id'),
|
||||
simulation_requirement=data.get('simulation_requirement'),
|
||||
chunk_size=data.get('chunk_size', 500),
|
||||
chunk_overlap=data.get('chunk_overlap', 50),
|
||||
error=data.get('error')
|
||||
created_at=data.get("created_at", ""),
|
||||
updated_at=data.get("updated_at", ""),
|
||||
files=data.get("files", []),
|
||||
total_text_length=data.get("total_text_length", 0),
|
||||
ontology=data.get("ontology"),
|
||||
analysis_summary=data.get("analysis_summary"),
|
||||
graph_id=data.get("graph_id"),
|
||||
graph_build_task_id=data.get("graph_build_task_id"),
|
||||
simulation_requirement=data.get("simulation_requirement"),
|
||||
chunk_size=data.get("chunk_size", 500),
|
||||
chunk_overlap=data.get("chunk_overlap", 50),
|
||||
error=data.get("error"),
|
||||
)
|
||||
|
||||
|
||||
class ProjectManager:
|
||||
"""项目管理器 - 负责项目的持久化存储和检索"""
|
||||
|
||||
# 项目存储根目录
|
||||
PROJECTS_DIR = os.path.join(Config.UPLOAD_FOLDER, 'projects')
|
||||
|
||||
"""Project manager - responsible for project persistence storage and retrieval"""
|
||||
|
||||
# Project storage root directory
|
||||
PROJECTS_DIR = os.path.join(Config.UPLOAD_FOLDER, "projects")
|
||||
|
||||
@classmethod
|
||||
def _ensure_projects_dir(cls):
|
||||
"""确保项目目录存在"""
|
||||
"""Ensure the projects directory exists"""
|
||||
os.makedirs(cls.PROJECTS_DIR, exist_ok=True)
|
||||
|
||||
|
||||
@classmethod
|
||||
def _get_project_dir(cls, project_id: str) -> str:
|
||||
"""获取项目目录路径"""
|
||||
"""Get the project directory path"""
|
||||
return os.path.join(cls.PROJECTS_DIR, project_id)
|
||||
|
||||
|
||||
@classmethod
|
||||
def _get_project_meta_path(cls, project_id: str) -> str:
|
||||
"""获取项目元数据文件路径"""
|
||||
return os.path.join(cls._get_project_dir(project_id), 'project.json')
|
||||
|
||||
"""Get the project metadata file path"""
|
||||
return os.path.join(cls._get_project_dir(project_id), "project.json")
|
||||
|
||||
@classmethod
|
||||
def _get_project_files_dir(cls, project_id: str) -> str:
|
||||
"""获取项目文件存储目录"""
|
||||
return os.path.join(cls._get_project_dir(project_id), 'files')
|
||||
|
||||
"""Get the project file storage directory"""
|
||||
return os.path.join(cls._get_project_dir(project_id), "files")
|
||||
|
||||
@classmethod
|
||||
def _get_project_text_path(cls, project_id: str) -> str:
|
||||
"""获取项目提取文本存储路径"""
|
||||
return os.path.join(cls._get_project_dir(project_id), 'extracted_text.txt')
|
||||
|
||||
"""Get the path where the project's extracted text is stored"""
|
||||
return os.path.join(cls._get_project_dir(project_id), "extracted_text.txt")
|
||||
|
||||
@classmethod
|
||||
def create_project(cls, name: str = "Unnamed Project") -> Project:
|
||||
"""
|
||||
创建新项目
|
||||
|
||||
Create a new project
|
||||
|
||||
Args:
|
||||
name: 项目名称
|
||||
|
||||
name: Project name
|
||||
|
||||
Returns:
|
||||
新创建的Project对象
|
||||
The newly created Project object
|
||||
"""
|
||||
cls._ensure_projects_dir()
|
||||
|
||||
|
||||
project_id = f"proj_{uuid.uuid4().hex[:12]}"
|
||||
now = datetime.now().isoformat()
|
||||
|
||||
|
||||
project = Project(
|
||||
project_id=project_id,
|
||||
name=name,
|
||||
status=ProjectStatus.CREATED,
|
||||
created_at=now,
|
||||
updated_at=now
|
||||
updated_at=now,
|
||||
)
|
||||
|
||||
# 创建项目目录结构
|
||||
|
||||
# Create project directory structure
|
||||
project_dir = cls._get_project_dir(project_id)
|
||||
files_dir = cls._get_project_files_dir(project_id)
|
||||
os.makedirs(project_dir, exist_ok=True)
|
||||
os.makedirs(files_dir, exist_ok=True)
|
||||
|
||||
# 保存项目元数据
|
||||
|
||||
# Save project metadata
|
||||
cls.save_project(project)
|
||||
|
||||
|
||||
return project
|
||||
|
||||
|
||||
@classmethod
|
||||
def save_project(cls, project: Project) -> None:
|
||||
"""保存项目元数据"""
|
||||
"""Save project metadata"""
|
||||
project.updated_at = datetime.now().isoformat()
|
||||
meta_path = cls._get_project_meta_path(project.project_id)
|
||||
|
||||
with open(meta_path, 'w', encoding='utf-8') as f:
|
||||
|
||||
with open(meta_path, "w", encoding="utf-8") as f:
|
||||
json.dump(project.to_dict(), f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
@classmethod
|
||||
def get_project(cls, project_id: str) -> Optional[Project]:
|
||||
"""
|
||||
获取项目
|
||||
|
||||
Get a project
|
||||
|
||||
Args:
|
||||
project_id: 项目ID
|
||||
|
||||
project_id: Project ID
|
||||
|
||||
Returns:
|
||||
Project对象，如果不存在返回None
|
||||
Project object, or None if it does not exist
|
||||
"""
|
||||
meta_path = cls._get_project_meta_path(project_id)
|
||||
|
||||
|
||||
if not os.path.exists(meta_path):
|
||||
return None
|
||||
|
||||
with open(meta_path, 'r', encoding='utf-8') as f:
|
||||
|
||||
with open(meta_path, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
|
||||
|
||||
return Project.from_dict(data)
|
||||
|
||||
|
||||
@classmethod
|
||||
def list_projects(cls, limit: Optional[int] = 50) -> List[Project]:
|
||||
def list_projects(cls, limit: int = 50) -> List[Project]:
|
||||
"""
|
||||
列出所有项目
|
||||
|
||||
List all projects
|
||||
|
||||
Args:
|
||||
limit: 返回数量限制
|
||||
|
||||
limit: Return count limit
|
||||
|
||||
Returns:
|
||||
项目列表，按创建时间倒序
|
||||
List of projects, sorted by creation time in descending order
|
||||
"""
|
||||
cls._ensure_projects_dir()
|
||||
|
||||
|
||||
projects = []
|
||||
for project_id in os.listdir(cls.PROJECTS_DIR):
|
||||
project = cls.get_project(project_id)
|
||||
if project:
|
||||
projects.append(project)
|
||||
|
||||
# 按创建时间倒序排序
|
||||
|
||||
# Sort by creation time in descending order
|
||||
projects.sort(key=lambda p: p.created_at, reverse=True)
|
||||
|
||||
return projects if limit is None else projects[:limit]
|
||||
|
||||
@classmethod
|
||||
def find_projects_by_graph_id(cls, graph_id: str) -> List[Project]:
|
||||
"""Return every persisted project that references a Cloud graph."""
|
||||
return projects[:limit]
|
||||
|
||||
return [
|
||||
project
|
||||
for project in cls.list_projects(limit=None)
|
||||
if project.graph_id == graph_id
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def delete_project(cls, project_id: str) -> bool:
|
||||
"""
|
||||
删除项目及其所有文件
|
||||
|
||||
Delete a project and all its files
|
||||
|
||||
Args:
|
||||
project_id: 项目ID
|
||||
|
||||
project_id: Project ID
|
||||
|
||||
Returns:
|
||||
是否删除成功
|
||||
Whether the deletion was successful
|
||||
"""
|
||||
project_dir = cls._get_project_dir(project_id)
|
||||
|
||||
|
||||
if not os.path.exists(project_dir):
|
||||
return False
|
||||
|
||||
|
||||
shutil.rmtree(project_dir)
|
||||
return True
|
||||
|
||||
|
||||
@classmethod
|
||||
def save_file_to_project(cls, project_id: str, file_storage, original_filename: str) -> Dict[str, str]:
|
||||
def save_file_to_project(
|
||||
cls, project_id: str, file_storage, original_filename: str
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
保存上传的文件到项目目录
|
||||
|
||||
Save an uploaded file to the project directory
|
||||
|
||||
Args:
|
||||
project_id: 项目ID
|
||||
file_storage: Flask的FileStorage对象
|
||||
original_filename: 原始文件名
|
||||
|
||||
project_id: Project ID
|
||||
file_storage: Flask FileStorage object
|
||||
original_filename: Original filename
|
||||
|
||||
Returns:
|
||||
文件信息字典 {filename, path, size}
|
||||
File info dictionary {filename, path, size}
|
||||
"""
|
||||
files_dir = cls._get_project_files_dir(project_id)
|
||||
os.makedirs(files_dir, exist_ok=True)
|
||||
|
||||
# 生成安全的文件名
|
||||
|
||||
# Generate a safe filename
|
||||
ext = os.path.splitext(original_filename)[1].lower()
|
||||
safe_filename = f"{uuid.uuid4().hex[:8]}{ext}"
|
||||
file_path = os.path.join(files_dir, safe_filename)
|
||||
|
||||
# 保存文件
|
||||
|
||||
# Save file
|
||||
file_storage.save(file_path)
|
||||
|
||||
# 获取文件大小
|
||||
|
||||
# Get file size
|
||||
file_size = os.path.getsize(file_path)
|
||||
|
||||
|
||||
return {
|
||||
"original_filename": original_filename,
|
||||
"saved_filename": safe_filename,
|
||||
"path": file_path,
|
||||
"size": file_size
|
||||
"size": file_size,
|
||||
}
|
||||
|
||||
|
||||
@classmethod
|
||||
def save_extracted_text(cls, project_id: str, text: str) -> None:
|
||||
"""保存提取的文本"""
|
||||
"""Save the extracted text"""
|
||||
text_path = cls._get_project_text_path(project_id)
|
||||
with open(text_path, 'w', encoding='utf-8') as f:
|
||||
with open(text_path, "w", encoding="utf-8") as f:
|
||||
f.write(text)
|
||||
|
||||
|
||||
@classmethod
|
||||
def get_extracted_text(cls, project_id: str) -> Optional[str]:
|
||||
"""获取提取的文本"""
|
||||
"""Get the extracted text"""
|
||||
text_path = cls._get_project_text_path(project_id)
|
||||
|
||||
|
||||
if not os.path.exists(text_path):
|
||||
return None
|
||||
|
||||
with open(text_path, 'r', encoding='utf-8') as f:
|
||||
|
||||
with open(text_path, "r", encoding="utf-8") as f:
|
||||
return f.read()
|
||||
|
||||
|
||||
@classmethod
|
||||
def get_project_files(cls, project_id: str) -> List[str]:
|
||||
"""获取项目的所有文件路径"""
|
||||
"""Get all file paths of the project"""
|
||||
files_dir = cls._get_project_files_dir(project_id)
|
||||
|
||||
|
||||
if not os.path.exists(files_dir):
|
||||
return []
|
||||
|
||||
|
||||
return [
|
||||
os.path.join(files_dir, f)
|
||||
for f in os.listdir(files_dir)
|
||||
os.path.join(files_dir, f)
|
||||
for f in os.listdir(files_dir)
|
||||
if os.path.isfile(os.path.join(files_dir, f))
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
"""
|
||||
任务状态管理
|
||||
用于跟踪长时间运行的任务（如图谱构建）
|
||||
Task status management
|
||||
Used to track long-running tasks (such as graph building)
|
||||
"""
|
||||
|
||||
import uuid
|
||||
|
|
@ -14,30 +14,32 @@ from ..utils.locale import t
|
|||
|
||||
|
||||
class TaskStatus(str, Enum):
|
||||
"""任务状态枚举"""
|
||||
PENDING = "pending" # 等待中
|
||||
PROCESSING = "processing" # 处理中
|
||||
COMPLETED = "completed" # 已完成
|
||||
FAILED = "failed" # 失败
|
||||
"""Task status enum"""
|
||||
|
||||
PENDING = "pending" # pending
|
||||
PROCESSING = "processing" # processing
|
||||
COMPLETED = "completed" # completed
|
||||
FAILED = "failed" # failed
|
||||
|
||||
|
||||
@dataclass
|
||||
class Task:
|
||||
"""任务数据类"""
|
||||
"""Task data class"""
|
||||
|
||||
task_id: str
|
||||
task_type: str
|
||||
status: TaskStatus
|
||||
created_at: datetime
|
||||
updated_at: datetime
|
||||
progress: int = 0 # 总进度百分比 0-100
|
||||
message: str = "" # 状态消息
|
||||
result: Optional[Dict] = None # 任务结果
|
||||
error: Optional[str] = None # 错误信息
|
||||
metadata: Dict = field(default_factory=dict) # 额外元数据
|
||||
progress_detail: Dict = field(default_factory=dict) # 详细进度信息
|
||||
|
||||
progress: int = 0 # overall progress percentage 0-100
|
||||
message: str = "" # status message
|
||||
result: Optional[Dict] = None # task result
|
||||
error: Optional[str] = None # error message
|
||||
metadata: Dict = field(default_factory=dict) # additional metadata
|
||||
progress_detail: Dict = field(default_factory=dict) # detailed progress info
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""转换为字典"""
|
||||
"""Convert to dict"""
|
||||
return {
|
||||
"task_id": self.task_id,
|
||||
"task_type": self.task_type,
|
||||
|
|
@ -55,56 +57,58 @@ class Task:
|
|||
|
||||
class TaskManager:
|
||||
"""
|
||||
任务管理器
|
||||
线程安全的任务状态管理
|
||||
Task manager
|
||||
Thread-safe task state management
|
||||
"""
|
||||
|
||||
|
||||
_instance = None
|
||||
_lock = threading.Lock()
|
||||
|
||||
_tasks: Dict[str, "Task"]
|
||||
_task_lock: threading.Lock
|
||||
|
||||
def __new__(cls):
|
||||
"""单例模式"""
|
||||
"""Singleton pattern"""
|
||||
if cls._instance is None:
|
||||
with cls._lock:
|
||||
if cls._instance is None:
|
||||
cls._instance = super().__new__(cls)
|
||||
cls._instance._tasks: Dict[str, Task] = {}
|
||||
cls._instance._tasks = {}
|
||||
cls._instance._task_lock = threading.Lock()
|
||||
return cls._instance
|
||||
|
||||
|
||||
def create_task(self, task_type: str, metadata: Optional[Dict] = None) -> str:
|
||||
"""
|
||||
创建新任务
|
||||
|
||||
Create a new task
|
||||
|
||||
Args:
|
||||
task_type: 任务类型
|
||||
metadata: 额外元数据
|
||||
|
||||
task_type: Task type
|
||||
metadata: Additional metadata
|
||||
|
||||
Returns:
|
||||
任务ID
|
||||
Task ID
|
||||
"""
|
||||
task_id = str(uuid.uuid4())
|
||||
now = datetime.now()
|
||||
|
||||
|
||||
task = Task(
|
||||
task_id=task_id,
|
||||
task_type=task_type,
|
||||
status=TaskStatus.PENDING,
|
||||
created_at=now,
|
||||
updated_at=now,
|
||||
metadata=metadata or {}
|
||||
metadata=metadata or {},
|
||||
)
|
||||
|
||||
|
||||
with self._task_lock:
|
||||
self._tasks[task_id] = task
|
||||
|
||||
|
||||
return task_id
|
||||
|
||||
|
||||
def get_task(self, task_id: str) -> Optional[Task]:
|
||||
"""获取任务"""
|
||||
"""Get a task"""
|
||||
with self._task_lock:
|
||||
return self._tasks.get(task_id)
|
||||
|
||||
|
||||
def update_task(
|
||||
self,
|
||||
task_id: str,
|
||||
|
|
@ -113,19 +117,19 @@ class TaskManager:
|
|||
message: Optional[str] = None,
|
||||
result: Optional[Dict] = None,
|
||||
error: Optional[str] = None,
|
||||
progress_detail: Optional[Dict] = None
|
||||
progress_detail: Optional[Dict] = None,
|
||||
):
|
||||
"""
|
||||
更新任务状态
|
||||
|
||||
Update task status
|
||||
|
||||
Args:
|
||||
task_id: 任务ID
|
||||
status: 新状态
|
||||
progress: 进度
|
||||
message: 消息
|
||||
result: 结果
|
||||
error: 错误信息
|
||||
progress_detail: 详细进度信息
|
||||
task_id: Task ID
|
||||
status: New status
|
||||
progress: Progress
|
||||
message: Message
|
||||
result: Result
|
||||
error: Error message
|
||||
progress_detail: Detailed progress info
|
||||
"""
|
||||
with self._task_lock:
|
||||
task = self._tasks.get(task_id)
|
||||
|
|
@ -143,44 +147,49 @@ class TaskManager:
|
|||
task.error = error
|
||||
if progress_detail is not None:
|
||||
task.progress_detail = progress_detail
|
||||
|
||||
|
||||
def complete_task(self, task_id: str, result: Dict):
|
||||
"""标记任务完成"""
|
||||
"""Mark a task as completed"""
|
||||
self.update_task(
|
||||
task_id,
|
||||
status=TaskStatus.COMPLETED,
|
||||
progress=100,
|
||||
message=t('progress.taskComplete'),
|
||||
result=result
|
||||
message=t("progress.taskComplete"),
|
||||
result=result,
|
||||
)
|
||||
|
||||
|
||||
def fail_task(self, task_id: str, error: str):
|
||||
"""标记任务失败"""
|
||||
"""Mark a task as failed"""
|
||||
self.update_task(
|
||||
task_id,
|
||||
status=TaskStatus.FAILED,
|
||||
message=t('progress.taskFailed'),
|
||||
error=error
|
||||
message=t("progress.taskFailed"),
|
||||
error=error,
|
||||
)
|
||||
|
||||
|
||||
def list_tasks(self, task_type: Optional[str] = None) -> list:
|
||||
"""列出任务"""
|
||||
"""List tasks"""
|
||||
with self._task_lock:
|
||||
tasks = list(self._tasks.values())
|
||||
if task_type:
|
||||
tasks = [t for t in tasks if t.task_type == task_type]
|
||||
return [t.to_dict() for t in sorted(tasks, key=lambda x: x.created_at, reverse=True)]
|
||||
|
||||
return [
|
||||
t.to_dict()
|
||||
for t in sorted(tasks, key=lambda x: x.created_at, reverse=True)
|
||||
]
|
||||
|
||||
def cleanup_old_tasks(self, max_age_hours: int = 24):
|
||||
"""清理旧任务"""
|
||||
"""Clean up old tasks"""
|
||||
from datetime import timedelta
|
||||
|
||||
cutoff = datetime.now() - timedelta(hours=max_age_hours)
|
||||
|
||||
|
||||
with self._task_lock:
|
||||
old_ids = [
|
||||
tid for tid, task in self._tasks.items()
|
||||
if task.created_at < cutoff and task.status in [TaskStatus.COMPLETED, TaskStatus.FAILED]
|
||||
tid
|
||||
for tid, task in self._tasks.items()
|
||||
if task.created_at < cutoff
|
||||
and task.status in [TaskStatus.COMPLETED, TaskStatus.FAILED]
|
||||
]
|
||||
for tid in old_ids:
|
||||
del self._tasks[tid]
|
||||
|
||||
|
|
|
|||
|
|
@ -1,32 +1,27 @@
|
|||
"""
|
||||
业务服务模块
|
||||
Business services module
|
||||
"""
|
||||
|
||||
from .ontology_generator import OntologyGenerator
|
||||
from .graph_builder import GraphBuilderService
|
||||
from .text_processor import TextProcessor
|
||||
from .zep_entity_reader import ZepEntityReader, EntityNode, FilteredEntities
|
||||
from .entity_reader import EntityReader, EntityNode, FilteredEntities
|
||||
from .oasis_profile_generator import OasisProfileGenerator, OasisAgentProfile
|
||||
from .simulation_manager import SimulationManager, SimulationState, SimulationStatus
|
||||
from .simulation_config_generator import (
|
||||
SimulationConfigGenerator,
|
||||
SimulationConfigGenerator,
|
||||
SimulationParameters,
|
||||
AgentActivityConfig,
|
||||
TimeSimulationConfig,
|
||||
EventConfig,
|
||||
PlatformConfig
|
||||
PlatformConfig,
|
||||
)
|
||||
from .simulation_runner import (
|
||||
SimulationRunner,
|
||||
SimulationRunState,
|
||||
RunnerStatus,
|
||||
AgentAction,
|
||||
RoundSummary
|
||||
)
|
||||
from .zep_graph_memory_updater import (
|
||||
ZepGraphMemoryUpdater,
|
||||
ZepGraphMemoryManager,
|
||||
AgentActivity
|
||||
RoundSummary,
|
||||
)
|
||||
from .simulation_ipc import (
|
||||
SimulationIPCClient,
|
||||
|
|
@ -34,40 +29,36 @@ from .simulation_ipc import (
|
|||
IPCCommand,
|
||||
IPCResponse,
|
||||
CommandType,
|
||||
CommandStatus
|
||||
CommandStatus,
|
||||
)
|
||||
|
||||
__all__ = [
|
||||
'OntologyGenerator',
|
||||
'GraphBuilderService',
|
||||
'TextProcessor',
|
||||
'ZepEntityReader',
|
||||
'EntityNode',
|
||||
'FilteredEntities',
|
||||
'OasisProfileGenerator',
|
||||
'OasisAgentProfile',
|
||||
'SimulationManager',
|
||||
'SimulationState',
|
||||
'SimulationStatus',
|
||||
'SimulationConfigGenerator',
|
||||
'SimulationParameters',
|
||||
'AgentActivityConfig',
|
||||
'TimeSimulationConfig',
|
||||
'EventConfig',
|
||||
'PlatformConfig',
|
||||
'SimulationRunner',
|
||||
'SimulationRunState',
|
||||
'RunnerStatus',
|
||||
'AgentAction',
|
||||
'RoundSummary',
|
||||
'ZepGraphMemoryUpdater',
|
||||
'ZepGraphMemoryManager',
|
||||
'AgentActivity',
|
||||
'SimulationIPCClient',
|
||||
'SimulationIPCServer',
|
||||
'IPCCommand',
|
||||
'IPCResponse',
|
||||
'CommandType',
|
||||
'CommandStatus',
|
||||
"OntologyGenerator",
|
||||
"GraphBuilderService",
|
||||
"TextProcessor",
|
||||
"EntityReader",
|
||||
"EntityNode",
|
||||
"FilteredEntities",
|
||||
"OasisProfileGenerator",
|
||||
"OasisAgentProfile",
|
||||
"SimulationManager",
|
||||
"SimulationState",
|
||||
"SimulationStatus",
|
||||
"SimulationConfigGenerator",
|
||||
"SimulationParameters",
|
||||
"AgentActivityConfig",
|
||||
"TimeSimulationConfig",
|
||||
"EventConfig",
|
||||
"PlatformConfig",
|
||||
"SimulationRunner",
|
||||
"SimulationRunState",
|
||||
"RunnerStatus",
|
||||
"AgentAction",
|
||||
"RoundSummary",
|
||||
"SimulationIPCClient",
|
||||
"SimulationIPCServer",
|
||||
"IPCCommand",
|
||||
"IPCResponse",
|
||||
"CommandType",
|
||||
"CommandStatus",
|
||||
]
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,292 @@
|
|||
"""
|
||||
Entity reader — reads and filters entities from local sqlite graph store.
|
||||
Replaces the former Zep-based entity reader (now uses local SQLite).
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional, Set
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from ..utils.logger import get_logger
|
||||
from .local_graph_store import LocalGraphStore
|
||||
|
||||
logger = get_logger("mirofish.entity_reader")
|
||||
|
||||
|
||||
@dataclass
|
||||
class EntityNode:
|
||||
"""Entity node data structure"""
|
||||
|
||||
uuid: str
|
||||
name: str
|
||||
labels: List[str]
|
||||
summary: str
|
||||
attributes: Dict[str, Any]
|
||||
related_edges: List[Dict[str, Any]] = field(default_factory=list)
|
||||
related_nodes: List[Dict[str, Any]] = field(default_factory=list)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"uuid": self.uuid,
|
||||
"name": self.name,
|
||||
"labels": self.labels,
|
||||
"summary": self.summary,
|
||||
"attributes": self.attributes,
|
||||
"related_edges": self.related_edges,
|
||||
"related_nodes": self.related_nodes,
|
||||
}
|
||||
|
||||
def get_entity_type(self) -> Optional[str]:
|
||||
"""Get entity type (excluding default Entity label)"""
|
||||
for label in self.labels:
|
||||
if label not in ["Entity", "Node"]:
|
||||
return label
|
||||
return None
|
||||
|
||||
|
||||
@dataclass
|
||||
class FilteredEntities:
|
||||
"""Filtered entity set"""
|
||||
|
||||
entities: List[EntityNode]
|
||||
entity_types: Set[str]
|
||||
total_count: int
|
||||
filtered_count: int
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"entities": [e.to_dict() for e in self.entities],
|
||||
"entity_types": list(self.entity_types),
|
||||
"total_count": self.total_count,
|
||||
"filtered_count": self.filtered_count,
|
||||
}
|
||||
|
||||
|
||||
class EntityReader:
|
||||
"""
|
||||
Entity reader and filter service
|
||||
|
||||
Reads nodes from the local graph store, filters by defined entity types.
|
||||
Kept class name for import compatibility.
|
||||
"""
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None):
|
||||
pass
|
||||
|
||||
def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]:
|
||||
"""Get all nodes from graph"""
|
||||
logger.info(f"Reading all nodes from graph {graph_id}...")
|
||||
store = LocalGraphStore(graph_id)
|
||||
nodes = store.get_all_nodes()
|
||||
nodes_data = [n.to_dict() for n in nodes]
|
||||
logger.info(f"Got {len(nodes_data)} nodes")
|
||||
return nodes_data
|
||||
|
||||
def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]:
|
||||
"""Get all edges from graph"""
|
||||
logger.info(f"Reading all edges from graph {graph_id}...")
|
||||
store = LocalGraphStore(graph_id)
|
||||
edges = store.get_all_edges()
|
||||
edges_data = []
|
||||
for edge in edges:
|
||||
edges_data.append(
|
||||
{
|
||||
"uuid": edge.uuid,
|
||||
"name": edge.name,
|
||||
"fact": edge.fact,
|
||||
"source_node_uuid": edge.source_node_uuid,
|
||||
"target_node_uuid": edge.target_node_uuid,
|
||||
"attributes": edge.attributes,
|
||||
}
|
||||
)
|
||||
logger.info(f"Got {len(edges_data)} edges")
|
||||
return edges_data
|
||||
|
||||
def get_node_edges(self, graph_id: str, node_uuid: str) -> List[Dict[str, Any]]:
|
||||
"""Get edges for a specific node"""
|
||||
store = LocalGraphStore(graph_id)
|
||||
edges = store.get_node_edges(node_uuid)
|
||||
edges_data = []
|
||||
for edge in edges:
|
||||
edges_data.append(
|
||||
{
|
||||
"uuid": edge.uuid,
|
||||
"name": edge.name,
|
||||
"fact": edge.fact,
|
||||
"source_node_uuid": edge.source_node_uuid,
|
||||
"target_node_uuid": edge.target_node_uuid,
|
||||
"attributes": edge.attributes,
|
||||
}
|
||||
)
|
||||
return edges_data
|
||||
|
||||
def filter_defined_entities(
|
||||
self,
|
||||
graph_id: str,
|
||||
defined_entity_types: Optional[List[str]] = None,
|
||||
enrich_with_edges: bool = True,
|
||||
) -> FilteredEntities:
|
||||
"""Filter nodes matching defined entity types"""
|
||||
logger.info(f"Filtering entities in graph {graph_id}...")
|
||||
|
||||
all_nodes = self.get_all_nodes(graph_id)
|
||||
total_count = len(all_nodes)
|
||||
|
||||
all_edges = self.get_all_edges(graph_id) if enrich_with_edges else []
|
||||
node_map = {n["uuid"]: n for n in all_nodes}
|
||||
|
||||
filtered_entities = []
|
||||
entity_types_found = set()
|
||||
|
||||
for node in all_nodes:
|
||||
labels = node.get("labels", [])
|
||||
custom_labels = [l for l in labels if l not in ["Entity", "Node"]]
|
||||
|
||||
if not custom_labels:
|
||||
continue
|
||||
|
||||
if defined_entity_types:
|
||||
matching_labels = [
|
||||
l for l in custom_labels if l in defined_entity_types
|
||||
]
|
||||
if not matching_labels:
|
||||
continue
|
||||
entity_type = matching_labels[0]
|
||||
else:
|
||||
entity_type = custom_labels[0]
|
||||
|
||||
entity_types_found.add(entity_type)
|
||||
|
||||
entity = EntityNode(
|
||||
uuid=node["uuid"],
|
||||
name=node["name"],
|
||||
labels=labels,
|
||||
summary=node["summary"],
|
||||
attributes=node["attributes"],
|
||||
)
|
||||
|
||||
if enrich_with_edges:
|
||||
related_edges = []
|
||||
related_node_uuids = set()
|
||||
|
||||
for edge in all_edges:
|
||||
if edge["source_node_uuid"] == node["uuid"]:
|
||||
related_edges.append(
|
||||
{
|
||||
"direction": "outgoing",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"target_node_uuid": edge["target_node_uuid"],
|
||||
}
|
||||
)
|
||||
related_node_uuids.add(edge["target_node_uuid"])
|
||||
elif edge["target_node_uuid"] == node["uuid"]:
|
||||
related_edges.append(
|
||||
{
|
||||
"direction": "incoming",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"source_node_uuid": edge["source_node_uuid"],
|
||||
}
|
||||
)
|
||||
related_node_uuids.add(edge["source_node_uuid"])
|
||||
|
||||
entity.related_edges = related_edges
|
||||
|
||||
related_nodes = []
|
||||
for related_uuid in related_node_uuids:
|
||||
if related_uuid in node_map:
|
||||
rn = node_map[related_uuid]
|
||||
related_nodes.append(
|
||||
{
|
||||
"uuid": rn["uuid"],
|
||||
"name": rn["name"],
|
||||
"labels": rn["labels"],
|
||||
"summary": rn.get("summary", ""),
|
||||
}
|
||||
)
|
||||
entity.related_nodes = related_nodes
|
||||
|
||||
filtered_entities.append(entity)
|
||||
|
||||
logger.info(
|
||||
f"Filter complete: {total_count} total, {len(filtered_entities)} matched, types: {entity_types_found}"
|
||||
)
|
||||
|
||||
return FilteredEntities(
|
||||
entities=filtered_entities,
|
||||
entity_types=entity_types_found,
|
||||
total_count=total_count,
|
||||
filtered_count=len(filtered_entities),
|
||||
)
|
||||
|
||||
def get_entity_with_context(
|
||||
self, graph_id: str, entity_uuid: str
|
||||
) -> Optional[EntityNode]:
|
||||
"""Get single entity with full context (edges + related nodes)"""
|
||||
store = LocalGraphStore(graph_id)
|
||||
node = store.get_node(entity_uuid)
|
||||
if not node:
|
||||
return None
|
||||
|
||||
edges = self.get_node_edges(graph_id, entity_uuid)
|
||||
all_nodes = self.get_all_nodes(graph_id)
|
||||
node_map = {n["uuid"]: n for n in all_nodes}
|
||||
|
||||
related_edges = []
|
||||
related_node_uuids = set()
|
||||
|
||||
for edge in edges:
|
||||
if edge["source_node_uuid"] == entity_uuid:
|
||||
related_edges.append(
|
||||
{
|
||||
"direction": "outgoing",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"target_node_uuid": edge["target_node_uuid"],
|
||||
}
|
||||
)
|
||||
related_node_uuids.add(edge["target_node_uuid"])
|
||||
else:
|
||||
related_edges.append(
|
||||
{
|
||||
"direction": "incoming",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"source_node_uuid": edge["source_node_uuid"],
|
||||
}
|
||||
)
|
||||
related_node_uuids.add(edge["source_node_uuid"])
|
||||
|
||||
related_nodes = []
|
||||
for related_uuid in related_node_uuids:
|
||||
if related_uuid in node_map:
|
||||
rn = node_map[related_uuid]
|
||||
related_nodes.append(
|
||||
{
|
||||
"uuid": rn["uuid"],
|
||||
"name": rn["name"],
|
||||
"labels": rn["labels"],
|
||||
"summary": rn.get("summary", ""),
|
||||
}
|
||||
)
|
||||
|
||||
return EntityNode(
|
||||
uuid=node.uuid,
|
||||
name=node.name,
|
||||
labels=node.labels,
|
||||
summary=node.summary,
|
||||
attributes=node.attributes,
|
||||
related_edges=related_edges,
|
||||
related_nodes=related_nodes,
|
||||
)
|
||||
|
||||
def get_entities_by_type(
|
||||
self, graph_id: str, entity_type: str, enrich_with_edges: bool = True
|
||||
) -> List[EntityNode]:
|
||||
"""Get all entities of a specific type"""
|
||||
result = self.filter_defined_entities(
|
||||
graph_id=graph_id,
|
||||
defined_entity_types=[entity_type],
|
||||
enrich_with_edges=enrich_with_edges,
|
||||
)
|
||||
return result.entities
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,146 @@
|
|||
"""
|
||||
Local graph extractor — LLM-based entity/relation extraction from text chunks.
|
||||
Replaces Zep Cloud server-side NLP extraction pipeline (now local LLM-based).
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import Dict, Any, List, Optional
|
||||
|
||||
from ..utils.llm_client import LLMClient
|
||||
from ..utils.logger import get_logger
|
||||
from ..utils.embeddings import embed
|
||||
from .local_graph_store import LocalGraphStore
|
||||
|
||||
logger = get_logger("mirofish.extractor")
|
||||
|
||||
EXTRACT_SYSTEM_PROMPT = """You are a knowledge-graph extraction engine. Given a text chunk and an ontology (entity types + relation types), extract entities and relations as valid JSON.
|
||||
|
||||
Output ONLY valid JSON in this exact format:
|
||||
{
|
||||
"entities": [
|
||||
{"name": "EntityName", "type": "EntityType", "summary": "One-sentence description", "attributes": {}}
|
||||
],
|
||||
"edges": [
|
||||
{"name": "relation_type", "fact": "Natural language fact statement", "source": "SourceEntityName", "target": "TargetEntityName", "attributes": {}}
|
||||
]
|
||||
}
|
||||
|
||||
Rules:
|
||||
1. Only use entity types and relation types defined in the ontology
|
||||
2. Entity names should be proper nouns or specific identifiers from the text
|
||||
3. Each edge fact must be a complete sentence describing the relationship
|
||||
4. If no entities/edges are found, return {"entities": [], "edges": []}
|
||||
5. Do not include any text outside the JSON object"""
|
||||
|
||||
|
||||
class LocalGraphExtractor:
|
||||
"""Extracts entities and relations from text using LLM, stores in LocalGraphStore."""
|
||||
|
||||
def __init__(self, graph_id: str, ontology: Dict[str, Any]):
|
||||
self.store = LocalGraphStore(graph_id)
|
||||
self.ontology = ontology
|
||||
self.llm = LLMClient()
|
||||
self._entity_index: Dict[str, str] = {} # name -> uuid
|
||||
|
||||
def extract_and_store(self, chunks: List[str], progress_callback=None) -> int:
|
||||
"""Extract entities/edges from text chunks, store in graph. Returns total extracted count."""
|
||||
entity_types = [e["name"] for e in self.ontology.get("entity_types", [])]
|
||||
edge_types = [e["name"] for e in self.ontology.get("edge_types", [])]
|
||||
ontology_summary = json.dumps(
|
||||
{"entity_types": entity_types, "edge_types": edge_types}, ensure_ascii=False
|
||||
)
|
||||
|
||||
total = 0
|
||||
for i, chunk in enumerate(chunks):
|
||||
if progress_callback:
|
||||
progress_callback(i + 1, len(chunks))
|
||||
|
||||
result = self._extract_from_chunk(chunk, ontology_summary)
|
||||
if not result:
|
||||
continue
|
||||
|
||||
for entity in result.get("entities", []):
|
||||
self._add_or_merge_entity(entity)
|
||||
|
||||
for edge in result.get("edges", []):
|
||||
self._add_edge(edge)
|
||||
|
||||
total += len(result.get("entities", [])) + len(result.get("edges", []))
|
||||
|
||||
return total
|
||||
|
||||
def _extract_from_chunk(
|
||||
self, chunk: str, ontology_summary: str
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
user_msg = f"Ontology types: {ontology_summary}\n\nText chunk:\n{chunk}"
|
||||
try:
|
||||
messages = [
|
||||
{"role": "system", "content": EXTRACT_SYSTEM_PROMPT},
|
||||
{"role": "user", "content": user_msg},
|
||||
]
|
||||
return self.llm.chat_json(messages, temperature=0.2, max_tokens=8192)
|
||||
except Exception as e:
|
||||
logger.warning(f"Extraction failed for chunk: {str(e)[:100]}")
|
||||
return None
|
||||
|
||||
def _add_or_merge_entity(self, entity: Dict[str, Any]):
|
||||
name = entity.get("name", "").strip()
|
||||
etype = entity.get("type", "Entity")
|
||||
if not name:
|
||||
return
|
||||
|
||||
existing = self.store.find_node_by_name(name, labels=[etype])
|
||||
if existing:
|
||||
return
|
||||
|
||||
labels = ["Entity", etype] if etype != "Entity" else ["Entity"]
|
||||
summary = entity.get("summary", "")
|
||||
attributes = entity.get("attributes", {})
|
||||
|
||||
emb = embed(f"{name} {summary}") if summary else embed(name)
|
||||
node_uuid = self.store.add_node(
|
||||
name=name,
|
||||
labels=labels,
|
||||
summary=summary,
|
||||
attributes=attributes,
|
||||
embedding=emb,
|
||||
)
|
||||
self._entity_index[f"{name}:{etype}"] = node_uuid
|
||||
|
||||
def _add_edge(self, edge: Dict[str, Any]):
|
||||
name = edge.get("name", "").strip()
|
||||
source_name = edge.get("source", "").strip()
|
||||
target_name = edge.get("target", "").strip()
|
||||
fact = edge.get("fact", "").strip()
|
||||
|
||||
if not name or not source_name or not target_name or not fact:
|
||||
return
|
||||
|
||||
source_uuid = self._find_entity_uuid(source_name)
|
||||
target_uuid = self._find_entity_uuid(target_name)
|
||||
|
||||
if not source_uuid or not target_uuid:
|
||||
return
|
||||
|
||||
emb = embed(fact)
|
||||
self.store.add_edge(
|
||||
name=name,
|
||||
fact=fact,
|
||||
source_node_uuid=source_uuid,
|
||||
target_node_uuid=target_uuid,
|
||||
fact_type=name,
|
||||
attributes=edge.get("attributes", {}),
|
||||
embedding=emb,
|
||||
)
|
||||
|
||||
def _find_entity_uuid(self, name: str) -> Optional[str]:
|
||||
for key, uuid in self._entity_index.items():
|
||||
if key.startswith(f"{name}:"):
|
||||
return uuid
|
||||
node = self.store.find_node_by_name(name)
|
||||
if node:
|
||||
self._entity_index[
|
||||
f"{node.name}:{node.labels[-1] if len(node.labels) > 1 else 'Entity'}"
|
||||
] = node.uuid
|
||||
return node.uuid
|
||||
return None
|
||||
|
|
@ -0,0 +1,426 @@
|
|||
"""
|
||||
Local graph store — sqlite-backed knowledge graph (replaces Zep Cloud).
|
||||
Stores nodes, edges, episodes, and embeddings per graph.
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import sqlite3
|
||||
import uuid
|
||||
from typing import Dict, Any, List, Optional
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from ..utils.logger import get_logger
|
||||
|
||||
logger = get_logger("mirofish.graph_store")
|
||||
|
||||
GRAPH_DIR = os.path.join(os.path.dirname(__file__), "../../uploads/graphs")
|
||||
|
||||
|
||||
def _ensure_graph_dir(graph_id: str) -> str:
|
||||
d = os.path.join(GRAPH_DIR, graph_id)
|
||||
os.makedirs(d, exist_ok=True)
|
||||
return d
|
||||
|
||||
|
||||
def _db_path(graph_id: str) -> str:
|
||||
return os.path.join(_ensure_graph_dir(graph_id), "graph.db")
|
||||
|
||||
|
||||
def _connect(graph_id: str) -> sqlite3.Connection:
|
||||
conn = sqlite3.connect(_db_path(graph_id))
|
||||
conn.row_factory = sqlite3.Row
|
||||
conn.execute("PRAGMA journal_mode=WAL")
|
||||
return conn
|
||||
|
||||
|
||||
SCHEMA = """
|
||||
CREATE TABLE IF NOT EXISTS graph_meta (
|
||||
graph_id TEXT PRIMARY KEY,
|
||||
name TEXT,
|
||||
description TEXT,
|
||||
ontology TEXT,
|
||||
created_at TEXT
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS nodes (
|
||||
uuid TEXT PRIMARY KEY,
|
||||
graph_id TEXT,
|
||||
name TEXT,
|
||||
labels TEXT,
|
||||
summary TEXT,
|
||||
attributes TEXT,
|
||||
embedding TEXT,
|
||||
created_at TEXT
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS edges (
|
||||
uuid TEXT PRIMARY KEY,
|
||||
graph_id TEXT,
|
||||
name TEXT,
|
||||
fact TEXT,
|
||||
fact_type TEXT,
|
||||
source_node_uuid TEXT,
|
||||
target_node_uuid TEXT,
|
||||
attributes TEXT,
|
||||
embedding TEXT,
|
||||
created_at TEXT,
|
||||
valid_at TEXT,
|
||||
invalid_at TEXT,
|
||||
expired_at TEXT,
|
||||
episodes TEXT
|
||||
);
|
||||
CREATE TABLE IF NOT EXISTS episodes (
|
||||
uuid TEXT PRIMARY KEY,
|
||||
graph_id TEXT,
|
||||
data TEXT,
|
||||
type TEXT,
|
||||
processed INTEGER DEFAULT 1,
|
||||
created_at TEXT
|
||||
);
|
||||
CREATE INDEX IF NOT EXISTS idx_nodes_graph ON nodes(graph_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_edges_graph ON edges(graph_id);
|
||||
CREATE INDEX IF NOT EXISTS idx_edges_source ON edges(source_node_uuid);
|
||||
CREATE INDEX IF NOT EXISTS idx_edges_target ON edges(target_node_uuid);
|
||||
"""
|
||||
|
||||
|
||||
@dataclass
|
||||
class LocalNode:
|
||||
uuid: str
|
||||
name: str
|
||||
labels: List[str]
|
||||
summary: str
|
||||
attributes: Dict[str, Any]
|
||||
created_at: Optional[str] = None
|
||||
embedding: Optional[List[float]] = None
|
||||
|
||||
@property
|
||||
def uuid_(self) -> str:
|
||||
return self.uuid
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"uuid": self.uuid,
|
||||
"name": self.name,
|
||||
"labels": self.labels,
|
||||
"summary": self.summary,
|
||||
"attributes": self.attributes,
|
||||
"created_at": self.created_at,
|
||||
}
|
||||
|
||||
|
||||
@dataclass
|
||||
class LocalEdge:
|
||||
uuid: str
|
||||
name: str
|
||||
fact: str
|
||||
fact_type: str
|
||||
source_node_uuid: str
|
||||
target_node_uuid: str
|
||||
attributes: Dict[str, Any]
|
||||
created_at: Optional[str] = None
|
||||
valid_at: Optional[str] = None
|
||||
invalid_at: Optional[str] = None
|
||||
expired_at: Optional[str] = None
|
||||
episodes: Optional[List[str]] = None
|
||||
embedding: Optional[List[float]] = None
|
||||
|
||||
@property
|
||||
def uuid_(self) -> str:
|
||||
return self.uuid
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"uuid": self.uuid,
|
||||
"name": self.name,
|
||||
"fact": self.fact,
|
||||
"fact_type": self.fact_type,
|
||||
"source_node_uuid": self.source_node_uuid,
|
||||
"target_node_uuid": self.target_node_uuid,
|
||||
"attributes": self.attributes,
|
||||
"created_at": self.created_at,
|
||||
"valid_at": self.valid_at,
|
||||
"invalid_at": self.invalid_at,
|
||||
"expired_at": self.expired_at,
|
||||
"episodes": self.episodes or [],
|
||||
}
|
||||
|
||||
|
||||
class LocalGraphStore:
|
||||
"""Sqlite-backed graph store. One DB file per graph."""
|
||||
|
||||
def __init__(self, graph_id: str):
|
||||
self.graph_id = graph_id
|
||||
self._init_schema()
|
||||
|
||||
def _init_schema(self):
|
||||
with _connect(self.graph_id) as conn:
|
||||
conn.executescript(SCHEMA)
|
||||
conn.commit()
|
||||
|
||||
def set_meta(
|
||||
self, name: str, description: str, ontology: Dict[str, Any], created_at: str
|
||||
):
|
||||
with _connect(self.graph_id) as conn:
|
||||
conn.execute(
|
||||
"INSERT OR REPLACE INTO graph_meta (graph_id, name, description, ontology, created_at) VALUES (?, ?, ?, ?, ?)",
|
||||
(
|
||||
self.graph_id,
|
||||
name,
|
||||
description,
|
||||
json.dumps(ontology, ensure_ascii=False),
|
||||
created_at,
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
|
||||
def get_ontology(self) -> Dict[str, Any]:
|
||||
with _connect(self.graph_id) as conn:
|
||||
row = conn.execute(
|
||||
"SELECT ontology FROM graph_meta WHERE graph_id=?", (self.graph_id,)
|
||||
).fetchone()
|
||||
if row:
|
||||
return json.loads(row["ontology"])
|
||||
return {}
|
||||
|
||||
def add_episode(self, data: str, episode_type: str = "text") -> str:
|
||||
ep_uuid = str(uuid.uuid4())
|
||||
from datetime import datetime
|
||||
|
||||
with _connect(self.graph_id) as conn:
|
||||
conn.execute(
|
||||
"INSERT INTO episodes (uuid, graph_id, data, type, processed, created_at) VALUES (?, ?, ?, ?, 1, ?)",
|
||||
(
|
||||
ep_uuid,
|
||||
self.graph_id,
|
||||
data,
|
||||
episode_type,
|
||||
datetime.now().isoformat(),
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
return ep_uuid
|
||||
|
||||
def add_node(
|
||||
self,
|
||||
name: str,
|
||||
labels: List[str],
|
||||
summary: str = "",
|
||||
attributes: Optional[Dict[str, Any]] = None,
|
||||
embedding: Optional[List[float]] = None,
|
||||
) -> str:
|
||||
node_uuid = str(uuid.uuid4())
|
||||
from datetime import datetime
|
||||
|
||||
with _connect(self.graph_id) as conn:
|
||||
conn.execute(
|
||||
"INSERT OR REPLACE INTO nodes (uuid, graph_id, name, labels, summary, attributes, embedding, created_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?)",
|
||||
(
|
||||
node_uuid,
|
||||
self.graph_id,
|
||||
name,
|
||||
json.dumps(labels, ensure_ascii=False),
|
||||
summary,
|
||||
json.dumps(attributes or {}, ensure_ascii=False),
|
||||
json.dumps(embedding) if embedding else None,
|
||||
datetime.now().isoformat(),
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
return node_uuid
|
||||
|
||||
def add_edge(
|
||||
self,
|
||||
name: str,
|
||||
fact: str,
|
||||
source_node_uuid: str,
|
||||
target_node_uuid: str,
|
||||
fact_type: str = "",
|
||||
attributes: Optional[Dict[str, Any]] = None,
|
||||
embedding: Optional[List[float]] = None,
|
||||
valid_at: Optional[str] = None,
|
||||
invalid_at: Optional[str] = None,
|
||||
expired_at: Optional[str] = None,
|
||||
) -> str:
|
||||
edge_uuid = str(uuid.uuid4())
|
||||
from datetime import datetime
|
||||
|
||||
with _connect(self.graph_id) as conn:
|
||||
conn.execute(
|
||||
"""INSERT OR REPLACE INTO edges
|
||||
(uuid, graph_id, name, fact, fact_type, source_node_uuid, target_node_uuid,
|
||||
attributes, embedding, created_at, valid_at, invalid_at, expired_at, episodes)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
|
||||
(
|
||||
edge_uuid,
|
||||
self.graph_id,
|
||||
name,
|
||||
fact,
|
||||
fact_type or name,
|
||||
source_node_uuid,
|
||||
target_node_uuid,
|
||||
json.dumps(attributes or {}, ensure_ascii=False),
|
||||
json.dumps(embedding) if embedding else None,
|
||||
datetime.now().isoformat(),
|
||||
valid_at,
|
||||
invalid_at,
|
||||
expired_at,
|
||||
"[]",
|
||||
),
|
||||
)
|
||||
conn.commit()
|
||||
return edge_uuid
|
||||
|
||||
def find_node_by_name(
|
||||
self, name: str, labels: Optional[List[str]] = None
|
||||
) -> Optional[LocalNode]:
|
||||
with _connect(self.graph_id) as conn:
|
||||
if labels:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM nodes WHERE graph_id=? AND name=?",
|
||||
(self.graph_id, name),
|
||||
).fetchall()
|
||||
for row in rows:
|
||||
node_labels = json.loads(row["labels"])
|
||||
if any(l in node_labels for l in labels):
|
||||
return self._row_to_node(row)
|
||||
if rows:
|
||||
return self._row_to_node(rows[0])
|
||||
else:
|
||||
row = conn.execute(
|
||||
"SELECT * FROM nodes WHERE graph_id=? AND name=? LIMIT 1",
|
||||
(self.graph_id, name),
|
||||
).fetchone()
|
||||
if row:
|
||||
return self._row_to_node(row)
|
||||
return None
|
||||
|
||||
def get_node(self, node_uuid: str) -> Optional[LocalNode]:
|
||||
with _connect(self.graph_id) as conn:
|
||||
row = conn.execute(
|
||||
"SELECT * FROM nodes WHERE uuid=?", (node_uuid,)
|
||||
).fetchone()
|
||||
if row:
|
||||
return self._row_to_node(row)
|
||||
return None
|
||||
|
||||
def get_all_nodes(self) -> List[LocalNode]:
|
||||
with _connect(self.graph_id) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM nodes WHERE graph_id=?", (self.graph_id,)
|
||||
).fetchall()
|
||||
return [self._row_to_node(r) for r in rows]
|
||||
|
||||
def get_all_edges(self) -> List[LocalEdge]:
|
||||
with _connect(self.graph_id) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM edges WHERE graph_id=?", (self.graph_id,)
|
||||
).fetchall()
|
||||
return [self._row_to_edge(r) for r in rows]
|
||||
|
||||
def get_node_edges(self, node_uuid: str) -> List[LocalEdge]:
|
||||
with _connect(self.graph_id) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM edges WHERE source_node_uuid=? OR target_node_uuid=?",
|
||||
(node_uuid, node_uuid),
|
||||
).fetchall()
|
||||
return [self._row_to_edge(r) for r in rows]
|
||||
|
||||
def search_nodes(self, query: str, limit: int = 20) -> List[LocalNode]:
|
||||
# ponytail: LIKE keyword search; O(n) scan per query, fine for graphs < 10k nodes;
|
||||
# upgrade to embedding cosine sim via embed() if recall matters
|
||||
pattern = f"%{query}%"
|
||||
with _connect(self.graph_id) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM nodes WHERE graph_id=? AND (name LIKE ? OR summary LIKE ?) LIMIT ?",
|
||||
(self.graph_id, pattern, pattern, limit),
|
||||
).fetchall()
|
||||
return [self._row_to_node(r) for r in rows]
|
||||
|
||||
def search_edges(self, query: str, limit: int = 20) -> List[LocalEdge]:
|
||||
pattern = f"%{query}%"
|
||||
with _connect(self.graph_id) as conn:
|
||||
rows = conn.execute(
|
||||
"SELECT * FROM edges WHERE graph_id=? AND (fact LIKE ? OR name LIKE ?) LIMIT ?",
|
||||
(self.graph_id, pattern, pattern, limit),
|
||||
).fetchall()
|
||||
return [self._row_to_edge(r) for r in rows]
|
||||
|
||||
def get_statistics(self) -> Dict[str, Any]:
|
||||
with _connect(self.graph_id) as conn:
|
||||
node_count = conn.execute(
|
||||
"SELECT COUNT(*) FROM nodes WHERE graph_id=?", (self.graph_id,)
|
||||
).fetchone()[0]
|
||||
edge_count = conn.execute(
|
||||
"SELECT COUNT(*) FROM edges WHERE graph_id=?", (self.graph_id,)
|
||||
).fetchone()[0]
|
||||
entity_types = set()
|
||||
for row in conn.execute(
|
||||
"SELECT labels FROM nodes WHERE graph_id=?", (self.graph_id,)
|
||||
).fetchall():
|
||||
labels = json.loads(row["labels"])
|
||||
for l in labels:
|
||||
if l not in ("Entity", "Node"):
|
||||
entity_types.add(l)
|
||||
return {
|
||||
"node_count": node_count,
|
||||
"edge_count": edge_count,
|
||||
"entity_types": list(entity_types),
|
||||
}
|
||||
|
||||
def delete(self):
|
||||
import shutil
|
||||
|
||||
d = os.path.join(GRAPH_DIR, self.graph_id)
|
||||
if os.path.exists(d):
|
||||
shutil.rmtree(d)
|
||||
|
||||
def _row_to_node(self, row: sqlite3.Row) -> LocalNode:
|
||||
embedding = None
|
||||
if row["embedding"]:
|
||||
try:
|
||||
embedding = json.loads(row["embedding"])
|
||||
except Exception:
|
||||
pass
|
||||
return LocalNode(
|
||||
uuid=row["uuid"],
|
||||
name=row["name"],
|
||||
labels=json.loads(row["labels"]) if row["labels"] else [],
|
||||
summary=row["summary"] or "",
|
||||
attributes=json.loads(row["attributes"]) if row["attributes"] else {},
|
||||
created_at=row["created_at"],
|
||||
embedding=embedding,
|
||||
)
|
||||
|
||||
def _row_to_edge(self, row: sqlite3.Row) -> LocalEdge:
|
||||
embedding = None
|
||||
if row["embedding"]:
|
||||
try:
|
||||
embedding = json.loads(row["embedding"])
|
||||
except Exception:
|
||||
pass
|
||||
episodes = []
|
||||
if row["episodes"]:
|
||||
try:
|
||||
episodes = json.loads(row["episodes"])
|
||||
except Exception:
|
||||
pass
|
||||
return LocalEdge(
|
||||
uuid=row["uuid"],
|
||||
name=row["name"] or "",
|
||||
fact=row["fact"] or "",
|
||||
fact_type=row["fact_type"] or "",
|
||||
source_node_uuid=row["source_node_uuid"],
|
||||
target_node_uuid=row["target_node_uuid"],
|
||||
attributes=json.loads(row["attributes"]) if row["attributes"] else {},
|
||||
created_at=row["created_at"],
|
||||
valid_at=row["valid_at"],
|
||||
invalid_at=row["invalid_at"],
|
||||
expired_at=row["expired_at"],
|
||||
episodes=episodes,
|
||||
embedding=embedding,
|
||||
)
|
||||
|
||||
|
||||
def delete_graph(graph_id: str):
|
||||
store = LocalGraphStore(graph_id)
|
||||
store.delete()
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -1,11 +1,11 @@
|
|||
"""
|
||||
模拟IPC通信模块
|
||||
用于Flask后端和模拟脚本之间的进程间通信
|
||||
Simulation IPC communication module
|
||||
Used for inter-process communication between the Flask backend and the simulation script
|
||||
|
||||
通过文件系统实现简单的命令/响应模式：
|
||||
1. Flask写入命令到 commands/ 目录
|
||||
2. 模拟脚本轮询命令目录，执行命令并写入响应到 responses/ 目录
|
||||
3. Flask轮询响应目录获取结果
|
||||
Implements a simple command/response pattern via the file system:
|
||||
1. Flask writes commands to the commands/ directory
|
||||
2. The simulation script polls the command directory, executes commands, and writes responses to the responses/ directory
|
||||
3. Flask polls the response directory to get results
|
||||
"""
|
||||
|
||||
import os
|
||||
|
|
@ -18,19 +18,22 @@ from datetime import datetime
|
|||
from enum import Enum
|
||||
|
||||
from ..utils.logger import get_logger
|
||||
from ..utils.locale import t
|
||||
|
||||
logger = get_logger('mirofish.simulation_ipc')
|
||||
logger = get_logger("mirofish.simulation_ipc")
|
||||
|
||||
|
||||
class CommandType(str, Enum):
|
||||
"""命令类型"""
|
||||
INTERVIEW = "interview" # 单个Agent采访
|
||||
BATCH_INTERVIEW = "batch_interview" # 批量采访
|
||||
CLOSE_ENV = "close_env" # 关闭环境
|
||||
"""Command type"""
|
||||
|
||||
INTERVIEW = "interview" # Single Agent interview
|
||||
BATCH_INTERVIEW = "batch_interview" # Batch interview
|
||||
CLOSE_ENV = "close_env" # Close environment
|
||||
|
||||
|
||||
class CommandStatus(str, Enum):
|
||||
"""命令状态"""
|
||||
"""Command status"""
|
||||
|
||||
PENDING = "pending"
|
||||
PROCESSING = "processing"
|
||||
COMPLETED = "completed"
|
||||
|
|
@ -39,246 +42,237 @@ class CommandStatus(str, Enum):
|
|||
|
||||
@dataclass
|
||||
class IPCCommand:
|
||||
"""IPC命令"""
|
||||
"""IPC command"""
|
||||
|
||||
command_id: str
|
||||
command_type: CommandType
|
||||
args: Dict[str, Any]
|
||||
timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
|
||||
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"command_id": self.command_id,
|
||||
"command_type": self.command_type.value,
|
||||
"args": self.args,
|
||||
"timestamp": self.timestamp
|
||||
"timestamp": self.timestamp,
|
||||
}
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> 'IPCCommand':
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "IPCCommand":
|
||||
return cls(
|
||||
command_id=data["command_id"],
|
||||
command_type=CommandType(data["command_type"]),
|
||||
args=data.get("args", {}),
|
||||
timestamp=data.get("timestamp", datetime.now().isoformat())
|
||||
timestamp=data.get("timestamp", datetime.now().isoformat()),
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class IPCResponse:
|
||||
"""IPC响应"""
|
||||
"""IPC response"""
|
||||
|
||||
command_id: str
|
||||
status: CommandStatus
|
||||
result: Optional[Dict[str, Any]] = None
|
||||
error: Optional[str] = None
|
||||
timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
|
||||
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"command_id": self.command_id,
|
||||
"status": self.status.value,
|
||||
"result": self.result,
|
||||
"error": self.error,
|
||||
"timestamp": self.timestamp
|
||||
"timestamp": self.timestamp,
|
||||
}
|
||||
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: Dict[str, Any]) -> 'IPCResponse':
|
||||
def from_dict(cls, data: Dict[str, Any]) -> "IPCResponse":
|
||||
return cls(
|
||||
command_id=data["command_id"],
|
||||
status=CommandStatus(data["status"]),
|
||||
result=data.get("result"),
|
||||
error=data.get("error"),
|
||||
timestamp=data.get("timestamp", datetime.now().isoformat())
|
||||
timestamp=data.get("timestamp", datetime.now().isoformat()),
|
||||
)
|
||||
|
||||
|
||||
class SimulationIPCClient:
|
||||
"""
|
||||
模拟IPC客户端（Flask端使用）
|
||||
|
||||
用于向模拟进程发送命令并等待响应
|
||||
Simulation IPC client (used on the Flask side)
|
||||
|
||||
Used to send commands to the simulation process and wait for responses
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, simulation_dir: str):
|
||||
"""
|
||||
初始化IPC客户端
|
||||
|
||||
Initialize the IPC client
|
||||
|
||||
Args:
|
||||
simulation_dir: 模拟数据目录
|
||||
simulation_dir: Simulation data directory
|
||||
"""
|
||||
self.simulation_dir = simulation_dir
|
||||
self.commands_dir = os.path.join(simulation_dir, "ipc_commands")
|
||||
self.responses_dir = os.path.join(simulation_dir, "ipc_responses")
|
||||
|
||||
# 确保目录存在
|
||||
|
||||
# Ensure directories exist
|
||||
os.makedirs(self.commands_dir, exist_ok=True)
|
||||
os.makedirs(self.responses_dir, exist_ok=True)
|
||||
|
||||
|
||||
def send_command(
|
||||
self,
|
||||
command_type: CommandType,
|
||||
args: Dict[str, Any],
|
||||
timeout: float = 60.0,
|
||||
poll_interval: float = 0.5
|
||||
poll_interval: float = 0.5,
|
||||
) -> IPCResponse:
|
||||
"""
|
||||
发送命令并等待响应
|
||||
|
||||
Send a command and wait for a response
|
||||
|
||||
Args:
|
||||
command_type: 命令类型
|
||||
args: 命令参数
|
||||
timeout: 超时时间（秒）
|
||||
poll_interval: 轮询间隔（秒）
|
||||
|
||||
command_type: Command type
|
||||
args: Command arguments
|
||||
timeout: Timeout duration (seconds)
|
||||
poll_interval: Polling interval (seconds)
|
||||
|
||||
Returns:
|
||||
IPCResponse
|
||||
|
||||
|
||||
Raises:
|
||||
TimeoutError: 等待响应超时
|
||||
TimeoutError: Timed out waiting for a response
|
||||
"""
|
||||
command_id = str(uuid.uuid4())
|
||||
command = IPCCommand(
|
||||
command_id=command_id,
|
||||
command_type=command_type,
|
||||
args=args
|
||||
command_id=command_id, command_type=command_type, args=args
|
||||
)
|
||||
|
||||
# 写入命令文件
|
||||
|
||||
# Write command file
|
||||
command_file = os.path.join(self.commands_dir, f"{command_id}.json")
|
||||
with open(command_file, 'w', encoding='utf-8') as f:
|
||||
with open(command_file, "w", encoding="utf-8") as f:
|
||||
json.dump(command.to_dict(), f, ensure_ascii=False, indent=2)
|
||||
|
||||
logger.info(f"发送IPC命令: {command_type.value}, command_id={command_id}")
|
||||
|
||||
# 等待响应
|
||||
|
||||
logger.info(
|
||||
f"Sending IPC command: {command_type.value}, command_id={command_id}"
|
||||
)
|
||||
|
||||
# Wait for response
|
||||
response_file = os.path.join(self.responses_dir, f"{command_id}.json")
|
||||
start_time = time.time()
|
||||
|
||||
|
||||
while time.time() - start_time < timeout:
|
||||
if os.path.exists(response_file):
|
||||
try:
|
||||
with open(response_file, 'r', encoding='utf-8') as f:
|
||||
with open(response_file, "r", encoding="utf-8") as f:
|
||||
response_data = json.load(f)
|
||||
response = IPCResponse.from_dict(response_data)
|
||||
|
||||
# 清理命令和响应文件
|
||||
|
||||
# Clean up command and response files
|
||||
try:
|
||||
os.remove(command_file)
|
||||
os.remove(response_file)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
logger.info(f"收到IPC响应: command_id={command_id}, status={response.status.value}")
|
||||
|
||||
logger.info(
|
||||
f"Received IPC response: command_id={command_id}, status={response.status.value}"
|
||||
)
|
||||
return response
|
||||
except (json.JSONDecodeError, KeyError) as e:
|
||||
logger.warning(f"解析响应失败: {e}")
|
||||
|
||||
logger.warning(f"Failed to parse response: {e}")
|
||||
|
||||
time.sleep(poll_interval)
|
||||
|
||||
# 超时
|
||||
logger.error(f"等待IPC响应超时: command_id={command_id}")
|
||||
|
||||
# 清理命令文件
|
||||
|
||||
# Timeout
|
||||
logger.error(f"Timed out waiting for IPC response: command_id={command_id}")
|
||||
|
||||
# Clean up command file
|
||||
try:
|
||||
os.remove(command_file)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
raise TimeoutError(f"等待命令响应超时 ({timeout}秒)")
|
||||
|
||||
|
||||
raise TimeoutError(t("api.ipcTimeout", timeout=timeout))
|
||||
|
||||
def send_interview(
|
||||
self,
|
||||
agent_id: int,
|
||||
prompt: str,
|
||||
platform: str = None,
|
||||
timeout: float = 60.0
|
||||
self, agent_id: int, prompt: str, platform: str = None, timeout: float = 60.0
|
||||
) -> IPCResponse:
|
||||
"""
|
||||
发送单个Agent采访命令
|
||||
|
||||
Send a single-Agent interview command
|
||||
|
||||
Args:
|
||||
agent_id: Agent ID
|
||||
prompt: 采访问题
|
||||
platform: 指定平台（可选）
|
||||
- "twitter": 只采访Twitter平台
|
||||
- "reddit": 只采访Reddit平台
|
||||
- None: 双平台模拟时同时采访两个平台，单平台模拟时采访该平台
|
||||
timeout: 超时时间
|
||||
|
||||
prompt: Interview question
|
||||
platform: Specified platform (optional)
|
||||
- "twitter": only interview the Twitter platform
|
||||
- "reddit": only interview the Reddit platform
|
||||
- None: in dual-platform simulation, interview both platforms simultaneously; in single-platform simulation, interview that platform
|
||||
timeout: Timeout duration
|
||||
|
||||
Returns:
|
||||
IPCResponse，result字段包含采访结果
|
||||
IPCResponse, the result field contains the interview result
|
||||
"""
|
||||
args = {
|
||||
"agent_id": agent_id,
|
||||
"prompt": prompt
|
||||
}
|
||||
args = {"agent_id": agent_id, "prompt": prompt}
|
||||
if platform:
|
||||
args["platform"] = platform
|
||||
|
||||
|
||||
return self.send_command(
|
||||
command_type=CommandType.INTERVIEW,
|
||||
args=args,
|
||||
timeout=timeout
|
||||
command_type=CommandType.INTERVIEW, args=args, timeout=timeout
|
||||
)
|
||||
|
||||
|
||||
def send_batch_interview(
|
||||
self,
|
||||
interviews: List[Dict[str, Any]],
|
||||
platform: str = None,
|
||||
timeout: float = 120.0
|
||||
timeout: float = 120.0,
|
||||
) -> IPCResponse:
|
||||
"""
|
||||
发送批量采访命令
|
||||
|
||||
Send a batch interview command
|
||||
|
||||
Args:
|
||||
interviews: 采访列表，每个元素包含 {"agent_id": int, "prompt": str, "platform": str(可选)}
|
||||
platform: 默认平台（可选，会被每个采访项的platform覆盖）
|
||||
- "twitter": 默认只采访Twitter平台
|
||||
- "reddit": 默认只采访Reddit平台
|
||||
- None: 双平台模拟时每个Agent同时采访两个平台
|
||||
timeout: 超时时间
|
||||
|
||||
interviews: Interview list, each element contains {"agent_id": int, "prompt": str, "platform": str (optional)}
|
||||
platform: Default platform (optional, overridden by each interview item's platform)
|
||||
- "twitter": default to only interview the Twitter platform
|
||||
- "reddit": default to only interview the Reddit platform
|
||||
- None: in dual-platform simulation, each Agent is interviewed on both platforms simultaneously
|
||||
timeout: Timeout duration
|
||||
|
||||
Returns:
|
||||
IPCResponse，result字段包含所有采访结果
|
||||
IPCResponse, the result field contains all interview results
|
||||
"""
|
||||
args = {"interviews": interviews}
|
||||
if platform:
|
||||
args["platform"] = platform
|
||||
|
||||
|
||||
return self.send_command(
|
||||
command_type=CommandType.BATCH_INTERVIEW,
|
||||
args=args,
|
||||
timeout=timeout
|
||||
command_type=CommandType.BATCH_INTERVIEW, args=args, timeout=timeout
|
||||
)
|
||||
|
||||
|
||||
def send_close_env(self, timeout: float = 30.0) -> IPCResponse:
|
||||
"""
|
||||
发送关闭环境命令
|
||||
|
||||
Send a close-environment command
|
||||
|
||||
Args:
|
||||
timeout: 超时时间
|
||||
|
||||
timeout: Timeout duration
|
||||
|
||||
Returns:
|
||||
IPCResponse
|
||||
"""
|
||||
return self.send_command(
|
||||
command_type=CommandType.CLOSE_ENV,
|
||||
args={},
|
||||
timeout=timeout
|
||||
command_type=CommandType.CLOSE_ENV, args={}, timeout=timeout
|
||||
)
|
||||
|
||||
|
||||
def check_env_alive(self) -> bool:
|
||||
"""
|
||||
检查模拟环境是否存活
|
||||
|
||||
通过检查 env_status.json 文件来判断
|
||||
Check whether the simulation environment is alive
|
||||
|
||||
Determined by checking the env_status.json file
|
||||
"""
|
||||
status_file = os.path.join(self.simulation_dir, "env_status.json")
|
||||
if not os.path.exists(status_file):
|
||||
return False
|
||||
|
||||
|
||||
try:
|
||||
with open(status_file, 'r', encoding='utf-8') as f:
|
||||
with open(status_file, "r", encoding="utf-8") as f:
|
||||
status = json.load(f)
|
||||
return status.get("status") == "alive"
|
||||
except (json.JSONDecodeError, OSError):
|
||||
|
|
@ -287,108 +281,108 @@ class SimulationIPCClient:
|
|||
|
||||
class SimulationIPCServer:
|
||||
"""
|
||||
模拟IPC服务器（模拟脚本端使用）
|
||||
|
||||
轮询命令目录，执行命令并返回响应
|
||||
Simulation IPC server (used on the simulation script side)
|
||||
|
||||
Polls the command directory, executes commands, and returns responses
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, simulation_dir: str):
|
||||
"""
|
||||
初始化IPC服务器
|
||||
|
||||
Initialize the IPC server
|
||||
|
||||
Args:
|
||||
simulation_dir: 模拟数据目录
|
||||
simulation_dir: Simulation data directory
|
||||
"""
|
||||
self.simulation_dir = simulation_dir
|
||||
self.commands_dir = os.path.join(simulation_dir, "ipc_commands")
|
||||
self.responses_dir = os.path.join(simulation_dir, "ipc_responses")
|
||||
|
||||
# 确保目录存在
|
||||
|
||||
# Ensure directories exist
|
||||
os.makedirs(self.commands_dir, exist_ok=True)
|
||||
os.makedirs(self.responses_dir, exist_ok=True)
|
||||
|
||||
# 环境状态
|
||||
|
||||
# Environment status
|
||||
self._running = False
|
||||
|
||||
|
||||
def start(self):
|
||||
"""标记服务器为运行状态"""
|
||||
"""Mark the server as running"""
|
||||
self._running = True
|
||||
self._update_env_status("alive")
|
||||
|
||||
|
||||
def stop(self):
|
||||
"""标记服务器为停止状态"""
|
||||
"""Mark the server as stopped"""
|
||||
self._running = False
|
||||
self._update_env_status("stopped")
|
||||
|
||||
|
||||
def _update_env_status(self, status: str):
|
||||
"""更新环境状态文件"""
|
||||
"""Update the environment status file"""
|
||||
status_file = os.path.join(self.simulation_dir, "env_status.json")
|
||||
with open(status_file, 'w', encoding='utf-8') as f:
|
||||
json.dump({
|
||||
"status": status,
|
||||
"timestamp": datetime.now().isoformat()
|
||||
}, f, ensure_ascii=False, indent=2)
|
||||
|
||||
with open(status_file, "w", encoding="utf-8") as f:
|
||||
json.dump(
|
||||
{"status": status, "timestamp": datetime.now().isoformat()},
|
||||
f,
|
||||
ensure_ascii=False,
|
||||
indent=2,
|
||||
)
|
||||
|
||||
def poll_commands(self) -> Optional[IPCCommand]:
|
||||
"""
|
||||
轮询命令目录，返回第一个待处理的命令
|
||||
|
||||
Poll the command directory and return the first pending command
|
||||
|
||||
Returns:
|
||||
IPCCommand 或 None
|
||||
IPCCommand or None
|
||||
"""
|
||||
if not os.path.exists(self.commands_dir):
|
||||
return None
|
||||
|
||||
# 按时间排序获取命令文件
|
||||
|
||||
# Sort command files by time
|
||||
command_files = []
|
||||
for filename in os.listdir(self.commands_dir):
|
||||
if filename.endswith('.json'):
|
||||
if filename.endswith(".json"):
|
||||
filepath = os.path.join(self.commands_dir, filename)
|
||||
command_files.append((filepath, os.path.getmtime(filepath)))
|
||||
|
||||
|
||||
command_files.sort(key=lambda x: x[1])
|
||||
|
||||
|
||||
for filepath, _ in command_files:
|
||||
try:
|
||||
with open(filepath, 'r', encoding='utf-8') as f:
|
||||
with open(filepath, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
return IPCCommand.from_dict(data)
|
||||
except (json.JSONDecodeError, KeyError, OSError) as e:
|
||||
logger.warning(f"读取命令文件失败: {filepath}, {e}")
|
||||
logger.warning(f"Failed to read command file: {filepath}, {e}")
|
||||
continue
|
||||
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def send_response(self, response: IPCResponse):
|
||||
"""
|
||||
发送响应
|
||||
|
||||
Send a response
|
||||
|
||||
Args:
|
||||
response: IPC响应
|
||||
response: IPC response
|
||||
"""
|
||||
response_file = os.path.join(self.responses_dir, f"{response.command_id}.json")
|
||||
with open(response_file, 'w', encoding='utf-8') as f:
|
||||
with open(response_file, "w", encoding="utf-8") as f:
|
||||
json.dump(response.to_dict(), f, ensure_ascii=False, indent=2)
|
||||
|
||||
# 删除命令文件
|
||||
|
||||
# Delete command file
|
||||
command_file = os.path.join(self.commands_dir, f"{response.command_id}.json")
|
||||
try:
|
||||
os.remove(command_file)
|
||||
except OSError:
|
||||
pass
|
||||
|
||||
|
||||
def send_success(self, command_id: str, result: Dict[str, Any]):
|
||||
"""发送成功响应"""
|
||||
self.send_response(IPCResponse(
|
||||
command_id=command_id,
|
||||
status=CommandStatus.COMPLETED,
|
||||
result=result
|
||||
))
|
||||
|
||||
"""Send a success response"""
|
||||
self.send_response(
|
||||
IPCResponse(
|
||||
command_id=command_id, status=CommandStatus.COMPLETED, result=result
|
||||
)
|
||||
)
|
||||
|
||||
def send_error(self, command_id: str, error: str):
|
||||
"""发送错误响应"""
|
||||
self.send_response(IPCResponse(
|
||||
command_id=command_id,
|
||||
status=CommandStatus.FAILED,
|
||||
error=error
|
||||
))
|
||||
"""Send an error response"""
|
||||
self.send_response(
|
||||
IPCResponse(command_id=command_id, status=CommandStatus.FAILED, error=error)
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
"""
|
||||
OASIS模拟管理器
|
||||
管理Twitter和Reddit双平台并行模拟
|
||||
使用预设脚本 + LLM智能生成配置参数
|
||||
OASIS simulation manager
|
||||
Manages parallel Twitter and Reddit dual-platform simulation
|
||||
Uses preset scripts + LLM-generated configuration parameters
|
||||
"""
|
||||
|
||||
import os
|
||||
|
|
@ -14,71 +14,72 @@ from enum import Enum
|
|||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
from .zep_entity_reader import ZepEntityReader, FilteredEntities
|
||||
from .entity_reader import EntityReader, FilteredEntities
|
||||
from .oasis_profile_generator import OasisProfileGenerator, OasisAgentProfile
|
||||
from .simulation_config_generator import SimulationConfigGenerator, SimulationParameters
|
||||
from ..utils.locale import t
|
||||
|
||||
logger = get_logger('mirofish.simulation')
|
||||
logger = get_logger("mirofish.simulation")
|
||||
|
||||
|
||||
class SimulationStatus(str, Enum):
|
||||
"""模拟状态"""
|
||||
"""Simulation status"""
|
||||
|
||||
CREATED = "created"
|
||||
PREPARING = "preparing"
|
||||
READY = "ready"
|
||||
RUNNING = "running"
|
||||
STOPPING = "stopping"
|
||||
PAUSED = "paused"
|
||||
STOPPED = "stopped" # 模拟被手动停止
|
||||
COMPLETED = "completed" # 模拟自然完成
|
||||
STOPPED = "stopped" # Simulation manually stopped
|
||||
COMPLETED = "completed" # Simulation naturally completed
|
||||
FAILED = "failed"
|
||||
|
||||
|
||||
class PlatformType(str, Enum):
|
||||
"""平台类型"""
|
||||
"""Platform type"""
|
||||
|
||||
TWITTER = "twitter"
|
||||
REDDIT = "reddit"
|
||||
|
||||
|
||||
@dataclass
|
||||
class SimulationState:
|
||||
"""模拟状态"""
|
||||
"""Simulation state"""
|
||||
|
||||
simulation_id: str
|
||||
project_id: str
|
||||
graph_id: str
|
||||
|
||||
# 平台启用状态
|
||||
|
||||
# Platform enable status
|
||||
enable_twitter: bool = True
|
||||
enable_reddit: bool = True
|
||||
|
||||
# 状态
|
||||
|
||||
# Status
|
||||
status: SimulationStatus = SimulationStatus.CREATED
|
||||
|
||||
# 准备阶段数据
|
||||
|
||||
# Preparation phase data
|
||||
entities_count: int = 0
|
||||
profiles_count: int = 0
|
||||
entity_types: List[str] = field(default_factory=list)
|
||||
|
||||
# 配置生成信息
|
||||
profiles_generated: bool = False
|
||||
|
||||
# Config generation info
|
||||
config_generated: bool = False
|
||||
config_reasoning: str = ""
|
||||
|
||||
# 运行时数据
|
||||
|
||||
# Runtime data
|
||||
current_round: int = 0
|
||||
twitter_status: str = "not_started"
|
||||
reddit_status: str = "not_started"
|
||||
|
||||
# 时间戳
|
||||
|
||||
# Timestamps
|
||||
created_at: str = field(default_factory=lambda: datetime.now().isoformat())
|
||||
updated_at: str = field(default_factory=lambda: datetime.now().isoformat())
|
||||
|
||||
# 错误信息
|
||||
|
||||
# Error info
|
||||
error: Optional[str] = None
|
||||
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""完整状态字典（内部使用）"""
|
||||
"""Full state dictionary (internal use)"""
|
||||
return {
|
||||
"simulation_id": self.simulation_id,
|
||||
"project_id": self.project_id,
|
||||
|
|
@ -89,7 +90,6 @@ class SimulationState:
|
|||
"entities_count": self.entities_count,
|
||||
"profiles_count": self.profiles_count,
|
||||
"entity_types": self.entity_types,
|
||||
"profiles_generated": self.profiles_generated,
|
||||
"config_generated": self.config_generated,
|
||||
"config_reasoning": self.config_reasoning,
|
||||
"current_round": self.current_round,
|
||||
|
|
@ -99,18 +99,9 @@ class SimulationState:
|
|||
"updated_at": self.updated_at,
|
||||
"error": self.error,
|
||||
}
|
||||
|
||||
def get_default_platform(self) -> str:
|
||||
"""根据启用状态返回默认平台"""
|
||||
if self.enable_twitter and self.enable_reddit:
|
||||
return "reddit" # 两者都启用时保持原默认
|
||||
elif self.enable_twitter:
|
||||
return "twitter"
|
||||
else:
|
||||
return "reddit"
|
||||
|
||||
def to_simple_dict(self) -> Dict[str, Any]:
|
||||
"""简化状态字典（API返回使用）"""
|
||||
"""Simplified state dictionary (for API responses)"""
|
||||
return {
|
||||
"simulation_id": self.simulation_id,
|
||||
"project_id": self.project_id,
|
||||
|
|
@ -119,7 +110,6 @@ class SimulationState:
|
|||
"entities_count": self.entities_count,
|
||||
"profiles_count": self.profiles_count,
|
||||
"entity_types": self.entity_types,
|
||||
"profiles_generated": self.profiles_generated,
|
||||
"config_generated": self.config_generated,
|
||||
"error": self.error,
|
||||
}
|
||||
|
|
@ -127,60 +117,59 @@ class SimulationState:
|
|||
|
||||
class SimulationManager:
|
||||
"""
|
||||
模拟管理器
|
||||
|
||||
核心功能：
|
||||
1. 从Zep图谱读取实体并过滤
|
||||
2. 生成OASIS Agent Profile
|
||||
3. 使用LLM智能生成模拟配置参数
|
||||
4. 准备预设脚本所需的所有文件
|
||||
Simulation manager
|
||||
|
||||
Core features:
|
||||
1. Read entities from the graph store and filter them
|
||||
2. Generate OASIS Agent Profiles
|
||||
3. Use LLM to intelligently generate simulation configuration parameters
|
||||
4. Prepare all files required by the preset scripts
|
||||
"""
|
||||
|
||||
# 模拟数据存储目录
|
||||
|
||||
# Simulation data storage directory
|
||||
SIMULATION_DATA_DIR = os.path.join(
|
||||
os.path.dirname(__file__),
|
||||
'../../uploads/simulations'
|
||||
os.path.dirname(__file__), "../../uploads/simulations"
|
||||
)
|
||||
|
||||
|
||||
def __init__(self):
|
||||
# 确保目录存在
|
||||
# Ensure directory exists
|
||||
os.makedirs(self.SIMULATION_DATA_DIR, exist_ok=True)
|
||||
|
||||
# 内存中的模拟状态缓存
|
||||
|
||||
# In-memory simulation state cache
|
||||
self._simulations: Dict[str, SimulationState] = {}
|
||||
|
||||
|
||||
def _get_simulation_dir(self, simulation_id: str) -> str:
|
||||
"""获取模拟数据目录"""
|
||||
"""Get the simulation data directory"""
|
||||
sim_dir = os.path.join(self.SIMULATION_DATA_DIR, simulation_id)
|
||||
os.makedirs(sim_dir, exist_ok=True)
|
||||
return sim_dir
|
||||
|
||||
|
||||
def _save_simulation_state(self, state: SimulationState):
|
||||
"""保存模拟状态到文件"""
|
||||
"""Save the simulation state to a file"""
|
||||
sim_dir = self._get_simulation_dir(state.simulation_id)
|
||||
state_file = os.path.join(sim_dir, "state.json")
|
||||
|
||||
|
||||
state.updated_at = datetime.now().isoformat()
|
||||
|
||||
with open(state_file, 'w', encoding='utf-8') as f:
|
||||
|
||||
with open(state_file, "w", encoding="utf-8") as f:
|
||||
json.dump(state.to_dict(), f, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
self._simulations[state.simulation_id] = state
|
||||
|
||||
|
||||
def _load_simulation_state(self, simulation_id: str) -> Optional[SimulationState]:
|
||||
"""从文件加载模拟状态"""
|
||||
"""Load the simulation state from a file"""
|
||||
if simulation_id in self._simulations:
|
||||
return self._simulations[simulation_id]
|
||||
|
||||
|
||||
sim_dir = self._get_simulation_dir(simulation_id)
|
||||
state_file = os.path.join(sim_dir, "state.json")
|
||||
|
||||
|
||||
if not os.path.exists(state_file):
|
||||
return None
|
||||
|
||||
with open(state_file, 'r', encoding='utf-8') as f:
|
||||
|
||||
with open(state_file, "r", encoding="utf-8") as f:
|
||||
data = json.load(f)
|
||||
|
||||
|
||||
state = SimulationState(
|
||||
simulation_id=simulation_id,
|
||||
project_id=data.get("project_id", ""),
|
||||
|
|
@ -191,7 +180,6 @@ class SimulationManager:
|
|||
entities_count=data.get("entities_count", 0),
|
||||
profiles_count=data.get("profiles_count", 0),
|
||||
entity_types=data.get("entity_types", []),
|
||||
profiles_generated=data.get("profiles_generated", False),
|
||||
config_generated=data.get("config_generated", False),
|
||||
config_reasoning=data.get("config_reasoning", ""),
|
||||
current_round=data.get("current_round", 0),
|
||||
|
|
@ -201,10 +189,10 @@ class SimulationManager:
|
|||
updated_at=data.get("updated_at", datetime.now().isoformat()),
|
||||
error=data.get("error"),
|
||||
)
|
||||
|
||||
|
||||
self._simulations[simulation_id] = state
|
||||
return state
|
||||
|
||||
|
||||
def create_simulation(
|
||||
self,
|
||||
project_id: str,
|
||||
|
|
@ -213,20 +201,21 @@ class SimulationManager:
|
|||
enable_reddit: bool = True,
|
||||
) -> SimulationState:
|
||||
"""
|
||||
创建新的模拟
|
||||
|
||||
Create a new simulation
|
||||
|
||||
Args:
|
||||
project_id: 项目ID
|
||||
graph_id: Zep图谱ID
|
||||
enable_twitter: 是否启用Twitter模拟
|
||||
enable_reddit: 是否启用Reddit模拟
|
||||
|
||||
project_id: Project ID
|
||||
graph_id: graph store ID
|
||||
enable_twitter: Whether to enable Twitter simulation
|
||||
enable_reddit: Whether to enable Reddit simulation
|
||||
|
||||
Returns:
|
||||
SimulationState
|
||||
"""
|
||||
import uuid
|
||||
|
||||
simulation_id = f"sim_{uuid.uuid4().hex[:12]}"
|
||||
|
||||
|
||||
state = SimulationState(
|
||||
simulation_id=simulation_id,
|
||||
project_id=project_id,
|
||||
|
|
@ -235,12 +224,14 @@ class SimulationManager:
|
|||
enable_reddit=enable_reddit,
|
||||
status=SimulationStatus.CREATED,
|
||||
)
|
||||
|
||||
|
||||
self._save_simulation_state(state)
|
||||
logger.info(f"创建模拟: {simulation_id}, project={project_id}, graph={graph_id}")
|
||||
|
||||
logger.info(
|
||||
f"Created simulation: {simulation_id}, project={project_id}, graph={graph_id}"
|
||||
)
|
||||
|
||||
return state
|
||||
|
||||
|
||||
def prepare_simulation(
|
||||
self,
|
||||
simulation_id: str,
|
||||
|
|
@ -249,102 +240,100 @@ class SimulationManager:
|
|||
defined_entity_types: Optional[List[str]] = None,
|
||||
use_llm_for_profiles: bool = True,
|
||||
progress_callback: Optional[callable] = None,
|
||||
parallel_profile_count: int = 3
|
||||
parallel_profile_count: int = 3,
|
||||
) -> SimulationState:
|
||||
"""
|
||||
准备模拟环境（全程自动化）
|
||||
|
||||
步骤：
|
||||
1. 从Zep图谱读取并过滤实体
|
||||
2. 为每个实体生成OASIS Agent Profile（可选LLM增强，支持并行）
|
||||
3. 使用LLM智能生成模拟配置参数（时间、活跃度、发言频率等）
|
||||
4. 保存配置文件和Profile文件
|
||||
5. 复制预设脚本到模拟目录
|
||||
|
||||
Prepare the simulation environment (fully automated)
|
||||
|
||||
Steps:
|
||||
1. Read and filter entities from the graph store
|
||||
2. Generate an OASIS Agent Profile for each entity (optional LLM enhancement, supports parallelism)
|
||||
3. Use LLM to intelligently generate simulation configuration parameters (time, activity, posting frequency, etc.)
|
||||
4. Save the configuration file and Profile files
|
||||
5. Copy preset scripts to the simulation directory
|
||||
|
||||
Args:
|
||||
simulation_id: 模拟ID
|
||||
simulation_requirement: 模拟需求描述（用于LLM生成配置）
|
||||
document_text: 原始文档内容（用于LLM理解背景）
|
||||
defined_entity_types: 预定义的实体类型（可选）
|
||||
use_llm_for_profiles: 是否使用LLM生成详细人设
|
||||
progress_callback: 进度回调函数 (stage, progress, message)
|
||||
parallel_profile_count: 并行生成人设的数量，默认3
|
||||
|
||||
simulation_id: Simulation ID
|
||||
simulation_requirement: Simulation requirement description (used for LLM configuration generation)
|
||||
document_text: Original document content (used for LLM to understand context)
|
||||
defined_entity_types: Predefined entity types (optional)
|
||||
use_llm_for_profiles: Whether to use LLM to generate detailed personas
|
||||
progress_callback: Progress callback function (stage, progress, message)
|
||||
parallel_profile_count: Number of personas to generate in parallel, default 3
|
||||
|
||||
Returns:
|
||||
SimulationState
|
||||
"""
|
||||
state = self._load_simulation_state(simulation_id)
|
||||
if not state:
|
||||
raise ValueError(f"模拟不存在: {simulation_id}")
|
||||
|
||||
raise ValueError(t("api.simulationNotFound", id=simulation_id))
|
||||
|
||||
try:
|
||||
state.status = SimulationStatus.PREPARING
|
||||
state.error = None
|
||||
state.profiles_generated = False
|
||||
state.config_generated = False
|
||||
state.config_reasoning = ""
|
||||
self._save_simulation_state(state)
|
||||
|
||||
|
||||
sim_dir = self._get_simulation_dir(simulation_id)
|
||||
|
||||
# ========== 阶段1: 读取并过滤实体 ==========
|
||||
|
||||
# ========== Phase 1: Read and filter entities ==========
|
||||
if progress_callback:
|
||||
progress_callback("reading", 0, t('progress.connectingZepGraph'))
|
||||
|
||||
reader = ZepEntityReader()
|
||||
|
||||
progress_callback("reading", 0, t("progress.connectingGraph"))
|
||||
|
||||
reader = EntityReader()
|
||||
|
||||
if progress_callback:
|
||||
progress_callback("reading", 30, t('progress.readingNodeData'))
|
||||
|
||||
progress_callback("reading", 30, t("progress.readingNodeData"))
|
||||
|
||||
filtered = reader.filter_defined_entities(
|
||||
graph_id=state.graph_id,
|
||||
defined_entity_types=defined_entity_types,
|
||||
enrich_with_edges=True
|
||||
enrich_with_edges=True,
|
||||
)
|
||||
|
||||
|
||||
state.entities_count = filtered.filtered_count
|
||||
state.entity_types = list(filtered.entity_types)
|
||||
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"reading", 100,
|
||||
t('progress.readingComplete', count=filtered.filtered_count),
|
||||
"reading",
|
||||
100,
|
||||
t("progress.readingComplete", count=filtered.filtered_count),
|
||||
current=filtered.filtered_count,
|
||||
total=filtered.filtered_count
|
||||
total=filtered.filtered_count,
|
||||
)
|
||||
|
||||
|
||||
if filtered.filtered_count == 0:
|
||||
state.status = SimulationStatus.FAILED
|
||||
state.error = "没有找到符合条件的实体，请检查图谱是否正确构建"
|
||||
state.error = "No matching entities found; please check whether the graph is correctly built"
|
||||
self._save_simulation_state(state)
|
||||
raise ValueError(state.error)
|
||||
|
||||
# ========== 阶段2: 生成Agent Profile ==========
|
||||
return state
|
||||
|
||||
# ========== Phase 2: Generate Agent Profiles ==========
|
||||
total_entities = len(filtered.entities)
|
||||
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_profiles", 0,
|
||||
t('progress.startGenerating'),
|
||||
"generating_profiles",
|
||||
0,
|
||||
t("progress.startGenerating"),
|
||||
current=0,
|
||||
total=total_entities
|
||||
total=total_entities,
|
||||
)
|
||||
|
||||
# 传入graph_id以启用Zep检索功能，获取更丰富的上下文
|
||||
|
||||
# Pass graph_id to enable graph retrieval for richer context
|
||||
generator = OasisProfileGenerator(graph_id=state.graph_id)
|
||||
|
||||
|
||||
def profile_progress(current, total, msg):
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_profiles",
|
||||
int(current / total * 100),
|
||||
"generating_profiles",
|
||||
int(current / total * 100),
|
||||
msg,
|
||||
current=current,
|
||||
total=total,
|
||||
item_name=msg
|
||||
item_name=msg,
|
||||
)
|
||||
|
||||
# 设置实时保存的文件路径（优先使用 Reddit JSON 格式）
|
||||
|
||||
# Set real-time save file path (prefer Reddit JSON format)
|
||||
realtime_output_path = None
|
||||
realtime_platform = "reddit"
|
||||
if state.enable_reddit:
|
||||
|
|
@ -353,73 +342,75 @@ class SimulationManager:
|
|||
elif state.enable_twitter:
|
||||
realtime_output_path = os.path.join(sim_dir, "twitter_profiles.csv")
|
||||
realtime_platform = "twitter"
|
||||
|
||||
|
||||
profiles = generator.generate_profiles_from_entities(
|
||||
entities=filtered.entities,
|
||||
use_llm=use_llm_for_profiles,
|
||||
progress_callback=profile_progress,
|
||||
graph_id=state.graph_id, # 传入graph_id用于Zep检索
|
||||
parallel_count=parallel_profile_count, # 并行生成数量
|
||||
realtime_output_path=realtime_output_path, # 实时保存路径
|
||||
output_platform=realtime_platform # 输出格式
|
||||
graph_id=state.graph_id, # Pass graph_id for graph retrieval
|
||||
parallel_count=parallel_profile_count, # Parallel generation count
|
||||
realtime_output_path=realtime_output_path, # Real-time save path
|
||||
output_platform=realtime_platform, # Output format
|
||||
)
|
||||
|
||||
|
||||
state.profiles_count = len(profiles)
|
||||
state.profiles_generated = len(profiles) > 0
|
||||
self._save_simulation_state(state)
|
||||
|
||||
# 保存Profile文件（注意：Twitter使用CSV格式，Reddit使用JSON格式）
|
||||
# Reddit 已经在生成过程中实时保存了，这里再保存一次确保完整性
|
||||
|
||||
# Save Profile files (note: Twitter uses CSV format, Reddit uses JSON format)
|
||||
# Reddit was already saved in real time during generation; save again here to ensure completeness
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_profiles", 95,
|
||||
t('progress.savingProfiles'),
|
||||
"generating_profiles",
|
||||
95,
|
||||
t("progress.savingProfiles"),
|
||||
current=total_entities,
|
||||
total=total_entities
|
||||
total=total_entities,
|
||||
)
|
||||
|
||||
|
||||
if state.enable_reddit:
|
||||
generator.save_profiles(
|
||||
profiles=profiles,
|
||||
file_path=os.path.join(sim_dir, "reddit_profiles.json"),
|
||||
platform="reddit"
|
||||
platform="reddit",
|
||||
)
|
||||
|
||||
|
||||
if state.enable_twitter:
|
||||
# Twitter使用CSV格式！这是OASIS的要求
|
||||
# Twitter uses CSV format! This is required by OASIS
|
||||
generator.save_profiles(
|
||||
profiles=profiles,
|
||||
file_path=os.path.join(sim_dir, "twitter_profiles.csv"),
|
||||
platform="twitter"
|
||||
platform="twitter",
|
||||
)
|
||||
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_profiles", 100,
|
||||
t('progress.profilesComplete', count=len(profiles)),
|
||||
"generating_profiles",
|
||||
100,
|
||||
t("progress.profilesComplete", count=len(profiles)),
|
||||
current=len(profiles),
|
||||
total=len(profiles)
|
||||
total=len(profiles),
|
||||
)
|
||||
|
||||
# ========== 阶段3: LLM智能生成模拟配置 ==========
|
||||
|
||||
# ========== Phase 3: LLM intelligent simulation config generation ==========
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_config", 0,
|
||||
t('progress.analyzingRequirements'),
|
||||
"generating_config",
|
||||
0,
|
||||
t("progress.analyzingRequirements"),
|
||||
current=0,
|
||||
total=3
|
||||
total=3,
|
||||
)
|
||||
|
||||
|
||||
config_generator = SimulationConfigGenerator()
|
||||
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_config", 30,
|
||||
t('progress.callingLLMConfig'),
|
||||
"generating_config",
|
||||
30,
|
||||
t("progress.callingLLMConfig"),
|
||||
current=1,
|
||||
total=3
|
||||
total=3,
|
||||
)
|
||||
|
||||
|
||||
sim_params = config_generator.generate_config(
|
||||
simulation_id=simulation_id,
|
||||
project_id=state.project_id,
|
||||
|
|
@ -428,123 +419,119 @@ class SimulationManager:
|
|||
document_text=document_text,
|
||||
entities=filtered.entities,
|
||||
enable_twitter=state.enable_twitter,
|
||||
enable_reddit=state.enable_reddit
|
||||
enable_reddit=state.enable_reddit,
|
||||
)
|
||||
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_config", 70,
|
||||
t('progress.savingConfigFiles'),
|
||||
"generating_config",
|
||||
70,
|
||||
t("progress.savingConfigFiles"),
|
||||
current=2,
|
||||
total=3
|
||||
total=3,
|
||||
)
|
||||
|
||||
# 保存配置文件
|
||||
|
||||
# Save config files
|
||||
config_path = os.path.join(sim_dir, "simulation_config.json")
|
||||
with open(config_path, 'w', encoding='utf-8') as f:
|
||||
with open(config_path, "w", encoding="utf-8") as f:
|
||||
f.write(sim_params.to_json())
|
||||
|
||||
|
||||
state.config_generated = True
|
||||
state.config_reasoning = sim_params.generation_reasoning
|
||||
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
"generating_config", 100,
|
||||
t('progress.configComplete'),
|
||||
"generating_config",
|
||||
100,
|
||||
t("progress.configComplete"),
|
||||
current=3,
|
||||
total=3
|
||||
total=3,
|
||||
)
|
||||
|
||||
# 注意：运行脚本保留在 backend/scripts/ 目录，不再复制到模拟目录
|
||||
# 启动模拟时，simulation_runner 会从 scripts/ 目录运行脚本
|
||||
|
||||
# 更新状态
|
||||
|
||||
# Note: run scripts stay in backend/scripts/, no longer copied to simulation dir
|
||||
# When starting simulation, simulation_runner runs scripts from scripts/ dir
|
||||
|
||||
# Update state
|
||||
state.status = SimulationStatus.READY
|
||||
self._save_simulation_state(state)
|
||||
|
||||
logger.info(f"模拟准备完成: {simulation_id}, "
|
||||
f"entities={state.entities_count}, profiles={state.profiles_count}")
|
||||
|
||||
|
||||
logger.info(
|
||||
f"Simulation preparation complete: {simulation_id}, "
|
||||
f"entities={state.entities_count}, profiles={state.profiles_count}"
|
||||
)
|
||||
|
||||
return state
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"模拟准备失败: {simulation_id}, error={str(e)}")
|
||||
logger.error(f"Simulation preparation failed: {simulation_id}, error={str(e)}")
|
||||
import traceback
|
||||
|
||||
logger.error(traceback.format_exc())
|
||||
state.status = SimulationStatus.FAILED
|
||||
state.error = str(e)
|
||||
self._save_simulation_state(state)
|
||||
raise
|
||||
|
||||
|
||||
def get_simulation(self, simulation_id: str) -> Optional[SimulationState]:
|
||||
"""获取模拟状态"""
|
||||
"""Get simulation state"""
|
||||
return self._load_simulation_state(simulation_id)
|
||||
|
||||
def list_simulations(self, project_id: Optional[str] = None) -> List[SimulationState]:
|
||||
"""列出所有模拟"""
|
||||
|
||||
def list_simulations(
|
||||
self, project_id: Optional[str] = None
|
||||
) -> List[SimulationState]:
|
||||
"""List all simulations"""
|
||||
simulations = []
|
||||
|
||||
|
||||
if os.path.exists(self.SIMULATION_DATA_DIR):
|
||||
for sim_id in os.listdir(self.SIMULATION_DATA_DIR):
|
||||
# 跳过隐藏文件（如 .DS_Store）和非目录文件
|
||||
# Skip hidden files (e.g. .DS_Store) and non-directory files
|
||||
sim_path = os.path.join(self.SIMULATION_DATA_DIR, sim_id)
|
||||
if sim_id.startswith('.') or not os.path.isdir(sim_path):
|
||||
if sim_id.startswith(".") or not os.path.isdir(sim_path):
|
||||
continue
|
||||
|
||||
|
||||
state = self._load_simulation_state(sim_id)
|
||||
if state:
|
||||
if project_id is None or state.project_id == project_id:
|
||||
simulations.append(state)
|
||||
|
||||
|
||||
return simulations
|
||||
|
||||
def get_profiles(self, simulation_id: str, platform: str = None) -> List[Dict[str, Any]]:
|
||||
"""获取模拟的Agent Profile"""
|
||||
|
||||
def get_profiles(
|
||||
self, simulation_id: str, platform: str = "reddit"
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""Get the simulation's Agent Profile"""
|
||||
state = self._load_simulation_state(simulation_id)
|
||||
if not state:
|
||||
raise ValueError(f"模拟不存在: {simulation_id}")
|
||||
|
||||
if platform is None:
|
||||
platform = state.get_default_platform()
|
||||
|
||||
if platform not in {"twitter", "reddit"}:
|
||||
raise ValueError(f"不支持的平台: {platform}")
|
||||
raise ValueError(t("api.simulationNotFound", id=simulation_id))
|
||||
|
||||
sim_dir = self._get_simulation_dir(simulation_id)
|
||||
profile_path = os.path.join(
|
||||
sim_dir,
|
||||
"twitter_profiles.csv" if platform == "twitter" else "reddit_profiles.json",
|
||||
)
|
||||
|
||||
profile_path = os.path.join(sim_dir, f"{platform}_profiles.json")
|
||||
|
||||
if not os.path.exists(profile_path):
|
||||
return []
|
||||
|
||||
if platform == "twitter":
|
||||
import csv
|
||||
|
||||
with open(profile_path, 'r', encoding='utf-8', newline='') as f:
|
||||
return list(csv.DictReader(f))
|
||||
|
||||
with open(profile_path, 'r', encoding='utf-8') as f:
|
||||
with open(profile_path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def get_simulation_config(self, simulation_id: str) -> Optional[Dict[str, Any]]:
|
||||
"""获取模拟配置"""
|
||||
"""Get the simulation configuration"""
|
||||
sim_dir = self._get_simulation_dir(simulation_id)
|
||||
config_path = os.path.join(sim_dir, "simulation_config.json")
|
||||
|
||||
|
||||
if not os.path.exists(config_path):
|
||||
return None
|
||||
|
||||
with open(config_path, 'r', encoding='utf-8') as f:
|
||||
|
||||
with open(config_path, "r", encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
def get_run_instructions(self, simulation_id: str) -> Dict[str, str]:
|
||||
"""获取运行说明"""
|
||||
"""Get run instructions"""
|
||||
sim_dir = self._get_simulation_dir(simulation_id)
|
||||
config_path = os.path.join(sim_dir, "simulation_config.json")
|
||||
scripts_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../scripts'))
|
||||
|
||||
scripts_dir = os.path.abspath(
|
||||
os.path.join(os.path.dirname(__file__), "../../scripts")
|
||||
)
|
||||
|
||||
return {
|
||||
"simulation_dir": sim_dir,
|
||||
"scripts_dir": scripts_dir,
|
||||
|
|
@ -555,10 +542,10 @@ class SimulationManager:
|
|||
"parallel": f"python {scripts_dir}/run_parallel_simulation.py --config {config_path}",
|
||||
},
|
||||
"instructions": (
|
||||
f"1. 激活conda环境: conda activate MiroFish\n"
|
||||
f"2. 运行模拟 (脚本位于 {scripts_dir}):\n"
|
||||
f" - 单独运行Twitter: python {scripts_dir}/run_twitter_simulation.py --config {config_path}\n"
|
||||
f" - 单独运行Reddit: python {scripts_dir}/run_reddit_simulation.py --config {config_path}\n"
|
||||
f" - 并行运行双平台: python {scripts_dir}/run_parallel_simulation.py --config {config_path}"
|
||||
)
|
||||
f"1. Activate conda environment: conda activate MiroFish\n"
|
||||
f"2. Run simulation (scripts located at {scripts_dir}):\n"
|
||||
f" - Run Twitter only: python {scripts_dir}/run_twitter_simulation.py --config {config_path}\n"
|
||||
f" - Run Reddit only: python {scripts_dir}/run_reddit_simulation.py --config {config_path}\n"
|
||||
f" - Run both platforms in parallel: python {scripts_dir}/run_parallel_simulation.py --config {config_path}"
|
||||
),
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,5 +1,5 @@
|
|||
"""
|
||||
文本处理服务
|
||||
Text processing service
|
||||
"""
|
||||
|
||||
from typing import List, Optional
|
||||
|
|
@ -7,65 +7,60 @@ from ..utils.file_parser import FileParser, split_text_into_chunks
|
|||
|
||||
|
||||
class TextProcessor:
|
||||
"""文本处理器"""
|
||||
|
||||
"""Text processor"""
|
||||
|
||||
@staticmethod
|
||||
def extract_from_files(file_paths: List[str]) -> str:
|
||||
"""从多个文件提取文本"""
|
||||
"""Extract text from multiple files"""
|
||||
return FileParser.extract_from_multiple(file_paths)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def split_text(
|
||||
text: str,
|
||||
chunk_size: int = 500,
|
||||
overlap: int = 50
|
||||
) -> List[str]:
|
||||
def split_text(text: str, chunk_size: int = 500, overlap: int = 50) -> List[str]:
|
||||
"""
|
||||
分割文本
|
||||
|
||||
Split text
|
||||
|
||||
Args:
|
||||
text: 原始文本
|
||||
chunk_size: 块大小
|
||||
overlap: 重叠大小
|
||||
|
||||
text: Original text
|
||||
chunk_size: Chunk size
|
||||
overlap: Overlap size
|
||||
|
||||
Returns:
|
||||
文本块列表
|
||||
List of text chunks
|
||||
"""
|
||||
return split_text_into_chunks(text, chunk_size, overlap)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def preprocess_text(text: str) -> str:
|
||||
"""
|
||||
预处理文本
|
||||
- 移除多余空白
|
||||
- 标准化换行
|
||||
|
||||
Preprocess text
|
||||
- Remove extra whitespace
|
||||
- Normalize line breaks
|
||||
|
||||
Args:
|
||||
text: 原始文本
|
||||
|
||||
text: Original text
|
||||
|
||||
Returns:
|
||||
处理后的文本
|
||||
Processed text
|
||||
"""
|
||||
import re
|
||||
|
||||
# 标准化换行
|
||||
text = text.replace('\r\n', '\n').replace('\r', '\n')
|
||||
|
||||
# 移除连续空行（保留最多两个换行）
|
||||
text = re.sub(r'\n{3,}', '\n\n', text)
|
||||
|
||||
# 移除行首行尾空白
|
||||
lines = [line.strip() for line in text.split('\n')]
|
||||
text = '\n'.join(lines)
|
||||
|
||||
|
||||
# Normalize line breaks
|
||||
text = text.replace("\r\n", "\n").replace("\r", "\n")
|
||||
|
||||
# Remove consecutive blank lines (keep at most two newlines)
|
||||
text = re.sub(r"\n{3,}", "\n\n", text)
|
||||
|
||||
# Strip leading and trailing whitespace from each line
|
||||
lines = [line.strip() for line in text.split("\n")]
|
||||
text = "\n".join(lines)
|
||||
|
||||
return text.strip()
|
||||
|
||||
|
||||
@staticmethod
|
||||
def get_text_stats(text: str) -> dict:
|
||||
"""获取文本统计信息"""
|
||||
"""Get text statistics"""
|
||||
return {
|
||||
"total_chars": len(text),
|
||||
"total_lines": text.count('\n') + 1,
|
||||
"total_lines": text.count("\n") + 1,
|
||||
"total_words": len(text.split()),
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,446 +0,0 @@
|
|||
"""
|
||||
Zep实体读取与过滤服务
|
||||
从Zep图谱中读取节点，筛选出符合预定义实体类型的节点
|
||||
"""
|
||||
|
||||
from typing import Dict, Any, List, Optional, Set, Callable, TypeVar
|
||||
from dataclasses import dataclass, field
|
||||
from zep_cloud import NotFoundError
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
|
||||
from ..utils.zep import call_zep_read_with_retry, get_zep_client
|
||||
|
||||
logger = get_logger('mirofish.zep_entity_reader')
|
||||
|
||||
# 用于泛型返回类型
|
||||
T = TypeVar('T')
|
||||
|
||||
|
||||
@dataclass
|
||||
class EntityNode:
|
||||
"""实体节点数据结构"""
|
||||
uuid: str
|
||||
name: str
|
||||
labels: List[str]
|
||||
summary: str
|
||||
attributes: Dict[str, Any]
|
||||
# 相关的边信息
|
||||
related_edges: List[Dict[str, Any]] = field(default_factory=list)
|
||||
# 相关的其他节点信息
|
||||
related_nodes: List[Dict[str, Any]] = field(default_factory=list)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"uuid": self.uuid,
|
||||
"name": self.name,
|
||||
"labels": self.labels,
|
||||
"summary": self.summary,
|
||||
"attributes": self.attributes,
|
||||
"related_edges": self.related_edges,
|
||||
"related_nodes": self.related_nodes,
|
||||
}
|
||||
|
||||
def get_entity_type(self) -> Optional[str]:
|
||||
"""获取实体类型（排除默认的Entity标签）"""
|
||||
for label in self.labels:
|
||||
if label not in ["Entity", "Node"]:
|
||||
return label
|
||||
return None
|
||||
|
||||
|
||||
@dataclass
|
||||
class FilteredEntities:
|
||||
"""过滤后的实体集合"""
|
||||
entities: List[EntityNode]
|
||||
entity_types: Set[str]
|
||||
total_count: int
|
||||
filtered_count: int
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
return {
|
||||
"entities": [e.to_dict() for e in self.entities],
|
||||
"entity_types": list(self.entity_types),
|
||||
"total_count": self.total_count,
|
||||
"filtered_count": self.filtered_count,
|
||||
}
|
||||
|
||||
|
||||
class ZepEntityReader:
|
||||
"""
|
||||
Zep实体读取与过滤服务
|
||||
|
||||
主要功能：
|
||||
1. 从Zep图谱读取所有节点
|
||||
2. 筛选出符合预定义实体类型的节点（Labels不只是Entity的节点）
|
||||
3. 获取每个实体的相关边和关联节点信息
|
||||
"""
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None):
|
||||
self.api_key = api_key or Config.ZEP_API_KEY
|
||||
if not self.api_key:
|
||||
raise ValueError("ZEP_API_KEY 未配置")
|
||||
|
||||
self.client = get_zep_client(self.api_key)
|
||||
|
||||
def _call_with_retry(
|
||||
self,
|
||||
func: Callable[[], T],
|
||||
operation_name: str,
|
||||
max_retries: int = 3,
|
||||
initial_delay: float = 2.0
|
||||
) -> T:
|
||||
"""
|
||||
带重试机制的Zep API调用
|
||||
|
||||
Args:
|
||||
func: 要执行的函数（无参数的lambda或callable）
|
||||
operation_name: 操作名称，用于日志
|
||||
max_retries: 最大重试次数（默认3次，即最多尝试3次）
|
||||
initial_delay: 初始延迟秒数
|
||||
|
||||
Returns:
|
||||
API调用结果
|
||||
"""
|
||||
return call_zep_read_with_retry(
|
||||
func,
|
||||
operation_name=operation_name,
|
||||
max_attempts=max_retries,
|
||||
initial_delay=initial_delay,
|
||||
)
|
||||
|
||||
def get_all_nodes(self, graph_id: str) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取图谱的所有节点（分页获取）
|
||||
|
||||
Args:
|
||||
graph_id: 图谱ID
|
||||
|
||||
Returns:
|
||||
节点列表
|
||||
"""
|
||||
logger.info(f"获取图谱 {graph_id} 的所有节点...")
|
||||
|
||||
nodes = fetch_all_nodes(self.client, graph_id)
|
||||
|
||||
nodes_data = []
|
||||
for node in nodes:
|
||||
nodes_data.append({
|
||||
"uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
|
||||
"name": node.name or "",
|
||||
"labels": node.labels or [],
|
||||
"summary": node.summary or "",
|
||||
"attributes": node.attributes or {},
|
||||
})
|
||||
|
||||
logger.info(f"共获取 {len(nodes_data)} 个节点")
|
||||
return nodes_data
|
||||
|
||||
def get_all_edges(self, graph_id: str) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取图谱的所有边（分页获取）
|
||||
|
||||
Args:
|
||||
graph_id: 图谱ID
|
||||
|
||||
Returns:
|
||||
边列表
|
||||
"""
|
||||
logger.info(f"获取图谱 {graph_id} 的所有边...")
|
||||
|
||||
edges = fetch_all_edges(self.client, graph_id)
|
||||
|
||||
edges_data = []
|
||||
for edge in edges:
|
||||
edges_data.append({
|
||||
"uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
|
||||
"name": edge.name or "",
|
||||
"fact": edge.fact or "",
|
||||
"source_node_uuid": edge.source_node_uuid,
|
||||
"target_node_uuid": edge.target_node_uuid,
|
||||
"attributes": edge.attributes or {},
|
||||
})
|
||||
|
||||
logger.info(f"共获取 {len(edges_data)} 条边")
|
||||
return edges_data
|
||||
|
||||
def get_node_edges(
|
||||
self,
|
||||
node_uuid: str,
|
||||
*,
|
||||
graph_id: Optional[str] = None,
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取指定节点的相关边。
|
||||
|
||||
Zep Cloud 3.25 的 ``graph.node.get_edges`` 实测只返回节点作为
|
||||
source 的边，尽管文档将其描述为“all edges”。需要完整上下文时必须
|
||||
提供 graph_id，以全图分页后同时筛选 incoming 和 outgoing 边。
|
||||
|
||||
Args:
|
||||
node_uuid: 节点UUID
|
||||
graph_id: 图谱ID；提供时保证返回双向完整关系
|
||||
|
||||
Returns:
|
||||
边列表
|
||||
"""
|
||||
try:
|
||||
if graph_id:
|
||||
return [
|
||||
edge
|
||||
for edge in self.get_all_edges(graph_id)
|
||||
if edge["source_node_uuid"] == node_uuid
|
||||
or edge["target_node_uuid"] == node_uuid
|
||||
]
|
||||
|
||||
# 使用重试机制调用Zep API
|
||||
edges = self._call_with_retry(
|
||||
func=lambda: self.client.graph.node.get_edges(node_uuid=node_uuid),
|
||||
operation_name=f"获取节点边(node={node_uuid[:8]}...)"
|
||||
)
|
||||
|
||||
edges_data = []
|
||||
for edge in edges:
|
||||
edges_data.append({
|
||||
"uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''),
|
||||
"name": edge.name or "",
|
||||
"fact": edge.fact or "",
|
||||
"source_node_uuid": edge.source_node_uuid,
|
||||
"target_node_uuid": edge.target_node_uuid,
|
||||
"attributes": edge.attributes or {},
|
||||
})
|
||||
|
||||
return edges_data
|
||||
except Exception as e:
|
||||
# An empty edge list is valid data. Authentication, permission and
|
||||
# transport failures must not be made indistinguishable from it.
|
||||
logger.error(f"获取节点 {node_uuid} 的边失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def filter_defined_entities(
|
||||
self,
|
||||
graph_id: str,
|
||||
defined_entity_types: Optional[List[str]] = None,
|
||||
enrich_with_edges: bool = True
|
||||
) -> FilteredEntities:
|
||||
"""
|
||||
筛选出符合预定义实体类型的节点
|
||||
|
||||
筛选逻辑：
|
||||
- 如果节点的Labels只有一个"Entity"，说明这个实体不符合我们预定义的类型，跳过
|
||||
- 如果节点的Labels包含除"Entity"和"Node"之外的标签，说明符合预定义类型，保留
|
||||
|
||||
Args:
|
||||
graph_id: 图谱ID
|
||||
defined_entity_types: 预定义的实体类型列表（可选，如果提供则只保留这些类型）
|
||||
enrich_with_edges: 是否获取每个实体的相关边信息
|
||||
|
||||
Returns:
|
||||
FilteredEntities: 过滤后的实体集合
|
||||
"""
|
||||
logger.info(f"开始筛选图谱 {graph_id} 的实体...")
|
||||
|
||||
# 获取所有节点
|
||||
all_nodes = self.get_all_nodes(graph_id)
|
||||
total_count = len(all_nodes)
|
||||
|
||||
# 获取所有边（用于后续关联查找）
|
||||
all_edges = self.get_all_edges(graph_id) if enrich_with_edges else []
|
||||
|
||||
# 构建节点UUID到节点数据的映射
|
||||
node_map = {n["uuid"]: n for n in all_nodes}
|
||||
|
||||
# 筛选符合条件的实体
|
||||
filtered_entities = []
|
||||
entity_types_found = set()
|
||||
|
||||
for node in all_nodes:
|
||||
labels = node.get("labels", [])
|
||||
|
||||
# 筛选逻辑：Labels必须包含除"Entity"和"Node"之外的标签
|
||||
custom_labels = [l for l in labels if l not in ["Entity", "Node"]]
|
||||
|
||||
if not custom_labels:
|
||||
# 只有默认标签，跳过
|
||||
continue
|
||||
|
||||
# 如果指定了预定义类型，检查是否匹配
|
||||
if defined_entity_types:
|
||||
matching_labels = [l for l in custom_labels if l in defined_entity_types]
|
||||
if not matching_labels:
|
||||
continue
|
||||
entity_type = matching_labels[0]
|
||||
else:
|
||||
entity_type = custom_labels[0]
|
||||
|
||||
entity_types_found.add(entity_type)
|
||||
|
||||
# 创建实体节点对象
|
||||
entity = EntityNode(
|
||||
uuid=node["uuid"],
|
||||
name=node["name"],
|
||||
labels=labels,
|
||||
summary=node["summary"],
|
||||
attributes=node["attributes"],
|
||||
)
|
||||
|
||||
# 获取相关边和节点
|
||||
if enrich_with_edges:
|
||||
related_edges = []
|
||||
related_node_uuids = set()
|
||||
|
||||
for edge in all_edges:
|
||||
if edge["source_node_uuid"] == node["uuid"]:
|
||||
related_edges.append({
|
||||
"direction": "outgoing",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"target_node_uuid": edge["target_node_uuid"],
|
||||
})
|
||||
related_node_uuids.add(edge["target_node_uuid"])
|
||||
elif edge["target_node_uuid"] == node["uuid"]:
|
||||
related_edges.append({
|
||||
"direction": "incoming",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"source_node_uuid": edge["source_node_uuid"],
|
||||
})
|
||||
related_node_uuids.add(edge["source_node_uuid"])
|
||||
|
||||
entity.related_edges = related_edges
|
||||
|
||||
# 获取关联节点的基本信息
|
||||
related_nodes = []
|
||||
for related_uuid in related_node_uuids:
|
||||
if related_uuid in node_map:
|
||||
related_node = node_map[related_uuid]
|
||||
related_nodes.append({
|
||||
"uuid": related_node["uuid"],
|
||||
"name": related_node["name"],
|
||||
"labels": related_node["labels"],
|
||||
"summary": related_node.get("summary", ""),
|
||||
})
|
||||
|
||||
entity.related_nodes = related_nodes
|
||||
|
||||
filtered_entities.append(entity)
|
||||
|
||||
logger.info(f"筛选完成: 总节点 {total_count}, 符合条件 {len(filtered_entities)}, "
|
||||
f"实体类型: {entity_types_found}")
|
||||
|
||||
return FilteredEntities(
|
||||
entities=filtered_entities,
|
||||
entity_types=entity_types_found,
|
||||
total_count=total_count,
|
||||
filtered_count=len(filtered_entities),
|
||||
)
|
||||
|
||||
def get_entity_with_context(
|
||||
self,
|
||||
graph_id: str,
|
||||
entity_uuid: str
|
||||
) -> Optional[EntityNode]:
|
||||
"""
|
||||
获取单个实体及其完整上下文（边和关联节点，带重试机制）
|
||||
|
||||
Args:
|
||||
graph_id: 图谱ID
|
||||
entity_uuid: 实体UUID
|
||||
|
||||
Returns:
|
||||
EntityNode或None
|
||||
"""
|
||||
try:
|
||||
# 使用重试机制获取节点
|
||||
node = self._call_with_retry(
|
||||
func=lambda: self.client.graph.node.get(uuid_=entity_uuid),
|
||||
operation_name=f"获取节点详情(uuid={entity_uuid[:8]}...)"
|
||||
)
|
||||
|
||||
if not node:
|
||||
return None
|
||||
|
||||
# 获取节点的边
|
||||
edges = self.get_node_edges(entity_uuid, graph_id=graph_id)
|
||||
|
||||
# 获取所有节点用于关联查找
|
||||
all_nodes = self.get_all_nodes(graph_id)
|
||||
node_map = {n["uuid"]: n for n in all_nodes}
|
||||
|
||||
# 处理相关边和节点
|
||||
related_edges = []
|
||||
related_node_uuids = set()
|
||||
|
||||
for edge in edges:
|
||||
if edge["source_node_uuid"] == entity_uuid:
|
||||
related_edges.append({
|
||||
"direction": "outgoing",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"target_node_uuid": edge["target_node_uuid"],
|
||||
})
|
||||
related_node_uuids.add(edge["target_node_uuid"])
|
||||
else:
|
||||
related_edges.append({
|
||||
"direction": "incoming",
|
||||
"edge_name": edge["name"],
|
||||
"fact": edge["fact"],
|
||||
"source_node_uuid": edge["source_node_uuid"],
|
||||
})
|
||||
related_node_uuids.add(edge["source_node_uuid"])
|
||||
|
||||
# 获取关联节点信息
|
||||
related_nodes = []
|
||||
for related_uuid in related_node_uuids:
|
||||
if related_uuid in node_map:
|
||||
related_node = node_map[related_uuid]
|
||||
related_nodes.append({
|
||||
"uuid": related_node["uuid"],
|
||||
"name": related_node["name"],
|
||||
"labels": related_node["labels"],
|
||||
"summary": related_node.get("summary", ""),
|
||||
})
|
||||
|
||||
return EntityNode(
|
||||
uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''),
|
||||
name=node.name or "",
|
||||
labels=node.labels or [],
|
||||
summary=node.summary or "",
|
||||
attributes=node.attributes or {},
|
||||
related_edges=related_edges,
|
||||
related_nodes=related_nodes,
|
||||
)
|
||||
|
||||
except NotFoundError:
|
||||
return None
|
||||
except Exception as e:
|
||||
# Only an actual Zep 404 means "entity not found". Propagate 401,
|
||||
# 403 and exhausted transport errors so callers cannot prepare a
|
||||
# simulation with silently incomplete graph context.
|
||||
logger.error(f"获取实体 {entity_uuid} 失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def get_entities_by_type(
|
||||
self,
|
||||
graph_id: str,
|
||||
entity_type: str,
|
||||
enrich_with_edges: bool = True
|
||||
) -> List[EntityNode]:
|
||||
"""
|
||||
获取指定类型的所有实体
|
||||
|
||||
Args:
|
||||
graph_id: 图谱ID
|
||||
entity_type: 实体类型（如 "Student", "PublicFigure" 等）
|
||||
enrich_with_edges: 是否获取相关边信息
|
||||
|
||||
Returns:
|
||||
实体列表
|
||||
"""
|
||||
result = self.filter_defined_entities(
|
||||
graph_id=graph_id,
|
||||
defined_entity_types=[entity_type],
|
||||
enrich_with_edges=enrich_with_edges
|
||||
)
|
||||
return result.entities
|
||||
|
|
@ -1,791 +0,0 @@
|
|||
"""
|
||||
Zep图谱记忆更新服务
|
||||
将模拟中的Agent活动动态更新到Zep图谱中
|
||||
"""
|
||||
|
||||
import time
|
||||
import threading
|
||||
from typing import Dict, Any, List, Optional
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
from queue import Queue, Empty
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
from ..utils.locale import get_locale, set_locale
|
||||
from ..utils.zep import (
|
||||
ZEP_INGESTION_WAIT_TIMEOUT_SECONDS,
|
||||
call_zep_read_with_retry,
|
||||
get_zep_client,
|
||||
)
|
||||
|
||||
logger = get_logger('mirofish.zep_graph_memory_updater')
|
||||
|
||||
|
||||
@dataclass
|
||||
class AgentActivity:
|
||||
"""Agent活动记录"""
|
||||
platform: str # twitter / reddit
|
||||
agent_id: int
|
||||
agent_name: str
|
||||
action_type: str # CREATE_POST, LIKE_POST, etc.
|
||||
action_args: Dict[str, Any]
|
||||
round_num: int
|
||||
timestamp: str
|
||||
|
||||
def to_episode_text(self) -> str:
|
||||
"""
|
||||
将活动转换为可以发送给Zep的文本描述
|
||||
|
||||
采用自然语言描述格式，让Zep能够从中提取实体和关系
|
||||
不添加模拟相关的前缀，避免误导图谱更新
|
||||
"""
|
||||
# 根据不同的动作类型生成不同的描述
|
||||
action_descriptions = {
|
||||
"CREATE_POST": self._describe_create_post,
|
||||
"LIKE_POST": self._describe_like_post,
|
||||
"DISLIKE_POST": self._describe_dislike_post,
|
||||
"REPOST": self._describe_repost,
|
||||
"QUOTE_POST": self._describe_quote_post,
|
||||
"FOLLOW": self._describe_follow,
|
||||
"CREATE_COMMENT": self._describe_create_comment,
|
||||
"LIKE_COMMENT": self._describe_like_comment,
|
||||
"DISLIKE_COMMENT": self._describe_dislike_comment,
|
||||
"SEARCH_POSTS": self._describe_search,
|
||||
"SEARCH_USER": self._describe_search_user,
|
||||
"MUTE": self._describe_mute,
|
||||
}
|
||||
|
||||
describe_func = action_descriptions.get(self.action_type, self._describe_generic)
|
||||
description = describe_func()
|
||||
|
||||
# Keep the event time in the source text as well as episode metadata so
|
||||
# temporal extraction does not collapse a multi-action batch.
|
||||
return (
|
||||
f"[{self.timestamp}] [{self.platform} round {self.round_num}] "
|
||||
f"{self.agent_name}: {description}"
|
||||
)
|
||||
|
||||
def _describe_create_post(self) -> str:
|
||||
content = self.action_args.get("content", "")
|
||||
if content:
|
||||
return f"发布了一条帖子：「{content}」"
|
||||
return "发布了一条帖子"
|
||||
|
||||
def _describe_like_post(self) -> str:
|
||||
"""点赞帖子 - 包含帖子原文和作者信息"""
|
||||
post_content = self.action_args.get("post_content", "")
|
||||
post_author = self.action_args.get("post_author_name", "")
|
||||
|
||||
if post_content and post_author:
|
||||
return f"点赞了{post_author}的帖子：「{post_content}」"
|
||||
elif post_content:
|
||||
return f"点赞了一条帖子：「{post_content}」"
|
||||
elif post_author:
|
||||
return f"点赞了{post_author}的一条帖子"
|
||||
return "点赞了一条帖子"
|
||||
|
||||
def _describe_dislike_post(self) -> str:
|
||||
"""踩帖子 - 包含帖子原文和作者信息"""
|
||||
post_content = self.action_args.get("post_content", "")
|
||||
post_author = self.action_args.get("post_author_name", "")
|
||||
|
||||
if post_content and post_author:
|
||||
return f"踩了{post_author}的帖子：「{post_content}」"
|
||||
elif post_content:
|
||||
return f"踩了一条帖子：「{post_content}」"
|
||||
elif post_author:
|
||||
return f"踩了{post_author}的一条帖子"
|
||||
return "踩了一条帖子"
|
||||
|
||||
def _describe_repost(self) -> str:
|
||||
"""转发帖子 - 包含原帖内容和作者信息"""
|
||||
original_content = self.action_args.get("original_content", "")
|
||||
original_author = self.action_args.get("original_author_name", "")
|
||||
|
||||
if original_content and original_author:
|
||||
return f"转发了{original_author}的帖子：「{original_content}」"
|
||||
elif original_content:
|
||||
return f"转发了一条帖子：「{original_content}」"
|
||||
elif original_author:
|
||||
return f"转发了{original_author}的一条帖子"
|
||||
return "转发了一条帖子"
|
||||
|
||||
def _describe_quote_post(self) -> str:
|
||||
"""引用帖子 - 包含原帖内容、作者信息和引用评论"""
|
||||
original_content = self.action_args.get("original_content", "")
|
||||
original_author = self.action_args.get("original_author_name", "")
|
||||
quote_content = self.action_args.get("quote_content", "") or self.action_args.get("content", "")
|
||||
|
||||
base = ""
|
||||
if original_content and original_author:
|
||||
base = f"引用了{original_author}的帖子「{original_content}」"
|
||||
elif original_content:
|
||||
base = f"引用了一条帖子「{original_content}」"
|
||||
elif original_author:
|
||||
base = f"引用了{original_author}的一条帖子"
|
||||
else:
|
||||
base = "引用了一条帖子"
|
||||
|
||||
if quote_content:
|
||||
base += f"，并评论道：「{quote_content}」"
|
||||
return base
|
||||
|
||||
def _describe_follow(self) -> str:
|
||||
"""关注用户 - 包含被关注用户的名称"""
|
||||
target_user_name = self.action_args.get("target_user_name", "")
|
||||
|
||||
if target_user_name:
|
||||
return f"关注了用户「{target_user_name}」"
|
||||
return "关注了一个用户"
|
||||
|
||||
def _describe_create_comment(self) -> str:
|
||||
"""发表评论 - 包含评论内容和所评论的帖子信息"""
|
||||
content = self.action_args.get("content", "")
|
||||
post_content = self.action_args.get("post_content", "")
|
||||
post_author = self.action_args.get("post_author_name", "")
|
||||
|
||||
if content:
|
||||
if post_content and post_author:
|
||||
return f"在{post_author}的帖子「{post_content}」下评论道：「{content}」"
|
||||
elif post_content:
|
||||
return f"在帖子「{post_content}」下评论道：「{content}」"
|
||||
elif post_author:
|
||||
return f"在{post_author}的帖子下评论道：「{content}」"
|
||||
return f"评论道：「{content}」"
|
||||
return "发表了评论"
|
||||
|
||||
def _describe_like_comment(self) -> str:
|
||||
"""点赞评论 - 包含评论内容和作者信息"""
|
||||
comment_content = self.action_args.get("comment_content", "")
|
||||
comment_author = self.action_args.get("comment_author_name", "")
|
||||
|
||||
if comment_content and comment_author:
|
||||
return f"点赞了{comment_author}的评论：「{comment_content}」"
|
||||
elif comment_content:
|
||||
return f"点赞了一条评论：「{comment_content}」"
|
||||
elif comment_author:
|
||||
return f"点赞了{comment_author}的一条评论"
|
||||
return "点赞了一条评论"
|
||||
|
||||
def _describe_dislike_comment(self) -> str:
|
||||
"""踩评论 - 包含评论内容和作者信息"""
|
||||
comment_content = self.action_args.get("comment_content", "")
|
||||
comment_author = self.action_args.get("comment_author_name", "")
|
||||
|
||||
if comment_content and comment_author:
|
||||
return f"踩了{comment_author}的评论：「{comment_content}」"
|
||||
elif comment_content:
|
||||
return f"踩了一条评论：「{comment_content}」"
|
||||
elif comment_author:
|
||||
return f"踩了{comment_author}的一条评论"
|
||||
return "踩了一条评论"
|
||||
|
||||
def _describe_search(self) -> str:
|
||||
"""搜索帖子 - 包含搜索关键词"""
|
||||
query = self.action_args.get("query", "") or self.action_args.get("keyword", "")
|
||||
return f"搜索了「{query}」" if query else "进行了搜索"
|
||||
|
||||
def _describe_search_user(self) -> str:
|
||||
"""搜索用户 - 包含搜索关键词"""
|
||||
query = self.action_args.get("query", "") or self.action_args.get("username", "")
|
||||
return f"搜索了用户「{query}」" if query else "搜索了用户"
|
||||
|
||||
def _describe_mute(self) -> str:
|
||||
"""屏蔽用户 - 包含被屏蔽用户的名称"""
|
||||
target_user_name = self.action_args.get("target_user_name", "")
|
||||
|
||||
if target_user_name:
|
||||
return f"屏蔽了用户「{target_user_name}」"
|
||||
return "屏蔽了一个用户"
|
||||
|
||||
def _describe_generic(self) -> str:
|
||||
# 对于未知的动作类型，生成通用描述
|
||||
return f"执行了{self.action_type}操作"
|
||||
|
||||
|
||||
class _DrainDeadlineExceeded(TimeoutError):
|
||||
def __init__(self, processed_count: int):
|
||||
super().__init__("Zep updater drain deadline elapsed")
|
||||
self.processed_count = processed_count
|
||||
|
||||
|
||||
class ZepGraphMemoryUpdater:
|
||||
"""
|
||||
Zep图谱记忆更新器
|
||||
|
||||
监控模拟的actions日志文件，将新的agent活动实时更新到Zep图谱中。
|
||||
按平台分组，每累积BATCH_SIZE条活动后批量发送到Zep。
|
||||
|
||||
所有有意义的行为都会被更新到Zep，action_args中会包含完整的上下文信息：
|
||||
- 点赞/踩的帖子原文
|
||||
- 转发/引用的帖子原文
|
||||
- 关注/屏蔽的用户名
|
||||
- 点赞/踩的评论原文
|
||||
"""
|
||||
|
||||
# 批量发送大小（每个平台累积多少条后发送）
|
||||
BATCH_SIZE = 5
|
||||
|
||||
# 平台名称映射（用于控制台显示）
|
||||
PLATFORM_DISPLAY_NAMES = {
|
||||
'twitter': '世界1',
|
||||
'reddit': '世界2',
|
||||
}
|
||||
|
||||
# 发送间隔（秒），避免请求过快
|
||||
SEND_INTERVAL = 0.5
|
||||
|
||||
# Zep recommends keeping an episode below 10,000 characters. Leave room
|
||||
# for future source formatting changes.
|
||||
MAX_EPISODE_CHARS = 9_500
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
graph_id: str,
|
||||
api_key: Optional[str] = None,
|
||||
simulation_id: Optional[str] = None,
|
||||
):
|
||||
"""
|
||||
初始化更新器
|
||||
|
||||
Args:
|
||||
graph_id: Zep图谱ID
|
||||
api_key: Zep API Key（可选，默认从配置读取）
|
||||
"""
|
||||
self.graph_id = graph_id
|
||||
self.simulation_id = simulation_id or "unknown"
|
||||
self.api_key = api_key or Config.ZEP_API_KEY
|
||||
|
||||
if not self.api_key:
|
||||
raise ValueError("ZEP_API_KEY未配置")
|
||||
|
||||
self.client = get_zep_client(self.api_key)
|
||||
|
||||
# 活动队列
|
||||
self._activity_queue: Queue = Queue()
|
||||
|
||||
# 按平台分组的活动缓冲区（每个平台各自累积到BATCH_SIZE后批量发送）
|
||||
self._platform_buffers: Dict[str, List[AgentActivity]] = {
|
||||
'twitter': [],
|
||||
'reddit': [],
|
||||
}
|
||||
self._buffer_lock = threading.Lock()
|
||||
self._acceptance_lock = threading.Lock()
|
||||
|
||||
# 控制标志
|
||||
self._running = False
|
||||
self._worker_thread: Optional[threading.Thread] = None
|
||||
|
||||
# 统计
|
||||
self._total_activities = 0 # 实际添加到队列的活动数
|
||||
self._total_sent = 0 # 成功发送到Zep的批次数
|
||||
self._total_items_sent = 0 # 成功发送到Zep的活动条数
|
||||
self._failed_count = 0 # 发送失败的批次数
|
||||
self._skipped_count = 0 # 被过滤跳过的活动数（DO_NOTHING）
|
||||
self._failed_batches: List[Dict[str, Any]] = []
|
||||
self._pending_episode_uuids: List[str] = []
|
||||
|
||||
logger.info(f"ZepGraphMemoryUpdater 初始化完成: graph_id={graph_id}, batch_size={self.BATCH_SIZE}")
|
||||
|
||||
def _get_platform_display_name(self, platform: str) -> str:
|
||||
"""获取平台的显示名称"""
|
||||
return self.PLATFORM_DISPLAY_NAMES.get(platform.lower(), platform)
|
||||
|
||||
def start(self):
|
||||
"""启动后台工作线程"""
|
||||
if self._running:
|
||||
return
|
||||
|
||||
# Capture locale before spawning background thread
|
||||
current_locale = get_locale()
|
||||
|
||||
self._running = True
|
||||
self._worker_thread = threading.Thread(
|
||||
target=self._worker_loop,
|
||||
args=(current_locale,),
|
||||
daemon=True,
|
||||
name=f"ZepMemoryUpdater-{self.graph_id[:8]}"
|
||||
)
|
||||
self._worker_thread.start()
|
||||
logger.info(f"ZepGraphMemoryUpdater 已启动: graph_id={self.graph_id}")
|
||||
|
||||
def stop(self):
|
||||
"""Drain the worker, flush tail events, and wait for Cloud ingestion."""
|
||||
deadline = time.time() + ZEP_INGESTION_WAIT_TIMEOUT_SECONDS
|
||||
# Serialize the accepting->closed transition with add_activity's
|
||||
# check+enqueue operation. This closes the small race where a producer
|
||||
# could enqueue after both the worker and final flush had exited.
|
||||
with self._acceptance_lock:
|
||||
self._running = False
|
||||
|
||||
if self._worker_thread and self._worker_thread.is_alive():
|
||||
join_timeout = max(0.0, deadline - time.time())
|
||||
self._worker_thread.join(timeout=join_timeout)
|
||||
if self._worker_thread.is_alive():
|
||||
raise TimeoutError(
|
||||
f"Zep updater worker did not stop within {join_timeout:.0f}s"
|
||||
)
|
||||
|
||||
# The worker has drained the queue. Only now is it safe to flush
|
||||
# buffers; doing this before join loses an item already dequeued by the
|
||||
# worker but not yet buffered.
|
||||
self._flush_remaining(deadline=deadline)
|
||||
|
||||
if self._failed_batches:
|
||||
raise RuntimeError(
|
||||
f"{len(self._failed_batches)} Zep activity batch(es) failed; "
|
||||
"simulation graph ingestion is incomplete"
|
||||
)
|
||||
|
||||
self._wait_for_pending_episodes(deadline=deadline)
|
||||
|
||||
logger.info(f"ZepGraphMemoryUpdater 已停止: graph_id={self.graph_id}, "
|
||||
f"total_activities={self._total_activities}, "
|
||||
f"batches_sent={self._total_sent}, "
|
||||
f"items_sent={self._total_items_sent}, "
|
||||
f"failed={self._failed_count}, "
|
||||
f"skipped={self._skipped_count}")
|
||||
|
||||
def add_activity(self, activity: AgentActivity):
|
||||
"""
|
||||
添加一个agent活动到队列
|
||||
|
||||
所有有意义的行为都会被添加到队列，包括：
|
||||
- CREATE_POST（发帖）
|
||||
- CREATE_COMMENT（评论）
|
||||
- QUOTE_POST（引用帖子）
|
||||
- SEARCH_POSTS（搜索帖子）
|
||||
- SEARCH_USER（搜索用户）
|
||||
- LIKE_POST/DISLIKE_POST（点赞/踩帖子）
|
||||
- REPOST（转发）
|
||||
- FOLLOW（关注）
|
||||
- MUTE（屏蔽）
|
||||
- LIKE_COMMENT/DISLIKE_COMMENT（点赞/踩评论）
|
||||
|
||||
action_args中会包含完整的上下文信息（如帖子原文、用户名等）。
|
||||
|
||||
Args:
|
||||
activity: Agent活动记录
|
||||
"""
|
||||
# 跳过DO_NOTHING类型的活动
|
||||
if activity.action_type == "DO_NOTHING":
|
||||
self._skipped_count += 1
|
||||
return
|
||||
|
||||
with self._acceptance_lock:
|
||||
if not self._running:
|
||||
raise RuntimeError("Zep graph updater is not running")
|
||||
self._activity_queue.put(activity)
|
||||
self._total_activities += 1
|
||||
logger.debug(f"添加活动到Zep队列: {activity.agent_name} - {activity.action_type}")
|
||||
|
||||
def add_activity_from_dict(self, data: Dict[str, Any], platform: str):
|
||||
"""
|
||||
从字典数据添加活动
|
||||
|
||||
Args:
|
||||
data: 从actions.jsonl解析的字典数据
|
||||
platform: 平台名称 (twitter/reddit)
|
||||
"""
|
||||
# 跳过事件类型的条目
|
||||
if "event_type" in data:
|
||||
return
|
||||
if data.get("success") is False:
|
||||
self._skipped_count += 1
|
||||
return
|
||||
|
||||
activity = AgentActivity(
|
||||
platform=platform,
|
||||
agent_id=data.get("agent_id", 0),
|
||||
agent_name=data.get("agent_name", ""),
|
||||
action_type=data.get("action_type", ""),
|
||||
action_args=data.get("action_args", {}),
|
||||
round_num=data.get("round", 0),
|
||||
timestamp=data.get("timestamp", datetime.now().isoformat()),
|
||||
)
|
||||
|
||||
self.add_activity(activity)
|
||||
|
||||
def _worker_loop(self, locale: str = 'zh'):
|
||||
"""后台工作循环 - 按平台批量发送活动到Zep"""
|
||||
set_locale(locale)
|
||||
while self._running or not self._activity_queue.empty():
|
||||
try:
|
||||
# 尝试从队列获取活动（超时1秒）
|
||||
try:
|
||||
activity = self._activity_queue.get(timeout=1)
|
||||
|
||||
# 将活动添加到对应平台的缓冲区
|
||||
platform = activity.platform.lower()
|
||||
batch = None
|
||||
with self._buffer_lock:
|
||||
if platform not in self._platform_buffers:
|
||||
self._platform_buffers[platform] = []
|
||||
self._platform_buffers[platform].append(activity)
|
||||
|
||||
# 检查该平台是否达到批量大小
|
||||
if len(self._platform_buffers[platform]) >= self.BATCH_SIZE:
|
||||
batch = self._platform_buffers[platform][:self.BATCH_SIZE]
|
||||
self._platform_buffers[platform] = self._platform_buffers[platform][self.BATCH_SIZE:]
|
||||
|
||||
# Never hold the buffer lock across network I/O or sleep.
|
||||
if batch:
|
||||
self._send_batch_activities(batch, platform)
|
||||
time.sleep(self.SEND_INTERVAL)
|
||||
|
||||
except Empty:
|
||||
pass
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"工作循环异常: {e}")
|
||||
time.sleep(1)
|
||||
|
||||
def _build_episode_payloads(
|
||||
self,
|
||||
activities: List[AgentActivity],
|
||||
) -> List[tuple[List[AgentActivity], str]]:
|
||||
payloads: List[tuple[List[AgentActivity], str]] = []
|
||||
current_activities: List[AgentActivity] = []
|
||||
current_lines: List[str] = []
|
||||
current_length = 0
|
||||
|
||||
for activity in activities:
|
||||
text = activity.to_episode_text()
|
||||
if len(text) > self.MAX_EPISODE_CHARS:
|
||||
marker = "... [truncated by MiroFish]"
|
||||
text = text[: self.MAX_EPISODE_CHARS - len(marker)] + marker
|
||||
projected_length = current_length + (1 if current_lines else 0) + len(text)
|
||||
if current_lines and projected_length > self.MAX_EPISODE_CHARS:
|
||||
payloads.append((current_activities, "\n".join(current_lines)))
|
||||
current_activities = []
|
||||
current_lines = []
|
||||
current_length = 0
|
||||
current_activities.append(activity)
|
||||
current_lines.append(text)
|
||||
current_length += (1 if len(current_lines) > 1 else 0) + len(text)
|
||||
|
||||
if current_lines:
|
||||
payloads.append((current_activities, "\n".join(current_lines)))
|
||||
return payloads
|
||||
|
||||
def _send_batch_activities(
|
||||
self,
|
||||
activities: List[AgentActivity],
|
||||
platform: str,
|
||||
*,
|
||||
deadline: float | None = None,
|
||||
) -> int:
|
||||
"""
|
||||
批量发送活动到Zep图谱（合并为一条文本）
|
||||
|
||||
Args:
|
||||
activities: Agent活动列表
|
||||
platform: 平台名称
|
||||
"""
|
||||
if not activities:
|
||||
return 0
|
||||
|
||||
processed_count = 0
|
||||
for payload_activities, combined_text in self._build_episode_payloads(activities):
|
||||
if deadline is not None and time.time() >= deadline:
|
||||
raise _DrainDeadlineExceeded(processed_count)
|
||||
try:
|
||||
episode = self.client.graph.add(
|
||||
graph_id=self.graph_id,
|
||||
type="text",
|
||||
data=combined_text,
|
||||
created_at=self._to_rfc3339(payload_activities[-1].timestamp),
|
||||
source_description="MiroFish simulation activity batch",
|
||||
metadata={
|
||||
"source": "mirofish_simulation",
|
||||
"simulation_id": self.simulation_id,
|
||||
"platform": platform,
|
||||
"activity_count": len(payload_activities),
|
||||
"first_round": min(a.round_num for a in payload_activities),
|
||||
"last_round": max(a.round_num for a in payload_activities),
|
||||
"agent_ids": ",".join(
|
||||
str(value)
|
||||
for value in sorted({a.agent_id for a in payload_activities})
|
||||
),
|
||||
"action_types": ",".join(
|
||||
value
|
||||
for value in sorted({a.action_type for a in payload_activities})
|
||||
if value
|
||||
) or "unknown",
|
||||
},
|
||||
)
|
||||
|
||||
episode_uuid = (
|
||||
getattr(episode, "uuid_", None)
|
||||
or getattr(episode, "uuid", None)
|
||||
)
|
||||
if not episode_uuid:
|
||||
raise RuntimeError("Zep graph.add returned no episode UUID")
|
||||
self._pending_episode_uuids.append(str(episode_uuid))
|
||||
self._total_sent += 1
|
||||
self._total_items_sent += len(payload_activities)
|
||||
display_name = self._get_platform_display_name(platform)
|
||||
logger.info(f"成功批量发送 {len(payload_activities)} 条{display_name}活动到图谱 {self.graph_id}")
|
||||
logger.debug(f"批量内容预览: {combined_text[:200]}...")
|
||||
|
||||
except Exception as e:
|
||||
# graph.add has no idempotency key. Replaying an ambiguous
|
||||
# response can duplicate extracted facts, so fail closed and
|
||||
# surface the incomplete batch to SimulationRunner.
|
||||
logger.error(f"批量发送到Zep失败，未自动重放非幂等写入: {e}")
|
||||
self._failed_count += 1
|
||||
self._failed_batches.append({
|
||||
"platform": platform,
|
||||
"activities": payload_activities,
|
||||
"error": str(e),
|
||||
})
|
||||
finally:
|
||||
# Successes have a confirmed episode UUID; failures are kept
|
||||
# durably in _failed_batches and must never be replayed. Either
|
||||
# way this payload is accounted for before moving on.
|
||||
processed_count += len(payload_activities)
|
||||
return processed_count
|
||||
|
||||
@staticmethod
|
||||
def _to_rfc3339(value: str) -> str:
|
||||
try:
|
||||
parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
|
||||
if parsed.tzinfo is None:
|
||||
parsed = parsed.astimezone()
|
||||
return parsed.isoformat()
|
||||
except (AttributeError, TypeError, ValueError):
|
||||
return datetime.now().astimezone().isoformat()
|
||||
|
||||
def _flush_remaining(self, *, deadline: float | None = None):
|
||||
"""发送队列和缓冲区中剩余的活动"""
|
||||
# 首先处理队列中剩余的活动，添加到缓冲区
|
||||
while not self._activity_queue.empty():
|
||||
try:
|
||||
activity = self._activity_queue.get_nowait()
|
||||
platform = activity.platform.lower()
|
||||
with self._buffer_lock:
|
||||
if platform not in self._platform_buffers:
|
||||
self._platform_buffers[platform] = []
|
||||
self._platform_buffers[platform].append(activity)
|
||||
except Empty:
|
||||
break
|
||||
|
||||
for platform in list(self._platform_buffers):
|
||||
with self._buffer_lock:
|
||||
buffer = list(self._platform_buffers.get(platform, []))
|
||||
if not buffer:
|
||||
continue
|
||||
display_name = self._get_platform_display_name(platform)
|
||||
logger.info(f"发送{display_name}平台剩余的 {len(buffer)} 条活动")
|
||||
if deadline is not None and time.time() >= deadline:
|
||||
raise TimeoutError(
|
||||
"Zep updater drain deadline elapsed before flushing all activities"
|
||||
)
|
||||
try:
|
||||
processed_count = self._send_batch_activities(
|
||||
buffer,
|
||||
platform,
|
||||
deadline=deadline,
|
||||
)
|
||||
except _DrainDeadlineExceeded as error:
|
||||
with self._buffer_lock:
|
||||
del self._platform_buffers[platform][:error.processed_count]
|
||||
raise TimeoutError(str(error)) from error
|
||||
else:
|
||||
with self._buffer_lock:
|
||||
del self._platform_buffers[platform][:processed_count]
|
||||
|
||||
def _wait_for_pending_episodes(self, *, deadline: float | None = None) -> None:
|
||||
pending = set(self._pending_episode_uuids)
|
||||
if not pending:
|
||||
return
|
||||
|
||||
if deadline is None:
|
||||
deadline = time.time() + ZEP_INGESTION_WAIT_TIMEOUT_SECONDS
|
||||
while pending:
|
||||
if time.time() >= deadline:
|
||||
raise TimeoutError(
|
||||
f"Zep simulation ingestion timed out with {len(pending)} "
|
||||
"episode(s) pending"
|
||||
)
|
||||
for episode_uuid in list(pending):
|
||||
episode = call_zep_read_with_retry(
|
||||
lambda: self.client.graph.episode.get(uuid_=episode_uuid),
|
||||
operation_name=f"poll simulation episode {episode_uuid}",
|
||||
)
|
||||
if getattr(episode, "processed", False):
|
||||
pending.remove(episode_uuid)
|
||||
if pending:
|
||||
time.sleep(3)
|
||||
self._pending_episode_uuids = []
|
||||
|
||||
def get_stats(self) -> Dict[str, Any]:
|
||||
"""获取统计信息"""
|
||||
with self._buffer_lock:
|
||||
buffer_sizes = {p: len(b) for p, b in self._platform_buffers.items()}
|
||||
|
||||
return {
|
||||
"graph_id": self.graph_id,
|
||||
"batch_size": self.BATCH_SIZE,
|
||||
"total_activities": self._total_activities, # 添加到队列的活动总数
|
||||
"batches_sent": self._total_sent, # 成功发送的批次数
|
||||
"items_sent": self._total_items_sent, # 成功发送的活动条数
|
||||
"failed_count": self._failed_count, # 发送失败的批次数
|
||||
"pending_episode_count": len(self._pending_episode_uuids),
|
||||
"skipped_count": self._skipped_count, # 被过滤跳过的活动数（DO_NOTHING）
|
||||
"queue_size": self._activity_queue.qsize(),
|
||||
"buffer_sizes": buffer_sizes, # 各平台缓冲区大小
|
||||
"running": self._running,
|
||||
}
|
||||
|
||||
|
||||
class ZepGraphMemoryManager:
|
||||
"""
|
||||
管理多个模拟的Zep图谱记忆更新器
|
||||
|
||||
每个模拟可以有自己的更新器实例
|
||||
"""
|
||||
|
||||
_updaters: Dict[str, ZepGraphMemoryUpdater] = {}
|
||||
_lock = threading.Lock()
|
||||
|
||||
@classmethod
|
||||
def create_updater(cls, simulation_id: str, graph_id: str) -> ZepGraphMemoryUpdater:
|
||||
"""
|
||||
为模拟创建图谱记忆更新器
|
||||
|
||||
Args:
|
||||
simulation_id: 模拟ID
|
||||
graph_id: Zep图谱ID
|
||||
|
||||
Returns:
|
||||
ZepGraphMemoryUpdater实例
|
||||
"""
|
||||
with cls._lock:
|
||||
# 如果已存在，先停止旧的
|
||||
if simulation_id in cls._updaters:
|
||||
cls._updaters[simulation_id].stop()
|
||||
|
||||
updater = ZepGraphMemoryUpdater(
|
||||
graph_id,
|
||||
simulation_id=simulation_id,
|
||||
)
|
||||
updater.start()
|
||||
cls._updaters[simulation_id] = updater
|
||||
cls._stop_all_done = False
|
||||
|
||||
logger.info(f"创建图谱记忆更新器: simulation_id={simulation_id}, graph_id={graph_id}")
|
||||
return updater
|
||||
|
||||
@classmethod
|
||||
def get_updater(cls, simulation_id: str) -> Optional[ZepGraphMemoryUpdater]:
|
||||
"""获取模拟的更新器"""
|
||||
with cls._lock:
|
||||
return cls._updaters.get(simulation_id)
|
||||
|
||||
@classmethod
|
||||
def get_simulation_ids_for_graph(cls, graph_id: str) -> List[str]:
|
||||
"""Return simulations whose updater still owns or drains this graph."""
|
||||
|
||||
with cls._lock:
|
||||
return sorted(
|
||||
simulation_id
|
||||
for simulation_id, updater in cls._updaters.items()
|
||||
if updater.graph_id == graph_id
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def get_simulation_ids(cls) -> List[str]:
|
||||
"""Return every simulation with a retained updater."""
|
||||
|
||||
with cls._lock:
|
||||
return sorted(cls._updaters)
|
||||
|
||||
@classmethod
|
||||
def discard_inactive_updater(cls, simulation_id: str) -> bool:
|
||||
"""Discard a failed, fully stopped updater during graph destruction."""
|
||||
|
||||
with cls._lock:
|
||||
updater = cls._updaters.get(simulation_id)
|
||||
if updater is None:
|
||||
return False
|
||||
worker_alive = bool(
|
||||
updater._worker_thread and updater._worker_thread.is_alive()
|
||||
)
|
||||
if updater._running or worker_alive:
|
||||
raise RuntimeError(
|
||||
f"Zep updater for {simulation_id} is still active"
|
||||
)
|
||||
cls._updaters.pop(simulation_id, None)
|
||||
logger.warning(
|
||||
"Discarded incomplete Zep updater during explicit graph deletion: "
|
||||
"simulation_id=%s, graph_id=%s",
|
||||
simulation_id,
|
||||
updater.graph_id,
|
||||
)
|
||||
return True
|
||||
|
||||
@classmethod
|
||||
def stop_updater(cls, simulation_id: str):
|
||||
"""停止并移除模拟的更新器"""
|
||||
with cls._lock:
|
||||
updater = cls._updaters.get(simulation_id)
|
||||
if updater is None:
|
||||
return
|
||||
|
||||
# Do not hold the manager lock through up to several minutes of Cloud
|
||||
# polling. Crucially, only remove the updater after a successful drain;
|
||||
# on failure it remains visible to report/deletion barriers and can be
|
||||
# stopped again.
|
||||
updater.stop()
|
||||
with cls._lock:
|
||||
if cls._updaters.get(simulation_id) is updater:
|
||||
cls._updaters.pop(simulation_id, None)
|
||||
logger.info(f"已停止图谱记忆更新器: simulation_id={simulation_id}")
|
||||
|
||||
# 防止 stop_all 重复调用的标志
|
||||
_stop_all_done = False
|
||||
|
||||
@classmethod
|
||||
def stop_all(cls):
|
||||
"""停止所有更新器"""
|
||||
# 防止重复调用
|
||||
if cls._stop_all_done:
|
||||
return
|
||||
|
||||
with cls._lock:
|
||||
simulation_ids = list(cls._updaters)
|
||||
|
||||
errors = []
|
||||
for simulation_id in simulation_ids:
|
||||
try:
|
||||
cls.stop_updater(simulation_id)
|
||||
except Exception as error:
|
||||
# Keep a failed updater registered so the caller can retry and
|
||||
# lifecycle/report guards still see the incomplete ingestion.
|
||||
logger.error(
|
||||
"停止更新器失败: simulation_id=%s, error=%s",
|
||||
simulation_id,
|
||||
error,
|
||||
)
|
||||
errors.append((simulation_id, error))
|
||||
|
||||
with cls._lock:
|
||||
cls._stop_all_done = not cls._updaters
|
||||
|
||||
if errors:
|
||||
details = "; ".join(
|
||||
f"{simulation_id}: {error}"
|
||||
for simulation_id, error in errors
|
||||
)
|
||||
raise RuntimeError(f"部分图谱更新器未完整停止: {details}")
|
||||
logger.info("已停止所有图谱记忆更新器")
|
||||
|
||||
@classmethod
|
||||
def get_all_stats(cls) -> Dict[str, Dict[str, Any]]:
|
||||
"""获取所有更新器的统计信息"""
|
||||
return {
|
||||
sim_id: updater.get_stats()
|
||||
for sim_id, updater in cls._updaters.items()
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,10 +1,16 @@
|
|||
"""
|
||||
工具模块
|
||||
Utilities module
|
||||
"""
|
||||
|
||||
from .file_parser import FileParser
|
||||
from .llm_client import LLMClient
|
||||
from .locale import t, get_locale, set_locale, get_language_instruction
|
||||
|
||||
__all__ = ['FileParser', 'LLMClient', 't', 'get_locale', 'set_locale', 'get_language_instruction']
|
||||
|
||||
__all__ = [
|
||||
"FileParser",
|
||||
"LLMClient",
|
||||
"t",
|
||||
"get_locale",
|
||||
"set_locale",
|
||||
"get_language_instruction",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -0,0 +1,55 @@
|
|||
"""
|
||||
Embeddings utility — uses Ollama /v1/embeddings or OpenAI-compatible endpoint.
|
||||
Falls back to None if embeddings endpoint unavailable.
|
||||
"""
|
||||
|
||||
import json
|
||||
from typing import List, Optional
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
|
||||
logger = get_logger("mirofish.embeddings")
|
||||
|
||||
_embedding_client = None
|
||||
|
||||
|
||||
def _get_client():
|
||||
global _embedding_client
|
||||
if _embedding_client is None:
|
||||
from openai import OpenAI
|
||||
|
||||
_embedding_client = OpenAI(
|
||||
api_key=Config.LLM_API_KEY or "ollama",
|
||||
base_url=Config.LLM_BASE_URL,
|
||||
)
|
||||
return _embedding_client
|
||||
|
||||
|
||||
def embed(text: str, model: Optional[str] = None) -> Optional[List[float]]:
|
||||
"""Embed text via the configured LLM endpoint. Returns None on failure."""
|
||||
if not text or not text.strip():
|
||||
return None
|
||||
embed_model = model or Config.LLM_MODEL_NAME
|
||||
try:
|
||||
client = _get_client()
|
||||
resp = client.embeddings.create(model=embed_model, input=text[:8000])
|
||||
if resp.data and len(resp.data) > 0:
|
||||
return resp.data[0].embedding
|
||||
except Exception as e:
|
||||
logger.debug(
|
||||
f"Embedding failed (non-fatal, keyword search will be used): {str(e)[:100]}"
|
||||
)
|
||||
return None
|
||||
|
||||
|
||||
def cosine_similarity(a: List[float], b: List[float]) -> float:
|
||||
"""Pure-Python cosine similarity. ponytail: O(d) per comparison, fine for small graphs."""
|
||||
if not a or not b or len(a) != len(b):
|
||||
return 0.0
|
||||
dot = sum(x * y for x, y in zip(a, b))
|
||||
norm_a = sum(x * x for x in a) ** 0.5
|
||||
norm_b = sum(x * x for x in b) ** 0.5
|
||||
if norm_a == 0 or norm_b == 0:
|
||||
return 0.0
|
||||
return dot / (norm_a * norm_b)
|
||||
|
|
@ -1,68 +1,72 @@
|
|||
"""
|
||||
文件解析工具
|
||||
支持PDF、Markdown、TXT文件的文本提取
|
||||
File parsing utility
|
||||
Supports text extraction from PDF, Markdown, TXT files
|
||||
"""
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import List, Optional
|
||||
|
||||
from .locale import t
|
||||
|
||||
|
||||
def _read_text_with_fallback(file_path: str) -> str:
|
||||
"""
|
||||
读取文本文件，UTF-8失败时自动探测编码。
|
||||
|
||||
采用多级回退策略：
|
||||
1. 首先尝试 UTF-8 解码
|
||||
2. 使用 charset_normalizer 检测编码
|
||||
3. 回退到 chardet 检测编码
|
||||
4. 最终使用 UTF-8 + errors='replace' 兜底
|
||||
|
||||
Read a text file, automatically detecting encoding when UTF-8 fails.
|
||||
|
||||
Uses a multi-level fallback strategy:
|
||||
1. First try UTF-8 decoding
|
||||
2. Use charset_normalizer to detect encoding
|
||||
3. Fall back to chardet to detect encoding
|
||||
4. Finally use UTF-8 + errors='replace' as a last resort
|
||||
|
||||
Args:
|
||||
file_path: 文件路径
|
||||
|
||||
file_path: File path
|
||||
|
||||
Returns:
|
||||
解码后的文本内容
|
||||
Decoded text content
|
||||
"""
|
||||
data = Path(file_path).read_bytes()
|
||||
|
||||
# 首先尝试 UTF-8
|
||||
|
||||
# First try UTF-8
|
||||
try:
|
||||
return data.decode('utf-8')
|
||||
return data.decode("utf-8")
|
||||
except UnicodeDecodeError:
|
||||
pass
|
||||
|
||||
# 尝试使用 charset_normalizer 检测编码
|
||||
|
||||
# Try using charset_normalizer to detect encoding
|
||||
encoding = None
|
||||
try:
|
||||
from charset_normalizer import from_bytes
|
||||
|
||||
best = from_bytes(data).best()
|
||||
if best and best.encoding:
|
||||
encoding = best.encoding
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 回退到 chardet
|
||||
|
||||
# Fall back to chardet
|
||||
if not encoding:
|
||||
try:
|
||||
import chardet
|
||||
|
||||
result = chardet.detect(data)
|
||||
encoding = result.get('encoding') if result else None
|
||||
encoding = result.get("encoding") if result else None
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 最终兜底：使用 UTF-8 + replace
|
||||
|
||||
# Final fallback: use UTF-8 + replace
|
||||
if not encoding:
|
||||
encoding = 'utf-8'
|
||||
|
||||
return data.decode(encoding, errors='replace')
|
||||
encoding = "utf-8"
|
||||
|
||||
return data.decode(encoding, errors="replace")
|
||||
|
||||
|
||||
class FileParser:
|
||||
"""文件解析器"""
|
||||
|
||||
SUPPORTED_EXTENSIONS = {'.pdf', '.md', '.markdown', '.txt'}
|
||||
|
||||
"""File parser"""
|
||||
|
||||
SUPPORTED_EXTENSIONS = {".pdf", ".md", ".markdown", ".txt"}
|
||||
|
||||
@classmethod
|
||||
def is_supported(cls, file_path: str) -> bool:
|
||||
"""
|
||||
|
|
@ -80,124 +84,134 @@ class FileParser:
|
|||
@classmethod
|
||||
def extract_text(cls, file_path: str) -> str:
|
||||
"""
|
||||
从文件中提取文本
|
||||
|
||||
Extract text from a file
|
||||
|
||||
Args:
|
||||
file_path: 文件路径
|
||||
|
||||
file_path: File path
|
||||
|
||||
Returns:
|
||||
提取的文本内容
|
||||
Extracted text content
|
||||
"""
|
||||
path = Path(file_path)
|
||||
|
||||
|
||||
if not path.exists():
|
||||
raise FileNotFoundError(f"文件不存在: {file_path}")
|
||||
|
||||
raise FileNotFoundError(t("api.fileNotFound", path=file_path))
|
||||
|
||||
suffix = path.suffix.lower()
|
||||
|
||||
|
||||
if suffix not in cls.SUPPORTED_EXTENSIONS:
|
||||
raise ValueError(f"不支持的文件格式: {suffix}")
|
||||
|
||||
if suffix == '.pdf':
|
||||
raise ValueError(t("api.unsupportedFileFormat", suffix=suffix))
|
||||
|
||||
if suffix == ".pdf":
|
||||
return cls._extract_from_pdf(file_path)
|
||||
elif suffix in {'.md', '.markdown'}:
|
||||
elif suffix in {".md", ".markdown"}:
|
||||
return cls._extract_from_md(file_path)
|
||||
elif suffix == '.txt':
|
||||
elif suffix == ".txt":
|
||||
return cls._extract_from_txt(file_path)
|
||||
|
||||
raise ValueError(f"无法处理的文件格式: {suffix}")
|
||||
|
||||
|
||||
raise ValueError(t("api.unsupportedFileFormatGeneric", suffix=suffix))
|
||||
|
||||
@staticmethod
|
||||
def _extract_from_pdf(file_path: str) -> str:
|
||||
"""从PDF提取文本"""
|
||||
"""Extract text from a PDF"""
|
||||
try:
|
||||
import fitz # PyMuPDF
|
||||
except ImportError:
|
||||
raise ImportError("需要安装PyMuPDF: pip install PyMuPDF")
|
||||
|
||||
raise ImportError("PyMuPDF is required: pip install PyMuPDF")
|
||||
|
||||
text_parts = []
|
||||
with fitz.open(file_path) as doc:
|
||||
for page in doc:
|
||||
text = page.get_text()
|
||||
if text.strip():
|
||||
text_parts.append(text)
|
||||
|
||||
|
||||
return "\n\n".join(text_parts)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _extract_from_md(file_path: str) -> str:
|
||||
"""从Markdown提取文本，支持自动编码检测"""
|
||||
"""Extract text from Markdown, with automatic encoding detection"""
|
||||
return _read_text_with_fallback(file_path)
|
||||
|
||||
|
||||
@staticmethod
|
||||
def _extract_from_txt(file_path: str) -> str:
|
||||
"""从TXT提取文本，支持自动编码检测"""
|
||||
"""Extract text from TXT, with automatic encoding detection"""
|
||||
return _read_text_with_fallback(file_path)
|
||||
|
||||
|
||||
@classmethod
|
||||
def extract_from_multiple(cls, file_paths: List[str]) -> str:
|
||||
"""
|
||||
从多个文件提取文本并合并
|
||||
|
||||
Extract text from multiple files and merge them
|
||||
|
||||
Args:
|
||||
file_paths: 文件路径列表
|
||||
|
||||
file_paths: List of file paths
|
||||
|
||||
Returns:
|
||||
合并后的文本
|
||||
Merged text
|
||||
"""
|
||||
all_texts = []
|
||||
|
||||
|
||||
for i, file_path in enumerate(file_paths, 1):
|
||||
try:
|
||||
text = cls.extract_text(file_path)
|
||||
filename = Path(file_path).name
|
||||
all_texts.append(f"=== 文档 {i}: {filename} ===\n{text}")
|
||||
all_texts.append(f"=== Document {i}: {filename} ===\n{text}")
|
||||
except Exception as e:
|
||||
all_texts.append(f"=== 文档 {i}: {file_path} (提取失败: {str(e)}) ===")
|
||||
|
||||
all_texts.append(
|
||||
f"=== Document {i}: {file_path} (extraction failed: {str(e)}) ==="
|
||||
)
|
||||
|
||||
return "\n\n".join(all_texts)
|
||||
|
||||
|
||||
def split_text_into_chunks(
|
||||
text: str,
|
||||
chunk_size: int = 500,
|
||||
overlap: int = 50
|
||||
text: str, chunk_size: int = 500, overlap: int = 50
|
||||
) -> List[str]:
|
||||
"""
|
||||
将文本分割成小块
|
||||
|
||||
Split text into chunks
|
||||
|
||||
Args:
|
||||
text: 原始文本
|
||||
chunk_size: 每块的字符数
|
||||
overlap: 重叠字符数
|
||||
|
||||
text: Original text
|
||||
chunk_size: Number of characters per chunk
|
||||
overlap: Number of overlapping characters
|
||||
|
||||
Returns:
|
||||
文本块列表
|
||||
List of text chunks
|
||||
"""
|
||||
if len(text) <= chunk_size:
|
||||
return [text] if text.strip() else []
|
||||
|
||||
|
||||
chunks = []
|
||||
start = 0
|
||||
|
||||
|
||||
while start < len(text):
|
||||
end = start + chunk_size
|
||||
|
||||
# 尝试在句子边界处分割
|
||||
|
||||
# Try to split at sentence boundaries
|
||||
if end < len(text):
|
||||
# 查找最近的句子结束符
|
||||
for sep in ['。', '！', '？', '.\n', '!\n', '?\n', '\n\n', '. ', '! ', '? ']:
|
||||
# Find the nearest sentence-ending separator
|
||||
for sep in [
|
||||
"。",
|
||||
"！",
|
||||
"？",
|
||||
".\n",
|
||||
"!\n",
|
||||
"?\n",
|
||||
"\n\n",
|
||||
". ",
|
||||
"! ",
|
||||
"? ",
|
||||
]:
|
||||
last_sep = text[start:end].rfind(sep)
|
||||
if last_sep != -1 and last_sep > chunk_size * 0.3:
|
||||
end = start + last_sep + len(sep)
|
||||
break
|
||||
|
||||
|
||||
chunk = text[start:end].strip()
|
||||
if chunk:
|
||||
chunks.append(chunk)
|
||||
|
||||
# 下一个块从重叠位置开始
|
||||
start = end - overlap if end < len(text) else len(text)
|
||||
|
||||
return chunks
|
||||
|
||||
# Next chunk starts from the overlap position
|
||||
start = end - overlap if end < len(text) else len(text)
|
||||
|
||||
return chunks
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
"""
|
||||
LLM客户端封装
|
||||
统一使用OpenAI格式调用
|
||||
LLM client wrapper
|
||||
Uses OpenAI-compatible API format
|
||||
"""
|
||||
|
||||
import json
|
||||
|
|
@ -9,48 +9,46 @@ from typing import Optional, Dict, Any, List
|
|||
from openai import OpenAI
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.locale import t
|
||||
from .openai_chat_compat import create_chat_completion, extract_chat_completion_text
|
||||
|
||||
|
||||
class LLMClient:
|
||||
"""LLM客户端"""
|
||||
|
||||
"""LLM client"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
api_key: Optional[str] = None,
|
||||
base_url: Optional[str] = None,
|
||||
model: Optional[str] = None
|
||||
model: Optional[str] = None,
|
||||
):
|
||||
self.api_key = api_key or Config.LLM_API_KEY
|
||||
self.base_url = base_url or Config.LLM_BASE_URL
|
||||
self.model = model or Config.LLM_MODEL_NAME
|
||||
|
||||
|
||||
if not self.api_key:
|
||||
raise ValueError("LLM_API_KEY 未配置")
|
||||
|
||||
self.client = OpenAI(
|
||||
api_key=self.api_key,
|
||||
base_url=self.base_url
|
||||
)
|
||||
|
||||
raise ValueError(t("api.llmApiKeyMissing"))
|
||||
|
||||
self.client = OpenAI(api_key=self.api_key, base_url=self.base_url)
|
||||
|
||||
def chat(
|
||||
self,
|
||||
messages: List[Dict[str, str]],
|
||||
temperature: float = 0.7,
|
||||
max_tokens: int = 4096,
|
||||
response_format: Optional[Dict] = None
|
||||
max_tokens: int = 8192,
|
||||
response_format: Optional[Dict] = None,
|
||||
) -> str:
|
||||
"""
|
||||
发送聊天请求
|
||||
|
||||
Send a chat request
|
||||
|
||||
Args:
|
||||
messages: 消息列表
|
||||
temperature: 温度参数
|
||||
max_tokens: 最大token数
|
||||
response_format: 响应格式（如JSON模式）
|
||||
|
||||
messages: List of messages
|
||||
temperature: Temperature parameter
|
||||
max_tokens: Maximum number of tokens
|
||||
response_format: Response format (e.g. JSON mode)
|
||||
|
||||
Returns:
|
||||
模型响应文本
|
||||
Model response text
|
||||
"""
|
||||
response = create_chat_completion(
|
||||
self.client,
|
||||
|
|
@ -61,41 +59,42 @@ class LLMClient:
|
|||
response_format=response_format,
|
||||
)
|
||||
content = extract_chat_completion_text(response)
|
||||
# 部分模型（如MiniMax M2.5）会在content中包含<think>思考内容，需要移除
|
||||
content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
|
||||
# Some models (e.g. MiniMax M2.5) include thinking content in the content; remove it
|
||||
content = re.sub(r"<think>[\s\S]*?</think>", "", content).strip()
|
||||
return content
|
||||
|
||||
|
||||
def chat_json(
|
||||
self,
|
||||
messages: List[Dict[str, str]],
|
||||
temperature: float = 0.3,
|
||||
max_tokens: int = 4096
|
||||
max_tokens: int = 8192,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
发送聊天请求并返回JSON
|
||||
|
||||
Send a chat request and return JSON
|
||||
|
||||
Args:
|
||||
messages: 消息列表
|
||||
temperature: 温度参数
|
||||
max_tokens: 最大token数
|
||||
|
||||
messages: List of messages
|
||||
temperature: Temperature parameter
|
||||
max_tokens: Maximum number of tokens
|
||||
|
||||
Returns:
|
||||
解析后的JSON对象
|
||||
Parsed JSON object
|
||||
"""
|
||||
response = self.chat(
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
response_format={"type": "json_object"}
|
||||
response_format={"type": "json_object"},
|
||||
)
|
||||
# 清理markdown代码块标记
|
||||
# Clean up markdown code block markers
|
||||
cleaned_response = response.strip()
|
||||
cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE)
|
||||
cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response)
|
||||
cleaned_response = re.sub(
|
||||
r"^```(?:json)?\s*\n?", "", cleaned_response, flags=re.IGNORECASE
|
||||
)
|
||||
cleaned_response = re.sub(r"\n?```\s*$", "", cleaned_response)
|
||||
cleaned_response = cleaned_response.strip()
|
||||
|
||||
try:
|
||||
return json.loads(cleaned_response)
|
||||
except json.JSONDecodeError:
|
||||
raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}")
|
||||
|
||||
raise ValueError(t("api.llmJsonInvalid", response=cleaned_response))
|
||||
|
|
@ -5,18 +5,18 @@ from flask import request, has_request_context
|
|||
|
||||
_thread_local = threading.local()
|
||||
|
||||
_locales_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'locales')
|
||||
_locales_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", "locales")
|
||||
|
||||
# Load language registry
|
||||
with open(os.path.join(_locales_dir, 'languages.json'), 'r', encoding='utf-8') as f:
|
||||
with open(os.path.join(_locales_dir, "languages.json"), "r", encoding="utf-8") as f:
|
||||
_languages = json.load(f)
|
||||
|
||||
# Load translation files
|
||||
_translations = {}
|
||||
for filename in os.listdir(_locales_dir):
|
||||
if filename.endswith('.json') and filename != 'languages.json':
|
||||
if filename.endswith(".json") and filename != "languages.json":
|
||||
locale_name = filename[:-5]
|
||||
with open(os.path.join(_locales_dir, filename), 'r', encoding='utf-8') as f:
|
||||
with open(os.path.join(_locales_dir, filename), "r", encoding="utf-8") as f:
|
||||
_translations[locale_name] = json.load(f)
|
||||
|
||||
|
||||
|
|
@ -27,17 +27,17 @@ def set_locale(locale: str):
|
|||
|
||||
def get_locale() -> str:
|
||||
if has_request_context():
|
||||
raw = request.headers.get('Accept-Language', 'zh')
|
||||
return raw if raw in _translations else 'zh'
|
||||
return getattr(_thread_local, 'locale', 'zh')
|
||||
raw = request.headers.get("Accept-Language", "en")
|
||||
return raw if raw in _translations else "en"
|
||||
return getattr(_thread_local, "locale", "en")
|
||||
|
||||
|
||||
def t(key: str, **kwargs) -> str:
|
||||
locale = get_locale()
|
||||
messages = _translations.get(locale, _translations.get('zh', {}))
|
||||
messages = _translations.get(locale, _translations.get("en", {}))
|
||||
|
||||
value = messages
|
||||
for part in key.split('.'):
|
||||
for part in key.split("."):
|
||||
if isinstance(value, dict):
|
||||
value = value.get(part)
|
||||
else:
|
||||
|
|
@ -45,8 +45,8 @@ def t(key: str, **kwargs) -> str:
|
|||
break
|
||||
|
||||
if value is None:
|
||||
value = _translations.get('zh', {})
|
||||
for part in key.split('.'):
|
||||
value = _translations.get("en", {})
|
||||
for part in key.split("."):
|
||||
if isinstance(value, dict):
|
||||
value = value.get(part)
|
||||
else:
|
||||
|
|
@ -58,12 +58,12 @@ def t(key: str, **kwargs) -> str:
|
|||
|
||||
if kwargs:
|
||||
for k, v in kwargs.items():
|
||||
value = value.replace(f'{{{k}}}', str(v))
|
||||
value = value.replace(f"{{{k}}}", str(v))
|
||||
|
||||
return value
|
||||
|
||||
|
||||
def get_language_instruction() -> str:
|
||||
locale = get_locale()
|
||||
lang_config = _languages.get(locale, _languages.get('zh', {}))
|
||||
return lang_config.get('llmInstruction', '请使用中文回答。')
|
||||
lang_config = _languages.get(locale, _languages.get("en", {}))
|
||||
return lang_config.get("llmInstruction", "Please respond in English.")
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
"""
|
||||
日志配置模块
|
||||
提供统一的日志管理，同时输出到控制台和文件
|
||||
Logging configuration module
|
||||
Provides unified log management, outputting to both console and file
|
||||
"""
|
||||
|
||||
import os
|
||||
|
|
@ -12,91 +12,92 @@ from logging.handlers import RotatingFileHandler
|
|||
|
||||
def _ensure_utf8_stdout():
|
||||
"""
|
||||
确保 stdout/stderr 使用 UTF-8 编码
|
||||
解决 Windows 控制台中文乱码问题
|
||||
Ensure stdout/stderr use UTF-8 encoding
|
||||
Fixes garbled Chinese output in the Windows console
|
||||
"""
|
||||
if sys.platform == 'win32':
|
||||
# Windows 下重新配置标准输出为 UTF-8
|
||||
if hasattr(sys.stdout, 'reconfigure'):
|
||||
sys.stdout.reconfigure(encoding='utf-8', errors='replace')
|
||||
if hasattr(sys.stderr, 'reconfigure'):
|
||||
sys.stderr.reconfigure(encoding='utf-8', errors='replace')
|
||||
if sys.platform == "win32":
|
||||
# Reconfigure standard output to UTF-8 on Windows
|
||||
if hasattr(sys.stdout, "reconfigure"):
|
||||
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
|
||||
if hasattr(sys.stderr, "reconfigure"):
|
||||
sys.stderr.reconfigure(encoding="utf-8", errors="replace")
|
||||
|
||||
|
||||
# 日志目录
|
||||
LOG_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'logs')
|
||||
# Log directory
|
||||
LOG_DIR = os.path.join(
|
||||
os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "logs"
|
||||
)
|
||||
|
||||
|
||||
def setup_logger(name: str = 'mirofish', level: int = logging.DEBUG) -> logging.Logger:
|
||||
def setup_logger(name: str = "mirofish", level: int = logging.DEBUG) -> logging.Logger:
|
||||
"""
|
||||
设置日志器
|
||||
|
||||
Set up a logger
|
||||
|
||||
Args:
|
||||
name: 日志器名称
|
||||
level: 日志级别
|
||||
|
||||
name: Logger name
|
||||
level: Log level
|
||||
|
||||
Returns:
|
||||
配置好的日志器
|
||||
Configured logger
|
||||
"""
|
||||
# 确保日志目录存在
|
||||
# Ensure log directory exists
|
||||
os.makedirs(LOG_DIR, exist_ok=True)
|
||||
|
||||
# 创建日志器
|
||||
|
||||
# Create logger
|
||||
logger = logging.getLogger(name)
|
||||
logger.setLevel(level)
|
||||
|
||||
# 阻止日志向上传播到根 logger，避免重复输出
|
||||
|
||||
# Prevent logs from propagating to the root logger to avoid duplicate output
|
||||
logger.propagate = False
|
||||
|
||||
# 如果已经有处理器，不重复添加
|
||||
|
||||
# If handlers already exist, do not add duplicates
|
||||
if logger.handlers:
|
||||
return logger
|
||||
|
||||
# 日志格式
|
||||
|
||||
# Log format
|
||||
detailed_formatter = logging.Formatter(
|
||||
'[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s',
|
||||
datefmt='%Y-%m-%d %H:%M:%S'
|
||||
"[%(asctime)s] %(levelname)s [%(name)s.%(funcName)s:%(lineno)d] %(message)s",
|
||||
datefmt="%Y-%m-%d %H:%M:%S",
|
||||
)
|
||||
|
||||
|
||||
simple_formatter = logging.Formatter(
|
||||
'[%(asctime)s] %(levelname)s: %(message)s',
|
||||
datefmt='%H:%M:%S'
|
||||
"[%(asctime)s] %(levelname)s: %(message)s", datefmt="%H:%M:%S"
|
||||
)
|
||||
|
||||
# 1. 文件处理器 - 详细日志（按日期命名，带轮转）
|
||||
log_filename = datetime.now().strftime('%Y-%m-%d') + '.log'
|
||||
|
||||
# 1. File handler - detailed logs (named by date, with rotation)
|
||||
log_filename = datetime.now().strftime("%Y-%m-%d") + ".log"
|
||||
file_handler = RotatingFileHandler(
|
||||
os.path.join(LOG_DIR, log_filename),
|
||||
maxBytes=10 * 1024 * 1024, # 10MB
|
||||
backupCount=5,
|
||||
encoding='utf-8'
|
||||
encoding="utf-8",
|
||||
)
|
||||
file_handler.setLevel(logging.DEBUG)
|
||||
file_handler.setFormatter(detailed_formatter)
|
||||
|
||||
# 2. 控制台处理器 - 简洁日志（INFO及以上）
|
||||
# 确保 Windows 下使用 UTF-8 编码，避免中文乱码
|
||||
|
||||
# 2. Console handler - concise logs (INFO and above)
|
||||
# Ensure UTF-8 encoding on Windows to avoid garbled Chinese characters
|
||||
_ensure_utf8_stdout()
|
||||
console_handler = logging.StreamHandler(sys.stdout)
|
||||
console_handler.setLevel(logging.INFO)
|
||||
console_handler.setFormatter(simple_formatter)
|
||||
|
||||
# 添加处理器
|
||||
|
||||
# Add handlers
|
||||
logger.addHandler(file_handler)
|
||||
logger.addHandler(console_handler)
|
||||
|
||||
|
||||
return logger
|
||||
|
||||
|
||||
def get_logger(name: str = 'mirofish') -> logging.Logger:
|
||||
def get_logger(name: str = "mirofish") -> logging.Logger:
|
||||
"""
|
||||
获取日志器（如果不存在则创建）
|
||||
|
||||
Get a logger (create one if it does not exist)
|
||||
|
||||
Args:
|
||||
name: 日志器名称
|
||||
|
||||
name: Logger name
|
||||
|
||||
Returns:
|
||||
日志器实例
|
||||
Logger instance
|
||||
"""
|
||||
logger = logging.getLogger(name)
|
||||
if not logger.handlers:
|
||||
|
|
@ -104,23 +105,26 @@ def get_logger(name: str = 'mirofish') -> logging.Logger:
|
|||
return logger
|
||||
|
||||
|
||||
# 创建默认日志器
|
||||
# Create default logger
|
||||
logger = setup_logger()
|
||||
|
||||
|
||||
# 便捷方法
|
||||
def debug(msg: str, *args, **kwargs) -> None:
|
||||
# Convenience methods
|
||||
def debug(msg, *args, **kwargs):
|
||||
logger.debug(msg, *args, **kwargs)
|
||||
|
||||
def info(msg: str, *args, **kwargs) -> None:
|
||||
|
||||
def info(msg, *args, **kwargs):
|
||||
logger.info(msg, *args, **kwargs)
|
||||
|
||||
def warning(msg: str, *args, **kwargs) -> None:
|
||||
|
||||
def warning(msg, *args, **kwargs):
|
||||
logger.warning(msg, *args, **kwargs)
|
||||
|
||||
def error(msg: str, *args, **kwargs) -> None:
|
||||
|
||||
def error(msg, *args, **kwargs):
|
||||
logger.error(msg, *args, **kwargs)
|
||||
|
||||
def critical(msg: str, *args, **kwargs) -> None:
|
||||
logger.critical(msg, *args, **kwargs)
|
||||
|
||||
def critical(msg, *args, **kwargs):
|
||||
logger.critical(msg, *args, **kwargs)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
"""
|
||||
API调用重试机制
|
||||
用于处理LLM等外部API调用的重试逻辑
|
||||
API call retry mechanism
|
||||
Used to handle retry logic for external API calls such as LLM
|
||||
"""
|
||||
|
||||
import time
|
||||
|
|
@ -9,7 +9,7 @@ import functools
|
|||
from typing import Callable, Any, Optional, Type, Tuple
|
||||
from ..utils.logger import get_logger
|
||||
|
||||
logger = get_logger('mirofish.retry')
|
||||
logger = get_logger("mirofish.retry")
|
||||
|
||||
|
||||
def retry_with_backoff(
|
||||
|
|
@ -19,61 +19,65 @@ def retry_with_backoff(
|
|||
backoff_factor: float = 2.0,
|
||||
jitter: bool = True,
|
||||
exceptions: Tuple[Type[Exception], ...] = (Exception,),
|
||||
on_retry: Optional[Callable[[Exception, int], None]] = None
|
||||
on_retry: Optional[Callable[[Exception, int], None]] = None,
|
||||
):
|
||||
"""
|
||||
带指数退避的重试装饰器
|
||||
|
||||
Retry decorator with exponential backoff
|
||||
|
||||
Args:
|
||||
max_retries: 最大重试次数
|
||||
initial_delay: 初始延迟（秒）
|
||||
max_delay: 最大延迟（秒）
|
||||
backoff_factor: 退避因子
|
||||
jitter: 是否添加随机抖动
|
||||
exceptions: 需要重试的异常类型
|
||||
on_retry: 重试时的回调函数 (exception, retry_count)
|
||||
|
||||
max_retries: Maximum number of retries
|
||||
initial_delay: Initial delay (seconds)
|
||||
max_delay: Maximum delay (seconds)
|
||||
backoff_factor: Backoff factor
|
||||
jitter: Whether to add random jitter
|
||||
exceptions: Exception types to retry on
|
||||
on_retry: Retry callback function (exception, retry_count)
|
||||
|
||||
Usage:
|
||||
@retry_with_backoff(max_retries=3)
|
||||
def call_llm_api():
|
||||
...
|
||||
"""
|
||||
|
||||
def decorator(func: Callable) -> Callable:
|
||||
@functools.wraps(func)
|
||||
def wrapper(*args, **kwargs) -> Any:
|
||||
last_exception = None
|
||||
delay = initial_delay
|
||||
|
||||
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
return func(*args, **kwargs)
|
||||
|
||||
|
||||
except exceptions as e:
|
||||
last_exception = e
|
||||
|
||||
|
||||
if attempt == max_retries:
|
||||
logger.error(f"函数 {func.__name__} 在 {max_retries} 次重试后仍失败: {str(e)}")
|
||||
logger.error(
|
||||
f"Function {func.__name__} still failed after {max_retries} retries: {str(e)}"
|
||||
)
|
||||
raise
|
||||
|
||||
# 计算延迟
|
||||
|
||||
# Calculate delay
|
||||
current_delay = min(delay, max_delay)
|
||||
if jitter:
|
||||
current_delay = current_delay * (0.5 + random.random())
|
||||
|
||||
|
||||
logger.warning(
|
||||
f"函数 {func.__name__} 第 {attempt + 1} 次尝试失败: {str(e)}, "
|
||||
f"{current_delay:.1f}秒后重试..."
|
||||
f"Function {func.__name__} attempt {attempt + 1} failed: {str(e)}, "
|
||||
f"retrying in {current_delay:.1f}s..."
|
||||
)
|
||||
|
||||
|
||||
if on_retry:
|
||||
on_retry(e, attempt + 1)
|
||||
|
||||
|
||||
time.sleep(current_delay)
|
||||
delay *= backoff_factor
|
||||
|
||||
|
||||
raise last_exception
|
||||
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
|
|
@ -84,155 +88,151 @@ def retry_with_backoff_async(
|
|||
backoff_factor: float = 2.0,
|
||||
jitter: bool = True,
|
||||
exceptions: Tuple[Type[Exception], ...] = (Exception,),
|
||||
on_retry: Optional[Callable[[Exception, int], None]] = None
|
||||
on_retry: Optional[Callable[[Exception, int], None]] = None,
|
||||
):
|
||||
"""
|
||||
异步版本的重试装饰器
|
||||
Async version of the retry decorator
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
|
||||
def decorator(func: Callable) -> Callable:
|
||||
@functools.wraps(func)
|
||||
async def wrapper(*args, **kwargs) -> Any:
|
||||
last_exception = None
|
||||
delay = initial_delay
|
||||
|
||||
|
||||
for attempt in range(max_retries + 1):
|
||||
try:
|
||||
return await func(*args, **kwargs)
|
||||
|
||||
|
||||
except exceptions as e:
|
||||
last_exception = e
|
||||
|
||||
|
||||
if attempt == max_retries:
|
||||
logger.error(f"异步函数 {func.__name__} 在 {max_retries} 次重试后仍失败: {str(e)}")
|
||||
logger.error(
|
||||
f"Async function {func.__name__} still failed after {max_retries} retries: {str(e)}"
|
||||
)
|
||||
raise
|
||||
|
||||
|
||||
current_delay = min(delay, max_delay)
|
||||
if jitter:
|
||||
current_delay = current_delay * (0.5 + random.random())
|
||||
|
||||
|
||||
logger.warning(
|
||||
f"异步函数 {func.__name__} 第 {attempt + 1} 次尝试失败: {str(e)}, "
|
||||
f"{current_delay:.1f}秒后重试..."
|
||||
f"Async function {func.__name__} attempt {attempt + 1} failed: {str(e)}, "
|
||||
f"retrying in {current_delay:.1f}s..."
|
||||
)
|
||||
|
||||
|
||||
if on_retry:
|
||||
on_retry(e, attempt + 1)
|
||||
|
||||
|
||||
await asyncio.sleep(current_delay)
|
||||
delay *= backoff_factor
|
||||
|
||||
|
||||
raise last_exception
|
||||
|
||||
|
||||
return wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
class RetryableAPIClient:
|
||||
"""
|
||||
可重试的API客户端封装
|
||||
Retryable API client wrapper
|
||||
"""
|
||||
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
max_retries: int = 3,
|
||||
initial_delay: float = 1.0,
|
||||
max_delay: float = 30.0,
|
||||
backoff_factor: float = 2.0
|
||||
backoff_factor: float = 2.0,
|
||||
):
|
||||
self.max_retries = max_retries
|
||||
self.initial_delay = initial_delay
|
||||
self.max_delay = max_delay
|
||||
self.backoff_factor = backoff_factor
|
||||
|
||||
|
||||
def call_with_retry(
|
||||
self,
|
||||
func: Callable,
|
||||
*args,
|
||||
exceptions: Tuple[Type[Exception], ...] = (Exception,),
|
||||
**kwargs
|
||||
**kwargs,
|
||||
) -> Any:
|
||||
"""
|
||||
执行函数调用并在失败时重试
|
||||
|
||||
Execute a function call and retry on failure
|
||||
|
||||
Args:
|
||||
func: 要调用的函数
|
||||
*args: 函数参数
|
||||
exceptions: 需要重试的异常类型
|
||||
**kwargs: 函数关键字参数
|
||||
|
||||
func: The function to call
|
||||
*args: Function arguments
|
||||
exceptions: Exception types to retry on
|
||||
**kwargs: Function keyword arguments
|
||||
|
||||
Returns:
|
||||
函数返回值
|
||||
Function return value
|
||||
"""
|
||||
last_exception = None
|
||||
delay = self.initial_delay
|
||||
|
||||
|
||||
for attempt in range(self.max_retries + 1):
|
||||
try:
|
||||
return func(*args, **kwargs)
|
||||
|
||||
|
||||
except exceptions as e:
|
||||
last_exception = e
|
||||
|
||||
|
||||
if attempt == self.max_retries:
|
||||
logger.error(f"API调用在 {self.max_retries} 次重试后仍失败: {str(e)}")
|
||||
logger.error(
|
||||
f"API call still failed after {self.max_retries} retries: {str(e)}"
|
||||
)
|
||||
raise
|
||||
|
||||
|
||||
current_delay = min(delay, self.max_delay)
|
||||
current_delay = current_delay * (0.5 + random.random())
|
||||
|
||||
|
||||
logger.warning(
|
||||
f"API调用第 {attempt + 1} 次尝试失败: {str(e)}, "
|
||||
f"{current_delay:.1f}秒后重试..."
|
||||
f"API call attempt {attempt + 1} failed: {str(e)}, "
|
||||
f"retrying in {current_delay:.1f}s..."
|
||||
)
|
||||
|
||||
|
||||
time.sleep(current_delay)
|
||||
delay *= self.backoff_factor
|
||||
|
||||
|
||||
raise last_exception
|
||||
|
||||
|
||||
def call_batch_with_retry(
|
||||
self,
|
||||
items: list,
|
||||
process_func: Callable,
|
||||
exceptions: Tuple[Type[Exception], ...] = (Exception,),
|
||||
continue_on_failure: bool = True
|
||||
continue_on_failure: bool = True,
|
||||
) -> Tuple[list, list]:
|
||||
"""
|
||||
批量调用并对每个失败项单独重试
|
||||
|
||||
Batch call with individual retry for each failed item
|
||||
|
||||
Args:
|
||||
items: 要处理的项目列表
|
||||
process_func: 处理函数，接收单个item作为参数
|
||||
exceptions: 需要重试的异常类型
|
||||
continue_on_failure: 单项失败后是否继续处理其他项
|
||||
|
||||
items: List of items to process
|
||||
process_func: Processing function, takes a single item as argument
|
||||
exceptions: Exception types to retry on
|
||||
continue_on_failure: Whether to continue processing other items after one fails
|
||||
|
||||
Returns:
|
||||
(成功结果列表, 失败项列表)
|
||||
(list of successful results, list of failed items)
|
||||
"""
|
||||
results = []
|
||||
failures = []
|
||||
|
||||
|
||||
for idx, item in enumerate(items):
|
||||
try:
|
||||
result = self.call_with_retry(
|
||||
process_func,
|
||||
item,
|
||||
exceptions=exceptions
|
||||
)
|
||||
result = self.call_with_retry(process_func, item, exceptions=exceptions)
|
||||
results.append(result)
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理第 {idx + 1} 项失败: {str(e)}")
|
||||
failures.append({
|
||||
"index": idx,
|
||||
"item": item,
|
||||
"error": str(e)
|
||||
})
|
||||
|
||||
logger.error(f"Failed to process item {idx + 1}: {str(e)}")
|
||||
failures.append({"index": idx, "item": item, "error": str(e)})
|
||||
|
||||
if not continue_on_failure:
|
||||
raise
|
||||
|
||||
return results, failures
|
||||
|
||||
return results, failures
|
||||
|
|
|
|||
|
|
@ -1,160 +0,0 @@
|
|||
"""Complete Zep Graph node/edge pagination using opaque response cursors."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
from typing import Any
|
||||
|
||||
from zep_cloud.client import Zep
|
||||
|
||||
from .logger import get_logger
|
||||
from .zep import call_zep_read_with_retry
|
||||
|
||||
logger = get_logger("mirofish.zep_paging")
|
||||
|
||||
_DEFAULT_PAGE_SIZE = 100
|
||||
_DEFAULT_MAX_RETRIES = 3
|
||||
_DEFAULT_RETRY_DELAY = 2.0
|
||||
_NEXT_CURSOR_HEADER = "zep-next-cursor"
|
||||
|
||||
|
||||
def _fetch_page_with_retry(
|
||||
api_call: Callable[..., Any],
|
||||
*args: Any,
|
||||
max_retries: int = _DEFAULT_MAX_RETRIES,
|
||||
retry_delay: float = _DEFAULT_RETRY_DELAY,
|
||||
page_description: str = "page",
|
||||
**kwargs: Any,
|
||||
) -> Any:
|
||||
"""Fetch one read-only page with the shared transient-error policy."""
|
||||
|
||||
return call_zep_read_with_retry(
|
||||
lambda: api_call(*args, **kwargs),
|
||||
operation_name=page_description,
|
||||
max_attempts=max_retries,
|
||||
initial_delay=retry_delay,
|
||||
)
|
||||
|
||||
|
||||
def _header_value(headers: Any, name: str) -> str | None:
|
||||
if not headers:
|
||||
return None
|
||||
direct = headers.get(name)
|
||||
if direct is not None:
|
||||
return str(direct)
|
||||
return next(
|
||||
(
|
||||
str(value)
|
||||
for header_name, value in headers.items()
|
||||
if str(header_name).lower() == name.lower()
|
||||
),
|
||||
None,
|
||||
)
|
||||
|
||||
|
||||
def _fetch_all(
|
||||
api_call: Callable[..., Any],
|
||||
graph_id: str,
|
||||
*,
|
||||
item_name: str,
|
||||
page_size: int,
|
||||
max_items: int | None,
|
||||
max_retries: int,
|
||||
retry_delay: float,
|
||||
) -> list[Any]:
|
||||
if not 1 <= page_size <= 100:
|
||||
raise ValueError("page_size must be between 1 and 100")
|
||||
if max_items is not None and max_items < 1:
|
||||
raise ValueError("max_items must be at least 1 when provided")
|
||||
|
||||
all_items: list[Any] = []
|
||||
cursor: str | None = None
|
||||
seen_cursors: set[str] = set()
|
||||
page_number = 0
|
||||
|
||||
while True:
|
||||
kwargs: dict[str, Any] = {"limit": page_size}
|
||||
if cursor is not None:
|
||||
kwargs["cursor"] = cursor
|
||||
|
||||
page_number += 1
|
||||
response = _fetch_page_with_retry(
|
||||
api_call,
|
||||
graph_id,
|
||||
max_retries=max_retries,
|
||||
retry_delay=retry_delay,
|
||||
page_description=(
|
||||
f"fetch {item_name} page {page_number} (graph={graph_id})"
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
batch = list(getattr(response, "data", None) or [])
|
||||
all_items.extend(batch)
|
||||
|
||||
if max_items is not None and len(all_items) >= max_items:
|
||||
if len(all_items) > max_items:
|
||||
all_items = all_items[:max_items]
|
||||
logger.warning(
|
||||
"Zep %s pagination reached explicit max_items=%s for graph %s",
|
||||
item_name,
|
||||
max_items,
|
||||
graph_id,
|
||||
)
|
||||
break
|
||||
|
||||
next_cursor = _header_value(
|
||||
getattr(response, "headers", None),
|
||||
_NEXT_CURSOR_HEADER,
|
||||
)
|
||||
if next_cursor is None:
|
||||
break
|
||||
if next_cursor in seen_cursors or next_cursor == cursor:
|
||||
raise RuntimeError(
|
||||
f"Zep {item_name} pagination cursor did not advance for graph {graph_id}"
|
||||
)
|
||||
seen_cursors.add(next_cursor)
|
||||
cursor = next_cursor
|
||||
|
||||
return all_items
|
||||
|
||||
|
||||
def fetch_all_nodes(
|
||||
client: Zep,
|
||||
graph_id: str,
|
||||
page_size: int = _DEFAULT_PAGE_SIZE,
|
||||
max_items: int | None = None,
|
||||
max_retries: int = _DEFAULT_MAX_RETRIES,
|
||||
retry_delay: float = _DEFAULT_RETRY_DELAY,
|
||||
) -> list[Any]:
|
||||
"""Fetch every graph node unless the caller supplies an explicit cap."""
|
||||
|
||||
return _fetch_all(
|
||||
client.graph.node.with_raw_response.get_by_graph_id,
|
||||
graph_id,
|
||||
item_name="nodes",
|
||||
page_size=page_size,
|
||||
max_items=max_items,
|
||||
max_retries=max_retries,
|
||||
retry_delay=retry_delay,
|
||||
)
|
||||
|
||||
|
||||
def fetch_all_edges(
|
||||
client: Zep,
|
||||
graph_id: str,
|
||||
page_size: int = _DEFAULT_PAGE_SIZE,
|
||||
max_retries: int = _DEFAULT_MAX_RETRIES,
|
||||
retry_delay: float = _DEFAULT_RETRY_DELAY,
|
||||
max_items: int | None = None,
|
||||
) -> list[Any]:
|
||||
"""Fetch every graph edge unless the caller supplies an explicit cap."""
|
||||
|
||||
return _fetch_all(
|
||||
client.graph.edge.with_raw_response.get_by_graph_id,
|
||||
graph_id,
|
||||
item_name="edges",
|
||||
page_size=page_size,
|
||||
max_items=max_items,
|
||||
max_retries=max_retries,
|
||||
retry_delay=retry_delay,
|
||||
)
|
||||
|
|
@ -0,0 +1,127 @@
|
|||
# Nedbank Africa Expansion — Public Sentiment Prediction
|
||||
|
||||
> **MiroFish Simulation Requirement Preset**
|
||||
> Version: v1.0.0 | Created: 2026-07-10
|
||||
|
||||
---
|
||||
|
||||
## Project Name
|
||||
|
||||
Nedbank Africa Expansion Sentiment Prediction
|
||||
|
||||
## Simulation Requirement
|
||||
|
||||
Nedbank, one of South Africa's largest retail banks, announces a strategic
|
||||
expansion into the rest of the African continent, opening branches and digital
|
||||
banking services in Nigeria, Kenya, Ghana, and Mozambique over the next 18
|
||||
months. The expansion includes partnerships with local telecom providers for
|
||||
mobile money integration, a US$200 million investment fund, and a marketing
|
||||
campaign positioning Nedbank as "the bank that understands Africa." Predict the
|
||||
public sentiment response across social media platforms (Twitter and Reddit) in
|
||||
the days following the announcement. Consider the perspectives of South African
|
||||
customers reacting to capital outflow concerns, Nigerian and Kenyan citizens'
|
||||
reception of a South African bank entering their markets, fintech competitors
|
||||
(like MTN MoMo, Flutterwave, OPay), financial analysts discussing the investment
|
||||
rationale, regulators in each target country, pan-African economic nationalists,
|
||||
and ordinary citizens concerned about data privacy and neo-colonial financial
|
||||
dominance. How does sentiment evolve over a 72-hour period across these diverse
|
||||
stakeholder groups?
|
||||
|
||||
## Additional Context
|
||||
|
||||
Key factors to model:
|
||||
|
||||
1. South-South investment dynamics and historical skepticism toward South African corporate expansion in Africa
|
||||
2. Competitive landscape with established fintech players (Flutterwave, OPay, Paystack, MPesa)
|
||||
3. Mobile money integration as a differentiator
|
||||
4. Regulatory uncertainty in each target market
|
||||
5. Currency volatility and capital controls concerns from SA depositors
|
||||
6. Nedbank's ESG commitments and "Africa-focused" brand positioning
|
||||
7. Data sovereignty concerns in target countries
|
||||
8. Comparison to prior pan-African bank expansions (Standard Bank, Absa/Barclays)
|
||||
|
||||
The simulation should capture the initial announcement hype, analyst
|
||||
commentary, competitive response, regulatory statements, and citizen reactions
|
||||
across geographic and demographic segments.
|
||||
|
||||
## Recommended Entity Types
|
||||
|
||||
Suggested agent personas for the knowledge graph:
|
||||
|
||||
| Entity Type | Description |
|
||||
|---|---|
|
||||
| Customer | General retail banking customer |
|
||||
| BankingCustomer | Nedbank-specific SA customer |
|
||||
| FintechCompetitor | Competitor fintech (Flutterwave, OPay, Paystack, MPesa, MTN MoMo) |
|
||||
| FinancialAnalyst | Market analyst covering African banking |
|
||||
| Regulator | Financial regulator in target country |
|
||||
| Journalist | Business/tech journalist covering the story |
|
||||
| EconomicNationalist | Pan-African economic sovereignty advocate |
|
||||
| Citizen | Ordinary citizen in a target market |
|
||||
| TelecomPartner | Local telecom partner providing mobile money rails |
|
||||
| GovernmentOfficial | Government official in target country |
|
||||
| Investor | Nedbank shareholder / institutional investor |
|
||||
| SocialMediaInfluencer | Influencer shaping public discourse |
|
||||
|
||||
## Recommended Platforms
|
||||
|
||||
| Platform | Enabled |
|
||||
|---|---|
|
||||
| Twitter | Yes |
|
||||
| Reddit | Yes |
|
||||
|
||||
## Recommended Time Configuration
|
||||
|
||||
| Parameter | Value |
|
||||
|---|---|
|
||||
| Total simulation hours | 72 |
|
||||
| Minutes per round | 30 |
|
||||
| Approx. total rounds | 144 |
|
||||
|
||||
## Suggested Source Documents
|
||||
|
||||
Upload these (or similar) documents when creating the MiroFish project so the
|
||||
ontology generator and knowledge graph have rich source material:
|
||||
|
||||
1. Nedbank annual report and Africa strategy disclosure (PDF)
|
||||
2. Press release on the expansion announcement (MD)
|
||||
3. Market analysis reports on African banking sector (PDF/MD)
|
||||
4. Articles on Standard Bank and Absa prior pan-African expansion outcomes (MD)
|
||||
5. Fintech landscape reports for Nigeria, Kenya, Ghana, Mozambique (MD)
|
||||
6. Social media sentiment studies on South African brands entering African markets (TXT)
|
||||
|
||||
## How to Use This Preset
|
||||
|
||||
1. **Create project** — In the MiroFish frontend, enter the **Project Name**
|
||||
above, paste the **Simulation Requirement** and **Additional Context** into
|
||||
the project creation form.
|
||||
2. **Upload documents** — Upload the suggested source documents (or equivalent
|
||||
real-world materials).
|
||||
3. **Generate ontology** — The pipeline auto-generates entity/edge types from
|
||||
the documents and requirement.
|
||||
4. **Build graph** — The knowledge graph is constructed from the ontology.
|
||||
5. **Generate profiles** — Agent personas are created for each entity in the
|
||||
graph.
|
||||
6. **Run simulation** — Twitter + Reddit simulation runs for 72 simulated hours.
|
||||
7. **Generate report** — The Report Agent produces a sentiment prediction report
|
||||
across all stakeholder groups.
|
||||
|
||||
## Usage via API
|
||||
|
||||
```bash
|
||||
# Create project with ontology generation (Endpoint 1)
|
||||
curl -X POST http://localhost:5001/api/graph/ontology/generate \
|
||||
-F "project_name=Nedbank Africa Expansion Sentiment Prediction" \
|
||||
-F "simulation_requirement=Nedbank, one of South Africa's largest retail banks, announces a strategic expansion into the rest of the African continent, opening branches and digital banking services in Nigeria, Kenya, Ghana, and Mozambique over the next 18 months. The expansion includes partnerships with local telecom providers for mobile money integration, a US\$200 million investment fund, and a marketing campaign positioning Nedbank as 'the bank that understands Africa.' Predict the public sentiment response across social media platforms (Twitter and Reddit) in the days following the announcement. Consider the perspectives of South African customers reacting to capital outflow concerns, Nigerian and Kenyan citizens' reception of a South African bank entering their markets, fintech competitors (like MTN MoMo, Flutterwave, OPay), financial analysts discussing the investment rationale, regulators in each target country, pan-African economic nationalists, and ordinary citizens concerned about data privacy and neo-colonial financial dominance. How does sentiment evolve over a 72-hour period across these diverse stakeholder groups?" \
|
||||
-F "additional_context=Key factors to model: (1) South-South investment dynamics and historical skepticism toward South African corporate expansion in Africa; (2) Competitive landscape with established fintech players (Flutterwave, OPay, Paystack, MPesa); (3) Mobile money integration as a differentiator; (4) Regulatory uncertainty in each target market; (5) Currency volatility and capital controls concerns from SA depositors; (6) Nedbank's ESG commitments and 'Africa-focused' brand positioning; (7) Data sovereignty concerns in target countries; (8) Comparison to prior pan-African bank expansions (Standard Bank, Absa/Barclays). The simulation should capture the initial announcement hype, analyst commentary, competitive response, regulatory statements, and citizen reactions across geographic and demographic segments." \
|
||||
-F "files=@nedbank_annual_report.pdf" \
|
||||
-F "files=@africa_banking_market_analysis.md" \
|
||||
-F "files=@fintech_landscape_west_africa.md"
|
||||
|
||||
# The response contains project_id — use it for subsequent steps:
|
||||
# 2. Build graph: POST /api/graph/build
|
||||
# 3. Create sim: POST /api/simulation/create
|
||||
# 4. Prepare sim: POST /api/simulation/prepare
|
||||
# 5. Start sim: POST /api/simulation/start
|
||||
# 6. Generate report: POST /api/report/generate
|
||||
```
|
||||
|
|
@ -1,36 +1,32 @@
|
|||
[project]
|
||||
name = "mirofish-backend"
|
||||
version = "0.1.0"
|
||||
description = "MiroFish - 简洁通用的群体智能引擎，预测万物"
|
||||
requires-python = ">=3.11,<3.13"
|
||||
description = "MiroFish - A concise general-purpose collective intelligence engine for predicting anything"
|
||||
requires-python = ">=3.11"
|
||||
license = { text = "AGPL-3.0" }
|
||||
authors = [
|
||||
{ name = "MiroFish Team" }
|
||||
]
|
||||
|
||||
dependencies = [
|
||||
# 核心框架
|
||||
# Core framework
|
||||
"flask>=3.0.0",
|
||||
"flask-cors>=6.0.0",
|
||||
|
||||
# LLM 相关
|
||||
# LLM related
|
||||
"openai>=1.0.0",
|
||||
|
||||
# Zep Cloud
|
||||
"zep-cloud==3.25.0",
|
||||
"httpx>=0.27.0",
|
||||
|
||||
# OASIS 社交媒体模拟
|
||||
|
||||
# OASIS social media simulation
|
||||
"camel-oasis==0.2.5",
|
||||
"camel-ai==0.2.78",
|
||||
|
||||
# 文件处理
|
||||
# File processing
|
||||
"PyMuPDF>=1.24.0",
|
||||
# 编码检测（支持非UTF-8编码的文本文件）
|
||||
# Encoding detection (supports non-UTF-8 encoded text files)
|
||||
"charset-normalizer>=3.0.0",
|
||||
"chardet>=5.0.0",
|
||||
|
||||
# 工具库
|
||||
# Utility libraries
|
||||
"python-dotenv>=1.0.0",
|
||||
"pydantic>=2.0.0",
|
||||
]
|
||||
|
|
@ -53,4 +49,4 @@ dev = [
|
|||
]
|
||||
|
||||
[tool.hatch.build.targets.wheel]
|
||||
packages = ["app"]
|
||||
packages = ["app"]
|
||||
|
|
@ -5,32 +5,28 @@
|
|||
# Install: pip install -r requirements.txt
|
||||
# ===========================================
|
||||
|
||||
# ============= 核心框架 =============
|
||||
# ============= Core Framework =============
|
||||
flask>=3.0.0
|
||||
flask-cors>=6.0.0
|
||||
|
||||
# ============= LLM 相关 =============
|
||||
# OpenAI SDK（统一使用 OpenAI 格式调用 LLM）
|
||||
# ============= LLM Related =============
|
||||
# OpenAI SDK (unified OpenAI format for LLM calls)
|
||||
openai>=1.0.0
|
||||
|
||||
# ============= Zep Cloud =============
|
||||
zep-cloud==3.25.0
|
||||
httpx>=0.27.0
|
||||
|
||||
# ============= OASIS 社交媒体模拟 =============
|
||||
# OASIS 社交模拟框架
|
||||
# ============= OASIS Social Media Simulation =============
|
||||
# OASIS social simulation framework
|
||||
camel-oasis==0.2.5
|
||||
camel-ai==0.2.78
|
||||
|
||||
# ============= 文件处理 =============
|
||||
# ============= File Processing =============
|
||||
PyMuPDF>=1.24.0
|
||||
# 编码检测（支持非UTF-8编码的文本文件）
|
||||
# Encoding detection (supports non-UTF-8 encoded text files)
|
||||
charset-normalizer>=3.0.0
|
||||
chardet>=5.0.0
|
||||
|
||||
# ============= 工具库 =============
|
||||
# 环境变量加载
|
||||
# ============= Utility Libraries =============
|
||||
# Environment variable loading
|
||||
python-dotenv>=1.0.0
|
||||
|
||||
# 数据验证
|
||||
pydantic>=2.0.0
|
||||
# Data validation
|
||||
pydantic>=2.0.0
|
||||
|
|
@ -1,21 +1,21 @@
|
|||
"""
|
||||
MiroFish Backend 启动入口
|
||||
MiroFish Backend entry point
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
# 解决 Windows 控制台中文乱码问题：在所有导入之前设置 UTF-8 编码
|
||||
if sys.platform == 'win32':
|
||||
# 设置环境变量确保 Python 使用 UTF-8
|
||||
os.environ.setdefault('PYTHONIOENCODING', 'utf-8')
|
||||
# 重新配置标准输出流为 UTF-8
|
||||
if hasattr(sys.stdout, 'reconfigure'):
|
||||
sys.stdout.reconfigure(encoding='utf-8', errors='replace')
|
||||
if hasattr(sys.stderr, 'reconfigure'):
|
||||
sys.stderr.reconfigure(encoding='utf-8', errors='replace')
|
||||
# Fix Chinese garbled output in Windows console: set UTF-8 encoding before all imports
|
||||
if sys.platform == "win32":
|
||||
# Set environment variable to ensure Python uses UTF-8
|
||||
os.environ.setdefault("PYTHONIOENCODING", "utf-8")
|
||||
# Reconfigure standard output streams to UTF-8
|
||||
if hasattr(sys.stdout, "reconfigure"):
|
||||
sys.stdout.reconfigure(encoding="utf-8", errors="replace")
|
||||
if hasattr(sys.stderr, "reconfigure"):
|
||||
sys.stderr.reconfigure(encoding="utf-8", errors="replace")
|
||||
|
||||
# 添加项目根目录到路径
|
||||
# Add project root directory to path
|
||||
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
|
||||
|
||||
from app import create_app
|
||||
|
|
@ -23,28 +23,27 @@ from app.config import Config
|
|||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
# 验证配置
|
||||
"""Main function"""
|
||||
# Validate configuration
|
||||
errors = Config.validate()
|
||||
if errors:
|
||||
print("配置错误:")
|
||||
print("Configuration errors:")
|
||||
for err in errors:
|
||||
print(f" - {err}")
|
||||
print("\n请检查 .env 文件中的配置")
|
||||
print("\nPlease check the configuration in the .env file")
|
||||
sys.exit(1)
|
||||
|
||||
# 创建应用
|
||||
|
||||
# Create application
|
||||
app = create_app()
|
||||
|
||||
# 获取运行配置
|
||||
host = os.environ.get('FLASK_HOST', '0.0.0.0')
|
||||
port = int(os.environ.get('FLASK_PORT', 5001))
|
||||
|
||||
# Get runtime config
|
||||
host = os.environ.get("FLASK_HOST", "0.0.0.0")
|
||||
port = int(os.environ.get("FLASK_PORT", 5001))
|
||||
debug = Config.DEBUG
|
||||
|
||||
# 启动服务
|
||||
|
||||
# Start service
|
||||
app.run(host=host, port=port, debug=debug, threaded=True)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
|
|
|||
|
|
@ -1,15 +1,15 @@
|
|||
"""
|
||||
动作日志记录器
|
||||
用于记录OASIS模拟中每个Agent的动作，供后端监控使用
|
||||
Action logger.
|
||||
Used to log each agent's actions in OASIS simulations for backend monitoring.
|
||||
|
||||
日志结构:
|
||||
Log structure:
|
||||
sim_xxx/
|
||||
├── twitter/
|
||||
│ └── actions.jsonl # Twitter 平台动作日志
|
||||
│ └── actions.jsonl # Twitter platform action log
|
||||
├── reddit/
|
||||
│ └── actions.jsonl # Reddit 平台动作日志
|
||||
├── simulation.log # 主模拟进程日志
|
||||
└── run_state.json # 运行状态（API 查询用）
|
||||
│ └── actions.jsonl # Reddit platform action log
|
||||
├── simulation.log # Main simulation process log
|
||||
└── run_state.json # Run state (for API queries)
|
||||
"""
|
||||
|
||||
import json
|
||||
|
|
@ -20,26 +20,26 @@ from typing import Dict, Any, Optional
|
|||
|
||||
|
||||
class PlatformActionLogger:
|
||||
"""单平台动作日志记录器"""
|
||||
|
||||
"""Single-platform action logger"""
|
||||
|
||||
def __init__(self, platform: str, base_dir: str):
|
||||
"""
|
||||
初始化日志记录器
|
||||
|
||||
Initialize the logger.
|
||||
|
||||
Args:
|
||||
platform: 平台名称 (twitter/reddit)
|
||||
base_dir: 模拟目录的基础路径
|
||||
platform: Platform name (twitter/reddit)
|
||||
base_dir: Base path of the simulation directory
|
||||
"""
|
||||
self.platform = platform
|
||||
self.base_dir = base_dir
|
||||
self.log_dir = os.path.join(base_dir, platform)
|
||||
self.log_path = os.path.join(self.log_dir, "actions.jsonl")
|
||||
self._ensure_dir()
|
||||
|
||||
|
||||
def _ensure_dir(self):
|
||||
"""确保目录存在"""
|
||||
"""Ensure the directory exists"""
|
||||
os.makedirs(self.log_dir, exist_ok=True)
|
||||
|
||||
|
||||
def log_action(
|
||||
self,
|
||||
round_num: int,
|
||||
|
|
@ -48,9 +48,9 @@ class PlatformActionLogger:
|
|||
action_type: str,
|
||||
action_args: Optional[Dict[str, Any]] = None,
|
||||
result: Optional[str] = None,
|
||||
success: bool = True
|
||||
success: bool = True,
|
||||
):
|
||||
"""记录一个动作"""
|
||||
"""Log an action"""
|
||||
entry = {
|
||||
"round": round_num,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
|
|
@ -61,49 +61,52 @@ class PlatformActionLogger:
|
|||
"result": result,
|
||||
"success": success,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_round_start(self, round_num: int, simulated_hour: int):
|
||||
"""记录轮次开始"""
|
||||
"""Log round start"""
|
||||
entry = {
|
||||
"round": round_num,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"event_type": "round_start",
|
||||
"simulated_hour": simulated_hour,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_round_end(self, round_num: int, actions_count: int):
|
||||
"""记录轮次结束"""
|
||||
"""Log round end"""
|
||||
entry = {
|
||||
"round": round_num,
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"event_type": "round_end",
|
||||
"actions_count": actions_count,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_simulation_start(self, config: Dict[str, Any]):
|
||||
"""记录模拟开始"""
|
||||
"""Log simulation start"""
|
||||
entry = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"event_type": "simulation_start",
|
||||
"platform": self.platform,
|
||||
"total_rounds": config.get("time_config", {}).get("total_simulation_hours", 72) * 2,
|
||||
"total_rounds": config.get("time_config", {}).get(
|
||||
"total_simulation_hours", 72
|
||||
)
|
||||
* 2,
|
||||
"agents_count": len(config.get("agent_configs", [])),
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_simulation_end(self, total_rounds: int, total_actions: int):
|
||||
"""记录模拟结束"""
|
||||
"""Log simulation end"""
|
||||
entry = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"event_type": "simulation_end",
|
||||
|
|
@ -111,108 +114,111 @@ class PlatformActionLogger:
|
|||
"total_rounds": total_rounds,
|
||||
"total_actions": total_actions,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
|
||||
class SimulationLogManager:
|
||||
"""
|
||||
模拟日志管理器
|
||||
统一管理所有日志文件，按平台分离
|
||||
Simulation log manager.
|
||||
Unified management of all log files, separated by platform.
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, simulation_dir: str):
|
||||
"""
|
||||
初始化日志管理器
|
||||
|
||||
Initialize the log manager.
|
||||
|
||||
Args:
|
||||
simulation_dir: 模拟目录路径
|
||||
simulation_dir: Simulation directory path
|
||||
"""
|
||||
self.simulation_dir = simulation_dir
|
||||
self.twitter_logger: Optional[PlatformActionLogger] = None
|
||||
self.reddit_logger: Optional[PlatformActionLogger] = None
|
||||
self._main_logger: Optional[logging.Logger] = None
|
||||
|
||||
# 设置主日志
|
||||
|
||||
# Set up main log
|
||||
self._setup_main_logger()
|
||||
|
||||
|
||||
def _setup_main_logger(self):
|
||||
"""设置主模拟日志"""
|
||||
"""Set up the main simulation log"""
|
||||
log_path = os.path.join(self.simulation_dir, "simulation.log")
|
||||
|
||||
# 创建 logger
|
||||
self._main_logger = logging.getLogger(f"simulation.{os.path.basename(self.simulation_dir)}")
|
||||
|
||||
# Create logger
|
||||
self._main_logger = logging.getLogger(
|
||||
f"simulation.{os.path.basename(self.simulation_dir)}"
|
||||
)
|
||||
self._main_logger.setLevel(logging.INFO)
|
||||
self._main_logger.handlers.clear()
|
||||
|
||||
# 文件处理器
|
||||
file_handler = logging.FileHandler(log_path, encoding='utf-8', mode='w')
|
||||
|
||||
# File handler
|
||||
file_handler = logging.FileHandler(log_path, encoding="utf-8", mode="w")
|
||||
file_handler.setLevel(logging.INFO)
|
||||
file_handler.setFormatter(logging.Formatter(
|
||||
'%(asctime)s - %(levelname)s - %(message)s',
|
||||
datefmt='%Y-%m-%d %H:%M:%S'
|
||||
))
|
||||
file_handler.setFormatter(
|
||||
logging.Formatter(
|
||||
"%(asctime)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
|
||||
)
|
||||
)
|
||||
self._main_logger.addHandler(file_handler)
|
||||
|
||||
# 控制台处理器
|
||||
|
||||
# Console handler
|
||||
console_handler = logging.StreamHandler()
|
||||
console_handler.setLevel(logging.INFO)
|
||||
console_handler.setFormatter(logging.Formatter(
|
||||
'[%(asctime)s] %(message)s',
|
||||
datefmt='%H:%M:%S'
|
||||
))
|
||||
console_handler.setFormatter(
|
||||
logging.Formatter("[%(asctime)s] %(message)s", datefmt="%H:%M:%S")
|
||||
)
|
||||
self._main_logger.addHandler(console_handler)
|
||||
|
||||
|
||||
self._main_logger.propagate = False
|
||||
|
||||
|
||||
def get_twitter_logger(self) -> PlatformActionLogger:
|
||||
"""获取 Twitter 平台日志记录器"""
|
||||
"""Get the Twitter platform logger"""
|
||||
if self.twitter_logger is None:
|
||||
self.twitter_logger = PlatformActionLogger("twitter", self.simulation_dir)
|
||||
return self.twitter_logger
|
||||
|
||||
|
||||
def get_reddit_logger(self) -> PlatformActionLogger:
|
||||
"""获取 Reddit 平台日志记录器"""
|
||||
"""Get the Reddit platform logger"""
|
||||
if self.reddit_logger is None:
|
||||
self.reddit_logger = PlatformActionLogger("reddit", self.simulation_dir)
|
||||
return self.reddit_logger
|
||||
|
||||
|
||||
def log(self, message: str, level: str = "info"):
|
||||
"""记录主日志"""
|
||||
"""Log a main log message"""
|
||||
if self._main_logger:
|
||||
getattr(self._main_logger, level.lower(), self._main_logger.info)(message)
|
||||
|
||||
|
||||
def info(self, message: str):
|
||||
self.log(message, "info")
|
||||
|
||||
|
||||
def warning(self, message: str):
|
||||
self.log(message, "warning")
|
||||
|
||||
|
||||
def error(self, message: str):
|
||||
self.log(message, "error")
|
||||
|
||||
|
||||
def debug(self, message: str):
|
||||
self.log(message, "debug")
|
||||
|
||||
|
||||
# ============ 兼容旧接口 ============
|
||||
# ============ Legacy interface compatibility ============
|
||||
|
||||
|
||||
class ActionLogger:
|
||||
"""
|
||||
动作日志记录器（兼容旧接口）
|
||||
建议使用 SimulationLogManager 代替
|
||||
Action logger (legacy interface compatible).
|
||||
Using SimulationLogManager is recommended instead.
|
||||
"""
|
||||
|
||||
|
||||
def __init__(self, log_path: str):
|
||||
self.log_path = log_path
|
||||
self._ensure_dir()
|
||||
|
||||
|
||||
def _ensure_dir(self):
|
||||
log_dir = os.path.dirname(self.log_path)
|
||||
if log_dir:
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
|
||||
|
||||
def log_action(
|
||||
self,
|
||||
round_num: int,
|
||||
|
|
@ -222,7 +228,7 @@ class ActionLogger:
|
|||
action_type: str,
|
||||
action_args: Optional[Dict[str, Any]] = None,
|
||||
result: Optional[str] = None,
|
||||
success: bool = True
|
||||
success: bool = True,
|
||||
):
|
||||
entry = {
|
||||
"round": round_num,
|
||||
|
|
@ -235,10 +241,10 @@ class ActionLogger:
|
|||
"result": result,
|
||||
"success": success,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_round_start(self, round_num: int, simulated_hour: int, platform: str):
|
||||
entry = {
|
||||
"round": round_num,
|
||||
|
|
@ -247,10 +253,10 @@ class ActionLogger:
|
|||
"event_type": "round_start",
|
||||
"simulated_hour": simulated_hour,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_round_end(self, round_num: int, actions_count: int, platform: str):
|
||||
entry = {
|
||||
"round": round_num,
|
||||
|
|
@ -259,22 +265,25 @@ class ActionLogger:
|
|||
"event_type": "round_end",
|
||||
"actions_count": actions_count,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_simulation_start(self, platform: str, config: Dict[str, Any]):
|
||||
entry = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
"platform": platform,
|
||||
"event_type": "simulation_start",
|
||||
"total_rounds": config.get("time_config", {}).get("total_simulation_hours", 72) * 2,
|
||||
"total_rounds": config.get("time_config", {}).get(
|
||||
"total_simulation_hours", 72
|
||||
)
|
||||
* 2,
|
||||
"agents_count": len(config.get("agent_configs", [])),
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
def log_simulation_end(self, platform: str, total_rounds: int, total_actions: int):
|
||||
entry = {
|
||||
"timestamp": datetime.now().isoformat(),
|
||||
|
|
@ -283,23 +292,23 @@ class ActionLogger:
|
|||
"total_rounds": total_rounds,
|
||||
"total_actions": total_actions,
|
||||
}
|
||||
|
||||
with open(self.log_path, 'a', encoding='utf-8') as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + '\n')
|
||||
|
||||
with open(self.log_path, "a", encoding="utf-8") as f:
|
||||
f.write(json.dumps(entry, ensure_ascii=False) + "\n")
|
||||
|
||||
|
||||
# 全局日志实例（兼容旧接口）
|
||||
# Global logger instance (legacy interface compatible)
|
||||
_global_logger: Optional[ActionLogger] = None
|
||||
|
||||
|
||||
def get_logger(log_path: Optional[str] = None) -> ActionLogger:
|
||||
"""获取全局日志实例（兼容旧接口）"""
|
||||
"""Get the global logger instance (legacy interface compatible)"""
|
||||
global _global_logger
|
||||
|
||||
|
||||
if log_path:
|
||||
_global_logger = ActionLogger(log_path)
|
||||
|
||||
|
||||
if _global_logger is None:
|
||||
_global_logger = ActionLogger("actions.jsonl")
|
||||
|
||||
|
||||
return _global_logger
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -1,8 +1,8 @@
|
|||
"""
|
||||
测试Profile格式生成是否符合OASIS要求
|
||||
验证：
|
||||
1. Twitter Profile生成CSV格式
|
||||
2. Reddit Profile生成JSON详细格式
|
||||
Test Profile format generation against OASIS requirements.
|
||||
Validation:
|
||||
1. Twitter Profile generates CSV format
|
||||
2. Reddit Profile generates detailed JSON format
|
||||
"""
|
||||
|
||||
import os
|
||||
|
|
@ -11,19 +11,22 @@ import json
|
|||
import csv
|
||||
import tempfile
|
||||
|
||||
# 添加项目路径
|
||||
# Add project path
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from app.services.oasis_profile_generator import OasisProfileGenerator, OasisAgentProfile
|
||||
from app.services.oasis_profile_generator import (
|
||||
OasisProfileGenerator,
|
||||
OasisAgentProfile,
|
||||
)
|
||||
|
||||
|
||||
def test_profile_formats():
|
||||
"""测试Profile格式"""
|
||||
"""Test Profile formats"""
|
||||
print("=" * 60)
|
||||
print("OASIS Profile格式测试")
|
||||
print("OASIS Profile format test")
|
||||
print("=" * 60)
|
||||
|
||||
# 创建测试Profile数据
|
||||
|
||||
# Create test Profile data
|
||||
test_profiles = [
|
||||
OasisAgentProfile(
|
||||
user_id=0,
|
||||
|
|
@ -60,87 +63,102 @@ def test_profile_formats():
|
|||
source_entity_type="University",
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
generator = OasisProfileGenerator.__new__(OasisProfileGenerator)
|
||||
|
||||
# 使用临时目录
|
||||
|
||||
# Use a temporary directory
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
twitter_path = os.path.join(temp_dir, "twitter_profiles.csv")
|
||||
reddit_path = os.path.join(temp_dir, "reddit_profiles.json")
|
||||
|
||||
# 测试Twitter CSV格式
|
||||
print("\n1. 测试Twitter Profile (CSV格式)")
|
||||
|
||||
# Test Twitter CSV format
|
||||
print("\n1. Test Twitter Profile (CSV format)")
|
||||
print("-" * 40)
|
||||
generator._save_twitter_csv(test_profiles, twitter_path)
|
||||
|
||||
# 读取并验证CSV
|
||||
with open(twitter_path, 'r', encoding='utf-8') as f:
|
||||
|
||||
# Read and validate CSV
|
||||
with open(twitter_path, "r", encoding="utf-8") as f:
|
||||
reader = csv.DictReader(f)
|
||||
rows = list(reader)
|
||||
|
||||
print(f" 文件: {twitter_path}")
|
||||
print(f" 行数: {len(rows)}")
|
||||
print(f" 表头: {list(rows[0].keys())}")
|
||||
print(f"\n 示例数据 (第1行):")
|
||||
|
||||
print(f" File: {twitter_path}")
|
||||
print(f" Row count: {len(rows)}")
|
||||
print(f" Headers: {list(rows[0].keys())}")
|
||||
print(f"\n Sample data (row 1):")
|
||||
for key, value in rows[0].items():
|
||||
print(f" {key}: {value}")
|
||||
|
||||
# 验证必需字段
|
||||
required_twitter_fields = ['user_id', 'user_name', 'name', 'bio',
|
||||
'friend_count', 'follower_count', 'statuses_count', 'created_at']
|
||||
|
||||
# Validate required fields
|
||||
required_twitter_fields = [
|
||||
"user_id",
|
||||
"user_name",
|
||||
"name",
|
||||
"bio",
|
||||
"friend_count",
|
||||
"follower_count",
|
||||
"statuses_count",
|
||||
"created_at",
|
||||
]
|
||||
missing = set(required_twitter_fields) - set(rows[0].keys())
|
||||
if missing:
|
||||
print(f"\n [错误] 缺少字段: {missing}")
|
||||
print(f"\n [ERROR] Missing fields: {missing}")
|
||||
else:
|
||||
print(f"\n [通过] 所有必需字段都存在")
|
||||
|
||||
# 测试Reddit JSON格式
|
||||
print("\n2. 测试Reddit Profile (JSON详细格式)")
|
||||
print(f"\n [PASS] All required fields are present")
|
||||
|
||||
# Test Reddit JSON format
|
||||
print("\n2. Test Reddit Profile (detailed JSON format)")
|
||||
print("-" * 40)
|
||||
generator._save_reddit_json(test_profiles, reddit_path)
|
||||
|
||||
# 读取并验证JSON
|
||||
with open(reddit_path, 'r', encoding='utf-8') as f:
|
||||
|
||||
# Read and validate JSON
|
||||
with open(reddit_path, "r", encoding="utf-8") as f:
|
||||
reddit_data = json.load(f)
|
||||
|
||||
print(f" 文件: {reddit_path}")
|
||||
print(f" 条目数: {len(reddit_data)}")
|
||||
print(f" 字段: {list(reddit_data[0].keys())}")
|
||||
print(f"\n 示例数据 (第1条):")
|
||||
|
||||
print(f" File: {reddit_path}")
|
||||
print(f" Entry count: {len(reddit_data)}")
|
||||
print(f" Fields: {list(reddit_data[0].keys())}")
|
||||
print(f"\n Sample data (entry 1):")
|
||||
print(json.dumps(reddit_data[0], ensure_ascii=False, indent=4))
|
||||
|
||||
# 验证详细格式字段
|
||||
required_reddit_fields = ['realname', 'username', 'bio', 'persona']
|
||||
optional_reddit_fields = ['age', 'gender', 'mbti', 'country', 'profession', 'interested_topics']
|
||||
|
||||
|
||||
# Validate detailed format fields
|
||||
required_reddit_fields = ["realname", "username", "bio", "persona"]
|
||||
optional_reddit_fields = [
|
||||
"age",
|
||||
"gender",
|
||||
"mbti",
|
||||
"country",
|
||||
"profession",
|
||||
"interested_topics",
|
||||
]
|
||||
|
||||
missing = set(required_reddit_fields) - set(reddit_data[0].keys())
|
||||
if missing:
|
||||
print(f"\n [错误] 缺少必需字段: {missing}")
|
||||
print(f"\n [ERROR] Missing required fields: {missing}")
|
||||
else:
|
||||
print(f"\n [通过] 所有必需字段都存在")
|
||||
|
||||
print(f"\n [PASS] All required fields are present")
|
||||
|
||||
present_optional = set(optional_reddit_fields) & set(reddit_data[0].keys())
|
||||
print(f" [信息] 可选字段: {present_optional}")
|
||||
|
||||
print(f" [INFO] Optional fields: {present_optional}")
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("测试完成!")
|
||||
print("Test completed!")
|
||||
print("=" * 60)
|
||||
|
||||
|
||||
def show_expected_formats():
|
||||
"""显示OASIS期望的格式"""
|
||||
"""Show the formats expected by OASIS"""
|
||||
print("\n" + "=" * 60)
|
||||
print("OASIS 期望的Profile格式参考")
|
||||
print("OASIS expected Profile format reference")
|
||||
print("=" * 60)
|
||||
|
||||
print("\n1. Twitter Profile (CSV格式)")
|
||||
|
||||
print("\n1. Twitter Profile (CSV format)")
|
||||
print("-" * 40)
|
||||
twitter_example = """user_id,user_name,name,bio,friend_count,follower_count,statuses_count,created_at
|
||||
0,user0,User Zero,I am user zero with interests in technology.,100,150,500,2023-01-01
|
||||
1,user1,User One,Tech enthusiast and coffee lover.,200,250,1000,2023-01-02"""
|
||||
print(twitter_example)
|
||||
|
||||
print("\n2. Reddit Profile (JSON详细格式)")
|
||||
|
||||
print("\n2. Reddit Profile (detailed JSON format)")
|
||||
print("-" * 40)
|
||||
reddit_example = [
|
||||
{
|
||||
|
|
@ -153,7 +171,7 @@ def show_expected_formats():
|
|||
"mbti": "ESTJ",
|
||||
"country": "UK",
|
||||
"profession": "Hospitality & Tourism",
|
||||
"interested_topics": ["Economics", "Business"]
|
||||
"interested_topics": ["Economics", "Business"],
|
||||
}
|
||||
]
|
||||
print(json.dumps(reddit_example, ensure_ascii=False, indent=2))
|
||||
|
|
@ -162,5 +180,3 @@ def show_expected_formats():
|
|||
if __name__ == "__main__":
|
||||
test_profile_formats()
|
||||
show_expected_formats()
|
||||
|
||||
|
||||
|
|
|
|||
754
backend/uv.lock
754
backend/uv.lock
|
|
@ -1,6 +1,6 @@
|
|||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.11, <3.13"
|
||||
requires-python = ">=3.11"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12'",
|
||||
"python_full_version < '3.12'",
|
||||
|
|
@ -30,7 +30,7 @@ version = "4.12.0"
|
|||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "idna" },
|
||||
{ name = "typing-extensions" },
|
||||
{ name = "typing-extensions", marker = "python_full_version < '3.13'" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
|
||||
wheels = [
|
||||
|
|
@ -234,6 +234,40 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/7b/2b/2b6435f76bfeb6bbf055596976da087377ede68df465419d192acf00c437/cffi-2.0.0-cp312-cp312-win32.whl", hash = "sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18", size = 172932, upload-time = "2025-09-08T23:22:57.188Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/f8/ed/13bd4418627013bec4ed6e54283b1959cf6db888048c7cf4b4c3b5b36002/cffi-2.0.0-cp312-cp312-win_amd64.whl", hash = "sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5", size = 183557, upload-time = "2025-09-08T23:22:58.351Z" },
|
||||
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[[package]]
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||||
|
|
@ -2774,19 +3486,3 @@ sdist = { url = "https://files.pythonhosted.org/packages/d4/c8/cc640404a0981e6c1
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|||
wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/8b/90/89a2ff242ccab6a24fbab18dbbabc67c51a6f0ed01f9a0f41689dc177419/yarg-0.1.9-py2.py3-none-any.whl", hash = "sha256:4f9cebdc00fac946c9bf2783d634e538a71c7d280a4d806d45fd4dc0ef441492", size = 19162, upload-time = "2014-08-11T22:01:41.104Z" },
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|
||||
[[package]]
|
||||
name = "zep-cloud"
|
||||
version = "3.25.0"
|
||||
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|
||||
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|
||||
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||||
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||||
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{ name = "python-dateutil" },
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||||
{ name = "typing-extensions" },
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wheels = [
|
||||
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]
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
services:
|
||||
mirofish:
|
||||
image: ghcr.io/666ghj/mirofish:latest
|
||||
# 加速镜像（如拉取缓慢可替换上方地址）
|
||||
# Mirror for faster pulling (replace the address above if pulling is slow)
|
||||
# image: ghcr.nju.edu.cn/666ghj/mirofish:latest
|
||||
container_name: mirofish
|
||||
env_file:
|
||||
|
|
|
|||
|
|
@ -1,15 +1,15 @@
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|||
<!doctype html>
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<html lang="zh">
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<html lang="en">
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<head>
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<link rel="preconnect" href="https://fonts.googleapis.com">
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<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
|
||||
<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@100..800&family=Noto+Sans+SC:wght@300;400;500;700;800;900&family=Space+Grotesk:wght@300..700&display=swap" rel="stylesheet">
|
||||
<link href="https://fonts.googleapis.com/css2?family=Fraunces:ital,opsz,wght@0,9..144,300;0,9..144,400;0,9..144,500;1,9..144,300;1,9..144,400&family=Inter:wght@300;400;500;600;700&family=JetBrains+Mono:wght@100..800&family=Space+Grotesk:wght@300..700&display=swap" rel="stylesheet">
|
||||
<meta charset="UTF-8" />
|
||||
<link rel="icon" type="image/png" href="/icon.png" />
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
|
||||
<meta name="description" content="MiroFish - 社交媒体舆论模拟系统" />
|
||||
<title>MiroFish - 预测万物</title>
|
||||
<meta name="description" content="MiroFish - Social Media Opinion Simulation System" />
|
||||
<title>MiroFish - Predict Everything</title>
|
||||
</head>
|
||||
<body>
|
||||
<div id="app"></div>
|
||||
|
|
|
|||
|
|
@ -3,11 +3,11 @@
|
|||
</template>
|
||||
|
||||
<script setup>
|
||||
// 使用 Vue Router 来管理页面
|
||||
// Use Vue Router for page management
|
||||
</script>
|
||||
|
||||
<style>
|
||||
/* 全局样式重置 */
|
||||
/* Global style reset */
|
||||
* {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
|
|
@ -15,33 +15,50 @@
|
|||
}
|
||||
|
||||
#app {
|
||||
font-family: 'JetBrains Mono', 'Space Grotesk', 'Noto Sans SC', monospace;
|
||||
font-family: 'Inter', system-ui, -apple-system, sans-serif;
|
||||
-webkit-font-smoothing: antialiased;
|
||||
-moz-osx-font-smoothing: grayscale;
|
||||
color: #000000;
|
||||
background-color: #ffffff;
|
||||
color: #0A1B2A;
|
||||
background-color: #EBEDF0;
|
||||
}
|
||||
|
||||
/* 滚动条样式 */
|
||||
/* Scrollbar styles */
|
||||
::-webkit-scrollbar {
|
||||
width: 8px;
|
||||
height: 8px;
|
||||
width: 10px;
|
||||
height: 10px;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-track {
|
||||
background: #f1f1f1;
|
||||
background: #E4E7EC;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-thumb {
|
||||
background: #000000;
|
||||
background: #56697D;
|
||||
}
|
||||
|
||||
::-webkit-scrollbar-thumb:hover {
|
||||
background: #333333;
|
||||
background: #1E3247;
|
||||
}
|
||||
|
||||
/* 全局按钮样式 */
|
||||
/* Global button styles */
|
||||
button {
|
||||
font-family: inherit;
|
||||
}
|
||||
</style>
|
||||
|
||||
/* Global focus visible styles */
|
||||
*:focus-visible {
|
||||
outline: 2px solid #C4751A;
|
||||
outline-offset: 2px;
|
||||
}
|
||||
|
||||
/* Respect reduced-motion */
|
||||
@media (prefers-reduced-motion: reduce) {
|
||||
*,
|
||||
*::before,
|
||||
*::after {
|
||||
animation-duration: 0.01ms !important;
|
||||
animation-iteration-count: 1 !important;
|
||||
transition-duration: 0.01ms !important;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
|
|
@ -1,8 +1,8 @@
|
|||
import service from './index'
|
||||
|
||||
/**
|
||||
* 生成本体（上传文档和模拟需求）
|
||||
* @param {Object} data - 包含files, simulation_requirement, project_name等
|
||||
* Generate ontology (upload documents and simulation requirement)
|
||||
* @param {Object} data - Contains files, simulation_requirement, project_name, etc.
|
||||
* @returns {Promise}
|
||||
*/
|
||||
export function generateOntology(formData) {
|
||||
|
|
@ -17,8 +17,8 @@ export function generateOntology(formData) {
|
|||
}
|
||||
|
||||
/**
|
||||
* 构建图谱
|
||||
* @param {Object} data - 包含project_id, graph_name等
|
||||
* Build graph
|
||||
* @param {Object} data - Contains project_id, graph_name, etc.
|
||||
* @returns {Promise}
|
||||
*/
|
||||
export function buildGraph(data) {
|
||||
|
|
@ -30,8 +30,8 @@ export function buildGraph(data) {
|
|||
}
|
||||
|
||||
/**
|
||||
* 查询任务状态
|
||||
* @param {String} taskId - 任务ID
|
||||
* Query task status
|
||||
* @param {String} taskId - Task ID
|
||||
* @returns {Promise}
|
||||
*/
|
||||
export function getTaskStatus(taskId) {
|
||||
|
|
@ -42,8 +42,8 @@ export function getTaskStatus(taskId) {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取图谱数据
|
||||
* @param {String} graphId - 图谱ID
|
||||
* Get graph data
|
||||
* @param {String} graphId - Graph ID
|
||||
* @returns {Promise}
|
||||
*/
|
||||
export function getGraphData(graphId) {
|
||||
|
|
@ -54,8 +54,8 @@ export function getGraphData(graphId) {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取项目信息
|
||||
* @param {String} projectId - 项目ID
|
||||
* Get project info
|
||||
* @param {String} projectId - Project ID
|
||||
* @returns {Promise}
|
||||
*/
|
||||
export function getProject(projectId) {
|
||||
|
|
|
|||
|
|
@ -1,16 +1,16 @@
|
|||
import axios from 'axios'
|
||||
import i18n from '../i18n'
|
||||
|
||||
// 创建axios实例
|
||||
// Create axios instance
|
||||
const service = axios.create({
|
||||
baseURL: import.meta.env.VITE_API_BASE_URL || 'http://localhost:5001',
|
||||
timeout: 300000, // 5分钟超时（本体生成可能需要较长时间）
|
||||
timeout: 300000, // 5 minute timeout (ontology generation may take longer)
|
||||
headers: {
|
||||
'Content-Type': 'application/json'
|
||||
}
|
||||
})
|
||||
|
||||
// 请求拦截器
|
||||
// Request interceptor
|
||||
service.interceptors.request.use(
|
||||
config => {
|
||||
config.headers['Accept-Language'] = i18n.global.locale.value
|
||||
|
|
@ -22,12 +22,12 @@ service.interceptors.request.use(
|
|||
}
|
||||
)
|
||||
|
||||
// 响应拦截器（容错重试机制）
|
||||
// Response interceptor (fault-tolerant retry mechanism)
|
||||
service.interceptors.response.use(
|
||||
response => {
|
||||
const res = response.data
|
||||
|
||||
// 如果返回的状态码不是success，则抛出错误
|
||||
// If returned status code is not success, throw error
|
||||
if (!res.success && res.success !== undefined) {
|
||||
console.error('API Error:', res.error || res.message || 'Unknown error')
|
||||
return Promise.reject(new Error(res.error || res.message || 'Error'))
|
||||
|
|
@ -38,12 +38,12 @@ service.interceptors.response.use(
|
|||
error => {
|
||||
console.error('Response error:', error)
|
||||
|
||||
// 处理超时
|
||||
// Handle timeout
|
||||
if (error.code === 'ECONNABORTED' && error.message.includes('timeout')) {
|
||||
console.error('Request timeout')
|
||||
}
|
||||
|
||||
// 处理网络错误
|
||||
// Handle network error
|
||||
if (error.message === 'Network Error') {
|
||||
console.error('Network error - please check your connection')
|
||||
}
|
||||
|
|
@ -52,4 +52,18 @@ service.interceptors.response.use(
|
|||
}
|
||||
)
|
||||
|
||||
// Request function with retry
|
||||
export const requestWithRetry = async (requestFn, maxRetries = 3, delay = 1000) => {
|
||||
for (let i = 0; i < maxRetries; i++) {
|
||||
try {
|
||||
return await requestFn()
|
||||
} catch (error) {
|
||||
if (i === maxRetries - 1) throw error
|
||||
|
||||
console.warn(`Request failed, retrying (${i + 1}/${maxRetries})...`)
|
||||
await new Promise(resolve => setTimeout(resolve, delay * Math.pow(2, i)))
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
export default service
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import service from './index'
|
||||
|
||||
/**
|
||||
* 开始报告生成
|
||||
* Start report generation
|
||||
* @param {Object} data - { simulation_id, force_regenerate? }
|
||||
*/
|
||||
export const generateReport = (data) => {
|
||||
|
|
@ -9,7 +9,7 @@ export const generateReport = (data) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取报告生成状态
|
||||
* Get report generation status
|
||||
* @param {string} reportId
|
||||
*/
|
||||
export const getReportStatus = (reportId) => {
|
||||
|
|
@ -17,25 +17,25 @@ export const getReportStatus = (reportId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取 Agent 日志（增量）
|
||||
* Get Agent log (incremental)
|
||||
* @param {string} reportId
|
||||
* @param {number} fromLine - 从第几行开始获取
|
||||
* @param {number} fromLine - Starting line number
|
||||
*/
|
||||
export const getAgentLog = (reportId, fromLine = 0) => {
|
||||
return service.get(`/api/report/${reportId}/agent-log`, { params: { from_line: fromLine } })
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取控制台日志（增量）
|
||||
* Get console log (incremental)
|
||||
* @param {string} reportId
|
||||
* @param {number} fromLine - 从第几行开始获取
|
||||
* @param {number} fromLine - Starting line number
|
||||
*/
|
||||
export const getConsoleLog = (reportId, fromLine = 0) => {
|
||||
return service.get(`/api/report/${reportId}/console-log`, { params: { from_line: fromLine } })
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取报告详情
|
||||
* Get report details
|
||||
* @param {string} reportId
|
||||
*/
|
||||
export const getReport = (reportId) => {
|
||||
|
|
@ -43,7 +43,7 @@ export const getReport = (reportId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 与 Report Agent 对话
|
||||
* Chat with Report Agent
|
||||
* @param {Object} data - { simulation_id, message, chat_history? }
|
||||
*/
|
||||
export const chatWithReport = (data) => {
|
||||
|
|
|
|||
|
|
@ -1,23 +1,23 @@
|
|||
import service from './index'
|
||||
import service, { requestWithRetry } from './index'
|
||||
|
||||
/**
|
||||
* 创建模拟
|
||||
* Create simulation
|
||||
* @param {Object} data - { project_id, graph_id?, enable_twitter?, enable_reddit? }
|
||||
*/
|
||||
export const createSimulation = (data) => {
|
||||
return service.post('/api/simulation/create', data)
|
||||
return requestWithRetry(() => service.post('/api/simulation/create', data), 3, 1000)
|
||||
}
|
||||
|
||||
/**
|
||||
* 准备模拟环境（异步任务）
|
||||
* Prepare simulation environment (async task)
|
||||
* @param {Object} data - { simulation_id, entity_types?, use_llm_for_profiles?, parallel_profile_count?, force_regenerate? }
|
||||
*/
|
||||
export const prepareSimulation = (data) => {
|
||||
return service.post('/api/simulation/prepare', data)
|
||||
return requestWithRetry(() => service.post('/api/simulation/prepare', data), 3, 1000)
|
||||
}
|
||||
|
||||
/**
|
||||
* 查询准备任务进度
|
||||
* Query preparation task progress
|
||||
* @param {Object} data - { task_id?, simulation_id? }
|
||||
*/
|
||||
export const getPrepareStatus = (data) => {
|
||||
|
|
@ -25,7 +25,7 @@ export const getPrepareStatus = (data) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取模拟状态
|
||||
* Get simulation status
|
||||
* @param {string} simulationId
|
||||
*/
|
||||
export const getSimulation = (simulationId) => {
|
||||
|
|
@ -33,27 +33,25 @@ export const getSimulation = (simulationId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取模拟的 Agent Profiles
|
||||
* Get simulation Agent Profiles
|
||||
* @param {string} simulationId
|
||||
* @param {string} [platform] - 'reddit' | 'twitter'（省略时由后端根据模拟配置自动选择）
|
||||
* @param {string} platform - 'reddit' | 'twitter'
|
||||
*/
|
||||
export const getSimulationProfiles = (simulationId, platform) => {
|
||||
const params = platform ? { platform } : {}
|
||||
return service.get(`/api/simulation/${simulationId}/profiles`, { params })
|
||||
export const getSimulationProfiles = (simulationId, platform = 'reddit') => {
|
||||
return service.get(`/api/simulation/${simulationId}/profiles`, { params: { platform } })
|
||||
}
|
||||
|
||||
/**
|
||||
* 实时获取生成中的 Agent Profiles
|
||||
* Get in-progress Agent Profiles in real-time
|
||||
* @param {string} simulationId
|
||||
* @param {string} [platform] - 'reddit' | 'twitter'（省略时由后端根据模拟配置自动选择）
|
||||
* @param {string} platform - 'reddit' | 'twitter'
|
||||
*/
|
||||
export const getSimulationProfilesRealtime = (simulationId, platform) => {
|
||||
const params = platform ? { platform } : {}
|
||||
return service.get(`/api/simulation/${simulationId}/profiles/realtime`, { params })
|
||||
export const getSimulationProfilesRealtime = (simulationId, platform = 'reddit') => {
|
||||
return service.get(`/api/simulation/${simulationId}/profiles/realtime`, { params: { platform } })
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取模拟配置
|
||||
* Get simulation config
|
||||
* @param {string} simulationId
|
||||
*/
|
||||
export const getSimulationConfig = (simulationId) => {
|
||||
|
|
@ -61,17 +59,17 @@ export const getSimulationConfig = (simulationId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 实时获取生成中的模拟配置
|
||||
* Get in-progress simulation config in real-time
|
||||
* @param {string} simulationId
|
||||
* @returns {Promise} 返回配置信息，包含元数据和配置内容
|
||||
* @returns {Promise} Returns config info including metadata and config content
|
||||
*/
|
||||
export const getSimulationConfigRealtime = (simulationId) => {
|
||||
return service.get(`/api/simulation/${simulationId}/config/realtime`)
|
||||
}
|
||||
|
||||
/**
|
||||
* 列出所有模拟
|
||||
* @param {string} projectId - 可选，按项目ID过滤
|
||||
* List all simulations
|
||||
* @param {string} projectId - Optional, filter by project ID
|
||||
*/
|
||||
export const listSimulations = (projectId) => {
|
||||
const params = projectId ? { project_id: projectId } : {}
|
||||
|
|
@ -79,15 +77,15 @@ export const listSimulations = (projectId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 启动模拟
|
||||
* Start simulation
|
||||
* @param {Object} data - { simulation_id, platform?, max_rounds?, enable_graph_memory_update? }
|
||||
*/
|
||||
export const startSimulation = (data) => {
|
||||
return service.post('/api/simulation/start', data)
|
||||
return requestWithRetry(() => service.post('/api/simulation/start', data), 3, 1000)
|
||||
}
|
||||
|
||||
/**
|
||||
* 停止模拟
|
||||
* Stop simulation
|
||||
* @param {Object} data - { simulation_id }
|
||||
*/
|
||||
export const stopSimulation = (data) => {
|
||||
|
|
@ -95,7 +93,7 @@ export const stopSimulation = (data) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取模拟运行实时状态
|
||||
* Get simulation run real-time status
|
||||
* @param {string} simulationId
|
||||
*/
|
||||
export const getRunStatus = (simulationId) => {
|
||||
|
|
@ -103,7 +101,7 @@ export const getRunStatus = (simulationId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取模拟运行详细状态（包含最近动作）
|
||||
* Get simulation run detailed status (including recent actions)
|
||||
* @param {string} simulationId
|
||||
*/
|
||||
export const getRunStatusDetail = (simulationId) => {
|
||||
|
|
@ -111,23 +109,23 @@ export const getRunStatusDetail = (simulationId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取模拟中的帖子
|
||||
* Get posts in simulation
|
||||
* @param {string} simulationId
|
||||
* @param {string} [platform] - 'reddit' | 'twitter'（省略时由后端根据模拟配置自动选择）
|
||||
* @param {number} limit - 返回数量
|
||||
* @param {number} offset - 偏移量
|
||||
* @param {string} platform - 'reddit' | 'twitter'
|
||||
* @param {number} limit - Return count
|
||||
* @param {number} offset - Offset
|
||||
*/
|
||||
export const getSimulationPosts = (simulationId, platform, limit = 50, offset = 0) => {
|
||||
const params = { limit, offset }
|
||||
if (platform) params.platform = platform
|
||||
return service.get(`/api/simulation/${simulationId}/posts`, { params })
|
||||
export const getSimulationPosts = (simulationId, platform = 'reddit', limit = 50, offset = 0) => {
|
||||
return service.get(`/api/simulation/${simulationId}/posts`, {
|
||||
params: { platform, limit, offset }
|
||||
})
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取模拟时间线（按轮次汇总）
|
||||
* Get simulation timeline (grouped by round)
|
||||
* @param {string} simulationId
|
||||
* @param {number} startRound - 起始轮次
|
||||
* @param {number} endRound - 结束轮次
|
||||
* @param {number} startRound - Start round
|
||||
* @param {number} endRound - End round
|
||||
*/
|
||||
export const getSimulationTimeline = (simulationId, startRound = 0, endRound = null) => {
|
||||
const params = { start_round: startRound }
|
||||
|
|
@ -138,7 +136,7 @@ export const getSimulationTimeline = (simulationId, startRound = 0, endRound = n
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取Agent统计信息
|
||||
* Get Agent statistics
|
||||
* @param {string} simulationId
|
||||
*/
|
||||
export const getAgentStats = (simulationId) => {
|
||||
|
|
@ -146,7 +144,7 @@ export const getAgentStats = (simulationId) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取模拟动作历史
|
||||
* Get simulation action history
|
||||
* @param {string} simulationId
|
||||
* @param {Object} params - { limit, offset, platform, agent_id, round_num }
|
||||
*/
|
||||
|
|
@ -155,7 +153,7 @@ export const getSimulationActions = (simulationId, params = {}) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 关闭模拟环境（优雅退出）
|
||||
* Close simulation environment (graceful exit)
|
||||
* @param {Object} data - { simulation_id, timeout? }
|
||||
*/
|
||||
export const closeSimulationEnv = (data) => {
|
||||
|
|
@ -163,7 +161,7 @@ export const closeSimulationEnv = (data) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 获取模拟环境状态
|
||||
* Get simulation environment status
|
||||
* @param {Object} data - { simulation_id }
|
||||
*/
|
||||
export const getEnvStatus = (data) => {
|
||||
|
|
@ -171,18 +169,19 @@ export const getEnvStatus = (data) => {
|
|||
}
|
||||
|
||||
/**
|
||||
* 批量采访 Agent
|
||||
* Batch interview Agents
|
||||
* @param {Object} data - { simulation_id, interviews: [{ agent_id, prompt }] }
|
||||
*/
|
||||
export const interviewAgents = (data) => {
|
||||
return service.post('/api/simulation/interview/batch', data)
|
||||
return requestWithRetry(() => service.post('/api/simulation/interview/batch', data), 3, 1000)
|
||||
}
|
||||
|
||||
/**
|
||||
* 获取历史模拟列表（带项目详情）
|
||||
* 用于首页历史项目展示
|
||||
* @param {number} limit - 返回数量限制
|
||||
* Get historical simulation list (with project details)
|
||||
* For home page historical project display
|
||||
* @param {number} limit - Return count limit
|
||||
*/
|
||||
export const getSimulationHistory = (limit = 20) => {
|
||||
return service.get('/api/simulation/history', { params: { limit } })
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
<div class="graph-panel">
|
||||
<div class="panel-header">
|
||||
<span class="panel-title">{{ $t('graph.panelTitle') }}</span>
|
||||
<!-- 顶部工具栏 (Internal Top Right) -->
|
||||
<!-- Top toolbar (Internal Top Right) -->
|
||||
<div class="header-tools">
|
||||
<button class="tool-btn" @click="$emit('refresh')" :disabled="loading" :title="$t('graph.refreshGraph')">
|
||||
<span class="icon-refresh" :class="{ 'spinning': loading }">↻</span>
|
||||
|
|
@ -15,11 +15,11 @@
|
|||
</div>
|
||||
|
||||
<div class="graph-container" ref="graphContainer">
|
||||
<!-- 图谱可视化 -->
|
||||
<!-- Graph visualization -->
|
||||
<div v-if="graphData" class="graph-view">
|
||||
<svg ref="graphSvg" class="graph-svg"></svg>
|
||||
|
||||
<!-- 构建中/模拟中提示 -->
|
||||
<!-- Building/simulating hint -->
|
||||
<div v-if="currentPhase === 1 || isSimulating" class="graph-building-hint">
|
||||
<div class="memory-icon-wrapper">
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="memory-icon">
|
||||
|
|
@ -30,7 +30,7 @@
|
|||
{{ isSimulating ? $t('graph.graphMemoryRealtime') : $t('graph.realtimeUpdating') }}
|
||||
</div>
|
||||
|
||||
<!-- 模拟结束后的提示 -->
|
||||
<!-- Post-simulation hint -->
|
||||
<div v-if="showSimulationFinishedHint" class="graph-building-hint finished-hint">
|
||||
<div class="hint-icon-wrapper">
|
||||
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" class="hint-icon">
|
||||
|
|
@ -48,7 +48,7 @@
|
|||
</button>
|
||||
</div>
|
||||
|
||||
<!-- 节点/边详情面板 -->
|
||||
<!-- Node/Edge detail panel -->
|
||||
<div v-if="selectedItem" class="detail-panel">
|
||||
<div class="detail-panel-header">
|
||||
<span class="detail-title">{{ selectedItem.type === 'node' ? $t('graph.nodeDetails') : $t('graph.relationship') }}</span>
|
||||
|
|
@ -58,7 +58,7 @@
|
|||
<button class="detail-close" @click="closeDetailPanel">×</button>
|
||||
</div>
|
||||
|
||||
<!-- 节点详情 -->
|
||||
<!-- Node details -->
|
||||
<div v-if="selectedItem.type === 'node'" class="detail-content">
|
||||
<div class="detail-row">
|
||||
<span class="detail-label">Name:</span>
|
||||
|
|
@ -101,9 +101,9 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 边详情 -->
|
||||
<!-- Edge details -->
|
||||
<div v-else class="detail-content">
|
||||
<!-- 自环组详情 -->
|
||||
<!-- Self-loop group details -->
|
||||
<template v-if="selectedItem.data.isSelfLoopGroup">
|
||||
<div class="edge-relation-header self-loop-header">
|
||||
{{ selectedItem.data.source_name }} - Self Relations
|
||||
|
|
@ -154,7 +154,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- 普通边详情 -->
|
||||
<!-- Regular edge details -->
|
||||
<template v-else>
|
||||
<div class="edge-relation-header">
|
||||
{{ selectedItem.data.source_name }} → {{ selectedItem.data.name || 'RELATED_TO' }} → {{ selectedItem.data.target_name }}
|
||||
|
|
@ -200,20 +200,20 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 加载状态 -->
|
||||
<!-- Loading state -->
|
||||
<div v-else-if="loading" class="graph-state">
|
||||
<div class="loading-spinner"></div>
|
||||
<p>{{ $t('graph.graphDataLoading') }}</p>
|
||||
</div>
|
||||
|
||||
<!-- 等待/空状态 -->
|
||||
<!-- Waiting/empty state -->
|
||||
<div v-else class="graph-state">
|
||||
<div class="empty-icon">❖</div>
|
||||
<p class="empty-text">{{ $t('graph.waitingOntology') }}</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 底部图例 (Bottom Left) -->
|
||||
<!-- Bottom legend (Bottom Left) -->
|
||||
<div v-if="graphData && entityTypes.length" class="graph-legend">
|
||||
<span class="legend-title">Entity Types</span>
|
||||
<div class="legend-items">
|
||||
|
|
@ -224,7 +224,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 显示边标签开关 -->
|
||||
<!-- Show edge labels toggle -->
|
||||
<div v-if="graphData" class="edge-labels-toggle">
|
||||
<label class="toggle-switch">
|
||||
<input type="checkbox" v-model="showEdgeLabels" />
|
||||
|
|
@ -251,26 +251,26 @@ const emit = defineEmits(['refresh', 'toggle-maximize'])
|
|||
const graphContainer = ref(null)
|
||||
const graphSvg = ref(null)
|
||||
const selectedItem = ref(null)
|
||||
const showEdgeLabels = ref(true) // 默认显示边标签
|
||||
const expandedSelfLoops = ref(new Set()) // 展开的自环项
|
||||
const showSimulationFinishedHint = ref(false) // 模拟结束后的提示
|
||||
const wasSimulating = ref(false) // 追踪之前是否在模拟中
|
||||
const showEdgeLabels = ref(true) // Show edge labels by default
|
||||
const expandedSelfLoops = ref(new Set()) // Expanded self-loop items
|
||||
const showSimulationFinishedHint = ref(false) // Post-simulation hint
|
||||
const wasSimulating = ref(false) // Track whether previously simulating
|
||||
|
||||
// 关闭模拟结束提示
|
||||
// Close post-simulation hint
|
||||
const dismissFinishedHint = () => {
|
||||
showSimulationFinishedHint.value = false
|
||||
}
|
||||
|
||||
// 监听 isSimulating 变化，检测模拟结束
|
||||
// Watch isSimulating changes, detect simulation end
|
||||
watch(() => props.isSimulating, (newValue, oldValue) => {
|
||||
if (wasSimulating.value && !newValue) {
|
||||
// 从模拟中变为非模拟状态，显示结束提示
|
||||
// Transition from simulating to non-simulating, show end hint
|
||||
showSimulationFinishedHint.value = true
|
||||
}
|
||||
wasSimulating.value = newValue
|
||||
}, { immediate: true })
|
||||
|
||||
// 切换自环项展开/折叠状态
|
||||
// Toggle self-loop item expand/collapse
|
||||
const toggleSelfLoop = (id) => {
|
||||
const newSet = new Set(expandedSelfLoops.value)
|
||||
if (newSet.has(id)) {
|
||||
|
|
@ -281,11 +281,11 @@ const toggleSelfLoop = (id) => {
|
|||
expandedSelfLoops.value = newSet
|
||||
}
|
||||
|
||||
// 计算实体类型用于图例
|
||||
// Calculate entity types for legend
|
||||
const entityTypes = computed(() => {
|
||||
if (!props.graphData?.nodes) return []
|
||||
const typeMap = {}
|
||||
// 美观的颜色调色板
|
||||
// Beautiful color palette
|
||||
const colors = ['#FF6B35', '#004E89', '#7B2D8E', '#1A936F', '#C5283D', '#E9724C', '#3498db', '#9b59b6', '#27ae60', '#f39c12']
|
||||
|
||||
props.graphData.nodes.forEach(node => {
|
||||
|
|
@ -298,7 +298,7 @@ const entityTypes = computed(() => {
|
|||
return Object.values(typeMap)
|
||||
})
|
||||
|
||||
// 格式化时间
|
||||
// Format time
|
||||
const formatDateTime = (dateStr) => {
|
||||
if (!dateStr) return ''
|
||||
try {
|
||||
|
|
@ -318,7 +318,7 @@ const formatDateTime = (dateStr) => {
|
|||
|
||||
const closeDetailPanel = () => {
|
||||
selectedItem.value = null
|
||||
expandedSelfLoops.value = new Set() // 重置展开状态
|
||||
expandedSelfLoops.value = new Set() // Reset expanded state
|
||||
}
|
||||
|
||||
let currentSimulation = null
|
||||
|
|
@ -328,7 +328,7 @@ let linkLabelBgRef = null
|
|||
const renderGraph = () => {
|
||||
if (!graphSvg.value || !props.graphData) return
|
||||
|
||||
// 停止之前的仿真
|
||||
// Stop previous simulation
|
||||
if (currentSimulation) {
|
||||
currentSimulation.stop()
|
||||
}
|
||||
|
|
@ -362,16 +362,16 @@ const renderGraph = () => {
|
|||
|
||||
const nodeIds = new Set(nodes.map(n => n.id))
|
||||
|
||||
// 处理边数据，计算同一对节点间的边数量和索引
|
||||
// Process edge data, calculate edge count and index between same node pair
|
||||
const edgePairCount = {}
|
||||
const selfLoopEdges = {} // 按节点分组的自环边
|
||||
const selfLoopEdges = {} // Self-loop edges grouped by node
|
||||
const tempEdges = edgesData
|
||||
.filter(e => nodeIds.has(e.source_node_uuid) && nodeIds.has(e.target_node_uuid))
|
||||
|
||||
// 统计每对节点之间的边数量，收集自环边
|
||||
// Count edges between each node pair, collect self-loop edges
|
||||
tempEdges.forEach(e => {
|
||||
if (e.source_node_uuid === e.target_node_uuid) {
|
||||
// 自环 - 收集到数组中
|
||||
// Self-loop - collect into array
|
||||
if (!selfLoopEdges[e.source_node_uuid]) {
|
||||
selfLoopEdges[e.source_node_uuid] = []
|
||||
}
|
||||
|
|
@ -386,9 +386,9 @@ const renderGraph = () => {
|
|||
}
|
||||
})
|
||||
|
||||
// 记录当前处理到每对节点的第几条边
|
||||
// Track which edge index for each node pair
|
||||
const edgePairIndex = {}
|
||||
const processedSelfLoopNodes = new Set() // 已处理的自环节点
|
||||
const processedSelfLoopNodes = new Set() // Processed self-loop nodes
|
||||
|
||||
const edges = []
|
||||
|
||||
|
|
@ -396,9 +396,9 @@ const renderGraph = () => {
|
|||
const isSelfLoop = e.source_node_uuid === e.target_node_uuid
|
||||
|
||||
if (isSelfLoop) {
|
||||
// 自环边 - 每个节点只添加一条合并的自环
|
||||
// Self-loop edge - only add one merged self-loop per node
|
||||
if (processedSelfLoopNodes.has(e.source_node_uuid)) {
|
||||
return // 已处理过，跳过
|
||||
return // Already processed, skip
|
||||
}
|
||||
processedSelfLoopNodes.add(e.source_node_uuid)
|
||||
|
||||
|
|
@ -417,7 +417,7 @@ const renderGraph = () => {
|
|||
source_name: nodeName,
|
||||
target_name: nodeName,
|
||||
selfLoopCount: allSelfLoops.length,
|
||||
selfLoopEdges: allSelfLoops // 存储所有自环边的详细信息
|
||||
selfLoopEdges: allSelfLoops // Store all self-loop edge details
|
||||
}
|
||||
})
|
||||
return
|
||||
|
|
@ -428,19 +428,19 @@ const renderGraph = () => {
|
|||
const currentIndex = edgePairIndex[pairKey] || 0
|
||||
edgePairIndex[pairKey] = currentIndex + 1
|
||||
|
||||
// 判断边的方向是否与标准化方向一致（源UUID < 目标UUID）
|
||||
// Check if edge direction matches normalized direction (source UUID < target UUID)
|
||||
const isReversed = e.source_node_uuid > e.target_node_uuid
|
||||
|
||||
// 计算曲率：多条边时分散开，单条边为直线
|
||||
// Calculate curvature: spread for multiple edges, straight for single edge
|
||||
let curvature = 0
|
||||
if (totalCount > 1) {
|
||||
// 均匀分布曲率，确保明显区分
|
||||
// 曲率范围根据边数量增加，边越多曲率范围越大
|
||||
// Evenly distribute curvature, ensure clear distinction
|
||||
// Curvature range increases with edge count, more edges = wider range
|
||||
const curvatureRange = Math.min(1.2, 0.6 + totalCount * 0.15)
|
||||
curvature = ((currentIndex / (totalCount - 1)) - 0.5) * curvatureRange * 2
|
||||
|
||||
// 如果边的方向与标准化方向相反，翻转曲率
|
||||
// 这样确保所有边在同一参考系下分布，不会因方向不同而重叠
|
||||
// If edge direction is opposite to normalized, flip curvature
|
||||
// This ensures all edges distribute in same reference frame, no overlap from direction differences
|
||||
if (isReversed) {
|
||||
curvature = -curvature
|
||||
}
|
||||
|
|
@ -468,11 +468,11 @@ const renderGraph = () => {
|
|||
entityTypes.value.forEach(t => colorMap[t.name] = t.color)
|
||||
const getColor = (type) => colorMap[type] || '#999'
|
||||
|
||||
// Simulation - 根据边数量动态调整节点间距
|
||||
// Simulation - dynamically adjust node spacing based on edge count
|
||||
const simulation = d3.forceSimulation(nodes)
|
||||
.force('link', d3.forceLink(edges).id(d => d.id).distance(d => {
|
||||
// 根据这对节点之间的边数量动态调整距离
|
||||
// 基础距离 150，每多一条边增加 40
|
||||
// Dynamically adjust distance based on edge count between this node pair
|
||||
// Base distance 150, +40 per additional edge
|
||||
const baseDistance = 150
|
||||
const edgeCount = d.pairTotal || 1
|
||||
return baseDistance + (edgeCount - 1) * 50
|
||||
|
|
@ -480,7 +480,7 @@ const renderGraph = () => {
|
|||
.force('charge', d3.forceManyBody().strength(-400))
|
||||
.force('center', d3.forceCenter(width / 2, height / 2))
|
||||
.force('collide', d3.forceCollide(50))
|
||||
// 添加向中心的引力，让独立的节点群聚集到中心区域
|
||||
// Add center gravity to pull isolated node clusters toward center
|
||||
.force('x', d3.forceX(width / 2).strength(0.04))
|
||||
.force('y', d3.forceY(height / 2).strength(0.04))
|
||||
|
||||
|
|
@ -493,39 +493,39 @@ const renderGraph = () => {
|
|||
g.attr('transform', event.transform)
|
||||
}))
|
||||
|
||||
// Links - 使用 path 支持曲线
|
||||
// Links - use path for curved support
|
||||
const linkGroup = g.append('g').attr('class', 'links')
|
||||
|
||||
// 计算曲线路径
|
||||
// Calculate curved path
|
||||
const getLinkPath = (d) => {
|
||||
const sx = d.source.x, sy = d.source.y
|
||||
const tx = d.target.x, ty = d.target.y
|
||||
|
||||
// 检测自环
|
||||
// Detect self-loop
|
||||
if (d.isSelfLoop) {
|
||||
// 自环：绘制一个圆弧从节点出发再返回
|
||||
// Self-loop: draw an arc from node and back
|
||||
const loopRadius = 30
|
||||
// 从节点右侧出发，绕一圈回来
|
||||
const x1 = sx + 8 // 起点偏移
|
||||
// Start from node right side, loop around and return
|
||||
const x1 = sx + 8 // Start offset
|
||||
const y1 = sy - 4
|
||||
const x2 = sx + 8 // 终点偏移
|
||||
const x2 = sx + 8 // End offset
|
||||
const y2 = sy + 4
|
||||
// 使用圆弧绘制自环（sweep-flag=1 顺时针）
|
||||
// Use arc for self-loop (sweep-flag=1 clockwise)
|
||||
return `M${x1},${y1} A${loopRadius},${loopRadius} 0 1,1 ${x2},${y2}`
|
||||
}
|
||||
|
||||
if (d.curvature === 0) {
|
||||
// 直线
|
||||
// Straight line
|
||||
return `M${sx},${sy} L${tx},${ty}`
|
||||
}
|
||||
|
||||
// 计算曲线控制点 - 根据边数量和距离动态调整
|
||||
// Calculate curve control point - dynamically adjust based on edge count and distance
|
||||
const dx = tx - sx, dy = ty - sy
|
||||
const dist = Math.sqrt(dx * dx + dy * dy)
|
||||
// 垂直于连线方向的偏移，根据距离比例计算，保证曲线明显可见
|
||||
// 边越多，偏移量占距离的比例越大
|
||||
// Perpendicular offset from connection line, calculated by distance ratio, ensures visible curve
|
||||
// More edges = larger offset ratio
|
||||
const pairTotal = d.pairTotal || 1
|
||||
const offsetRatio = 0.25 + pairTotal * 0.05 // 基础25%，每多一条边增加5%
|
||||
const offsetRatio = 0.25 + pairTotal * 0.05 // Base 25%, +5% per additional edge
|
||||
const baseOffset = Math.max(35, dist * offsetRatio)
|
||||
const offsetX = -dy / dist * d.curvature * baseOffset
|
||||
const offsetY = dx / dist * d.curvature * baseOffset
|
||||
|
|
@ -535,14 +535,14 @@ const renderGraph = () => {
|
|||
return `M${sx},${sy} Q${cx},${cy} ${tx},${ty}`
|
||||
}
|
||||
|
||||
// 计算曲线中点（用于标签定位）
|
||||
// Calculate curve midpoint (for label positioning)
|
||||
const getLinkMidpoint = (d) => {
|
||||
const sx = d.source.x, sy = d.source.y
|
||||
const tx = d.target.x, ty = d.target.y
|
||||
|
||||
// 检测自环
|
||||
// Detect self-loop
|
||||
if (d.isSelfLoop) {
|
||||
// 自环标签位置：节点右侧
|
||||
// Self-loop label position: right of node
|
||||
return { x: sx + 70, y: sy }
|
||||
}
|
||||
|
||||
|
|
@ -550,7 +550,7 @@ const renderGraph = () => {
|
|||
return { x: (sx + tx) / 2, y: (sy + ty) / 2 }
|
||||
}
|
||||
|
||||
// 二次贝塞尔曲线的中点 t=0.5
|
||||
// Quadratic bezier midpoint t=0.5
|
||||
const dx = tx - sx, dy = ty - sy
|
||||
const dist = Math.sqrt(dx * dx + dy * dy)
|
||||
const pairTotal = d.pairTotal || 1
|
||||
|
|
@ -561,7 +561,7 @@ const renderGraph = () => {
|
|||
const cx = (sx + tx) / 2 + offsetX
|
||||
const cy = (sy + ty) / 2 + offsetY
|
||||
|
||||
// 二次贝塞尔曲线公式 B(t) = (1-t)²P0 + 2(1-t)tP1 + t²P2, t=0.5
|
||||
// Quadratic bezier formula B(t) = (1-t)²P0 + 2(1-t)tP1 + t²P2, t=0.5
|
||||
const midX = 0.25 * sx + 0.5 * cx + 0.25 * tx
|
||||
const midY = 0.25 * sy + 0.5 * cy + 0.25 * ty
|
||||
|
||||
|
|
@ -577,11 +577,11 @@ const renderGraph = () => {
|
|||
.style('cursor', 'pointer')
|
||||
.on('click', (event, d) => {
|
||||
event.stopPropagation()
|
||||
// 重置之前选中边的样式
|
||||
// Reset previously selected edge style
|
||||
linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
|
||||
linkLabelBg.attr('fill', 'rgba(255,255,255,0.95)')
|
||||
linkLabels.attr('fill', '#666')
|
||||
// 高亮当前选中的边
|
||||
// Highlight currently selected edge
|
||||
d3.select(event.target).attr('stroke', '#3498db').attr('stroke-width', 3)
|
||||
|
||||
selectedItem.value = {
|
||||
|
|
@ -590,7 +590,7 @@ const renderGraph = () => {
|
|||
}
|
||||
})
|
||||
|
||||
// Link labels background (白色背景使文字更清晰)
|
||||
// Link labels background (white background for clearer text)
|
||||
const linkLabelBg = linkGroup.selectAll('rect')
|
||||
.data(edges)
|
||||
.enter().append('rect')
|
||||
|
|
@ -605,7 +605,7 @@ const renderGraph = () => {
|
|||
linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
|
||||
linkLabelBg.attr('fill', 'rgba(255,255,255,0.95)')
|
||||
linkLabels.attr('fill', '#666')
|
||||
// 高亮对应的边
|
||||
// Highlight corresponding edge
|
||||
link.filter(l => l === d).attr('stroke', '#3498db').attr('stroke-width', 3)
|
||||
d3.select(event.target).attr('fill', 'rgba(52, 152, 219, 0.1)')
|
||||
|
||||
|
|
@ -633,7 +633,7 @@ const renderGraph = () => {
|
|||
linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
|
||||
linkLabelBg.attr('fill', 'rgba(255,255,255,0.95)')
|
||||
linkLabels.attr('fill', '#666')
|
||||
// 高亮对应的边
|
||||
// Highlight corresponding edge
|
||||
link.filter(l => l === d).attr('stroke', '#3498db').attr('stroke-width', 3)
|
||||
d3.select(event.target).attr('fill', '#3498db')
|
||||
|
||||
|
|
@ -643,7 +643,7 @@ const renderGraph = () => {
|
|||
}
|
||||
})
|
||||
|
||||
// 保存引用供外部控制显隐
|
||||
// Save reference for external show/hide control
|
||||
linkLabelsRef = linkLabels
|
||||
linkLabelBgRef = linkLabelBg
|
||||
|
||||
|
|
@ -661,7 +661,7 @@ const renderGraph = () => {
|
|||
.style('cursor', 'pointer')
|
||||
.call(d3.drag()
|
||||
.on('start', (event, d) => {
|
||||
// 只记录位置，不重启仿真（区分点击和拖拽）
|
||||
// Only record position, don't restart simulation (distinguish click vs drag)
|
||||
d.fx = d.x
|
||||
d.fy = d.y
|
||||
d._dragStartX = event.x
|
||||
|
|
@ -669,13 +669,13 @@ const renderGraph = () => {
|
|||
d._isDragging = false
|
||||
})
|
||||
.on('drag', (event, d) => {
|
||||
// 检测是否真正开始拖拽（移动超过阈值）
|
||||
// Detect if actual drag started (moved beyond threshold)
|
||||
const dx = event.x - d._dragStartX
|
||||
const dy = event.y - d._dragStartY
|
||||
const distance = Math.sqrt(dx * dx + dy * dy)
|
||||
|
||||
if (!d._isDragging && distance > 3) {
|
||||
// 首次检测到真正拖拽，才重启仿真
|
||||
// First detection of actual drag, then restart simulation
|
||||
d._isDragging = true
|
||||
simulation.alphaTarget(0.3).restart()
|
||||
}
|
||||
|
|
@ -686,7 +686,7 @@ const renderGraph = () => {
|
|||
}
|
||||
})
|
||||
.on('end', (event, d) => {
|
||||
// 只有真正拖拽过才让仿真逐渐停止
|
||||
// Only gradually stop simulation if actual drag occurred
|
||||
if (d._isDragging) {
|
||||
simulation.alphaTarget(0)
|
||||
}
|
||||
|
|
@ -697,12 +697,12 @@ const renderGraph = () => {
|
|||
)
|
||||
.on('click', (event, d) => {
|
||||
event.stopPropagation()
|
||||
// 重置所有节点样式
|
||||
// Reset all node styles
|
||||
node.attr('stroke', '#fff').attr('stroke-width', 2.5)
|
||||
linkGroup.selectAll('path').attr('stroke', '#C0C0C0').attr('stroke-width', 1.5)
|
||||
// 高亮选中节点
|
||||
// Highlight selected node
|
||||
d3.select(event.target).attr('stroke', '#E91E63').attr('stroke-width', 4)
|
||||
// 高亮与此节点相连的边
|
||||
// Highlight edges connected to this node
|
||||
link.filter(l => l.source.id === d.id || l.target.id === d.id)
|
||||
.attr('stroke', '#E91E63')
|
||||
.attr('stroke-width', 2.5)
|
||||
|
|
@ -739,19 +739,19 @@ const renderGraph = () => {
|
|||
.style('font-family', 'system-ui, sans-serif')
|
||||
|
||||
simulation.on('tick', () => {
|
||||
// 更新曲线路径
|
||||
// Update curved paths
|
||||
link.attr('d', d => getLinkPath(d))
|
||||
|
||||
// 更新边标签位置（无旋转，水平显示更清晰）
|
||||
// Update edge label positions (no rotation, horizontal for clarity)
|
||||
linkLabels.each(function(d) {
|
||||
const mid = getLinkMidpoint(d)
|
||||
d3.select(this)
|
||||
.attr('x', mid.x)
|
||||
.attr('y', mid.y)
|
||||
.attr('transform', '') // 移除旋转，保持水平
|
||||
.attr('transform', '') // Remove rotation, keep horizontal
|
||||
})
|
||||
|
||||
// 更新边标签背景
|
||||
// Update edge label background
|
||||
linkLabelBg.each(function(d, i) {
|
||||
const mid = getLinkMidpoint(d)
|
||||
const textEl = linkLabels.nodes()[i]
|
||||
|
|
@ -761,7 +761,7 @@ const renderGraph = () => {
|
|||
.attr('y', mid.y - bbox.height / 2 - 2)
|
||||
.attr('width', bbox.width + 8)
|
||||
.attr('height', bbox.height + 4)
|
||||
.attr('transform', '') // 移除旋转
|
||||
.attr('transform', '') // Remove rotation
|
||||
})
|
||||
|
||||
node
|
||||
|
|
@ -773,7 +773,7 @@ const renderGraph = () => {
|
|||
.attr('y', d => d.y)
|
||||
})
|
||||
|
||||
// 点击空白处关闭详情面板
|
||||
// Click empty space to close detail panel
|
||||
svg.on('click', () => {
|
||||
selectedItem.value = null
|
||||
node.attr('stroke', '#fff').attr('stroke-width', 2.5)
|
||||
|
|
@ -787,7 +787,7 @@ watch(() => props.graphData, () => {
|
|||
nextTick(renderGraph)
|
||||
}, { deep: true })
|
||||
|
||||
// 监听边标签显示开关
|
||||
// Watch edge label display toggle
|
||||
watch(showEdgeLabels, (newVal) => {
|
||||
if (linkLabelsRef) {
|
||||
linkLabelsRef.style('display', newVal ? 'block' : 'none')
|
||||
|
|
@ -1250,7 +1250,7 @@ input:checked + .slider:before {
|
|||
50% { opacity: 1; transform: scale(1.15); filter: drop-shadow(0 0 8px rgba(76, 175, 80, 0.6)); }
|
||||
}
|
||||
|
||||
/* 模拟结束后的提示样式 */
|
||||
/* Post-simulation hint styles */
|
||||
.graph-building-hint.finished-hint {
|
||||
background: rgba(0, 0, 0, 0.65);
|
||||
border: 1px solid rgba(255, 255, 255, 0.1);
|
||||
|
|
|
|||
|
|
@ -4,20 +4,20 @@
|
|||
:class="{ 'no-projects': projects.length === 0 && !loading }"
|
||||
ref="historyContainer"
|
||||
>
|
||||
<!-- 背景装饰：技术网格线（只在有项目时显示） -->
|
||||
<!-- Background decoration: tech grid lines (only shown when projects exist) -->
|
||||
<div v-if="projects.length > 0 || loading" class="tech-grid-bg">
|
||||
<div class="grid-pattern"></div>
|
||||
<div class="gradient-overlay"></div>
|
||||
</div>
|
||||
|
||||
<!-- 标题区域 -->
|
||||
<!-- Title area -->
|
||||
<div class="section-header">
|
||||
<div class="section-line"></div>
|
||||
<span class="section-title">{{ $t('history.title') }}</span>
|
||||
<div class="section-line"></div>
|
||||
</div>
|
||||
|
||||
<!-- 卡片容器（只在有项目时显示） -->
|
||||
<!-- Card container (only shown when projects exist) -->
|
||||
<div v-if="projects.length > 0" class="cards-container" :class="{ expanded: isExpanded }" :style="containerStyle">
|
||||
<div
|
||||
v-for="(project, index) in projects"
|
||||
|
|
@ -29,7 +29,7 @@
|
|||
@mouseleave="hoveringCard = null"
|
||||
@click="navigateToProject(project)"
|
||||
>
|
||||
<!-- 卡片头部：simulation_id 和 功能可用状态 -->
|
||||
<!-- Card header: simulation_id and feature availability status -->
|
||||
<div class="card-header">
|
||||
<span class="card-id">{{ formatSimulationId(project.simulation_id) }}</span>
|
||||
<div class="card-status-icons">
|
||||
|
|
@ -50,12 +50,12 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 文件列表区域 -->
|
||||
<!-- File list area -->
|
||||
<div class="card-files-wrapper">
|
||||
<!-- 角落装饰 - 取景框风格 -->
|
||||
<!-- Corner decoration - viewfinder style -->
|
||||
<div class="corner-mark top-left-only"></div>
|
||||
|
||||
<!-- 文件列表 -->
|
||||
<!-- File list -->
|
||||
<div class="files-list" v-if="project.files && project.files.length > 0">
|
||||
<div
|
||||
v-for="(file, fileIndex) in project.files.slice(0, 3)"
|
||||
|
|
@ -65,25 +65,25 @@
|
|||
<span class="file-tag" :class="getFileType(file.filename)">{{ getFileTypeLabel(file.filename) }}</span>
|
||||
<span class="file-name">{{ truncateFilename(file.filename, 20) }}</span>
|
||||
</div>
|
||||
<!-- 如果有更多文件，显示提示 -->
|
||||
<!-- If more files exist, show hint -->
|
||||
<div v-if="project.files.length > 3" class="files-more">
|
||||
{{ $t('history.moreFiles', { count: project.files.length - 3 }) }}
|
||||
</div>
|
||||
</div>
|
||||
<!-- 无文件时的占位 -->
|
||||
<!-- Placeholder when no files -->
|
||||
<div class="files-empty" v-else>
|
||||
<span class="empty-file-icon">◇</span>
|
||||
<span class="empty-file-text">{{ $t('history.noFiles') }}</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 卡片标题（使用模拟需求的前20字作为标题） -->
|
||||
<!-- Card title (first 20 chars of simulation requirement as title) -->
|
||||
<h3 class="card-title">{{ getSimulationTitle(project.simulation_requirement) }}</h3>
|
||||
|
||||
<!-- 卡片描述（模拟需求完整展示） -->
|
||||
<!-- Card description (full simulation requirement) -->
|
||||
<p class="card-desc">{{ truncateText(project.simulation_requirement, 55) }}</p>
|
||||
|
||||
<!-- 卡片底部 -->
|
||||
<!-- Card bottom -->
|
||||
<div class="card-footer">
|
||||
<div class="card-datetime">
|
||||
<span class="card-date">{{ formatDate(project.created_at) }}</span>
|
||||
|
|
@ -94,23 +94,23 @@
|
|||
</span>
|
||||
</div>
|
||||
|
||||
<!-- 底部装饰线 (hover时展开) -->
|
||||
<!-- Bottom decorative line (expands on hover) -->
|
||||
<div class="card-bottom-line"></div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 加载状态 -->
|
||||
<!-- Loading state -->
|
||||
<div v-if="loading" class="loading-state">
|
||||
<span class="loading-spinner"></span>
|
||||
<span class="loading-text">{{ $t('history.loadingText') }}</span>
|
||||
</div>
|
||||
|
||||
<!-- 历史回放详情弹窗 -->
|
||||
<!-- History replay detail modal -->
|
||||
<Teleport to="body">
|
||||
<Transition name="modal">
|
||||
<div v-if="selectedProject" class="modal-overlay" @click.self="closeModal">
|
||||
<div class="modal-content">
|
||||
<!-- 弹窗头部 -->
|
||||
<!-- Modal header -->
|
||||
<div class="modal-header">
|
||||
<div class="modal-title-section">
|
||||
<span class="modal-id">{{ formatSimulationId(selectedProject.simulation_id) }}</span>
|
||||
|
|
@ -122,15 +122,15 @@
|
|||
<button class="modal-close" @click="closeModal">×</button>
|
||||
</div>
|
||||
|
||||
<!-- 弹窗内容 -->
|
||||
<!-- Modal content -->
|
||||
<div class="modal-body">
|
||||
<!-- 模拟需求 -->
|
||||
<!-- Simulation requirement -->
|
||||
<div class="modal-section">
|
||||
<div class="modal-label">{{ $t('history.simRequirement') }}</div>
|
||||
<div class="modal-requirement">{{ selectedProject.simulation_requirement || $t('common.none') }}</div>
|
||||
</div>
|
||||
|
||||
<!-- 文件列表 -->
|
||||
<!-- File list -->
|
||||
<div class="modal-section">
|
||||
<div class="modal-label">{{ $t('history.relatedFiles') }}</div>
|
||||
<div class="modal-files" v-if="selectedProject.files && selectedProject.files.length > 0">
|
||||
|
|
@ -143,14 +143,14 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 推演回放分割线 -->
|
||||
<!-- Simulation replay divider -->
|
||||
<div class="modal-divider">
|
||||
<span class="divider-line"></span>
|
||||
<span class="divider-text">{{ $t('history.replayTitle') }}</span>
|
||||
<span class="divider-line"></span>
|
||||
</div>
|
||||
|
||||
<!-- 导航按钮 -->
|
||||
<!-- Navigation buttons -->
|
||||
<div class="modal-actions">
|
||||
<button
|
||||
class="modal-btn btn-project"
|
||||
|
|
@ -179,7 +179,7 @@
|
|||
<span class="btn-text">{{ $t('history.step4Button') }}</span>
|
||||
</button>
|
||||
</div>
|
||||
<!-- 不可回放提示 -->
|
||||
<!-- Non-replayable hint -->
|
||||
<div class="modal-playback-hint">
|
||||
<span class="hint-text">{{ $t('history.replayHint') }}</span>
|
||||
</div>
|
||||
|
|
@ -200,56 +200,56 @@ const router = useRouter()
|
|||
const route = useRoute()
|
||||
const { t } = useI18n()
|
||||
|
||||
// 状态
|
||||
// State
|
||||
const projects = ref([])
|
||||
const loading = ref(true)
|
||||
const isExpanded = ref(false)
|
||||
const hoveringCard = ref(null)
|
||||
const historyContainer = ref(null)
|
||||
const selectedProject = ref(null) // 当前选中的项目（用于弹窗）
|
||||
const selectedProject = ref(null) // Currently selected project (for modal)
|
||||
let observer = null
|
||||
let isAnimating = false // 动画锁，防止闪烁
|
||||
let expandDebounceTimer = null // 防抖定时器
|
||||
let pendingState = null // 记录待执行的目标状态
|
||||
let isAnimating = false // Animation lock, prevents flicker
|
||||
let expandDebounceTimer = null // Debounce timer
|
||||
let pendingState = null // Record pending target state
|
||||
|
||||
// 卡片布局配置 - 调整为更宽的比例
|
||||
// Card layout config - adjusted for wider ratio
|
||||
const CARDS_PER_ROW = 4
|
||||
const CARD_WIDTH = 280
|
||||
const CARD_HEIGHT = 280
|
||||
const CARD_GAP = 24
|
||||
|
||||
// 动态计算容器高度样式
|
||||
// Dynamically calculate container height style
|
||||
const containerStyle = computed(() => {
|
||||
if (!isExpanded.value) {
|
||||
// 折叠态：固定高度
|
||||
// Collapsed: fixed height
|
||||
return { minHeight: '420px' }
|
||||
}
|
||||
|
||||
// 展开态：根据卡片数量动态计算高度
|
||||
// Expanded: dynamically calculate height based on card count
|
||||
const total = projects.value.length
|
||||
if (total === 0) {
|
||||
return { minHeight: '280px' }
|
||||
}
|
||||
|
||||
const rows = Math.ceil(total / CARDS_PER_ROW)
|
||||
// 计算实际需要的高度：行数 * 卡片高度 + (行数-1) * 间距 + 少量底部间距
|
||||
// Calculate actual needed height: rows * card height + (rows-1) * gap + small bottom padding
|
||||
const expandedHeight = rows * CARD_HEIGHT + (rows - 1) * CARD_GAP + 10
|
||||
|
||||
return { minHeight: `${expandedHeight}px` }
|
||||
})
|
||||
|
||||
// 获取卡片样式
|
||||
// Get card style
|
||||
const getCardStyle = (index) => {
|
||||
const total = projects.value.length
|
||||
|
||||
if (isExpanded.value) {
|
||||
// 展开态：网格布局
|
||||
// Expanded: grid layout
|
||||
const transition = 'transform 700ms cubic-bezier(0.23, 1, 0.32, 1), opacity 700ms cubic-bezier(0.23, 1, 0.32, 1), box-shadow 0.3s ease, border-color 0.3s ease'
|
||||
|
||||
const col = index % CARDS_PER_ROW
|
||||
const row = Math.floor(index / CARDS_PER_ROW)
|
||||
|
||||
// 计算当前行的卡片数量，确保每行居中
|
||||
// Calculate cards per row, ensure centered
|
||||
const currentRowStart = row * CARDS_PER_ROW
|
||||
const currentRowCards = Math.min(CARDS_PER_ROW, total - currentRowStart)
|
||||
|
||||
|
|
@ -259,7 +259,7 @@ const getCardStyle = (index) => {
|
|||
const colInRow = index % CARDS_PER_ROW
|
||||
const x = startX + colInRow * (CARD_WIDTH + CARD_GAP)
|
||||
|
||||
// 向下展开，增加与标题的间距
|
||||
// Expand downward, increase spacing from title
|
||||
const y = 20 + row * (CARD_HEIGHT + CARD_GAP)
|
||||
|
||||
return {
|
||||
|
|
@ -269,14 +269,14 @@ const getCardStyle = (index) => {
|
|||
transition: transition
|
||||
}
|
||||
} else {
|
||||
// 折叠态：扇形堆叠
|
||||
// Collapsed: fan stack
|
||||
const transition = 'transform 700ms cubic-bezier(0.23, 1, 0.32, 1), opacity 700ms cubic-bezier(0.23, 1, 0.32, 1), box-shadow 0.3s ease, border-color 0.3s ease'
|
||||
|
||||
const centerIndex = (total - 1) / 2
|
||||
const offset = index - centerIndex
|
||||
|
||||
const x = offset * 35
|
||||
// 调整起始位置，靠近标题但保持适当间距
|
||||
// Adjust start position, close to title but with proper spacing
|
||||
const y = 25 + Math.abs(offset) * 8
|
||||
const r = offset * 3
|
||||
const s = 0.95 - Math.abs(offset) * 0.05
|
||||
|
|
@ -290,24 +290,24 @@ const getCardStyle = (index) => {
|
|||
}
|
||||
}
|
||||
|
||||
// 根据轮数进度获取样式类
|
||||
// Get style class based on round progress
|
||||
const getProgressClass = (simulation) => {
|
||||
const current = simulation.current_round || 0
|
||||
const total = simulation.total_rounds || 0
|
||||
|
||||
if (total === 0 || current === 0) {
|
||||
// 未开始
|
||||
// Not started
|
||||
return 'not-started'
|
||||
} else if (current >= total) {
|
||||
// 已完成
|
||||
// Completed
|
||||
return 'completed'
|
||||
} else {
|
||||
// 进行中
|
||||
// In progress
|
||||
return 'in-progress'
|
||||
}
|
||||
}
|
||||
|
||||
// 格式化日期（只显示日期部分）
|
||||
// Format date (show date portion only)
|
||||
const formatDate = (dateStr) => {
|
||||
if (!dateStr) return ''
|
||||
try {
|
||||
|
|
@ -324,7 +324,7 @@ const formatDate = (dateStr) => {
|
|||
}
|
||||
}
|
||||
|
||||
// 格式化时间（显示时:分）
|
||||
// Format time (show HH:MM)
|
||||
const formatTime = (dateStr) => {
|
||||
if (!dateStr) return ''
|
||||
try {
|
||||
|
|
@ -337,27 +337,27 @@ const formatTime = (dateStr) => {
|
|||
}
|
||||
}
|
||||
|
||||
// 截断文本
|
||||
// Truncate text
|
||||
const truncateText = (text, maxLength) => {
|
||||
if (!text) return ''
|
||||
return text.length > maxLength ? text.slice(0, maxLength) + '...' : text
|
||||
}
|
||||
|
||||
// 从模拟需求生成标题（取前20字）
|
||||
// Generate title from simulation requirement (first 20 chars)
|
||||
const getSimulationTitle = (requirement) => {
|
||||
if (!requirement) return t('history.untitledSimulation')
|
||||
const title = requirement.slice(0, 20)
|
||||
return requirement.length > 20 ? title + '...' : title
|
||||
}
|
||||
|
||||
// 格式化 simulation_id 显示（截取前6位）
|
||||
// Format simulation_id display (first 6 chars)
|
||||
const formatSimulationId = (simulationId) => {
|
||||
if (!simulationId) return 'SIM_UNKNOWN'
|
||||
const prefix = simulationId.replace('sim_', '').slice(0, 6)
|
||||
return `SIM_${prefix.toUpperCase()}`
|
||||
}
|
||||
|
||||
// 格式化轮数显示（当前轮/总轮数）
|
||||
// Format round display (current/total)
|
||||
const formatRounds = (simulation) => {
|
||||
const current = simulation.current_round || 0
|
||||
const total = simulation.total_rounds || 0
|
||||
|
|
@ -365,7 +365,7 @@ const formatRounds = (simulation) => {
|
|||
return t('history.roundsProgress', { current, total })
|
||||
}
|
||||
|
||||
// 获取文件类型（用于样式）
|
||||
// Get file type (for styling)
|
||||
const getFileType = (filename) => {
|
||||
if (!filename) return 'other'
|
||||
const ext = filename.split('.').pop()?.toLowerCase()
|
||||
|
|
@ -381,14 +381,14 @@ const getFileType = (filename) => {
|
|||
return typeMap[ext] || 'other'
|
||||
}
|
||||
|
||||
// 获取文件类型标签文本
|
||||
// Get file type label text
|
||||
const getFileTypeLabel = (filename) => {
|
||||
if (!filename) return 'FILE'
|
||||
const ext = filename.split('.').pop()?.toUpperCase()
|
||||
return ext || 'FILE'
|
||||
}
|
||||
|
||||
// 截断文件名（保留扩展名）
|
||||
// Truncate filename (preserve extension)
|
||||
const truncateFilename = (filename, maxLength) => {
|
||||
if (!filename) return t('history.unknownFile')
|
||||
if (filename.length <= maxLength) return filename
|
||||
|
|
@ -399,17 +399,17 @@ const truncateFilename = (filename, maxLength) => {
|
|||
return truncatedName + ext
|
||||
}
|
||||
|
||||
// 打开项目详情弹窗
|
||||
// Open project detail modal
|
||||
const navigateToProject = (simulation) => {
|
||||
selectedProject.value = simulation
|
||||
}
|
||||
|
||||
// 关闭弹窗
|
||||
// Close modal
|
||||
const closeModal = () => {
|
||||
selectedProject.value = null
|
||||
}
|
||||
|
||||
// 导航到图谱构建页面（Project）
|
||||
// Navigate to graph build page (Project)
|
||||
const goToProject = () => {
|
||||
if (selectedProject.value?.project_id) {
|
||||
router.push({
|
||||
|
|
@ -420,7 +420,7 @@ const goToProject = () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 导航到环境配置页面（Simulation）
|
||||
// Navigate to environment config page (Simulation)
|
||||
const goToSimulation = () => {
|
||||
if (selectedProject.value?.simulation_id) {
|
||||
router.push({
|
||||
|
|
@ -431,7 +431,7 @@ const goToSimulation = () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 导航到分析报告页面（Report）
|
||||
// Navigate to analysis report page (Report)
|
||||
const goToReport = () => {
|
||||
if (selectedProject.value?.report_id) {
|
||||
router.push({
|
||||
|
|
@ -442,7 +442,7 @@ const goToReport = () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 加载历史项目
|
||||
// Load historical projects
|
||||
const loadHistory = async () => {
|
||||
try {
|
||||
loading.value = true
|
||||
|
|
@ -451,14 +451,14 @@ const loadHistory = async () => {
|
|||
projects.value = response.data || []
|
||||
}
|
||||
} catch (error) {
|
||||
console.error('加载历史项目失败:', error)
|
||||
console.error('Failed to load historical projects:', error)
|
||||
projects.value = []
|
||||
} finally {
|
||||
loading.value = false
|
||||
}
|
||||
}
|
||||
|
||||
// 初始化 IntersectionObserver
|
||||
// Initialize IntersectionObserver
|
||||
const initObserver = () => {
|
||||
if (observer) {
|
||||
observer.disconnect()
|
||||
|
|
@ -469,47 +469,47 @@ const initObserver = () => {
|
|||
entries.forEach((entry) => {
|
||||
const shouldExpand = entry.isIntersecting
|
||||
|
||||
// 更新待执行的目标状态（无论是否在动画中都要记录最新的目标状态）
|
||||
// Update pending target state (always record latest target state regardless of animation)
|
||||
pendingState = shouldExpand
|
||||
|
||||
// 清除之前的防抖定时器（新的滚动意图会覆盖旧的）
|
||||
// Clear previous debounce timer (new scroll intent overrides old)
|
||||
if (expandDebounceTimer) {
|
||||
clearTimeout(expandDebounceTimer)
|
||||
expandDebounceTimer = null
|
||||
}
|
||||
|
||||
// 如果正在动画中，只记录状态，等动画结束后处理
|
||||
// If animating, only record state, process after animation ends
|
||||
if (isAnimating) return
|
||||
|
||||
// 如果目标状态与当前状态相同，不需要处理
|
||||
// If target state equals current state, no processing needed
|
||||
if (shouldExpand === isExpanded.value) {
|
||||
pendingState = null
|
||||
return
|
||||
}
|
||||
|
||||
// 使用防抖延迟状态切换，防止快速闪烁
|
||||
// 展开时延迟较短(50ms)，收起时延迟较长(200ms)以增加稳定性
|
||||
// Use debounce to delay state switch, prevent rapid flicker
|
||||
// Shorter delay on expand (50ms), longer on collapse (200ms) for stability
|
||||
const delay = shouldExpand ? 50 : 200
|
||||
|
||||
expandDebounceTimer = setTimeout(() => {
|
||||
// 检查是否正在动画
|
||||
// Check if currently animating
|
||||
if (isAnimating) return
|
||||
|
||||
// 检查待执行状态是否仍需要执行（可能已被后续滚动覆盖）
|
||||
// Check if pending state still needs execution (may have been overridden by subsequent scroll)
|
||||
if (pendingState === null || pendingState === isExpanded.value) return
|
||||
|
||||
// 设置动画锁
|
||||
// Set animation lock
|
||||
isAnimating = true
|
||||
isExpanded.value = pendingState
|
||||
pendingState = null
|
||||
|
||||
// 动画完成后解除锁定，并检查是否有待处理的状态变化
|
||||
// Release lock after animation, check for pending state changes
|
||||
setTimeout(() => {
|
||||
isAnimating = false
|
||||
|
||||
// 动画结束后，检查是否有新的待执行状态
|
||||
// After animation ends, check for new pending state
|
||||
if (pendingState !== null && pendingState !== isExpanded.value) {
|
||||
// 延迟一小段时间再执行，避免太快切换
|
||||
// Delay briefly before executing, avoid too-rapid switching
|
||||
expandDebounceTimer = setTimeout(() => {
|
||||
if (pendingState !== null && pendingState !== isExpanded.value) {
|
||||
isAnimating = true
|
||||
|
|
@ -526,20 +526,20 @@ const initObserver = () => {
|
|||
})
|
||||
},
|
||||
{
|
||||
// 使用多个阈值，使检测更平滑
|
||||
// Use multiple thresholds for smoother detection
|
||||
threshold: [0.4, 0.6, 0.8],
|
||||
// 调整 rootMargin，视口底部向上收缩，需要滚动更多才触发展开
|
||||
// Adjust rootMargin, shrink viewport bottom, require more scroll to trigger expand
|
||||
rootMargin: '0px 0px -150px 0px'
|
||||
}
|
||||
)
|
||||
|
||||
// 开始观察
|
||||
// Start observing
|
||||
if (historyContainer.value) {
|
||||
observer.observe(historyContainer.value)
|
||||
}
|
||||
}
|
||||
|
||||
// 监听路由变化，当返回首页时重新加载数据
|
||||
// Watch route changes, reload data when returning to home
|
||||
watch(() => route.path, (newPath) => {
|
||||
if (newPath === '/') {
|
||||
loadHistory()
|
||||
|
|
@ -547,28 +547,28 @@ watch(() => route.path, (newPath) => {
|
|||
})
|
||||
|
||||
onMounted(async () => {
|
||||
// 确保 DOM 渲染完成后再加载数据
|
||||
// Ensure DOM renders before loading data
|
||||
await nextTick()
|
||||
await loadHistory()
|
||||
|
||||
// 等待 DOM 渲染后初始化观察器
|
||||
// Wait for DOM render then init observer
|
||||
setTimeout(() => {
|
||||
initObserver()
|
||||
}, 100)
|
||||
})
|
||||
|
||||
// 如果使用 keep-alive，在组件激活时重新加载数据
|
||||
// If using keep-alive, reload data on component activation
|
||||
onActivated(() => {
|
||||
loadHistory()
|
||||
})
|
||||
|
||||
onUnmounted(() => {
|
||||
// 清理 Intersection Observer
|
||||
// Clean up Intersection Observer
|
||||
if (observer) {
|
||||
observer.disconnect()
|
||||
observer = null
|
||||
}
|
||||
// 清理防抖定时器
|
||||
// Clean up debounce timer
|
||||
if (expandDebounceTimer) {
|
||||
clearTimeout(expandDebounceTimer)
|
||||
expandDebounceTimer = null
|
||||
|
|
@ -577,7 +577,7 @@ onUnmounted(() => {
|
|||
</script>
|
||||
|
||||
<style scoped>
|
||||
/* 容器 */
|
||||
/* Container */
|
||||
.history-database {
|
||||
position: relative;
|
||||
width: 100%;
|
||||
|
|
@ -587,13 +587,13 @@ onUnmounted(() => {
|
|||
overflow: visible;
|
||||
}
|
||||
|
||||
/* 无项目时简化显示 */
|
||||
/* Simplified display when no projects */
|
||||
.history-database.no-projects {
|
||||
min-height: auto;
|
||||
padding: 40px 0 20px;
|
||||
}
|
||||
|
||||
/* 技术网格背景 */
|
||||
/* Tech grid background */
|
||||
.tech-grid-bg {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
|
|
@ -604,7 +604,7 @@ onUnmounted(() => {
|
|||
pointer-events: none;
|
||||
}
|
||||
|
||||
/* 使用CSS背景图案创建固定间距的正方形网格 */
|
||||
/* Use CSS background pattern to create fixed-spacing square grid */
|
||||
.grid-pattern {
|
||||
position: absolute;
|
||||
top: 0;
|
||||
|
|
@ -615,7 +615,7 @@ onUnmounted(() => {
|
|||
linear-gradient(to right, rgba(0, 0, 0, 0.05) 1px, transparent 1px),
|
||||
linear-gradient(to bottom, rgba(0, 0, 0, 0.05) 1px, transparent 1px);
|
||||
background-size: 50px 50px;
|
||||
/* 从左上角开始定位，高度变化时只在底部扩展，不影响已有网格位置 */
|
||||
/* Position from top-left, only expand at bottom on height change, preserve existing grid positions */
|
||||
background-position: top left;
|
||||
}
|
||||
|
||||
|
|
@ -631,7 +631,7 @@ onUnmounted(() => {
|
|||
pointer-events: none;
|
||||
}
|
||||
|
||||
/* 标题区域 */
|
||||
/* Title area */
|
||||
.section-header {
|
||||
position: relative;
|
||||
z-index: 100;
|
||||
|
|
@ -659,7 +659,7 @@ onUnmounted(() => {
|
|||
text-transform: uppercase;
|
||||
}
|
||||
|
||||
/* 卡片容器 */
|
||||
/* Card container */
|
||||
.cards-container {
|
||||
position: relative;
|
||||
display: flex;
|
||||
|
|
@ -667,10 +667,10 @@ onUnmounted(() => {
|
|||
align-items: flex-start;
|
||||
padding: 0 40px;
|
||||
transition: min-height 700ms cubic-bezier(0.23, 1, 0.32, 1);
|
||||
/* min-height 由 JS 动态计算，根据卡片数量自适应 */
|
||||
/* min-height dynamically calculated by JS, adapts to card count */
|
||||
}
|
||||
|
||||
/* 项目卡片 */
|
||||
/* Project card */
|
||||
.project-card {
|
||||
position: absolute;
|
||||
width: 280px;
|
||||
|
|
@ -693,7 +693,7 @@ onUnmounted(() => {
|
|||
z-index: 1000 !important;
|
||||
}
|
||||
|
||||
/* 卡片头部 */
|
||||
/* Card header */
|
||||
.card-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
|
|
@ -711,7 +711,7 @@ onUnmounted(() => {
|
|||
font-weight: 500;
|
||||
}
|
||||
|
||||
/* 功能状态图标组 */
|
||||
/* Feature status icon group */
|
||||
.card-status-icons {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
|
|
@ -728,17 +728,17 @@ onUnmounted(() => {
|
|||
opacity: 1;
|
||||
}
|
||||
|
||||
/* 不同功能的颜色 */
|
||||
.status-icon:nth-child(1).available { color: #3B82F6; } /* 图谱构建 - 蓝色 */
|
||||
.status-icon:nth-child(2).available { color: #F59E0B; } /* 环境搭建 - 橙色 */
|
||||
.status-icon:nth-child(3).available { color: #10B981; } /* 分析报告 - 绿色 */
|
||||
/* Colors for different features */
|
||||
.status-icon:nth-child(1).available { color: #3B82F6; } /* Graph Build - blue */
|
||||
.status-icon:nth-child(2).available { color: #F59E0B; } /* Env Setup - orange */
|
||||
.status-icon:nth-child(3).available { color: #10B981; } /* Analysis Report - green */
|
||||
|
||||
.status-icon.unavailable {
|
||||
color: #D1D5DB;
|
||||
opacity: 0.5;
|
||||
}
|
||||
|
||||
/* 轮数进度显示 */
|
||||
/* Round progress display */
|
||||
.card-progress {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
|
|
@ -752,13 +752,13 @@ onUnmounted(() => {
|
|||
font-size: 0.5rem;
|
||||
}
|
||||
|
||||
/* 进度状态颜色 */
|
||||
.card-progress.completed { color: #10B981; } /* 已完成 - 绿色 */
|
||||
.card-progress.in-progress { color: #F59E0B; } /* 进行中 - 橙色 */
|
||||
.card-progress.not-started { color: #9CA3AF; } /* 未开始 - 灰色 */
|
||||
/* Progress status colors */
|
||||
.card-progress.completed { color: #10B981; } /* Completed - green */
|
||||
.card-progress.in-progress { color: #F59E0B; } /* In Progress - orange */
|
||||
.card-progress.not-started { color: #9CA3AF; } /* Not Started - gray */
|
||||
.card-status.pending { color: #9CA3AF; }
|
||||
|
||||
/* 文件列表区域 */
|
||||
/* File list area */
|
||||
.card-files-wrapper {
|
||||
position: relative;
|
||||
width: 100%;
|
||||
|
|
@ -778,7 +778,7 @@ onUnmounted(() => {
|
|||
gap: 4px;
|
||||
}
|
||||
|
||||
/* 更多文件提示 */
|
||||
/* More files hint */
|
||||
.files-more {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
|
|
@ -808,7 +808,7 @@ onUnmounted(() => {
|
|||
border-color: #e5e7eb;
|
||||
}
|
||||
|
||||
/* 简约文件标签样式 */
|
||||
/* Minimal file tag styles */
|
||||
.file-tag {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
|
|
@ -826,7 +826,7 @@ onUnmounted(() => {
|
|||
min-width: 28px;
|
||||
}
|
||||
|
||||
/* 低饱和度配色方案 - Morandi色系 */
|
||||
/* Low saturation color scheme - Morandi palette */
|
||||
.file-tag.pdf { background: #f2e6e6; color: #a65a5a; }
|
||||
.file-tag.doc { background: #e6eff5; color: #5a7ea6; }
|
||||
.file-tag.xls { background: #e6f2e8; color: #5aa668; }
|
||||
|
|
@ -847,7 +847,7 @@ onUnmounted(() => {
|
|||
letter-spacing: 0.1px;
|
||||
}
|
||||
|
||||
/* 无文件时的占位 */
|
||||
/* Placeholder when no files */
|
||||
.files-empty {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
|
|
@ -868,13 +868,13 @@ onUnmounted(() => {
|
|||
letter-spacing: 0.5px;
|
||||
}
|
||||
|
||||
/* 悬停时文件区域效果 */
|
||||
/* File area hover effect */
|
||||
.project-card:hover .card-files-wrapper {
|
||||
border-color: #d1d5db;
|
||||
background: linear-gradient(135deg, #ffffff 0%, #f8f9fa 100%);
|
||||
}
|
||||
|
||||
/* 角落装饰 */
|
||||
/* Corner decoration */
|
||||
.corner-mark.top-left-only {
|
||||
position: absolute;
|
||||
top: 6px;
|
||||
|
|
@ -887,7 +887,7 @@ onUnmounted(() => {
|
|||
z-index: 10;
|
||||
}
|
||||
|
||||
/* 卡片标题 */
|
||||
/* Card title */
|
||||
.card-title {
|
||||
font-family: 'Inter', -apple-system, sans-serif;
|
||||
font-size: 0.9rem;
|
||||
|
|
@ -905,7 +905,7 @@ onUnmounted(() => {
|
|||
color: #2563EB;
|
||||
}
|
||||
|
||||
/* 卡片描述 */
|
||||
/* Card description */
|
||||
.card-desc {
|
||||
font-family: 'Inter', sans-serif;
|
||||
font-size: 0.75rem;
|
||||
|
|
@ -919,7 +919,7 @@ onUnmounted(() => {
|
|||
-webkit-box-orient: vertical;
|
||||
}
|
||||
|
||||
/* 卡片底部 */
|
||||
/* Card bottom */
|
||||
.card-footer {
|
||||
position: relative;
|
||||
display: flex;
|
||||
|
|
@ -933,14 +933,14 @@ onUnmounted(() => {
|
|||
font-weight: 500;
|
||||
}
|
||||
|
||||
/* 日期时间组合 */
|
||||
/* Date/time combo */
|
||||
.card-datetime {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
/* 底部轮数进度显示 */
|
||||
/* Bottom round progress display */
|
||||
.card-footer .card-progress {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
|
|
@ -954,12 +954,12 @@ onUnmounted(() => {
|
|||
font-size: 0.5rem;
|
||||
}
|
||||
|
||||
/* 进度状态颜色 - 底部 */
|
||||
/* Progress status colors - bottom */
|
||||
.card-footer .card-progress.completed { color: #10B981; }
|
||||
.card-footer .card-progress.in-progress { color: #F59E0B; }
|
||||
.card-footer .card-progress.not-started { color: #9CA3AF; }
|
||||
|
||||
/* 底部装饰线 */
|
||||
/* Bottom decorative line */
|
||||
.card-bottom-line {
|
||||
position: absolute;
|
||||
bottom: 0;
|
||||
|
|
@ -975,7 +975,7 @@ onUnmounted(() => {
|
|||
width: 100%;
|
||||
}
|
||||
|
||||
/* 空状态 */
|
||||
/* Empty state */
|
||||
.empty-state, .loading-state {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
|
|
@ -1003,7 +1003,7 @@ onUnmounted(() => {
|
|||
to { transform: rotate(360deg); }
|
||||
}
|
||||
|
||||
/* 响应式 */
|
||||
/* Responsive */
|
||||
@media (max-width: 1200px) {
|
||||
.project-card {
|
||||
width: 240px;
|
||||
|
|
@ -1019,7 +1019,7 @@ onUnmounted(() => {
|
|||
}
|
||||
}
|
||||
|
||||
/* ===== 历史回放详情弹窗样式 ===== */
|
||||
/* ===== History Replay Detail Modal Styles ===== */
|
||||
.modal-overlay {
|
||||
position: fixed;
|
||||
top: 0;
|
||||
|
|
@ -1045,7 +1045,7 @@ onUnmounted(() => {
|
|||
box-shadow: 0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
|
||||
}
|
||||
|
||||
/* 动画过渡 */
|
||||
/* Animation transition */
|
||||
.modal-enter-active,
|
||||
.modal-leave-active {
|
||||
transition: opacity 0.3s ease;
|
||||
|
|
@ -1074,7 +1074,7 @@ onUnmounted(() => {
|
|||
opacity: 0;
|
||||
}
|
||||
|
||||
/* 弹窗头部 */
|
||||
/* Modal header */
|
||||
.modal-header {
|
||||
display: flex;
|
||||
justify-content: space-between;
|
||||
|
|
@ -1141,7 +1141,7 @@ onUnmounted(() => {
|
|||
color: #111827;
|
||||
}
|
||||
|
||||
/* 弹窗内容 */
|
||||
/* Modal content */
|
||||
.modal-body {
|
||||
padding: 24px 32px;
|
||||
}
|
||||
|
|
@ -1183,7 +1183,7 @@ onUnmounted(() => {
|
|||
padding-right: 4px;
|
||||
}
|
||||
|
||||
/* 自定义滚动条样式 */
|
||||
/* Custom scrollbar styles */
|
||||
.modal-files::-webkit-scrollbar {
|
||||
width: 4px;
|
||||
}
|
||||
|
|
@ -1237,7 +1237,7 @@ onUnmounted(() => {
|
|||
text-align: center;
|
||||
}
|
||||
|
||||
/* 推演回放分割线 */
|
||||
/* Simulation replay divider */
|
||||
.modal-divider {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
|
|
@ -1261,7 +1261,7 @@ onUnmounted(() => {
|
|||
white-space: nowrap;
|
||||
}
|
||||
|
||||
/* 导航按钮 */
|
||||
/* Navigation buttons */
|
||||
.modal-actions {
|
||||
display: flex;
|
||||
gap: 16px;
|
||||
|
|
@ -1328,7 +1328,7 @@ onUnmounted(() => {
|
|||
color: #111827;
|
||||
}
|
||||
|
||||
/* 不可回放提示 */
|
||||
/* Non-replayable hint */
|
||||
.modal-playback-hint {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
|
|
|
|||
|
|
@ -210,10 +210,10 @@ const selectedOntologyItem = ref(null)
|
|||
const logContent = ref(null)
|
||||
const creatingSimulation = ref(false)
|
||||
|
||||
// 进入环境搭建 - 创建 simulation 并跳转
|
||||
// Enter environment setup - create simulation and navigate
|
||||
const handleEnterEnvSetup = async () => {
|
||||
if (!props.projectData?.project_id || !props.projectData?.graph_id) {
|
||||
console.error('缺少项目或图谱信息')
|
||||
console.error('Missing project or graph info')
|
||||
return
|
||||
}
|
||||
|
||||
|
|
@ -228,17 +228,17 @@ const handleEnterEnvSetup = async () => {
|
|||
})
|
||||
|
||||
if (res.success && res.data?.simulation_id) {
|
||||
// 跳转到 simulation 页面
|
||||
// Navigate to simulation page
|
||||
router.push({
|
||||
name: 'Simulation',
|
||||
params: { simulationId: res.data.simulation_id }
|
||||
})
|
||||
} else {
|
||||
console.error('创建模拟失败:', res.error)
|
||||
console.error('Failed to create simulation:', res.error)
|
||||
alert(t('step1.createSimulationFailed', { error: res.error || t('common.unknownError') }))
|
||||
}
|
||||
} catch (err) {
|
||||
console.error('创建模拟异常:', err)
|
||||
console.error('Simulation creation error:', err)
|
||||
alert(t('step1.createSimulationException', { error: err.message }))
|
||||
} finally {
|
||||
creatingSimulation.value = false
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
<template>
|
||||
<div class="env-setup-panel">
|
||||
<div class="scroll-container">
|
||||
<!-- Step 01: 模拟实例 -->
|
||||
<!-- Step 01: Simulation Instance -->
|
||||
<div class="step-card" :class="{ 'active': phase === 0, 'completed': phase > 0 }">
|
||||
<div class="card-header">
|
||||
<div class="step-info">
|
||||
|
|
@ -41,7 +41,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Step 02: 生成 Agent 人设 -->
|
||||
<!-- Step 02: Generate Agent Personas -->
|
||||
<div class="step-card" :class="{ 'active': phase === 1, 'completed': phase > 1 }">
|
||||
<div class="card-header">
|
||||
<div class="step-info">
|
||||
|
|
@ -113,7 +113,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Step 03: 生成双平台模拟配置 -->
|
||||
<!-- Step 03: Generate Dual-Platform Config -->
|
||||
<div class="step-card" :class="{ 'active': phase === 2, 'completed': phase > 2 }">
|
||||
<div class="card-header">
|
||||
<div class="step-info">
|
||||
|
|
@ -135,7 +135,7 @@
|
|||
|
||||
<!-- Config Preview -->
|
||||
<div v-if="simulationConfig" class="config-detail-panel">
|
||||
<!-- 时间配置 -->
|
||||
<!-- Time Config -->
|
||||
<div class="config-block">
|
||||
<div class="config-grid">
|
||||
<div class="config-item">
|
||||
|
|
@ -179,7 +179,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Agent 配置 -->
|
||||
<!-- Agent Config -->
|
||||
<div class="config-block">
|
||||
<div class="config-block-header">
|
||||
<span class="config-block-title">{{ $t('step2.agentConfig') }}</span>
|
||||
|
|
@ -191,7 +191,7 @@
|
|||
:key="agent.agent_id"
|
||||
class="agent-card"
|
||||
>
|
||||
<!-- 卡片头部 -->
|
||||
<!-- Card header -->
|
||||
<div class="agent-card-header">
|
||||
<div class="agent-identity">
|
||||
<span class="agent-id">Agent {{ agent.agent_id }}</span>
|
||||
|
|
@ -203,7 +203,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 活跃时间轴 -->
|
||||
<!-- Activity timeline -->
|
||||
<div class="agent-timeline">
|
||||
<span class="timeline-label">{{ $t('step2.activeTimePeriod') }}</span>
|
||||
<div class="mini-timeline">
|
||||
|
|
@ -224,7 +224,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 行为参数 -->
|
||||
<!-- Behavior params -->
|
||||
<div class="agent-params">
|
||||
<div class="param-group">
|
||||
<div class="param-item">
|
||||
|
|
@ -264,7 +264,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 平台配置 -->
|
||||
<!-- Platform Config -->
|
||||
<div class="config-block">
|
||||
<div class="config-block-header">
|
||||
<span class="config-block-title">{{ $t('step2.recommendAlgoConfig') }}</span>
|
||||
|
|
@ -327,7 +327,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- LLM 配置推理 -->
|
||||
<!-- LLM Config Reasoning -->
|
||||
<div v-if="simulationConfig.generation_reasoning" class="config-block">
|
||||
<div class="config-block-header">
|
||||
<span class="config-block-title">{{ $t('step2.llmConfigReasoning') }}</span>
|
||||
|
|
@ -346,7 +346,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Step 04: 初始激活编排 -->
|
||||
<!-- Step 04: Initial Activation Orchestration -->
|
||||
<div class="step-card" :class="{ 'active': phase === 3, 'completed': phase > 3 }">
|
||||
<div class="card-header">
|
||||
<div class="step-info">
|
||||
|
|
@ -367,7 +367,7 @@
|
|||
</p>
|
||||
|
||||
<div v-if="simulationConfig?.event_config" class="orchestration-content">
|
||||
<!-- 叙事方向 -->
|
||||
<!-- Narrative Direction -->
|
||||
<div class="narrative-box">
|
||||
<span class="box-label narrative-label">
|
||||
<svg width="20" height="20" viewBox="0 0 24 24" fill="none" xmlns="http://www.w3.org/2000/svg" class="special-icon">
|
||||
|
|
@ -385,7 +385,7 @@
|
|||
<p class="narrative-text">{{ simulationConfig.event_config.narrative_direction }}</p>
|
||||
</div>
|
||||
|
||||
<!-- 热点话题 -->
|
||||
<!-- Hot Topics -->
|
||||
<div class="topics-section">
|
||||
<span class="box-label">{{ $t('step2.initialHotTopics') }}</span>
|
||||
<div class="hot-topics-grid">
|
||||
|
|
@ -395,7 +395,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 初始帖子流 -->
|
||||
<!-- Initial Post Stream -->
|
||||
<div class="initial-posts-section">
|
||||
<span class="box-label">{{ $t('step2.initialActivationSeq', { count: simulationConfig.event_config.initial_posts.length }) }}</span>
|
||||
<div class="posts-timeline">
|
||||
|
|
@ -418,7 +418,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Step 05: 准备完成 -->
|
||||
<!-- Step 05: Setup Complete -->
|
||||
<div class="step-card" :class="{ 'active': phase === 4 }">
|
||||
<div class="card-header">
|
||||
<div class="step-info">
|
||||
|
|
@ -435,7 +435,7 @@
|
|||
<p class="api-note">POST /api/simulation/start</p>
|
||||
<p class="description">{{ $t('step2.setupCompleteDesc') }}</p>
|
||||
|
||||
<!-- 模拟轮数配置 - 只有在配置生成完成且轮数计算出来后才显示 -->
|
||||
<!-- Simulation rounds config - only shown after config generation and rounds calculated -->
|
||||
<div v-if="simulationConfig && autoGeneratedRounds" class="rounds-config-section">
|
||||
<div class="rounds-header">
|
||||
<div class="header-left">
|
||||
|
|
@ -544,7 +544,7 @@
|
|||
</div>
|
||||
|
||||
<div class="modal-body">
|
||||
<!-- 基本信息 -->
|
||||
<!-- Basic Info -->
|
||||
<div class="modal-info-grid">
|
||||
<div class="info-item">
|
||||
<span class="info-label">{{ $t('step2.profileModalAge') }}</span>
|
||||
|
|
@ -564,13 +564,13 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 简介 -->
|
||||
<!-- Bio -->
|
||||
<div class="modal-section">
|
||||
<span class="section-label">{{ $t('step2.profileModalBio') }}</span>
|
||||
<p class="section-bio">{{ selectedProfile.bio || $t('step2.noBio') }}</p>
|
||||
</div>
|
||||
|
||||
<!-- 关注话题 -->
|
||||
<!-- Followed Topics -->
|
||||
<div class="modal-section" v-if="selectedProfile.interested_topics?.length">
|
||||
<span class="section-label">{{ $t('step2.profileModalTopics') }}</span>
|
||||
<div class="topics-grid">
|
||||
|
|
@ -582,11 +582,11 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- 详细人设 -->
|
||||
<!-- Detailed Persona -->
|
||||
<div class="modal-section" v-if="selectedProfile.persona">
|
||||
<span class="section-label">{{ $t('step2.profileModalPersona') }}</span>
|
||||
|
||||
<!-- 人设维度概览 -->
|
||||
<!-- Persona Dimension Overview -->
|
||||
<div class="persona-dimensions">
|
||||
<div class="dimension-card">
|
||||
<span class="dim-title">{{ $t('step2.personaDimExperience') }}</span>
|
||||
|
|
@ -645,7 +645,7 @@ import {
|
|||
const { t } = useI18n()
|
||||
|
||||
const props = defineProps({
|
||||
simulationId: String, // 从父组件传入
|
||||
simulationId: String, // Passed from parent component
|
||||
projectData: Object,
|
||||
graphData: Object,
|
||||
systemLogs: Array
|
||||
|
|
@ -654,7 +654,7 @@ const props = defineProps({
|
|||
const emit = defineEmits(['go-back', 'next-step', 'add-log', 'update-status'])
|
||||
|
||||
// State
|
||||
const phase = ref(0) // 0: 初始化, 1: 生成人设, 2: 生成配置, 3: 完成
|
||||
const phase = ref(0) // 0: Init, 1: Generating personas, 2: Generating config, 3: Complete
|
||||
const taskId = ref(null)
|
||||
const prepareProgress = ref(0)
|
||||
const currentStage = ref('')
|
||||
|
|
@ -666,43 +666,43 @@ const simulationConfig = ref(null)
|
|||
const selectedProfile = ref(null)
|
||||
const showProfilesDetail = ref(true)
|
||||
|
||||
// 日志去重：记录上一次输出的关键信息
|
||||
// Log dedup: record last output key info
|
||||
let lastLoggedMessage = ''
|
||||
let lastLoggedProfileCount = 0
|
||||
let lastLoggedConfigStage = ''
|
||||
|
||||
// 模拟轮数配置
|
||||
const useCustomRounds = ref(false) // 默认使用自动配置轮数
|
||||
const customMaxRounds = ref(40) // 默认推荐40轮
|
||||
// Simulation rounds config
|
||||
const useCustomRounds = ref(false) // Default: use auto-configured rounds
|
||||
const customMaxRounds = ref(40) // Default recommended: 40 rounds
|
||||
|
||||
// Watch stage to update phase
|
||||
watch(currentStage, (newStage) => {
|
||||
if (newStage === '生成Agent人设' || newStage === 'generating_profiles') {
|
||||
if (newStage === 'Generating Agent Personas' || newStage === 'generating_profiles') {
|
||||
phase.value = 1
|
||||
} else if (newStage === '生成模拟配置' || newStage === 'generating_config') {
|
||||
} else if (newStage === 'Generating Simulation Config' || newStage === 'generating_config') {
|
||||
phase.value = 2
|
||||
// 进入配置生成阶段，开始轮询配置
|
||||
// Enter config generation phase, start polling config
|
||||
if (!configTimer) {
|
||||
addLog(t('log.startGeneratingConfig'))
|
||||
startConfigPolling()
|
||||
}
|
||||
} else if (newStage === '准备模拟脚本' || newStage === 'copying_scripts') {
|
||||
phase.value = 2 // 仍属于配置阶段
|
||||
} else if (newStage === 'Preparing Simulation Scripts' || newStage === 'copying_scripts') {
|
||||
phase.value = 2 // Still in config phase
|
||||
}
|
||||
})
|
||||
|
||||
// 从配置中计算自动生成的轮数（不使用硬编码默认值）
|
||||
// Calculate auto-generated rounds from config (no hardcoded defaults)
|
||||
const autoGeneratedRounds = computed(() => {
|
||||
if (!simulationConfig.value?.time_config) {
|
||||
return null // 配置未生成时返回 null
|
||||
return null // Return null when config not yet generated
|
||||
}
|
||||
const totalHours = simulationConfig.value.time_config.total_simulation_hours
|
||||
const minutesPerRound = simulationConfig.value.time_config.minutes_per_round
|
||||
if (!totalHours || !minutesPerRound) {
|
||||
return null // 配置数据不完整时返回 null
|
||||
return null // Return null when config data incomplete
|
||||
}
|
||||
const calculatedRounds = Math.floor((totalHours * 60) / minutesPerRound)
|
||||
// 确保最大轮数不小于40（推荐值），避免滑动条范围异常
|
||||
// Ensure max rounds >= 40 (recommended), avoid slider range anomaly
|
||||
return Math.max(calculatedRounds, 40)
|
||||
})
|
||||
|
||||
|
|
@ -719,7 +719,7 @@ const displayProfiles = computed(() => {
|
|||
return profiles.value.slice(0, 6)
|
||||
})
|
||||
|
||||
// 根据agent_id获取对应的username
|
||||
// Get corresponding username by agent_id
|
||||
const getAgentUsername = (agentId) => {
|
||||
if (profiles.value && profiles.value.length > agentId && agentId >= 0) {
|
||||
const profile = profiles.value[agentId]
|
||||
|
|
@ -728,7 +728,7 @@ const getAgentUsername = (agentId) => {
|
|||
return `agent_${agentId}`
|
||||
}
|
||||
|
||||
// 计算所有人设的关联话题总数
|
||||
// Calculate total related topics across all personas
|
||||
const totalTopicsCount = computed(() => {
|
||||
return profiles.value.reduce((sum, p) => {
|
||||
return sum + (p.interested_topics?.length || 0)
|
||||
|
|
@ -748,17 +748,17 @@ const handlePrepareFailure = (message) => {
|
|||
emit('update-status', 'error')
|
||||
}
|
||||
|
||||
// 处理开始模拟按钮点击
|
||||
// Handle start simulation button click
|
||||
const handleStartSimulation = () => {
|
||||
// 构建传递给父组件的参数
|
||||
// Build params to pass to parent component
|
||||
const params = {}
|
||||
|
||||
if (useCustomRounds.value) {
|
||||
// 用户自定义轮数，传递 max_rounds 参数
|
||||
// User custom rounds, pass max_rounds param
|
||||
params.maxRounds = customMaxRounds.value
|
||||
addLog(t('log.startSimCustomRounds', { rounds: customMaxRounds.value }))
|
||||
} else {
|
||||
// 用户选择保持自动生成的轮数，不传递 max_rounds 参数
|
||||
// User chose auto-generated rounds, don't pass max_rounds param
|
||||
addLog(t('log.startSimAutoRounds', { rounds: autoGeneratedRounds.value }))
|
||||
}
|
||||
|
||||
|
|
@ -776,7 +776,7 @@ const selectProfile = (profile) => {
|
|||
selectedProfile.value = profile
|
||||
}
|
||||
|
||||
// 自动开始准备模拟
|
||||
// Auto-start simulation preparation
|
||||
const startPrepareSimulation = async () => {
|
||||
if (!props.simulationId) {
|
||||
addLog(t('log.errorMissingSimId'))
|
||||
|
|
@ -784,7 +784,7 @@ const startPrepareSimulation = async () => {
|
|||
return
|
||||
}
|
||||
|
||||
// 标记第一步完成，开始第二步
|
||||
// Mark step 1 complete, start step 2
|
||||
phase.value = 1
|
||||
addLog(t('log.simInstanceCreated', { id: props.simulationId }))
|
||||
addLog(t('log.preparingSimEnv'))
|
||||
|
|
@ -808,19 +808,19 @@ const startPrepareSimulation = async () => {
|
|||
addLog(t('log.prepareTaskStarted'))
|
||||
addLog(t('log.prepareTaskId', { taskId: res.data.task_id }))
|
||||
|
||||
// 立即设置预期Agent总数（从prepare接口返回值获取）
|
||||
// Immediately set expected Agent total (from prepare API return value)
|
||||
if (res.data.expected_entities_count) {
|
||||
expectedTotal.value = res.data.expected_entities_count
|
||||
addLog(t('log.zepEntitiesFound', { count: res.data.expected_entities_count }))
|
||||
addLog(t('log.graphEntitiesFound', { count: res.data.expected_entities_count }))
|
||||
if (res.data.entity_types && res.data.entity_types.length > 0) {
|
||||
addLog(t('log.entityTypes', { types: res.data.entity_types.join(', ') }))
|
||||
}
|
||||
}
|
||||
|
||||
addLog(t('log.startPollingProgress'))
|
||||
// 开始轮询进度
|
||||
// Start polling progress
|
||||
startPolling()
|
||||
// 开始实时获取 Profiles
|
||||
// Start real-time Profile fetching
|
||||
startProfilesPolling()
|
||||
} else {
|
||||
addLog(t('log.prepareFailed', { error: res.error || t('common.unknownError') }))
|
||||
|
|
@ -866,15 +866,15 @@ const pollPrepareStatus = async () => {
|
|||
if (res.success && res.data) {
|
||||
const data = res.data
|
||||
|
||||
// 更新进度
|
||||
// Update progress
|
||||
prepareProgress.value = data.progress || 0
|
||||
progressMessage.value = data.message || ''
|
||||
|
||||
// 解析阶段信息并输出详细日志
|
||||
// Parse phase info and output detailed log
|
||||
if (data.progress_detail) {
|
||||
currentStage.value = data.progress_detail.current_stage_name || ''
|
||||
|
||||
// 输出详细进度日志（避免重复）
|
||||
// Output detailed progress log (avoid duplicates)
|
||||
const detail = data.progress_detail
|
||||
const logKey = `${detail.current_stage}-${detail.current_item}-${detail.total_items}`
|
||||
if (logKey !== lastLoggedMessage && detail.item_description) {
|
||||
|
|
@ -887,19 +887,19 @@ const pollPrepareStatus = async () => {
|
|||
}
|
||||
}
|
||||
} else if (data.message) {
|
||||
// 从消息中提取阶段
|
||||
// Extract phase from message
|
||||
const match = data.message.match(/\[(\d+)\/(\d+)\]\s*([^:]+)/)
|
||||
if (match) {
|
||||
currentStage.value = match[3].trim()
|
||||
}
|
||||
// 输出消息日志（避免重复）
|
||||
// Output message log (avoid duplicates)
|
||||
if (data.message !== lastLoggedMessage) {
|
||||
lastLoggedMessage = data.message
|
||||
addLog(data.message)
|
||||
}
|
||||
}
|
||||
|
||||
// 检查是否完成
|
||||
// Check if complete
|
||||
if (data.status === 'completed' || data.status === 'ready' || data.already_prepared) {
|
||||
addLog(t('log.prepareComplete'))
|
||||
stopPolling()
|
||||
|
|
@ -910,7 +910,7 @@ const pollPrepareStatus = async () => {
|
|||
}
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn('轮询状态失败:', err)
|
||||
console.warn('Poll status failed:', err)
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -923,19 +923,19 @@ const fetchProfilesRealtime = async () => {
|
|||
if (res.success && res.data) {
|
||||
const prevCount = profiles.value.length
|
||||
profiles.value = res.data.profiles || []
|
||||
// 只有当 API 返回有效值时才更新，避免覆盖已有的有效值
|
||||
// Only update when API returns valid value, avoid overwriting existing valid value
|
||||
if (res.data.total_expected) {
|
||||
expectedTotal.value = res.data.total_expected
|
||||
}
|
||||
|
||||
// 提取实体类型
|
||||
// Extract entity types
|
||||
const types = new Set()
|
||||
profiles.value.forEach(p => {
|
||||
if (p.entity_type) types.add(p.entity_type)
|
||||
})
|
||||
entityTypes.value = Array.from(types)
|
||||
|
||||
// 输出 Profile 生成进度日志（仅当数量变化时）
|
||||
// Output Profile generation progress log (only when count changes)
|
||||
const currentCount = profiles.value.length
|
||||
if (currentCount > 0 && currentCount !== lastLoggedProfileCount) {
|
||||
lastLoggedProfileCount = currentCount
|
||||
|
|
@ -947,18 +947,18 @@ const fetchProfilesRealtime = async () => {
|
|||
}
|
||||
addLog(t('log.agentProfile', { current: currentCount, total: total, name: profileName, profession: latestProfile?.profession || t('step2.unknownProfession') }))
|
||||
|
||||
// 如果全部生成完成
|
||||
// If all generation complete
|
||||
if (expectedTotal.value && currentCount >= expectedTotal.value) {
|
||||
addLog(t('log.allProfilesComplete', { count: currentCount }))
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn('获取 Profiles 失败:', err)
|
||||
console.warn('Failed to get Profiles:', err)
|
||||
}
|
||||
}
|
||||
|
||||
// 配置轮询
|
||||
// Config polling
|
||||
const startConfigPolling = () => {
|
||||
configTimer = setInterval(fetchConfigRealtime, 2000)
|
||||
}
|
||||
|
|
@ -984,7 +984,7 @@ const fetchConfigRealtime = async () => {
|
|||
return
|
||||
}
|
||||
|
||||
// 输出配置生成阶段日志（避免重复）
|
||||
// Output config generation phase log (avoid duplicates)
|
||||
if (data.generation_stage && data.generation_stage !== lastLoggedConfigStage) {
|
||||
lastLoggedConfigStage = data.generation_stage
|
||||
if (data.generation_stage === 'generating_profiles') {
|
||||
|
|
@ -994,12 +994,12 @@ const fetchConfigRealtime = async () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 如果配置已生成
|
||||
// If config generated
|
||||
if (data.config_generated && data.config) {
|
||||
simulationConfig.value = data.config
|
||||
addLog(t('log.configComplete'))
|
||||
|
||||
// 显示详细配置摘要
|
||||
// Show detailed config summary
|
||||
if (data.summary) {
|
||||
addLog(t('log.configSummaryAgents', { count: data.summary.total_agents }))
|
||||
addLog(t('log.configSummaryHours', { hours: data.summary.simulation_hours }))
|
||||
|
|
@ -1008,13 +1008,13 @@ const fetchConfigRealtime = async () => {
|
|||
addLog(t('log.configSummaryPlatforms', { twitter: data.summary.has_twitter_config ? '✓' : '✗', reddit: data.summary.has_reddit_config ? '✓' : '✗' }))
|
||||
}
|
||||
|
||||
// 显示时间配置详情
|
||||
// Show time config details
|
||||
if (data.config.time_config) {
|
||||
const tc = data.config.time_config
|
||||
addLog(t('log.timeConfigDetail', { minutes: tc.minutes_per_round, rounds: Math.floor((tc.total_simulation_hours * 60) / tc.minutes_per_round) }))
|
||||
}
|
||||
|
||||
// 显示事件配置
|
||||
// Show event config
|
||||
if (data.config.event_config?.narrative_direction) {
|
||||
const narrative = data.config.event_config.narrative_direction
|
||||
addLog(t('log.narrativeDirection', { direction: narrative.length > 50 ? narrative.substring(0, 50) + '...' : narrative }))
|
||||
|
|
@ -1027,7 +1027,7 @@ const fetchConfigRealtime = async () => {
|
|||
}
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn('获取 Config 失败:', err)
|
||||
console.warn('Failed to get Config:', err)
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -1035,11 +1035,11 @@ const loadPreparedData = async () => {
|
|||
phase.value = 2
|
||||
addLog(t('log.loadingExistingConfig'))
|
||||
|
||||
// 最后获取一次 Profiles
|
||||
// Final Profile fetch
|
||||
await fetchProfilesRealtime()
|
||||
addLog(t('log.loadedAgentProfiles', { count: profiles.value.length }))
|
||||
|
||||
// 获取配置（使用实时接口）
|
||||
// Get config (using real-time API)
|
||||
try {
|
||||
const res = await getSimulationConfigRealtime(props.simulationId)
|
||||
if (res.success && res.data) {
|
||||
|
|
@ -1054,7 +1054,7 @@ const loadPreparedData = async () => {
|
|||
simulationConfig.value = configState.config
|
||||
addLog(t('log.configLoadSuccess'))
|
||||
|
||||
// 显示详细配置摘要
|
||||
// Show detailed config summary
|
||||
if (configState.summary) {
|
||||
addLog(t('log.configSummaryAgents', { count: configState.summary.total_agents }))
|
||||
addLog(t('log.configSummaryHours', { hours: configState.summary.simulation_hours }))
|
||||
|
|
@ -1087,7 +1087,7 @@ watch(() => props.systemLogs?.length, () => {
|
|||
})
|
||||
|
||||
onMounted(() => {
|
||||
// 自动开始准备流程
|
||||
// Auto-start preparation flow
|
||||
if (props.simulationId) {
|
||||
addLog(t('log.step2Init'))
|
||||
startPrepareSimulation()
|
||||
|
|
@ -1923,7 +1923,7 @@ onUnmounted(() => {
|
|||
flex: 1;
|
||||
}
|
||||
|
||||
/* 基本信息网格 */
|
||||
/* Basic info grid */
|
||||
.modal-info-grid {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(2, 1fr);
|
||||
|
|
@ -1959,7 +1959,7 @@ onUnmounted(() => {
|
|||
color: #FF5722;
|
||||
}
|
||||
|
||||
/* 模块区域 */
|
||||
/* Module area */
|
||||
.modal-section {
|
||||
margin-bottom: 28px;
|
||||
}
|
||||
|
|
@ -1985,7 +1985,7 @@ onUnmounted(() => {
|
|||
border-left: 3px solid #E0E0E0;
|
||||
}
|
||||
|
||||
/* 话题标签 */
|
||||
/* Topic tags */
|
||||
.topics-grid {
|
||||
display: flex;
|
||||
flex-wrap: wrap;
|
||||
|
|
@ -2007,7 +2007,7 @@ onUnmounted(() => {
|
|||
color: #0D47A1;
|
||||
}
|
||||
|
||||
/* 详细人设 */
|
||||
/* Detailed persona */
|
||||
.persona-dimensions {
|
||||
display: grid;
|
||||
grid-template-columns: repeat(2, 1fr);
|
||||
|
|
@ -2293,7 +2293,7 @@ onUnmounted(() => {
|
|||
margin: 0;
|
||||
}
|
||||
|
||||
/* 模拟轮数配置样式 */
|
||||
/* Simulation rounds config styles */
|
||||
.rounds-config-section {
|
||||
margin: 24px 0;
|
||||
padding-top: 24px;
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@
|
|||
<!-- Top Control Bar -->
|
||||
<div class="control-bar">
|
||||
<div class="status-group">
|
||||
<!-- Twitter 平台进度 -->
|
||||
<!-- Twitter Platform Progress -->
|
||||
<div class="platform-status twitter" :class="{ active: runStatus.twitter_running, completed: runStatus.twitter_completed }">
|
||||
<div class="platform-header">
|
||||
<svg class="platform-icon" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
|
||||
|
|
@ -30,7 +30,7 @@
|
|||
<span class="stat-value mono">{{ runStatus.twitter_actions_count || 0 }}</span>
|
||||
</span>
|
||||
</div>
|
||||
<!-- 可用动作提示 -->
|
||||
<!-- Available actions hint -->
|
||||
<div class="actions-tooltip">
|
||||
<div class="tooltip-title">Available Actions</div>
|
||||
<div class="tooltip-actions">
|
||||
|
|
@ -44,7 +44,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Reddit 平台进度 -->
|
||||
<!-- Reddit Platform Progress -->
|
||||
<div class="platform-status reddit" :class="{ active: runStatus.reddit_running, completed: runStatus.reddit_completed }">
|
||||
<div class="platform-header">
|
||||
<svg class="platform-icon" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
|
||||
|
|
@ -71,7 +71,7 @@
|
|||
<span class="stat-value mono">{{ runStatus.reddit_actions_count || 0 }}</span>
|
||||
</span>
|
||||
</div>
|
||||
<!-- 可用动作提示 -->
|
||||
<!-- Available actions hint -->
|
||||
<div class="actions-tooltip">
|
||||
<div class="tooltip-title">Available Actions</div>
|
||||
<div class="tooltip-actions">
|
||||
|
|
@ -157,12 +157,12 @@
|
|||
</div>
|
||||
|
||||
<div class="card-body">
|
||||
<!-- CREATE_POST: 发布帖子 -->
|
||||
<!-- CREATE_POST: Create post -->
|
||||
<div v-if="action.action_type === 'CREATE_POST' && action.action_args?.content" class="content-text main-text">
|
||||
{{ action.action_args.content }}
|
||||
</div>
|
||||
|
||||
<!-- QUOTE_POST: 引用帖子 -->
|
||||
<!-- QUOTE_POST: Quote post -->
|
||||
<template v-if="action.action_type === 'QUOTE_POST'">
|
||||
<div v-if="action.action_args?.quote_content" class="content-text">
|
||||
{{ action.action_args.quote_content }}
|
||||
|
|
@ -178,7 +178,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- REPOST: 转发帖子 -->
|
||||
<!-- REPOST: Repost -->
|
||||
<template v-if="action.action_type === 'REPOST'">
|
||||
<div class="repost-info">
|
||||
<svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><polyline points="17 1 21 5 17 9"></polyline><path d="M3 11V9a4 4 0 0 1 4-4h14"></path><polyline points="7 23 3 19 7 15"></polyline><path d="M21 13v2a4 4 0 0 1-4 4H3"></path></svg>
|
||||
|
|
@ -189,7 +189,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- LIKE_POST: 点赞帖子 -->
|
||||
<!-- LIKE_POST: Like post -->
|
||||
<template v-if="action.action_type === 'LIKE_POST'">
|
||||
<div class="like-info">
|
||||
<svg class="icon-small filled" viewBox="0 0 24 24" width="14" height="14" fill="currentColor"><path d="M20.84 4.61a5.5 5.5 0 0 0-7.78 0L12 5.67l-1.06-1.06a5.5 5.5 0 0 0-7.78 7.78l1.06 1.06L12 21.23l7.78-7.78 1.06-1.06a5.5 5.5 0 0 0 0-7.78z"></path></svg>
|
||||
|
|
@ -200,7 +200,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- CREATE_COMMENT: 发表评论 -->
|
||||
<!-- CREATE_COMMENT: Create comment -->
|
||||
<template v-if="action.action_type === 'CREATE_COMMENT'">
|
||||
<div v-if="action.action_args?.content" class="content-text">
|
||||
{{ action.action_args.content }}
|
||||
|
|
@ -211,7 +211,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- SEARCH_POSTS: 搜索帖子 -->
|
||||
<!-- SEARCH_POSTS: Search posts -->
|
||||
<template v-if="action.action_type === 'SEARCH_POSTS'">
|
||||
<div class="search-info">
|
||||
<svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><circle cx="11" cy="11" r="8"></circle><line x1="21" y1="21" x2="16.65" y2="16.65"></line></svg>
|
||||
|
|
@ -220,7 +220,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- FOLLOW: 关注用户 -->
|
||||
<!-- FOLLOW: Follow user -->
|
||||
<template v-if="action.action_type === 'FOLLOW'">
|
||||
<div class="follow-info">
|
||||
<svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><path d="M16 21v-2a4 4 0 0 0-4-4H5a4 4 0 0 0-4 4v2"></path><circle cx="8.5" cy="7" r="4"></circle><line x1="20" y1="8" x2="20" y2="14"></line><line x1="23" y1="11" x2="17" y2="11"></line></svg>
|
||||
|
|
@ -240,7 +240,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- DO_NOTHING: 无操作（静默） -->
|
||||
<!-- DO_NOTHING: No action (silent) -->
|
||||
<template v-if="action.action_type === 'DO_NOTHING'">
|
||||
<div class="idle-info">
|
||||
<svg class="icon-small" viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2"><circle cx="12" cy="12" r="10"></circle><line x1="12" y1="8" x2="12" y2="12"></line><line x1="12" y1="16" x2="12.01" y2="16"></line></svg>
|
||||
|
|
@ -248,7 +248,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- 通用回退：未知类型或有 content 但未被上述处理 -->
|
||||
<!-- Generic fallback: unknown type or has content but not handled above -->
|
||||
<div v-if="!['CREATE_POST', 'QUOTE_POST', 'REPOST', 'LIKE_POST', 'CREATE_COMMENT', 'SEARCH_POSTS', 'FOLLOW', 'UPVOTE_POST', 'DOWNVOTE_POST', 'DO_NOTHING'].includes(action.action_type) && action.action_args?.content" class="content-text">
|
||||
{{ action.action_args.content }}
|
||||
</div>
|
||||
|
|
@ -301,10 +301,10 @@ const { t } = useI18n()
|
|||
|
||||
const props = defineProps({
|
||||
simulationId: String,
|
||||
maxRounds: Number, // 从Step2传入的最大轮数
|
||||
maxRounds: Number, // Max rounds passed from Step2
|
||||
minutesPerRound: {
|
||||
type: Number,
|
||||
default: 30 // 默认每轮30分钟
|
||||
default: 30 // Default: 30 minutes per round
|
||||
},
|
||||
projectData: Object,
|
||||
graphData: Object,
|
||||
|
|
@ -317,22 +317,22 @@ const router = useRouter()
|
|||
|
||||
// State
|
||||
const isGeneratingReport = ref(false)
|
||||
const phase = ref(0) // 0: 未开始, 1: 运行中, 2: 已完成
|
||||
const phase = ref(0) // 0: Not started, 1: Running, 2: Completed
|
||||
const isStarting = ref(false)
|
||||
const isStopping = ref(false)
|
||||
const startError = ref(null)
|
||||
const runStatus = ref({})
|
||||
const allActions = ref([]) // 所有动作（增量累积）
|
||||
const actionIds = ref(new Set()) // 用于去重的动作ID集合
|
||||
const allActions = ref([]) // All actions (incremental accumulation)
|
||||
const actionIds = ref(new Set()) // Action ID set for deduplication
|
||||
const scrollContainer = ref(null)
|
||||
|
||||
// Computed
|
||||
// 按时间顺序显示动作（最新的在最后面，即底部）
|
||||
// Show actions in chronological order (newest at bottom)
|
||||
const chronologicalActions = computed(() => {
|
||||
return allActions.value
|
||||
})
|
||||
|
||||
// 各平台动作计数
|
||||
// Per-platform action counts
|
||||
const twitterActionsCount = computed(() => {
|
||||
return allActions.value.filter(a => a.platform === 'twitter').length
|
||||
})
|
||||
|
|
@ -341,7 +341,7 @@ const redditActionsCount = computed(() => {
|
|||
return allActions.value.filter(a => a.platform === 'reddit').length
|
||||
})
|
||||
|
||||
// 格式化模拟流逝时间（根据轮次和每轮分钟数计算）
|
||||
// Format simulated elapsed time (calculated from rounds and minutes per round)
|
||||
const formatElapsedTime = (currentRound) => {
|
||||
if (!currentRound || currentRound <= 0) return '0h 0m'
|
||||
const totalMinutes = currentRound * props.minutesPerRound
|
||||
|
|
@ -350,12 +350,12 @@ const formatElapsedTime = (currentRound) => {
|
|||
return `${hours}h ${minutes}m`
|
||||
}
|
||||
|
||||
// Twitter平台的模拟流逝时间
|
||||
// Twitter platform simulated elapsed time
|
||||
const twitterElapsedTime = computed(() => {
|
||||
return formatElapsedTime(runStatus.value.twitter_current_round || 0)
|
||||
})
|
||||
|
||||
// Reddit平台的模拟流逝时间
|
||||
// Reddit platform simulated elapsed time
|
||||
const redditElapsedTime = computed(() => {
|
||||
return formatElapsedTime(runStatus.value.reddit_current_round || 0)
|
||||
})
|
||||
|
|
@ -365,7 +365,7 @@ const addLog = (msg) => {
|
|||
emit('add-log', msg)
|
||||
}
|
||||
|
||||
// 重置所有状态（用于重新启动模拟）
|
||||
// Reset all state (for simulation restart)
|
||||
const resetAllState = () => {
|
||||
phase.value = 0
|
||||
runStatus.value = {}
|
||||
|
|
@ -376,17 +376,17 @@ const resetAllState = () => {
|
|||
startError.value = null
|
||||
isStarting.value = false
|
||||
isStopping.value = false
|
||||
stopPolling() // 停止之前可能存在的轮询
|
||||
stopPolling() // Stop any previous polling
|
||||
}
|
||||
|
||||
// 启动模拟
|
||||
// Start simulation
|
||||
const doStartSimulation = async () => {
|
||||
if (!props.simulationId) {
|
||||
addLog(t('log.errorMissingSimId'))
|
||||
return
|
||||
}
|
||||
|
||||
// 先重置所有状态，确保不会受到上一次模拟的影响
|
||||
// Reset all state first to avoid interference from previous simulation
|
||||
resetAllState()
|
||||
|
||||
isStarting.value = true
|
||||
|
|
@ -398,8 +398,8 @@ const doStartSimulation = async () => {
|
|||
const params = {
|
||||
simulation_id: props.simulationId,
|
||||
platform: 'parallel',
|
||||
force: true, // 强制重新开始
|
||||
enable_graph_memory_update: true // 开启动态图谱更新
|
||||
force: true, // Force restart
|
||||
enable_graph_memory_update: true // Enable dynamic graph update
|
||||
}
|
||||
|
||||
if (props.maxRounds) {
|
||||
|
|
@ -424,7 +424,7 @@ const doStartSimulation = async () => {
|
|||
startStatusPolling()
|
||||
startDetailPolling()
|
||||
} else {
|
||||
startError.value = res.error || '启动失败'
|
||||
startError.value = res.error || 'Start failed'
|
||||
addLog(t('log.startFailed', { error: res.error || t('common.unknownError') }))
|
||||
emit('update-status', 'error')
|
||||
}
|
||||
|
|
@ -437,7 +437,7 @@ const doStartSimulation = async () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 停止模拟
|
||||
// Stop simulation
|
||||
const handleStopSimulation = async () => {
|
||||
if (!props.simulationId) return
|
||||
|
||||
|
|
@ -462,7 +462,7 @@ const handleStopSimulation = async () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 轮询状态
|
||||
// Poll status
|
||||
let statusTimer = null
|
||||
let detailTimer = null
|
||||
|
||||
|
|
@ -485,7 +485,7 @@ const stopPolling = () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 追踪各平台的上一次轮次，用于检测变化并输出日志
|
||||
// Track last round per platform for change detection and logging
|
||||
const prevTwitterRound = ref(0)
|
||||
const prevRedditRound = ref(0)
|
||||
|
||||
|
|
@ -500,7 +500,7 @@ const fetchRunStatus = async () => {
|
|||
|
||||
runStatus.value = data
|
||||
|
||||
// 分别检测各平台的轮次变化并输出日志
|
||||
// Detect round changes per platform and log
|
||||
if (data.twitter_current_round > prevTwitterRound.value) {
|
||||
addLog(`[Plaza] R${data.twitter_current_round}/${data.total_rounds} | T:${data.twitter_simulated_hours || 0}h | A:${data.twitter_actions_count}`)
|
||||
prevTwitterRound.value = data.twitter_current_round
|
||||
|
|
@ -511,18 +511,23 @@ const fetchRunStatus = async () => {
|
|||
prevRedditRound.value = data.reddit_current_round
|
||||
}
|
||||
|
||||
// 检测模拟是否已完成（通过 runner_status 或平台完成状态判断）
|
||||
// Check if simulation completed (via runner_status or platform completion status)
|
||||
const isCompleted = data.runner_status === 'completed' || data.runner_status === 'stopped'
|
||||
const isFailed = data.runner_status === 'failed'
|
||||
|
||||
// runner_status is authoritative because the backend only publishes a
|
||||
// terminal state after the Zep ingestion barrier has completed.
|
||||
// Extra check: if backend hasn't updated runner_status yet but platform reports completed
|
||||
// Detect via twitter_completed and reddit_completed status
|
||||
const platformsCompleted = checkPlatformsCompleted(data)
|
||||
|
||||
if (isFailed) {
|
||||
addLog(t('log.simFailed') + (data.error ? `: ${data.error}` : ''))
|
||||
phase.value = 2
|
||||
stopPolling()
|
||||
emit('update-status', 'error')
|
||||
} else if (isCompleted) {
|
||||
} else if (isCompleted || platformsCompleted) {
|
||||
if (platformsCompleted && !isCompleted) {
|
||||
addLog(t('log.allPlatformsCompleted'))
|
||||
}
|
||||
addLog(t('log.simCompleted'))
|
||||
phase.value = 2
|
||||
stopPolling()
|
||||
|
|
@ -530,28 +535,28 @@ const fetchRunStatus = async () => {
|
|||
}
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn('获取运行状态失败:', err)
|
||||
console.warn('Failed to get run state:', err)
|
||||
}
|
||||
}
|
||||
|
||||
// 检查所有启用的平台是否已完成
|
||||
// Check if all enabled platforms have completed
|
||||
const checkPlatformsCompleted = (data) => {
|
||||
// 如果没有任何平台数据，返回 false
|
||||
// If no platform data, return false
|
||||
if (!data) return false
|
||||
|
||||
// 检查各平台的完成状态
|
||||
// Check completion status per platform
|
||||
const twitterCompleted = data.twitter_completed === true
|
||||
const redditCompleted = data.reddit_completed === true
|
||||
|
||||
// 如果至少有一个平台完成了，检查是否所有启用的平台都完成了
|
||||
// 通过 actions_count 判断平台是否被启用（如果 count > 0 或 running 曾为 true）
|
||||
// If at least one platform completed, check if all enabled platforms completed
|
||||
// Determine if platform is enabled via actions_count (if count > 0 or running was true)
|
||||
const twitterEnabled = (data.twitter_actions_count > 0) || data.twitter_running || twitterCompleted
|
||||
const redditEnabled = (data.reddit_actions_count > 0) || data.reddit_running || redditCompleted
|
||||
|
||||
// 如果没有任何平台被启用，返回 false
|
||||
// If no platform is enabled, return false
|
||||
if (!twitterEnabled && !redditEnabled) return false
|
||||
|
||||
// 检查所有启用的平台是否都已完成
|
||||
// Check if all enabled platforms have completed
|
||||
if (twitterEnabled && !twitterCompleted) return false
|
||||
if (redditEnabled && !redditCompleted) return false
|
||||
|
||||
|
|
@ -565,13 +570,13 @@ const fetchRunStatusDetail = async () => {
|
|||
const res = await getRunStatusDetail(props.simulationId)
|
||||
|
||||
if (res.success && res.data) {
|
||||
// 使用 all_actions 获取完整的动作列表
|
||||
// Use all_actions to get full action list
|
||||
const serverActions = res.data.all_actions || []
|
||||
|
||||
// 增量添加新动作（去重）
|
||||
// Incrementally add new actions (dedup)
|
||||
let newActionsAdded = 0
|
||||
serverActions.forEach(action => {
|
||||
// 生成唯一ID
|
||||
// Generate unique ID
|
||||
const actionId = action.id || `${action.timestamp}-${action.platform}-${action.agent_id}-${action.action_type}`
|
||||
|
||||
if (!actionIds.value.has(actionId)) {
|
||||
|
|
@ -584,11 +589,11 @@ const fetchRunStatusDetail = async () => {
|
|||
}
|
||||
})
|
||||
|
||||
// 不自动滚动，让用户自由查看时间轴
|
||||
// 新动作会在底部追加
|
||||
// Don't auto-scroll, let user freely browse timeline
|
||||
// New actions appended at bottom
|
||||
}
|
||||
} catch (err) {
|
||||
console.warn('获取详细状态失败:', err)
|
||||
console.warn('Failed to get detailed status:', err)
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -666,7 +671,7 @@ const handleNextStep = async () => {
|
|||
const reportId = res.data.report_id
|
||||
addLog(t('log.reportGenTaskStarted', { reportId }))
|
||||
|
||||
// 跳转到报告页面
|
||||
// Navigate to report page
|
||||
router.push({ name: 'Report', params: { reportId } })
|
||||
} else {
|
||||
addLog(t('log.reportGenFailed', { error: res.error || t('common.unknownError') }))
|
||||
|
|
|
|||
|
|
@ -127,7 +127,7 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<!-- Next Step Button - 在完成后显示 -->
|
||||
<!-- Next Step Button - shown after completion -->
|
||||
<button v-if="isComplete" class="next-step-btn" @click="goToInteraction">
|
||||
<span>{{ $t('step4.goToInteraction') }}</span>
|
||||
<svg viewBox="0 0 24 24" width="16" height="16" fill="none" stroke="currentColor" stroke-width="2">
|
||||
|
|
@ -194,7 +194,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- Section Content Generated (内容生成完成，但整个章节可能还没完成) -->
|
||||
<!-- Section Content Generated (content generation complete, but the entire section may not be done yet) -->
|
||||
<template v-if="log.action === 'section_content'">
|
||||
<div class="section-tag content-ready">
|
||||
<svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
|
||||
|
|
@ -205,7 +205,7 @@
|
|||
</div>
|
||||
</template>
|
||||
|
||||
<!-- Section Complete (章节生成完成) -->
|
||||
<!-- Section Complete (section generation complete) -->
|
||||
<template v-if="log.action === 'section_complete'">
|
||||
<div class="section-tag completed">
|
||||
<svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
|
||||
|
|
@ -315,7 +315,7 @@
|
|||
Final: {{ log.details?.has_final_answer ? 'Yes' : 'No' }}
|
||||
</span>
|
||||
</div>
|
||||
<!-- 当是最终答案时，显示特殊提示 -->
|
||||
<!-- When it's the final answer, show a special hint -->
|
||||
<div v-if="log.details?.has_final_answer" class="final-answer-hint">
|
||||
<svg viewBox="0 0 24 24" width="14" height="14" fill="none" stroke="currentColor" stroke-width="2">
|
||||
<polyline points="20 6 9 17 4 12"></polyline>
|
||||
|
|
@ -433,22 +433,22 @@ const showRawResult = reactive({})
|
|||
|
||||
// Toggle functions
|
||||
const toggleRawResult = (timestamp, event) => {
|
||||
// 保存按钮相对于视口的位置
|
||||
// Save the button's position relative to the viewport
|
||||
const button = event?.target
|
||||
const buttonRect = button?.getBoundingClientRect()
|
||||
const buttonTopBeforeToggle = buttonRect?.top
|
||||
|
||||
// 切换状态
|
||||
// Toggle state
|
||||
showRawResult[timestamp] = !showRawResult[timestamp]
|
||||
|
||||
// 等待 DOM 更新后，调整滚动位置以保持按钮在相同位置
|
||||
// Wait for DOM update, then adjust scroll position to keep the button in the same position
|
||||
if (button && buttonTopBeforeToggle !== undefined && rightPanel.value) {
|
||||
nextTick(() => {
|
||||
const newButtonRect = button.getBoundingClientRect()
|
||||
const buttonTopAfterToggle = newButtonRect.top
|
||||
const scrollDelta = buttonTopAfterToggle - buttonTopBeforeToggle
|
||||
|
||||
// 调整滚动位置
|
||||
// Adjust scroll position
|
||||
rightPanel.value.scrollTop += scrollDelta
|
||||
})
|
||||
}
|
||||
|
|
@ -466,7 +466,7 @@ const toggleSectionContent = (idx) => {
|
|||
}
|
||||
|
||||
const toggleSectionCollapse = (idx) => {
|
||||
// 只有已完成的章节才能折叠
|
||||
// Only completed sections can be collapsed
|
||||
if (!generatedSections.value[idx + 1]) return
|
||||
const newSet = new Set(collapsedSections.value)
|
||||
if (newSet.has(idx)) {
|
||||
|
|
@ -499,32 +499,32 @@ const toolConfig = {
|
|||
'insight_forge': {
|
||||
name: 'Deep Insight',
|
||||
color: 'purple',
|
||||
icon: 'lightbulb' // 灯泡图标 - 代表洞察
|
||||
icon: 'lightbulb' // lightbulb icon - represents insight
|
||||
},
|
||||
'panorama_search': {
|
||||
name: 'Panorama Search',
|
||||
color: 'blue',
|
||||
icon: 'globe' // 地球图标 - 代表全景搜索
|
||||
icon: 'globe' // globe icon - represents panoramic search
|
||||
},
|
||||
'interview_agents': {
|
||||
name: 'Agent Interview',
|
||||
color: 'green',
|
||||
icon: 'users' // 用户图标 - 代表对话
|
||||
icon: 'users' // users icon - represents conversation
|
||||
},
|
||||
'quick_search': {
|
||||
name: 'Quick Search',
|
||||
color: 'orange',
|
||||
icon: 'zap' // 闪电图标 - 代表快速
|
||||
icon: 'zap' // lightning icon - represents speed
|
||||
},
|
||||
'get_graph_statistics': {
|
||||
name: 'Graph Stats',
|
||||
color: 'cyan',
|
||||
icon: 'chart' // 图表图标 - 代表统计
|
||||
icon: 'chart' // chart icon - represents statistics
|
||||
},
|
||||
'get_entities_by_type': {
|
||||
name: 'Entity Query',
|
||||
color: 'pink',
|
||||
icon: 'database' // 数据库图标 - 代表实体
|
||||
icon: 'database' // Database icon - represents entities
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -553,31 +553,31 @@ const parseInsightForge = (text) => {
|
|||
}
|
||||
|
||||
try {
|
||||
// 提取分析问题
|
||||
const queryMatch = text.match(/分析问题:\s*(.+?)(?:\n|$)/)
|
||||
// Extract analysis question
|
||||
const queryMatch = text.match(/Analysis Question:\s*(.+?)(?:\n|$)/)
|
||||
if (queryMatch) result.query = queryMatch[1].trim()
|
||||
|
||||
// 提取预测场景
|
||||
const reqMatch = text.match(/预测场景:\s*(.+?)(?:\n|$)/)
|
||||
// Extract prediction scenario
|
||||
const reqMatch = text.match(/Prediction Scenario:\s*(.+?)(?:\n|$)/)
|
||||
if (reqMatch) result.simulationRequirement = reqMatch[1].trim()
|
||||
|
||||
// 提取统计数据 - 匹配"相关预测事实: X条"格式
|
||||
const factMatch = text.match(/相关预测事实:\s*(\d+)/)
|
||||
const entityMatch = text.match(/涉及实体:\s*(\d+)/)
|
||||
const relMatch = text.match(/关系链:\s*(\d+)/)
|
||||
// Extract statistics - match "Related prediction facts: X items" format
|
||||
const factMatch = text.match(/Related Prediction Facts:\s*(\d+)/)
|
||||
const entityMatch = text.match(/Entities Involved:\s*(\d+)/)
|
||||
const relMatch = text.match(/Relationship Chains:\s*(\d+)/)
|
||||
if (factMatch) result.stats.facts = parseInt(factMatch[1])
|
||||
if (entityMatch) result.stats.entities = parseInt(entityMatch[1])
|
||||
if (relMatch) result.stats.relationships = parseInt(relMatch[1])
|
||||
|
||||
// 提取子问题 - 完整提取，不限制数量
|
||||
const subQSection = text.match(/### 分析的子问题\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Extract sub-questions - full extraction, no limit
|
||||
const subQSection = text.match(/### Analysis Sub-Questions\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (subQSection) {
|
||||
const lines = subQSection[1].split('\n').filter(l => l.match(/^\d+\./))
|
||||
result.subQueries = lines.map(l => l.replace(/^\d+\.\s*/, '').trim()).filter(Boolean)
|
||||
}
|
||||
|
||||
// 提取关键事实 - 完整提取，不限制数量
|
||||
const factsSection = text.match(/### 【关键事实】[\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Extract key facts - full extraction, no limit
|
||||
const factsSection = text.match(/### [Key Facts][\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (factsSection) {
|
||||
const lines = factsSection[1].split('\n').filter(l => l.match(/^\d+\./))
|
||||
result.facts = lines.map(l => {
|
||||
|
|
@ -586,16 +586,16 @@ const parseInsightForge = (text) => {
|
|||
}).filter(Boolean)
|
||||
}
|
||||
|
||||
// 提取核心实体 - 完整提取，包含摘要和相关事实数
|
||||
const entitySection = text.match(/### 【核心实体】\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Extract core entities - full extraction, including summary and related fact count
|
||||
const entitySection = text.match(/### [Core Entities]\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (entitySection) {
|
||||
const entityText = entitySection[1]
|
||||
// 按 "- **" 分割实体块
|
||||
// Split entity blocks by "- **"
|
||||
const entityBlocks = entityText.split(/\n(?=- \*\*)/).filter(b => b.trim().startsWith('- **'))
|
||||
result.entities = entityBlocks.map(block => {
|
||||
const nameMatch = block.match(/^-\s*\*\*(.+?)\*\*\s*\((.+?)\)/)
|
||||
const summaryMatch = block.match(/摘要:\s*"?(.+?)"?(?:\n|$)/)
|
||||
const relatedMatch = block.match(/相关事实:\s*(\d+)/)
|
||||
const summaryMatch = block.match(/Summary:\s*"?(.+?)"?(?:\n|$)/)
|
||||
const relatedMatch = block.match(/Related Facts:\s*(\d+)/)
|
||||
return {
|
||||
name: nameMatch ? nameMatch[1].trim() : '',
|
||||
type: nameMatch ? nameMatch[2].trim() : '',
|
||||
|
|
@ -605,8 +605,8 @@ const parseInsightForge = (text) => {
|
|||
}).filter(e => e.name)
|
||||
}
|
||||
|
||||
// 提取关系链 - 完整提取，不限制数量
|
||||
const relSection = text.match(/### 【关系链】\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Extract relationship chains - full extraction, no limit
|
||||
const relSection = text.match(/### [Relationship Chains]\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (relSection) {
|
||||
const lines = relSection[1].split('\n').filter(l => l.trim().startsWith('-'))
|
||||
result.relations = lines.map(l => {
|
||||
|
|
@ -634,33 +634,33 @@ const parsePanorama = (text) => {
|
|||
}
|
||||
|
||||
try {
|
||||
// 提取查询
|
||||
const queryMatch = text.match(/查询:\s*(.+?)(?:\n|$)/)
|
||||
// Extract query
|
||||
const queryMatch = text.match(/Query:\s*(.+?)(?:\n|$)/)
|
||||
if (queryMatch) result.query = queryMatch[1].trim()
|
||||
|
||||
// 提取统计数据
|
||||
const nodesMatch = text.match(/总节点数:\s*(\d+)/)
|
||||
const edgesMatch = text.match(/总边数:\s*(\d+)/)
|
||||
const activeMatch = text.match(/当前有效事实:\s*(\d+)/)
|
||||
const histMatch = text.match(/历史\/过期事实:\s*(\d+)/)
|
||||
// Extract statistics
|
||||
const nodesMatch = text.match(/Total Nodes:\s*(\d+)/)
|
||||
const edgesMatch = text.match(/Total Edges:\s*(\d+)/)
|
||||
const activeMatch = text.match(/Currently Active Facts:\s*(\d+)/)
|
||||
const histMatch = text.match(/Historical\/Expired Facts:\s*(\d+)/)
|
||||
if (nodesMatch) result.stats.nodes = parseInt(nodesMatch[1])
|
||||
if (edgesMatch) result.stats.edges = parseInt(edgesMatch[1])
|
||||
if (activeMatch) result.stats.activeFacts = parseInt(activeMatch[1])
|
||||
if (histMatch) result.stats.historicalFacts = parseInt(histMatch[1])
|
||||
|
||||
// 提取当前有效事实 - 完整提取，不限制数量
|
||||
const activeSection = text.match(/### 【当前有效事实】[\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Extract currently active facts - full extraction, no limit
|
||||
const activeSection = text.match(/### [Currently Active Facts][\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (activeSection) {
|
||||
const lines = activeSection[1].split('\n').filter(l => l.match(/^\d+\./))
|
||||
result.activeFacts = lines.map(l => {
|
||||
// 移除编号和引号
|
||||
// Remove numbering and quotes
|
||||
const factText = l.replace(/^\d+\.\s*/, '').replace(/^"|"$/g, '').trim()
|
||||
return factText
|
||||
}).filter(Boolean)
|
||||
}
|
||||
|
||||
// 提取历史/过期事实 - 完整提取，不限制数量
|
||||
const histSection = text.match(/### 【历史\/过期事实】[\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Extract historical/expired facts - full extraction, no limit
|
||||
const histSection = text.match(/### [Historical\/Expired Facts][\s\S]*?\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (histSection) {
|
||||
const lines = histSection[1].split('\n').filter(l => l.match(/^\d+\./))
|
||||
result.historicalFacts = lines.map(l => {
|
||||
|
|
@ -669,8 +669,8 @@ const parsePanorama = (text) => {
|
|||
}).filter(Boolean)
|
||||
}
|
||||
|
||||
// 提取涉及实体 - 完整提取，不限制数量
|
||||
const entitySection = text.match(/### 【涉及实体】\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Extract involved entities - full extraction, no limit
|
||||
const entitySection = text.match(/### [Involved Entities]\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (entitySection) {
|
||||
const lines = entitySection[1].split('\n').filter(l => l.trim().startsWith('-'))
|
||||
result.entities = lines.map(l => {
|
||||
|
|
@ -698,25 +698,25 @@ const parseInterview = (text) => {
|
|||
}
|
||||
|
||||
try {
|
||||
// 提取采访主题
|
||||
const topicMatch = text.match(/\*\*采访主题:\*\*\s*(.+?)(?:\n|$)/)
|
||||
// Extract interview topic
|
||||
const topicMatch = text.match(/\*\*Interview Topic:\*\*\s*(.+?)(?:\n|$)/)
|
||||
if (topicMatch) result.topic = topicMatch[1].trim()
|
||||
|
||||
// 提取采访人数（如 "5 / 9 位模拟Agent"）
|
||||
const countMatch = text.match(/\*\*采访人数:\*\*\s*(\d+)\s*\/\s*(\d+)/)
|
||||
// Extract interview count (e.g. "5 / 9 simulated Agents")
|
||||
const countMatch = text.match(/\*\*Interview Count:\*\*\s*(\d+)\s*\/\s*(\d+)/)
|
||||
if (countMatch) {
|
||||
result.successCount = parseInt(countMatch[1])
|
||||
result.totalCount = parseInt(countMatch[2])
|
||||
result.agentCount = `${countMatch[1]} / ${countMatch[2]}`
|
||||
}
|
||||
|
||||
// 提取采访对象选择理由
|
||||
const reasonMatch = text.match(/### 采访对象选择理由\n([\s\S]*?)(?=\n---\n|\n### 采访实录)/)
|
||||
// Extract interviewee selection rationale
|
||||
const reasonMatch = text.match(/### Interviewee Selection Rationale\n([\s\S]*?)(?=\n---\n|\n### Interview Transcript)/)
|
||||
if (reasonMatch) {
|
||||
result.selectionReason = reasonMatch[1].trim()
|
||||
}
|
||||
|
||||
// 解析每个人的选择理由
|
||||
// Parse each person's selection rationale
|
||||
const parseIndividualReasons = (reasonText) => {
|
||||
const reasons = {}
|
||||
if (!reasonText) return reasons
|
||||
|
|
@ -730,26 +730,26 @@ const parseInterview = (text) => {
|
|||
let name = null
|
||||
let reasonStart = null
|
||||
|
||||
// 格式1: 数字. **名字（index=X）**：理由
|
||||
// 例如: 1. **校友_345（index=1）**：作为武大校友...
|
||||
// Format 1: Number. **Name (index=X)**: rationale
|
||||
// e.g.: 1. **Alumni_345 (index=1)**: As a Wuhan University alumni...
|
||||
headerMatch = line.match(/^\d+\.\s*\*\*([^*（(]+)(?:[（(]index\s*=?\s*\d+[)）])?\*\*[：:]\s*(.*)/)
|
||||
if (headerMatch) {
|
||||
name = headerMatch[1].trim()
|
||||
reasonStart = headerMatch[2]
|
||||
}
|
||||
|
||||
// 格式2: - 选择名字（index X）：理由
|
||||
// 例如: - 选择家长_601（index 0）：作为家长群体代表...
|
||||
// Format 2: - Select Name (index X): rationale
|
||||
// e.g.: - Select Parent_601 (index 0): As a parent group representative...
|
||||
if (!headerMatch) {
|
||||
headerMatch = line.match(/^-\s*选择([^（(]+)(?:[（(]index\s*=?\s*\d+[)）])?[：:]\s*(.*)/)
|
||||
headerMatch = line.match(/^-\s*Select([^（(]+)(?:[（(]index\s*=?\s*\d+[)）])?[：:]\s*(.*)/)
|
||||
if (headerMatch) {
|
||||
name = headerMatch[1].trim()
|
||||
reasonStart = headerMatch[2]
|
||||
}
|
||||
}
|
||||
|
||||
// 格式3: - **名字（index X）**：理由
|
||||
// 例如: - **家长_601（index 0）**：作为家长群体代表...
|
||||
// Format 3: - **Name (index X)**: rationale
|
||||
// e.g.: - **Parent_601 (index 0)**: As a parent group representative...
|
||||
if (!headerMatch) {
|
||||
headerMatch = line.match(/^-\s*\*\*([^*（(]+)(?:[（(]index\s*=?\s*\d+[)）])?\*\*[：:]\s*(.*)/)
|
||||
if (headerMatch) {
|
||||
|
|
@ -759,20 +759,20 @@ const parseInterview = (text) => {
|
|||
}
|
||||
|
||||
if (name) {
|
||||
// 保存上一个人的理由
|
||||
// Save previous person's rationale
|
||||
if (currentName && currentReason.length > 0) {
|
||||
reasons[currentName] = currentReason.join(' ').trim()
|
||||
}
|
||||
// 开始新的人
|
||||
// Start new person
|
||||
currentName = name
|
||||
currentReason = reasonStart ? [reasonStart.trim()] : []
|
||||
} else if (currentName && line.trim() && !line.match(/^未选|^综上|^最终选择/)) {
|
||||
// 理由的续行（排除结尾总结段落）
|
||||
} else if (currentName && line.trim() && !line.match(/^Not selected|^In summary|^Final selection/)) {
|
||||
// Continuation of rationale (exclude closing summary paragraph)
|
||||
currentReason.push(line.trim())
|
||||
}
|
||||
}
|
||||
|
||||
// 保存最后一个人的理由
|
||||
// Save last person's rationale
|
||||
if (currentName && currentReason.length > 0) {
|
||||
reasons[currentName] = currentReason.join(' ').trim()
|
||||
}
|
||||
|
|
@ -782,8 +782,8 @@ const parseInterview = (text) => {
|
|||
|
||||
const individualReasons = parseIndividualReasons(result.selectionReason)
|
||||
|
||||
// 提取每个采访记录
|
||||
const interviewBlocks = text.split(/#### 采访 #\d+:/).slice(1)
|
||||
// Extract each interview record
|
||||
const interviewBlocks = text.split(/#### Interview #\d+:/).slice(1)
|
||||
|
||||
interviewBlocks.forEach((block, index) => {
|
||||
const interview = {
|
||||
|
|
@ -799,33 +799,33 @@ const parseInterview = (text) => {
|
|||
quotes: []
|
||||
}
|
||||
|
||||
// 提取标题（如 "学生"、"教育从业者" 等）
|
||||
// Extract title (e.g. "Student", "Education Professional", etc.)
|
||||
const titleMatch = block.match(/^(.+?)\n/)
|
||||
if (titleMatch) interview.title = titleMatch[1].trim()
|
||||
|
||||
// 提取姓名和角色
|
||||
// Extract name and role
|
||||
const nameRoleMatch = block.match(/\*\*(.+?)\*\*\s*\((.+?)\)/)
|
||||
if (nameRoleMatch) {
|
||||
interview.name = nameRoleMatch[1].trim()
|
||||
interview.role = nameRoleMatch[2].trim()
|
||||
// 设置该人的选择理由
|
||||
// Set this person's selection rationale
|
||||
interview.selectionReason = individualReasons[interview.name] || ''
|
||||
}
|
||||
|
||||
// 提取简介
|
||||
const bioMatch = block.match(/_简介:\s*([\s\S]*?)_\n/)
|
||||
// Extract bio
|
||||
const bioMatch = block.match(/_Bio:\s*([\s\S]*?)_\n/)
|
||||
if (bioMatch) {
|
||||
interview.bio = bioMatch[1].trim().replace(/\.\.\.$/, '...')
|
||||
}
|
||||
|
||||
// 提取问题列表
|
||||
// Extract question list
|
||||
const qMatch = block.match(/\*\*Q:\*\*\s*([\s\S]*?)(?=\n\n\*\*A:\*\*|\*\*A:\*\*)/)
|
||||
if (qMatch) {
|
||||
const qText = qMatch[1].trim()
|
||||
// 按数字编号分割问题
|
||||
// Split questions by number
|
||||
const questions = qText.split(/\n\d+\.\s+/).filter(q => q.trim())
|
||||
if (questions.length > 0) {
|
||||
// 如果第一个问题前面有"1."，需要特殊处理
|
||||
// If first question has "1." prefix, handle specially
|
||||
const firstQ = qText.match(/^1\.\s+(.+)/)
|
||||
if (firstQ) {
|
||||
interview.questions = [firstQ[1].trim(), ...questions.slice(1).map(q => q.trim())]
|
||||
|
|
@ -835,14 +835,14 @@ const parseInterview = (text) => {
|
|||
}
|
||||
}
|
||||
|
||||
// 提取回答 - 分Twitter和Reddit
|
||||
const answerMatch = block.match(/\*\*A:\*\*\s*([\s\S]*?)(?=\*\*关键引言|$)/)
|
||||
// Extract answers - separate Twitter and Reddit
|
||||
const answerMatch = block.match(/\*\*A:\*\*\s*([\s\S]*?)(?=\*\*Key Quotes|$)/)
|
||||
if (answerMatch) {
|
||||
const answerText = answerMatch[1].trim()
|
||||
|
||||
// 分离Twitter和Reddit回答
|
||||
const twitterMatch = answerText.match(/【Twitter平台回答】\n?([\s\S]*?)(?=【Reddit平台回答】|$)/)
|
||||
const redditMatch = answerText.match(/【Reddit平台回答】\n?([\s\S]*?)$/)
|
||||
// Separate Twitter and Reddit answers
|
||||
const twitterMatch = answerText.match(/[Twitter Platform Answer]\n?([\s\S]*?)(?=[Reddit Platform Answer]|$)/)
|
||||
const redditMatch = answerText.match(/[Reddit Platform Answer]\n?([\s\S]*?)$/)
|
||||
|
||||
if (twitterMatch) {
|
||||
interview.twitterAnswer = twitterMatch[1].trim()
|
||||
|
|
@ -851,29 +851,29 @@ const parseInterview = (text) => {
|
|||
interview.redditAnswer = redditMatch[1].trim()
|
||||
}
|
||||
|
||||
// 平台回退逻辑（兼容旧格式：只有一个平台标记的情况）
|
||||
// Platform fallback logic (backward compat: single platform marker)
|
||||
if (!twitterMatch && redditMatch) {
|
||||
// 只有 Reddit 回答，仅在非占位文本时复制为默认显示
|
||||
if (interview.redditAnswer && interview.redditAnswer !== '（该平台未获得回复）') {
|
||||
// Only Reddit answer, copy as default display only if not placeholder text
|
||||
if (interview.redditAnswer && interview.redditAnswer !== '(No response from this platform)') {
|
||||
interview.twitterAnswer = interview.redditAnswer
|
||||
}
|
||||
} else if (twitterMatch && !redditMatch) {
|
||||
if (interview.twitterAnswer && interview.twitterAnswer !== '（该平台未获得回复）') {
|
||||
if (interview.twitterAnswer && interview.twitterAnswer !== '(No response from this platform)') {
|
||||
interview.redditAnswer = interview.twitterAnswer
|
||||
}
|
||||
} else if (!twitterMatch && !redditMatch) {
|
||||
// 没有分平台标记（极旧格式），整体作为回答
|
||||
// No platform markers (very old format), use entire text as answer
|
||||
interview.twitterAnswer = answerText
|
||||
}
|
||||
}
|
||||
|
||||
// 提取关键引言（兼容多种引号格式）
|
||||
const quotesMatch = block.match(/\*\*关键引言:\*\*\n([\s\S]*?)(?=\n---|\n####|$)/)
|
||||
// Extract key quotes (compatible with multiple quote formats)
|
||||
const quotesMatch = block.match(/\*\*Key Quotes:\*\*\n([\s\S]*?)(?=\n---|\n####|$)/)
|
||||
if (quotesMatch) {
|
||||
const quotesText = quotesMatch[1]
|
||||
// 优先匹配 > "text" 格式
|
||||
// Prefer > "text" format
|
||||
let quoteMatches = quotesText.match(/> "([^"]+)"/g)
|
||||
// 回退：匹配 > "text" 或 > \u201Ctext\u201D（中文引号）
|
||||
// Fallback: match > "text" or > \u201Ctext\u201D (Chinese quotes)
|
||||
if (!quoteMatches) {
|
||||
quoteMatches = quotesText.match(/> [\u201C""]([^\u201D""]+)[\u201D""]/g)
|
||||
}
|
||||
|
|
@ -889,8 +889,8 @@ const parseInterview = (text) => {
|
|||
}
|
||||
})
|
||||
|
||||
// 提取采访摘要
|
||||
const summaryMatch = text.match(/### 采访摘要与核心观点\n([\s\S]*?)$/)
|
||||
// Extract interview summary
|
||||
const summaryMatch = text.match(/### Interview Summary & Key Insights\n([\s\S]*?)$/)
|
||||
if (summaryMatch) {
|
||||
result.summary = summaryMatch[1].trim()
|
||||
}
|
||||
|
|
@ -911,23 +911,23 @@ const parseQuickSearch = (text) => {
|
|||
}
|
||||
|
||||
try {
|
||||
// 提取搜索查询
|
||||
const queryMatch = text.match(/搜索查询:\s*(.+?)(?:\n|$)/)
|
||||
// Extract search query
|
||||
const queryMatch = text.match(/Search Query:\s*(.+?)(?:\n|$)/)
|
||||
if (queryMatch) result.query = queryMatch[1].trim()
|
||||
|
||||
// 提取结果数量
|
||||
const countMatch = text.match(/找到\s*(\d+)\s*条/)
|
||||
// Extract result count
|
||||
const countMatch = text.match(/Found\s*(\d+)\s*results/)
|
||||
if (countMatch) result.count = parseInt(countMatch[1])
|
||||
|
||||
// 提取相关事实 - 完整提取，不限制数量
|
||||
const factsSection = text.match(/### 相关事实:\n([\s\S]*)$/)
|
||||
// Extract related facts - full extraction, no limit
|
||||
const factsSection = text.match(/### Related Facts:\n([\s\S]*)$/)
|
||||
if (factsSection) {
|
||||
const lines = factsSection[1].split('\n').filter(l => l.match(/^\d+\./))
|
||||
result.facts = lines.map(l => l.replace(/^\d+\.\s*/, '').trim()).filter(Boolean)
|
||||
}
|
||||
|
||||
// 尝试提取边信息（如果有）
|
||||
const edgesSection = text.match(/### 相关边:\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Try extracting edge info (if present)
|
||||
const edgesSection = text.match(/### Related Edges:\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (edgesSection) {
|
||||
const lines = edgesSection[1].split('\n').filter(l => l.trim().startsWith('-'))
|
||||
result.edges = lines.map(l => {
|
||||
|
|
@ -939,8 +939,8 @@ const parseQuickSearch = (text) => {
|
|||
}).filter(Boolean)
|
||||
}
|
||||
|
||||
// 尝试提取节点信息（如果有）
|
||||
const nodesSection = text.match(/### 相关节点:\n([\s\S]*?)(?=\n###|$)/)
|
||||
// Try extracting node info (if present)
|
||||
const nodesSection = text.match(/### Related Nodes:\n([\s\S]*?)(?=\n###|$)/)
|
||||
if (nodesSection) {
|
||||
const lines = nodesSection[1].split('\n').filter(l => l.trim().startsWith('-'))
|
||||
result.nodes = lines.map(l => {
|
||||
|
|
@ -1229,7 +1229,7 @@ const PanoramaDisplay = {
|
|||
h('div', { class: 'fact-item historical', key: i }, [
|
||||
h('span', { class: 'fact-number' }, i + 1),
|
||||
h('div', { class: 'fact-content' }, [
|
||||
// 尝试提取时间信息 [time - time]
|
||||
// Try extracting time info [time - time]
|
||||
(() => {
|
||||
const timeMatch = fact.match(/^\[(.+?)\]\s*(.*)$/)
|
||||
if (timeMatch) {
|
||||
|
|
@ -1296,16 +1296,16 @@ const InterviewDisplay = {
|
|||
|
||||
const activeIndex = ref(0)
|
||||
const expandedAnswers = ref(new Set())
|
||||
// 为每个问题-回答对维护独立的平台选择状态
|
||||
// Maintain independent platform selection state per Q&A pair
|
||||
const platformTabs = reactive({}) // { 'agentIdx-qIdx': 'twitter' | 'reddit' }
|
||||
|
||||
// 获取某个问题的当前平台选择
|
||||
// Get current platform selection for a question
|
||||
const getPlatformTab = (agentIdx, qIdx) => {
|
||||
const key = `${agentIdx}-${qIdx}`
|
||||
return platformTabs[key] || 'twitter'
|
||||
}
|
||||
|
||||
// 设置某个问题的平台选择
|
||||
// Set platform selection for a question
|
||||
const setPlatformTab = (agentIdx, qIdx, platform) => {
|
||||
const key = `${agentIdx}-${qIdx}`
|
||||
platformTabs[key] = platform
|
||||
|
|
@ -1327,26 +1327,26 @@ const InterviewDisplay = {
|
|||
return text.substring(0, 400) + '...'
|
||||
}
|
||||
|
||||
// 检查是否为平台占位文本
|
||||
// Check if platform placeholder text
|
||||
const isPlaceholderText = (text) => {
|
||||
if (!text) return true
|
||||
const t = text.trim()
|
||||
return t === '（该平台未获得回复）' || t === '(该平台未获得回复)' || t === '[无回复]'
|
||||
return t === '(No response from this platform)' || t === '(No response from this platform)' || t === '[No response]'
|
||||
}
|
||||
|
||||
// 尝试按问题编号分割回答
|
||||
// Try splitting answer by question number
|
||||
const splitAnswerByQuestions = (answerText, questionCount) => {
|
||||
if (!answerText || questionCount <= 0) return [answerText]
|
||||
if (isPlaceholderText(answerText)) return ['']
|
||||
|
||||
// 支持两种编号格式：
|
||||
// 1. "问题X：" 或 "问题X:" （中文格式，后端新格式）
|
||||
// 2. "1. " 或 "\n1. " （数字+点，旧格式兼容）
|
||||
// Support two numbering formats:
|
||||
// 1. "Question X:" or "Question X:" (Chinese format, new backend format)
|
||||
// 2. "1. " or "\n1. " (number + dot, old format compat)
|
||||
let matches = []
|
||||
let match
|
||||
|
||||
// 优先尝试 "问题X：" 格式
|
||||
const cnPattern = /(?:^|[\r\n]+)问题(\d+)[：:]\s*/g
|
||||
// Prefer "Question X:" format
|
||||
const cnPattern = /(?:^|[\r\n]+)Question(\d+)[：:]\s*/g
|
||||
while ((match = cnPattern.exec(answerText)) !== null) {
|
||||
matches.push({
|
||||
num: parseInt(match[1]),
|
||||
|
|
@ -1355,7 +1355,7 @@ const InterviewDisplay = {
|
|||
})
|
||||
}
|
||||
|
||||
// 如果没匹配到，回退到 "数字." 格式
|
||||
// If no match, fall back to "number." format
|
||||
if (matches.length === 0) {
|
||||
const numPattern = /(?:^|[\r\n]+)(\d+)\.\s+/g
|
||||
while ((match = numPattern.exec(answerText)) !== null) {
|
||||
|
|
@ -1367,16 +1367,16 @@ const InterviewDisplay = {
|
|||
}
|
||||
}
|
||||
|
||||
// 如果没有找到编号或只找到一个，返回整体
|
||||
// If no numbering found or only one found, return whole
|
||||
if (matches.length <= 1) {
|
||||
const cleaned = answerText
|
||||
.replace(/^问题\d+[：:]\s*/, '')
|
||||
.replace(/^Question\d+[：:]\s*/, '')
|
||||
.replace(/^\d+\.\s+/, '')
|
||||
.trim()
|
||||
return [cleaned || answerText]
|
||||
}
|
||||
|
||||
// 按编号提取各部分
|
||||
// Extract parts by number
|
||||
const parts = []
|
||||
for (let i = 0; i < matches.length; i++) {
|
||||
const current = matches[i]
|
||||
|
|
@ -1397,7 +1397,7 @@ const InterviewDisplay = {
|
|||
return [answerText]
|
||||
}
|
||||
|
||||
// 获取某个问题对应的回答
|
||||
// Get answer for a specific question
|
||||
const getAnswerForQuestion = (interview, qIdx, platform) => {
|
||||
const answer = platform === 'twitter' ? interview.twitterAnswer : (interview.redditAnswer || interview.twitterAnswer)
|
||||
if (!answer || isPlaceholderText(answer)) return answer || ''
|
||||
|
|
@ -1405,21 +1405,21 @@ const InterviewDisplay = {
|
|||
const questionCount = interview.questions?.length || 1
|
||||
const answers = splitAnswerByQuestions(answer, questionCount)
|
||||
|
||||
// 分割成功且索引有效
|
||||
// Split succeeded and index valid
|
||||
if (answers.length > 1 && qIdx < answers.length) {
|
||||
return answers[qIdx] || ''
|
||||
}
|
||||
|
||||
// 分割失败：第一个问题返回完整回答，其余返回空
|
||||
// Split failed: first question returns full answer, rest return empty
|
||||
return qIdx === 0 ? answer : ''
|
||||
}
|
||||
|
||||
// 检查某个问题是否有双平台回答（过滤占位文本）
|
||||
// Check if a question has dual-platform answers (filter placeholders)
|
||||
const hasMultiplePlatforms = (interview, qIdx) => {
|
||||
if (!interview.twitterAnswer || !interview.redditAnswer) return false
|
||||
const twitterAnswer = getAnswerForQuestion(interview, qIdx, 'twitter')
|
||||
const redditAnswer = getAnswerForQuestion(interview, qIdx, 'reddit')
|
||||
// 两个平台都有真实回答（非占位文本）且内容不同
|
||||
// Both platforms have real answers (non-placeholder) and content differs
|
||||
return !isPlaceholderText(twitterAnswer) && !isPlaceholderText(redditAnswer) && twitterAnswer !== redditAnswer
|
||||
}
|
||||
|
||||
|
|
@ -1469,13 +1469,13 @@ const InterviewDisplay = {
|
|||
])
|
||||
]),
|
||||
|
||||
// Selection Reason - 选择理由
|
||||
// Selection Reason
|
||||
props.result.interviews[activeIndex.value]?.selectionReason && h('div', { class: 'selection-reason' }, [
|
||||
h('div', { class: 'reason-label' }, '选择理由'),
|
||||
h('div', { class: 'reason-label' }, 'Selection Reason'),
|
||||
h('div', { class: 'reason-content' }, props.result.interviews[activeIndex.value].selectionReason)
|
||||
]),
|
||||
|
||||
// Q&A Conversation Thread - 一问一答样式
|
||||
// Q&A Conversation Thread - Q&A style
|
||||
h('div', { class: 'qa-thread' },
|
||||
(props.result.interviews[activeIndex.value]?.questions?.length > 0
|
||||
? props.result.interviews[activeIndex.value].questions
|
||||
|
|
@ -1505,7 +1505,7 @@ const InterviewDisplay = {
|
|||
h('div', { class: 'qa-content' }, [
|
||||
h('div', { class: 'qa-answer-header' }, [
|
||||
h('div', { class: 'qa-sender' }, interview?.name || 'Agent'),
|
||||
// 双平台切换按钮（仅在有真实双平台回答时显示）
|
||||
// Dual-platform toggle button (only shown when real dual-platform answers exist)
|
||||
hasDualPlatform && h('div', { class: 'platform-switch' }, [
|
||||
h('button', {
|
||||
class: ['platform-btn', { active: currentPlatform === 'twitter' }],
|
||||
|
|
@ -1537,7 +1537,7 @@ const InterviewDisplay = {
|
|||
.replace(/\*\*(.+?)\*\*/g, '<strong>$1</strong>')
|
||||
.replace(/\n/g, '<br>')
|
||||
}),
|
||||
// Expand/Collapse Button（占位文本不显示）
|
||||
// Expand/Collapse Button (not shown for placeholder text)
|
||||
!isPlaceholder && answerText.length > 400 && h('button', {
|
||||
class: 'expand-answer-btn',
|
||||
onClick: () => toggleAnswer(expandKey)
|
||||
|
|
@ -1769,19 +1769,19 @@ const isFinalizing = computed(() => {
|
|||
return !isComplete.value && isPlanningDone.value && totalSections.value > 0 && completedSections.value >= totalSections.value
|
||||
})
|
||||
|
||||
// 当前活跃的步骤（用于顶部显示）
|
||||
// Currently active step (for top display)
|
||||
const activeStep = computed(() => {
|
||||
const steps = workflowSteps.value
|
||||
// 找到当前 active 的步骤
|
||||
// Find currently active step
|
||||
const active = steps.find(s => s.status === 'active')
|
||||
if (active) return active
|
||||
|
||||
// 如果没有 active，返回最后一个 done 的步骤
|
||||
// If no active, return last done step
|
||||
const doneSteps = steps.filter(s => s.status === 'done')
|
||||
if (doneSteps.length > 0) return doneSteps[doneSteps.length - 1]
|
||||
|
||||
// 否则返回第一个步骤
|
||||
return steps[0] || { noLabel: '--', title: '等待开始', status: 'todo', meta: '' }
|
||||
// Otherwise return first step
|
||||
return steps[0] || { noLabel: '--', title: 'Waiting to start', status: 'todo', meta: '' }
|
||||
})
|
||||
|
||||
const workflowSteps = computed(() => {
|
||||
|
|
@ -1874,25 +1874,25 @@ const truncateText = (text, maxLen) => {
|
|||
const renderMarkdown = (content) => {
|
||||
if (!content) return ''
|
||||
|
||||
// 去掉开头的二级标题（## xxx），因为章节标题已在外层显示
|
||||
// Remove leading level-2 headings (## xxx), as section titles are shown in outer layer
|
||||
let processedContent = content.replace(/^##\s+.+\n+/, '')
|
||||
|
||||
// 处理代码块
|
||||
// Process code blocks
|
||||
let html = processedContent.replace(/```(\w*)\n([\s\S]*?)```/g, '<pre class="code-block"><code>$2</code></pre>')
|
||||
|
||||
// 处理行内代码
|
||||
// Process inline code
|
||||
html = html.replace(/`([^`]+)`/g, '<code class="inline-code">$1</code>')
|
||||
|
||||
// 处理标题
|
||||
// Process headings
|
||||
html = html.replace(/^#### (.+)$/gm, '<h5 class="md-h5">$1</h5>')
|
||||
html = html.replace(/^### (.+)$/gm, '<h4 class="md-h4">$1</h4>')
|
||||
html = html.replace(/^## (.+)$/gm, '<h3 class="md-h3">$1</h3>')
|
||||
html = html.replace(/^# (.+)$/gm, '<h2 class="md-h2">$1</h2>')
|
||||
|
||||
// 处理引用块
|
||||
// Process blockquotes
|
||||
html = html.replace(/^> (.+)$/gm, '<blockquote class="md-quote">$1</blockquote>')
|
||||
|
||||
// 处理列表 - 支持子列表
|
||||
// Process lists - support sub-lists
|
||||
html = html.replace(/^(\s*)- (.+)$/gm, (match, indent, text) => {
|
||||
const level = Math.floor(indent.length / 2)
|
||||
return `<li class="md-li" data-level="${level}">${text}</li>`
|
||||
|
|
@ -1902,52 +1902,52 @@ const renderMarkdown = (content) => {
|
|||
return `<li class="md-oli" data-level="${level}">${text}</li>`
|
||||
})
|
||||
|
||||
// 包装无序列表
|
||||
// Wrap unordered list
|
||||
html = html.replace(/(<li class="md-li"[^>]*>.*?<\/li>\s*)+/g, '<ul class="md-ul">$&</ul>')
|
||||
// 包装有序列表
|
||||
// Wrap ordered list
|
||||
html = html.replace(/(<li class="md-oli"[^>]*>.*?<\/li>\s*)+/g, '<ol class="md-ol">$&</ol>')
|
||||
|
||||
// 清理列表项之间的所有空白
|
||||
// Clean all whitespace between list items
|
||||
html = html.replace(/<\/li>\s+<li/g, '</li><li')
|
||||
// 清理列表开始标签后的空白
|
||||
// Clean whitespace after list start tag
|
||||
html = html.replace(/<ul class="md-ul">\s+/g, '<ul class="md-ul">')
|
||||
html = html.replace(/<ol class="md-ol">\s+/g, '<ol class="md-ol">')
|
||||
// 清理列表结束标签前的空白
|
||||
// Clean whitespace before list end tag
|
||||
html = html.replace(/\s+<\/ul>/g, '</ul>')
|
||||
html = html.replace(/\s+<\/ol>/g, '</ol>')
|
||||
|
||||
// 处理粗体和斜体
|
||||
// Process bold and italic
|
||||
html = html.replace(/\*\*(.+?)\*\*/g, '<strong>$1</strong>')
|
||||
html = html.replace(/\*(.+?)\*/g, '<em>$1</em>')
|
||||
html = html.replace(/_(.+?)_/g, '<em>$1</em>')
|
||||
|
||||
// 处理分隔线
|
||||
// Process horizontal rules
|
||||
html = html.replace(/^---$/gm, '<hr class="md-hr">')
|
||||
|
||||
// 处理换行 - 空行变成段落分隔，单换行变成 <br>
|
||||
// Process line breaks - blank lines become paragraph separators, single line breaks become <br>
|
||||
html = html.replace(/\n\n/g, '</p><p class="md-p">')
|
||||
html = html.replace(/\n/g, '<br>')
|
||||
|
||||
// 包装在段落中
|
||||
// Wrap in paragraphs
|
||||
html = '<p class="md-p">' + html + '</p>'
|
||||
|
||||
// 清理空段落
|
||||
// Clean empty paragraphs
|
||||
html = html.replace(/<p class="md-p"><\/p>/g, '')
|
||||
html = html.replace(/<p class="md-p">(<h[2-5])/g, '$1')
|
||||
html = html.replace(/(<\/h[2-5]>)<\/p>/g, '$1')
|
||||
html = html.replace(/<p class="md-p">(<ul|<ol|<blockquote|<pre|<hr)/g, '$1')
|
||||
html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>|<\/pre>)<\/p>/g, '$1')
|
||||
// 清理块级元素前后的 <br> 标签
|
||||
// Clean <br> tags around block-level elements
|
||||
html = html.replace(/<br>\s*(<ul|<ol|<blockquote)/g, '$1')
|
||||
html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>)\s*<br>/g, '$1')
|
||||
// 清理 <p><br> 紧跟块级元素的情况（多余空行导致）
|
||||
// Clean <p><br> immediately following block-level elements (caused by extra blank lines)
|
||||
html = html.replace(/<p class="md-p">(<br>\s*)+(<ul|<ol|<blockquote|<pre|<hr)/g, '$2')
|
||||
// 清理连续的 <br> 标签
|
||||
// Clean consecutive <br> tags
|
||||
html = html.replace(/(<br>\s*){2,}/g, '<br>')
|
||||
// 清理块级元素后紧跟的段落开始标签前的 <br>
|
||||
// Clean <br> before paragraph start tag following block-level element
|
||||
html = html.replace(/(<\/ol>|<\/ul>|<\/blockquote>)<br>(<p|<div)/g, '$1$2')
|
||||
|
||||
// 修复非连续有序列表的编号：当单项 <ol> 被段落内容隔开时，保持编号递增
|
||||
// Fix non-consecutive ordered list numbering: when single-item <ol> is separated by paragraph content, keep numbering incrementing
|
||||
const tokens = html.split(/(<ol class="md-ol">(?:<li class="md-oli"[^>]*>[\s\S]*?<\/li>)+<\/ol>)/g)
|
||||
let olCounter = 0
|
||||
let inSequence = false
|
||||
|
|
@ -2011,9 +2011,9 @@ const getActionLabel = (action) => {
|
|||
}
|
||||
|
||||
const getLogLevelClass = (log) => {
|
||||
if (log.includes('ERROR') || log.includes('错误')) return 'error'
|
||||
if (log.includes('WARNING') || log.includes('警告')) return 'warning'
|
||||
// INFO 使用默认颜色，不标记为 success
|
||||
if (log.includes('ERROR') || log.includes('Error')) return 'error'
|
||||
if (log.includes('WARNING') || log.includes('Warning')) return 'warning'
|
||||
// INFO uses default color, not marked as success
|
||||
return ''
|
||||
}
|
||||
|
||||
|
|
@ -2042,11 +2042,11 @@ const fetchAgentLog = async () => {
|
|||
currentSectionIndex.value = log.section_index
|
||||
}
|
||||
|
||||
// section_complete - 章节生成完成
|
||||
// section_complete - section generation complete
|
||||
if (log.action === 'section_complete') {
|
||||
if (log.details?.content) {
|
||||
generatedSections.value[log.section_index] = log.details.content
|
||||
// 自动展开刚生成的章节
|
||||
// Auto-expand newly generated section
|
||||
expandedContent.value.add(log.section_index - 1)
|
||||
currentSectionIndex.value = null
|
||||
}
|
||||
|
|
@ -2054,10 +2054,10 @@ const fetchAgentLog = async () => {
|
|||
|
||||
if (log.action === 'report_complete') {
|
||||
isComplete.value = true
|
||||
currentSectionIndex.value = null // 确保清除 loading 状态
|
||||
currentSectionIndex.value = null // Ensure loading state is cleared
|
||||
emit('update-status', 'completed')
|
||||
stopPolling()
|
||||
// 滚动逻辑统一在循环结束后的 nextTick 中处理
|
||||
// Scroll logic handled uniformly in nextTick after loop ends
|
||||
}
|
||||
|
||||
if (log.action === 'report_start') {
|
||||
|
|
@ -2069,7 +2069,7 @@ const fetchAgentLog = async () => {
|
|||
|
||||
nextTick(() => {
|
||||
if (rightPanel.value) {
|
||||
// 如果任务已完成，滚动到顶部；否则滚动到底部跟随最新日志
|
||||
// If task completed, scroll to top; otherwise scroll to bottom to follow latest logs
|
||||
if (isComplete.value) {
|
||||
rightPanel.value.scrollTop = 0
|
||||
} else {
|
||||
|
|
@ -2084,39 +2084,39 @@ const fetchAgentLog = async () => {
|
|||
}
|
||||
}
|
||||
|
||||
// 提取最终答案内容 - 从 LLM response 中提取章节内容
|
||||
// Extract final answer content - extract section content from LLM response
|
||||
const extractFinalContent = (response) => {
|
||||
if (!response) return null
|
||||
|
||||
// 尝试提取 <final_answer> 标签内的内容
|
||||
// Try extracting content inside <final_answer> tags
|
||||
const finalAnswerTagMatch = response.match(/<final_answer>([\s\S]*?)<\/final_answer>/)
|
||||
if (finalAnswerTagMatch) {
|
||||
return finalAnswerTagMatch[1].trim()
|
||||
}
|
||||
|
||||
// 尝试找 Final Answer: 后面的内容（支持多种格式）
|
||||
// 格式1: Final Answer:\n\n内容
|
||||
// 格式2: Final Answer: 内容
|
||||
// Try finding content after Final Answer: (supports multiple formats)
|
||||
// Format 1: Final Answer:\n\ncontent
|
||||
// Format 2: Final Answer: content
|
||||
const finalAnswerMatch = response.match(/Final\s*Answer:\s*\n*([\s\S]*)$/i)
|
||||
if (finalAnswerMatch) {
|
||||
return finalAnswerMatch[1].trim()
|
||||
}
|
||||
|
||||
// 尝试找 最终答案: 后面的内容
|
||||
const chineseFinalMatch = response.match(/最终答案[:：]\s*\n*([\s\S]*)$/i)
|
||||
// Try finding content after Final Answer: (Chinese)
|
||||
const chineseFinalMatch = response.match(/Final Answer[:：]\s*\n*([\s\S]*)$/i)
|
||||
if (chineseFinalMatch) {
|
||||
return chineseFinalMatch[1].trim()
|
||||
}
|
||||
|
||||
// 如果以 ## 或 # 或 > 开头，可能是直接的 markdown 内容
|
||||
// If starts with ## or # or >, may be direct markdown content
|
||||
const trimmedResponse = response.trim()
|
||||
if (trimmedResponse.match(/^[#>]/)) {
|
||||
return trimmedResponse
|
||||
}
|
||||
|
||||
// 如果内容较长且包含markdown格式，尝试移除思考过程后返回
|
||||
// If content is long and contains markdown, try removing thinking process and return
|
||||
if (response.length > 300 && (response.includes('**') || response.includes('>'))) {
|
||||
// 移除 Thought: 开头的思考过程
|
||||
// Remove thinking process starting with Thought:
|
||||
const thoughtMatch = response.match(/^Thought:[\s\S]*?(?=\n\n[^T]|\n\n$)/i)
|
||||
if (thoughtMatch) {
|
||||
const afterThought = response.substring(thoughtMatch[0].length).trim()
|
||||
|
|
@ -2461,7 +2461,7 @@ watch(() => props.reportId, (newId) => {
|
|||
.section-number {
|
||||
font-family: 'JetBrains Mono', monospace;
|
||||
font-size: 16px;
|
||||
color: #9CA3AF; /* 深灰色，不随状态变化 */
|
||||
color: #9CA3AF; /* Dark gray, doesn't change with status */
|
||||
font-weight: 500;
|
||||
}
|
||||
|
||||
|
|
@ -3903,7 +3903,7 @@ watch(() => props.reportId, (newId) => {
|
|||
overflow: hidden;
|
||||
}
|
||||
|
||||
/* Selection Reason - 选择理由 */
|
||||
/* Selection Reason */
|
||||
:deep(.interview-display .selection-reason) {
|
||||
background: #F8FAFC;
|
||||
border: 1px solid #E2E8F0;
|
||||
|
|
@ -5102,7 +5102,7 @@ watch(() => props.reportId, (newId) => {
|
|||
border-radius: 4px;
|
||||
}
|
||||
|
||||
/* Console Logs - 与 Step3Simulation.vue 保持一致 */
|
||||
/* Console Logs - consistent with Step3Simulation.vue */
|
||||
.console-logs {
|
||||
background: #000;
|
||||
color: #DDD;
|
||||
|
|
|
|||
|
|
@ -437,7 +437,7 @@ const showToolsDetail = ref(true)
|
|||
// Chat State
|
||||
const chatInput = ref('')
|
||||
const chatHistory = ref([])
|
||||
const chatHistoryCache = ref({}) // 缓存所有对话记录: { 'report_agent': [], 'agent_0': [], 'agent_1': [], ... }
|
||||
const chatHistoryCache = ref({}) // Cache all chat records: { 'report_agent': [], 'agent_0': [], 'agent_1': [], ... }
|
||||
const isSending = ref(false)
|
||||
const chatMessages = ref(null)
|
||||
const chatInputRef = ref(null)
|
||||
|
|
@ -487,7 +487,7 @@ const selectChatTarget = (target) => {
|
|||
}
|
||||
}
|
||||
|
||||
// 保存当前对话记录到缓存
|
||||
// Save current chat records to cache
|
||||
const saveChatHistory = () => {
|
||||
if (chatHistory.value.length === 0) return
|
||||
|
||||
|
|
@ -499,7 +499,7 @@ const saveChatHistory = () => {
|
|||
}
|
||||
|
||||
const selectReportAgentChat = () => {
|
||||
// 保存当前对话记录
|
||||
// Save current chat records
|
||||
saveChatHistory()
|
||||
|
||||
activeTab.value = 'chat'
|
||||
|
|
@ -508,7 +508,7 @@ const selectReportAgentChat = () => {
|
|||
selectedAgentIndex.value = null
|
||||
showAgentDropdown.value = false
|
||||
|
||||
// 恢复 Report Agent 的对话记录
|
||||
// Restore Report Agent chat records
|
||||
chatHistory.value = chatHistoryCache.value['report_agent'] || []
|
||||
}
|
||||
|
||||
|
|
@ -528,7 +528,7 @@ const toggleAgentDropdown = () => {
|
|||
}
|
||||
|
||||
const selectAgent = (agent, idx) => {
|
||||
// 保存当前对话记录
|
||||
// Save current chat records
|
||||
saveChatHistory()
|
||||
|
||||
selectedAgent.value = agent
|
||||
|
|
@ -536,7 +536,7 @@ const selectAgent = (agent, idx) => {
|
|||
chatTarget.value = 'agent'
|
||||
showAgentDropdown.value = false
|
||||
|
||||
// 恢复该 Agent 的对话记录
|
||||
// Restore this Agent's chat records
|
||||
chatHistory.value = chatHistoryCache.value[`agent_${idx}`] || []
|
||||
addLog(t('log.selectChatTarget', { name: agent.username }))
|
||||
}
|
||||
|
|
@ -566,7 +566,7 @@ const renderMarkdown = (content) => {
|
|||
html = html.replace(/^# (.+)$/gm, '<h2 class="md-h2">$1</h2>')
|
||||
html = html.replace(/^> (.+)$/gm, '<blockquote class="md-quote">$1</blockquote>')
|
||||
|
||||
// 处理列表 - 支持子列表
|
||||
// Process lists - support sub-lists
|
||||
html = html.replace(/^(\s*)- (.+)$/gm, (match, indent, text) => {
|
||||
const level = Math.floor(indent.length / 2)
|
||||
return `<li class="md-li" data-level="${level}">${text}</li>`
|
||||
|
|
@ -576,17 +576,17 @@ const renderMarkdown = (content) => {
|
|||
return `<li class="md-oli" data-level="${level}">${text}</li>`
|
||||
})
|
||||
|
||||
// 包装无序列表
|
||||
// Wrap unordered list
|
||||
html = html.replace(/(<li class="md-li"[^>]*>.*?<\/li>\s*)+/g, '<ul class="md-ul">$&</ul>')
|
||||
// 包装有序列表
|
||||
// Wrap ordered list
|
||||
html = html.replace(/(<li class="md-oli"[^>]*>.*?<\/li>\s*)+/g, '<ol class="md-ol">$&</ol>')
|
||||
|
||||
// 清理列表项之间的所有空白
|
||||
// Clean all whitespace between list items
|
||||
html = html.replace(/<\/li>\s+<li/g, '</li><li')
|
||||
// 清理列表开始标签后的空白
|
||||
// Clean whitespace after list start tag
|
||||
html = html.replace(/<ul class="md-ul">\s+/g, '<ul class="md-ul">')
|
||||
html = html.replace(/<ol class="md-ol">\s+/g, '<ol class="md-ol">')
|
||||
// 清理列表结束标签前的空白
|
||||
// Clean whitespace before list end tag
|
||||
html = html.replace(/\s+<\/ul>/g, '</ul>')
|
||||
html = html.replace(/\s+<\/ol>/g, '</ol>')
|
||||
|
||||
|
|
@ -602,17 +602,17 @@ const renderMarkdown = (content) => {
|
|||
html = html.replace(/(<\/h[2-5]>)<\/p>/g, '$1')
|
||||
html = html.replace(/<p class="md-p">(<ul|<ol|<blockquote|<pre|<hr)/g, '$1')
|
||||
html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>|<\/pre>)<\/p>/g, '$1')
|
||||
// 清理块级元素前后的 <br> 标签
|
||||
// Clean <br> tags around block-level elements
|
||||
html = html.replace(/<br>\s*(<ul|<ol|<blockquote)/g, '$1')
|
||||
html = html.replace(/(<\/ul>|<\/ol>|<\/blockquote>)\s*<br>/g, '$1')
|
||||
// 清理 <p><br> 紧跟块级元素的情况（多余空行导致）
|
||||
// Clean <p><br> immediately following block-level elements (caused by extra blank lines)
|
||||
html = html.replace(/<p class="md-p">(<br>\s*)+(<ul|<ol|<blockquote|<pre|<hr)/g, '$2')
|
||||
// 清理连续的 <br> 标签
|
||||
// Clean consecutive <br> tags
|
||||
html = html.replace(/(<br>\s*){2,}/g, '<br>')
|
||||
// 清理块级元素后紧跟的段落开始标签前的 <br>
|
||||
// Clean <br> before paragraph start tag following block-level element
|
||||
html = html.replace(/(<\/ol>|<\/ul>|<\/blockquote>)<br>(<p|<div)/g, '$1$2')
|
||||
|
||||
// 修复非连续有序列表的编号：当单项 <ol> 被段落内容隔开时，保持编号递增
|
||||
// Fix non-consecutive ordered list numbering: when single-item <ol> is separated by paragraph content, keep numbering incrementing
|
||||
const tokens = html.split(/(<ol class="md-ol">(?:<li class="md-oli"[^>]*>[\s\S]*?<\/li>)+<\/ol>)/g)
|
||||
let olCounter = 0
|
||||
let inSequence = false
|
||||
|
|
@ -674,7 +674,7 @@ const sendMessage = async () => {
|
|||
} finally {
|
||||
isSending.value = false
|
||||
scrollToBottom()
|
||||
// 自动保存对话记录到缓存
|
||||
// Auto-save chat records to cache
|
||||
saveChatHistory()
|
||||
}
|
||||
}
|
||||
|
|
@ -722,9 +722,9 @@ const sendToAgent = async (message) => {
|
|||
const historyContext = chatHistory.value
|
||||
.slice(0, -1)
|
||||
.slice(-6)
|
||||
.map(msg => `${msg.role === 'user' ? '提问者' : '你'}：${msg.content}`)
|
||||
.map(msg => `${msg.role === 'user' ? 'Questioner' : 'You'}: ${msg.content}`)
|
||||
.join('\n')
|
||||
prompt = `以下是我们之前的对话：\n${historyContext}\n\n现在我的新问题是：${message}`
|
||||
prompt = `The following is our previous conversation:\n${historyContext}\n\nMy new question now is: ${message}`
|
||||
}
|
||||
|
||||
const res = await interviewAgents({
|
||||
|
|
@ -736,17 +736,17 @@ const sendToAgent = async (message) => {
|
|||
})
|
||||
|
||||
if (res.success && res.data) {
|
||||
// 正确的数据路径: res.data.result.results 是一个对象字典
|
||||
// 格式: {"twitter_0": {...}, "reddit_0": {...}} 或单平台 {"reddit_0": {...}}
|
||||
// Correct data path: res.data.result.results is an object dictionary
|
||||
// Format: {"twitter_0": {...}, "reddit_0": {...}} or single platform {"reddit_0": {...}}
|
||||
const resultData = res.data.result || res.data
|
||||
const resultsDict = resultData.results || resultData
|
||||
|
||||
// 将对象字典转换为数组，优先获取 reddit 平台的回复
|
||||
// Convert object dictionary to array, prefer reddit platform responses
|
||||
let responseContent = null
|
||||
const agentId = selectedAgentIndex.value
|
||||
|
||||
if (typeof resultsDict === 'object' && !Array.isArray(resultsDict)) {
|
||||
// 优先使用 reddit 平台回复，其次 twitter
|
||||
// Prefer reddit platform response, then twitter
|
||||
const redditKey = `reddit_${agentId}`
|
||||
const twitterKey = `twitter_${agentId}`
|
||||
const agentResult = resultsDict[redditKey] || resultsDict[twitterKey] || Object.values(resultsDict)[0]
|
||||
|
|
@ -754,7 +754,7 @@ const sendToAgent = async (message) => {
|
|||
responseContent = agentResult.response || agentResult.answer
|
||||
}
|
||||
} else if (Array.isArray(resultsDict) && resultsDict.length > 0) {
|
||||
// 兼容数组格式
|
||||
// Compatible with array format
|
||||
responseContent = resultsDict[0].response || resultsDict[0].answer
|
||||
}
|
||||
|
||||
|
|
@ -820,19 +820,19 @@ const submitSurvey = async () => {
|
|||
})
|
||||
|
||||
if (res.success && res.data) {
|
||||
// 正确的数据路径: res.data.result.results 是一个对象字典
|
||||
// 格式: {"twitter_0": {...}, "reddit_0": {...}, "twitter_1": {...}, ...}
|
||||
// Correct data path: res.data.result.results is an object dictionary
|
||||
// Format: {"twitter_0": {...}, "reddit_0": {...}, "twitter_1": {...}, ...}
|
||||
const resultData = res.data.result || res.data
|
||||
const resultsDict = resultData.results || resultData
|
||||
|
||||
// 将对象字典转换为数组格式
|
||||
// Convert object dictionary to array format
|
||||
const surveyResultsList = []
|
||||
|
||||
for (const interview of interviews) {
|
||||
const agentIdx = interview.agent_id
|
||||
const agent = profiles.value[agentIdx]
|
||||
|
||||
// 优先使用 reddit 平台回复，其次 twitter
|
||||
// Prefer reddit platform response, then twitter
|
||||
let responseContent = t('step5.noResponse')
|
||||
|
||||
if (typeof resultsDict === 'object' && !Array.isArray(resultsDict)) {
|
||||
|
|
@ -843,7 +843,7 @@ const submitSurvey = async () => {
|
|||
responseContent = agentResult.response || agentResult.answer || t('step5.noResponse')
|
||||
}
|
||||
} else if (Array.isArray(resultsDict)) {
|
||||
// 兼容数组格式
|
||||
// Compatible with array format
|
||||
const matchedResult = resultsDict.find(r => r.agent_id === agentIdx)
|
||||
if (matchedResult) {
|
||||
responseContent = matchedResult.response || matchedResult.answer || t('step5.noResponse')
|
||||
|
|
@ -983,7 +983,7 @@ watch(() => props.simulationId, (newId) => {
|
|||
overflow: hidden;
|
||||
}
|
||||
|
||||
/* Left Panel - Report Style (与 Step4Report.vue 完全一致) */
|
||||
/* Left Panel - Report Style (identical to Step4Report.vue) */
|
||||
.left-panel.report-style {
|
||||
width: 45%;
|
||||
min-width: 450px;
|
||||
|
|
@ -2031,7 +2031,7 @@ watch(() => props.simulationId, (newId) => {
|
|||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
/* 修复有序列表编号 - 使用 CSS 计数器让多个 ol 连续编号 */
|
||||
/* Fix ordered list numbering - use CSS counters for consecutive numbering across multiple ol */
|
||||
.message-text {
|
||||
counter-reset: list-counter;
|
||||
}
|
||||
|
|
@ -2057,7 +2057,7 @@ watch(() => props.simulationId, (newId) => {
|
|||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
/* 无序列表样式 */
|
||||
/* Unordered list styles */
|
||||
.message-text :deep(.md-ul) {
|
||||
padding-left: 20px;
|
||||
margin: 8px 0;
|
||||
|
|
@ -2536,7 +2536,7 @@ watch(() => props.simulationId, (newId) => {
|
|||
margin: 6px 0;
|
||||
}
|
||||
|
||||
/* 聊天/问卷区域的引用样式 */
|
||||
/* Chat/Survey area quote styles */
|
||||
.chat-messages :deep(.md-quote),
|
||||
.result-answer :deep(.md-quote) {
|
||||
margin: 12px 0;
|
||||
|
|
|
|||
|
|
@ -14,12 +14,12 @@ for (const path in localeFiles) {
|
|||
}
|
||||
}
|
||||
|
||||
const savedLocale = localStorage.getItem('locale') || 'zh'
|
||||
const savedLocale = localStorage.getItem('locale') || 'en'
|
||||
|
||||
const i18n = createI18n({
|
||||
legacy: false,
|
||||
locale: savedLocale,
|
||||
fallbackLocale: 'zh',
|
||||
fallbackLocale: 'en',
|
||||
messages
|
||||
})
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
/**
|
||||
* 临时存储待上传的文件和需求
|
||||
* 用于首页点击启动引擎后立即跳转，在Process页面再进行API调用
|
||||
* Temporarily store pending upload files and requirements
|
||||
* Used to jump immediately after clicking start engine on home page, then make API calls on Process page
|
||||
*/
|
||||
import { reactive } from 'vue'
|
||||
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -49,7 +49,7 @@
|
|||
/>
|
||||
</div>
|
||||
|
||||
<!-- Right Panel: Step5 深度互动 -->
|
||||
<!-- Right Panel: Step5 Deep Interaction -->
|
||||
<div class="panel-wrapper right" :style="rightPanelStyle">
|
||||
<Step5Interaction
|
||||
:reportId="currentReportId"
|
||||
|
|
@ -83,7 +83,7 @@ const props = defineProps({
|
|||
reportId: String
|
||||
})
|
||||
|
||||
// Layout State - 默认切换到工作台视角
|
||||
// Layout State - default to workbench view
|
||||
const viewMode = ref('workbench')
|
||||
|
||||
// Data State
|
||||
|
|
@ -147,26 +147,26 @@ const loadReportData = async () => {
|
|||
try {
|
||||
addLog(t('log.loadReportData', { id: currentReportId.value }))
|
||||
|
||||
// 获取 report 信息以获取 simulation_id
|
||||
// Get report info to obtain simulation_id
|
||||
const reportRes = await getReport(currentReportId.value)
|
||||
if (reportRes.success && reportRes.data) {
|
||||
const reportData = reportRes.data
|
||||
simulationId.value = reportData.simulation_id
|
||||
|
||||
if (simulationId.value) {
|
||||
// 获取 simulation 信息
|
||||
// Get simulation info
|
||||
const simRes = await getSimulation(simulationId.value)
|
||||
if (simRes.success && simRes.data) {
|
||||
const simData = simRes.data
|
||||
|
||||
// 获取 project 信息
|
||||
// Get project info
|
||||
if (simData.project_id) {
|
||||
const projRes = await getProject(simData.project_id)
|
||||
if (projRes.success && projRes.data) {
|
||||
projectData.value = projRes.data
|
||||
addLog(t('log.projectLoadSuccess', { id: projRes.data.project_id }))
|
||||
|
||||
// 获取 graph 数据
|
||||
// Get graph data
|
||||
if (projRes.data.graph_id) {
|
||||
await loadGraph(projRes.data.graph_id)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@
|
|||
|
||||
<!-- Right Panel: Step Components -->
|
||||
<div class="panel-wrapper right" :style="rightPanelStyle">
|
||||
<!-- Step 1: 图谱构建 -->
|
||||
<!-- Step 1: Graph Build -->
|
||||
<Step1GraphBuild
|
||||
v-if="currentStep === 1"
|
||||
:currentPhase="currentPhase"
|
||||
|
|
@ -61,7 +61,7 @@
|
|||
:systemLogs="systemLogs"
|
||||
@next-step="handleNextStep"
|
||||
/>
|
||||
<!-- Step 2: 环境搭建 -->
|
||||
<!-- Step 2: Environment Setup -->
|
||||
<Step2EnvSetup
|
||||
v-else-if="currentStep === 2"
|
||||
:projectData="projectData"
|
||||
|
|
@ -95,7 +95,7 @@ const { t, tm } = useI18n()
|
|||
const viewMode = ref('split') // graph | split | workbench
|
||||
|
||||
// Step State
|
||||
const currentStep = ref(1) // 1: 图谱构建, 2: 环境搭建, 3: 开始模拟, 4: 报告生成, 5: 深度互动
|
||||
const currentStep = ref(1) // 1: Graph Build, 2: Env Setup, 3: Run Simulation, 4: Report Generation, 5: Deep Interaction
|
||||
const stepNames = computed(() => tm('main.stepNames'))
|
||||
|
||||
// Data State
|
||||
|
|
@ -166,7 +166,7 @@ const handleNextStep = (params = {}) => {
|
|||
currentStep.value++
|
||||
addLog(t('log.enterStep', { step: currentStep.value, name: stepNames.value[currentStep.value - 1] }))
|
||||
|
||||
// 如果是从 Step 2 进入 Step 3，记录模拟轮数配置
|
||||
// If entering Step 3 from Step 2, record simulation rounds config
|
||||
if (currentStep.value === 3 && params.maxRounds) {
|
||||
addLog(t('log.customSimRounds', { rounds: params.maxRounds }))
|
||||
}
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -49,7 +49,7 @@
|
|||
/>
|
||||
</div>
|
||||
|
||||
<!-- Right Panel: Step4 报告生成 -->
|
||||
<!-- Right Panel: Step4 Report Generation -->
|
||||
<div class="panel-wrapper right" :style="rightPanelStyle">
|
||||
<Step4Report
|
||||
:reportId="currentReportId"
|
||||
|
|
@ -83,7 +83,7 @@ const props = defineProps({
|
|||
reportId: String
|
||||
})
|
||||
|
||||
// Layout State - 默认切换到工作台视角
|
||||
// Layout State - default to workbench view
|
||||
const viewMode = ref('workbench')
|
||||
|
||||
// Data State
|
||||
|
|
@ -146,26 +146,26 @@ const loadReportData = async () => {
|
|||
try {
|
||||
addLog(t('log.loadReportData', { id: currentReportId.value }))
|
||||
|
||||
// 获取 report 信息以获取 simulation_id
|
||||
// Get report info to obtain simulation_id
|
||||
const reportRes = await getReport(currentReportId.value)
|
||||
if (reportRes.success && reportRes.data) {
|
||||
const reportData = reportRes.data
|
||||
simulationId.value = reportData.simulation_id
|
||||
|
||||
if (simulationId.value) {
|
||||
// 获取 simulation 信息
|
||||
// Get simulation info
|
||||
const simRes = await getSimulation(simulationId.value)
|
||||
if (simRes.success && simRes.data) {
|
||||
const simData = simRes.data
|
||||
|
||||
// 获取 project 信息
|
||||
// Get project info
|
||||
if (simData.project_id) {
|
||||
const projRes = await getProject(simData.project_id)
|
||||
if (projRes.success && projRes.data) {
|
||||
projectData.value = projRes.data
|
||||
addLog(t('log.projectLoadSuccess', { id: projRes.data.project_id }))
|
||||
|
||||
// 获取 graph 数据
|
||||
// Get graph data
|
||||
if (projRes.data.graph_id) {
|
||||
await loadGraph(projRes.data.graph_id)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -49,7 +49,7 @@
|
|||
/>
|
||||
</div>
|
||||
|
||||
<!-- Right Panel: Step3 开始模拟 -->
|
||||
<!-- Right Panel: Step3 Run Simulation -->
|
||||
<div class="panel-wrapper right" :style="rightPanelStyle">
|
||||
<Step3Simulation
|
||||
:simulationId="currentSimulationId"
|
||||
|
|
@ -92,9 +92,9 @@ const viewMode = ref('split')
|
|||
|
||||
// Data State
|
||||
const currentSimulationId = ref(route.params.simulationId)
|
||||
// 直接在初始化时从 query 参数获取 maxRounds，确保子组件能立即获取到值
|
||||
// Get maxRounds from query params at init, ensuring child component gets value immediately
|
||||
const maxRounds = ref(route.query.maxRounds ? parseInt(route.query.maxRounds) : null)
|
||||
const minutesPerRound = ref(30) // 默认每轮30分钟
|
||||
const minutesPerRound = ref(30) // Default: 30 minutes per round
|
||||
const projectData = ref(null)
|
||||
const graphData = ref(null)
|
||||
const graphLoading = ref(false)
|
||||
|
|
@ -150,14 +150,14 @@ const toggleMaximize = (target) => {
|
|||
}
|
||||
|
||||
const handleGoBack = async () => {
|
||||
// 在返回 Step 2 之前，先关闭正在运行的模拟
|
||||
// Before returning to Step 2, close running simulation first
|
||||
addLog(t('log.preparingGoBack'))
|
||||
|
||||
// 停止轮询
|
||||
// Stop polling
|
||||
stopGraphRefresh()
|
||||
|
||||
try {
|
||||
// 先尝试优雅关闭模拟环境
|
||||
// Try graceful close of simulation environment first
|
||||
const envStatusRes = await getEnvStatus({ simulation_id: currentSimulationId.value })
|
||||
|
||||
if (envStatusRes.success && envStatusRes.data?.env_alive) {
|
||||
|
|
@ -178,7 +178,7 @@ const handleGoBack = async () => {
|
|||
}
|
||||
}
|
||||
} else {
|
||||
// 环境未运行，检查是否需要停止进程
|
||||
// Environment not running, check if process needs stopping
|
||||
if (isSimulating.value) {
|
||||
addLog(t('log.stoppingSimProcess'))
|
||||
try {
|
||||
|
|
@ -193,13 +193,13 @@ const handleGoBack = async () => {
|
|||
addLog(t('log.checkStatusFailed', { error: err.message }))
|
||||
}
|
||||
|
||||
// 返回到 Step 2 (环境搭建)
|
||||
// Return to Step 2 (Environment Setup)
|
||||
router.push({ name: 'Simulation', params: { simulationId: currentSimulationId.value } })
|
||||
}
|
||||
|
||||
const handleNextStep = () => {
|
||||
// Step3Simulation 组件会直接处理报告生成和路由跳转
|
||||
// 这个方法仅作为备用
|
||||
// Step3Simulation component handles report generation and routing directly
|
||||
// This method is only a fallback
|
||||
addLog(t('log.enterStep4'))
|
||||
}
|
||||
|
||||
|
|
@ -208,12 +208,12 @@ const loadSimulationData = async () => {
|
|||
try {
|
||||
addLog(t('log.loadingSimData', { id: currentSimulationId.value }))
|
||||
|
||||
// 获取 simulation 信息
|
||||
// Get simulation info
|
||||
const simRes = await getSimulation(currentSimulationId.value)
|
||||
if (simRes.success && simRes.data) {
|
||||
const simData = simRes.data
|
||||
|
||||
// 获取 simulation config 以获取 minutes_per_round
|
||||
// Get simulation config to obtain minutes_per_round
|
||||
try {
|
||||
const configRes = await getSimulationConfig(currentSimulationId.value)
|
||||
if (configRes.success && configRes.data?.time_config?.minutes_per_round) {
|
||||
|
|
@ -224,14 +224,14 @@ const loadSimulationData = async () => {
|
|||
addLog(t('log.timeConfigFetchFailed', { minutes: minutesPerRound.value }))
|
||||
}
|
||||
|
||||
// 获取 project 信息
|
||||
// Get project info
|
||||
if (simData.project_id) {
|
||||
const projRes = await getProject(simData.project_id)
|
||||
if (projRes.success && projRes.data) {
|
||||
projectData.value = projRes.data
|
||||
addLog(t('log.projectLoadSuccess', { id: projRes.data.project_id }))
|
||||
|
||||
// 获取 graph 数据
|
||||
// Get graph data
|
||||
if (projRes.data.graph_id) {
|
||||
await loadGraph(projRes.data.graph_id)
|
||||
}
|
||||
|
|
@ -246,8 +246,8 @@ const loadSimulationData = async () => {
|
|||
}
|
||||
|
||||
const loadGraph = async (graphId) => {
|
||||
// 当正在模拟时，自动刷新不显示全屏 loading，以免闪烁
|
||||
// 手动刷新或初始加载时显示 loading
|
||||
// When simulation is running, auto-refresh doesn't show fullscreen loading to avoid flicker
|
||||
// Show loading on manual refresh or initial load
|
||||
if (!isSimulating.value) {
|
||||
graphLoading.value = true
|
||||
}
|
||||
|
|
@ -279,7 +279,7 @@ let graphRefreshTimer = null
|
|||
const startGraphRefresh = () => {
|
||||
if (graphRefreshTimer) return
|
||||
addLog(t('log.graphRealtimeRefreshStart'))
|
||||
// 立即刷新一次，然后每30秒刷新
|
||||
// Refresh immediately once, then every 30 seconds
|
||||
graphRefreshTimer = setInterval(refreshGraph, 30000)
|
||||
}
|
||||
|
||||
|
|
@ -302,7 +302,7 @@ watch(isSimulating, (newValue) => {
|
|||
onMounted(() => {
|
||||
addLog(t('log.simRunViewInit'))
|
||||
|
||||
// 记录 maxRounds 配置（值已在初始化时从 query 参数获取）
|
||||
// Record maxRounds config (value already obtained from query params at init)
|
||||
if (maxRounds.value) {
|
||||
addLog(t('log.customRounds', { rounds: maxRounds.value }))
|
||||
}
|
||||
|
|
|
|||
|
|
@ -48,7 +48,7 @@
|
|||
/>
|
||||
</div>
|
||||
|
||||
<!-- Right Panel: Step2 环境搭建 -->
|
||||
<!-- Right Panel: Step2 Environment Setup -->
|
||||
<div class="panel-wrapper right" :style="rightPanelStyle">
|
||||
<Step2EnvSetup
|
||||
:simulationId="currentSimulationId"
|
||||
|
|
@ -142,7 +142,7 @@ const toggleMaximize = (target) => {
|
|||
}
|
||||
|
||||
const handleGoBack = () => {
|
||||
// 返回到 process 页面
|
||||
// Return to process page
|
||||
if (projectData.value?.project_id) {
|
||||
router.push({ name: 'Process', params: { projectId: projectData.value.project_id } })
|
||||
} else {
|
||||
|
|
@ -153,65 +153,65 @@ const handleGoBack = () => {
|
|||
const handleNextStep = (params = {}) => {
|
||||
addLog(t('log.enterStep3'))
|
||||
|
||||
// 记录模拟轮数配置
|
||||
// Record simulation rounds config
|
||||
if (params.maxRounds) {
|
||||
addLog(t('log.customRoundsConfig', { rounds: params.maxRounds }))
|
||||
} else {
|
||||
addLog(t('log.useAutoRounds'))
|
||||
}
|
||||
|
||||
// 构建路由参数
|
||||
// Build route params
|
||||
const routeParams = {
|
||||
name: 'SimulationRun',
|
||||
params: { simulationId: currentSimulationId.value }
|
||||
}
|
||||
|
||||
// 如果有自定义轮数，通过 query 参数传递
|
||||
// If custom rounds, pass via query params
|
||||
if (params.maxRounds) {
|
||||
routeParams.query = { maxRounds: params.maxRounds }
|
||||
}
|
||||
|
||||
// 跳转到 Step 3 页面
|
||||
// Navigate to Step 3 page
|
||||
router.push(routeParams)
|
||||
}
|
||||
|
||||
// --- Data Logic ---
|
||||
|
||||
/**
|
||||
* 检查并关闭正在运行的模拟
|
||||
* 当用户从 Step 3 返回到 Step 2 时，默认用户要退出模拟
|
||||
* Check and close running simulation
|
||||
* When user returns from Step 3 to Step 2, assume user wants to exit simulation
|
||||
*/
|
||||
const checkAndStopRunningSimulation = async () => {
|
||||
if (!currentSimulationId.value) return
|
||||
|
||||
try {
|
||||
// 先检查模拟环境是否存活
|
||||
// First check if simulation environment is alive
|
||||
const envStatusRes = await getEnvStatus({ simulation_id: currentSimulationId.value })
|
||||
|
||||
if (envStatusRes.success && envStatusRes.data?.env_alive) {
|
||||
addLog(t('log.detectedSimEnvRunning'))
|
||||
|
||||
// 尝试优雅关闭模拟环境
|
||||
// Try graceful close of simulation environment
|
||||
try {
|
||||
const closeRes = await closeSimulationEnv({
|
||||
simulation_id: currentSimulationId.value,
|
||||
timeout: 10 // 10秒超时
|
||||
timeout: 10 // 10 second timeout
|
||||
})
|
||||
|
||||
if (closeRes.success) {
|
||||
addLog(t('log.simEnvClosed'))
|
||||
} else {
|
||||
addLog(t('log.closeSimEnvFailedWithError', { error: closeRes.error || t('common.unknownError') }))
|
||||
// 如果优雅关闭失败，尝试强制停止
|
||||
// If graceful close fails, try force stop
|
||||
await forceStopSimulation()
|
||||
}
|
||||
} catch (closeErr) {
|
||||
addLog(t('log.closeSimEnvException', { error: closeErr.message }))
|
||||
// 如果优雅关闭异常，尝试强制停止
|
||||
// If graceful close throws, try force stop
|
||||
await forceStopSimulation()
|
||||
}
|
||||
} else {
|
||||
// 环境未运行，但可能进程还在，检查模拟状态
|
||||
// Environment not running, but process may still exist, checking simulation status
|
||||
const simRes = await getSimulation(currentSimulationId.value)
|
||||
if (simRes.success && simRes.data?.status === 'running') {
|
||||
addLog(t('log.detectedSimRunning'))
|
||||
|
|
@ -219,13 +219,13 @@ const checkAndStopRunningSimulation = async () => {
|
|||
}
|
||||
}
|
||||
} catch (err) {
|
||||
// 检查环境状态失败不影响后续流程
|
||||
console.warn('检查模拟状态失败:', err)
|
||||
// Environment status check failure doesn't affect subsequent flow
|
||||
console.warn('Failed to check simulation status:', err)
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 强制停止模拟
|
||||
* Force stop simulation
|
||||
*/
|
||||
const forceStopSimulation = async () => {
|
||||
try {
|
||||
|
|
@ -244,19 +244,19 @@ const loadSimulationData = async () => {
|
|||
try {
|
||||
addLog(t('log.loadingSimData', { id: currentSimulationId.value }))
|
||||
|
||||
// 获取 simulation 信息
|
||||
// Get simulation info
|
||||
const simRes = await getSimulation(currentSimulationId.value)
|
||||
if (simRes.success && simRes.data) {
|
||||
const simData = simRes.data
|
||||
|
||||
// 获取 project 信息
|
||||
// Get project info
|
||||
if (simData.project_id) {
|
||||
const projRes = await getProject(simData.project_id)
|
||||
if (projRes.success && projRes.data) {
|
||||
projectData.value = projRes.data
|
||||
addLog(t('log.projectLoadSuccess', { id: projRes.data.project_id }))
|
||||
|
||||
// 获取 graph 数据
|
||||
// Get graph data
|
||||
if (projRes.data.graph_id) {
|
||||
await loadGraph(projRes.data.graph_id)
|
||||
}
|
||||
|
|
@ -294,10 +294,10 @@ const refreshGraph = () => {
|
|||
onMounted(async () => {
|
||||
addLog(t('log.simViewInit'))
|
||||
|
||||
// 检查并关闭正在运行的模拟（用户从 Step 3 返回时）
|
||||
// Check and close running simulation (when user returns from Step 3)
|
||||
await checkAndStopRunningSimulation()
|
||||
|
||||
// 加载模拟数据
|
||||
// Load simulation data
|
||||
loadSimulationData()
|
||||
})
|
||||
</script>
|
||||
|
|
|
|||
153
locales/en.json
153
locales/en.json
|
|
@ -33,43 +33,45 @@
|
|||
"visitGithub": "Visit our Github page"
|
||||
},
|
||||
"home": {
|
||||
"tagline": "Concise & Universal Swarm Intelligence Engine",
|
||||
"version": "/ v0.1-Preview",
|
||||
"heroTitle1": "Upload Reports,",
|
||||
"heroTitle2": "Predict the Future",
|
||||
"heroDesc": "From a single document, {brand} extracts reality seeds to auto-generate a parallel world with up to {agentScale}. Inject variables from a god's-eye view to find the {optimalSolution} in complex group dynamics.",
|
||||
"eyebrow": "Sentiment Intelligence Platform",
|
||||
"version": "v0.1 · Preview",
|
||||
"heroTitle1": "Know how the public",
|
||||
"heroTitle2": "will respond",
|
||||
"heroTitle3": "before you commit.",
|
||||
"heroDesc": "{brand} ingests policy briefs and regulatory documents, then runs multi-agent simulations across social platforms to forecast how real stakeholder groups react over time. Decision-makers see the {optimalSolution} before a single line is published.",
|
||||
"heroDescBrand": "MiroFish",
|
||||
"heroDescAgentScale": "million-scale Agents",
|
||||
"heroDescOptimalSolution": "\"local optimum\"",
|
||||
"slogan": "Let Agents rehearse the future, let decisions prevail",
|
||||
"systemStatus": "System Status",
|
||||
"systemReady": "Ready",
|
||||
"systemReadyDesc": "Prediction engine on standby. Upload unstructured data to initialize a simulation sequence.",
|
||||
"metricLowCost": "Low Cost",
|
||||
"metricLowCostDesc": "Avg. $5/sim",
|
||||
"metricHighAvail": "Scalable",
|
||||
"metricHighAvailDesc": "Millions of Agents",
|
||||
"workflowSequence": "Workflow",
|
||||
"step01Title": "Graph Build",
|
||||
"step01Desc": "Seed extraction & memory injection & GraphRAG construction",
|
||||
"step02Title": "Env Setup",
|
||||
"step02Desc": "Entity extraction & persona generation & Agent config injection",
|
||||
"step03Title": "Simulation",
|
||||
"step03Desc": "Dual-platform parallel sim & auto-parse requirements & temporal memory",
|
||||
"heroDescOptimalSolution": "sentiment trajectory",
|
||||
"waveformLabelNoise": "raw signal",
|
||||
"waveformLabelSignal": "extracted intelligence",
|
||||
"heroPrimaryCta": "Initiate Assessment",
|
||||
"heroSecondaryCta": "View Methodology",
|
||||
"methodologyLabel": "Methodology",
|
||||
"methodologyTitle": "Five-Phase Intelligence Cycle",
|
||||
"methodologyDesc": "Every assessment passes through the same disciplined sequence, from document ingestion to deep interrogation of the simulated population.",
|
||||
"step01Title": "Ingest",
|
||||
"step01Desc": "Seed extraction, memory injection, and GraphRAG construction from source documents",
|
||||
"step02Title": "Model",
|
||||
"step02Desc": "Entity extraction, persona generation, and agent configuration injection",
|
||||
"step03Title": "Simulate",
|
||||
"step03Desc": "Dual-platform parallel simulation with temporal memory and auto-parsed requirements",
|
||||
"step04Title": "Report",
|
||||
"step04Desc": "ReportAgent interacts with the post-simulation environment via rich tools",
|
||||
"step05Title": "Interaction",
|
||||
"step05Desc": "Chat with any simulated individual & converse with ReportAgent",
|
||||
"realitySeed": "01 / Reality Seed",
|
||||
"supportedFormats": "Formats: PDF, MD, TXT",
|
||||
"dragToUpload": "Drag files to upload",
|
||||
"orBrowse": "or click to browse files",
|
||||
"inputParams": "Input Parameters",
|
||||
"simulationPrompt": ">_ 02 / Simulation Prompt",
|
||||
"promptPlaceholder": "// Describe your simulation or prediction requirement in natural language",
|
||||
"engineBadge": "Engine: MiroFish-V1.0",
|
||||
"startEngine": "Start Engine",
|
||||
"initializing": "Initializing..."
|
||||
"step04Desc": "ReportAgent interacts with the post-simulation environment via a rich tool suite",
|
||||
"step05Title": "Interrogate",
|
||||
"step05Desc": "Chat directly with any simulated individual or converse with ReportAgent",
|
||||
"deployLabel": "Deploy",
|
||||
"deployTitle": "New Assessment",
|
||||
"deployDesc": "Upload source documents and describe what you need to predict. The engine handles the rest.",
|
||||
"realitySeed": "Source Documents",
|
||||
"supportedFormats": "PDF · MD · TXT",
|
||||
"dragToUpload": "Drag files here or click to browse",
|
||||
"orBrowse": "No files selected — PDF, MD, or TXT accepted",
|
||||
"simulationPrompt": "Assessment Brief",
|
||||
"promptPlaceholder": "Describe the scenario you need to predict. e.g. 'If the central bank announces a 50bp rate cut, how will retail banking customers respond on social media over the following 72 hours?'",
|
||||
"engineBadge": "MiroFish v1.0",
|
||||
"startEngine": "Initiate Sentiment Assessment",
|
||||
"initializing": "Initializing...",
|
||||
"fileCount": "{count} files attached",
|
||||
"removeFile": "Remove"
|
||||
},
|
||||
"main": {
|
||||
"layoutGraph": "Graph",
|
||||
|
|
@ -85,7 +87,7 @@
|
|||
"ontologyDesc": "LLM analyzes document content and simulation requirements, extracts reality seeds, and auto-generates a suitable ontology structure",
|
||||
"analyzingDocs": "Analyzing documents...",
|
||||
"graphRagBuild": "GraphRAG Build",
|
||||
"graphRagDesc": "Based on the generated ontology, documents are auto-chunked and sent to Zep to build a knowledge graph, extracting entities and relations, forming temporal memory and community summaries",
|
||||
"graphRagDesc": "Based on the generated ontology, documents are auto-chunked and sent to the local graph store to build a knowledge graph, extracting entities and relations, forming temporal memory and community summaries",
|
||||
"entityNodes": "Entity Nodes",
|
||||
"relationEdges": "Relation Edges",
|
||||
"schemaTypes": "Schema Types",
|
||||
|
|
@ -297,6 +299,30 @@
|
|||
"toggleMaximize": "Maximize/Restore",
|
||||
"closeHint": "Close hint"
|
||||
},
|
||||
"process": {
|
||||
"liveKnowledgeGraph": "Live Knowledge Graph",
|
||||
"nodes": "nodes",
|
||||
"relations": "relations",
|
||||
"refreshGraph": "Refresh graph",
|
||||
"exitFullscreen": "Exit fullscreen",
|
||||
"enterFullscreen": "Fullscreen",
|
||||
"buildingGraph": "Building graph...",
|
||||
"buildingPhase": "Graph Build",
|
||||
"graphBuildStarted": "Graph build task started...",
|
||||
"startingGraphBuild": "Starting graph build...",
|
||||
"graphBuildFailed": "Graph build failed",
|
||||
"graphBuildFailedWithError": "Graph build failed: {error}",
|
||||
"graphBuildComplete": "Graph build complete, loading full data...",
|
||||
"unknownError": "unknown error",
|
||||
"uploadDesc": "After uploading documents, the LLM analyzes content and auto-generates an ontology structure (entity types + relation types) suited for public opinion simulation",
|
||||
"generatedRelationTypes": "Generated Relation Types ({count})",
|
||||
"moreRelations": "+{count} more relations...",
|
||||
"graphBuildPhaseDesc": "Based on the generated ontology, documents are chunked and sent to the graph engine to build a knowledge graph, extracting entities and relations",
|
||||
"envSetupInProgress": "Environment setup feature in development...",
|
||||
"unnamed": "Unnamed",
|
||||
"unknown": "Unknown",
|
||||
"startFailed": "Start failed"
|
||||
},
|
||||
"history": {
|
||||
"title": "Simulation History",
|
||||
"graphBuild": "Graph Build",
|
||||
|
|
@ -328,7 +354,7 @@
|
|||
"noDocProcessed": "No documents were processed successfully. Please check file formats.",
|
||||
"requireProjectId": "Please provide project_id",
|
||||
"configError": "Configuration error: {details}",
|
||||
"zepApiKeyMissing": "ZEP_API_KEY not configured",
|
||||
"graphApiKeyMissing": "Graph API key not configured",
|
||||
"ontologyNotGenerated": "Ontology not yet generated. Please call /ontology/generate first.",
|
||||
"graphBuilding": "Graph build in progress. Do not resubmit. To force rebuild, add force: true.",
|
||||
"textNotFound": "Extracted text content not found",
|
||||
|
|
@ -388,15 +414,45 @@
|
|||
"envRunning": "Environment is running and ready for Interview commands",
|
||||
"envNotRunningShort": "Environment not running or closed",
|
||||
"requireGraphIdAndQuery": "Please provide graph_id and query",
|
||||
"initReportAgent": "Initializing Report Agent..."
|
||||
"initReportAgent": "Initializing Report Agent...",
|
||||
"llmApiKeyMissing": "LLM_API_KEY not configured",
|
||||
"ipcTimeout": "Timed out waiting for command response ({timeout}s)",
|
||||
"simAlreadyRunning": "Simulation is already running: {id}",
|
||||
"simConfigNotFoundPrepare": "Simulation config not found. Please call /prepare first.",
|
||||
"graphMemoryRequiresGraphId": "Graph memory update requires a graph_id",
|
||||
"scriptNotFound": "Script not found: {path}",
|
||||
"simConfigNotFound": "Simulation config not found: {id}",
|
||||
"simConfigNoAgents": "No agents in simulation config: {id}",
|
||||
"fileNotFound": "File not found: {path}",
|
||||
"unsupportedFileFormat": "Unsupported file format: {suffix}",
|
||||
"unsupportedFileFormatGeneric": "Cannot process file format: {suffix}",
|
||||
"llmJsonInvalid": "LLM returned invalid JSON format: {response}",
|
||||
"simRunningHint": "Simulation is running. Stop it first, or use force=true to restart.",
|
||||
"simNotRunning": "Simulation is not running: {id}, status={status}",
|
||||
"envNotRunningInterview": "Simulation environment not running or closed. Cannot execute interview: {id}",
|
||||
"simDirNotExist": "Simulation directory does not exist",
|
||||
"simMissingFiles": "Missing required files",
|
||||
"simNotPrepared": "Status not in prepared list or config_generated is false: status={status}, config_generated={config_generated}",
|
||||
"simStateReadFailed": "Failed to read state file: {error}",
|
||||
"envReadyForInterview": "Environment is running and ready for interview commands",
|
||||
"envCloseCommandSent": "Environment close command sent",
|
||||
"envCloseCommandSentTimeout": "Environment close command sent (response timed out, environment may be closing)",
|
||||
"simDirNotExistNoClean": "Simulation directory does not exist, no cleanup needed",
|
||||
"envAlreadyClosed": "Environment is already closed",
|
||||
"unknownType": "Unknown type",
|
||||
"unknown": "Unknown",
|
||||
"unknownError": "Unknown error",
|
||||
"unknownTool": "Unknown tool: {name}. Please use one of: insight_forge, panorama_search, quick_search",
|
||||
"defaultReportTitle": "Simulation Analysis Report",
|
||||
"unknownProfession": "Unknown profession"
|
||||
},
|
||||
"progress": {
|
||||
"initGraphService": "Initializing graph build service...",
|
||||
"textChunking": "Chunking text...",
|
||||
"creatingZepGraph": "Creating Zep graph...",
|
||||
"creatingGraph": "Creating knowledge graph...",
|
||||
"settingOntology": "Setting ontology definition...",
|
||||
"addingChunks": "Adding {count} text chunks...",
|
||||
"waitingZepProcess": "Waiting for Zep to process data...",
|
||||
"waitingGraphProcess": "Waiting for graph processing...",
|
||||
"fetchingGraphData": "Fetching graph data...",
|
||||
"graphBuildComplete": "Graph build complete",
|
||||
"buildFailed": "Build failed: {error}",
|
||||
|
|
@ -410,12 +466,13 @@
|
|||
"noEpisodesWait": "No episodes to wait for",
|
||||
"waitingEpisodes": "Waiting for {count} text chunks to process...",
|
||||
"episodesTimeout": "Some chunks timed out, {completed}/{total} completed",
|
||||
"zepProcessing": "Zep processing... {completed}/{total} done, {pending} pending ({elapsed}s)",
|
||||
"graphProcessing": "Processing... {completed}/{total} done, {pending} pending ({elapsed}s)",
|
||||
"processingComplete": "Processing complete: {completed}/{total}",
|
||||
"extractingEntities": "Extracting entities via LLM... {done}/{total} chunks",
|
||||
"taskComplete": "Task complete",
|
||||
"taskFailed": "Task failed",
|
||||
"startPreparingEnv": "Preparing simulation environment...",
|
||||
"connectingZepGraph": "Connecting to Zep graph...",
|
||||
"connectingGraph": "Connecting to graph store...",
|
||||
"readingNodeData": "Reading node data...",
|
||||
"readingComplete": "Done, {count} entities found",
|
||||
"startGenerating": "Starting generation...",
|
||||
|
|
@ -441,7 +498,7 @@
|
|||
"generatingEventConfig": "Generating event config and hot topics...",
|
||||
"generatingAgentConfig": "Generating agent config ({start}-{end}/{total})...",
|
||||
"generatingPlatformConfig": "Generating platform config...",
|
||||
"zepSearchQuery": "All information, activities, events, relationships and background about {name}",
|
||||
"graphSearchQuery": "All information, activities, events, relationships and background about {name}",
|
||||
"timeConfigLabel": "Time Config",
|
||||
"eventConfigLabel": "Event Config",
|
||||
"agentConfigResult": "Agent Config: {count} generated",
|
||||
|
|
@ -492,7 +549,7 @@
|
|||
"detectedExistingPrep": "Detected existing preparation, using it directly",
|
||||
"prepareTaskStarted": "Preparation task started",
|
||||
"prepareTaskId": " └─ Task ID: {taskId}",
|
||||
"zepEntitiesFound": "Found {count} entities from Zep graph",
|
||||
"graphEntitiesFound": "Found {count} entities from graph store",
|
||||
"entityTypes": " └─ Entity types: {types}",
|
||||
"startPollingProgress": "Polling preparation progress...",
|
||||
"prepareFailed": "Preparation failed: {error}",
|
||||
|
|
@ -612,13 +669,13 @@
|
|||
"redirectToInsightForge": "get_simulation_context redirected to insight_forge"
|
||||
},
|
||||
"console": {
|
||||
"zepToolsInitialized": "ZepToolsService initialized",
|
||||
"zepRetryAttempt": "Zep {operation} attempt {attempt} failed: {error}, retrying in {delay}s...",
|
||||
"zepAllRetriesFailed": "Zep {operation} failed after {retries} attempts: {error}",
|
||||
"graphToolsInitialized": "GraphToolsService initialized",
|
||||
"graphRetryAttempt": "{operation} attempt {attempt} failed: {error}, retrying in {delay}s...",
|
||||
"graphAllRetriesFailed": "{operation} failed after {retries} attempts: {error}",
|
||||
"graphSearch": "Graph search: graph_id={graphId}, query={query}...",
|
||||
"graphSearchOp": "Graph search (graph={graphId})",
|
||||
"searchComplete": "Search complete: found {count} relevant facts",
|
||||
"zepSearchApiFallback": "Zep Search API failed, falling back to local search: {error}",
|
||||
"graphSearchApiFallback": "Graph search API failed, falling back to local search: {error}",
|
||||
"usingLocalSearch": "Using local search: query={query}...",
|
||||
"localSearchComplete": "Local search complete: found {count} relevant facts",
|
||||
"localSearchFailed": "Local search failed: {error}",
|
||||
|
|
|
|||
|
|
@ -1,8 +1,4 @@
|
|||
{
|
||||
"zh": {
|
||||
"label": "中文",
|
||||
"llmInstruction": "请使用中文回答。"
|
||||
},
|
||||
"en": {
|
||||
"label": "English",
|
||||
"llmInstruction": "Please respond in English."
|
||||
|
|
|
|||
667
locales/zh.json
667
locales/zh.json
|
|
@ -1,667 +0,0 @@
|
|||
{
|
||||
"common": {
|
||||
"confirm": "确认",
|
||||
"cancel": "取消",
|
||||
"loading": "加载中...",
|
||||
"error": "错误",
|
||||
"success": "成功",
|
||||
"completed": "已完成",
|
||||
"processing": "生成中",
|
||||
"pending": "等待",
|
||||
"ready": "就绪",
|
||||
"running": "运行中",
|
||||
"failed": "失败",
|
||||
"unknown": "未知",
|
||||
"unknownError": "未知错误",
|
||||
"none": "无",
|
||||
"close": "关闭",
|
||||
"back": "返回",
|
||||
"next": "下一步",
|
||||
"retry": "重试",
|
||||
"noData": "暂无数据",
|
||||
"hours": "小时",
|
||||
"minutes": "分钟",
|
||||
"rounds": "轮",
|
||||
"items": "个",
|
||||
"files": "个文件"
|
||||
},
|
||||
"meta": {
|
||||
"title": "MiroFish - 预测万物",
|
||||
"description": "MiroFish - 社交媒体舆论模拟系统"
|
||||
},
|
||||
"nav": {
|
||||
"visitGithub": "访问我们的Github主页"
|
||||
},
|
||||
"home": {
|
||||
"tagline": "简洁通用的群体智能引擎",
|
||||
"version": "/ v0.1-预览版",
|
||||
"heroTitle1": "上传任意报告",
|
||||
"heroTitle2": "即刻推演未来",
|
||||
"heroDesc": "即使只有一段文字，{brand} 也能基于其中的现实种子，全自动生成与之对应的至多{agentScale}构成的平行世界。通过上帝视角注入变量，在复杂的群体交互中寻找动态环境下的{optimalSolution}",
|
||||
"heroDescBrand": "MiroFish",
|
||||
"heroDescAgentScale": "百万级Agent",
|
||||
"heroDescOptimalSolution": "\"局部最优解\"",
|
||||
"slogan": "让未来在 Agent 群中预演，让决策在百战后胜出",
|
||||
"systemStatus": "系统状态",
|
||||
"systemReady": "准备就绪",
|
||||
"systemReadyDesc": "预测引擎待命中，可上传多份非结构化数据以初始化模拟序列",
|
||||
"metricLowCost": "低成本",
|
||||
"metricLowCostDesc": "常规模拟平均5$/次",
|
||||
"metricHighAvail": "高可用",
|
||||
"metricHighAvailDesc": "最多百万级Agent模拟",
|
||||
"workflowSequence": "工作流序列",
|
||||
"step01Title": "图谱构建",
|
||||
"step01Desc": "现实种子提取 & 个体与群体记忆注入 & GraphRAG构建",
|
||||
"step02Title": "环境搭建",
|
||||
"step02Desc": "实体关系抽取 & 人设生成 & 环境配置Agent注入仿真参数",
|
||||
"step03Title": "开始模拟",
|
||||
"step03Desc": "双平台并行模拟 & 自动解析预测需求 & 动态更新时序记忆",
|
||||
"step04Title": "报告生成",
|
||||
"step04Desc": "ReportAgent拥有丰富的工具集与模拟后环境进行深度交互",
|
||||
"step05Title": "深度互动",
|
||||
"step05Desc": "与模拟世界中的任意一位进行对话 & 与ReportAgent进行对话",
|
||||
"realitySeed": "01 / 现实种子",
|
||||
"supportedFormats": "支持格式: PDF, MD, TXT",
|
||||
"dragToUpload": "拖拽文件上传",
|
||||
"orBrowse": "或点击浏览文件系统",
|
||||
"inputParams": "输入参数",
|
||||
"simulationPrompt": ">_ 02 / 模拟提示词",
|
||||
"promptPlaceholder": "// 用自然语言输入模拟或预测需求（例.武大若发布撤销肖某处分的公告，会引发什么舆情走向）",
|
||||
"engineBadge": "引擎: MiroFish-V1.0",
|
||||
"startEngine": "启动引擎",
|
||||
"initializing": "初始化中..."
|
||||
},
|
||||
"main": {
|
||||
"layoutGraph": "图谱",
|
||||
"layoutSplit": "双栏",
|
||||
"layoutWorkbench": "工作台",
|
||||
"stepNames": ["图谱构建", "环境搭建", "开始模拟", "报告生成", "深度互动"]
|
||||
},
|
||||
"step1": {
|
||||
"ontologyGeneration": "本体生成",
|
||||
"ontologyCompleted": "已完成",
|
||||
"ontologyGenerating": "生成中",
|
||||
"ontologyPending": "等待",
|
||||
"ontologyDesc": "LLM分析文档内容与模拟需求，提取出现实种子，自动生成合适的本体结构",
|
||||
"analyzingDocs": "正在分析文档...",
|
||||
"graphRagBuild": "GraphRAG构建",
|
||||
"graphRagDesc": "基于生成的本体，将文档自动分块后调用 Zep 构建知识图谱，提取实体和关系，并形成时序记忆与社区摘要",
|
||||
"entityNodes": "实体节点",
|
||||
"relationEdges": "关系边",
|
||||
"schemaTypes": "SCHEMA类型",
|
||||
"buildComplete": "构建完成",
|
||||
"buildCompleteDesc": "图谱构建已完成，请进入下一步进行模拟环境搭建",
|
||||
"inProgress": "进行中",
|
||||
"creating": "创建中...",
|
||||
"enterEnvSetup": "进入环境搭建",
|
||||
"createSimulationFailed": "创建模拟失败: {error}",
|
||||
"createSimulationException": "创建模拟异常: {error}"
|
||||
},
|
||||
"step2": {
|
||||
"simInstanceInit": "模拟实例初始化",
|
||||
"simInstanceDesc": "新建simulation实例，拉取模拟世界参数模版",
|
||||
"asyncTaskDone": "异步任务已完成",
|
||||
"generateAgentPersona": "生成 Agent 人设",
|
||||
"generateAgentPersonaDesc": "结合上下文，自动调用工具从知识图谱梳理实体与关系，初始化模拟个体，并基于现实种子赋予他们独特的行为与记忆",
|
||||
"currentAgentCount": "当前Agent数",
|
||||
"expectedAgentTotal": "预期Agent总数",
|
||||
"relatedTopicsCount": "现实种子当前关联话题数",
|
||||
"generatedAgentPersonas": "已生成的 Agent 人设",
|
||||
"unknownProfession": "未知职业",
|
||||
"noBio": "暂无简介",
|
||||
"dualPlatformConfig": "生成双平台模拟配置",
|
||||
"dualPlatformConfigDesc": "LLM 根据模拟需求与现实种子，智能设置世界时间流速、推荐算法、每个个体的活跃时间段、发言频率、事件触发等参数",
|
||||
"simulationDuration": "模拟时长",
|
||||
"roundDuration": "每轮时长",
|
||||
"totalRounds": "总轮次",
|
||||
"activePerHour": "每小时活跃",
|
||||
"peakHours": "高峰时段",
|
||||
"workHours": "工作时段",
|
||||
"morningHours": "早间时段",
|
||||
"offPeakHours": "低谷时段",
|
||||
"agentConfig": "Agent 配置",
|
||||
"activeTimePeriod": "活跃时段",
|
||||
"postsPerHour": "发帖/时",
|
||||
"commentsPerHour": "评论/时",
|
||||
"responseDelay": "响应延迟",
|
||||
"activityLevel": "活跃度",
|
||||
"sentimentBias": "情感倾向",
|
||||
"influenceWeight": "影响力",
|
||||
"recommendAlgoConfig": "推荐算法配置",
|
||||
"platform1Name": "平台 1：广场 / 信息流",
|
||||
"platform2Name": "平台 2：话题 / 社区",
|
||||
"recencyWeight": "时效权重",
|
||||
"popularityWeight": "热度权重",
|
||||
"relevanceWeight": "相关性权重",
|
||||
"viralThreshold": "病毒阈值",
|
||||
"echoChamberStrength": "回音室强度",
|
||||
"llmConfigReasoning": "LLM 配置推理",
|
||||
"initialActivation": "初始激活编排",
|
||||
"initialActivationDesc": "基于叙事方向，自动生成初始激活事件与热点话题，引导模拟世界的初始状态",
|
||||
"orchestrating": "编排中",
|
||||
"narrativeDirection": "叙事引导方向",
|
||||
"initialHotTopics": "初始热点话题",
|
||||
"initialActivationSeq": "初始激活序列 ({count})",
|
||||
"setupComplete": "准备完成",
|
||||
"setupCompleteDesc": "模拟环境已准备完成，可以开始运行模拟",
|
||||
"roundsConfig": "模拟轮数设定",
|
||||
"roundsConfigDesc": "MiroFish 自动规划推演现实 {hours} 小时，每轮代表现实 {minutesPerRound} 分钟时间流逝",
|
||||
"customToggle": "自定义",
|
||||
"roundsUnit": "轮",
|
||||
"estimatedDuration": "若Agent规模为100：预计耗时约 {minutes} 分钟",
|
||||
"estimatedDurationFull": "若Agent规模为100：预计耗时 {minutes} 分钟",
|
||||
"recommendedRounds": "{rounds} (推荐)",
|
||||
"customTip": "若首次运行，强烈建议切换至'自定义模式'减少模拟轮数，以便快速预览效果并降低报错风险",
|
||||
"backToGraphBuild": "返回图谱构建",
|
||||
"startDualWorldSim": "开始双世界并行模拟",
|
||||
"profileModalAge": "事件外显年龄",
|
||||
"profileModalGender": "事件外显性别",
|
||||
"profileModalCountry": "国家/地区",
|
||||
"profileModalMbti": "事件外显MBTI",
|
||||
"profileModalBio": "人设简介",
|
||||
"profileModalTopics": "现实种子关联话题",
|
||||
"profileModalPersona": "详细人设背景",
|
||||
"personaDimExperience": "事件全景经历",
|
||||
"personaDimExperienceDesc": "在此事件中的完整行为轨迹",
|
||||
"personaDimBehavior": "行为模式侧写",
|
||||
"personaDimBehaviorDesc": "经验总结与行事风格偏好",
|
||||
"personaDimMemory": "独特记忆印记",
|
||||
"personaDimMemoryDesc": "基于现实种子形成的记忆",
|
||||
"personaDimSocial": "社会关系网络",
|
||||
"personaDimSocialDesc": "个体链接与交互图谱",
|
||||
"genderMale": "男",
|
||||
"genderFemale": "女",
|
||||
"genderOther": "其他",
|
||||
"yearsOld": "岁",
|
||||
"initializing": "初始化",
|
||||
"generating": "生成中"
|
||||
},
|
||||
"step3": {
|
||||
"startGenerateReport": "开始生成结果报告",
|
||||
"generatingReport": "启动中...",
|
||||
"waitingForActions": "Waiting for agent actions...",
|
||||
"errorMissingSimId": "错误：缺少 simulationId",
|
||||
"startingDualSim": "正在启动双平台并行模拟...",
|
||||
"graphMemoryUpdateEnabled": "已开启动态图谱更新模式",
|
||||
"setMaxRounds": "设置最大模拟轮数: {rounds}",
|
||||
"oldSimCleared": "已清理旧的模拟日志，重新开始模拟",
|
||||
"engineStarted": "模拟引擎启动成功",
|
||||
"startFailed": "启动失败: {error}",
|
||||
"startException": "启动异常: {error}",
|
||||
"stoppingSim": "正在停止模拟...",
|
||||
"simStopped": "模拟已停止",
|
||||
"stopFailed": "停止失败: {error}",
|
||||
"stopException": "停止异常: {error}",
|
||||
"allPlatformsCompleted": "检测到所有平台模拟已结束",
|
||||
"simCompleted": "模拟已完成",
|
||||
"graphRealtimeRefresh": "开启图谱实时刷新 (30s)",
|
||||
"graphRefreshStopped": "停止图谱实时刷新",
|
||||
"preparingGoBack": "准备返回 Step 2，正在关闭模拟...",
|
||||
"closingSimEnv": "正在关闭模拟环境...",
|
||||
"simEnvClosed": "模拟环境已关闭",
|
||||
"closeFailed": "关闭模拟环境失败，尝试强制停止...",
|
||||
"stoppingProcess": "正在停止模拟进程...",
|
||||
"checkStatusFailed": "检查模拟状态失败: {error}",
|
||||
"forceStopSuccess": "模拟已强制停止",
|
||||
"forceStopFailed": "强制停止失败: {error}",
|
||||
"startGenerateReportBtn": "开始生成结果报告",
|
||||
"generatingReportBtn": "启动中..."
|
||||
},
|
||||
"step4": {
|
||||
"generatingSection": "正在生成{title}...",
|
||||
"goToInteraction": "进入深度互动",
|
||||
"waitingForReportAgent": "Waiting for Report Agent...",
|
||||
"collapse": "收起 ▲",
|
||||
"expandAll": "展开全部 {count} 条 ▼",
|
||||
"expandAllEntities": "展开全部 {count} 个 ▼",
|
||||
"scenarioLabel": "预测场景: ",
|
||||
"tabKeyFacts": "当前关键记忆 ({count})",
|
||||
"tabCoreEntities": "核心实体 ({count})",
|
||||
"tabRelationChains": "关系链 ({count})",
|
||||
"tabSubQueries": "子问题 ({count})",
|
||||
"panelKeyFacts": "时序记忆中所关联的最新关键事实",
|
||||
"totalCount": "共 {count} 条",
|
||||
"totalEntityCount": "共 {count} 个",
|
||||
"panelCoreEntities": "核心实体",
|
||||
"factCount": "{count}条",
|
||||
"panelRelationChains": "关系链",
|
||||
"panelSubQueries": "漂移查询生成分析子问题",
|
||||
"emptyKeyFacts": "暂无当前关键记忆",
|
||||
"emptyCoreEntities": "暂无核心实体",
|
||||
"emptyRelationChains": "暂无关系链",
|
||||
"tabActiveFacts": "当前有效记忆 ({count})",
|
||||
"tabHistoricalFacts": "历史记忆 ({count})",
|
||||
"tabEntities": "涉及实体 ({count})",
|
||||
"panelActiveFacts": "当前有效记忆",
|
||||
"emptyActiveFacts": "暂无当前有效记忆",
|
||||
"panelHistoricalFacts": "历史记忆",
|
||||
"emptyHistoricalFacts": "暂无历史记忆",
|
||||
"panelEntities": "涉及实体",
|
||||
"emptyEntities": "暂无涉及实体",
|
||||
"searchLabel": "搜索: ",
|
||||
"tabFacts": "事实 ({count})",
|
||||
"tabEdges": "关系 ({count})",
|
||||
"tabNodes": "节点 ({count})",
|
||||
"panelSearchResults": "搜索结果",
|
||||
"emptySearchResults": "未找到相关结果",
|
||||
"panelRelatedEdges": "相关关系",
|
||||
"panelRelatedNodes": "相关节点",
|
||||
"world1": "世界1",
|
||||
"world2": "世界2"
|
||||
},
|
||||
"step5": {
|
||||
"interactiveTools": "Interactive Tools",
|
||||
"agentsAvailable": "{count} agents available",
|
||||
"chatWithReportAgent": "与Report Agent对话",
|
||||
"chatWithAgent": "与世界中任意个体对话",
|
||||
"selectChatTarget": "选择对话对象",
|
||||
"sendSurvey": "发送问卷调查到世界中",
|
||||
"reportAgentChat": "Report Agent - Chat",
|
||||
"reportAgentDesc": "报告生成智能体的快速对话版本，可调用 4 种专业工具，拥有MiroFish的完整记忆",
|
||||
"toolInsightForge": "InsightForge 深度归因",
|
||||
"toolInsightForgeDesc": "对齐现实世界种子数据与模拟环境状态，结合Global/Local Memory机制，提供跨时空的深度归因分析",
|
||||
"toolPanoramaSearch": "PanoramaSearch 全景追踪",
|
||||
"toolPanoramaSearchDesc": "基于图结构的广度遍历算法，重构事件传播路径，捕获全量信息流动的拓扑结构",
|
||||
"toolQuickSearch": "QuickSearch 快速检索",
|
||||
"toolQuickSearchDesc": "基于 GraphRAG 的即时查询接口，优化索引效率，用于快速提取具体的节点属性与离散事实",
|
||||
"toolInterviewSubAgent": "InterviewSubAgent 虚拟访谈",
|
||||
"toolInterviewSubAgentDesc": "自主式访谈，能够并行与模拟世界中个体进行多轮对话，采集非结构化的观点数据与心理状态",
|
||||
"profileBio": "简介",
|
||||
"chatEmptyReportAgent": "与 Report Agent 对话，深入了解报告内容",
|
||||
"chatEmptyAgent": "与模拟个体对话，了解他们的观点",
|
||||
"chatInputPlaceholder": "输入您的问题...",
|
||||
"selectSurveyTarget": "选择调查对象",
|
||||
"selectedCount": "已选 {selected} / {total}",
|
||||
"surveyQuestions": "问卷问题",
|
||||
"surveyInputPlaceholder": "输入您想问所有被选中对象的问题...",
|
||||
"submitSurvey": "发送问卷",
|
||||
"surveyResults": "调查结果",
|
||||
"surveyResultsCount": "{count} 条回复",
|
||||
"selectAll": "全选",
|
||||
"clearSelection": "清空",
|
||||
"errorOccurred": "抱歉，发生了错误: {error}",
|
||||
"noResponse": "无响应",
|
||||
"requestFailed": "请求失败",
|
||||
"selectAgentFirst": "请先选择一个模拟个体"
|
||||
},
|
||||
"graph": {
|
||||
"panelTitle": "Graph Relationship Visualization",
|
||||
"refreshGraph": "刷新图谱",
|
||||
"graphMemoryRealtime": "GraphRAG长短期记忆实时更新中",
|
||||
"realtimeUpdating": "实时更新中...",
|
||||
"pendingContentHint": "还有少量内容处理中，建议稍后手动刷新图谱",
|
||||
"nodeDetails": "Node Details",
|
||||
"relationship": "Relationship",
|
||||
"graphDataLoading": "图谱数据加载中...",
|
||||
"waitingOntology": "等待本体生成...",
|
||||
"toggleMaximize": "最大化/还原",
|
||||
"closeHint": "关闭提示"
|
||||
},
|
||||
"history": {
|
||||
"title": "推演记录",
|
||||
"graphBuild": "图谱构建",
|
||||
"envSetup": "环境搭建",
|
||||
"analysisReport": "分析报告",
|
||||
"moreFiles": "+{count} 个文件",
|
||||
"noFiles": "暂无文件",
|
||||
"loadingText": "加载中...",
|
||||
"simRequirement": "模拟需求",
|
||||
"relatedFiles": "关联文件",
|
||||
"noRelatedFiles": "暂无关联文件",
|
||||
"replayTitle": "推演回放",
|
||||
"step1Button": "图谱构建",
|
||||
"step2Button": "环境搭建",
|
||||
"step4Button": "分析报告",
|
||||
"replayHint": "Step3「开始模拟」与 Step5「深度互动」需在运行中启动，不支持历史回放",
|
||||
"notStarted": "未开始",
|
||||
"roundsProgress": "{current}/{total} 轮",
|
||||
"untitledSimulation": "未命名模拟",
|
||||
"unknownFile": "未知文件"
|
||||
},
|
||||
"api": {
|
||||
"projectNotFound": "项目不存在: {id}",
|
||||
"projectDeleteFailed": "项目不存在或删除失败: {id}",
|
||||
"projectDeleted": "项目已删除: {id}",
|
||||
"projectReset": "项目已重置: {id}",
|
||||
"requireSimulationRequirement": "请提供模拟需求描述 (simulation_requirement)",
|
||||
"requireFileUpload": "请至少上传一个文档文件",
|
||||
"noDocProcessed": "没有成功处理任何文档，请检查文件格式",
|
||||
"requireProjectId": "请提供 project_id",
|
||||
"configError": "配置错误: {details}",
|
||||
"zepApiKeyMissing": "ZEP_API_KEY未配置",
|
||||
"ontologyNotGenerated": "项目尚未生成本体，请先调用 /ontology/generate",
|
||||
"graphBuilding": "图谱正在构建中，请勿重复提交。如需强制重建，请添加 force: true",
|
||||
"textNotFound": "未找到提取的文本内容",
|
||||
"ontologyNotFound": "未找到本体定义",
|
||||
"graphBuildStarted": "图谱构建任务已启动，请通过 /task/{taskId} 查询进度",
|
||||
"graphBuildComplete": "图谱构建完成",
|
||||
"buildFailed": "构建失败: {error}",
|
||||
"taskNotFound": "任务不存在: {id}",
|
||||
"graphDeleted": "图谱已删除: {id}",
|
||||
"entityNotFound": "实体不存在: {id}",
|
||||
"graphNotBuilt": "项目尚未构建图谱，请先调用 /api/graph/build",
|
||||
"requireSimulationId": "请提供 simulation_id",
|
||||
"simulationNotFound": "模拟不存在: {id}",
|
||||
"projectMissingRequirement": "项目缺少模拟需求描述 (simulation_requirement)",
|
||||
"prepareStarted": "准备任务已启动，请通过 /api/simulation/prepare/status 查询进度",
|
||||
"alreadyPrepared": "已有完成的准备工作，无需重复生成",
|
||||
"notStartedPrepare": "尚未开始准备，请调用 /api/simulation/prepare 开始",
|
||||
"taskCompletedPrepared": "任务已完成（准备工作已存在）",
|
||||
"requireTaskOrSimId": "请提供 task_id 或 simulation_id",
|
||||
"configNotFound": "模拟配置不存在，请先调用 /prepare 接口",
|
||||
"configFileNotFound": "配置文件不存在，请先调用 /prepare 接口",
|
||||
"unknownScript": "未知脚本: {name}，可选: {allowed}",
|
||||
"scriptFileNotFound": "脚本文件不存在: {name}",
|
||||
"requireGraphId": "请提供 graph_id",
|
||||
"noMatchingEntities": "没有找到符合条件的实体",
|
||||
"maxRoundsPositive": "max_rounds 必须是正整数",
|
||||
"maxRoundsInvalid": "max_rounds 必须是有效的整数",
|
||||
"invalidPlatform": "无效的平台类型: {platform}，可选: twitter/reddit/parallel",
|
||||
"simRunningForceHint": "模拟正在运行中，请先调用 /stop 接口停止，或使用 force=true 强制重新开始",
|
||||
"simNotReady": "模拟未准备好，当前状态: {status}，请先调用 /prepare 接口",
|
||||
"graphIdRequiredForMemory": "启用图谱记忆更新需要有效的 graph_id，请确保项目已构建图谱",
|
||||
"dbNotExist": "数据库不存在，模拟可能尚未运行",
|
||||
"requireMessage": "请提供 message",
|
||||
"missingGraphId": "缺少图谱ID",
|
||||
"missingGraphIdEnsure": "缺少图谱ID，请确保已构建图谱",
|
||||
"missingSimRequirement": "缺少模拟需求描述",
|
||||
"reportAlreadyExists": "报告已存在",
|
||||
"reportGenerateStarted": "报告生成任务已启动，请通过 /api/report/generate/status 查询进度",
|
||||
"reportGenerated": "报告已生成",
|
||||
"reportNotFound": "报告不存在: {id}",
|
||||
"noReportForSim": "该模拟暂无报告: {id}",
|
||||
"reportDeleted": "报告已删除: {id}",
|
||||
"reportGenerateFailed": "报告生成失败",
|
||||
"sectionNotFound": "章节不存在: section_{index}.md",
|
||||
"reportProgressNotAvail": "报告不存在或进度信息不可用: {id}",
|
||||
"requireAgentId": "请提供 agent_id",
|
||||
"requirePrompt": "请提供 prompt（采访问题）",
|
||||
"invalidInterviewPlatform": "platform 参数只能是 'twitter' 或 'reddit'",
|
||||
"envNotRunning": "模拟环境未运行或已关闭。请确保模拟已完成并进入等待命令模式。",
|
||||
"interviewTimeout": "等待Interview响应超时: {error}",
|
||||
"requireInterviews": "请提供 interviews（采访列表）",
|
||||
"interviewListMissingAgentId": "采访列表第{index}项缺少 agent_id",
|
||||
"interviewListMissingPrompt": "采访列表第{index}项缺少 prompt",
|
||||
"interviewListInvalidPlatform": "采访列表第{index}项的platform只能是 'twitter' 或 'reddit'",
|
||||
"batchInterviewTimeout": "等待批量Interview响应超时: {error}",
|
||||
"globalInterviewTimeout": "等待全局Interview响应超时: {error}",
|
||||
"envRunning": "环境正在运行，可以接收Interview命令",
|
||||
"envNotRunningShort": "环境未运行或已关闭",
|
||||
"requireGraphIdAndQuery": "请提供 graph_id 和 query",
|
||||
"initReportAgent": "初始化Report Agent..."
|
||||
},
|
||||
"progress": {
|
||||
"initGraphService": "初始化图谱构建服务...",
|
||||
"textChunking": "文本分块中...",
|
||||
"creatingZepGraph": "创建Zep图谱...",
|
||||
"settingOntology": "设置本体定义...",
|
||||
"addingChunks": "开始添加 {count} 个文本块...",
|
||||
"waitingZepProcess": "等待Zep处理数据...",
|
||||
"fetchingGraphData": "获取图谱数据...",
|
||||
"graphBuildComplete": "图谱构建完成",
|
||||
"buildFailed": "构建失败: {error}",
|
||||
"startBuildingGraph": "开始构建图谱...",
|
||||
"graphCreated": "图谱已创建: {graphId}",
|
||||
"ontologySet": "本体已设置",
|
||||
"textSplit": "文本已分割为 {count} 个块",
|
||||
"fetchingGraphInfo": "获取图谱信息...",
|
||||
"sendingBatch": "发送第 {current}/{total} 批数据 ({chunks} 块)...",
|
||||
"batchFailed": "批次 {batch} 发送失败: {error}",
|
||||
"noEpisodesWait": "无需等待（没有 episode）",
|
||||
"waitingEpisodes": "开始等待 {count} 个文本块处理...",
|
||||
"episodesTimeout": "部分文本块超时，已完成 {completed}/{total}",
|
||||
"zepProcessing": "Zep处理中... {completed}/{total} 完成, {pending} 待处理 ({elapsed}秒)",
|
||||
"processingComplete": "处理完成: {completed}/{total}",
|
||||
"taskComplete": "任务完成",
|
||||
"taskFailed": "任务失败",
|
||||
"startPreparingEnv": "开始准备模拟环境...",
|
||||
"connectingZepGraph": "正在连接Zep图谱...",
|
||||
"readingNodeData": "正在读取节点数据...",
|
||||
"readingComplete": "完成，共 {count} 个实体",
|
||||
"startGenerating": "开始生成...",
|
||||
"analyzingRequirements": "正在分析模拟需求...",
|
||||
"generatingOutline": "正在生成报告大纲...",
|
||||
"parsingOutline": "正在解析大纲结构...",
|
||||
"outlinePlanComplete": "大纲规划完成",
|
||||
"deepSearchAndWrite": "深度检索与撰写中 ({current}/{max})",
|
||||
"initReport": "初始化报告...",
|
||||
"startPlanningOutline": "开始规划报告大纲...",
|
||||
"outlineDone": "大纲规划完成，共{count}个章节",
|
||||
"generatingSection": "正在生成章节: {title} ({current}/{total})",
|
||||
"sectionDone": "章节 {title} 已完成",
|
||||
"assemblingReport": "正在组装完整报告...",
|
||||
"reportComplete": "报告生成完成",
|
||||
"reportFailed": "报告生成失败: {error}",
|
||||
"savingProfiles": "保存Profile文件...",
|
||||
"profilesComplete": "完成，共 {count} 个Profile",
|
||||
"callingLLMConfig": "正在调用LLM生成配置...",
|
||||
"savingConfigFiles": "正在保存配置文件...",
|
||||
"configComplete": "配置生成完成",
|
||||
"generatingTimeConfig": "生成时间配置...",
|
||||
"generatingEventConfig": "生成事件配置和热点话题...",
|
||||
"generatingAgentConfig": "生成Agent配置 ({start}-{end}/{total})...",
|
||||
"generatingPlatformConfig": "生成平台配置...",
|
||||
"zepSearchQuery": "关于{name}的所有信息、活动、事件、关系和背景",
|
||||
"timeConfigLabel": "时间配置",
|
||||
"eventConfigLabel": "事件配置",
|
||||
"agentConfigResult": "Agent配置: 成功生成 {count} 个",
|
||||
"postAssignResult": "初始帖子分配: {count} 个帖子已分配发布者",
|
||||
"profileGenerated": "[已生成] {name} ({type})",
|
||||
"readingGraphEntities": "读取图谱实体",
|
||||
"generatingProfiles": "生成Agent人设",
|
||||
"generatingSimConfig": "生成模拟配置",
|
||||
"preparingScripts": "准备模拟脚本"
|
||||
},
|
||||
"log": {
|
||||
"preparingGoBack": "准备返回 Step 2，正在关闭模拟...",
|
||||
"closingSimEnv": "正在关闭模拟环境...",
|
||||
"simEnvClosed": "✓ 模拟环境已关闭",
|
||||
"closeSimEnvFailed": "关闭模拟环境失败，尝试强制停止...",
|
||||
"simForceStopSuccess": "✓ 模拟已强制停止",
|
||||
"forceStopFailed": "强制停止失败: {error}",
|
||||
"stoppingSimProcess": "正在停止模拟进程...",
|
||||
"simStopped": "✓ 模拟已停止",
|
||||
"stopSimFailed": "停止模拟失败: {error}",
|
||||
"checkStatusFailed": "检查模拟状态失败: {error}",
|
||||
"enterStep4": "进入 Step 4: 报告生成",
|
||||
"loadingSimData": "加载模拟数据: {id}",
|
||||
"timeConfig": "时间配置: 每轮 {minutes} 分钟",
|
||||
"timeConfigFetchFailed": "获取时间配置失败，使用默认值: {minutes}分钟/轮",
|
||||
"projectLoadSuccess": "项目加载成功: {id}",
|
||||
"loadSimDataFailed": "加载模拟数据失败: {error}",
|
||||
"loadException": "加载异常: {error}",
|
||||
"graphDataLoadSuccess": "图谱数据加载成功",
|
||||
"graphLoadFailed": "图谱加载失败: {error}",
|
||||
"graphRealtimeRefreshStart": "开启图谱实时刷新 (30s)",
|
||||
"graphRealtimeRefreshStop": "停止图谱实时刷新",
|
||||
"simRunViewInit": "SimulationRunView 初始化",
|
||||
"customRounds": "自定义模拟轮数: {rounds}",
|
||||
"enterStep3": "进入 Step 3: 开始模拟",
|
||||
"customRoundsConfig": "自定义模拟轮数: {rounds} 轮",
|
||||
"useAutoRounds": "使用自动配置的模拟轮数",
|
||||
"detectedSimEnvRunning": "检测到模拟环境正在运行，正在关闭...",
|
||||
"closeSimEnvFailedWithError": "关闭模拟环境失败: {error}",
|
||||
"closeSimEnvException": "关闭模拟环境异常: {error}",
|
||||
"detectedSimRunning": "检测到模拟状态为运行中，正在停止...",
|
||||
"forceStopSimFailed": "强制停止模拟失败: {error}",
|
||||
"forceStopSimException": "强制停止模拟异常: {error}",
|
||||
"simViewInit": "SimulationView 初始化",
|
||||
"errorMissingSimId": "错误：缺少 simulationId",
|
||||
"simInstanceCreated": "模拟实例已创建: {id}",
|
||||
"preparingSimEnv": "正在准备模拟环境...",
|
||||
"detectedExistingPrep": "检测到已有完成的准备工作，直接使用",
|
||||
"prepareTaskStarted": "准备任务已启动",
|
||||
"prepareTaskId": " └─ Task ID: {taskId}",
|
||||
"zepEntitiesFound": "从Zep图谱读取到 {count} 个实体",
|
||||
"entityTypes": " └─ 实体类型: {types}",
|
||||
"startPollingProgress": "开始轮询准备进度...",
|
||||
"prepareFailed": "准备失败: {error}",
|
||||
"prepareException": "准备异常: {error}",
|
||||
"prepareComplete": "✓ 准备工作已完成",
|
||||
"prepareFailedWithError": "✗ 准备失败: {error}",
|
||||
"startGeneratingConfig": "开始生成双平台模拟配置...",
|
||||
"generatingAgentProfileConfig": "正在生成Agent人设配置...",
|
||||
"generatingLLMConfig": "正在调用LLM生成模拟配置参数...",
|
||||
"configComplete": "✓ 模拟配置生成完成",
|
||||
"configSummaryAgents": " ├─ Agent数量: {count}个",
|
||||
"configSummaryHours": " ├─ 模拟时长: {hours}小时",
|
||||
"configSummaryPosts": " ├─ 初始帖子: {count}条",
|
||||
"configSummaryTopics": " ├─ 热点话题: {count}个",
|
||||
"configSummaryPlatforms": " └─ 平台配置: Twitter {twitter}, Reddit {reddit}",
|
||||
"timeConfigDetail": "时间配置: 每轮{minutes}分钟, 共{rounds}轮",
|
||||
"narrativeDirection": "叙事方向: {direction}",
|
||||
"envSetupComplete": "✓ 环境搭建完成，可以开始模拟",
|
||||
"startSimCustomRounds": "开始模拟，自定义轮数: {rounds} 轮",
|
||||
"startSimAutoRounds": "开始模拟，使用自动配置轮数: {rounds} 轮",
|
||||
"startGeneratingAgentProfiles": "开始生成Agent人设...",
|
||||
"agentProfile": "→ Agent人设 {current}/{total}: {name} ({profession})",
|
||||
"allProfilesComplete": "✓ 全部 {count} 个Agent人设生成完成",
|
||||
"loadingExistingConfig": "正在加载已有配置数据...",
|
||||
"loadedAgentProfiles": "已加载 {count} 个Agent人设",
|
||||
"configLoadSuccess": "✓ 模拟配置加载成功",
|
||||
"configSummaryPostsAlt": " └─ 初始帖子: {count}条",
|
||||
"configGenerating": "配置生成中，开始轮询等待...",
|
||||
"configNotGenerating": "模拟配置尚未生成，且后端已不再生成配置",
|
||||
"loadConfigFailed": "加载配置失败: {error}",
|
||||
"step2Init": "Step2 环境搭建初始化",
|
||||
"step3Init": "Step3 模拟运行初始化",
|
||||
"startingDualSim": "正在启动双平台并行模拟...",
|
||||
"setMaxRounds": "设置最大模拟轮数: {rounds}",
|
||||
"graphMemoryUpdateEnabled": "已开启动态图谱更新模式",
|
||||
"oldSimCleared": "✓ 已清理旧的模拟日志，重新开始模拟",
|
||||
"engineStarted": "✓ 模拟引擎启动成功",
|
||||
"startFailed": "✗ 启动失败: {error}",
|
||||
"startException": "✗ 启动异常: {error}",
|
||||
"stoppingSim": "正在停止模拟...",
|
||||
"simStoppedSuccess": "✓ 模拟已停止",
|
||||
"stopFailed": "停止失败: {error}",
|
||||
"stopException": "停止异常: {error}",
|
||||
"allPlatformsCompleted": "✓ 检测到所有平台模拟已结束",
|
||||
"simCompleted": "✓ 模拟已完成",
|
||||
"simFailed": "✗ 模拟失败",
|
||||
"reportRequestSent": "报告生成请求已发送，请稍候...",
|
||||
"startingReportGen": "正在启动报告生成...",
|
||||
"reportGenTaskStarted": "✓ 报告生成任务已启动: {reportId}",
|
||||
"reportGenFailed": "✗ 启动报告生成失败: {error}",
|
||||
"reportGenException": "✗ 启动报告生成异常: {error}",
|
||||
"step5Init": "Step5 深度互动初始化",
|
||||
"selectChatTarget": "选择对话对象: {name}",
|
||||
"sendFailed": "发送失败: {error}",
|
||||
"sendToReportAgent": "向 Report Agent 发送: {message}...",
|
||||
"reportAgentReplied": "Report Agent 已回复",
|
||||
"sendToAgent": "向 {name} 发送: {message}...",
|
||||
"agentReplied": "{name} 已回复",
|
||||
"sendSurvey": "发送问卷给 {count} 个对象...",
|
||||
"receivedReplies": "收到 {count} 条回复",
|
||||
"surveySendFailed": "问卷发送失败: {error}",
|
||||
"loadReportData": "加载报告数据: {id}",
|
||||
"loadReportFailed": "加载报告失败: {error}",
|
||||
"reportDataLoaded": "报告数据加载完成",
|
||||
"loadReportLogFailed": "加载报告日志失败: {error}",
|
||||
"loadedProfiles": "加载了 {count} 个模拟个体",
|
||||
"loadProfilesFailed": "加载模拟个体失败: {error}",
|
||||
"interactionViewInit": "InteractionView 初始化",
|
||||
"reportViewInit": "ReportView 初始化",
|
||||
"getReportInfoFailed": "获取报告信息失败: {error}",
|
||||
"enterStep": "进入 Step {step}: {name}",
|
||||
"returnToStep": "返回 Step {step}: {name}",
|
||||
"customSimRounds": "自定义模拟轮数: {rounds} 轮"
|
||||
},
|
||||
"report": {
|
||||
"taskStarted": "报告生成任务开始",
|
||||
"planningStart": "开始规划报告大纲",
|
||||
"fetchSimContext": "获取模拟上下文信息",
|
||||
"planningComplete": "大纲规划完成",
|
||||
"sectionStart": "开始生成章节: {title}",
|
||||
"reactThought": "ReACT 第{iteration}轮思考",
|
||||
"toolCall": "调用工具: {toolName}",
|
||||
"toolResult": "工具 {toolName} 返回结果",
|
||||
"llmResponse": "LLM 响应 (工具调用: {hasToolCalls}, 最终答案: {hasFinalAnswer})",
|
||||
"sectionContentDone": "章节 {title} 内容生成完成",
|
||||
"sectionComplete": "章节 {title} 生成完成",
|
||||
"reportComplete": "报告生成完成",
|
||||
"errorOccurred": "发生错误: {error}",
|
||||
"agentInitDone": "ReportAgent 初始化完成: graph_id={graphId}, simulation_id={simulationId}",
|
||||
"executingTool": "执行工具: {toolName}, 参数: {params}",
|
||||
"toolExecFailed": "工具执行失败: {toolName}, 错误: {error}",
|
||||
"startPlanningOutline": "开始规划报告大纲...",
|
||||
"outlinePlanDone": "大纲规划完成: {count} 个章节",
|
||||
"outlinePlanFailed": "大纲规划失败: {error}",
|
||||
"reactGenerateSection": "ReACT生成章节: {title}",
|
||||
"sectionIterNone": "章节 {title} 第 {iteration} 次迭代: LLM 返回 None",
|
||||
"sectionConflict": "章节 {title} 第 {iteration} 轮: LLM 同时输出工具调用和 Final Answer（第 {conflictCount} 次冲突）",
|
||||
"sectionConflictDowngrade": "章节 {title}: 连续 {conflictCount} 次冲突，降级为截断执行第一个工具调用",
|
||||
"sectionGenDone": "章节 {title} 生成完成（工具调用: {count}次）",
|
||||
"multiToolOnlyFirst": "LLM 尝试调用 {total} 个工具，只执行第一个: {toolName}",
|
||||
"sectionNoPrefix": "章节 {title} 未检测到 'Final Answer:' 前缀，直接采纳LLM输出作为最终内容（工具调用: {count}次）",
|
||||
"sectionMaxIter": "章节 {title} 达到最大迭代次数，强制生成",
|
||||
"sectionForceFailed": "章节 {title} 强制收尾时 LLM 返回 None，使用默认错误提示",
|
||||
"sectionGenFailedContent": "（本章节生成失败：LLM 返回空响应，请稍后重试）",
|
||||
"outlineSavedToFile": "大纲已保存到文件: {reportId}/outline.json",
|
||||
"sectionSaved": "章节已保存: {reportId}/section_{sectionNum}.md",
|
||||
"reportGenDone": "报告生成完成: {reportId}",
|
||||
"reportGenFailed": "报告生成失败: {error}",
|
||||
"agentChat": "Report Agent对话: {message}...",
|
||||
"fetchReportFailed": "获取报告内容失败: {error}",
|
||||
"outlineSaved": "大纲已保存: {reportId}",
|
||||
"sectionFileSaved": "章节已保存: {reportId}/{fileSuffix}",
|
||||
"fullReportAssembled": "完整报告已组装: {reportId}",
|
||||
"reportSaved": "报告已保存: {reportId}",
|
||||
"reportFolderDeleted": "报告文件夹已删除: {reportId}",
|
||||
"redirectToQuickSearch": "search_graph 已重定向到 quick_search",
|
||||
"redirectToInsightForge": "get_simulation_context 已重定向到 insight_forge"
|
||||
},
|
||||
"console": {
|
||||
"zepToolsInitialized": "ZepToolsService 初始化完成",
|
||||
"zepRetryAttempt": "Zep {operation} 第 {attempt} 次尝试失败: {error}, {delay}秒后重试...",
|
||||
"zepAllRetriesFailed": "Zep {operation} 在 {retries} 次尝试后仍失败: {error}",
|
||||
"graphSearch": "图谱搜索: graph_id={graphId}, query={query}...",
|
||||
"graphSearchOp": "图谱搜索(graph={graphId})",
|
||||
"searchComplete": "搜索完成: 找到 {count} 条相关事实",
|
||||
"zepSearchApiFallback": "Zep Search API失败，降级为本地搜索: {error}",
|
||||
"usingLocalSearch": "使用本地搜索: query={query}...",
|
||||
"localSearchComplete": "本地搜索完成: 找到 {count} 条相关事实",
|
||||
"localSearchFailed": "本地搜索失败: {error}",
|
||||
"fetchingAllNodes": "获取图谱 {graphId} 的所有节点...",
|
||||
"fetchedNodes": "获取到 {count} 个节点",
|
||||
"fetchingAllEdges": "获取图谱 {graphId} 的所有边...",
|
||||
"fetchedEdges": "获取到 {count} 条边",
|
||||
"fetchingNodeDetail": "获取节点详情: {uuid}...",
|
||||
"fetchNodeDetailOp": "获取节点详情(uuid={uuid}...)",
|
||||
"fetchNodeDetailFailed": "获取节点详情失败: {error}",
|
||||
"fetchingNodeEdges": "获取节点 {uuid}... 的相关边",
|
||||
"foundNodeEdges": "找到 {count} 条与节点相关的边",
|
||||
"fetchNodeEdgesFailed": "获取节点边失败: {error}",
|
||||
"fetchingEntitiesByType": "获取类型为 {type} 的实体...",
|
||||
"foundEntitiesByType": "找到 {count} 个 {type} 类型的实体",
|
||||
"fetchingEntitySummary": "获取实体 {name} 的关系摘要...",
|
||||
"fetchingGraphStats": "获取图谱 {graphId} 的统计信息...",
|
||||
"fetchingSimContext": "获取模拟上下文: {requirement}...",
|
||||
"insightForgeStart": "InsightForge 深度洞察检索: {query}...",
|
||||
"generatedSubQueries": "生成 {count} 个子问题",
|
||||
"insightForgeComplete": "InsightForge完成: {facts}条事实, {entities}个实体, {relationships}条关系",
|
||||
"generateSubQueriesFailed": "生成子问题失败: {error}，使用默认子问题",
|
||||
"panoramaSearchStart": "PanoramaSearch 广度搜索: {query}...",
|
||||
"panoramaSearchComplete": "PanoramaSearch完成: {active}条有效, {historical}条历史",
|
||||
"quickSearchStart": "QuickSearch 简单搜索: {query}...",
|
||||
"quickSearchComplete": "QuickSearch完成: {count}条结果",
|
||||
"interviewAgentsStart": "InterviewAgents 深度采访（真实API）: {requirement}...",
|
||||
"profilesNotFound": "未找到模拟 {simId} 的人设文件",
|
||||
"loadedProfiles": "加载到 {count} 个Agent人设",
|
||||
"selectedAgentsForInterview": "选择了 {count} 个Agent进行采访: {indices}",
|
||||
"generatedInterviewQuestions": "生成了 {count} 个采访问题",
|
||||
"callingBatchInterviewApi": "调用批量采访API（双平台）: {count} 个Agent",
|
||||
"interviewApiReturned": "采访API返回: {count} 个结果, success={success}",
|
||||
"interviewApiReturnedFailure": "采访API返回失败: {error}",
|
||||
"interviewApiCallFailed": "采访API调用失败（环境未运行？）: {error}",
|
||||
"interviewApiCallException": "采访API调用异常: {error}",
|
||||
"interviewAgentsComplete": "InterviewAgents完成: 采访了 {count} 个Agent（双平台）",
|
||||
"loadedRedditProfiles": "从 reddit_profiles.json 加载了 {count} 个人设",
|
||||
"readRedditProfilesFailed": "读取 reddit_profiles.json 失败: {error}",
|
||||
"loadedTwitterProfiles": "从 twitter_profiles.csv 加载了 {count} 个人设",
|
||||
"readTwitterProfilesFailed": "读取 twitter_profiles.csv 失败: {error}",
|
||||
"llmSelectAgentFailed": "LLM选择Agent失败，使用默认选择: {error}",
|
||||
"generateInterviewQuestionsFailed": "生成采访问题失败: {error}",
|
||||
"generateInterviewSummaryFailed": "生成采访摘要失败: {error}"
|
||||
}
|
||||
}
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
{
|
||||
"name": "mirofish",
|
||||
"version": "0.1.0",
|
||||
"description": "MiroFish - 简洁通用的群体智能引擎，预测万物",
|
||||
"description": "MiroFish - A Simple and Universal Swarm Intelligence Engine, Predicting Anything",
|
||||
"scripts": {
|
||||
"setup": "npm install && cd frontend && npm install",
|
||||
"setup:backend": "cd backend && uv sync",
|
||||
|
|
|
|||
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Before Width: | Height: | Size: 67 KiB After Width: | Height: | Size: 67 KiB |
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Before Width: | Height: | Size: 246 KiB After Width: | Height: | Size: 246 KiB |
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Before Width: | Height: | Size: 255 KiB After Width: | Height: | Size: 255 KiB |
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Before Width: | Height: | Size: 77 KiB After Width: | Height: | Size: 77 KiB |
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Before Width: | Height: | Size: 120 KiB After Width: | Height: | Size: 120 KiB |
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Before Width: | Height: | Size: 450 KiB After Width: | Height: | Size: 450 KiB |
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Before Width: | Height: | Size: 118 KiB After Width: | Height: | Size: 118 KiB |
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Before Width: | Height: | Size: 454 KiB After Width: | Height: | Size: 454 KiB |
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Before Width: | Height: | Size: 199 KiB After Width: | Height: | Size: 199 KiB |
Loading…
Reference in New Issue