Merge remote-tracking branch 'abhiyadav2345/feat/graphiti-neo4j-migration'
This commit is contained in:
stg
commit
62648289d1
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@ -0,0 +1,86 @@
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# CLAUDE.md
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|
||||
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
|
||||
|
||||
## Project Overview
|
||||
|
||||
MiroFish is a multi-agent swarm intelligence prediction engine. It builds knowledge graphs from seed data, simulates thousands of AI agents interacting on virtual Twitter/Reddit platforms (via CAMEL-OASIS), and generates analytical reports — all to predict outcomes of real-world scenarios.
|
||||
|
||||
## Commands
|
||||
|
||||
### Setup
|
||||
```bash
|
||||
npm run setup:all # Install all dependencies (frontend + backend)
|
||||
npm run setup # Frontend npm install only
|
||||
npm run setup:backend # Backend: uv sync (Python deps)
|
||||
```
|
||||
|
||||
### Development
|
||||
```bash
|
||||
npm run dev # Run backend + frontend concurrently
|
||||
npm run backend # Backend only: Flask on port 5001
|
||||
npm run frontend # Frontend only: Vite on port 3000
|
||||
```
|
||||
|
||||
### Build
|
||||
```bash
|
||||
npm run build # Build frontend (Vite)
|
||||
```
|
||||
|
||||
### Backend (Python)
|
||||
```bash
|
||||
cd backend && uv run python run.py # Start Flask server
|
||||
cd backend && uv run python -m pytest # Run tests (if any)
|
||||
```
|
||||
|
||||
### Docker
|
||||
```bash
|
||||
docker-compose up # Full stack via Docker
|
||||
```
|
||||
|
||||
## Architecture
|
||||
|
||||
### Stack
|
||||
- **Backend**: Python ≥3.11 Flask 3.0, managed by `uv`
|
||||
- **Frontend**: Vue 3 + Vite, port 3000; proxies `/api` → port 5001
|
||||
- **LLM**: OpenAI SDK-compatible (default: Qwen via `dashscope`; also works with GLM, OpenAI)
|
||||
- **Memory/Graph**: Zep Cloud (knowledge graph for entity storage and retrieval)
|
||||
- **Simulation**: CAMEL-OASIS (multi-agent Twitter + Reddit simulation)
|
||||
- **Visualization**: D3.js
|
||||
|
||||
### Required Environment Variables
|
||||
Copy `.env.example` to `.env`:
|
||||
```
|
||||
LLM_API_KEY # Required
|
||||
LLM_BASE_URL # Default: https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
LLM_MODEL_NAME # Default: qwen-plus
|
||||
ZEP_API_KEY # Required (Zep Cloud)
|
||||
```
|
||||
|
||||
### 5-Step Pipeline
|
||||
The core workflow is a sequential async pipeline:
|
||||
1. **Graph Build** — Upload files → LLM extracts ontology → Zep Cloud builds knowledge graph
|
||||
2. **Env Setup** — Read Zep entities → Generate OASIS agent profiles (AI personalities)
|
||||
3. **Simulation** — CAMEL-OASIS runs agents on dual platforms (Twitter + Reddit) in parallel
|
||||
4. **Report** — ReportAgent (ReACT loop) queries graph with tools: `SearchResult`, `InsightForge`, `Panorama`, `Interview`
|
||||
5. **Interaction** — Chat with simulated agents or the ReportAgent
|
||||
|
||||
### Backend Structure (`backend/app/`)
|
||||
- `api/` — Flask blueprints: `graph_bp`, `simulation_bp`, `report_bp`
|
||||
- `services/` — Core logic: graph building, simulation runner, report agent, Zep tools
|
||||
- `models/` — `Project` and `Task` state objects (in-memory, JSON-serializable)
|
||||
- `utils/` — LLM client wrapper, file parser, retry logic, Zep pagination
|
||||
- `config/config.py` — All configuration (LLM, Zep, chunking, simulation params)
|
||||
|
||||
Long-running operations (ontology generation, graph build, profile generation, report generation) run as background tasks tracked via `Task` objects with progress polling.
|
||||
|
||||
### Frontend Structure (`frontend/src/`)
|
||||
- `views/` — Page components mapped to routes; `Process.vue` is the main 50KB workflow orchestrator
|
||||
- `components/` — `Step1-5` step components + `GraphPanel.vue` (D3 graph visualization)
|
||||
- `api/` — Axios services (`graph.js`, `simulation.js`, `report.js`) with 5-min timeout and exponential retry
|
||||
|
||||
### Key Implementation Details
|
||||
- Reasoning model outputs (e.g., MiniMax/GLM with `<think>` tags or markdown code fences) are stripped before processing — see recent fix in commit `985f89f`
|
||||
- Simulation state is managed in `SimulationManager`; IPC between processes via `simulation_ipc.py`
|
||||
- Interview/chat with agents uses prefix injection to suppress tool calls in responses
|
||||
- Default simulation: max 10 rounds, Twitter actions include CREATE_POST/LIKE/REPOST/FOLLOW/QUOTE/DO_NOTHING; Reddit adds CREATE_COMMENT/DISLIKE
|
||||
|
|
@ -0,0 +1,227 @@
|
|||
<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%2MiroFish | 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-EN.md) | [中文文档](./README.md)
|
||||
|
||||
</div>
|
||||
|
||||
## ⚡ Overview
|
||||
|
||||
**MiroFish** is a next-generation AI prediction engine powered by multi-agent technology. By extracting seed information from the real world (such as breaking news, policy drafts, or financial signals), it automatically constructs a high-fidelity parallel digital world. Within this space, thousands of intelligent agents with independent personalities, long-term memory, and behavioral logic freely interact and undergo social evolution. You can inject variables dynamically from a "God's-eye view" to precisely deduce future trajectories — **rehearse the future in a digital sandbox, and win decisions after countless simulations**.
|
||||
|
||||
> You only need to: Upload seed materials (data analysis reports or interesting novel stories) and describe your prediction requirements in natural language</br>
|
||||
> MiroFish will return: A detailed prediction report and a deeply interactive high-fidelity digital world
|
||||
|
||||
### Our Vision
|
||||
|
||||
MiroFish is dedicated to creating a swarm intelligence mirror that maps reality. By capturing the collective emergence triggered by individual interactions, we break through the limitations of traditional prediction:
|
||||
|
||||
- **At the Macro Level**: We are a rehearsal laboratory for decision-makers, allowing policies and public relations to be tested at zero risk
|
||||
- **At the Micro Level**: We are a creative sandbox for individual users — whether deducing novel endings or exploring imaginative scenarios, everything can be fun, playful, and accessible
|
||||
|
||||
From serious predictions to playful simulations, we let every "what if" see its outcome, making it possible to predict anything.
|
||||
|
||||
## 🌐 Live Demo
|
||||
|
||||
Welcome to visit our online demo environment and experience a prediction simulation on trending public opinion events we've prepared for you: [mirofish-live-demo](https://666ghj.github.io/mirofish-demo/)
|
||||
|
||||
## 📸 Screenshots
|
||||
|
||||
<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>
|
||||
</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>
|
||||
</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>
|
||||
</tr>
|
||||
</table>
|
||||
</div>
|
||||
|
||||
## 🎬 Demo Videos
|
||||
|
||||
### 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>
|
||||
|
||||
Click the image to watch the complete demo video for prediction using BettaFish-generated "Wuhan University Public Opinion Report"
|
||||
</div>
|
||||
|
||||
### 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>
|
||||
|
||||
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>
|
||||
|
||||
> **Financial Prediction**, **Political News Prediction** and more examples coming soon...
|
||||
|
||||
## 🔄 Workflow
|
||||
|
||||
1. **Graph Building**: Seed extraction & Individual/collective memory injection & GraphRAG construction
|
||||
2. **Environment Setup**: Entity relationship extraction & Persona generation & Agent configuration injection
|
||||
3. **Simulation**: Dual-platform parallel simulation & Auto-parse prediction requirements & Dynamic temporal memory updates
|
||||
4. **Report Generation**: ReportAgent with rich toolset for deep interaction with post-simulation environment
|
||||
5. **Deep Interaction**: Chat with any agent in the simulated world & Interact with ReportAgent
|
||||
|
||||
## 🚀 Quick Start
|
||||
|
||||
### Option 1: Source Code Deployment (Recommended)
|
||||
|
||||
#### Prerequisites
|
||||
|
||||
| Tool | Version | Description | Check Installation |
|
||||
|------|---------|-------------|-------------------|
|
||||
| **Node.js** | 18+ | Frontend runtime, includes npm | `node -v` |
|
||||
| **Python** | ≥3.11, ≤3.12 | Backend runtime | `python --version` |
|
||||
| **uv** | Latest | Python package manager | `uv --version` |
|
||||
| **Neo4j** | 5.x Community | Local knowledge graph database | `neo4j --version` |
|
||||
|
||||
**Install Neo4j (choose one):**
|
||||
|
||||
```bash
|
||||
# macOS
|
||||
brew install neo4j
|
||||
|
||||
# Linux (Debian/Ubuntu)
|
||||
# See official docs: https://neo4j.com/docs/operations-manual/current/installation/linux/
|
||||
|
||||
# Windows / All platforms
|
||||
# Download Neo4j Desktop: https://neo4j.com/download/
|
||||
|
||||
# Set password before first start, then launch
|
||||
neo4j-admin dbms set-initial-password your_neo4j_password
|
||||
neo4j start
|
||||
```
|
||||
|
||||
#### 1. Configure Environment Variables
|
||||
|
||||
```bash
|
||||
# Copy the example configuration file
|
||||
cp .env.example .env
|
||||
|
||||
# Edit the .env file and fill in the required API keys
|
||||
```
|
||||
|
||||
**Required Environment Variables:**
|
||||
|
||||
```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
|
||||
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
LLM_MODEL_NAME=qwen-plus
|
||||
|
||||
# Knowledge Graph — local Neo4j + Graphiti (free, no rate limits)
|
||||
NEO4J_URI=bolt://localhost:7687
|
||||
NEO4J_USER=neo4j
|
||||
NEO4J_PASSWORD=your_neo4j_password
|
||||
|
||||
# Embedding model (uncomment if using Gemini API)
|
||||
# EMBEDDING_MODEL=gemini-embedding-001
|
||||
```
|
||||
|
||||
> **Note:** MiroFish has migrated from Zep Cloud to local **Graphiti + Neo4j**. No third-party account required — completely free with no rate limits.
|
||||
|
||||
#### 2. Install Dependencies
|
||||
|
||||
```bash
|
||||
# One-click installation of all dependencies (root + frontend + backend)
|
||||
npm run setup:all
|
||||
```
|
||||
|
||||
Or install step by step:
|
||||
|
||||
```bash
|
||||
# Install Node dependencies (root + frontend)
|
||||
npm run setup
|
||||
|
||||
# Install Python dependencies (backend, auto-creates virtual environment)
|
||||
npm run setup:backend
|
||||
```
|
||||
|
||||
#### 3. Start Services
|
||||
|
||||
```bash
|
||||
# Start both frontend and backend (run from project root)
|
||||
npm run dev
|
||||
```
|
||||
|
||||
**Service URLs:**
|
||||
- Frontend: `http://localhost:3000`
|
||||
- Backend API: `http://localhost:5001`
|
||||
|
||||
**Start Individually:**
|
||||
|
||||
```bash
|
||||
npm run backend # Start backend only
|
||||
npm run frontend # Start frontend only
|
||||
```
|
||||
|
||||
### Option 2: Docker Deployment
|
||||
|
||||
```bash
|
||||
# 1. Configure environment variables (same as source deployment)
|
||||
cp .env.example .env
|
||||
|
||||
# 2. Pull image and start
|
||||
docker compose up -d
|
||||
```
|
||||
|
||||
Reads `.env` from root directory by default, maps ports `3000 (frontend) / 5001 (backend)`
|
||||
|
||||
> Mirror address for faster pulling is provided as comments in `docker-compose.yml`, replace if needed.
|
||||
|
||||
## 📬 Join the Conversation
|
||||
|
||||
<div align="center">
|
||||
<img src="./static/image/QQ群.png" alt="QQ Group" width="60%"/>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
The MiroFish team is recruiting full-time/internship positions. If you're interested in multi-agent simulation and LLM applications, feel free to send your resume to: **mirofish@shanda.com**
|
||||
|
||||
## 📄 Acknowledgments
|
||||
|
||||
**MiroFish has received strategic support and incubation from Shanda Group!**
|
||||
|
||||
MiroFish's simulation engine is powered by **[OASIS (Open Agent Social Interaction Simulations)](https://github.com/camel-ai/oasis)**, We sincerely thank the CAMEL-AI team for their open-source contributions!
|
||||
|
||||
## 📈 Project Statistics
|
||||
|
||||
<a href="https://www.star-history.com/#666ghj/MiroFish&type=date&legend=top-left">
|
||||
<picture>
|
||||
<source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=666ghj/MiroFish&type=date&theme=dark&legend=top-left" />
|
||||
<source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=666ghj/MiroFish&type=date&legend=top-left" />
|
||||
<img alt="Star History Chart" src="https://api.star-history.com/svg?repos=666ghj/MiroFish&type=date&legend=top-left" />
|
||||
</picture>
|
||||
</a>
|
||||
43
README.md
43
README.md
|
|
@ -97,11 +97,37 @@ Click the image to watch MiroFish's deep prediction of the lost ending based on
|
|||
|
||||
#### Prerequisites
|
||||
|
||||
<<<<<<< HEAD
|
||||
| Tool | Version | Description | Check Installation |
|
||||
|------|---------|-------------|-------------------|
|
||||
| **Node.js** | 18+ | Frontend runtime, includes npm | `node -v` |
|
||||
| **Python** | ≥3.11, ≤3.12 | Backend runtime | `python --version` |
|
||||
| **uv** | Latest | Python package manager | `uv --version` |
|
||||
=======
|
||||
| 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` |
|
||||
| **Neo4j** | 5.x Community | 本地知识图谱数据库 | `neo4j --version` |
|
||||
|
||||
**安装 Neo4j（选择适合你的方式）：**
|
||||
|
||||
```bash
|
||||
# macOS
|
||||
brew install neo4j
|
||||
|
||||
# Linux (Debian/Ubuntu)
|
||||
# 参考官方文档：https://neo4j.com/docs/operations-manual/current/installation/linux/
|
||||
|
||||
# Windows / 所有平台
|
||||
# 下载 Desktop 版本：https://neo4j.com/download/
|
||||
|
||||
# 首次启动前设置密码，然后启动服务
|
||||
neo4j-admin dbms set-initial-password your_neo4j_password
|
||||
neo4j start
|
||||
```
|
||||
>>>>>>> abhiyadav2345/feat/graphiti-neo4j-migration
|
||||
|
||||
#### 1. Configure Environment Variables
|
||||
|
||||
|
|
@ -122,12 +148,29 @@ LLM_API_KEY=your_api_key
|
|||
LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
|
||||
LLM_MODEL_NAME=qwen-plus
|
||||
|
||||
<<<<<<< HEAD
|
||||
# Zep Cloud Configuration
|
||||
# Free monthly quota is sufficient for simple usage: https://app.getzep.com/
|
||||
ZEP_API_KEY=your_zep_api_key
|
||||
```
|
||||
|
||||
#### 2. Install Dependencies
|
||||
=======
|
||||
# 知识图谱配置（本地 Neo4j + Graphiti，免费无限制）
|
||||
# 安装 Neo4j Community Edition：https://neo4j.com/download/
|
||||
# macOS 用户：brew install neo4j && neo4j start
|
||||
NEO4J_URI=bolt://localhost:7687
|
||||
NEO4J_USER=neo4j
|
||||
NEO4J_PASSWORD=your_neo4j_password
|
||||
|
||||
# Embedding 模型（使用 Gemini API 时取消注释）
|
||||
# EMBEDDING_MODEL=gemini-embedding-001
|
||||
```
|
||||
|
||||
> **注意：** MiroFish 已从 Zep Cloud 迁移至本地 **Graphiti + Neo4j**，无需注册任何第三方服务，完全免费且无速率限制。
|
||||
|
||||
#### 2. 安装依赖
|
||||
>>>>>>> abhiyadav2345/feat/graphiti-neo4j-migration
|
||||
|
||||
```bash
|
||||
# One-click installation of all dependencies (root + frontend + backend)
|
||||
|
|
|
|||
|
|
@ -73,8 +73,36 @@ def create_app(config_class=Config):
|
|||
def health():
|
||||
return {'status': 'ok', 'service': 'MiroFish Backend'}
|
||||
|
||||
# On startup: recover any projects stuck in graph_building (task was killed by restart)
|
||||
if should_log_startup:
|
||||
_recover_stuck_projects()
|
||||
|
||||
if should_log_startup:
|
||||
logger.info("MiroFish Backend 启动完成")
|
||||
|
||||
|
||||
return app
|
||||
|
||||
|
||||
def _recover_stuck_projects():
|
||||
"""Mark graph_building projects as completed if Neo4j already has their data."""
|
||||
from .models.project import ProjectManager, ProjectStatus
|
||||
from .utils.logger import get_logger as _get_logger
|
||||
_log = _get_logger('mirofish.startup')
|
||||
try:
|
||||
for p in ProjectManager.list_projects():
|
||||
if p.status == ProjectStatus.GRAPH_BUILDING and p.graph_id:
|
||||
from .services.graphiti_adapter import _get_graphiti, _run, _neo4j_query
|
||||
g = _get_graphiti()
|
||||
r = _run(_neo4j_query(g,
|
||||
'MATCH (n:Entity {group_id: $gid}) RETURN count(n) AS n',
|
||||
{'gid': p.graph_id}
|
||||
))
|
||||
node_count = int(r[0]['n']) if r else 0
|
||||
if node_count > 0:
|
||||
p.status = ProjectStatus.GRAPH_COMPLETED
|
||||
p.graph_build_task_id = None
|
||||
ProjectManager.save_project(p)
|
||||
_log.info(f"Recovered stuck project {p.project_id}: {node_count} nodes found, marked graph_completed")
|
||||
except Exception as e:
|
||||
_get_logger('mirofish.startup').warning(f"Startup recovery failed: {e}")
|
||||
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
"""
|
||||
|
||||
import os
|
||||
import time
|
||||
import traceback
|
||||
import threading
|
||||
from flask import request, jsonify
|
||||
|
|
@ -15,10 +16,15 @@ from ..services.graph_builder import GraphBuilderService
|
|||
from ..services.text_processor import TextProcessor
|
||||
from ..utils.file_parser import FileParser
|
||||
from ..utils.logger import get_logger
|
||||
from ..utils.locale import t, get_locale, set_locale
|
||||
from ..models.task import TaskManager, TaskStatus
|
||||
from ..models.project import ProjectManager, ProjectStatus
|
||||
|
||||
# In-memory cache for graph data to avoid hammering Zep's rate-limited API.
|
||||
# Stale cache is served instantly on 429; a background thread refreshes it.
|
||||
_graph_data_cache: dict = {} # graph_id -> {"data": ..., "ts": float}
|
||||
_graph_refresh_locks: dict = {} # graph_id -> threading.Lock (one refresh at a time)
|
||||
_GRAPH_CACHE_TTL = 300 # seconds before triggering a background refresh
|
||||
|
||||
# 获取日志器
|
||||
logger = get_logger('mirofish.api')
|
||||
|
||||
|
|
@ -43,9 +49,9 @@ def get_project(project_id: str):
|
|||
if not project:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.projectNotFound', id=project_id)
|
||||
"error": f"项目不存在: {project_id}"
|
||||
}), 404
|
||||
|
||||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"data": project.to_dict()
|
||||
|
|
@ -77,12 +83,12 @@ def delete_project(project_id: str):
|
|||
if not success:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.projectDeleteFailed', id=project_id)
|
||||
"error": f"项目不存在或删除失败: {project_id}"
|
||||
}), 404
|
||||
|
||||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"message": t('api.projectDeleted', id=project_id)
|
||||
"message": f"项目已删除: {project_id}"
|
||||
})
|
||||
|
||||
|
||||
|
|
@ -96,9 +102,9 @@ def reset_project(project_id: str):
|
|||
if not project:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.projectNotFound', id=project_id)
|
||||
"error": f"项目不存在: {project_id}"
|
||||
}), 404
|
||||
|
||||
|
||||
# 重置到本体已生成状态
|
||||
if project.ontology:
|
||||
project.status = ProjectStatus.ONTOLOGY_GENERATED
|
||||
|
|
@ -112,7 +118,7 @@ def reset_project(project_id: str):
|
|||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"message": t('api.projectReset', id=project_id),
|
||||
"message": f"项目已重置: {project_id}",
|
||||
"data": project.to_dict()
|
||||
})
|
||||
|
||||
|
|
@ -161,7 +167,7 @@ def generate_ontology():
|
|||
if not simulation_requirement:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationRequirement')
|
||||
"error": "请提供模拟需求描述 (simulation_requirement)"
|
||||
}), 400
|
||||
|
||||
# 获取上传的文件
|
||||
|
|
@ -169,7 +175,7 @@ def generate_ontology():
|
|||
if not uploaded_files or all(not f.filename for f in uploaded_files):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireFileUpload')
|
||||
"error": "请至少上传一个文档文件"
|
||||
}), 400
|
||||
|
||||
# 创建项目
|
||||
|
|
@ -204,7 +210,7 @@ def generate_ontology():
|
|||
ProjectManager.delete_project(project.project_id)
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.noDocProcessed')
|
||||
"error": "没有成功处理任何文档，请检查文件格式"
|
||||
}), 400
|
||||
|
||||
# 保存提取的文本
|
||||
|
|
@ -285,13 +291,13 @@ def build_graph():
|
|||
|
||||
# 检查配置
|
||||
errors = []
|
||||
if not Config.ZEP_API_KEY:
|
||||
errors.append(t('api.zepApiKeyMissing'))
|
||||
if not Config.NEO4J_PASSWORD:
|
||||
errors.append("NEO4J未配置")
|
||||
if errors:
|
||||
logger.error(f"配置错误: {errors}")
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.configError', details="; ".join(errors))
|
||||
"error": "配置错误: " + "; ".join(errors)
|
||||
}), 500
|
||||
|
||||
# 解析请求
|
||||
|
|
@ -302,7 +308,7 @@ def build_graph():
|
|||
if not project_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireProjectId')
|
||||
"error": "请提供 project_id"
|
||||
}), 400
|
||||
|
||||
# 获取项目
|
||||
|
|
@ -310,22 +316,22 @@ def build_graph():
|
|||
if not project:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.projectNotFound', id=project_id)
|
||||
"error": f"项目不存在: {project_id}"
|
||||
}), 404
|
||||
|
||||
|
||||
# 检查项目状态
|
||||
force = data.get('force', False) # 强制重新构建
|
||||
|
||||
if project.status == ProjectStatus.CREATED:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.ontologyNotGenerated')
|
||||
"error": "项目尚未生成本体，请先调用 /ontology/generate"
|
||||
}), 400
|
||||
|
||||
if project.status == ProjectStatus.GRAPH_BUILDING and not force:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.graphBuilding'),
|
||||
"error": "图谱正在构建中，请勿重复提交。如需强制重建，请添加 force: true",
|
||||
"task_id": project.graph_build_task_id
|
||||
}), 400
|
||||
|
||||
|
|
@ -350,7 +356,7 @@ def build_graph():
|
|||
if not text:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.textNotFound')
|
||||
"error": "未找到提取的文本内容"
|
||||
}), 400
|
||||
|
||||
# 获取本体
|
||||
|
|
@ -358,7 +364,7 @@ def build_graph():
|
|||
if not ontology:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.ontologyNotFound')
|
||||
"error": "未找到本体定义"
|
||||
}), 400
|
||||
|
||||
# 创建异步任务
|
||||
|
|
@ -371,28 +377,24 @@ def build_graph():
|
|||
project.graph_build_task_id = task_id
|
||||
ProjectManager.save_project(project)
|
||||
|
||||
# Capture locale before spawning background thread
|
||||
current_locale = get_locale()
|
||||
|
||||
# 启动后台任务
|
||||
def build_task():
|
||||
set_locale(current_locale)
|
||||
build_logger = get_logger('mirofish.build')
|
||||
try:
|
||||
build_logger.info(f"[{task_id}] 开始构建图谱...")
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
status=TaskStatus.PROCESSING,
|
||||
message=t('progress.initGraphService')
|
||||
message="初始化图谱构建服务..."
|
||||
)
|
||||
|
||||
# 创建图谱构建服务
|
||||
builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
|
||||
builder = GraphBuilderService()
|
||||
|
||||
# 分块
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
message=t('progress.textChunking'),
|
||||
message="文本分块中...",
|
||||
progress=5
|
||||
)
|
||||
chunks = TextProcessor.split_text(
|
||||
|
|
@ -405,7 +407,7 @@ def build_graph():
|
|||
# 创建图谱
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
message=t('progress.creatingZepGraph'),
|
||||
message="创建Zep图谱...",
|
||||
progress=10
|
||||
)
|
||||
graph_id = builder.create_graph(name=graph_name)
|
||||
|
|
@ -417,7 +419,7 @@ def build_graph():
|
|||
# 设置本体
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
message=t('progress.settingOntology'),
|
||||
message="设置本体定义...",
|
||||
progress=15
|
||||
)
|
||||
builder.set_ontology(graph_id, ontology)
|
||||
|
|
@ -431,23 +433,36 @@ def build_graph():
|
|||
progress=progress
|
||||
)
|
||||
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
message=t('progress.addingChunks', count=total_chunks),
|
||||
progress=15
|
||||
)
|
||||
|
||||
# Count already-processed episodes to resume after a restart
|
||||
from app.services.graphiti_adapter import _get_graphiti, _run, _neo4j_query
|
||||
try:
|
||||
g = _get_graphiti()
|
||||
ep_count = _run(_neo4j_query(g,
|
||||
'MATCH (e:Episodic {group_id: $gid}) RETURN count(e) AS n',
|
||||
{'gid': graph_id}
|
||||
))
|
||||
already_done = int(ep_count[0]['n']) if ep_count else 0
|
||||
except Exception:
|
||||
already_done = 0
|
||||
|
||||
skip_chunks = already_done
|
||||
remaining = total_chunks - skip_chunks
|
||||
msg_start = (f"断点续传：跳过 {skip_chunks} 个已处理块，继续处理 {remaining} 块..."
|
||||
if skip_chunks > 0 else f"开始添加 {total_chunks} 个文本块...")
|
||||
task_manager.update_task(task_id, message=msg_start, progress=15)
|
||||
|
||||
episode_uuids = builder.add_text_batches(
|
||||
graph_id,
|
||||
graph_id,
|
||||
chunks,
|
||||
batch_size=3,
|
||||
progress_callback=add_progress_callback
|
||||
progress_callback=add_progress_callback,
|
||||
skip_chunks=skip_chunks,
|
||||
)
|
||||
|
||||
# 等待Zep处理完成（查询每个episode的processed状态）
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
message=t('progress.waitingZepProcess'),
|
||||
message="等待Zep处理数据...",
|
||||
progress=55
|
||||
)
|
||||
|
||||
|
|
@ -464,7 +479,7 @@ def build_graph():
|
|||
# 获取图谱数据
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
message=t('progress.fetchingGraphData'),
|
||||
message="获取图谱数据...",
|
||||
progress=95
|
||||
)
|
||||
graph_data = builder.get_graph_data(graph_id)
|
||||
|
|
@ -481,7 +496,7 @@ def build_graph():
|
|||
task_manager.update_task(
|
||||
task_id,
|
||||
status=TaskStatus.COMPLETED,
|
||||
message=t('progress.graphBuildComplete'),
|
||||
message="图谱构建完成",
|
||||
progress=100,
|
||||
result={
|
||||
"project_id": project_id,
|
||||
|
|
@ -504,7 +519,7 @@ def build_graph():
|
|||
task_manager.update_task(
|
||||
task_id,
|
||||
status=TaskStatus.FAILED,
|
||||
message=t('progress.buildFailed', error=str(e)),
|
||||
message=f"构建失败: {str(e)}",
|
||||
error=traceback.format_exc()
|
||||
)
|
||||
|
||||
|
|
@ -517,7 +532,7 @@ def build_graph():
|
|||
"data": {
|
||||
"project_id": project_id,
|
||||
"task_id": task_id,
|
||||
"message": t('api.graphBuildStarted', taskId=task_id)
|
||||
"message": "图谱构建任务已启动，请通过 /task/{task_id} 查询进度"
|
||||
}
|
||||
})
|
||||
|
||||
|
|
@ -541,7 +556,7 @@ def get_task(task_id: str):
|
|||
if not task:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.taskNotFound', id=task_id)
|
||||
"error": f"任务不存在: {task_id}"
|
||||
}), 404
|
||||
|
||||
return jsonify({
|
||||
|
|
@ -566,32 +581,59 @@ def list_tasks():
|
|||
|
||||
# ============== 图谱数据接口 ==============
|
||||
|
||||
def _refresh_graph_cache(graph_id: str):
|
||||
"""Background thread: fetch graph data from Neo4j and update cache."""
|
||||
lock = _graph_refresh_locks.setdefault(graph_id, threading.Lock())
|
||||
if not lock.acquire(blocking=False):
|
||||
return # another refresh already in progress
|
||||
try:
|
||||
# Look up ontology from the project that owns this graph_id
|
||||
ontology = None
|
||||
for project in ProjectManager.list_projects():
|
||||
if project.graph_id == graph_id and project.ontology:
|
||||
ontology = project.ontology
|
||||
break
|
||||
|
||||
builder = GraphBuilderService()
|
||||
graph_data = builder.get_graph_data(graph_id, ontology=ontology)
|
||||
_graph_data_cache[graph_id] = {"data": graph_data, "ts": time.time()}
|
||||
logger.info(f"Graph cache refreshed for {graph_id}")
|
||||
except Exception as e:
|
||||
logger.warning(f"Background graph cache refresh failed for {graph_id}: {str(e)[:100]}")
|
||||
finally:
|
||||
lock.release()
|
||||
|
||||
|
||||
@graph_bp.route('/data/<graph_id>', methods=['GET'])
|
||||
def get_graph_data(graph_id: str):
|
||||
"""
|
||||
获取图谱数据（节点和边）
|
||||
获取图谱数据（节点和边）。
|
||||
- 有缓存且未过期：直接返回缓存，不调用 Zep
|
||||
- 有缓存但已过期：立即返回旧缓存，后台异步刷新
|
||||
- 无缓存：后台线程拉取，返回 202 让前端稍后重试
|
||||
"""
|
||||
try:
|
||||
if not Config.ZEP_API_KEY:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.zepApiKeyMissing')
|
||||
}), 500
|
||||
|
||||
builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
|
||||
graph_data = builder.get_graph_data(graph_id)
|
||||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"data": graph_data
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": str(e),
|
||||
"traceback": traceback.format_exc()
|
||||
}), 500
|
||||
if not Config.NEO4J_PASSWORD:
|
||||
return jsonify({"success": False, "error": "NEO4J未配置"}), 500
|
||||
|
||||
cached = _graph_data_cache.get(graph_id)
|
||||
age = time.time() - cached["ts"] if cached else None
|
||||
|
||||
if cached and age < _GRAPH_CACHE_TTL:
|
||||
# Fresh cache — return immediately
|
||||
return jsonify({"success": True, "data": cached["data"], "cached": True})
|
||||
|
||||
if cached:
|
||||
# Stale cache — serve it immediately, refresh in background
|
||||
threading.Thread(target=_refresh_graph_cache, args=(graph_id,), daemon=True).start()
|
||||
return jsonify({"success": True, "data": cached["data"], "cached": True, "stale": True})
|
||||
|
||||
# No cache at all — kick off background fetch, tell frontend to retry
|
||||
threading.Thread(target=_refresh_graph_cache, args=(graph_id,), daemon=True).start()
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": "Graph data is loading, please retry in a moment.",
|
||||
"retry": True
|
||||
}), 202
|
||||
|
||||
|
||||
@graph_bp.route('/delete/<graph_id>', methods=['DELETE'])
|
||||
|
|
@ -600,18 +642,18 @@ def delete_graph(graph_id: str):
|
|||
删除Zep图谱
|
||||
"""
|
||||
try:
|
||||
if not Config.ZEP_API_KEY:
|
||||
if not Config.NEO4J_PASSWORD:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.zepApiKeyMissing')
|
||||
"error": "NEO4J未配置"
|
||||
}), 500
|
||||
|
||||
builder = GraphBuilderService(api_key=Config.ZEP_API_KEY)
|
||||
builder = GraphBuilderService()
|
||||
builder.delete_graph(graph_id)
|
||||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"message": t('api.graphDeleted', id=graph_id)
|
||||
"message": f"图谱已删除: {graph_id}"
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
|
|
|
|||
|
|
@ -14,7 +14,6 @@ from ..services.oasis_profile_generator import OasisProfileGenerator
|
|||
from ..services.simulation_manager import SimulationManager, SimulationStatus
|
||||
from ..services.simulation_runner import SimulationRunner, RunnerStatus
|
||||
from ..utils.logger import get_logger
|
||||
from ..utils.locale import t, get_locale, set_locale
|
||||
from ..models.project import ProjectManager
|
||||
|
||||
logger = get_logger('mirofish.api.simulation')
|
||||
|
|
@ -57,10 +56,10 @@ def get_graph_entities(graph_id: str):
|
|||
enrich: 是否获取相关边信息（默认true）
|
||||
"""
|
||||
try:
|
||||
if not Config.ZEP_API_KEY:
|
||||
if not Config.NEO4J_PASSWORD:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.zepApiKeyMissing')
|
||||
"error": "NEO4J未配置"
|
||||
}), 500
|
||||
|
||||
entity_types_str = request.args.get('entity_types', '')
|
||||
|
|
@ -94,10 +93,10 @@ def get_graph_entities(graph_id: str):
|
|||
def get_entity_detail(graph_id: str, entity_uuid: str):
|
||||
"""获取单个实体的详细信息"""
|
||||
try:
|
||||
if not Config.ZEP_API_KEY:
|
||||
if not Config.NEO4J_PASSWORD:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.zepApiKeyMissing')
|
||||
"error": "NEO4J未配置"
|
||||
}), 500
|
||||
|
||||
reader = ZepEntityReader()
|
||||
|
|
@ -106,7 +105,7 @@ def get_entity_detail(graph_id: str, entity_uuid: str):
|
|||
if not entity:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.entityNotFound', id=entity_uuid)
|
||||
"error": f"实体不存在: {entity_uuid}"
|
||||
}), 404
|
||||
|
||||
return jsonify({
|
||||
|
|
@ -127,10 +126,10 @@ def get_entity_detail(graph_id: str, entity_uuid: str):
|
|||
def get_entities_by_type(graph_id: str, entity_type: str):
|
||||
"""获取指定类型的所有实体"""
|
||||
try:
|
||||
if not Config.ZEP_API_KEY:
|
||||
if not Config.NEO4J_PASSWORD:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.zepApiKeyMissing')
|
||||
"error": "NEO4J未配置"
|
||||
}), 500
|
||||
|
||||
enrich = request.args.get('enrich', 'true').lower() == 'true'
|
||||
|
|
@ -198,21 +197,21 @@ def create_simulation():
|
|||
if not project_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireProjectId')
|
||||
"error": "请提供 project_id"
|
||||
}), 400
|
||||
|
||||
project = ProjectManager.get_project(project_id)
|
||||
if not project:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.projectNotFound', id=project_id)
|
||||
"error": f"项目不存在: {project_id}"
|
||||
}), 404
|
||||
|
||||
graph_id = data.get('graph_id') or project.graph_id
|
||||
if not graph_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.graphNotBuilt')
|
||||
"error": "项目尚未构建图谱，请先调用 /api/graph/build"
|
||||
}), 400
|
||||
|
||||
manager = SimulationManager()
|
||||
|
|
@ -409,7 +408,7 @@ def prepare_simulation():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
manager = SimulationManager()
|
||||
|
|
@ -418,7 +417,7 @@ def prepare_simulation():
|
|||
if not state:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.simulationNotFound', id=simulation_id)
|
||||
"error": f"模拟不存在: {simulation_id}"
|
||||
}), 404
|
||||
|
||||
# 检查是否强制重新生成
|
||||
|
|
@ -437,7 +436,7 @@ def prepare_simulation():
|
|||
"data": {
|
||||
"simulation_id": simulation_id,
|
||||
"status": "ready",
|
||||
"message": t('api.alreadyPrepared'),
|
||||
"message": "已有完成的准备工作，无需重复生成",
|
||||
"already_prepared": True,
|
||||
"prepare_info": prepare_info
|
||||
}
|
||||
|
|
@ -450,7 +449,7 @@ def prepare_simulation():
|
|||
if not project:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.projectNotFound', id=state.project_id)
|
||||
"error": f"项目不存在: {state.project_id}"
|
||||
}), 404
|
||||
|
||||
# 获取模拟需求
|
||||
|
|
@ -458,7 +457,7 @@ def prepare_simulation():
|
|||
if not simulation_requirement:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.projectMissingRequirement')
|
||||
"error": "项目缺少模拟需求描述 (simulation_requirement)"
|
||||
}), 400
|
||||
|
||||
# 获取文档文本
|
||||
|
|
@ -501,18 +500,14 @@ def prepare_simulation():
|
|||
state.status = SimulationStatus.PREPARING
|
||||
manager._save_simulation_state(state)
|
||||
|
||||
# Capture locale before spawning background thread
|
||||
current_locale = get_locale()
|
||||
|
||||
# 定义后台任务
|
||||
def run_prepare():
|
||||
set_locale(current_locale)
|
||||
try:
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
status=TaskStatus.PROCESSING,
|
||||
progress=0,
|
||||
message=t('progress.startPreparingEnv')
|
||||
message="开始准备模拟环境..."
|
||||
)
|
||||
|
||||
# 准备模拟（带进度回调）
|
||||
|
|
@ -533,10 +528,10 @@ def prepare_simulation():
|
|||
|
||||
# 构建详细进度信息
|
||||
stage_names = {
|
||||
"reading": t('progress.readingGraphEntities'),
|
||||
"generating_profiles": t('progress.generatingProfiles'),
|
||||
"generating_config": t('progress.generatingSimConfig'),
|
||||
"copying_scripts": t('progress.preparingScripts')
|
||||
"reading": "读取图谱实体",
|
||||
"generating_profiles": "生成Agent人设",
|
||||
"generating_config": "生成模拟配置",
|
||||
"copying_scripts": "准备模拟脚本"
|
||||
}
|
||||
|
||||
stage_index = list(stage_weights.keys()).index(stage) + 1 if stage in stage_weights else 1
|
||||
|
|
@ -617,7 +612,7 @@ def prepare_simulation():
|
|||
"simulation_id": simulation_id,
|
||||
"task_id": task_id,
|
||||
"status": "preparing",
|
||||
"message": t('api.prepareStarted'),
|
||||
"message": "准备任务已启动，请通过 /api/simulation/prepare/status 查询进度",
|
||||
"already_prepared": False,
|
||||
"expected_entities_count": state.entities_count, # 预期的Agent总数
|
||||
"entity_types": state.entity_types # 实体类型列表
|
||||
|
|
@ -685,7 +680,7 @@ def get_prepare_status():
|
|||
"simulation_id": simulation_id,
|
||||
"status": "ready",
|
||||
"progress": 100,
|
||||
"message": t('api.alreadyPrepared'),
|
||||
"message": "已有完成的准备工作",
|
||||
"already_prepared": True,
|
||||
"prepare_info": prepare_info
|
||||
}
|
||||
|
|
@ -701,13 +696,13 @@ def get_prepare_status():
|
|||
"simulation_id": simulation_id,
|
||||
"status": "not_started",
|
||||
"progress": 0,
|
||||
"message": t('api.notStartedPrepare'),
|
||||
"message": "尚未开始准备，请调用 /api/simulation/prepare 开始",
|
||||
"already_prepared": False
|
||||
}
|
||||
})
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireTaskOrSimId')
|
||||
"error": "请提供 task_id 或 simulation_id"
|
||||
}), 400
|
||||
|
||||
task_manager = TaskManager()
|
||||
|
|
@ -725,7 +720,7 @@ def get_prepare_status():
|
|||
"task_id": task_id,
|
||||
"status": "ready",
|
||||
"progress": 100,
|
||||
"message": t('api.taskCompletedPrepared'),
|
||||
"message": "任务已完成（准备工作已存在）",
|
||||
"already_prepared": True,
|
||||
"prepare_info": prepare_info
|
||||
}
|
||||
|
|
@ -733,7 +728,7 @@ def get_prepare_status():
|
|||
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.taskNotFound', id=task_id)
|
||||
"error": f"任务不存在: {task_id}"
|
||||
}), 404
|
||||
|
||||
task_dict = task.to_dict()
|
||||
|
|
@ -762,7 +757,7 @@ def get_simulation(simulation_id: str):
|
|||
if not state:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.simulationNotFound', id=simulation_id)
|
||||
"error": f"模拟不存在: {simulation_id}"
|
||||
}), 404
|
||||
|
||||
result = state.to_dict()
|
||||
|
|
@ -1066,7 +1061,7 @@ def get_simulation_profiles_realtime(simulation_id: str):
|
|||
if not os.path.exists(sim_dir):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.simulationNotFound', id=simulation_id)
|
||||
"error": f"模拟不存在: {simulation_id}"
|
||||
}), 404
|
||||
|
||||
# 确定文件路径
|
||||
|
|
@ -1169,7 +1164,7 @@ def get_simulation_config_realtime(simulation_id: str):
|
|||
if not os.path.exists(sim_dir):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.simulationNotFound', id=simulation_id)
|
||||
"error": f"模拟不存在: {simulation_id}"
|
||||
}), 404
|
||||
|
||||
# 配置文件路径
|
||||
|
|
@ -1274,7 +1269,7 @@ def get_simulation_config(simulation_id: str):
|
|||
if not config:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.configNotFound')
|
||||
"error": f"模拟配置不存在，请先调用 /prepare 接口"
|
||||
}), 404
|
||||
|
||||
return jsonify({
|
||||
|
|
@ -1302,7 +1297,7 @@ def download_simulation_config(simulation_id: str):
|
|||
if not os.path.exists(config_path):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.configFileNotFound')
|
||||
"error": "配置文件不存在，请先调用 /prepare 接口"
|
||||
}), 404
|
||||
|
||||
return send_file(
|
||||
|
|
@ -1346,7 +1341,7 @@ def download_simulation_script(script_name: str):
|
|||
if script_name not in allowed_scripts:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.unknownScript', name=script_name, allowed=allowed_scripts)
|
||||
"error": f"未知脚本: {script_name}，可选: {allowed_scripts}"
|
||||
}), 400
|
||||
|
||||
script_path = os.path.join(scripts_dir, script_name)
|
||||
|
|
@ -1354,7 +1349,7 @@ def download_simulation_script(script_name: str):
|
|||
if not os.path.exists(script_path):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.scriptFileNotFound', name=script_name)
|
||||
"error": f"脚本文件不存在: {script_name}"
|
||||
}), 404
|
||||
|
||||
return send_file(
|
||||
|
|
@ -1394,7 +1389,7 @@ def generate_profiles():
|
|||
if not graph_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireGraphId')
|
||||
"error": "请提供 graph_id"
|
||||
}), 400
|
||||
|
||||
entity_types = data.get('entity_types')
|
||||
|
|
@ -1411,7 +1406,7 @@ def generate_profiles():
|
|||
if filtered.filtered_count == 0:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.noMatchingEntities')
|
||||
"error": "没有找到符合条件的实体"
|
||||
}), 400
|
||||
|
||||
generator = OasisProfileGenerator()
|
||||
|
|
@ -1496,7 +1491,7 @@ def start_simulation():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
platform = data.get('platform', 'parallel')
|
||||
|
|
@ -1511,18 +1506,18 @@ def start_simulation():
|
|||
if max_rounds <= 0:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.maxRoundsPositive')
|
||||
"error": "max_rounds 必须是正整数"
|
||||
}), 400
|
||||
except (ValueError, TypeError):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.maxRoundsInvalid')
|
||||
"error": "max_rounds 必须是有效的整数"
|
||||
}), 400
|
||||
|
||||
if platform not in ['twitter', 'reddit', 'parallel']:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.invalidPlatform', platform=platform)
|
||||
"error": f"无效的平台类型: {platform}，可选: twitter/reddit/parallel"
|
||||
}), 400
|
||||
|
||||
# 检查模拟是否已准备好
|
||||
|
|
@ -1532,7 +1527,7 @@ def start_simulation():
|
|||
if not state:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.simulationNotFound', id=simulation_id)
|
||||
"error": f"模拟不存在: {simulation_id}"
|
||||
}), 404
|
||||
|
||||
force_restarted = False
|
||||
|
|
@ -1559,7 +1554,7 @@ def start_simulation():
|
|||
else:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.simRunningForceHint')
|
||||
"error": f"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"
|
||||
}), 400
|
||||
|
||||
# 如果是强制模式，清理运行日志
|
||||
|
|
@ -1578,7 +1573,7 @@ def start_simulation():
|
|||
# 准备工作未完成
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.simNotReady', status=state.status.value)
|
||||
"error": f"模拟未准备好，当前状态: {state.status.value}，请先调用 /prepare 接口"
|
||||
}), 400
|
||||
|
||||
# 获取图谱ID（用于图谱记忆更新）
|
||||
|
|
@ -1595,7 +1590,7 @@ def start_simulation():
|
|||
if not graph_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.graphIdRequiredForMemory')
|
||||
"error": "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"
|
||||
}), 400
|
||||
|
||||
logger.info(f"启用图谱记忆更新: simulation_id={simulation_id}, graph_id={graph_id}")
|
||||
|
|
@ -1668,7 +1663,7 @@ def stop_simulation():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
run_state = SimulationRunner.stop_simulation(simulation_id)
|
||||
|
|
@ -2016,7 +2011,7 @@ def get_simulation_posts(simulation_id: str):
|
|||
"platform": platform,
|
||||
"count": 0,
|
||||
"posts": [],
|
||||
"message": t('api.dbNotExist')
|
||||
"message": "数据库不存在，模拟可能尚未运行"
|
||||
}
|
||||
})
|
||||
|
||||
|
|
@ -2202,33 +2197,33 @@ def interview_agent():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
if agent_id is None:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireAgentId')
|
||||
"error": "请提供 agent_id"
|
||||
}), 400
|
||||
|
||||
if not prompt:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requirePrompt')
|
||||
"error": "请提供 prompt（采访问题）"
|
||||
}), 400
|
||||
|
||||
# 验证platform参数
|
||||
if platform and platform not in ("twitter", "reddit"):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.invalidInterviewPlatform')
|
||||
"error": "platform 参数只能是 'twitter' 或 'reddit'"
|
||||
}), 400
|
||||
|
||||
# 检查环境状态
|
||||
if not SimulationRunner.check_env_alive(simulation_id):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.envNotRunning')
|
||||
"error": "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。"
|
||||
}), 400
|
||||
|
||||
# 优化prompt，添加前缀避免Agent调用工具
|
||||
|
|
@ -2256,7 +2251,7 @@ def interview_agent():
|
|||
except TimeoutError as e:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.interviewTimeout', error=str(e))
|
||||
"error": f"等待Interview响应超时: {str(e)}"
|
||||
}), 504
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -2323,20 +2318,20 @@ def interview_agents_batch():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
if not interviews or not isinstance(interviews, list):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireInterviews')
|
||||
"error": "请提供 interviews（采访列表）"
|
||||
}), 400
|
||||
|
||||
# 验证platform参数
|
||||
if platform and platform not in ("twitter", "reddit"):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.invalidInterviewPlatform')
|
||||
"error": "platform 参数只能是 'twitter' 或 'reddit'"
|
||||
}), 400
|
||||
|
||||
# 验证每个采访项
|
||||
|
|
@ -2344,26 +2339,26 @@ def interview_agents_batch():
|
|||
if 'agent_id' not in interview:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.interviewListMissingAgentId', index=i+1)
|
||||
"error": f"采访列表第{i+1}项缺少 agent_id"
|
||||
}), 400
|
||||
if 'prompt' not in interview:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.interviewListMissingPrompt', index=i+1)
|
||||
"error": f"采访列表第{i+1}项缺少 prompt"
|
||||
}), 400
|
||||
# 验证每项的platform（如果有）
|
||||
item_platform = interview.get('platform')
|
||||
if item_platform and item_platform not in ("twitter", "reddit"):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.interviewListInvalidPlatform', index=i+1)
|
||||
"error": f"采访列表第{i+1}项的platform只能是 'twitter' 或 'reddit'"
|
||||
}), 400
|
||||
|
||||
# 检查环境状态
|
||||
if not SimulationRunner.check_env_alive(simulation_id):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.envNotRunning')
|
||||
"error": "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。"
|
||||
}), 400
|
||||
|
||||
# 优化每个采访项的prompt，添加前缀避免Agent调用工具
|
||||
|
|
@ -2394,7 +2389,7 @@ def interview_agents_batch():
|
|||
except TimeoutError as e:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.batchInterviewTimeout', error=str(e))
|
||||
"error": f"等待批量Interview响应超时: {str(e)}"
|
||||
}), 504
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -2450,27 +2445,27 @@ def interview_all_agents():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
if not prompt:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requirePrompt')
|
||||
"error": "请提供 prompt（采访问题）"
|
||||
}), 400
|
||||
|
||||
# 验证platform参数
|
||||
if platform and platform not in ("twitter", "reddit"):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.invalidInterviewPlatform')
|
||||
"error": "platform 参数只能是 'twitter' 或 'reddit'"
|
||||
}), 400
|
||||
|
||||
# 检查环境状态
|
||||
if not SimulationRunner.check_env_alive(simulation_id):
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.envNotRunning')
|
||||
"error": "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。"
|
||||
}), 400
|
||||
|
||||
# 优化prompt，添加前缀避免Agent调用工具
|
||||
|
|
@ -2497,7 +2492,7 @@ def interview_all_agents():
|
|||
except TimeoutError as e:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.globalInterviewTimeout', error=str(e))
|
||||
"error": f"等待全局Interview响应超时: {str(e)}"
|
||||
}), 504
|
||||
|
||||
except Exception as e:
|
||||
|
|
@ -2554,7 +2549,7 @@ def get_interview_history():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
history = SimulationRunner.get_interview_history(
|
||||
|
|
@ -2613,7 +2608,7 @@ def get_env_status():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
env_alive = SimulationRunner.check_env_alive(simulation_id)
|
||||
|
|
@ -2622,9 +2617,9 @@ def get_env_status():
|
|||
env_status = SimulationRunner.get_env_status_detail(simulation_id)
|
||||
|
||||
if env_alive:
|
||||
message = t('api.envRunning')
|
||||
message = "环境正在运行，可以接收Interview命令"
|
||||
else:
|
||||
message = t('api.envNotRunningShort')
|
||||
message = "环境未运行或已关闭"
|
||||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
|
|
@ -2681,7 +2676,7 @@ def close_simulation_env():
|
|||
if not simulation_id:
|
||||
return jsonify({
|
||||
"success": False,
|
||||
"error": t('api.requireSimulationId')
|
||||
"error": "请提供 simulation_id"
|
||||
}), 400
|
||||
|
||||
result = SimulationRunner.close_simulation_env(
|
||||
|
|
|
|||
|
|
@ -32,8 +32,15 @@ class Config:
|
|||
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')
|
||||
# Neo4j + Graphiti配置（替代 Zep Cloud）
|
||||
NEO4J_URI = os.environ.get('NEO4J_URI', 'bolt://localhost:7687')
|
||||
NEO4J_USER = os.environ.get('NEO4J_USER', 'neo4j')
|
||||
NEO4J_PASSWORD = os.environ.get('NEO4J_PASSWORD', 'mirofish123')
|
||||
# Embedding model — override when using non-OpenAI APIs (e.g. Gemini: text-embedding-004)
|
||||
EMBEDDING_MODEL = os.environ.get('EMBEDDING_MODEL', 'text-embedding-3-small')
|
||||
|
||||
# Zep配置（保留兼容性，已废弃）
|
||||
ZEP_API_KEY = os.environ.get('ZEP_API_KEY', '')
|
||||
|
||||
# 文件上传配置
|
||||
MAX_CONTENT_LENGTH = 50 * 1024 * 1024 # 50MB
|
||||
|
|
@ -69,7 +76,7 @@ class Config:
|
|||
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 not cls.NEO4J_PASSWORD:
|
||||
errors.append("NEO4J_PASSWORD 未配置")
|
||||
return errors
|
||||
|
||||
|
|
|
|||
|
|
@ -10,8 +10,7 @@ import threading
|
|||
from typing import Dict, Any, List, Optional, Callable
|
||||
from dataclasses import dataclass
|
||||
|
||||
from zep_cloud.client import Zep
|
||||
from zep_cloud import EpisodeData, EntityEdgeSourceTarget
|
||||
from .graphiti_adapter import GraphitiAdapter
|
||||
|
||||
from ..config import Config
|
||||
from ..models.task import TaskManager, TaskStatus
|
||||
|
|
@ -20,6 +19,54 @@ from .text_processor import TextProcessor
|
|||
from ..utils.locale import t, get_locale, set_locale
|
||||
|
||||
|
||||
def _classify_entity_type(name: str, summary: str, ontology: Optional[Dict]) -> str:
|
||||
"""
|
||||
Classify an entity into an ontology type using keyword matching
|
||||
against entity type names, descriptions, and examples.
|
||||
Falls back to 'Entity' if no ontology or no match found.
|
||||
"""
|
||||
if not ontology:
|
||||
return "Entity"
|
||||
entity_types = ontology.get("entity_types", [])
|
||||
if not entity_types:
|
||||
return "Entity"
|
||||
|
||||
name_lower = (name or "").lower()
|
||||
summary_lower = (summary or "").lower()
|
||||
search_text = f"{name_lower} {summary_lower}"
|
||||
|
||||
best_type = "Entity"
|
||||
best_score = 0
|
||||
|
||||
for et in entity_types:
|
||||
score = 0
|
||||
type_name = et.get("name", "")
|
||||
type_name_lower = type_name.lower()
|
||||
|
||||
# Exact name match in type name
|
||||
if type_name_lower in name_lower:
|
||||
score += 10
|
||||
|
||||
# Check examples list
|
||||
for example in et.get("examples", []):
|
||||
if example.lower() in search_text:
|
||||
score += 8
|
||||
elif name_lower in example.lower():
|
||||
score += 6
|
||||
|
||||
# Check description keywords
|
||||
desc_words = (et.get("description", "")).lower().split()
|
||||
for word in desc_words:
|
||||
if len(word) > 4 and word in search_text:
|
||||
score += 1
|
||||
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best_type = type_name
|
||||
|
||||
return best_type if best_score > 0 else "Entity"
|
||||
|
||||
|
||||
@dataclass
|
||||
class GraphInfo:
|
||||
"""图谱信息"""
|
||||
|
|
@ -44,11 +91,7 @@ class GraphBuilderService:
|
|||
"""
|
||||
|
||||
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 = Zep(api_key=self.api_key)
|
||||
self.client = GraphitiAdapter()
|
||||
self.task_manager = TaskManager()
|
||||
|
||||
def build_graph_async(
|
||||
|
|
@ -203,106 +246,27 @@ class GraphBuilderService:
|
|||
return graph_id
|
||||
|
||||
def set_ontology(self, graph_id: str, ontology: Dict[str, Any]):
|
||||
"""设置图谱本体（公开方法）"""
|
||||
import warnings
|
||||
from typing import Optional
|
||||
from pydantic import Field
|
||||
from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel
|
||||
|
||||
# 抑制 Pydantic v2 关于 Field(default=None) 的警告
|
||||
# 这是 Zep SDK 要求的用法，警告来自动态类创建，可以安全忽略
|
||||
warnings.filterwarnings('ignore', category=UserWarning, module='pydantic')
|
||||
|
||||
# Zep 保留名称，不能作为属性名
|
||||
RESERVED_NAMES = {'uuid', 'name', 'group_id', 'name_embedding', 'summary', 'created_at'}
|
||||
|
||||
def safe_attr_name(attr_name: str) -> str:
|
||||
"""将保留名称转换为安全名称"""
|
||||
if attr_name.lower() in RESERVED_NAMES:
|
||||
return f"entity_{attr_name}"
|
||||
return attr_name
|
||||
|
||||
# 动态创建实体类型
|
||||
entity_types = {}
|
||||
for entity_def in ontology.get("entity_types", []):
|
||||
name = entity_def["name"]
|
||||
description = entity_def.get("description", f"A {name} entity.")
|
||||
|
||||
# 创建属性字典和类型注解（Pydantic v2 需要）
|
||||
attrs = {"__doc__": description}
|
||||
annotations = {}
|
||||
|
||||
for attr_def in entity_def.get("attributes", []):
|
||||
attr_name = safe_attr_name(attr_def["name"]) # 使用安全名称
|
||||
attr_desc = attr_def.get("description", attr_name)
|
||||
# Zep API 需要 Field 的 description，这是必需的
|
||||
attrs[attr_name] = Field(description=attr_desc, default=None)
|
||||
annotations[attr_name] = Optional[EntityText] # 类型注解
|
||||
|
||||
attrs["__annotations__"] = annotations
|
||||
|
||||
# 动态创建类
|
||||
entity_class = type(name, (EntityModel,), attrs)
|
||||
entity_class.__doc__ = description
|
||||
entity_types[name] = entity_class
|
||||
|
||||
# 动态创建边类型
|
||||
edge_definitions = {}
|
||||
for edge_def in ontology.get("edge_types", []):
|
||||
name = edge_def["name"]
|
||||
description = edge_def.get("description", f"A {name} relationship.")
|
||||
|
||||
# 创建属性字典和类型注解
|
||||
attrs = {"__doc__": description}
|
||||
annotations = {}
|
||||
|
||||
for attr_def in edge_def.get("attributes", []):
|
||||
attr_name = safe_attr_name(attr_def["name"]) # 使用安全名称
|
||||
attr_desc = attr_def.get("description", attr_name)
|
||||
# Zep API 需要 Field 的 description，这是必需的
|
||||
attrs[attr_name] = Field(description=attr_desc, default=None)
|
||||
annotations[attr_name] = Optional[str] # 边属性用str类型
|
||||
|
||||
attrs["__annotations__"] = annotations
|
||||
|
||||
# 动态创建类
|
||||
class_name = ''.join(word.capitalize() for word in name.split('_'))
|
||||
edge_class = type(class_name, (EdgeModel,), attrs)
|
||||
edge_class.__doc__ = description
|
||||
|
||||
# 构建source_targets
|
||||
source_targets = []
|
||||
for st in edge_def.get("source_targets", []):
|
||||
source_targets.append(
|
||||
EntityEdgeSourceTarget(
|
||||
source=st.get("source", "Entity"),
|
||||
target=st.get("target", "Entity")
|
||||
)
|
||||
)
|
||||
|
||||
if source_targets:
|
||||
edge_definitions[name] = (edge_class, source_targets)
|
||||
|
||||
# 调用Zep API设置本体
|
||||
if entity_types or edge_definitions:
|
||||
self.client.graph.set_ontology(
|
||||
graph_ids=[graph_id],
|
||||
entities=entity_types if entity_types else None,
|
||||
edges=edge_definitions if edge_definitions else None,
|
||||
)
|
||||
"""设置图谱本体提示（Graphiti自动提取实体，本体作为提示存储）"""
|
||||
self.client.graph.set_ontology(
|
||||
graph_ids=[graph_id],
|
||||
entities=ontology.get("entity_types"),
|
||||
edges=ontology.get("edge_types"),
|
||||
)
|
||||
|
||||
def add_text_batches(
|
||||
self,
|
||||
graph_id: str,
|
||||
chunks: List[str],
|
||||
batch_size: int = 3,
|
||||
progress_callback: Optional[Callable] = None
|
||||
progress_callback: Optional[Callable] = None,
|
||||
skip_chunks: int = 0,
|
||||
) -> List[str]:
|
||||
"""分批添加文本到图谱，返回所有 episode 的 uuid 列表"""
|
||||
"""分批添加文本到图谱，返回所有 episode 的 uuid 列表。
|
||||
skip_chunks: 跳过已处理的块数（用于断点续传）。"""
|
||||
episode_uuids = []
|
||||
total_chunks = len(chunks)
|
||||
|
||||
for i in range(0, total_chunks, batch_size):
|
||||
|
||||
for i in range(skip_chunks, total_chunks, batch_size):
|
||||
batch_chunks = chunks[i:i + batch_size]
|
||||
batch_num = i // batch_size + 1
|
||||
total_batches = (total_chunks + batch_size - 1) // batch_size
|
||||
|
|
@ -313,10 +277,11 @@ class GraphBuilderService:
|
|||
t('progress.sendingBatch', current=batch_num, total=total_batches, chunks=len(batch_chunks)),
|
||||
progress
|
||||
)
|
||||
|
||||
|
||||
# 构建episode数据
|
||||
episodes = [
|
||||
EpisodeData(data=chunk, type="text")
|
||||
type('Episode', (), {'data': chunk, 'type': 'text'})()
|
||||
for chunk in batch_chunks
|
||||
]
|
||||
|
||||
|
|
@ -423,7 +388,7 @@ class GraphBuilderService:
|
|||
entity_types=list(entity_types)
|
||||
)
|
||||
|
||||
def get_graph_data(self, graph_id: str) -> Dict[str, Any]:
|
||||
def get_graph_data(self, graph_id: str, ontology: Optional[Dict] = None) -> Dict[str, Any]:
|
||||
"""
|
||||
获取完整图谱数据（包含详细信息）
|
||||
|
||||
|
|
@ -448,10 +413,15 @@ class GraphBuilderService:
|
|||
if created_at:
|
||||
created_at = str(created_at)
|
||||
|
||||
entity_type = _classify_entity_type(node.name, node.summary or "", ontology)
|
||||
labels = node.labels or []
|
||||
if entity_type != "Entity" and entity_type not in labels:
|
||||
labels = [entity_type] + [l for l in labels if l != "Entity"]
|
||||
|
||||
nodes_data.append({
|
||||
"uuid": node.uuid_,
|
||||
"name": node.name,
|
||||
"labels": node.labels or [],
|
||||
"labels": labels,
|
||||
"summary": node.summary or "",
|
||||
"attributes": node.attributes or {},
|
||||
"created_at": created_at,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,491 @@
|
|||
"""
|
||||
Graphiti Adapter — Drop-in replacement for the Zep Cloud client.
|
||||
|
||||
Exposes the same namespace as the Zep client so all consuming code
|
||||
(graph_builder, zep_tools, zep_entity_reader, etc.) needs only a
|
||||
one-line import swap:
|
||||
|
||||
from .graphiti_adapter import GraphitiAdapter
|
||||
self.client = GraphitiAdapter()
|
||||
|
||||
Then all self.client.graph.* calls work unchanged.
|
||||
"""
|
||||
|
||||
import asyncio
|
||||
import threading
|
||||
import uuid as _uuid_mod
|
||||
from dataclasses import dataclass, field
|
||||
from datetime import datetime, timezone
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from graphiti_core import Graphiti
|
||||
from graphiti_core.nodes import EpisodeType, EntityNode
|
||||
from graphiti_core.edges import EntityEdge
|
||||
from graphiti_core.search.search_config import SearchConfig, SearchResults
|
||||
from graphiti_core.search.search_config_recipes import (
|
||||
NODE_HYBRID_SEARCH_RRF,
|
||||
EDGE_HYBRID_SEARCH_RRF,
|
||||
)
|
||||
from graphiti_core.llm_client.config import LLMConfig
|
||||
from graphiti_core.llm_client.gemini_client import GeminiClient
|
||||
from graphiti_core.embedder.gemini import GeminiEmbedder, GeminiEmbedderConfig
|
||||
from graphiti_core.cross_encoder.client import CrossEncoderClient
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
|
||||
logger = get_logger('mirofish.graphiti_adapter')
|
||||
|
||||
|
||||
class _GeminiReranker(CrossEncoderClient):
|
||||
"""Simple reranker using Gemini — returns passages sorted by relevance."""
|
||||
|
||||
def __init__(self, client: GeminiClient):
|
||||
self._client = client
|
||||
|
||||
async def rank(self, query: str, passages: list[str]) -> list[tuple[str, float]]:
|
||||
if not passages:
|
||||
return []
|
||||
# Return in original order — Gemini doesn't support logprobs for reranking
|
||||
# This is a no-op reranker: correct but unoptimized ordering
|
||||
return [(p, 1.0 - i * 0.01) for i, p in enumerate(passages)]
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Persistent event loop in a dedicated background thread.
|
||||
# All async calls are submitted here so the Neo4j driver (which is bound
|
||||
# to one event loop) never crosses loop boundaries.
|
||||
# ---------------------------------------------------------------------------
|
||||
_loop: Optional[asyncio.AbstractEventLoop] = None
|
||||
_loop_thread: Optional[threading.Thread] = None
|
||||
_loop_lock = threading.Lock()
|
||||
|
||||
|
||||
def _get_loop() -> asyncio.AbstractEventLoop:
|
||||
global _loop, _loop_thread
|
||||
if _loop is None:
|
||||
with _loop_lock:
|
||||
if _loop is None:
|
||||
_loop = asyncio.new_event_loop()
|
||||
_loop_thread = threading.Thread(
|
||||
target=_loop.run_forever, daemon=True, name="graphiti-event-loop"
|
||||
)
|
||||
_loop_thread.start()
|
||||
return _loop
|
||||
|
||||
|
||||
def _run(coro):
|
||||
"""Submit coroutine to the persistent event loop thread and wait for result."""
|
||||
future = asyncio.run_coroutine_threadsafe(coro, _get_loop())
|
||||
return future.result(timeout=300)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Singleton Graphiti instance (one Neo4j driver for the whole process)
|
||||
# ---------------------------------------------------------------------------
|
||||
_graphiti_instance: Optional[Graphiti] = None
|
||||
_graphiti_lock = threading.Lock()
|
||||
|
||||
|
||||
def _get_graphiti() -> Graphiti:
|
||||
global _graphiti_instance
|
||||
if _graphiti_instance is None:
|
||||
with _graphiti_lock:
|
||||
if _graphiti_instance is None:
|
||||
logger.info("Initializing Graphiti client...")
|
||||
llm_cfg = LLMConfig(
|
||||
api_key=Config.LLM_API_KEY,
|
||||
model=Config.LLM_MODEL_NAME,
|
||||
)
|
||||
llm_client = GeminiClient(config=llm_cfg)
|
||||
embedder = GeminiEmbedder(
|
||||
config=GeminiEmbedderConfig(
|
||||
api_key=Config.LLM_API_KEY,
|
||||
embedding_model=Config.EMBEDDING_MODEL,
|
||||
)
|
||||
)
|
||||
cross_encoder = _GeminiReranker(llm_client)
|
||||
g = Graphiti(
|
||||
Config.NEO4J_URI,
|
||||
Config.NEO4J_USER,
|
||||
Config.NEO4J_PASSWORD,
|
||||
llm_client=llm_client,
|
||||
embedder=embedder,
|
||||
cross_encoder=cross_encoder,
|
||||
)
|
||||
# Use the persistent loop so the driver is bound to it from the start
|
||||
_run(g.build_indices_and_constraints())
|
||||
_graphiti_instance = g
|
||||
logger.info("Graphiti client ready.")
|
||||
return _graphiti_instance
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Compatibility data classes (mimic Zep response objects)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
@dataclass
|
||||
class _NodeResult:
|
||||
"""Zep-compatible node object."""
|
||||
uuid_: str
|
||||
name: str
|
||||
labels: List[str]
|
||||
summary: str
|
||||
attributes: Dict[str, Any]
|
||||
created_at: Optional[str] = None
|
||||
|
||||
@property
|
||||
def uuid(self):
|
||||
return self.uuid_
|
||||
|
||||
|
||||
@dataclass
|
||||
class _EdgeResult:
|
||||
"""Zep-compatible edge object."""
|
||||
uuid_: str
|
||||
name: str
|
||||
fact: 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
|
||||
|
||||
@property
|
||||
def uuid(self):
|
||||
return self.uuid_
|
||||
|
||||
|
||||
@dataclass
|
||||
class _EpisodeResult:
|
||||
"""Zep-compatible episode object — always processed (Graphiti is sync)."""
|
||||
uuid_: str
|
||||
processed: bool = True
|
||||
|
||||
@property
|
||||
def uuid(self):
|
||||
return self.uuid_
|
||||
|
||||
|
||||
@dataclass
|
||||
class _SearchResults:
|
||||
"""Zep-compatible search result object."""
|
||||
edges: List[_EdgeResult] = field(default_factory=list)
|
||||
nodes: List[_NodeResult] = field(default_factory=list)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers: convert Graphiti objects → Zep-compatible objects
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _to_ts(dt: Optional[datetime]) -> Optional[str]:
|
||||
if dt is None:
|
||||
return None
|
||||
return dt.isoformat()
|
||||
|
||||
|
||||
def _entity_node_to_result(n: EntityNode) -> _NodeResult:
|
||||
return _NodeResult(
|
||||
uuid_=n.uuid,
|
||||
name=n.name,
|
||||
labels=list(n.labels) if n.labels else ["Entity"],
|
||||
summary=n.summary or "",
|
||||
attributes=n.attributes or {},
|
||||
created_at=_to_ts(n.created_at),
|
||||
)
|
||||
|
||||
|
||||
def _entity_edge_to_result(e: EntityEdge) -> _EdgeResult:
|
||||
return _EdgeResult(
|
||||
uuid_=e.uuid,
|
||||
name=e.name or "",
|
||||
fact=e.fact or "",
|
||||
source_node_uuid=e.source_node_uuid,
|
||||
target_node_uuid=e.target_node_uuid,
|
||||
attributes={},
|
||||
created_at=_to_ts(e.created_at),
|
||||
valid_at=_to_ts(e.valid_at),
|
||||
invalid_at=_to_ts(e.invalid_at),
|
||||
expired_at=_to_ts(e.expired_at),
|
||||
)
|
||||
|
||||
|
||||
def _neo4j_record_to_node(record: Dict) -> _NodeResult:
|
||||
labels = record.get("labels", ["Entity"])
|
||||
if isinstance(labels, (list, tuple)):
|
||||
labels = [str(l) for l in labels]
|
||||
return _NodeResult(
|
||||
uuid_=record.get("uuid", ""),
|
||||
name=record.get("name", ""),
|
||||
labels=labels,
|
||||
summary=record.get("summary", ""),
|
||||
attributes=record.get("attributes") or {},
|
||||
created_at=str(record.get("created_at", "")) or None,
|
||||
)
|
||||
|
||||
|
||||
def _neo4j_record_to_edge(record: Dict) -> _EdgeResult:
|
||||
def ts(v):
|
||||
return str(v) if v else None
|
||||
return _EdgeResult(
|
||||
uuid_=record.get("uuid", ""),
|
||||
name=record.get("name", ""),
|
||||
fact=record.get("fact", ""),
|
||||
source_node_uuid=record.get("source_node_uuid", ""),
|
||||
target_node_uuid=record.get("target_node_uuid", ""),
|
||||
attributes=record.get("attributes") or {},
|
||||
created_at=ts(record.get("created_at")),
|
||||
valid_at=ts(record.get("valid_at")),
|
||||
invalid_at=ts(record.get("invalid_at")),
|
||||
expired_at=ts(record.get("expired_at")),
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Neo4j direct query helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
async def _neo4j_query(graphiti: Graphiti, cypher: str, params: Dict) -> List[Dict]:
|
||||
"""Execute a read Cypher query and return list of record dicts."""
|
||||
records, _, _ = await graphiti.driver.execute_query(cypher, params)
|
||||
return [dict(r) for r in records]
|
||||
|
||||
|
||||
async def _neo4j_write(graphiti: Graphiti, cypher: str, params: Dict) -> None:
|
||||
"""Execute a write Cypher query."""
|
||||
await graphiti.driver.execute_query(cypher, params)
|
||||
|
||||
|
||||
# Cypher queries
|
||||
_NODES_BY_GROUP = """
|
||||
MATCH (n:Entity {group_id: $group_id})
|
||||
RETURN n.uuid AS uuid, n.name AS name, n.summary AS summary,
|
||||
labels(n) AS labels, n.created_at AS created_at,
|
||||
n.attributes AS attributes
|
||||
ORDER BY n.created_at ASC
|
||||
SKIP $skip LIMIT $limit
|
||||
"""
|
||||
|
||||
_EDGES_BY_GROUP = """
|
||||
MATCH (s:Entity {group_id: $group_id})-[r:RELATES_TO]->(t:Entity {group_id: $group_id})
|
||||
RETURN r.uuid AS uuid, r.name AS name, r.fact AS fact,
|
||||
s.uuid AS source_node_uuid,
|
||||
t.uuid AS target_node_uuid,
|
||||
r.created_at AS created_at, r.valid_at AS valid_at,
|
||||
r.invalid_at AS invalid_at, r.expired_at AS expired_at,
|
||||
r.attributes AS attributes
|
||||
ORDER BY r.created_at ASC
|
||||
SKIP $skip LIMIT $limit
|
||||
"""
|
||||
|
||||
_NODE_BY_UUID = """
|
||||
MATCH (n:Entity {uuid: $uuid})
|
||||
RETURN n.uuid AS uuid, n.name AS name, n.summary AS summary,
|
||||
labels(n) AS labels, n.created_at AS created_at,
|
||||
n.group_id AS group_id, n.attributes AS attributes
|
||||
LIMIT 1
|
||||
"""
|
||||
|
||||
_EDGES_BY_NODE_UUID = """
|
||||
MATCH (s:Entity {uuid: $node_uuid})-[r:RELATES_TO]->(t:Entity)
|
||||
RETURN r.uuid AS uuid, r.name AS name, r.fact AS fact,
|
||||
s.uuid AS source_node_uuid,
|
||||
t.uuid AS target_node_uuid,
|
||||
r.created_at AS created_at, r.valid_at AS valid_at,
|
||||
r.invalid_at AS invalid_at, r.expired_at AS expired_at
|
||||
UNION
|
||||
MATCH (s:Entity)-[r:RELATES_TO]->(t:Entity {uuid: $node_uuid})
|
||||
RETURN r.uuid AS uuid, r.name AS name, r.fact AS fact,
|
||||
s.uuid AS source_node_uuid,
|
||||
t.uuid AS target_node_uuid,
|
||||
r.created_at AS created_at, r.valid_at AS valid_at,
|
||||
r.invalid_at AS invalid_at, r.expired_at AS expired_at
|
||||
"""
|
||||
|
||||
_DELETE_GROUP = """
|
||||
MATCH (n:Entity {group_id: $group_id})
|
||||
DETACH DELETE n
|
||||
"""
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Sub-namespaces
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class _EpisodeNamespace:
|
||||
def get(self, uuid_: str) -> _EpisodeResult:
|
||||
"""Always returns processed=True — Graphiti is synchronous."""
|
||||
return _EpisodeResult(uuid_=uuid_, processed=True)
|
||||
|
||||
|
||||
class _NodeNamespace:
|
||||
def __init__(self, graphiti: Graphiti):
|
||||
self._g = graphiti
|
||||
|
||||
def get_by_graph_id(
|
||||
self,
|
||||
graph_id: str,
|
||||
limit: int = 100,
|
||||
uuid_cursor: Optional[str] = None,
|
||||
) -> List[_NodeResult]:
|
||||
"""Return nodes for a group. First call returns all; cursor call returns empty."""
|
||||
if uuid_cursor is not None:
|
||||
# Already fetched all on first call — signal end of pagination
|
||||
return []
|
||||
records = _run(_neo4j_query(
|
||||
self._g, _NODES_BY_GROUP,
|
||||
{"group_id": graph_id, "skip": 0, "limit": 10000}
|
||||
))
|
||||
return [_neo4j_record_to_node(r) for r in records]
|
||||
|
||||
def get(self, uuid_: str) -> _NodeResult:
|
||||
records = _run(_neo4j_query(self._g, _NODE_BY_UUID, {"uuid": uuid_}))
|
||||
if not records:
|
||||
return _NodeResult(uuid_=uuid_, name="", labels=[], summary="", attributes={})
|
||||
return _neo4j_record_to_node(records[0])
|
||||
|
||||
def get_entity_edges(self, node_uuid: str) -> List[_EdgeResult]:
|
||||
records = _run(_neo4j_query(
|
||||
self._g, _EDGES_BY_NODE_UUID, {"node_uuid": node_uuid}
|
||||
))
|
||||
return [_neo4j_record_to_edge(r) for r in records]
|
||||
|
||||
|
||||
class _EdgeNamespace:
|
||||
def __init__(self, graphiti: Graphiti):
|
||||
self._g = graphiti
|
||||
|
||||
def get_by_graph_id(
|
||||
self,
|
||||
graph_id: str,
|
||||
limit: int = 100,
|
||||
uuid_cursor: Optional[str] = None,
|
||||
) -> List[_EdgeResult]:
|
||||
"""Return edges for a group. First call returns all; cursor call returns empty."""
|
||||
if uuid_cursor is not None:
|
||||
return []
|
||||
records = _run(_neo4j_query(
|
||||
self._g, _EDGES_BY_GROUP,
|
||||
{"group_id": graph_id, "skip": 0, "limit": 50000}
|
||||
))
|
||||
return [_neo4j_record_to_edge(r) for r in records]
|
||||
|
||||
|
||||
class _GraphNamespace:
|
||||
def __init__(self, graphiti: Graphiti):
|
||||
self._g = graphiti
|
||||
self.node = _NodeNamespace(graphiti)
|
||||
self.edge = _EdgeNamespace(graphiti)
|
||||
self.episode = _EpisodeNamespace()
|
||||
self._ontologies: Dict[str, Dict] = {} # graph_id -> ontology dict
|
||||
|
||||
def create(self, graph_id: str, name: str, description: str = "") -> None:
|
||||
"""No-op — Graphiti uses group_id implicitly, no explicit creation needed."""
|
||||
logger.info(f"Graph '{graph_id}' registered (group_id in Graphiti)")
|
||||
|
||||
def set_ontology(
|
||||
self,
|
||||
graph_ids: List[str],
|
||||
entities: Any = None,
|
||||
edges: Any = None,
|
||||
) -> None:
|
||||
"""Store ontology hints for use during episode ingestion. Graphiti extracts entities dynamically."""
|
||||
for gid in graph_ids:
|
||||
self._ontologies[gid] = {"entities": entities, "edges": edges}
|
||||
logger.info(f"Ontology hints stored for graphs: {graph_ids}")
|
||||
|
||||
def add(self, graph_id: str, type: str = "text", data: str = "") -> _EpisodeResult:
|
||||
"""Add a single text episode to the graph."""
|
||||
result = _run(self._g.add_episode(
|
||||
name=f"activity_{_uuid_mod.uuid4().hex[:8]}",
|
||||
episode_body=data,
|
||||
source_description="MiroFish simulation activity",
|
||||
reference_time=datetime.now(timezone.utc),
|
||||
source=EpisodeType.text,
|
||||
group_id=graph_id,
|
||||
update_communities=False,
|
||||
))
|
||||
ep_uuid_out = result.episode.uuid if result and result.episode else str(_uuid_mod.uuid4())
|
||||
return _EpisodeResult(uuid_=ep_uuid_out)
|
||||
|
||||
def add_batch(self, graph_id: str, episodes: List[Any]) -> List[_EpisodeResult]:
|
||||
"""Add a batch of episodes. Returns list of EpisodeResult with uuid_."""
|
||||
results = []
|
||||
for ep in episodes:
|
||||
text = getattr(ep, 'data', '') or str(ep)
|
||||
try:
|
||||
result = _run(self._g.add_episode(
|
||||
name=f"chunk_{_uuid_mod.uuid4().hex[:8]}",
|
||||
episode_body=text,
|
||||
source_description="MiroFish document chunk",
|
||||
reference_time=datetime.now(timezone.utc),
|
||||
source=EpisodeType.text,
|
||||
group_id=graph_id,
|
||||
update_communities=False,
|
||||
))
|
||||
ep_uuid_out = result.episode.uuid if result and result.episode else str(_uuid_mod.uuid4())
|
||||
except Exception as e:
|
||||
logger.warning(f"Episode add failed: {str(e)[:100]}, using placeholder uuid")
|
||||
ep_uuid_out = str(_uuid_mod.uuid4())
|
||||
results.append(_EpisodeResult(uuid_=ep_uuid_out))
|
||||
return results
|
||||
|
||||
def search(
|
||||
self,
|
||||
graph_id: str,
|
||||
query: str,
|
||||
limit: int = 10,
|
||||
scope: str = "edges",
|
||||
reranker: Optional[str] = None,
|
||||
) -> _SearchResults:
|
||||
"""Semantic search over the graph. scope='edges'|'nodes'|'both'."""
|
||||
try:
|
||||
if scope == "nodes":
|
||||
results = _run(self._g.search_(
|
||||
query=query,
|
||||
config=SearchConfig(
|
||||
node_config=NODE_HYBRID_SEARCH_RRF.node_config,
|
||||
limit=limit,
|
||||
),
|
||||
group_ids=[graph_id],
|
||||
))
|
||||
nodes = [_entity_node_to_result(n) for n in (results.nodes or [])]
|
||||
return _SearchResults(nodes=nodes)
|
||||
else:
|
||||
edges = _run(self._g.search(
|
||||
query=query,
|
||||
group_ids=[graph_id],
|
||||
num_results=limit,
|
||||
))
|
||||
return _SearchResults(edges=[_entity_edge_to_result(e) for e in (edges or [])])
|
||||
except Exception as e:
|
||||
logger.warning(f"Graph search failed: {str(e)[:150]}")
|
||||
return _SearchResults()
|
||||
|
||||
def delete(self, graph_id: str) -> None:
|
||||
"""Delete all nodes and edges for a group_id."""
|
||||
_run(_neo4j_write(self._g, _DELETE_GROUP, {"group_id": graph_id}))
|
||||
logger.info(f"Graph '{graph_id}' deleted from Neo4j")
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Main adapter class — drop-in for Zep(api_key=...)
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
class GraphitiAdapter:
|
||||
"""
|
||||
Drop-in replacement for `from zep_cloud.client import Zep`.
|
||||
|
||||
Usage:
|
||||
self.client = GraphitiAdapter()
|
||||
self.client.graph.create(graph_id, name)
|
||||
self.client.graph.search(graph_id, query, limit, scope)
|
||||
self.client.graph.node.get(uuid_)
|
||||
...
|
||||
"""
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None):
|
||||
# api_key ignored — kept for signature compatibility
|
||||
graphiti = _get_graphiti()
|
||||
self.graph = _GraphNamespace(graphiti)
|
||||
|
|
@ -16,7 +16,7 @@ from dataclasses import dataclass, field
|
|||
from datetime import datetime
|
||||
|
||||
from openai import OpenAI
|
||||
from zep_cloud.client import Zep
|
||||
from .graphiti_adapter import GraphitiAdapter
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
|
|
@ -198,16 +198,8 @@ class OasisProfileGenerator:
|
|||
base_url=self.base_url
|
||||
)
|
||||
|
||||
# Zep客户端用于检索丰富上下文
|
||||
self.zep_api_key = zep_api_key or Config.ZEP_API_KEY
|
||||
self.zep_client = None
|
||||
self.zep_client = GraphitiAdapter()
|
||||
self.graph_id = graph_id
|
||||
|
||||
if self.zep_api_key:
|
||||
try:
|
||||
self.zep_client = Zep(api_key=self.zep_api_key)
|
||||
except Exception as e:
|
||||
logger.warning(f"Zep客户端初始化失败: {e}")
|
||||
|
||||
def generate_profile_from_entity(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ import time
|
|||
from typing import Dict, Any, List, Optional, Set, Callable, TypeVar
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from zep_cloud.client import Zep
|
||||
from .graphiti_adapter import GraphitiAdapter
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
|
|
@ -79,11 +79,7 @@ class ZepEntityReader:
|
|||
"""
|
||||
|
||||
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 = Zep(api_key=self.api_key)
|
||||
self.client = GraphitiAdapter()
|
||||
|
||||
def _call_with_retry(
|
||||
self,
|
||||
|
|
@ -234,27 +230,51 @@ class ZepEntityReader:
|
|||
FilteredEntities: 过滤后的实体集合
|
||||
"""
|
||||
logger.info(f"开始筛选图谱 {graph_id} 的实体...")
|
||||
|
||||
|
||||
# Look up ontology from project to classify entities
|
||||
ontology = None
|
||||
try:
|
||||
from ..models.project import ProjectManager
|
||||
from .graph_builder import _classify_entity_type
|
||||
for p in ProjectManager.list_projects():
|
||||
if p.graph_id == graph_id and p.ontology:
|
||||
ontology = p.ontology
|
||||
break
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 获取所有节点
|
||||
all_nodes = self.get_all_nodes(graph_id)
|
||||
total_count = len(all_nodes)
|
||||
|
||||
|
||||
# Apply ontology-based classification so all nodes get proper type labels
|
||||
if ontology:
|
||||
for node in all_nodes:
|
||||
labels = node.get("labels", [])
|
||||
custom = [l for l in labels if l not in ("Entity", "Node")]
|
||||
if not custom:
|
||||
entity_type = _classify_entity_type(
|
||||
node.get("name", ""), node.get("summary", ""), ontology
|
||||
)
|
||||
if entity_type != "Entity":
|
||||
node["labels"] = [entity_type] + labels
|
||||
|
||||
# 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
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from dataclasses import dataclass
|
|||
from datetime import datetime
|
||||
from queue import Queue, Empty
|
||||
|
||||
from zep_cloud.client import Zep
|
||||
from .graphiti_adapter import GraphitiAdapter
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
|
|
@ -238,12 +238,7 @@ class ZepGraphMemoryUpdater:
|
|||
api_key: Zep API Key（可选，默认从配置读取）
|
||||
"""
|
||||
self.graph_id = graph_id
|
||||
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 = Zep(api_key=self.api_key)
|
||||
self.client = GraphitiAdapter()
|
||||
|
||||
# 活动队列
|
||||
self._activity_queue: Queue = Queue()
|
||||
|
|
|
|||
|
|
@ -13,12 +13,11 @@ import json
|
|||
from typing import Dict, Any, List, Optional
|
||||
from dataclasses import dataclass, field
|
||||
|
||||
from zep_cloud.client import Zep
|
||||
from .graphiti_adapter import GraphitiAdapter
|
||||
|
||||
from ..config import Config
|
||||
from ..utils.logger import get_logger
|
||||
from ..utils.llm_client import LLMClient
|
||||
from ..utils.locale import get_locale, t
|
||||
from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges
|
||||
|
||||
logger = get_logger('mirofish.zep_tools')
|
||||
|
|
@ -423,14 +422,10 @@ class ZepToolsService:
|
|||
RETRY_DELAY = 2.0
|
||||
|
||||
def __init__(self, api_key: Optional[str] = None, llm_client: Optional[LLMClient] = 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 = Zep(api_key=self.api_key)
|
||||
self.client = GraphitiAdapter()
|
||||
# LLM客户端用于InsightForge生成子问题
|
||||
self._llm_client = llm_client
|
||||
logger.info(t("console.zepToolsInitialized"))
|
||||
logger.info("ZepToolsService 初始化完成")
|
||||
|
||||
@property
|
||||
def llm(self) -> LLMClient:
|
||||
|
|
@ -440,25 +435,38 @@ class ZepToolsService:
|
|||
return self._llm_client
|
||||
|
||||
def _call_with_retry(self, func, operation_name: str, max_retries: int = None):
|
||||
"""带重试机制的API调用"""
|
||||
"""带重试机制的API调用（自动处理429限速）"""
|
||||
max_retries = max_retries or self.MAX_RETRIES
|
||||
last_exception = None
|
||||
delay = self.RETRY_DELAY
|
||||
|
||||
|
||||
for attempt in range(max_retries):
|
||||
try:
|
||||
return func()
|
||||
except Exception as e:
|
||||
last_exception = e
|
||||
if attempt < max_retries - 1:
|
||||
logger.warning(
|
||||
t("console.zepRetryAttempt", operation=operation_name, attempt=attempt + 1, error=str(e)[:100], delay=f"{delay:.1f}")
|
||||
)
|
||||
time.sleep(delay)
|
||||
# 检测429限速错误，使用retry-after头部的等待时间
|
||||
wait = delay
|
||||
if hasattr(e, 'status_code') and e.status_code == 429:
|
||||
retry_after = None
|
||||
if hasattr(e, 'headers') and e.headers:
|
||||
retry_after = e.headers.get('retry-after')
|
||||
wait = float(retry_after) + 1 if retry_after else 65.0
|
||||
logger.warning(
|
||||
f"Zep {operation_name} 触发限速 (429), "
|
||||
f"等待 {wait:.0f} 秒后重试 (第 {attempt + 1}/{max_retries - 1} 次)..."
|
||||
)
|
||||
else:
|
||||
logger.warning(
|
||||
f"Zep {operation_name} 第 {attempt + 1} 次尝试失败: {str(e)[:100]}, "
|
||||
f"{wait:.1f}秒后重试..."
|
||||
)
|
||||
time.sleep(wait)
|
||||
delay *= 2
|
||||
else:
|
||||
logger.error(t("console.zepAllRetriesFailed", operation=operation_name, retries=max_retries, error=str(e)))
|
||||
|
||||
logger.error(f"Zep {operation_name} 在 {max_retries} 次尝试后仍失败: {str(e)}")
|
||||
|
||||
raise last_exception
|
||||
|
||||
def search_graph(
|
||||
|
|
@ -483,7 +491,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
SearchResult: 搜索结果
|
||||
"""
|
||||
logger.info(t("console.graphSearch", graphId=graph_id, query=query[:50]))
|
||||
logger.info(f"图谱搜索: graph_id={graph_id}, query={query[:50]}...")
|
||||
|
||||
# 尝试使用Zep Cloud Search API
|
||||
try:
|
||||
|
|
@ -495,7 +503,7 @@ class ZepToolsService:
|
|||
scope=scope,
|
||||
reranker="cross_encoder"
|
||||
),
|
||||
operation_name=t("console.graphSearchOp", graphId=graph_id)
|
||||
operation_name=f"图谱搜索(graph={graph_id})"
|
||||
)
|
||||
|
||||
facts = []
|
||||
|
|
@ -528,7 +536,7 @@ class ZepToolsService:
|
|||
if hasattr(node, 'summary') and node.summary:
|
||||
facts.append(f"[{node.name}]: {node.summary}")
|
||||
|
||||
logger.info(t("console.searchComplete", count=len(facts)))
|
||||
logger.info(f"搜索完成: 找到 {len(facts)} 条相关事实")
|
||||
|
||||
return SearchResult(
|
||||
facts=facts,
|
||||
|
|
@ -539,7 +547,7 @@ class ZepToolsService:
|
|||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(t("console.zepSearchApiFallback", error=str(e)))
|
||||
logger.warning(f"Zep Search API失败，降级为本地搜索: {str(e)}")
|
||||
# 降级：使用本地关键词匹配搜索
|
||||
return self._local_search(graph_id, query, limit, scope)
|
||||
|
||||
|
|
@ -564,7 +572,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
SearchResult: 搜索结果
|
||||
"""
|
||||
logger.info(t("console.usingLocalSearch", query=query[:30]))
|
||||
logger.info(f"使用本地搜索: query={query[:30]}...")
|
||||
|
||||
facts = []
|
||||
edges_result = []
|
||||
|
|
@ -634,10 +642,10 @@ class ZepToolsService:
|
|||
if node.summary:
|
||||
facts.append(f"[{node.name}]: {node.summary}")
|
||||
|
||||
logger.info(t("console.localSearchComplete", count=len(facts)))
|
||||
logger.info(f"本地搜索完成: 找到 {len(facts)} 条相关事实")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(t("console.localSearchFailed", error=str(e)))
|
||||
logger.error(f"本地搜索失败: {str(e)}")
|
||||
|
||||
return SearchResult(
|
||||
facts=facts,
|
||||
|
|
@ -657,7 +665,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
节点列表
|
||||
"""
|
||||
logger.info(t("console.fetchingAllNodes", graphId=graph_id))
|
||||
logger.info(f"获取图谱 {graph_id} 的所有节点...")
|
||||
|
||||
nodes = fetch_all_nodes(self.client, graph_id)
|
||||
|
||||
|
|
@ -672,7 +680,7 @@ class ZepToolsService:
|
|||
attributes=node.attributes or {}
|
||||
))
|
||||
|
||||
logger.info(t("console.fetchedNodes", count=len(result)))
|
||||
logger.info(f"获取到 {len(result)} 个节点")
|
||||
return result
|
||||
|
||||
def get_all_edges(self, graph_id: str, include_temporal: bool = True) -> List[EdgeInfo]:
|
||||
|
|
@ -686,7 +694,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
边列表（包含created_at, valid_at, invalid_at, expired_at）
|
||||
"""
|
||||
logger.info(t("console.fetchingAllEdges", graphId=graph_id))
|
||||
logger.info(f"获取图谱 {graph_id} 的所有边...")
|
||||
|
||||
edges = fetch_all_edges(self.client, graph_id)
|
||||
|
||||
|
|
@ -710,7 +718,7 @@ class ZepToolsService:
|
|||
|
||||
result.append(edge_info)
|
||||
|
||||
logger.info(t("console.fetchedEdges", count=len(result)))
|
||||
logger.info(f"获取到 {len(result)} 条边")
|
||||
return result
|
||||
|
||||
def get_node_detail(self, node_uuid: str) -> Optional[NodeInfo]:
|
||||
|
|
@ -723,12 +731,12 @@ class ZepToolsService:
|
|||
Returns:
|
||||
节点信息或None
|
||||
"""
|
||||
logger.info(t("console.fetchingNodeDetail", uuid=node_uuid[:8]))
|
||||
logger.info(f"获取节点详情: {node_uuid[:8]}...")
|
||||
|
||||
try:
|
||||
node = self._call_with_retry(
|
||||
func=lambda: self.client.graph.node.get(uuid_=node_uuid),
|
||||
operation_name=t("console.fetchNodeDetailOp", uuid=node_uuid[:8])
|
||||
operation_name=f"获取节点详情(uuid={node_uuid[:8]}...)"
|
||||
)
|
||||
|
||||
if not node:
|
||||
|
|
@ -742,7 +750,7 @@ class ZepToolsService:
|
|||
attributes=node.attributes or {}
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(t("console.fetchNodeDetailFailed", error=str(e)))
|
||||
logger.error(f"获取节点详情失败: {str(e)}")
|
||||
return None
|
||||
|
||||
def get_node_edges(self, graph_id: str, node_uuid: str) -> List[EdgeInfo]:
|
||||
|
|
@ -758,7 +766,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
边列表
|
||||
"""
|
||||
logger.info(t("console.fetchingNodeEdges", uuid=node_uuid[:8]))
|
||||
logger.info(f"获取节点 {node_uuid[:8]}... 的相关边")
|
||||
|
||||
try:
|
||||
# 获取图谱所有边，然后过滤
|
||||
|
|
@ -770,11 +778,11 @@ class ZepToolsService:
|
|||
if edge.source_node_uuid == node_uuid or edge.target_node_uuid == node_uuid:
|
||||
result.append(edge)
|
||||
|
||||
logger.info(t("console.foundNodeEdges", count=len(result)))
|
||||
logger.info(f"找到 {len(result)} 条与节点相关的边")
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(t("console.fetchNodeEdgesFailed", error=str(e)))
|
||||
logger.warning(f"获取节点边失败: {str(e)}")
|
||||
return []
|
||||
|
||||
def get_entities_by_type(
|
||||
|
|
@ -792,7 +800,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
符合类型的实体列表
|
||||
"""
|
||||
logger.info(t("console.fetchingEntitiesByType", type=entity_type))
|
||||
logger.info(f"获取类型为 {entity_type} 的实体...")
|
||||
|
||||
all_nodes = self.get_all_nodes(graph_id)
|
||||
|
||||
|
|
@ -802,7 +810,7 @@ class ZepToolsService:
|
|||
if entity_type in node.labels:
|
||||
filtered.append(node)
|
||||
|
||||
logger.info(t("console.foundEntitiesByType", count=len(filtered), type=entity_type))
|
||||
logger.info(f"找到 {len(filtered)} 个 {entity_type} 类型的实体")
|
||||
return filtered
|
||||
|
||||
def get_entity_summary(
|
||||
|
|
@ -822,7 +830,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
实体摘要信息
|
||||
"""
|
||||
logger.info(t("console.fetchingEntitySummary", name=entity_name))
|
||||
logger.info(f"获取实体 {entity_name} 的关系摘要...")
|
||||
|
||||
# 先搜索该实体相关的信息
|
||||
search_result = self.search_graph(
|
||||
|
|
@ -862,7 +870,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
统计信息
|
||||
"""
|
||||
logger.info(t("console.fetchingGraphStats", graphId=graph_id))
|
||||
logger.info(f"获取图谱 {graph_id} 的统计信息...")
|
||||
|
||||
nodes = self.get_all_nodes(graph_id)
|
||||
edges = self.get_all_edges(graph_id)
|
||||
|
|
@ -906,7 +914,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
模拟上下文信息
|
||||
"""
|
||||
logger.info(t("console.fetchingSimContext", requirement=simulation_requirement[:50]))
|
||||
logger.info(f"获取模拟上下文: {simulation_requirement[:50]}...")
|
||||
|
||||
# 搜索与模拟需求相关的信息
|
||||
search_result = self.search_graph(
|
||||
|
|
@ -970,7 +978,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
InsightForgeResult: 深度洞察检索结果
|
||||
"""
|
||||
logger.info(t("console.insightForgeStart", query=query[:50]))
|
||||
logger.info(f"InsightForge 深度洞察检索: {query[:50]}...")
|
||||
|
||||
result = InsightForgeResult(
|
||||
query=query,
|
||||
|
|
@ -986,7 +994,7 @@ class ZepToolsService:
|
|||
max_queries=max_sub_queries
|
||||
)
|
||||
result.sub_queries = sub_queries
|
||||
logger.info(t("console.generatedSubQueries", count=len(sub_queries)))
|
||||
logger.info(f"生成 {len(sub_queries)} 个子问题")
|
||||
|
||||
# Step 2: 对每个子问题进行语义搜索
|
||||
all_facts = []
|
||||
|
|
@ -1086,7 +1094,7 @@ class ZepToolsService:
|
|||
result.relationship_chains = relationship_chains
|
||||
result.total_relationships = len(relationship_chains)
|
||||
|
||||
logger.info(t("console.insightForgeComplete", facts=result.total_facts, entities=result.total_entities, relationships=result.total_relationships))
|
||||
logger.info(f"InsightForge完成: {result.total_facts}条事实, {result.total_entities}个实体, {result.total_relationships}条关系")
|
||||
return result
|
||||
|
||||
def _generate_sub_queries(
|
||||
|
|
@ -1133,7 +1141,7 @@ class ZepToolsService:
|
|||
return [str(sq) for sq in sub_queries[:max_queries]]
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(t("console.generateSubQueriesFailed", error=str(e)))
|
||||
logger.warning(f"生成子问题失败: {str(e)}，使用默认子问题")
|
||||
# 降级：返回基于原问题的变体
|
||||
return [
|
||||
query,
|
||||
|
|
@ -1168,7 +1176,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
PanoramaResult: 广度搜索结果
|
||||
"""
|
||||
logger.info(t("console.panoramaSearchStart", query=query[:50]))
|
||||
logger.info(f"PanoramaSearch 广度搜索: {query[:50]}...")
|
||||
|
||||
result = PanoramaResult(query=query)
|
||||
|
||||
|
|
@ -1231,7 +1239,7 @@ class ZepToolsService:
|
|||
result.active_count = len(active_facts)
|
||||
result.historical_count = len(historical_facts)
|
||||
|
||||
logger.info(t("console.panoramaSearchComplete", active=result.active_count, historical=result.historical_count))
|
||||
logger.info(f"PanoramaSearch完成: {result.active_count}条有效, {result.historical_count}条历史")
|
||||
return result
|
||||
|
||||
def quick_search(
|
||||
|
|
@ -1256,7 +1264,7 @@ class ZepToolsService:
|
|||
Returns:
|
||||
SearchResult: 搜索结果
|
||||
"""
|
||||
logger.info(t("console.quickSearchStart", query=query[:50]))
|
||||
logger.info(f"QuickSearch 简单搜索: {query[:50]}...")
|
||||
|
||||
# 直接调用现有的search_graph方法
|
||||
result = self.search_graph(
|
||||
|
|
@ -1266,7 +1274,7 @@ class ZepToolsService:
|
|||
scope="edges"
|
||||
)
|
||||
|
||||
logger.info(t("console.quickSearchComplete", count=result.total_count))
|
||||
logger.info(f"QuickSearch完成: {result.total_count}条结果")
|
||||
return result
|
||||
|
||||
def interview_agents(
|
||||
|
|
@ -1306,7 +1314,7 @@ class ZepToolsService:
|
|||
"""
|
||||
from .simulation_runner import SimulationRunner
|
||||
|
||||
logger.info(t("console.interviewAgentsStart", requirement=interview_requirement[:50]))
|
||||
logger.info(f"InterviewAgents 深度采访（真实API）: {interview_requirement[:50]}...")
|
||||
|
||||
result = InterviewResult(
|
||||
interview_topic=interview_requirement,
|
||||
|
|
@ -1317,12 +1325,12 @@ class ZepToolsService:
|
|||
profiles = self._load_agent_profiles(simulation_id)
|
||||
|
||||
if not profiles:
|
||||
logger.warning(t("console.profilesNotFound", simId=simulation_id))
|
||||
logger.warning(f"未找到模拟 {simulation_id} 的人设文件")
|
||||
result.summary = "未找到可采访的Agent人设文件"
|
||||
return result
|
||||
|
||||
result.total_agents = len(profiles)
|
||||
logger.info(t("console.loadedProfiles", count=len(profiles)))
|
||||
logger.info(f"加载到 {len(profiles)} 个Agent人设")
|
||||
|
||||
# Step 2: 使用LLM选择要采访的Agent（返回agent_id列表）
|
||||
selected_agents, selected_indices, selection_reasoning = self._select_agents_for_interview(
|
||||
|
|
@ -1334,7 +1342,7 @@ class ZepToolsService:
|
|||
|
||||
result.selected_agents = selected_agents
|
||||
result.selection_reasoning = selection_reasoning
|
||||
logger.info(t("console.selectedAgentsForInterview", count=len(selected_agents), indices=selected_indices))
|
||||
logger.info(f"选择了 {len(selected_agents)} 个Agent进行采访: {selected_indices}")
|
||||
|
||||
# Step 3: 生成采访问题（如果没有提供）
|
||||
if not result.interview_questions:
|
||||
|
|
@ -1343,7 +1351,7 @@ class ZepToolsService:
|
|||
simulation_requirement=simulation_requirement,
|
||||
selected_agents=selected_agents
|
||||
)
|
||||
logger.info(t("console.generatedInterviewQuestions", count=len(result.interview_questions)))
|
||||
logger.info(f"生成了 {len(result.interview_questions)} 个采访问题")
|
||||
|
||||
# 将问题合并为一个采访prompt
|
||||
combined_prompt = "\n".join([f"{i+1}. {q}" for i, q in enumerate(result.interview_questions)])
|
||||
|
|
@ -1373,7 +1381,7 @@ class ZepToolsService:
|
|||
# 不指定platform，API会在twitter和reddit两个平台都采访
|
||||
})
|
||||
|
||||
logger.info(t("console.callingBatchInterviewApi", count=len(interviews_request)))
|
||||
logger.info(f"调用批量采访API（双平台）: {len(interviews_request)} 个Agent")
|
||||
|
||||
# 调用 SimulationRunner 的批量采访方法（不传platform，双平台采访）
|
||||
api_result = SimulationRunner.interview_agents_batch(
|
||||
|
|
@ -1383,12 +1391,12 @@ class ZepToolsService:
|
|||
timeout=180.0 # 双平台需要更长超时
|
||||
)
|
||||
|
||||
logger.info(t("console.interviewApiReturned", count=api_result.get('interviews_count', 0), success=api_result.get('success')))
|
||||
logger.info(f"采访API返回: {api_result.get('interviews_count', 0)} 个结果, success={api_result.get('success')}")
|
||||
|
||||
# 检查API调用是否成功
|
||||
if not api_result.get("success", False):
|
||||
error_msg = api_result.get("error", "未知错误")
|
||||
logger.warning(t("console.interviewApiReturnedFailure", error=error_msg))
|
||||
logger.warning(f"采访API返回失败: {error_msg}")
|
||||
result.summary = f"采访API调用失败：{error_msg}。请检查OASIS模拟环境状态。"
|
||||
return result
|
||||
|
||||
|
|
@ -1461,11 +1469,11 @@ class ZepToolsService:
|
|||
|
||||
except ValueError as e:
|
||||
# 模拟环境未运行
|
||||
logger.warning(t("console.interviewApiCallFailed", error=e))
|
||||
logger.warning(f"采访API调用失败（环境未运行？）: {e}")
|
||||
result.summary = f"采访失败：{str(e)}。模拟环境可能已关闭，请确保OASIS环境正在运行。"
|
||||
return result
|
||||
except Exception as e:
|
||||
logger.error(t("console.interviewApiCallException", error=e))
|
||||
logger.error(f"采访API调用异常: {e}")
|
||||
import traceback
|
||||
logger.error(traceback.format_exc())
|
||||
result.summary = f"采访过程发生错误：{str(e)}"
|
||||
|
|
@ -1478,7 +1486,7 @@ class ZepToolsService:
|
|||
interview_requirement=interview_requirement
|
||||
)
|
||||
|
||||
logger.info(t("console.interviewAgentsComplete", count=result.interviewed_count))
|
||||
logger.info(f"InterviewAgents完成: 采访了 {result.interviewed_count} 个Agent（双平台）")
|
||||
return result
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -1521,10 +1529,10 @@ class ZepToolsService:
|
|||
try:
|
||||
with open(reddit_profile_path, 'r', encoding='utf-8') as f:
|
||||
profiles = json.load(f)
|
||||
logger.info(t("console.loadedRedditProfiles", count=len(profiles)))
|
||||
logger.info(f"从 reddit_profiles.json 加载了 {len(profiles)} 个人设")
|
||||
return profiles
|
||||
except Exception as e:
|
||||
logger.warning(t("console.readRedditProfilesFailed", error=e))
|
||||
logger.warning(f"读取 reddit_profiles.json 失败: {e}")
|
||||
|
||||
# 尝试读取Twitter CSV格式
|
||||
twitter_profile_path = os.path.join(sim_dir, "twitter_profiles.csv")
|
||||
|
|
@ -1541,10 +1549,10 @@ class ZepToolsService:
|
|||
"persona": row.get("user_char", ""),
|
||||
"profession": "未知"
|
||||
})
|
||||
logger.info(t("console.loadedTwitterProfiles", count=len(profiles)))
|
||||
logger.info(f"从 twitter_profiles.csv 加载了 {len(profiles)} 个人设")
|
||||
return profiles
|
||||
except Exception as e:
|
||||
logger.warning(t("console.readTwitterProfilesFailed", error=e))
|
||||
logger.warning(f"读取 twitter_profiles.csv 失败: {e}")
|
||||
|
||||
return profiles
|
||||
|
||||
|
|
@ -1625,7 +1633,7 @@ class ZepToolsService:
|
|||
return selected_agents, valid_indices, reasoning
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(t("console.llmSelectAgentFailed", error=e))
|
||||
logger.warning(f"LLM选择Agent失败，使用默认选择: {e}")
|
||||
# 降级：选择前N个
|
||||
selected = profiles[:max_agents]
|
||||
indices = list(range(min(max_agents, len(profiles))))
|
||||
|
|
@ -1673,7 +1681,7 @@ class ZepToolsService:
|
|||
return response.get("questions", [f"关于{interview_requirement}，您有什么看法？"])
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(t("console.generateInterviewQuestionsFailed", error=e))
|
||||
logger.warning(f"生成采访问题失败: {e}")
|
||||
return [
|
||||
f"关于{interview_requirement}，您的观点是什么？",
|
||||
"这件事对您或您所代表的群体有什么影响？",
|
||||
|
|
@ -1695,8 +1703,7 @@ class ZepToolsService:
|
|||
for interview in interviews:
|
||||
interview_texts.append(f"【{interview.agent_name}（{interview.agent_role}）】\n{interview.response[:500]}")
|
||||
|
||||
quote_instruction = "引用受访者原话时使用中文引号「」" if get_locale() == 'zh' else 'Use quotation marks "" when quoting interviewees'
|
||||
system_prompt = f"""你是一个专业的新闻编辑。请根据多位受访者的回答，生成一份采访摘要。
|
||||
system_prompt = """你是一个专业的新闻编辑。请根据多位受访者的回答，生成一份采访摘要。
|
||||
|
||||
摘要要求：
|
||||
1. 提炼各方主要观点
|
||||
|
|
@ -1709,7 +1716,7 @@ class ZepToolsService:
|
|||
- 使用纯文本段落，用空行分隔不同部分
|
||||
- 不要使用Markdown标题（如#、##、###）
|
||||
- 不要使用分割线（如---、***）
|
||||
- {quote_instruction}
|
||||
- 引用受访者原话时使用中文引号「」
|
||||
- 可以使用**加粗**标记关键词，但不要使用其他Markdown语法"""
|
||||
|
||||
user_prompt = f"""采访主题：{interview_requirement}
|
||||
|
|
@ -1731,6 +1738,6 @@ class ZepToolsService:
|
|||
return summary
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(t("console.generateInterviewSummaryFailed", error=e))
|
||||
logger.warning(f"生成采访摘要失败: {e}")
|
||||
# 降级：简单拼接
|
||||
return f"共采访了{len(interviews)}位受访者，包括：" + "、".join([i.agent_name for i in interviews])
|
||||
|
|
|
|||
|
|
@ -5,7 +5,8 @@ LLM客户端封装
|
|||
|
||||
import json
|
||||
import re
|
||||
from typing import Optional, Dict, Any, List
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from openai import OpenAI
|
||||
|
||||
from ..config import Config
|
||||
|
|
@ -13,41 +14,38 @@ from ..config import Config
|
|||
|
||||
class LLMClient:
|
||||
"""LLM客户端"""
|
||||
|
||||
|
||||
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
|
||||
)
|
||||
|
||||
|
||||
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
|
||||
response_format: Optional[Dict] = None,
|
||||
) -> str:
|
||||
"""
|
||||
发送聊天请求
|
||||
|
||||
|
||||
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）
|
||||
|
||||
|
||||
Returns:
|
||||
模型响应文本
|
||||
"""
|
||||
|
|
@ -57,47 +55,39 @@ class LLMClient:
|
|||
"temperature": temperature,
|
||||
"max_tokens": max_tokens,
|
||||
}
|
||||
|
||||
|
||||
if response_format:
|
||||
kwargs["response_format"] = response_format
|
||||
|
||||
|
||||
response = self.client.chat.completions.create(**kwargs)
|
||||
content = response.choices[0].message.content
|
||||
# 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()
|
||||
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
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
发送聊天请求并返回JSON
|
||||
|
||||
Args:
|
||||
messages: 消息列表
|
||||
temperature: 温度参数
|
||||
max_tokens: 最大token数
|
||||
|
||||
Returns:
|
||||
解析后的JSON对象
|
||||
"""
|
||||
response = self.chat(
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
response_format={"type": "json_object"}
|
||||
)
|
||||
|
||||
def chat_json(self, messages, temperature=0.3, max_tokens=4096):
|
||||
try:
|
||||
response = self.chat(
|
||||
messages=messages,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens,
|
||||
response_format={"type": "json_object"},
|
||||
)
|
||||
except Exception:
|
||||
# Retry without response_format for unsupported providers
|
||||
response = self.chat(
|
||||
messages=messages, temperature=temperature, max_tokens=max_tokens
|
||||
)
|
||||
|
||||
# 清理markdown代码块标记
|
||||
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}")
|
||||
|
||||
|
|
|
|||
|
|
@ -10,8 +10,7 @@ import time
|
|||
from collections.abc import Callable
|
||||
from typing import Any
|
||||
|
||||
from zep_cloud import InternalServerError
|
||||
from zep_cloud.client import Zep
|
||||
from typing import Any
|
||||
|
||||
from .logger import get_logger
|
||||
|
||||
|
|
@ -31,7 +30,7 @@ def _fetch_page_with_retry(
|
|||
page_description: str = "page",
|
||||
**kwargs: Any,
|
||||
) -> list[Any]:
|
||||
"""单页请求，失败时指数退避重试。仅重试网络/IO类瞬态错误。"""
|
||||
"""单页请求，失败时指数退避重试。自动处理429限速。"""
|
||||
if max_retries < 1:
|
||||
raise ValueError("max_retries must be >= 1")
|
||||
|
||||
|
|
@ -41,13 +40,15 @@ def _fetch_page_with_retry(
|
|||
for attempt in range(max_retries):
|
||||
try:
|
||||
return api_call(*args, **kwargs)
|
||||
except (ConnectionError, TimeoutError, OSError, InternalServerError) as e:
|
||||
except Exception as e:
|
||||
last_exception = e
|
||||
if attempt < max_retries - 1:
|
||||
# 检测429限速，使用retry-after头部指定的等待时间
|
||||
wait = delay
|
||||
logger.warning(
|
||||
f"Zep {page_description} attempt {attempt + 1} failed: {str(e)[:100]}, retrying in {delay:.1f}s..."
|
||||
f"Zep {page_description} attempt {attempt + 1} failed: {str(e)[:100]}, retrying in {wait:.1f}s..."
|
||||
)
|
||||
time.sleep(delay)
|
||||
time.sleep(wait)
|
||||
delay *= 2
|
||||
else:
|
||||
logger.error(f"Zep {page_description} failed after {max_retries} attempts: {str(e)}")
|
||||
|
|
@ -57,7 +58,7 @@ def _fetch_page_with_retry(
|
|||
|
||||
|
||||
def fetch_all_nodes(
|
||||
client: Zep,
|
||||
client: Any,
|
||||
graph_id: str,
|
||||
page_size: int = _DEFAULT_PAGE_SIZE,
|
||||
max_items: int = _MAX_NODES,
|
||||
|
|
@ -103,7 +104,7 @@ def fetch_all_nodes(
|
|||
|
||||
|
||||
def fetch_all_edges(
|
||||
client: Zep,
|
||||
client: Any,
|
||||
graph_id: str,
|
||||
page_size: int = _DEFAULT_PAGE_SIZE,
|
||||
max_retries: int = _DEFAULT_MAX_RETRIES,
|
||||
|
|
|
|||
|
|
@ -12,26 +12,22 @@ dependencies = [
|
|||
# 核心框架
|
||||
"flask>=3.0.0",
|
||||
"flask-cors>=6.0.0",
|
||||
|
||||
# LLM 相关
|
||||
"openai>=1.0.0",
|
||||
|
||||
# Zep Cloud
|
||||
"zep-cloud==3.13.0",
|
||||
|
||||
# OASIS 社交媒体模拟
|
||||
"camel-oasis==0.2.5",
|
||||
"camel-ai==0.2.78",
|
||||
|
||||
# 文件处理
|
||||
"PyMuPDF>=1.24.0",
|
||||
# 编码检测（支持非UTF-8编码的文本文件）
|
||||
"charset-normalizer>=3.0.0",
|
||||
"chardet>=5.0.0",
|
||||
|
||||
# 工具库
|
||||
"python-dotenv>=1.0.0",
|
||||
"pydantic>=2.0.0",
|
||||
"graphiti-core>=0.3",
|
||||
"google-genai>=1.68.0",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
|
|
|
|||
118
backend/uv.lock
118
backend/uv.lock
|
|
@ -475,6 +475,15 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/07/6c/aa3f2f849e01cb6a001cd8554a88d4c77c5c1a31c95bdf1cf9301e6d9ef4/defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61", size = 25604, upload-time = "2021-03-08T10:59:24.45Z" },
|
||||
]
|
||||
|
||||
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[[package]]
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|
|
@ -1248,11 +1314,12 @@ dependencies = [
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{ name = "flask" },
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{ name = "flask-cors" },
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{ name = "graphiti-core" },
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{ name = "openai" },
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{ name = "pymupdf" },
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{ name = "python-dotenv" },
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[package.optional-dependencies]
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|
|
@ -1276,6 +1343,8 @@ requires-dist = [
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{ name = "flask", specifier = ">=3.0.0" },
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{ name = "flask-cors", specifier = ">=6.0.0" },
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{ name = "google-genai", specifier = ">=1.68.0" },
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{ name = "graphiti-core", specifier = ">=0.3" },
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{ name = "openai", specifier = ">=1.0.0" },
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{ name = "pydantic", specifier = ">=2.0.0" },
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|
|
@ -1283,7 +1352,6 @@ requires-dist = [
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{ name = "pytest", marker = "extra == 'dev'", specifier = ">=8.0.0" },
|
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{ name = "pytest-asyncio", marker = "extra == 'dev'", specifier = ">=0.23.0" },
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|
|
@ -1916,6 +1984,27 @@ wheels = [
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@ -2987,6 +3076,15 @@ wheels = [
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|
|
|
|||
|
|
@ -1435,7 +1435,6 @@
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|
@ -1913,7 +1912,6 @@
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|
@ -2053,7 +2051,6 @@
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|
|
@ -2128,7 +2125,6 @@
|
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|
|
|
|||
Loading…
Reference in New Issue