From e1241c8f5c99c06f5b79c7d553f48a304f821475 Mon Sep 17 00:00:00 2001 From: duwanze Date: Mon, 15 Jun 2026 09:28:38 +0800 Subject: [PATCH] docs: rewrite readme for neo4j edition --- README-ZH.md | 323 ++++++++++++++++++++++++++++----------------------- README.md | 314 ++++++++++++++++++++++++++----------------------- 2 files changed, 347 insertions(+), 290 deletions(-) diff --git a/README-ZH.md b/README-ZH.md index 13fbcb4d..bfc529cb 100644 --- a/README-ZH.md +++ b/README-ZH.md @@ -1,203 +1,236 @@ -
+# MiroFish Neo4j 二开版 -MiroFish Logo +[English](./README.md) | 中文 -666ghj%2FMiroFish | Trendshift +本项目基于原始开源仓库 [666ghj/MiroFish](https://github.com/666ghj/MiroFish) 进行二次开发。 -简洁通用的群体智能引擎，预测万物 -
-A Simple and Universal Swarm Intelligence Engine, Predicting Anything +本版本的核心改动是：将原项目中的 Zep Cloud 图谱记忆与检索依赖替换为本地 Neo4j 后端，使项目可以使用本地图数据库完成实体关系存储、图谱检索、报告工具调用和模拟后的记忆更新，同时保留原 MiroFish 的多智能体模拟流程。 -666ghj%2FMiroFish | Shanda +本项目遵守原仓库一致的开源协议：**AGPL-3.0**。 -[![GitHub Stars](https://img.shields.io/github/stars/666ghj/MiroFish?style=flat-square&color=DAA520)](https://github.com/666ghj/MiroFish/stargazers) -[![GitHub Watchers](https://img.shields.io/github/watchers/666ghj/MiroFish?style=flat-square)](https://github.com/666ghj/MiroFish/watchers) -[![GitHub Forks](https://img.shields.io/github/forks/666ghj/MiroFish?style=flat-square)](https://github.com/666ghj/MiroFish/network) -[![Docker](https://img.shields.io/badge/Docker-Build-2496ED?style=flat-square&logo=docker&logoColor=white)](https://hub.docker.com/) -[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/666ghj/MiroFish) +## 主要改动 -[![Discord](https://img.shields.io/badge/Discord-Join-5865F2?style=flat-square&logo=discord&logoColor=white)](http://discord.gg/ePf5aPaHnA) -[![X](https://img.shields.io/badge/X-Follow-000000?style=flat-square&logo=x&logoColor=white)](https://x.com/mirofish_ai) -[![Instagram](https://img.shields.io/badge/Instagram-Follow-E4405F?style=flat-square&logo=instagram&logoColor=white)](https://www.instagram.com/mirofish_ai/) +- 使用本地 Neo4j 替代 Zep Cloud 图谱存储与检索。 +- 新增 Neo4j 图谱构建、实体读取、记忆更新和搜索服务。 +- 新增图数据库后端工厂，支持按配置切换图谱后端。 +- 修复并增强报告工具输出： + - Deep Insight + - Panorama Search + - Quick Search +- 新增 LLM 429 限流等待与重试逻辑。 +- 新增足球场景的比分概率计算与报告注入。 +- 新增本地 Neo4j Docker Compose 配置。 -[English](./README.md) | [中文文档](./README-ZH.md) +## 功能说明 -
+- 上传种子文档并抽取实体关系。 +- 将抽取结果写入本地 Neo4j 图谱。 +- 生成模拟 Agent 和社交行为配置。 +- 运行双平台社交模拟。 +- 基于图谱检索生成预测报告。 +- 在足球比赛场景中输出胜平负概率、最可能比分、期望进球和比分矩阵。 -## ⚡ 项目概述 +## 项目结构 -**MiroFish** 是一款基于多智能体技术的新一代 AI 预测引擎。通过提取现实世界的种子信息（如突发新闻、政策草案、金融信号），自动构建出高保真的平行数字世界。在此空间内，成千上万个具备独立人格、长期记忆与行为逻辑的智能体进行自由交互与社会演化。你可透过「上帝视角」动态注入变量，精准推演未来走向——**让未来在数字沙盘中预演，助决策在百战模拟后胜出**。 - -> 你只需：上传种子材料（数据分析报告或者有趣的小说故事），并用自然语言描述预测需求
-> MiroFish 将返回：一份详尽的预测报告，以及一个可深度交互的高保真数字世界 - -### 我们的愿景 - -MiroFish 致力于打造映射现实的群体智能镜像，通过捕捉个体互动引发的群体涌现，突破传统预测的局限： - -- **于宏观**：我们是决策者的预演实验室，让政策与公关在零风险中试错 -- **于微观**：我们是个人用户的创意沙盘，无论是推演小说结局还是探索脑洞，皆可有趣、好玩、触手可及 - -从严肃预测到趣味仿真，我们让每一个如果都能看见结果，让预测万物成为可能。 - -## 🌐 在线体验 - -欢迎访问在线 Demo 演示环境，体验我们为你准备的一次关于热点舆情事件的推演预测：[mirofish-live-demo](https://666ghj.github.io/mirofish-demo/) - -## 📸 系统截图 - -
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- -## 🎬 演示视频 - -### 1. 武汉大学舆情推演预测 + MiroFish项目讲解 - -
-MiroFish Demo Video - -点击图片查看使用微舆BettaFish生成的《武大舆情报告》进行预测的完整演示视频 -
- -### 2. 《红楼梦》失传结局推演预测 - -
-MiroFish Demo Video - -点击图片查看基于《红楼梦》前80回数十万字，MiroFish深度预测失传结局 -
- -> **金融方向推演预测**、**时政要闻推演预测**等示例陆续更新中... - -## 🔄 工作流程 - -1. **图谱构建**：现实种子提取 & 个体与群体记忆注入 & GraphRAG构建 -2. **环境搭建**：实体关系抽取 & 人设生成 & 环境配置Agent注入仿真参数 -3. **开始模拟**：双平台并行模拟 & 自动解析预测需求 & 动态更新时序记忆 -4. **报告生成**：ReportAgent拥有丰富的工具集与模拟后环境进行深度交互 -5. **深度互动**：与模拟世界中的任意一位进行对话 & 与ReportAgent进行对话 - -## 🚀 快速开始 - -### 一、源码部署（推荐） - -#### 前置要求 - -| 工具 | 版本要求 | 说明 | 安装检查 | -|------|---------|------|---------| -| **Node.js** | 18+ | 前端运行环境，包含 npm | `node -v` | -| **Python** | ≥3.11, ≤3.12 | 后端运行环境 | `python --version` | -| **uv** | 最新版 | Python 包管理器 | `uv --version` | - -#### 1. 配置环境变量 - -```bash -# 复制示例配置文件 -cp .env.example .env - -# 编辑 .env 文件，填入必要的 API 密钥 +```text +frontend/ Vue + Vite 前端 +backend/ Flask 后端 +backend/app/services/ 模拟、报告、图谱与适配服务 +backend/app/services/adapters/ + Neo4j 图谱适配实现 +backend/app/utils/neo4j/ Neo4j 驱动与 Schema 工具 +locales/ 多语言文案 +static/ 静态图片资源 ``` -**必需的环境变量：** +## 环境要求 + +- Node.js 18+ +- Python 3.11 - 3.12 +- uv +- Docker（用于快速启动 Neo4j） +- 一个兼容 OpenAI SDK 格式的 LLM API Key + +## 环境变量配置 + +复制示例文件： + +```bash +cp .env.example .env +``` + +最小本地配置示例： ```env -# LLM API配置（支持 OpenAI SDK 格式的任意 LLM API） -# 推荐使用阿里百炼平台qwen-plus模型：https://bailian.console.aliyun.com/ -# 注意消耗较大，可先进行小于40轮的模拟尝试 -LLM_API_KEY=your_api_key +LLM_API_KEY=your_api_key_here LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 LLM_MODEL_NAME=qwen-plus -# Zep Cloud 配置 -# 每月免费额度即可支撑简单使用：https://app.getzep.com/ -ZEP_API_KEY=your_zep_api_key +GRAPH_BACKEND=neo4j +NEO4J_URI=bolt://localhost:7687 +NEO4J_USERNAME=neo4j +NEO4J_PASSWORD=password +NEO4J_DATABASE=neo4j ``` -#### 2. 安装依赖 +可选的 LLM 限流重试配置： + +```env +LLM_RATE_LIMIT_MAX_ATTEMPTS=20 +LLM_RATE_LIMIT_INITIAL_DELAY=30 +LLM_RATE_LIMIT_MAX_DELAY=180 +LLM_RATE_LIMIT_BACKOFF_FACTOR=1.5 +``` + +## 启动 Neo4j + +项目提供了本地 Neo4j 的 Docker Compose 文件： + +```bash +docker compose -f docker-compose.neo4j.yml up -d +``` + +默认地址： + +- Neo4j Browser：`http://localhost:7474` +- Bolt URI：`bolt://localhost:7687` +- 默认账号：`neo4j / password` + +请确保 `.env` 中的 `NEO4J_PASSWORD` 与 Docker Compose 中配置的密码一致。 + +## 安装依赖 + +一次性安装前后端依赖： ```bash -# 一键安装所有依赖（根目录 + 前端 + 后端） npm run setup:all ``` -或者分步安装： +也可以分开安装： ```bash -# 安装 Node 依赖（根目录 + 前端） npm run setup - -# 安装 Python 依赖（后端，自动创建虚拟环境） npm run setup:backend ``` -#### 3. 启动服务 +## 运行项目 + +同时启动前端和后端： ```bash -# 同时启动前后端（在项目根目录执行） npm run dev ``` -**服务地址：** +服务地址： + - 前端：`http://localhost:3000` - 后端 API：`http://localhost:5001` -**单独启动：** +单独启动： ```bash -npm run backend # 仅启动后端 -npm run frontend # 仅启动前端 +npm run backend +npm run frontend ``` -### 二、Docker 部署 +## Docker 运行 + +根目录的 `docker-compose.yml` 可用于启动应用容器。若使用本地 Neo4j 图谱后端，建议先启动 Neo4j： ```bash -# 1. 配置环境变量（同源码部署） -cp .env.example .env +docker compose -f docker-compose.neo4j.yml up -d +``` -# 2. 拉取镜像并启动 +再启动应用： + +```bash docker compose up -d ``` -默认会读取根目录下的 `.env`，并映射端口 `3000（前端）/5001（后端）` +默认会读取根目录下的 `.env`，并映射端口 `3000（前端）/5001（后端）`。 -> 在 `docker-compose.yml` 中已通过注释提供加速镜像地址，可按需替换 +## 基本使用流程 -## 📬 更多交流 +1. 启动 Neo4j。 +2. 启动后端和前端。 +3. 打开 `http://localhost:3000`。 +4. 创建或打开项目。 +5. 上传种子文档。 +6. 构建图谱。 +7. 生成模拟配置。 +8. 启动模拟。 +9. 生成预测报告。 -
-QQ交流群 -
+## 报告检索工具 -  +报告 Agent 可以调用以下图谱检索工具： -MiroFish团队长期招募全职/实习，如果你对多Agent应用感兴趣，欢迎投递简历至：**mirofish@shanda.com** +- **Deep Insight**：将问题拆解为多个子问题，并收集相关事实、实体和关系链。 +- **Panorama Search**：返回图谱中的全景实体和事实。 +- **Quick Search**：对图谱事实、节点名称和节点摘要进行轻量关键词搜索。 +- **Interview Agents**：结合模拟或图谱上下文生成面向 Agent 的回答。 -## 📄 致谢 +在 Neo4j 二开版中，检索工具会输出前端可直接解析的文本结构，因此报告时间线能够展示事实、实体和关系链，而不是空白 JSON。 -**MiroFish 得到了盛大集团的战略支持和孵化！** +## 足球比分概率报告 -MiroFish 的仿真引擎由 **[OASIS](https://github.com/camel-ai/oasis)** 驱动，我们衷心感谢 CAMEL-AI 团队的开源贡献！ +当模拟需求或种子数据包含足球、比分、泊松、lambda、xG、expected goals 等信号时，后端会尝试抽取： -## 📈 项目统计 +- `lambda_home` +- `lambda_away` +- xG / expected goals +- 主客队信息 - - - - - Star History Chart - - +并生成： + +- 主胜 / 平局 / 客胜概率 +- 最可能比分 Top Scorelines +- 期望进球 +- 比分概率矩阵 + +该部分会作为确定性计算结果注入报告，避免报告只停留在 LLM 的泛化描述。 + +## 日志位置 + +常用本地日志： + +```text +log/backend-restart.out.log +log/backend-restart.err.log +log/frontend-direct.out.log +log/frontend-direct.err.log +backend/uploads/reports//agent_log.jsonl +backend/uploads/reports//console_log.txt +``` + +## 常见问题 + +### Neo4j 连接失败 + +确认 Neo4j 已启动，并检查 `.env` 中的 Neo4j 配置是否与 Docker Compose 一致。 + +```bash +docker compose -f docker-compose.neo4j.yml ps +``` + +### LLM 返回 429 + +后端已内置限流重试逻辑。遇到 429 时会根据 `LLM_RATE_LIMIT_*` 配置等待后继续重试。 + +### Deep Insight / Panorama Search / Quick Search 内容为空 + +请确认项目已经成功构建图谱。Neo4j 检索会搜索关系事实、节点名称和节点摘要；如果图谱本身没有内容，工具也无法返回有效结果。 + +### 报告缺少比分预测 + +请确认模拟需求或种子数据包含足球相关关键词，并提供可抽取的 `lambda_home`、`lambda_away`、xG 或 expected goals 信息。 + +## 开源协议 + +本项目继承原仓库协议，使用 **AGPL-3.0**。 + +如果你部署、分发，或通过网络提供该软件服务，请遵守 AGPL-3.0 的相关要求。 + +## 致谢 + +本项目基于 [666ghj/MiroFish](https://github.com/666ghj/MiroFish) 进行二次开发。感谢原作者及所有贡献者的开源工作。 diff --git a/README.md b/README.md index de082935..9966b827 100644 --- a/README.md +++ b/README.md @@ -1,203 +1,227 @@ -
+# MiroFish Neo4j Edition -MiroFish Logo +English | [中文](./README-ZH.md) -666ghj%2FMiroFish | Trendshift +This project is a secondary development based on the original open-source repository [666ghj/MiroFish](https://github.com/666ghj/MiroFish). -简洁通用的群体智能引擎，预测万物 -
-A Simple and Universal Swarm Intelligence Engine, Predicting Anything +The main change in this edition is replacing the Zep Cloud graph-memory dependency with a local Neo4j backend, so the project can run with local graph storage and local graph search while keeping the original MiroFish multi-agent simulation workflow. -666ghj%2FMiroFish | Shanda +The project follows the same source license as the original repository: **AGPL-3.0**. -[![GitHub Stars](https://img.shields.io/github/stars/666ghj/MiroFish?style=flat-square&color=DAA520)](https://github.com/666ghj/MiroFish/stargazers) -[![GitHub Watchers](https://img.shields.io/github/watchers/666ghj/MiroFish?style=flat-square)](https://github.com/666ghj/MiroFish/watchers) -[![GitHub Forks](https://img.shields.io/github/forks/666ghj/MiroFish?style=flat-square)](https://github.com/666ghj/MiroFish/network) -[![Docker](https://img.shields.io/badge/Docker-Build-2496ED?style=flat-square&logo=docker&logoColor=white)](https://hub.docker.com/) -[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/666ghj/MiroFish) +## What Changed -[![Discord](https://img.shields.io/badge/Discord-Join-5865F2?style=flat-square&logo=discord&logoColor=white)](http://discord.gg/ePf5aPaHnA) -[![X](https://img.shields.io/badge/X-Follow-000000?style=flat-square&logo=x&logoColor=white)](https://x.com/mirofish_ai) -[![Instagram](https://img.shields.io/badge/Instagram-Follow-E4405F?style=flat-square&logo=instagram&logoColor=white)](https://www.instagram.com/mirofish_ai/) +- Replaced Zep Cloud graph storage/search with local Neo4j adapters. +- Added Neo4j graph builder, entity reader, memory updater, and search service. +- Added local graph service factory for switching graph backends. +- Improved report-agent search tool output for: + - Deep Insight + - Panorama Search + - Quick Search +- Added LLM 429 rate-limit waiting and retry logic. +- Added deterministic football score probability reporting for football simulation scenarios. +- Added local Neo4j Docker Compose configuration. -[English](./README.md) | [中文文档](./README-ZH.md) +## Features -
+- Upload seed documents and build a graph from extracted entities and relations. +- Generate simulation agents and social behavior profiles. +- Run dual-platform social simulation. +- Generate prediction reports with graph search tools. +- Use local Neo4j as the default graph backend. +- Produce football score probabilities when the scenario contains football/score/Poisson/lambda signals. -## ⚡ Overview +## Architecture -**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
-> 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 - -
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- -## 🎬 Demo Videos - -### 1. Wuhan University Public Opinion Simulation + MiroFish Project Introduction - -
-MiroFish Demo Video - -Click the image to watch the complete demo video for prediction using BettaFish-generated "Wuhan University Public Opinion Report" -
- -### 2. Dream of the Red Chamber Lost Ending Simulation - -
-MiroFish Demo Video - -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" -
- -> **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` | - -#### 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 +```text +frontend/ Vue + Vite frontend +backend/ Flask backend +backend/app/services/ Simulation, report, graph, and adapter services +backend/app/services/adapters/ + Neo4j graph adapter implementation +backend/app/utils/neo4j/ Neo4j driver and schema helpers +locales/ i18n text +static/ Static images ``` -**Required Environment Variables:** +## Requirements + +- Node.js 18+ +- Python 3.11 - 3.12 +- uv +- Docker, if you want to run Neo4j with Docker Compose +- An OpenAI-compatible LLM API key + +## Environment Configuration + +Copy the example environment file: + +```bash +cp .env.example .env +``` + +Minimal local configuration: ```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_API_KEY=your_api_key_here LLM_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 LLM_MODEL_NAME=qwen-plus -# Zep Cloud Configuration -# Free monthly quota is sufficient for simple usage: https://app.getzep.com/ -ZEP_API_KEY=your_zep_api_key +GRAPH_BACKEND=neo4j +NEO4J_URI=bolt://localhost:7687 +NEO4J_USERNAME=neo4j +NEO4J_PASSWORD=password +NEO4J_DATABASE=neo4j ``` -#### 2. Install Dependencies +Optional LLM rate-limit settings: + +```env +LLM_RATE_LIMIT_MAX_ATTEMPTS=20 +LLM_RATE_LIMIT_INITIAL_DELAY=30 +LLM_RATE_LIMIT_MAX_DELAY=180 +LLM_RATE_LIMIT_BACKOFF_FACTOR=1.5 +``` + +## Start Neo4j + +Use the included local Neo4j Compose file: + +```bash +docker compose -f docker-compose.neo4j.yml up -d +``` + +Default endpoints: + +- Neo4j Browser: `http://localhost:7474` +- Bolt URI: `bolt://localhost:7687` +- Default account: `neo4j / password` + +Make sure the password matches `NEO4J_PASSWORD` in `.env`. + +## Install Dependencies + +Install frontend and backend dependencies: ```bash -# One-click installation of all dependencies (root + frontend + backend) npm run setup:all ``` -Or install step by step: +Or install them separately: ```bash -# Install Node dependencies (root + frontend) npm run setup - -# Install Python dependencies (backend, auto-creates virtual environment) npm run setup:backend ``` -#### 3. Start Services +## Run the Project + +Run frontend and backend together: ```bash -# Start both frontend and backend (run from project root) npm run dev ``` -**Service URLs:** +Service URLs: + - Frontend: `http://localhost:3000` - Backend API: `http://localhost:5001` -**Start Individually:** +Run services separately: ```bash -npm run backend # Start backend only -npm run frontend # Start frontend only +npm run backend +npm run frontend ``` -### Option 2: Docker Deployment +## Docker + +The root `docker-compose.yml` can start the app container. For local graph storage, start Neo4j separately with: ```bash -# 1. Configure environment variables (same as source deployment) -cp .env.example .env +docker compose -f docker-compose.neo4j.yml up -d +``` -# 2. Pull image and start +Then run the application: + +```bash docker compose up -d ``` -Reads `.env` from root directory by default, maps ports `3000 (frontend) / 5001 (backend)` +## Basic Workflow -> Mirror address for faster pulling is provided as comments in `docker-compose.yml`, replace if needed. +1. Start Neo4j. +2. Start the backend and frontend. +3. Open `http://localhost:3000`. +4. Create or open a project. +5. Upload seed documents. +6. Build the graph. +7. Generate simulation configuration. +8. Run simulation. +9. Generate the prediction report. -## 📬 Join the Conversation +## Report Search Tools -
-QQ Group -
+The report agent can call several graph-search tools: -  +- **Deep Insight**: decomposes a question and gathers supporting graph facts. +- **Panorama Search**: returns a broad view of graph entities and facts. +- **Quick Search**: performs lightweight keyword search over graph facts and nodes. +- **Interview Agents**: uses simulation/graph context for agent-oriented responses. -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** +In the Neo4j edition, tool outputs are rendered in the text format expected by the frontend, so the report timeline can display facts, entities, and relation chains directly. -## 📄 Acknowledgments +## Football Score Probability -**MiroFish has received strategic support and incubation from Shanda Group!** +For football simulation prompts, the backend can extract usable signals such as `lambda_home`, `lambda_away`, xG, or prose score priors from simulation config/actions and generate: -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! +- Home/draw/away probabilities +- Top scorelines +- Expected goals +- Score distribution matrix -## 📈 Project Statistics +This is injected into the report as a deterministic computed section so the report does not depend only on LLM prose. - - - - - Star History Chart - - \ No newline at end of file +## Logs + +Common local log files: + +```text +log/backend-restart.out.log +log/backend-restart.err.log +log/frontend-direct.out.log +log/frontend-direct.err.log +backend/uploads/reports//agent_log.jsonl +backend/uploads/reports//console_log.txt +``` + +## Troubleshooting + +### Neo4j connection failed + +Check that Neo4j is running and the `.env` values match the Docker Compose credentials. + +```bash +docker compose -f docker-compose.neo4j.yml ps +``` + +### LLM returns 429 + +The backend includes rate-limit retry logic. It will sleep and retry according to the `LLM_RATE_LIMIT_*` settings. + +### Search tools return empty results + +Make sure the project has built a graph successfully. The Neo4j search tools search graph facts, node names, and node summaries. + +### Football report has no score prediction + +Make sure the simulation requirement or seed data includes football-related terms and usable scoring priors such as `lambda_home`, `lambda_away`, xG, or expected goals. + +## License + +This project follows the original repository license: **AGPL-3.0**. + +If you deploy, distribute, or provide this software over a network, please comply with the AGPL-3.0 requirements. + +## Acknowledgements + +This project is based on the original [666ghj/MiroFish](https://github.com/666ghj/MiroFish). Thanks to the original authors and contributors for their open-source work.