diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000000000000000000000000000000000000..6e8e325b246907dee937fe855f3cf8c95ff7abf5
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,93 @@
+# ======================================
+# GitHub Agent Demo - 环境变量配置
+# ======================================
+
+# --- LLM 供应商选择 ---
+# 支持: openai, deepseek, anthropic, gemini
+# 默认: deepseek
+LLM_PROVIDER=deepseek
+
+# --- API Keys (根据选择的供应商配置对应的 Key) ---
+
+# OpenAI (如果 LLM_PROVIDER=openai)
+OPENAI_API_KEY=
+# OPENAI_BASE_URL= # 可选: 自定义端点 (如 Azure OpenAI)
+
+# DeepSeek (如果 LLM_PROVIDER=deepseek)
+DEEPSEEK_API_KEY=
+# DEEPSEEK_BASE_URL=https://api.deepseek.com # 可选: 默认值
+
+# Anthropic Claude (如果 LLM_PROVIDER=anthropic)
+ANTHROPIC_API_KEY=
+
+# Google Gemini (如果 LLM_PROVIDER=gemini)
+GEMINI_API_KEY=
+# GEMINI_BASE_URL= # 可选: OpenAI 兼容端点
+
+# --- 模型配置 ---
+# 如果不指定，将使用各供应商的默认模型:
+# - openai: gpt-4o-mini
+# - deepseek: deepseek-chat
+# - anthropic: claude-3-5-sonnet-20241022
+# - gemini: gemini-1.5-flash
+# MODEL_NAME=deepseek-chat
+
+# --- GitHub Token ---
+# 用于访问 GitHub API，提高请求限制
+GITHUB_TOKEN=
+
+# --- Embedding 服务 ---
+# SiliconFlow API Key (用于 BGE-M3 Embedding)
+SILICON_API_KEY=
+
+# --- Langfuse 追踪配置 (可选) ---
+# LANGFUSE_ENABLED=true
+# LANGFUSE_HOST=http://localhost:3000
+# LANGFUSE_PUBLIC_KEY=
+# LANGFUSE_SECRET_KEY=
+
+# --- Qdrant 向量数据库配置 ---
+# 模式选择: "local" | "server" | "cloud"
+# - local: 本地嵌入式存储 (开发环境, 单 Worker)
+# - server: Qdrant Server Docker (生产环境, 多 Worker)
+# - cloud: Qdrant Cloud 托管服务
+QDRANT_MODE=local
+QDRANT_LOCAL_PATH=data/qdrant_db
+
+# Server 模式: 连接 Qdrant Server (Docker)
+# QDRANT_MODE=server
+# QDRANT_URL=http://localhost:6333
+# 或分开配置:
+# QDRANT_HOST=localhost
+# QDRANT_PORT=6333
+
+# Cloud 模式: 连接 Qdrant Cloud
+# QDRANT_MODE=cloud
+# QDRANT_URL=https://xxx.qdrant.tech
+# QDRANT_API_KEY=your-api-key
+
+# 向量维度 (BGE-M3 = 1024)
+# QDRANT_VECTOR_SIZE=1024
+
+# --- Gunicorn Worker 配置 ---
+# 2核2G服务器建议设为 2
+# 4核8G服务器可设为 4
+GUNICORN_WORKERS=2
+
+# --- 分布式锁配置 ---
+# 锁后端: "memory" | "file" | "redis"
+# - memory: 内存锁 (单进程)
+# - file: 文件锁 (多 Worker 单节点)
+# - redis: Redis 分布式锁 (多节点)
+LOCK_BACKEND=file
+LOCK_DIR=data/locks
+# REDIS_URL=redis://localhost:6379/0
+
+# --- 服务配置 ---
+HOST=0.0.0.0
+PORT=8000
+
+# --- LLM 参数 (可选) ---
+# LLM_TEMPERATURE=0.1
+# LLM_MAX_TOKENS=4096
+# LLM_TIMEOUT=600
\ No newline at end of file
diff --git a/.github/workflows/sync_to_hub.yml b/.github/workflows/sync_to_hub.yml
new file mode 100644
index 0000000000000000000000000000000000000000..a07b8c3c4bb7f452273787d9d1ebe3f62d23e4d4
--- /dev/null
+++ b/.github/workflows/sync_to_hub.yml
@@ -0,0 +1,58 @@
+name: Sync to Hugging Face hub
+on:
+ push:
+ branches: [main]
+ workflow_dispatch:
+
+jobs:
+ sync-to-hub:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ with:
+ fetch-depth: 0
+
+ - name: Push to hub
+ env:
+ HF_TOKEN: ${{ secrets.HF_TOKEN }}
+ HF_USERNAME: realdexter
+ SPACE_NAME: RepoReaper
+ run: |
+ echo "🚀 Starting deployment to Hugging Face..."
+
+ # 1. 配置 Git
+ git config --global user.email "bot@github.com"
+ git config --global user.name "GitHub Actions Bot"
+
+ # 2. 【核心魔法】动态生成 Hugging Face 专用的 README
+ # 这一步会在发送给 HF 之前，强行在 README.md 顶部插入配置头
+ # GitHub 本地的文件不会受影响，依然保持干净漂亮
+ echo "---" > hf_header.yml
+ echo "title: RepoReaper" >> hf_header.yml
+ echo "emoji: 💀" >> hf_header.yml
+ echo "colorFrom: blue" >> hf_header.yml
+ echo "colorTo: indigo" >> hf_header.yml
+ echo "sdk: docker" >> hf_header.yml
+ echo "pinned: false" >> hf_header.yml
+ echo "app_port: 8000" >> hf_header.yml # 👈 关键：这里指定端口，你就不用改代码了
+ echo "---" >> hf_header.yml
+ echo "" >> hf_header.yml
+
+ # 将配置头和原 README 内容拼接
+ cat hf_header.yml README.md > README_temp.md
+ mv README_temp.md README.md
+
+ # 3. 清理不需要的文件
+ rm -rf docs/
+ rm -f *.jpg *.png *.gif hf_header.yml
+ rm -rf .git
+
+ # 4. 初始化新仓库并推送
+ git init -b main
+ git add .
+ git commit -m "deploy: auto-inject hf config & sync"
+
+ git remote add space https://$HF_USERNAME:$HF_TOKEN@huggingface.co/spaces/$HF_USERNAME/$SPACE_NAME
+ git push --force space main
+
+ echo "✅ Deployment successful! Config header injected on-the-fly."
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..e1f6e29f6e3f6d7b89de136deb4bb2e5215dc444
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,43 @@
+# .gitignore
+__pycache__/
+*.py[cod]
+.env
+.venv/
+venv/
+.DS_Store
+data/
+# Vue 构建输出
+#frontend-dist/
+frontend-vue/node_modules/
+frontend-vue/dist/
+
+# 锁文件目录
+data/locks/
+
+# 日志
+logs/
+*.log
+
+# IDE
+.idea/
+.vscode/
+*.swp
+
+# 临时文件
+*.tmp
+*.bak
+QUICKSTART.md
+docs/INTERVIEW_QA.md
+docs/ROADMAP.md
+docs/TECHNICAL_REPORT.md
+evaluation/000_START_HERE.md
+evaluation/golden_dataset.json
+evaluation/HIGH_QUALITY_QUESTIONS.md
+
+evaluation/README_EVALUATION_SYSTEM.md
+evaluation/ragas_eval_dataset.json
+evaluation/sft_data/eval_results.jsonl
+evaluation/sft_data/negative_samples.jsonl
+evaluation/sft_data/positive_samples.jsonl
+evaluation/sft_data/skipped_samples.jsonl
+evaluation/sft_data/cleaned/rejected_20260128_010745.jsonl
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..67ffb714e5c9bf3a0006a52635d7ea0c479c8e1a
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,45 @@
+# 1. 基础镜像：选择 Python 3.10 的轻量版 (Slim)
+FROM python:3.10-slim
+
+# 2. 设置环境变量
+ENV PYTHONDONTWRITEBYTECODE=1 \
+ PYTHONUNBUFFERED=1 \
+ # 默认 LLM 供应商 (可通过 docker run -e 覆盖)
+ LLM_PROVIDER=deepseek
+
+# 3. 设置工作目录
+WORKDIR /app
+
+# 4. 安装系统级依赖
+# build-essential: ChromaDB 编译需要
+# curl: 健康检查
+# git: 某些 pip 包可能需要
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ build-essential \
+ curl \
+ git \
+ && rm -rf /var/lib/apt/lists/* \
+ && apt-get clean
+
+# 5. 复制依赖文件并安装 (利用 Docker 层缓存)
+COPY requirements.txt .
+
+# 6. 安装 Python 依赖
+RUN pip install --no-cache-dir --upgrade pip && \
+ pip install --no-cache-dir -r requirements.txt
+
+# 7. 复制项目代码
+COPY . .
+
+# 8. 创建数据目录 (Qdrant 本地存储 + 上下文缓存)
+RUN mkdir -p /app/data/qdrant_db /app/data/contexts
+
+# 9. 暴露端口
+EXPOSE 8000
+
+# 10. 健康检查
+HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
+ CMD curl -f http://localhost:8000/health || exit 1
+
+# 11. 启动命令
+CMD ["gunicorn", "-c", "gunicorn_conf.py", "app.main:app"]
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..242fa6488fcde1ae993f6959de666741a3f71eab
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 tzzp1224
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..05f8c1528f6176026d9e06e4eec497f5086b488b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,224 @@
+---
+title: RepoReaper
+emoji: 💀
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+pinned: false
+app_port: 8000
+---
+
+
+
+
+
+
RepoReaper
+
+
💀 Harvest Logic. Dissect Architecture. Chat with Code.
+
+
+ English •
+ 简体中文
+
+
+
+ 
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 👇 Live Demo / 在线体验 👇
+
+
+
+ 
+
+
+
+ 
+
+
+
+
+
+ ⚠️ Public demos use shared API quotas. Deploy locally for the best experience.
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/README_zh.md b/README_zh.md
new file mode 100644
index 0000000000000000000000000000000000000000..647001b61060e425ccf3d93f487f9816e4ea3db1
--- /dev/null
+++ b/README_zh.md
@@ -0,0 +1,212 @@
+
+
+
+
+
RepoReaper
+
+
💀 Harvest Logic. Dissect Architecture. Chat with Code.
+
+
+ English •
+ 简体中文
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 👇 在线体验 👇
+
+
+
+
+
+
+
+ 
+
+
+
+
+
+ ⚠️ 中国用户请使用 Seoul Server。如遇限流，建议本地部署。
+
+
+
+
+
+
+
+
+
`, `/`, `(`, `)`, `&`, `<`, `>` 等
+ 4. **使用简短英文ID**: 节点ID用简短英文如 `A`, `B`, `Client`, `API`
+ 5. **subgraph 标题也需引号**: `subgraph "核心服务"` ✓
+ 6. **数据库节点**: 使用 `[("数据库")]` 格式
+
+ - **正确示例**:
+ ```mermaid
+ graph TD
+ Client["客户端"] -->|"HTTP请求"| API["API网关"]
+ API --> Service["业务服务"]
+ Service --> DB[("数据库")]
+ Service -->|"调用"| External["外部服务"]
+ ```
+
+ ## 3. 核心逻辑分析 (Table)
+ (总结关键模块，不要列出所有文件，只列最重要的)
+
+ | 组件/文件 | 职责 (它做什么？) | 关键设计模式/逻辑 |
+ | :--- | :--- | :--- |
+ | 例如 `auth_service.py` | 处理JWT颁发与验证 | 单例模式, 路由装饰器 |
+ | ... | ... | ... |
+
+ ## 4. 🔬 核心方法深度解析
+ (精选 3-5 个最关键的 `.py` 文件。针对每个文件，列出驱动逻辑的 Top 2-3 个方法)
+
+ ### 4.1 `[文件名]`
+ * **`[方法名]`**: [解释它做什么以及为什么重要，不要贴代码]
+ * **`[方法名]`**: [解释...]
+
+ ## 5. 主要工作流 (Mermaid)
+ 选择**一个最重要**的业务流程 (Happy Path)。
+ 创建一个 `sequenceDiagram`。
+ - 参与者应该是高层概念 (如 User, API, DB)，不要用具体变量名。
+
+ **⚠️ sequenceDiagram 语法要求**:
+ 1. **participant 别名格式**: `participant API as "API服务"` ✓
+ 2. **消息文本用双引号**: `User->>API: "发起请求"` ✓
+ 3. **避免特殊字符**: 不要在消息中使用 `/`, `&`, `<`, `>` 等
+
+ - **正确示例**:
+ ```mermaid
+ sequenceDiagram
+ participant User as "用户"
+ participant API as "API服务"
+ participant DB as "数据库"
+ User->>API: "发起请求"
+ API->>DB: "查询数据"
+ DB-->>API: "返回结果"
+ API-->>User: "响应数据"
+ ```
+
+ ## 6. 快速开始 (Quick Start)
+ - **前置条件**: (如 Docker, Python 3.9+, .env 配置)
+ - **入口**: (如何启动主逻辑？如 `python main.py`)
+ """
+ else:
+ analysis_user_content = f"""
+ [Role]
+ You are a **Pragmatic Tech Lead**. Your goal is to create a **"3-Pages" Architecture Overview** for a developer who wants to understand this repo in 5 minutes.
+ [Input Data]
+ {repo_map_injection}
+
+ Files analyzed: {list(visited_files)}
+
+ [Code Knowledge & Key Snippets]
+ {enhanced_context}
+
+ [Strict Constraints]
+ 1. **NO Code Review**: Do NOT list bugs, issues, missing features, or recommendations.
+ 2. **NO Critique**: Do not judge the code quality. Focus on HOW it works.
+ 3. **Tone**: Professional, descriptive, and structural.
+ 4. **NO "FLUFF"**: Do NOT add unrequested sections like "Security", "Scalability", "Data Models", "Future Enhancements", etc.
+
+ [Required Output Format (Markdown)]
+
+ # Project Analysis Report
+
+ ## 1. Executive Summary
+ - **Purpose**: (What specific problem does this project solve? 1-2 sentences)
+ - **Key Features**: (Bullet points of top 3 features)
+ - **Tech Stack**: (List languages, frameworks, databases, and key libs)
+
+ ## 2. System Architecture
+ Create a `graph TD` diagram.
+ - Show high-level components (e.g., Client, API Server, Database, Worker, External Service).
+ - Label the edges with data flow (e.g., "HTTP", "SQL").
+ - **Style**: Keep it simple and conceptual. Limit to 8 nodes max.
+
+ **⚠️ Mermaid Syntax Rules (v10.x - MUST FOLLOW)**:
+ 1. **Wrap ALL node text in double quotes**: `A["User Client"]` ✓, `A[User Client]` ✗
+ 2. **Wrap ALL edge labels in double quotes**: `-->|"HTTP Request"|` ✓, `-->|HTTP Request|` ✗
+ 3. **NO special characters in text**: Avoid `/`, `()`, `&`, `<>`, `
` in labels
+ 4. **Use short alphanumeric IDs**: e.g., `A`, `B`, `Client`, `API`, `DB`
+ 5. **Subgraph titles need quotes**: `subgraph "Core Services"` ✓
+ 6. **Database node format**: Use `[("Database")]` for cylinder shape
+
+ - **Correct Example**:
+ ```mermaid
+ graph TD
+ Client["User Client"] -->|"HTTP Request"| API["API Gateway"]
+ API --> Service["Business Service"]
+ Service --> DB[("Database")]
+ Service -->|"Calls"| External["External API"]
+ ```
+
+ ## 3. Core Logic Analysis
+ (Create a Markdown Table to summarize key modules. Do not list every file, only the most important ones.)
+
+ | Component/File | Responsibility (What does it do?) | Key Design Pattern / Logic |
+ | :--- | :--- | :--- |
+ | e.g. `auth_service.py` | Handles JWT issuance and verification | Singleton, Decorator for routes |
+ | ... | ... | ... |
+
+ ## 4. Core Methods Deep Dive
+ (Select the 3-5 most critical `.py` files. For each, list the top 2-3 methods that drive the logic.)
+
+ ### 4.1 `[Filename, e.g., agent_service.py]`
+ * **`[Method Name]`**: [Explanation of what it does and why it matters. No code.]
+ * **`[Method Name]`**: [Explanation...]
+
+ ### 4.2 `[Filename, e.g., vector_service.py]`
+ * **`[Method Name]`**: [Explanation...]
+ * ...
+
+ ## 5. Main Workflow (Mermaid)
+ Select the **Single Most Important** business flow (The "Happy Path").
+ Create a `sequenceDiagram`.
+ - Participants should be high-level (e.g., User, API, DB), not specific variable names.
+
+ **⚠️ sequenceDiagram Syntax Rules**:
+ 1. **Wrap participant aliases in quotes**: `participant API as "API Server"` ✓
+ 2. **Wrap message text in quotes**: `User->>API: "Send Request"` ✓
+ 3. **NO special characters**: Avoid `/`, `&`, `<`, `>` in messages
+
+ - **Correct Example**:
+ ```mermaid
+ sequenceDiagram
+ participant User as "User"
+ participant API as "API Server"
+ participant DB as "Database"
+ User->>API: "Send Request"
+ API->>DB: "Query Data"
+ DB-->>API: "Return Result"
+ API-->>User: "Send Response"
+ ```
+
+ ## 6. Quick Start Guide
+ - **Prerequisites**: (e.g. Docker, Python 3.9+, .env file)
+ - **Entry Point**: (How to run the main logic? e.g. `python main.py` or `uvicorn`)
+
+ """
+
+ # === 增加 timeout 防止长文本生成时断连 ===
+ report_messages = [
+ {"role": "system", "content": "You are a pragmatic Tech Lead. Focus on architecture and data flow, not implementation details."},
+ {"role": "user", "content": analysis_user_content}
+ ]
+
+ stream_start_time = time.time()
+ stream = await client.chat.completions.create(
+ model=settings.default_model_name,
+ messages=report_messages,
+ stream=True,
+ timeout=settings.LLM_TIMEOUT # 使用统一配置
+ )
+
+ # === TTFT & Token Tracking ===
+ first_token_received = False
+ ttft_ms = None
+ generated_text = ""
+ completion_tokens_estimate = 0
+
+ # === 增加 try-except 捕获流式传输中断 ===
+ try:
+ async for chunk in stream:
+ if chunk.choices[0].delta.content:
+ content = chunk.choices[0].delta.content
+
+ # 记录 TTFT (首 Token 时间)
+ if not first_token_received:
+ ttft_ms = (time.time() - stream_start_time) * 1000
+ tracing_service.record_ttft(
+ ttft_ms=ttft_ms,
+ model=settings.default_model_name,
+ metadata={"step": "report_generation"}
+ )
+ first_token_received = True
+
+ generated_text += content
+ completion_tokens_estimate += 1 # 粗略估计每个 chunk 约 1 token
+ yield json.dumps({"step": "report_chunk", "chunk": content})
+ except (httpx.ReadError, httpx.ConnectError) as e:
+ yield json.dumps({"step": "error", "message": f"⚠️ Network Timeout during generation: {str(e)}"})
+ return
+
+ # 流结束后记录完整的 LLM 生成信息
+ total_latency_ms = (time.time() - stream_start_time) * 1000
+ tracing_service.record_llm_generation(
+ model=settings.default_model_name,
+ prompt_messages=report_messages,
+ generated_text=generated_text,
+ ttft_ms=ttft_ms,
+ total_latency_ms=total_latency_ms,
+ completion_tokens=completion_tokens_estimate,
+ is_streaming=True,
+ metadata={"step": "report_generation", "generated_chars": len(generated_text)}
+ )
+
+ # === 保存报告 (按语言存储，异步避免阻塞) ===
+ await vector_db.save_report(generated_text, language)
+
+ yield json.dumps({"step": "finish", "message": "✅ Analysis Complete!"})
+
+ except Exception as e:
+ # === 全局异常捕获 ===
+ import traceback
+ traceback.print_exc()
+
+ # 提取友好的错误信息
+ error_msg = str(e)
+ if "401" in error_msg:
+ ui_msg = "❌ GitHub Token Invalid. Please check your settings."
+ elif "403" in error_msg:
+ ui_msg = "❌ GitHub API Rate Limit Exceeded. Try again later or add a Token."
+ elif "404" in error_msg:
+ ui_msg = "❌ Repository Not Found. Check the URL."
+ elif "Timeout" in error_msg or "ConnectError" in error_msg:
+ ui_msg = "❌ Network Timeout. LLM or GitHub is not responding."
+ else:
+ ui_msg = f"💥 System Error: {error_msg}"
+
+ yield json.dumps({"step": "error", "message": ui_msg})
+ return # 终止流
\ No newline at end of file
diff --git a/app/services/auto_evaluation_service.py b/app/services/auto_evaluation_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebd205cee8dd4df99a846ae3b968d6b8d8c625aa
--- /dev/null
+++ b/app/services/auto_evaluation_service.py
@@ -0,0 +1,481 @@
+# 文件路径: app/services/auto_evaluation_service.py
+"""
+自动评估服务 - Phase 1
+在后台异步进行评估，不阻塞用户请求
+
+工作流程:
+ 1. 用户调用 /chat 或 /analyze
+ 2. 获得立即响应
+ 3. 后台异步执行评估
+ 4. 评估结果存储到 evaluation/sft_data/
+"""
+
+import asyncio
+import json
+import os
+from datetime import datetime
+from typing import Optional
+from dataclasses import dataclass
+
+from evaluation.evaluation_framework import (
+ EvaluationEngine,
+ EvaluationResult,
+ DataRoutingEngine,
+ DataQualityTier
+)
+from evaluation.utils import is_chatty_query, has_code_indicators
+from app.services.tracing_service import tracing_service
+
+
+@dataclass
+class EvaluationConfig:
+ """
+ 自动评估配置
+
+ 数据路由阈值说明（与 data_router.py 一致）:
+ - score > 0.9 → Gold → positive_samples.jsonl
+ - score > 0.6 → Silver → positive_samples.jsonl
+ - score > 0.4 → Bronze → negative_samples.jsonl
+ - score <= 0.4 → Rejected → 不存储
+ """
+ enabled: bool = True # 是否启用自动评估
+ use_ragas: bool = False # 是否使用 Ragas 进行 sanity check
+ custom_weight: float = 0.7 # custom_eval 的权重
+ ragas_weight: float = 0.3 # ragas_eval 的权重
+ diff_threshold: float = 0.2 # 差异阈值（超过则标记 needs_review）
+ min_quality_score: float = 0.4 # 最低质量分数（<=0.4 才拒绝）
+ async_evaluation: bool = True # 是否异步执行（推荐 True）
+ min_query_length: int = 10 # 最小 query 长度
+ min_answer_length: int = 100 # 最小 answer 长度
+ require_repo_url: bool = True # 是否要求有仓库 URL
+ require_code_in_context: bool = True # 是否要求上下文包含代码
+
+
+class AutoEvaluationService:
+ """自动评估服务"""
+
+ def __init__(
+ self,
+ eval_engine: EvaluationEngine,
+ data_router: DataRoutingEngine,
+ config: EvaluationConfig = None
+ ):
+ self.eval_engine = eval_engine
+ self.data_router = data_router
+ self.config = config or EvaluationConfig()
+ self.needs_review_queue: list = [] # 需要人工审查的样本队列
+ self._evaluated_keys: set = set() # 防重复评估（session_id:query_hash）
+
+ # 被过滤数据的记录文件
+ self.skipped_samples_file = "evaluation/sft_data/skipped_samples.jsonl"
+ os.makedirs(os.path.dirname(self.skipped_samples_file), exist_ok=True)
+
+ def _record_skipped(self, reason: str, query: str, session_id: str,
+ repo_url: str = "", context_len: int = 0, answer_len: int = 0) -> None:
+ """记录被跳过的样本（供日后分析）"""
+ record = {
+ "timestamp": datetime.now().isoformat(),
+ "reason": reason,
+ "session_id": session_id,
+ "query": query[:200] if query else "",
+ "repo_url": repo_url,
+ "context_length": context_len,
+ "answer_length": answer_len
+ }
+ try:
+ with open(self.skipped_samples_file, 'a', encoding='utf-8') as f:
+ f.write(json.dumps(record, ensure_ascii=False) + '\n')
+ except Exception as e:
+ print(f" ⚠️ 记录跳过样本失败: {e}")
+
+ def _validate_input(
+ self,
+ query: str,
+ retrieved_context: str,
+ generated_answer: str,
+ session_id: str,
+ repo_url: str
+ ) -> tuple[bool, Optional[str]]:
+ """
+ 验证输入是否满足评估条件
+
+ Returns:
+ (is_valid, skip_reason) - 如果有效返回 (True, None)，否则返回 (False, reason)
+ """
+ context_len = len(retrieved_context) if retrieved_context else 0
+ answer_len = len(generated_answer) if generated_answer else 0
+
+ # Query 验证
+ if not query or not query.strip():
+ self._record_skipped("query_empty", query or "", session_id, repo_url, context_len, answer_len)
+ return False, "query 为空"
+
+ if len(query.strip()) < self.config.min_query_length:
+ self._record_skipped("query_too_short", query, session_id, repo_url, context_len, answer_len)
+ return False, f"query 太短 ({len(query)} < {self.config.min_query_length})"
+
+ if is_chatty_query(query):
+ self._record_skipped("chatty_query", query, session_id, repo_url, context_len, answer_len)
+ return False, f"闲聊/无效 query: {query[:30]}"
+
+ # Repo URL 验证
+ if self.config.require_repo_url and not repo_url:
+ self._record_skipped("missing_repo_url", query, session_id, repo_url, context_len, answer_len)
+ return False, "缺少 repo_url"
+
+ # Answer 验证
+ if not generated_answer or len(generated_answer.strip()) < self.config.min_answer_length:
+ self._record_skipped("answer_too_short", query, session_id, repo_url, context_len, answer_len)
+ return False, f"回答太短 ({answer_len} < {self.config.min_answer_length})"
+
+ # Context 验证
+ if self.config.require_code_in_context and not has_code_indicators(retrieved_context):
+ self._record_skipped("no_code_in_context", query, session_id, repo_url, context_len, answer_len)
+ return False, "上下文中未检测到代码"
+
+ return True, None
+
+ def _check_duplicate(self, query: str, session_id: str) -> bool:
+ """检查是否重复评估，返回 True 表示是重复的"""
+ import hashlib
+ query_hash = hashlib.md5(query.encode()).hexdigest()[:8]
+ eval_key = f"{session_id}:{query_hash}"
+
+ if eval_key in self._evaluated_keys:
+ return True
+
+ self._evaluated_keys.add(eval_key)
+
+ # 限制缓存大小，防止内存泄漏
+ if len(self._evaluated_keys) > 1000:
+ self._evaluated_keys = set(list(self._evaluated_keys)[-500:])
+
+ return False
+
+ async def auto_evaluate(
+ self,
+ query: str,
+ retrieved_context: str,
+ generated_answer: str,
+ session_id: str = "auto",
+ repo_url: str = "",
+ language: str = "en"
+ ) -> Optional[str]:
+ """
+ 自动评估单个查询-回答对
+
+ Returns:
+ 质量等级 (gold/silver/bronze/rejected/needs_review) 或 None
+ """
+ # 输入验证
+ is_valid, skip_reason = self._validate_input(
+ query, retrieved_context, generated_answer, session_id, repo_url
+ )
+ if not is_valid:
+ print(f" ⚠️ [AutoEval] 跳过: {skip_reason}")
+ return None
+
+ # 防重复评估
+ if self._check_duplicate(query, session_id):
+ print(f" ⏭️ [AutoEval] 跳过重复评估: {query[:30]}...")
+ return None
+
+ start_time = datetime.now()
+
+ try:
+ # Step 1: 自定义评估
+ print(f"📊 [AutoEval] 开始评估: {query[:50]}...")
+
+ custom_metrics = await self.eval_engine.evaluate_generation(
+ query=query,
+ retrieved_context=retrieved_context,
+ generated_answer=generated_answer
+ )
+ custom_score = custom_metrics.overall_score()
+
+ print(f" ✓ Custom Score: {custom_score:.3f}")
+ print(f" - Faithfulness: {custom_metrics.faithfulness:.3f}")
+ print(f" - Answer Relevance: {custom_metrics.answer_relevance:.3f}")
+ print(f" - Completeness: {custom_metrics.answer_completeness:.3f}")
+
+ # Step 2: Ragas Sanity Check (如果启用)
+ ragas_score = None
+ ragas_details = None
+
+ if self.config.use_ragas:
+ try:
+ ragas_score, ragas_details = await self._ragas_eval(
+ query=query,
+ context=retrieved_context,
+ answer=generated_answer
+ )
+ print(f" ✓ Ragas Score: {ragas_score:.3f}")
+ if ragas_details:
+ print(f" - {ragas_details}")
+ except Exception as e:
+ print(f" ⚠️ Ragas 评估失败: {e}")
+ # Ragas 失败不应该中断主流程
+
+ # ============================================================
+ # Step 3: 混合评估 + 异常检测
+ # ============================================================
+ final_score, quality_status = self._compute_final_score(
+ custom_score=custom_score,
+ ragas_score=ragas_score
+ )
+
+ print(f" ✓ Final Score: {final_score:.3f} | Status: {quality_status}")
+
+ # ============================================================
+ # Step 4: 构建评估结果并存储
+ # ============================================================
+ eval_result = EvaluationResult(
+ session_id=session_id,
+ query=query,
+ repo_url=repo_url,
+ timestamp=start_time,
+ language=language,
+ generation_metrics=custom_metrics,
+ notes=f"ragas_score={ragas_score:.3f}" if ragas_score else ""
+ )
+
+ # 设置综合得分
+ eval_result.overall_score = final_score
+
+ # 根据状态和得分确定质量等级
+ print(f" [DEBUG] quality_status={quality_status}, final_score={final_score:.3f}, threshold={self.config.min_quality_score}")
+
+ if quality_status == "needs_review":
+ eval_result.data_quality_tier = DataQualityTier.BRONZE
+ eval_result.notes += " | needs_review=true"
+ # 加入审查队列
+ self.needs_review_queue.append({
+ "eval_result": eval_result,
+ "custom_score": custom_score,
+ "ragas_score": ragas_score,
+ "diff": abs(custom_score - (ragas_score or custom_score)),
+ "timestamp": start_time.isoformat()
+ })
+ print(f" ⚠️ 需要人工审查 (needs_review)，暂存队列")
+ # 同时也路由到数据存储，便于后续分析
+ self.data_router.route_sample(eval_result)
+ elif final_score > self.config.min_quality_score:
+ # score > 0.4: 路由到 positive (>0.6) 或 negative (0.4-0.6)
+ print(f" ✓ 路由到 data_router (score {final_score:.2f} > {self.config.min_quality_score})")
+ self.data_router.route_sample(eval_result)
+ else:
+ # score <= 0.4: 质量太差，直接拒绝
+ eval_result.data_quality_tier = DataQualityTier.REJECTED
+ print(f" ❌ 评分过低 ({final_score:.2f} <= {self.config.min_quality_score})，拒绝存储")
+
+ # 记录到 tracing
+ tracing_service.add_event("auto_evaluation_completed", {
+ "query": query[:100],
+ "custom_score": custom_score,
+ "ragas_score": ragas_score,
+ "final_score": final_score,
+ "status": quality_status,
+ "quality_tier": eval_result.data_quality_tier.value
+ })
+
+ print(f" ✅ 评估完成\n")
+
+ return eval_result.data_quality_tier.value
+
+ except Exception as e:
+ print(f" ❌ 自动评估异常: {e}")
+ import traceback
+ traceback.print_exc()
+ return None
+
+ async def auto_evaluate_async(
+ self,
+ query: str,
+ retrieved_context: str,
+ generated_answer: str,
+ session_id: str = "auto",
+ repo_url: str = "",
+ language: str = "en"
+ ) -> None:
+ """
+ 异步版本 - 不阻塞主流程
+
+ 在后台执行评估，不等待结果
+ """
+ if not self.config.async_evaluation:
+ # 同步模式（不推荐在生产环境）
+ await self.auto_evaluate(
+ query=query,
+ retrieved_context=retrieved_context,
+ generated_answer=generated_answer,
+ session_id=session_id,
+ repo_url=repo_url,
+ language=language
+ )
+ else:
+ # 异步模式（推荐）- 在后台执行
+ asyncio.create_task(
+ self._eval_task(
+ query=query,
+ retrieved_context=retrieved_context,
+ generated_answer=generated_answer,
+ session_id=session_id,
+ repo_url=repo_url,
+ language=language
+ )
+ )
+
+ async def _eval_task(
+ self,
+ query: str,
+ retrieved_context: str,
+ generated_answer: str,
+ session_id: str,
+ repo_url: str,
+ language: str
+ ) -> None:
+ """后台评估任务包装"""
+ try:
+ await asyncio.sleep(0.1) # 让用户请求先返回
+ await self.auto_evaluate(
+ query=query,
+ retrieved_context=retrieved_context,
+ generated_answer=generated_answer,
+ session_id=session_id,
+ repo_url=repo_url,
+ language=language
+ )
+ except Exception as e:
+ print(f"❌ Background eval task failed: {e}")
+
+ def _compute_final_score(
+ self,
+ custom_score: float,
+ ragas_score: Optional[float]
+ ) -> tuple[float, str]:
+ """
+ 计算最终得分和状态
+
+ Returns:
+ (final_score, status)
+ status: "normal" / "needs_review" / "high_confidence"
+ """
+
+ if ragas_score is None:
+ # 没有 Ragas 分数，直接用 custom 分数
+ return custom_score, "normal"
+
+ # 计算差异
+ diff = abs(custom_score - ragas_score)
+
+ # 判断异常
+ if diff > self.config.diff_threshold:
+ # 差异过大，标记为需要审查
+ return custom_score, "needs_review"
+
+ # 混合评分
+ final_score = (
+ self.config.custom_weight * custom_score +
+ self.config.ragas_weight * ragas_score
+ )
+
+ # 两者都高分 → 高置信度
+ if custom_score > 0.75 and ragas_score > 0.75:
+ status = "high_confidence"
+ else:
+ status = "normal"
+
+ return final_score, status
+
+ async def _ragas_eval(
+ self,
+ query: str,
+ context: str,
+ answer: str
+ ) -> tuple[Optional[float], Optional[str]]:
+ """
+ 使用 Ragas 进行 sanity check
+
+ Returns:
+ (score, details)
+ """
+ try:
+ from ragas.metrics import faithfulness, answer_relevancy
+ from ragas import evaluate
+
+ # 构造 Ragas 数据集
+ dataset_dict = {
+ "question": [query],
+ "contexts": [[context]],
+ "answer": [answer]
+ }
+
+ # 执行评估
+ result = evaluate(
+ dataset=dataset_dict,
+ metrics=[faithfulness, answer_relevancy]
+ )
+
+ # 提取分数
+ faithfulness_score = result["faithfulness"][0] if "faithfulness" in result else 0.5
+ relevancy_score = result["answer_relevancy"][0] if "answer_relevancy" in result else 0.5
+
+ # 平均得分
+ ragas_score = (faithfulness_score + relevancy_score) / 2
+
+ details = f"Ragas: faithfulness={faithfulness_score:.3f}, relevancy={relevancy_score:.3f}"
+
+ return ragas_score, details
+
+ except ImportError:
+ print("⚠️ Ragas 未安装，跳过 sanity check")
+ return None, None
+ except Exception as e:
+ print(f"⚠️ Ragas 评估异常: {e}")
+ return None, None
+
+ def get_review_queue(self) -> list:
+ """获取需要审查的样本列表"""
+ return self.needs_review_queue
+
+ def clear_review_queue(self) -> None:
+ """清空审查队列"""
+ self.needs_review_queue.clear()
+
+ def approve_sample(self, index: int) -> None:
+ """人工批准某个样本"""
+ if 0 <= index < len(self.needs_review_queue):
+ item = self.needs_review_queue[index]
+ # 直接存储到评估结果
+ self.data_router.route_sample(item["eval_result"])
+ print(f"✅ 样本 {index} 已批准")
+
+ def reject_sample(self, index: int) -> None:
+ """人工拒绝某个样本"""
+ if 0 <= index < len(self.needs_review_queue):
+ print(f"❌ 样本 {index} 已拒绝")
+ self.needs_review_queue.pop(index)
+
+
+# 全局实例
+auto_eval_service: Optional[AutoEvaluationService] = None
+
+
+def init_auto_evaluation_service(
+ eval_engine: EvaluationEngine,
+ data_router: DataRoutingEngine,
+ config: EvaluationConfig = None
+) -> AutoEvaluationService:
+ """初始化自动评估服务"""
+ global auto_eval_service
+ auto_eval_service = AutoEvaluationService(
+ eval_engine=eval_engine,
+ data_router=data_router,
+ config=config
+ )
+ return auto_eval_service
+
+
+def get_auto_evaluation_service() -> Optional[AutoEvaluationService]:
+ """获取自动评估服务实例"""
+ return auto_eval_service
diff --git a/app/services/chat_service.py b/app/services/chat_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..99c4904a4a98a745527e9a37ecfadf708ff502f6
--- /dev/null
+++ b/app/services/chat_service.py
@@ -0,0 +1,601 @@
+# 文件路径: app/services/chat_service.py
+import json
+import asyncio
+import re
+import time
+from dataclasses import dataclass, field
+from typing import Dict, Optional, AsyncGenerator, List, Set
+from app.core.config import settings
+from app.utils.llm_client import client
+from app.services.vector_service import store_manager
+from app.services.github_service import get_file_content
+from app.services.chunking_service import UniversalChunker, ChunkingConfig
+from app.services.tracing_service import tracing_service
+from app.utils.session import get_conversation_memory, ConversationMemory
+
+
+# ============================================================
+# 配置类 - 解耦所有可调参数
+# ============================================================
+
+@dataclass
+class ChatConfig:
+ """Chat 服务配置 - 集中管理所有参数"""
+ # JIT 动态加载配置
+ max_jit_rounds: int = 2 # 最大 JIT 轮数
+ max_files_per_round: int = 3 # 每轮最多加载文件数
+
+ # LLM 配置
+ temperature_thinking: float = 0.1 # 思考阶段温度
+ temperature_final: float = 0.2 # 最终回答温度
+ max_tokens: int = 4096 # 最大 token 数
+
+ # 检索配置
+ retrieval_top_k: int = 6 # RAG 检索 top-k
+ context_max_chars: int = 2000 # 单文档最大字符数
+
+ # 对话上下文配置
+ max_history_turns: int = 6 # 保留最近 N 轮对话
+ summary_threshold: int = 10 # 超过 N 轮开始压缩
+
+ # 调试配置
+ show_debug_info: bool = False # 是否显示调试信息
+
+
+# 全局配置实例
+chat_config = ChatConfig()
+
+
+@dataclass
+class ChatResult:
+ """聊天结果 - 用于后续自动评估"""
+ answer: str # 最终回答
+ retrieved_context: str # 检索到的上下文
+ generation_latency_ms: float # 生成耗时
+ retrieval_latency_ms: float = 0 # 检索耗时
+
+
+# === 评估数据存储 (供 main.py 获取) ===
+# 存储每个 session 的评估数据，key 为 session_id
+_eval_data_store: Dict[str, ChatResult] = {}
+
+def get_eval_data(session_id: str) -> Optional[ChatResult]:
+ """获取指定 session 的评估数据"""
+ return _eval_data_store.get(session_id)
+
+def clear_eval_data(session_id: str) -> None:
+ """清除指定 session 的评估数据"""
+ if session_id in _eval_data_store:
+ del _eval_data_store[session_id]
+
+
+# [Fix 2] 使用 Config 对象初始化，而非直接传参
+# 之前的写法: chunker = UniversalChunker(min_chunk_size=100)
+# 现在的写法:
+chunker = UniversalChunker(config=ChunkingConfig(min_chunk_size=100))
+
+# === 新增：简单的中文检测 ===
+def is_chinese_query(text: str) -> bool:
+ """检测字符串中是否包含中文字符"""
+ for char in text:
+ if '\u4e00' <= char <= '\u9fff':
+ return True
+ return False
+
+# === 优化 2：查询重写 (解决中英文检索不匹配问题) ===
+async def _rewrite_query(user_query: str):
+ """
+ 使用 LLM 将用户的自然语言（可能是中文）转换为 3-5 个代码搜索关键词（英文）。
+ """
+ prompt = f"""
+ You are a Code Search Expert.
+ Task: Convert the user's query into 3-5 English keywords for code search (BM25/Vector).
+
+ User Query: "{user_query}"
+
+ Rules:
+ 1. Output ONLY a JSON list of strings.
+ 2. Translate concepts to technical terms (e.g., "鉴权" -> "auth", "login", "middleware").
+ 3. Keep it short.
+
+ Example Output: ["authentication", "login_handler", "jwt_verify"]
+ """
+ try:
+ response = await client.chat.completions.create(
+ model=settings.default_model_name,
+ messages=[{"role": "user", "content": prompt}],
+ temperature=0.1,
+ max_tokens=100
+ )
+ content = response.choices[0].message.content
+ # 简单清洗
+ content = re.sub(r"^```(json)?|```$", "", content.strip(), flags=re.MULTILINE).strip()
+ keywords = json.loads(content)
+ if isinstance(keywords, list):
+ return " ".join(keywords) # 返回空格分隔的字符串供 BM25 使用
+ return user_query
+ except Exception as e:
+ print(f"⚠️ Query Rewrite Failed: {e}")
+ return user_query # 降级：直接用原句
+
+async def process_chat_stream(user_query: str, session_id: str):
+ """
+ 处理聊天流 - 支持多轮 JIT 动态加载文件 + 对话上下文记忆
+
+ 流程:
+ 1. 获取对话记忆，构建上下文
+ 2. 初始检索 RAG 上下文
+ 3. LLM 思考并回答，可能请求文件
+ 4. 如果请求文件，加载后继续对话 (最多 max_jit_rounds 轮)
+ 5. 最终生成答案并保存到对话记忆
+ """
+ vector_db = store_manager.get_store(session_id)
+ cfg = chat_config # 使用全局配置
+
+ # === 获取对话记忆 ===
+ memory = get_conversation_memory(session_id)
+ memory.add_user_message(user_query) # 立即记录用户消息
+
+ # 检查是否需要摘要压缩
+ if memory.needs_summarization():
+ yield "> 📝 *Compressing conversation history...*\n\n"
+ await _compress_conversation_history(memory)
+
+ # === 评估数据收集变量 ===
+ collected_context = ""
+ collected_response = ""
+ collected_retrieval_latency = 0.0
+ collected_generation_latency = 0.0
+
+ # === JIT 状态跟踪 ===
+ all_loaded_files: Set[str] = set() # 所有已加载的文件
+ all_failed_files: Set[str] = set() # 所有失败的文件
+ jit_round = 0 # 当前 JIT 轮数
+
+ # === 语言环境检测 ===
+ use_chinese = is_chinese_query(user_query)
+
+ # UI 提示语
+ ui_msgs = _get_ui_messages(use_chinese)
+
+ # === 步骤 0: 查询重写 ===
+ search_query = await _rewrite_query(user_query)
+ yield f"{ui_msgs['thinking']}`{search_query}`...\n\n"
+
+ # === 步骤 1: 初始 RAG 检索 ===
+ retrieval_start = time.time()
+ relevant_docs = await vector_db.search_hybrid(search_query, top_k=cfg.retrieval_top_k)
+ retrieval_latency_ms = (time.time() - retrieval_start) * 1000
+ collected_retrieval_latency = retrieval_latency_ms
+ tracing_service.add_event("retrieval_completed", {
+ "latency_ms": retrieval_latency_ms,
+ "documents_retrieved": len(relevant_docs) if relevant_docs else 0
+ })
+
+ rag_context = _build_context(relevant_docs, cfg.context_max_chars)
+ collected_context = rag_context
+
+ # === 步骤 2: 构建初始 Prompt ===
+ global_context = vector_db.global_context or {}
+ file_tree = global_context.get("file_tree", "(File tree not available.)")
+ agent_summary = global_context.get("summary", "")
+
+ # 获取对话历史上下文
+ conversation_context = _build_conversation_context(memory)
+
+ system_instruction = _build_system_prompt(
+ file_tree=file_tree,
+ agent_summary=agent_summary,
+ rag_context=rag_context,
+ use_chinese=use_chinese,
+ is_final_round=False,
+ conversation_context=conversation_context
+ )
+
+ augmented_user_query = f"""
+ {user_query}
+
+ (System Note: Priority 1: Answer using context. Priority 2: Use ONLY if critical info is missing.)
+ """
+
+ if not client:
+ yield "❌ LLM Error: Client not initialized"
+ return
+
+ # 初始化对话历史
+ messages = [
+ {"role": "system", "content": system_instruction},
+ {"role": "user", "content": augmented_user_query}
+ ]
+
+ try:
+ generation_start = time.time()
+
+ # === 多轮 JIT 循环 ===
+ while jit_round <= cfg.max_jit_rounds:
+ is_final_round = (jit_round == cfg.max_jit_rounds)
+
+ # 如果是最终轮，更新系统提示禁用工具
+ if is_final_round and jit_round > 0:
+ # 更新系统消息，告知这是最后一轮
+ messages[0] = {"role": "system", "content": _build_system_prompt(
+ file_tree=file_tree,
+ agent_summary=agent_summary,
+ rag_context=collected_context,
+ use_chinese=use_chinese,
+ is_final_round=True,
+ failed_files=list(all_failed_files)
+ )}
+
+ # LLM 流式生成
+ stream = await client.chat.completions.create(
+ model=settings.default_model_name,
+ messages=messages,
+ stream=True,
+ temperature=cfg.temperature_final if is_final_round else cfg.temperature_thinking,
+ max_tokens=cfg.max_tokens
+ )
+
+ buffer = ""
+ round_response = ""
+ requested_files: Set[str] = set()
+
+ async for chunk in stream:
+ content = chunk.choices[0].delta.content or ""
+ if not content:
+ continue
+
+ buffer += content
+ round_response += content
+ collected_response += content
+
+ # 检测 tool_code 标签
+ if "" in buffer:
+ matches = re.findall(r"\s*(.*?)\s*", buffer, re.DOTALL)
+ for f in matches:
+ clean_f = f.strip().replace("'", "").replace('"', "").replace("`", "")
+ # 过滤已加载和已失败的文件
+ if clean_f and clean_f not in all_loaded_files and clean_f not in all_failed_files:
+ requested_files.add(clean_f)
+ yield content
+ buffer = ""
+ else:
+ yield content
+
+ # 处理缓冲区残留
+ if "" in buffer:
+ matches = re.findall(r"\s*(.*?)\s*", buffer, re.DOTALL)
+ for f in matches:
+ clean_f = f.strip().replace("'", "").replace('"', "").replace("`", "")
+ if clean_f and clean_f not in all_loaded_files and clean_f not in all_failed_files:
+ requested_files.add(clean_f)
+
+ # === 判断是否需要继续 JIT ===
+ if not requested_files or is_final_round:
+ # 没有新文件请求，或已达最大轮数，结束循环
+ break
+
+ # === JIT 文件加载 ===
+ jit_round += 1
+
+ # 限制每轮文件数
+ files_to_load = list(requested_files)[:cfg.max_files_per_round]
+ file_list_str = ", ".join([f"`{f}`" for f in files_to_load])
+
+ yield f"\n\n> 🔍 **[JIT Round {jit_round}/{cfg.max_jit_rounds}]** {ui_msgs['action_short']}{file_list_str}...\n\n"
+
+ if not vector_db.repo_url:
+ yield ui_msgs['error_url']
+ break
+
+ # 加载文件
+ round_loaded_docs = []
+ round_failed_files = []
+
+ for file_path in files_to_load:
+ if file_path in vector_db.indexed_files:
+ docs = vector_db.get_documents_by_file(file_path)
+ round_loaded_docs.extend(docs)
+ all_loaded_files.add(file_path)
+ yield f"> ✅ Loaded: `{file_path}`\n"
+ else:
+ success = await _download_and_index(vector_db, file_path)
+ if success:
+ docs = vector_db.get_documents_by_file(file_path)
+ round_loaded_docs.extend(docs)
+ all_loaded_files.add(file_path)
+ yield f"> ✅ Downloaded: `{file_path}`\n"
+ else:
+ round_failed_files.append(file_path)
+ all_failed_files.add(file_path)
+ yield f"> ⚠️ Failed: `{file_path}`\n"
+
+ # 构建后续消息
+ if round_loaded_docs:
+ new_context = _build_context(round_loaded_docs, cfg.context_max_chars)
+ collected_context += f"\n\n[JIT Round {jit_round} Context]\n{new_context}"
+
+ # 构建状态消息
+ status_msg = _build_jit_status_message(
+ loaded_count=len(round_loaded_docs),
+ failed_files=round_failed_files,
+ remaining_rounds=cfg.max_jit_rounds - jit_round,
+ use_chinese=use_chinese
+ )
+
+ context_section = f"\n\n[New Code Context]\n{_build_context(round_loaded_docs, cfg.context_max_chars)}" if round_loaded_docs else ""
+
+ # 更新对话历史，继续对话
+ messages.append({"role": "assistant", "content": round_response})
+ messages.append({"role": "user", "content": f"{status_msg}{context_section}\n\nPlease continue your analysis."})
+
+ yield "\n\n" # 分隔符
+
+ # === 生成完成 ===
+ generation_latency_ms = (time.time() - generation_start) * 1000
+ collected_generation_latency = generation_latency_ms
+
+ tracing_service.add_event("generation_completed", {
+ "latency_ms": generation_latency_ms,
+ "jit_rounds": jit_round,
+ "files_loaded": len(all_loaded_files),
+ "files_failed": len(all_failed_files)
+ })
+
+ # === 保存助手回复到对话记忆 ===
+ memory.add_assistant_message(collected_response)
+
+ # 存储评估数据
+ _eval_data_store[session_id] = ChatResult(
+ answer=collected_response,
+ retrieved_context=collected_context,
+ generation_latency_ms=collected_generation_latency,
+ retrieval_latency_ms=collected_retrieval_latency
+ )
+ print(f"📦 [EvalData] Session {session_id}: {len(collected_context)} chars context, {len(collected_response)} chars answer, {jit_round} JIT rounds, {memory.get_turn_count()} turns")
+
+ except Exception as e:
+ import traceback
+ traceback.print_exc()
+ error_msg = str(e)
+ # 即使出错也保存部分回复
+ if collected_response:
+ memory.add_assistant_message(collected_response + f"\n\n[Error: {error_msg}]")
+ tracing_service.add_event("generation_error", {
+ "error": error_msg,
+ "error_type": type(e).__name__,
+ "jit_round": jit_round
+ })
+ yield f"\n\n❌ System Error: {error_msg}"
+
+
+# ============================================================
+# 辅助函数
+# ============================================================
+
+def _get_ui_messages(use_chinese: bool) -> Dict[str, str]:
+ """获取 UI 消息（根据语言）"""
+ if use_chinese:
+ return {
+ "thinking": "> 🧠 **思考中:** 正在检索相关代码: ",
+ "action_short": "正在读取文件: ",
+ "error_url": "> ⚠️ 错误: 仓库链接丢失。\n",
+ }
+ else:
+ return {
+ "thinking": "> 🧠 **Thinking:** Searching for code related to: ",
+ "action_short": "Retrieving files: ",
+ "error_url": "> ⚠️ Error: Repository URL lost.\n",
+ }
+
+
+def _build_system_prompt(
+ file_tree: str,
+ agent_summary: str,
+ rag_context: str,
+ use_chinese: bool,
+ is_final_round: bool,
+ failed_files: List[str] = None,
+ conversation_context: str = ""
+) -> str:
+ """构建系统提示词"""
+ lang_instruction = (
+ "IMPORTANT: The user is asking in Chinese. You MUST reply in Simplified Chinese (简体中文)."
+ if use_chinese else "Reply in English."
+ )
+
+ if is_final_round:
+ tool_instruction = """
+ [INSTRUCTIONS - FINAL ROUND]
+ This is your FINAL response. You MUST provide a complete answer NOW.
+ - DO NOT request any more files
+ - DO NOT use tags
+ - Synthesize all available context and give your best answer
+ - If some files were not accessible, explain what information is missing and provide the best possible answer with what you have
+ """
+ if failed_files:
+ tool_instruction += f"\n Note: The following files could not be accessed: {', '.join(failed_files)}"
+ else:
+ tool_instruction = """
+ [INSTRUCTIONS]
+ 1. **CHECK CONTEXT FIRST**: Look at the [Current Code Context]. Does it contain the answer?
+ 2. **IF YES**: Answer directly. DO NOT use tools.
+ 3. **IF NO**: Request missing files using tags: path/to/file
+ """
+
+ # 添加对话历史上下文
+ conversation_section = ""
+ if conversation_context:
+ conversation_section = f"""
+ [Previous Conversation]
+ {conversation_context}
+ """
+
+ return f"""
+ You are a Senior GitHub Repository Analyst.
+ {lang_instruction}
+
+ [Global Context - Repo Map]
+ {file_tree}
+
+ [Agent Analysis Summary]
+ {agent_summary}
+ {conversation_section}
+ [Current Code Context (Retrieved)]
+ {rag_context}
+ {tool_instruction}
+ """
+
+
+def _build_conversation_context(memory: ConversationMemory) -> str:
+ """
+ 构建对话历史上下文字符串
+
+ 只包含最近几轮对话的摘要，用于 system prompt
+ """
+ messages = memory.get_context_messages()
+
+ if len(messages) <= 2:
+ # 只有当前轮，不需要历史
+ return ""
+
+ # 排除最后一条（当前用户消息）
+ history_messages = messages[:-1]
+
+ if not history_messages:
+ return ""
+
+ context_parts = []
+ for msg in history_messages[-6:]: # 最多 6 条（3 轮）
+ role = "User" if msg["role"] == "user" else "Assistant"
+ # 截断过长的内容
+ content = msg["content"][:500]
+ if len(msg["content"]) > 500:
+ content += "..."
+ context_parts.append(f"{role}: {content}")
+
+ return "\n".join(context_parts)
+
+
+async def _compress_conversation_history(memory: ConversationMemory) -> None:
+ """
+ 压缩对话历史 - 使用 LLM 生成摘要
+ """
+ messages_to_summarize = memory.get_messages_to_summarize()
+
+ if not messages_to_summarize:
+ return
+
+ # 构建摘要请求
+ conversation_text = "\n".join([
+ f"{'User' if m['role'] == 'user' else 'Assistant'}: {m['content'][:300]}"
+ for m in messages_to_summarize
+ ])
+
+ prompt = f"""Summarize the following conversation in 2-3 sentences, focusing on:
+1. What questions were asked
+2. Key information discovered
+3. Important conclusions
+
+Conversation:
+{conversation_text}
+
+Summary (be concise):"""
+
+ try:
+ response = await client.chat.completions.create(
+ model=settings.default_model_name,
+ messages=[{"role": "user", "content": prompt}],
+ temperature=0.3,
+ max_tokens=200
+ )
+ summary = response.choices[0].message.content.strip()
+
+ # 保存摘要
+ end_idx = len(memory._messages) - chat_config.max_history_turns * 2
+ memory.set_summary(summary, end_idx)
+
+ print(f"📝 Conversation compressed: {len(messages_to_summarize)} messages -> summary")
+ except Exception as e:
+ print(f"⚠️ Failed to compress conversation: {e}")
+
+
+def _build_jit_status_message(
+ loaded_count: int,
+ failed_files: List[str],
+ remaining_rounds: int,
+ use_chinese: bool
+) -> str:
+ """构建 JIT 状态消息"""
+ if use_chinese:
+ if loaded_count > 0 and not failed_files:
+ return f"系统通知: 成功加载 {loaded_count} 个文件。"
+ elif loaded_count > 0 and failed_files:
+ failed_list = ", ".join(failed_files)
+ return f"系统通知: 加载了 {loaded_count} 个文件，但以下文件无法访问: {failed_list}。"
+ else:
+ failed_list = ", ".join(failed_files)
+ if remaining_rounds > 0:
+ return f"系统通知: 文件 ({failed_list}) 无法访问。你还有 {remaining_rounds} 次机会请求其他文件，或者基于现有上下文回答。"
+ else:
+ return f"系统通知: 文件 ({failed_list}) 无法访问。请基于现有上下文给出最佳回答。"
+ else:
+ if loaded_count > 0 and not failed_files:
+ return f"System Notification: Successfully loaded {loaded_count} files."
+ elif loaded_count > 0 and failed_files:
+ failed_list = ", ".join(failed_files)
+ return f"System Notification: Loaded {loaded_count} files, but the following could not be accessed: {failed_list}."
+ else:
+ failed_list = ", ".join(failed_files)
+ if remaining_rounds > 0:
+ return f"System Notification: Files ({failed_list}) could not be accessed. You have {remaining_rounds} more attempts to request other files, or answer based on available context."
+ else:
+ return f"System Notification: Files ({failed_list}) could not be accessed. Please provide the best possible answer based on existing context."
+
+async def _download_and_index(vector_db, file_path):
+ """下载并索引文件"""
+ try:
+ content = await get_file_content(vector_db.repo_url, file_path)
+ if not content: return False
+
+ chunks = await asyncio.to_thread(chunker.chunk_file, content, file_path)
+ if not chunks:
+ chunks = [{
+ "content": content,
+ "metadata": {"file": file_path, "type": "text", "name": "root", "class": ""}
+ }]
+
+ documents = [c["content"] for c in chunks]
+ metadatas = []
+ for c in chunks:
+ meta = c["metadata"]
+ metadatas.append({
+ "file": meta["file"],
+ "type": meta["type"],
+ "name": meta.get("name", ""),
+ "class": meta.get("class") or ""
+ })
+ await vector_db.add_documents(documents, metadatas)
+ return True
+ except Exception as e:
+ print(f"Download Error: {e}")
+ return False
+
+
+def _build_context(docs: List[Dict], max_chars: int = 2000) -> str:
+ """构建上下文字符串"""
+ if not docs:
+ return "(No relevant code snippets found yet)"
+
+ context = ""
+ for doc in docs:
+ file_info = doc.get('file', 'unknown')
+ metadata = doc.get('metadata', {})
+
+ if 'class' in metadata and metadata['class']:
+ file_info += f" (Class: {metadata['class']})"
+
+ content = doc.get('content', '')[:max_chars]
+ context += f"\n--- File: {file_info} ---\n{content}\n"
+
+ return context
\ No newline at end of file
diff --git a/app/services/chunking_service.py b/app/services/chunking_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..a0d7ca53a6931808f38441437cb145b7642bb98c
--- /dev/null
+++ b/app/services/chunking_service.py
@@ -0,0 +1,372 @@
+import ast
+import re
+import os
+from dataclasses import dataclass
+
+# --- 配置类 ---
+@dataclass
+class ChunkingConfig:
+ """
+ 统一管理切分服务的配置参数
+ """
+ min_chunk_size: int = 50 # 最小分块阈值 (chars)
+ max_chunk_size: int = 2000 # 最大分块阈值 (chars)
+ fallback_line_size: int = 100 # 兜底策略的行数 (lines)
+ max_context_chars: int = 500 # 允许注入到每个Chunk的上下文最大长度
+ # 超过此长度则不再注入，避免冗余内容撑爆 Token
+
+class UniversalChunker:
+ def __init__(self, config: ChunkingConfig = None):
+ # 如果未传入配置，使用默认配置
+ self.config = config if config else ChunkingConfig()
+
+ def chunk_file(self, content: str, file_path: str):
+ if not content:
+ return []
+
+ ext = os.path.splitext(file_path)[1].lower()
+
+ if ext == '.py':
+ return self._chunk_python(content, file_path)
+
+ # 2. C-Style 语言优化
+ elif ext in ['.java', '.js', '.ts', '.jsx', '.tsx', '.go', '.cpp', '.c', '.h', '.cs', '.php', '.rs']:
+ return self._chunk_c_style(content, file_path)
+
+ else:
+ return self._fallback_chunking(content, file_path)
+
+ def _chunk_python(self, content, file_path):
+ """
+ 分级注入策略
+ """
+ chunks = []
+ try:
+ tree = ast.parse(content)
+ except SyntaxError:
+ return self._fallback_chunking(content, file_path)
+
+ import_nodes = []
+ other_nodes = []
+ function_class_chunks = []
+
+ # A. 遍历与分类
+ for node in tree.body:
+ if isinstance(node, ast.ClassDef):
+ class_code = ast.get_source_segment(content, node)
+ if not class_code: continue
+ if len(class_code) <= self.config.max_chunk_size:
+ function_class_chunks.append(self._create_chunk(
+ class_code, file_path, "class", node.name, node.lineno, node.name
+ ))
+ else:
+ # function_class_chunks 包含了从大类中拆分出的方法
+ function_class_chunks.extend(
+ self._chunk_large_python_class(node, content, file_path)
+ )
+
+ elif isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+ func_code = ast.get_source_segment(content, node)
+ if func_code and len(func_code) >= self.config.min_chunk_size:
+ function_class_chunks.append(self._create_chunk(
+ func_code, file_path, "function", node.name, node.lineno
+ ))
+
+ else:
+ segment = ast.get_source_segment(content, node)
+ if segment and len(segment.strip()) > 0:
+ if isinstance(node, (ast.Import, ast.ImportFrom)):
+ import_nodes.append(segment)
+ else:
+ other_nodes.append(segment)
+
+ # B. 决策准备
+ has_core_code = len(function_class_chunks) > 0
+ others_text = "\n".join(other_nodes).strip()
+ should_inject_others = len(others_text) <= self.config.max_context_chars
+
+ # C. 构建 Context Header
+ context_parts = []
+ # 1. Import 永远注入
+ if import_nodes:
+ context_parts.append("\n".join(import_nodes))
+ # 2. Globals 按需注入
+ if others_text and should_inject_others:
+ context_parts.append(others_text)
+
+ full_header = "\n".join(context_parts).strip()
+ if full_header:
+ full_header = f"# --- Context ---\n{full_header}\n# ---------------\n"
+
+ # D. 注入 Header 到核心 Chunk (函数/类)
+ # 此时 function_class_chunks 已经包含了大类拆分出来的方法
+ # 这里的循环会给它们都加上 Import/Global Context
+ for chunk in function_class_chunks:
+ chunk["content"] = full_header + chunk["content"]
+
+ # E. 处理溢出 (仅当有核心代码时，才独立存储溢出的 Globals)
+ if has_core_code and others_text and not should_inject_others:
+ chunks.append(self._create_chunk(
+ others_text, file_path, "global_context", "globals", 1
+ ))
+
+ # F. 纯脚本兜底
+ if not has_core_code:
+ # 这是一个纯脚本文件 (只有 Import 和 顶层逻辑)
+ full_script = (("\n".join(import_nodes) + "\n") if import_nodes else "") + others_text
+ if full_script.strip():
+ # 如果脚本太长，不要硬切成一个大块，而是走 Fallback 按行切分
+ if len(full_script) > self.config.max_chunk_size * 1.5: # 1.5倍宽容度
+ return self._fallback_chunking(content, file_path)
+ else:
+ chunks.append(self._create_chunk(
+ full_script, file_path, "script", "main", 1
+ ))
+
+ chunks.extend(function_class_chunks)
+
+ if not chunks and len(content.strip()) > 0:
+ return self._fallback_chunking(content, file_path)
+
+ return chunks
+
+ def _chunk_large_python_class(self, class_node, content, file_path):
+ chunks = []
+ class_name = class_node.name
+ docstring = ast.get_docstring(class_node) or ""
+
+ # === 尝试收集类级别的变量定义 ===
+ class_vars = []
+ for node in class_node.body:
+ # 如果是赋值语句，且在方法定义之前 (通常 AST 是有序的)
+ if isinstance(node, (ast.Assign, ast.AnnAssign)):
+ seg = ast.get_source_segment(content, node)
+ if seg: class_vars.append(seg)
+ # 一旦遇到函数，就停止收集变量，避免把乱七八糟的逻辑也收进去
+ elif isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+ break
+
+ vars_text = "\n ".join(class_vars)
+ if vars_text:
+ vars_text = "\n " + vars_text # 缩进对齐
+
+ # 将变量拼接到 Header 中
+ context_header = f"class {class_name}:{vars_text}\n \"\"\"{docstring}\"\"\"\n # ... (Parent Context)\n"
+
+ for node in class_node.body:
+ if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
+ method_code = ast.get_source_segment(content, node)
+ if not method_code: continue
+
+ full_chunk_content = context_header + "\n" + method_code
+ chunks.append(self._create_chunk(
+ full_chunk_content, file_path, "method", node.name, node.lineno, class_name
+ ))
+ return chunks
+
+ def _chunk_c_style(self, content, file_path):
+ """
+ 解决宏干扰、全局变量丢失、跨行函数头问题
+ """
+ chunks = []
+ if not content: return []
+
+ # === 1. 定义正则 Token ===
+ # 使用 Named Groups 避免 startswith 的模糊匹配
+ # 顺序至关重要：长匹配优先
+ token_pattern = re.compile(
+ r'(?P/\*.*?\*/)|' # 块注释
+ r'(?P//[^\n]*)|' # 行注释
+ r'(?P"(?:\\.|[^"\\])*")|' # 双引号字符串
+ r'(?P\'(?:\\.|[^\'\\])*\')|' # 单引号字符
+ r'(?P`(?:\\.|[^`\\])*`)|' # 反引号模板 (JS/Go)
+ r'(?P^\s*#.*(?:\\\n.*)*)|' # 宏定义 (支持跨行)
+ r'(?P\{)|' # 开括号
+ r'(?P\})|' # 闭括号
+ r'(?P;)', # 分号 (用于分割全局变量和函数头)
+ re.DOTALL | re.MULTILINE
+ )
+
+ # 全局上下文收集器
+ global_context_parts = []
+
+ last_index = 0 # 上一个 Token 结束位置
+ block_start_index = 0 # 当前 Block (函数/类) 的签名开始位置
+
+ brace_balance = 0
+ in_structural_block = False # 是否在最外层的类/函数块内
+
+ # 暂存当前块的前置文本 (从上一个块结束 到 当前块开始)
+ # 这段文本里可能混杂着：全局变量、Import、以及当前函数的签名
+ pending_pre_text_start = 0
+
+ # 扫描
+ for match in token_pattern.finditer(content):
+ kind = match.lastgroup
+ start, end = match.span()
+
+ # 跳过非结构化 Token (注释、字符串、宏)
+ if kind in ('BLOCK_COMMENT', 'LINE_COMMENT', 'STRING', 'CHAR', 'TEMPLATE', 'MACRO'):
+ continue
+
+ # 忽略括号 () 和 []，只认 {}。
+ # C-style 语言只有 {} 定义 Scope Body。忽略 () [] 是为了防止 if(a[i]){...} 误判。
+ # 只要 regex 不匹配 () []，它们就被视为普通文本，不会影响 brace_balance。
+ if kind == 'BRACE_OPEN':
+ if brace_balance == 0:
+ # === 发现一个新的顶层 Block ===
+ in_structural_block = True
+
+ # 1. 分析 "空隙文本" (从上一个块结束 到 这个 { 之前)
+ gap_text = content[pending_pre_text_start:start]
+
+ # [策略] 拆分 Global Context 和 Signature
+ # 寻找最后一个分号 ';' 或 '}' (在 gap_text 内部的逻辑结束点)
+ # 倒序查找比较安全。
+ # 如果找不到，说明整段 gap 都是签名 (e.g. void foo() {)
+ # 如果找到，分号前是 Global，分号后是 Signature
+ split_idx = gap_text.rfind(';')
+ if split_idx != -1:
+ # 分号前：归入全局上下文
+ global_part = gap_text[:split_idx+1].strip()
+ if global_part:
+ global_context_parts.append(global_part)
+ # 分号后：是当前函数的签名
+ # 自动处理了跨行函数头，因为 gap_text 包含换行
+ block_signature_start = pending_pre_text_start + split_idx + 1
+ else:
+ # 没有分号，假设全是签名 (e.g. 紧接着上一个块，或者是文件开头)
+ # 但要小心 include/import 等没有分号的语句 (Python 思维在 C 里不适用，C 几乎都有分号)
+ # Go 语言除外 (Go 没分号)。这里做一个简单的 heuristic:
+ # 如果是 Go/JS/TS，可能没有分号。暂且全部视为 Signature，
+ # 除非它看起来像 import。
+ # 这是一个 trade-off。
+ block_signature_start = pending_pre_text_start
+
+ # 记录当前 Block 真正的“视觉开始点” (包含签名)
+ block_start_index = block_signature_start
+
+ brace_balance += 1
+
+ elif kind == 'BRACE_CLOSE':
+ brace_balance -= 1
+
+ if brace_balance == 0 and in_structural_block:
+ # === 顶层 Block 结束 ===
+ in_structural_block = False
+
+ # 提取完整代码块 (Signature + Body)
+ # 范围：block_start_index -> end
+ full_block_text = content[block_start_index:end]
+
+ # 小块合并策略
+ # 如果块太小 (e.g. Getter/Setter)，暂不生成 Chunk
+ # 架构决策：为了代码完整性，工业界 RAG 通常不建议丢弃小块，
+ # 尤其是 Getter/Setter 可能包含关键字段名。
+ # 这里我们生成 Chunk，但后续入库时可以由 Embedding 模型决定权重。
+
+ # 提取元数据
+ meta = self._extract_c_style_metadata(full_block_text)
+ start_line = content.count('\n', 0, block_start_index) + 1
+
+ chunks.append(self._create_chunk(
+ full_block_text, # 暂时不加 Global Header，最后统一加
+ file_path, meta["type"], meta["name"], start_line
+ ))
+
+ # 更新游标：下一个块的前置文本从这里开始
+ pending_pre_text_start = end
+
+ # === 循环结束后的收尾 ===
+ # 处理文件末尾的剩余文本 (Tail)
+ tail_text = content[pending_pre_text_start:].strip()
+ if tail_text:
+ global_context_parts.append(tail_text)
+
+ # === Global Context 重排序 ===
+ # 目标顺序: Includes > Macros (#define) > Others (Typedefs/Vars)
+ # 简单策略：基于字符串内容的优先级排序
+
+ def context_priority(text):
+ text = text.strip()
+ if text.startswith("#include") or text.startswith("import") or text.startswith("using"):
+ return 0 # 最高优先级
+ if text.startswith("#define") or text.startswith("#macro"):
+ return 1 # 宏定义
+ if text.startswith("typedef") or text.startswith("enum") or text.startswith("struct"):
+ return 2 # 类型定义
+ return 3 # 普通全局变量和其他
+
+ # 稳定排序
+ global_context_parts.sort(key=context_priority)
+
+ # === 组装与注入 ===
+ full_global_context = "\n".join(global_context_parts).strip()
+
+ should_inject = len(full_global_context) <= self.config.max_context_chars
+
+ context_header = ""
+ if full_global_context and should_inject:
+ context_header = f"/* --- Global Context --- */\n{full_global_context}\n/* ---------------------- */\n"
+
+ for chunk in chunks:
+ chunk["content"] = context_header + chunk["content"]
+
+ if (full_global_context and not should_inject) or (not chunks and full_global_context):
+ chunks.insert(0, self._create_chunk(
+ full_global_context, file_path, "global_context", "header", 1
+ ))
+
+ if not chunks:
+ return self._fallback_chunking(content, file_path)
+
+ return chunks
+
+ def _extract_c_style_metadata(self, code_block):
+ """
+ 从包含签名的代码块中提取元数据 (支持多行签名)
+ """
+ # 截取到第一个 { 为止
+ header_part = code_block.split('{')[0]
+ # 压缩多余空白，变成单行以便正则匹配
+ header_clean = " ".join(header_part.split())
+
+ # 1. Class/Struct/Interface
+ type_pattern = re.compile(r'\b(class|struct|interface|enum|record|type)\s+([a-zA-Z0-9_]+)')
+ match = type_pattern.search(header_clean)
+ if match:
+ return {"type": "class", "name": match.group(2)}
+
+ # 2. Function
+ # 匹配: 单词 + (
+ # 排除关键字: if, for, while, switch, catch, return
+ func_pattern = re.compile(r'\b([a-zA-Z0-9_]+)\s*\(')
+ for match in func_pattern.finditer(header_clean):
+ name = match.group(1)
+ if name not in {'if', 'for', 'while', 'switch', 'catch', 'return', 'sizeof'}:
+ return {"type": "function", "name": name}
+
+ return {"type": "code_block", "name": "anonymous"}
+
+ def _fallback_chunking(self, content, file_path):
+ """兜底策略：使用 Config 中的行数设置"""
+ chunks = []
+ lines = content.split('\n')
+ chunk_size = self.config.fallback_line_size
+
+ for i in range(0, len(lines), chunk_size):
+ chunk_content = "\n".join(lines[i:i+chunk_size])
+ chunks.append(self._create_chunk(chunk_content, file_path, "text_chunk", f"chunk_{i}", i+1))
+ return chunks
+
+ def _create_chunk(self, content, file_path, type_, name, start_line, class_name=""):
+ return {
+ "content": content,
+ "metadata": {
+ "file": file_path,
+ "type": type_,
+ "name": name,
+ "start_line": start_line,
+ "class": class_name
+ }
+ }
\ No newline at end of file
diff --git a/app/services/github_service.py b/app/services/github_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..78d5a2a82e81bc78058d7c88e08b10160e4e2a41
--- /dev/null
+++ b/app/services/github_service.py
@@ -0,0 +1,210 @@
+# -*- coding: utf-8 -*-
+"""
+GitHub 服务层
+
+职责:
+- 提供业务级别的 GitHub 操作
+- 封装底层客户端，提供简洁 API
+- 保持向后兼容的函数签名
+"""
+
+import logging
+from typing import List, Optional, Dict
+
+from app.utils.github_client import (
+ GitHubClient,
+ GitHubRepo,
+ GitHubFile,
+ FileFilter,
+ GitHubError,
+ GitHubNotFoundError,
+ get_github_client,
+ parse_repo_url,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ============================================================
+# 服务类
+# ============================================================
+
+class GitHubService:
+ """
+ GitHub 服务
+
+ 提供高层业务操作，内部使用异步客户端。
+
+ 使用示例:
+ ```python
+ service = GitHubService()
+
+ # 获取仓库文件列表
+ files = await service.get_repo_structure("https://github.com/owner/repo")
+
+ # 获取文件内容
+ content = await service.get_file_content(
+ "https://github.com/owner/repo",
+ "src/main.py"
+ )
+
+ # 批量获取文件
+ contents = await service.get_files_content(
+ "https://github.com/owner/repo",
+ ["README.md", "src/main.py", "requirements.txt"]
+ )
+ ```
+ """
+
+ def __init__(self, client: Optional[GitHubClient] = None):
+ self._client = client
+
+ @property
+ def client(self) -> GitHubClient:
+ """获取客户端 (延迟初始化)"""
+ if self._client is None:
+ self._client = get_github_client()
+ return self._client
+
+ async def _get_repo_from_url(self, repo_url: str) -> GitHubRepo:
+ """从 URL 获取仓库对象"""
+ parsed = parse_repo_url(repo_url)
+ if not parsed:
+ raise ValueError(f"无效的 GitHub URL: {repo_url}")
+
+ owner, name = parsed
+ return await self.client.get_repo(owner, name)
+
+ async def get_repo_structure(
+ self,
+ repo_url: str,
+ file_filter: Optional[FileFilter] = None
+ ) -> List[str]:
+ """
+ 获取仓库文件列表
+
+ Args:
+ repo_url: GitHub 仓库 URL
+ file_filter: 自定义文件过滤器
+
+ Returns:
+ 文件路径列表
+ """
+ repo = await self._get_repo_from_url(repo_url)
+ files = await self.client.get_repo_tree(repo, file_filter)
+ return [f.path for f in files]
+
+ async def get_file_content(
+ self,
+ repo_url: str,
+ file_path: str
+ ) -> Optional[str]:
+ """
+ 获取单个文件内容
+
+ Args:
+ repo_url: GitHub 仓库 URL
+ file_path: 文件路径
+
+ Returns:
+ 文件内容，失败返回 None
+ """
+ repo = await self._get_repo_from_url(repo_url)
+ return await self.client.get_file_content(repo, file_path)
+
+ async def get_files_content(
+ self,
+ repo_url: str,
+ file_paths: List[str]
+ ) -> Dict[str, Optional[str]]:
+ """
+ 批量获取文件内容 (并发)
+
+ Args:
+ repo_url: GitHub 仓库 URL
+ file_paths: 文件路径列表
+
+ Returns:
+ {path: content} 字典
+ """
+ repo = await self._get_repo_from_url(repo_url)
+ return await self.client.get_files_content(repo, file_paths, show_progress=True)
+
+ async def get_repo_info(self, repo_url: str) -> GitHubRepo:
+ """
+ 获取仓库基本信息
+
+ Args:
+ repo_url: GitHub 仓库 URL
+
+ Returns:
+ GitHubRepo 对象
+ """
+ return await self._get_repo_from_url(repo_url)
+
+
+# ============================================================
+# 全局服务实例
+# ============================================================
+
+_github_service: Optional[GitHubService] = None
+
+
+def get_github_service() -> GitHubService:
+ """获取 GitHub 服务单例"""
+ global _github_service
+ if _github_service is None:
+ _github_service = GitHubService()
+ return _github_service
+
+
+# ============================================================
+# 兼容旧接口 (同步风格的函数签名，但返回协程)
+# ============================================================
+
+# 保留 parse_repo_url 的旧签名兼容
+def parse_repo_url_compat(url: str) -> Optional[str]:
+ """
+ 解析 GitHub URL (兼容旧接口)
+
+ Returns:
+ "owner/repo" 字符串，无效返回 None
+ """
+ result = parse_repo_url(url)
+ if result:
+ return f"{result[0]}/{result[1]}"
+ return None
+
+
+async def get_repo_structure(repo_url: str) -> List[str]:
+ """
+ 获取仓库文件列表 (兼容旧接口)
+
+ 注意: 这是一个异步函数，需要 await 调用
+ """
+ service = get_github_service()
+ return await service.get_repo_structure(repo_url)
+
+
+async def get_file_content(repo_url: str, file_path: str) -> Optional[str]:
+ """
+ 获取文件内容 (兼容旧接口)
+
+ 注意: 这是一个异步函数，需要 await 调用
+ """
+ service = get_github_service()
+ return await service.get_file_content(repo_url, file_path)
+
+
+# 导出
+__all__ = [
+ "GitHubService",
+ "get_github_service",
+ "get_repo_structure",
+ "get_file_content",
+ "parse_repo_url_compat",
+ "GitHubError",
+ "GitHubNotFoundError",
+ "FileFilter",
+ "GitHubRepo",
+]
\ No newline at end of file
diff --git a/app/services/tracing_service.py b/app/services/tracing_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..05969c0fb5acede0581b938617293361a768ad64
--- /dev/null
+++ b/app/services/tracing_service.py
@@ -0,0 +1,549 @@
+# 文件路径: app/services/tracing_service.py
+"""
+Langfuse集成模块 - 用于端到端追踪和观测
+
+核心能力:
+1. 自动捕获每一步的延迟、Token成本、输入输出
+2. 记录完整的调用链路: Query -> Rewrite -> Retrieval -> Generation
+3. 记录Tool调用和参数
+4. 集成到评估流程
+
+Langfuse支持:
+- 本地部署 (docker run ... langfuse)
+- 云端托管 (app.langfuse.com)
+
+Author: Dexter
+Date: 2025-01-27
+"""
+
+import time
+import json
+import os
+from typing import Dict, Any, Optional, List, Callable
+from functools import wraps
+from datetime import datetime
+from dataclasses import dataclass
+
+
+# ============================================================================
+# 第一部分: Langfuse客户端初始化 (可选)
+# ============================================================================
+
+LANGFUSE_IMPORT_ERROR = None
+_LANGFUSE_ENABLED_ENV = os.getenv("LANGFUSE_ENABLED", "true").strip().lower()
+_LANGFUSE_ENABLED = _LANGFUSE_ENABLED_ENV not in {"0", "false", "no", "off"}
+
+if _LANGFUSE_ENABLED:
+ try:
+ from langfuse import Langfuse
+ from langfuse.decorators import observe, langfuse_context
+ LANGFUSE_AVAILABLE = True
+ except Exception as e:
+ LANGFUSE_IMPORT_ERROR = e
+ LANGFUSE_AVAILABLE = False
+else:
+ LANGFUSE_AVAILABLE = False
+
+
+@dataclass
+class TracingConfig:
+ """追踪配置"""
+ enabled: bool = True
+ backend: str = "langfuse" # "langfuse" or "local"
+ langfuse_host: str = os.getenv("LANGFUSE_HOST", "http://localhost:3000")
+ langfuse_public_key: str = os.getenv("LANGFUSE_PUBLIC_KEY", "")
+ langfuse_secret_key: str = os.getenv("LANGFUSE_SECRET_KEY", "")
+ capture_token_usage: bool = True
+ capture_latency: bool = True
+ local_log_dir: str = "logs/traces"
+
+
+class TracingService:
+ """
+ 统一的追踪服务
+ 支持Langfuse和本地日志两种后端
+ """
+
+ def __init__(self, config: TracingConfig = None):
+ self.config = config or TracingConfig()
+ self.langfuse_client = None
+ self.current_trace_id = None
+
+ if self.config.enabled and self.config.backend == "langfuse":
+ if not LANGFUSE_AVAILABLE:
+ print("⚠️ Langfuse not installed. Install with: pip install langfuse. Falling back to local logging.")
+ self.config.backend = "local"
+ else:
+ try:
+ self.langfuse_client = Langfuse(
+ host=self.config.langfuse_host,
+ public_key=self.config.langfuse_public_key,
+ secret_key=self.config.langfuse_secret_key,
+ enabled=True,
+ debug=False
+ )
+ print("✅ Langfuse client initialized successfully")
+ except Exception as e:
+ print(f"⚠️ Langfuse initialization failed: {e}. Falling back to local logging.")
+ self.config.backend = "local"
+
+ # 创建本地日志目录
+ os.makedirs(self.config.local_log_dir, exist_ok=True)
+
+ def start_trace(self, trace_name: str, session_id: str, metadata: Dict = None) -> str:
+ """启动一个新的追踪链"""
+ import uuid
+ trace_id = str(uuid.uuid4())
+ self.current_trace_id = trace_id
+
+ if self.langfuse_client:
+ self.langfuse_client.trace(
+ name=trace_name,
+ input=metadata or {},
+ session_id=session_id
+ )
+ print(f"📍 Trace started: {trace_id}")
+ else:
+ self._log_locally("trace_start", {
+ "trace_id": trace_id,
+ "name": trace_name,
+ "session_id": session_id,
+ "metadata": metadata,
+ "timestamp": datetime.now().isoformat()
+ })
+
+ return trace_id
+
+ def record_span(
+ self,
+ span_name: str,
+ operation: str,
+ input_data: Any,
+ output_data: Any,
+ latency_ms: float,
+ token_usage: Dict[str, int] = None,
+ metadata: Dict = None
+ ) -> None:
+ """记录一个操作的跨度"""
+
+ span_record = {
+ "span_name": span_name,
+ "operation": operation,
+ "latency_ms": latency_ms,
+ "timestamp": datetime.now().isoformat(),
+ "token_usage": token_usage or {},
+ "metadata": metadata or {}
+ }
+
+ if self.langfuse_client:
+ try:
+ # Langfuse:记录到云端
+ self.langfuse_client.span(
+ name=span_name,
+ input=input_data,
+ output=output_data,
+ metadata={
+ "operation": operation,
+ "latency_ms": latency_ms,
+ **(token_usage or {}),
+ **(metadata or {})
+ }
+ )
+ except Exception as e:
+ print(f"⚠️ Failed to record span to Langfuse: {e}")
+
+ # 本地日志
+ self._log_locally("span", span_record)
+
+ def record_tool_call(
+ self,
+ tool_name: str,
+ parameters: Dict,
+ result: Any,
+ latency_ms: float,
+ success: bool,
+ error: str = None
+ ) -> None:
+ """记录工具调用"""
+
+ tool_record = {
+ "tool_name": tool_name,
+ "parameters": parameters,
+ "result": str(result)[:500] if result else None,
+ "latency_ms": latency_ms,
+ "success": success,
+ "error": error,
+ "timestamp": datetime.now().isoformat()
+ }
+
+ if self.langfuse_client:
+ try:
+ self.langfuse_client.event(
+ name=f"tool_call:{tool_name}",
+ input=parameters,
+ output=result,
+ metadata={
+ "latency_ms": latency_ms,
+ "success": success,
+ "error": error
+ }
+ )
+ except Exception as e:
+ print(f"⚠️ Failed to record tool call: {e}")
+
+ self._log_locally("tool_call", tool_record)
+
+ def record_retrieval_debug(
+ self,
+ query: str,
+ retrieved_files: List[str],
+ vector_scores: List[float],
+ bm25_scores: List[float],
+ latency_ms: float
+ ) -> None:
+ """记录检索过程的调试信息"""
+
+ retrieval_record = {
+ "query": query,
+ "retrieved_count": len(retrieved_files),
+ "files": retrieved_files,
+ "vector_scores": vector_scores,
+ "bm25_scores": bm25_scores,
+ "latency_ms": latency_ms,
+ "timestamp": datetime.now().isoformat()
+ }
+
+ if self.langfuse_client:
+ try:
+ self.langfuse_client.event(
+ name="retrieval_debug",
+ input={"query": query},
+ output={"files": retrieved_files},
+ metadata=retrieval_record
+ )
+ except Exception as e:
+ print(f"⚠️ Failed to record retrieval debug: {e}")
+
+ self._log_locally("retrieval", retrieval_record)
+
+ def record_llm_generation(
+ self,
+ model: str,
+ prompt_messages: List[Dict],
+ generated_text: str,
+ ttft_ms: float = None,
+ total_latency_ms: float = None,
+ prompt_tokens: int = None,
+ completion_tokens: int = None,
+ total_tokens: int = None,
+ is_streaming: bool = False,
+ metadata: Dict = None
+ ) -> None:
+ """
+ 记录 LLM 生成的完整信息，包括 Token 消耗和 TTFT
+
+ Args:
+ model: 模型名称 (如 "gpt-4", "claude-3")
+ prompt_messages: 发送给 LLM 的消息列表
+ generated_text: 生成的文本（可截断）
+ ttft_ms: Time To First Token，首 token 延迟（毫秒）
+ total_latency_ms: 总生成延迟（毫秒）
+ prompt_tokens: 输入 token 数
+ completion_tokens: 输出 token 数
+ total_tokens: 总 token 数
+ is_streaming: 是否流式输出
+ metadata: 额外元数据
+ """
+ llm_record = {
+ "model": model,
+ "is_streaming": is_streaming,
+ "prompt_preview": str(prompt_messages)[:500], # 截断避免日志过大
+ "generated_preview": generated_text[:500] if generated_text else "",
+ "generated_length": len(generated_text) if generated_text else 0,
+ # Token 统计
+ "token_usage": {
+ "prompt_tokens": prompt_tokens,
+ "completion_tokens": completion_tokens,
+ "total_tokens": total_tokens
+ },
+ # 延迟统计
+ "latency": {
+ "ttft_ms": ttft_ms, # Time To First Token
+ "total_ms": total_latency_ms,
+ "tokens_per_second": round(completion_tokens / (total_latency_ms / 1000), 2)
+ if completion_tokens and total_latency_ms and total_latency_ms > 0 else None
+ },
+ "timestamp": datetime.now().isoformat(),
+ "metadata": metadata or {}
+ }
+
+ if self.langfuse_client:
+ try:
+ self.langfuse_client.generation(
+ name="llm_generation",
+ model=model,
+ input=prompt_messages,
+ output=generated_text[:1000] if generated_text else "",
+ usage={
+ "prompt_tokens": prompt_tokens or 0,
+ "completion_tokens": completion_tokens or 0,
+ "total_tokens": total_tokens or 0
+ },
+ metadata={
+ "ttft_ms": ttft_ms,
+ "total_latency_ms": total_latency_ms,
+ "is_streaming": is_streaming,
+ **(metadata or {})
+ }
+ )
+ except Exception as e:
+ print(f"⚠️ Failed to record LLM generation to Langfuse: {e}")
+
+ self._log_locally("llm_generation", llm_record)
+
+ def record_ttft(self, ttft_ms: float, model: str = None, metadata: Dict = None) -> None:
+ """
+ 单独记录 TTFT (Time To First Token)
+ 用于流式生成时在收到第一个 token 时立即记录
+
+ Args:
+ ttft_ms: 首 token 延迟（毫秒）
+ model: 模型名称
+ metadata: 额外元数据
+ """
+ ttft_record = {
+ "ttft_ms": ttft_ms,
+ "model": model,
+ "timestamp": datetime.now().isoformat(),
+ "metadata": metadata or {}
+ }
+
+ if self.langfuse_client:
+ try:
+ self.langfuse_client.event(
+ name="ttft",
+ input={},
+ output={"ttft_ms": ttft_ms},
+ metadata=ttft_record
+ )
+ except Exception as e:
+ print(f"⚠️ Failed to record TTFT: {e}")
+
+ self._log_locally("ttft", ttft_record)
+
+ def add_event(self, event_name: str, event_data: Dict[str, Any] = None) -> None:
+ """
+ 添加事件记录
+
+ Args:
+ event_name: 事件名称 (如 "repo_map_generated", "file_read_failed" 等)
+ event_data: 事件相关数据
+ """
+ event_record = {
+ "event_name": event_name,
+ "event_data": event_data or {},
+ "timestamp": datetime.now().isoformat()
+ }
+
+ if self.langfuse_client:
+ try:
+ self.langfuse_client.event(
+ name=event_name,
+ input={},
+ output=event_data or {},
+ metadata=event_data or {}
+ )
+ except Exception as e:
+ print(f"⚠️ Failed to record event '{event_name}': {e}")
+
+ self._log_locally("event", event_record)
+
+ def _log_locally(self, log_type: str, data: Dict) -> None:
+ """本地日志记录"""
+ log_file = os.path.join(
+ self.config.local_log_dir,
+ f"{log_type}_{datetime.now().strftime('%Y%m%d')}.jsonl"
+ )
+
+ with open(log_file, 'a', encoding='utf-8') as f:
+ f.write(json.dumps(data, ensure_ascii=False, default=str) + '\n')
+
+ def get_trace_url(self, trace_id: str = None) -> str:
+ """获取Langfuse中该trace的URL (用于前端跳转)"""
+ if not self.langfuse_client or not trace_id:
+ return None
+
+ # Langfuse云端URL格式
+ return f"{self.config.langfuse_host}/traces/{trace_id}"
+
+
+# ============================================================================
+# 第二部分: 装饰器 - 自动追踪
+# ============================================================================
+
+def traced(operation_name: str, capture_args: List[str] = None):
+ """
+ 装饰器: 自动为被装饰函数添加追踪
+
+ 使用示例:
+ @traced("query_rewrite", capture_args=["user_query"])
+ async def rewrite_query(user_query: str):
+ ...
+ """
+
+ def decorator(func: Callable):
+ @wraps(func)
+ async def async_wrapper(*args, **kwargs):
+ start_time = time.time()
+
+ # 捕获输入参数
+ input_data = {}
+ if capture_args:
+ for arg_name in capture_args:
+ if arg_name in kwargs:
+ input_data[arg_name] = kwargs[arg_name]
+
+ try:
+ result = await func(*args, **kwargs)
+ latency_ms = (time.time() - start_time) * 1000
+
+ # 记录跨度
+ tracing_service.record_span(
+ span_name=operation_name,
+ operation=func.__name__,
+ input_data=input_data,
+ output_data={"success": True},
+ latency_ms=latency_ms
+ )
+
+ return result
+ except Exception as e:
+ latency_ms = (time.time() - start_time) * 1000
+ tracing_service.record_span(
+ span_name=operation_name,
+ operation=func.__name__,
+ input_data=input_data,
+ output_data={"error": str(e)},
+ latency_ms=latency_ms,
+ metadata={"error": True}
+ )
+ raise
+
+ @wraps(func)
+ def sync_wrapper(*args, **kwargs):
+ start_time = time.time()
+
+ input_data = {}
+ if capture_args:
+ for arg_name in capture_args:
+ if arg_name in kwargs:
+ input_data[arg_name] = kwargs[arg_name]
+
+ try:
+ result = func(*args, **kwargs)
+ latency_ms = (time.time() - start_time) * 1000
+
+ tracing_service.record_span(
+ span_name=operation_name,
+ operation=func.__name__,
+ input_data=input_data,
+ output_data={"success": True},
+ latency_ms=latency_ms
+ )
+
+ return result
+ except Exception as e:
+ latency_ms = (time.time() - start_time) * 1000
+ tracing_service.record_span(
+ span_name=operation_name,
+ operation=func.__name__,
+ input_data=input_data,
+ output_data={"error": str(e)},
+ latency_ms=latency_ms,
+ metadata={"error": True}
+ )
+ raise
+
+ # 判断是async还是sync
+ if asyncio.iscoroutinefunction(func):
+ return async_wrapper
+ else:
+ return sync_wrapper
+
+ return decorator
+
+
+# ============================================================================
+# 第三部分: 全局实例
+# ============================================================================
+
+tracing_config = TracingConfig(
+ enabled=True,
+ backend="langfuse" if LANGFUSE_AVAILABLE else "local"
+)
+
+tracing_service = TracingService(config=tracing_config)
+
+
+# ============================================================================
+# 第四部分: 集成示例 (如何在agent_service.py中使用)
+# ============================================================================
+
+"""
+在你的agent_service.py中添加:
+
+1. 导入追踪服务:
+ from app.services.tracing_service import tracing_service
+
+2. 在agent_stream函数开始:
+ trace_id = tracing_service.start_trace(
+ trace_name="github_agent_analysis",
+ session_id=session_id,
+ metadata={"repo_url": repo_url, "language": language}
+ )
+
+3. 在generate_repo_map函数周围:
+ start_time = time.time()
+ file_tree_str, mapped_files = await generate_repo_map(repo_url, file_list, limit=limit)
+ latency_ms = (time.time() - start_time) * 1000
+
+ tracing_service.record_span(
+ span_name="generate_repo_map",
+ operation="repo_mapping",
+ input_data={"file_count": len(file_list), "limit": limit},
+ output_data={"files_in_map": len(mapped_files)},
+ latency_ms=latency_ms
+ )
+
+4. 在process_single_file中记录检索:
+ tracing_service.record_retrieval_debug(
+ query=search_query,
+ retrieved_files=valid_files,
+ vector_scores=vector_scores,
+ bm25_scores=bm25_scores,
+ latency_ms=search_latency
+ )
+
+5. 工具调用记录:
+ start_time = time.time()
+ try:
+ result = get_file_content(repo_url, file_path)
+ tracing_service.record_tool_call(
+ tool_name="get_file_content",
+ parameters={"file_path": file_path},
+ result=result[:100] if result else None,
+ latency_ms=(time.time() - start_time) * 1000,
+ success=True
+ )
+ except Exception as e:
+ tracing_service.record_tool_call(
+ tool_name="get_file_content",
+ parameters={"file_path": file_path},
+ result=None,
+ latency_ms=(time.time() - start_time) * 1000,
+ success=False,
+ error=str(e)
+ )
+"""
+
+import asyncio
diff --git a/app/services/vector_service.py b/app/services/vector_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d2ecd56908d3730c34098766964c0822f217c7e
--- /dev/null
+++ b/app/services/vector_service.py
@@ -0,0 +1,676 @@
+# -*- coding: utf-8 -*-
+"""
+向量服务层 - Qdrant 版
+
+特性:
+1. 混合搜索 - Qdrant 向量 + BM25 关键词，RRF 融合
+2. 异步原生 - 全链路异步
+3. 会话隔离 - 每个 session 独立集合
+4. 状态持久化 - 仓库信息、BM25 索引缓存
+"""
+
+import asyncio
+import json
+import logging
+import os
+import pickle
+import re
+import tempfile
+import time
+from dataclasses import dataclass, field
+from typing import List, Dict, Any, Optional, Set
+
+from rank_bm25 import BM25Okapi
+
+from app.core.config import settings
+from app.storage.base import Document, SearchResult, CollectionStats
+from app.storage.qdrant_store import QdrantVectorStore, QdrantConfig, get_qdrant_factory
+from app.utils.embedding import get_embedding_service, EmbeddingConfig
+
+logger = logging.getLogger(__name__)
+
+
+# ============================================================
+# 使用统一配置
+# ============================================================
+
+from app.core.config import vector_config as config
+
+# 确保目录存在
+os.makedirs(config.context_dir, exist_ok=True)
+
+# === 向后兼容导出 (供 main.py 使用) ===
+vector_config = config # 兼容旧名称
+CONTEXT_DIR = config.context_dir
+QDRANT_DIR = config.data_dir # Qdrant 数据目录
+
+
+# ============================================================
+# Embedding 服务
+# ============================================================
+
+_embedding_service = None
+
+def get_embedding():
+ """获取 Embedding 服务单例"""
+ global _embedding_service
+ if _embedding_service is None:
+ emb_config = EmbeddingConfig(
+ api_base_url=config.embedding_api_url,
+ model_name=config.embedding_model,
+ batch_size=config.embedding_batch_size,
+ max_text_length=config.embedding_max_length,
+ max_concurrent_batches=config.embedding_concurrency,
+ )
+ _embedding_service = get_embedding_service(emb_config)
+ return _embedding_service
+
+
+# ============================================================
+# 向量存储服务
+# ============================================================
+
+class VectorStore:
+ """
+ 向量存储服务
+
+ 整合 Qdrant 向量搜索和 BM25 关键词搜索
+
+ 使用示例:
+ ```python
+ store = VectorStore("session_123")
+ await store.initialize()
+
+ # 重置 (分析新仓库时)
+ await store.reset()
+
+ # 添加文档
+ await store.add_documents(documents, metadatas)
+
+ # 混合搜索
+ results = await store.search_hybrid("how does auth work?")
+
+ await store.close()
+ ```
+ """
+
+ def __init__(self, session_id: str):
+ self.session_id = self._sanitize_id(session_id)
+ self.collection_name = f"repo_{self.session_id}"
+
+ # Qdrant 存储
+ self._qdrant: Optional[QdrantVectorStore] = None
+
+ # BM25 索引 (内存)
+ self._bm25: Optional[BM25Okapi] = None
+ self._doc_store: List[Document] = []
+ self._indexed_files: Set[str] = set()
+
+ # 上下文
+ self.repo_url: Optional[str] = None
+ self.global_context: Dict[str, Any] = {}
+
+ # 文件路径
+ self._context_file = os.path.join(config.context_dir, f"{self.session_id}.json")
+ self._cache_file = os.path.join(config.context_dir, f"{self.session_id}_bm25.pkl")
+
+ self._initialized = False
+
+ @staticmethod
+ def _sanitize_id(session_id: str) -> str:
+ """清理 session ID"""
+ clean = re.sub(r'[^a-zA-Z0-9_-]', '', session_id)
+ if not clean:
+ raise ValueError("Invalid session_id")
+ return clean
+
+ async def initialize(self) -> None:
+ """初始化存储"""
+ if self._initialized:
+ return
+
+ # 初始化 Qdrant
+ factory = get_qdrant_factory()
+ self._qdrant = factory.create(self.collection_name)
+ await self._qdrant.initialize()
+
+ # 加载本地状态
+ await self._load_state()
+
+ self._initialized = True
+ logger.debug(f"✅ VectorStore 初始化: {self.session_id}")
+
+ async def close(self) -> None:
+ """关闭连接"""
+ if self._qdrant:
+ await self._qdrant.close()
+ self._qdrant = None
+ self._initialized = False
+
+ async def _load_state(self) -> None:
+ """加载状态"""
+ # 1. 加载上下文 JSON
+ if os.path.exists(self._context_file):
+ try:
+ with open(self._context_file, 'r', encoding='utf-8') as f:
+ data = json.load(f)
+ self.repo_url = data.get("repo_url")
+ self.global_context = data.get("global_context", {})
+ except Exception as e:
+ logger.warning(f"加载上下文失败: {e}")
+
+ # 2. 尝试加载 BM25 缓存
+ cache_loaded = False
+ if os.path.exists(self._cache_file):
+ try:
+ with open(self._cache_file, 'rb') as f:
+ cache = pickle.load(f)
+ if isinstance(cache, dict) and cache.get("version") == config.cache_version:
+ self._bm25 = cache.get("bm25")
+ self._doc_store = cache.get("doc_store", [])
+ self._indexed_files = cache.get("indexed_files", set())
+ cache_loaded = True
+ logger.debug(f"📦 BM25 缓存命中: {len(self._doc_store)} 文档")
+ except Exception as e:
+ logger.warning(f"BM25 缓存损坏: {e}")
+ os.remove(self._cache_file)
+
+ # 3. 缓存未命中: 从 Qdrant 重建
+ if not cache_loaded and self._qdrant:
+ await self._rebuild_bm25_index()
+
+ async def _rebuild_bm25_index(self) -> None:
+ """从 Qdrant 重建 BM25 索引"""
+ logger.info(f"🔄 重建 BM25 索引: {self.session_id}")
+
+ documents = await self._qdrant.get_all_documents()
+
+ if documents:
+ self._doc_store = documents
+ self._indexed_files = {doc.file_path for doc in documents if doc.file_path}
+
+ tokenized = [self._tokenize(doc.content) for doc in documents]
+ if tokenized:
+ self._bm25 = BM25Okapi(tokenized)
+
+ self._save_bm25_cache()
+ logger.info(f"✅ BM25 索引重建完成: {len(documents)} 文档")
+
+ def _save_bm25_cache(self) -> None:
+ """保存 BM25 缓存 (原子写入)"""
+ if not self._doc_store:
+ return
+
+ try:
+ fd, tmp_path = tempfile.mkstemp(dir=config.context_dir)
+ with os.fdopen(fd, 'wb') as f:
+ pickle.dump({
+ "version": config.cache_version,
+ "bm25": self._bm25,
+ "doc_store": self._doc_store,
+ "indexed_files": self._indexed_files,
+ }, f)
+
+ if os.path.exists(self._cache_file):
+ os.remove(self._cache_file)
+ os.rename(tmp_path, self._cache_file)
+
+ except Exception as e:
+ logger.error(f"保存 BM25 缓存失败: {e}")
+
+ def _tokenize(self, text: str) -> List[str]:
+ """分词"""
+ return [
+ t.lower() for t in re.split(config.tokenize_regex, text)
+ if t.strip()
+ ]
+
+ async def save_context(self, repo_url: str, context_data: Dict[str, Any]) -> None:
+ """保存仓库上下文 (异步，不阻塞事件循环)"""
+ self.repo_url = repo_url
+ self.global_context = context_data
+ await asyncio.to_thread(self._write_context_file, {
+ "repo_url": repo_url,
+ "global_context": context_data,
+ })
+
+ def _write_context_file(self, updates: Dict[str, Any]) -> None:
+ """写入上下文文件 (同步，供线程池调用)"""
+ try:
+ existing = {}
+ if os.path.exists(self._context_file):
+ with open(self._context_file, 'r', encoding='utf-8') as f:
+ existing = json.load(f)
+ existing.update(updates)
+ with open(self._context_file, 'w', encoding='utf-8') as f:
+ json.dump(existing, f, ensure_ascii=False, indent=2)
+ except Exception as e:
+ logger.error(f"写入上下文失败: {e}")
+
+ async def save_report(self, report: str, language: str = "en") -> None:
+ """保存技术报告 (异步，不阻塞事件循环)"""
+ await asyncio.to_thread(self._write_report, report, language)
+
+ def _write_report(self, report: str, language: str) -> None:
+ """写入报告 (同步，供线程池调用)"""
+ try:
+ existing = {}
+ if os.path.exists(self._context_file):
+ with open(self._context_file, 'r', encoding='utf-8') as f:
+ existing = json.load(f)
+
+ if "reports" not in existing:
+ existing["reports"] = {}
+ existing["reports"][language] = report
+ existing["report"] = report
+ existing["report_language"] = language
+
+ with open(self._context_file, 'w', encoding='utf-8') as f:
+ json.dump(existing, f, ensure_ascii=False, indent=2)
+ logger.info(f"📝 报告已保存: {self.session_id} ({language})")
+ except Exception as e:
+ logger.error(f"保存报告失败: {e}")
+
+ def get_report(self, language: str = "en") -> Optional[str]:
+ """
+ 获取指定语言的报告
+
+ Args:
+ language: 语言代码 ('en', 'zh')
+
+ Returns:
+ 报告内容，不存在返回 None
+ """
+ context = self.load_context()
+ if not context:
+ return None
+
+ # 优先从 reports 字典获取
+ reports = context.get("reports", {})
+ if language in reports:
+ return reports[language]
+
+ # 兼容旧格式：如果只有 report 字段且语言匹配
+ if "report" in context:
+ stored_lang = context.get("report_language", "en")
+ if stored_lang == language:
+ return context["report"]
+
+ return None
+
+ def get_available_languages(self) -> List[str]:
+ """获取已有报告的语言列表"""
+ context = self.load_context()
+ if not context:
+ return []
+
+ reports = context.get("reports", {})
+ return list(reports.keys())
+
+ def load_context(self) -> Optional[Dict[str, Any]]:
+ """
+ 加载仓库上下文
+
+ Returns:
+ 包含 repo_url, global_context, report 等的字典，不存在返回 None
+ """
+ if not os.path.exists(self._context_file):
+ return None
+
+ try:
+ with open(self._context_file, 'r', encoding='utf-8') as f:
+ data = json.load(f)
+
+ # 恢复内存状态
+ self.repo_url = data.get("repo_url")
+ self.global_context = data.get("global_context", {})
+
+ return data
+ except Exception as e:
+ logger.error(f"加载上下文失败: {e}")
+ return None
+
+ def has_index(self) -> bool:
+ """检查是否已有索引"""
+ context = self.load_context()
+ return context is not None and context.get("repo_url") is not None
+
+ async def reset(self) -> None:
+ """重置存储 (分析新仓库时调用)"""
+ await self.initialize()
+
+ # 删除 Qdrant 集合
+ if self._qdrant:
+ await self._qdrant.delete_collection()
+ await self._qdrant.initialize()
+
+ # 清理本地文件
+ for f in [self._context_file, self._cache_file]:
+ if os.path.exists(f):
+ os.remove(f)
+
+ # 重置内存状态
+ self._bm25 = None
+ self._doc_store = []
+ self._indexed_files = set()
+ self.repo_url = None
+ self.global_context = {}
+
+ logger.info(f"🗑️ 重置存储: {self.session_id}")
+
+ # 兼容旧接口
+ def reset_collection(self) -> None:
+ """同步重置 (兼容旧代码)"""
+ asyncio.get_event_loop().run_until_complete(self.reset())
+
+ async def add_documents(
+ self,
+ documents: List[str],
+ metadatas: List[Dict[str, Any]]
+ ) -> int:
+ """
+ 添加文档
+
+ Args:
+ documents: 文档内容列表
+ metadatas: 元数据列表
+
+ Returns:
+ 成功添加的数量
+ """
+ if not documents:
+ return 0
+
+ await self.initialize()
+
+ # 1. 批量获取 Embedding
+ logger.info(f"📊 Embedding: {len(documents)} 个文档")
+ embedding_service = get_embedding()
+ embeddings = await embedding_service.embed_batch(documents, show_progress=True)
+
+ # 过滤无效的
+ valid_indices = [i for i, emb in enumerate(embeddings) if emb]
+ if not valid_indices:
+ logger.error("所有 Embedding 都失败了")
+ return 0
+
+ # 2. 构建 Document 对象
+ docs = []
+ for i in valid_indices:
+ doc_id = f"{metadatas[i].get('file', 'unknown')}_{len(self._doc_store) + len(docs)}"
+ doc = Document(
+ id=doc_id,
+ content=documents[i],
+ metadata=metadatas[i],
+ )
+ docs.append(doc)
+
+ valid_embeddings = [embeddings[i] for i in valid_indices]
+
+ # 3. 写入 Qdrant
+ added = await self._qdrant.add_documents(docs, valid_embeddings)
+
+ # 4. 更新 BM25 索引 (放入线程池，避免阻塞)
+ self._doc_store.extend(docs)
+ self._indexed_files.update(doc.file_path for doc in docs)
+
+ await asyncio.to_thread(self._rebuild_bm25_sync)
+
+ return added
+
+ def _rebuild_bm25_sync(self) -> None:
+ """重建 BM25 索引 (同步，用于线程池)"""
+ tokenized = [self._tokenize(doc.content) for doc in self._doc_store]
+ self._bm25 = BM25Okapi(tokenized)
+ self._save_bm25_cache()
+
+ async def embed_text(self, text: str) -> List[float]:
+ """获取文本 Embedding"""
+ embedding_service = get_embedding()
+ return await embedding_service.embed_text(text)
+
+ async def search_hybrid(
+ self,
+ query: str,
+ top_k: int = None
+ ) -> List[Dict[str, Any]]:
+ """
+ 混合搜索 (向量 + BM25，RRF 融合)
+
+ Args:
+ query: 查询文本
+ top_k: 返回数量
+
+ Returns:
+ 搜索结果列表
+ """
+ await self.initialize()
+
+ top_k = top_k or config.default_top_k
+ candidate_k = top_k * config.search_oversample
+
+ # 1. 向量搜索
+ vector_results: List[SearchResult] = []
+ query_embedding = await self.embed_text(query)
+
+ if query_embedding and self._qdrant:
+ vector_results = await self._qdrant.search(
+ query_embedding,
+ top_k=candidate_k
+ )
+
+ # 2. BM25 搜索
+ bm25_results: List[SearchResult] = []
+ if self._bm25 and self._doc_store:
+ tokens = self._tokenize(query)
+ if not tokens:
+ tokens = [""]
+
+ try:
+ scores = self._bm25.get_scores(tokens)
+ top_indices = sorted(
+ range(len(scores)),
+ key=lambda i: scores[i],
+ reverse=True
+ )[:candidate_k]
+
+ for idx in top_indices:
+ if scores[idx] > 0:
+ doc = self._doc_store[idx]
+ bm25_results.append(SearchResult(
+ document=doc,
+ score=scores[idx],
+ source="bm25",
+ ))
+ except Exception as e:
+ logger.error(f"BM25 搜索失败: {e}")
+
+ # 3. RRF 融合
+ fused = self._rrf_fusion(vector_results, bm25_results)
+
+ # 4. 格式化输出 (兼容旧接口)
+ results = []
+ for item in fused[:top_k]:
+ doc = item.document
+ results.append({
+ "id": doc.id,
+ "content": doc.content,
+ "file": doc.file_path,
+ "metadata": doc.metadata,
+ "score": item.score,
+ })
+
+ return results
+
+ def _rrf_fusion(
+ self,
+ vector_results: List[SearchResult],
+ bm25_results: List[SearchResult]
+ ) -> List[SearchResult]:
+ """RRF (Reciprocal Rank Fusion) 融合"""
+ k = config.rrf_k
+ fused: Dict[str, Dict] = {}
+
+ # 向量结果
+ for rank, result in enumerate(vector_results):
+ doc_id = result.document.id
+ if doc_id not in fused:
+ fused[doc_id] = {"result": result, "score": 0}
+ fused[doc_id]["score"] += config.rrf_weight_vector / (k + rank + 1)
+
+ # BM25 结果
+ for rank, result in enumerate(bm25_results):
+ doc_id = result.document.id
+ if doc_id not in fused:
+ fused[doc_id] = {"result": result, "score": 0}
+ fused[doc_id]["score"] += config.rrf_weight_bm25 / (k + rank + 1)
+
+ # 排序
+ sorted_items = sorted(
+ fused.values(),
+ key=lambda x: x["score"],
+ reverse=True
+ )
+
+ return [
+ SearchResult(
+ document=item["result"].document,
+ score=item["score"],
+ source="hybrid",
+ )
+ for item in sorted_items
+ ]
+
+ def get_documents_by_file(self, file_path: str) -> List[Dict[str, Any]]:
+ """根据文件路径获取文档 (兼容旧接口)"""
+ docs = [
+ doc for doc in self._doc_store
+ if doc.file_path == file_path
+ ]
+
+ result = []
+ for doc in sorted(docs, key=lambda d: d.metadata.get("start_line", 0)):
+ result.append({
+ "id": doc.id,
+ "content": doc.content,
+ "file": doc.file_path,
+ "metadata": doc.metadata,
+ "score": 1.0,
+ })
+
+ return result
+
+ @property
+ def indexed_files(self) -> Set[str]:
+ """已索引的文件"""
+ return self._indexed_files
+
+
+# ============================================================
+# 管理器 - LRU Cache + 过期清理
+# ============================================================
+
+class SessionEntry:
+ """Session 条目 - 包含存储实例和访问时间"""
+ __slots__ = ('store', 'last_access', 'created_at')
+
+ def __init__(self, store: VectorStore):
+ self.store = store
+ self.last_access = time.time()
+ self.created_at = time.time()
+
+ def touch(self) -> None:
+ """更新访问时间"""
+ self.last_access = time.time()
+
+
+class VectorStoreManager:
+ """
+ 向量存储管理器 - LRU Cache 实现
+
+ 特性:
+ 1. LRU 淘汰 - 超过 max_count 时淘汰最久未访问的内存中的 session
+ 2. 仓库数据永久存储 - 不清理仓库索引和报告
+ 3. 线程安全 - 使用 asyncio.Lock
+ """
+
+ def __init__(self, max_count: int = None):
+ self._max_count = max_count or config.session_max_count
+ self._sessions: Dict[str, SessionEntry] = {}
+ self._lock = asyncio.Lock()
+
+ def get_store(self, session_id: str) -> VectorStore:
+ """
+ 获取或创建存储实例 (同步接口，兼容现有代码)
+
+ 会触发 LRU 淘汰检查
+ """
+ if session_id in self._sessions:
+ entry = self._sessions[session_id]
+ entry.touch()
+ # 移动到最后（模拟 LRU）
+ self._sessions.pop(session_id)
+ self._sessions[session_id] = entry
+ return entry.store
+
+ # 创建新 session
+ store = VectorStore(session_id)
+ entry = SessionEntry(store)
+ self._sessions[session_id] = entry
+
+ # 检查是否需要 LRU 淘汰（异步执行）
+ if len(self._sessions) > self._max_count:
+ asyncio.create_task(self._evict_lru())
+
+ logger.info(f"📦 Session 创建: {session_id} (总数: {len(self._sessions)})")
+ return store
+
+ async def _evict_lru(self) -> None:
+ """淘汰最久未访问的 session"""
+ async with self._lock:
+ while len(self._sessions) > self._max_count:
+ # 找到最久未访问的
+ oldest_id = min(
+ self._sessions.keys(),
+ key=lambda k: self._sessions[k].last_access
+ )
+ entry = self._sessions.pop(oldest_id)
+ await entry.store.close()
+ logger.info(f"🗑️ LRU 淘汰: {oldest_id}")
+
+ async def close_session(self, session_id: str) -> None:
+ """关闭指定 session"""
+ async with self._lock:
+ if session_id in self._sessions:
+ entry = self._sessions.pop(session_id)
+ await entry.store.close()
+ logger.info(f"🔒 Session 关闭: {session_id}")
+
+ async def close_all(self) -> None:
+ """关闭所有连接"""
+ async with self._lock:
+ for session_id, entry in list(self._sessions.items()):
+ await entry.store.close()
+ self._sessions.clear()
+ logger.info("🔒 所有 Session 已关闭")
+
+ def get_stats(self) -> Dict[str, Any]:
+ """获取管理器统计信息"""
+ now = time.time()
+ sessions_info = []
+ for sid, entry in self._sessions.items():
+ sessions_info.append({
+ "session_id": sid,
+ "age_hours": round((now - entry.created_at) / 3600, 2),
+ "idle_minutes": round((now - entry.last_access) / 60, 2),
+ })
+
+ return {
+ "total_sessions": len(self._sessions),
+ "max_sessions": self._max_count,
+ "sessions": sorted(sessions_info, key=lambda x: x["idle_minutes"], reverse=True)
+ }
+
+
+# 全局管理器
+store_manager = VectorStoreManager()
diff --git a/app/storage/__init__.py b/app/storage/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f981e63cf1d56e2e6c50a14120ac7685908b42a
--- /dev/null
+++ b/app/storage/__init__.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+"""
+存储层模块
+
+提供向量存储的抽象和实现
+"""
+
+from app.storage.base import (
+ Document,
+ SearchResult,
+ CollectionStats,
+ StorageBackend,
+ BaseVectorStore,
+)
+from app.storage.qdrant_store import (
+ QdrantConfig,
+ QdrantVectorStore,
+ QdrantStoreFactory,
+ get_qdrant_factory,
+)
+
+__all__ = [
+ # 基础类型
+ "Document",
+ "SearchResult",
+ "CollectionStats",
+ "StorageBackend",
+ "BaseVectorStore",
+ # Qdrant
+ "QdrantConfig",
+ "QdrantVectorStore",
+ "QdrantStoreFactory",
+ "get_qdrant_factory",
+]
diff --git a/app/storage/base.py b/app/storage/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1dc2576c90e12b1ca9401c59d0e85cfc171430d
--- /dev/null
+++ b/app/storage/base.py
@@ -0,0 +1,159 @@
+# -*- coding: utf-8 -*-
+"""
+向量存储抽象层
+
+设计原则:
+1. 接口与实现分离 - 易于切换存储后端
+2. 异步优先 - 所有 I/O 操作都是异步的
+3. 类型安全 - 完整的类型注解
+4. 可观测 - 内置指标收集
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import List, Dict, Any, Optional, Set
+from enum import Enum
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+# ============================================================
+# 数据模型
+# ============================================================
+
+@dataclass
+class Document:
+ """文档数据模型"""
+ id: str
+ content: str
+ metadata: Dict[str, Any] = field(default_factory=dict)
+ embedding: Optional[List[float]] = None
+
+ @property
+ def file_path(self) -> str:
+ return self.metadata.get("file", "")
+
+ def to_dict(self) -> Dict[str, Any]:
+ return {
+ "id": self.id,
+ "content": self.content,
+ "metadata": self.metadata,
+ }
+
+
+@dataclass
+class SearchResult:
+ """搜索结果"""
+ document: Document
+ score: float
+ source: str = "vector" # "vector" | "bm25" | "hybrid"
+
+ def to_dict(self) -> Dict[str, Any]:
+ return {
+ "id": self.document.id,
+ "content": self.document.content,
+ "file": self.document.file_path,
+ "metadata": self.document.metadata,
+ "score": self.score,
+ "source": self.source,
+ }
+
+
+@dataclass
+class CollectionStats:
+ """集合统计信息"""
+ name: str
+ document_count: int
+ indexed_files: Set[str] = field(default_factory=set)
+ vector_dimension: int = 0
+
+
+class StorageBackend(Enum):
+ """存储后端类型"""
+ QDRANT = "qdrant"
+ CHROMA = "chroma" # 保留兼容性
+
+
+# ============================================================
+# 抽象基类
+# ============================================================
+
+class BaseVectorStore(ABC):
+ """
+ 向量存储抽象基类
+
+ 所有存储后端必须实现这些方法
+ """
+
+ @abstractmethod
+ async def initialize(self) -> None:
+ """初始化存储连接"""
+ pass
+
+ @abstractmethod
+ async def close(self) -> None:
+ """关闭连接"""
+ pass
+
+ @abstractmethod
+ async def add_documents(
+ self,
+ documents: List[Document],
+ embeddings: List[List[float]]
+ ) -> int:
+ """
+ 添加文档
+
+ Args:
+ documents: 文档列表
+ embeddings: 对应的嵌入向量
+
+ Returns:
+ 成功添加的文档数量
+ """
+ pass
+
+ @abstractmethod
+ async def search(
+ self,
+ query_embedding: List[float],
+ top_k: int = 10,
+ filter_conditions: Optional[Dict[str, Any]] = None
+ ) -> List[SearchResult]:
+ """
+ 向量相似度搜索
+
+ Args:
+ query_embedding: 查询向量
+ top_k: 返回数量
+ filter_conditions: 过滤条件
+
+ Returns:
+ 搜索结果列表
+ """
+ pass
+
+ @abstractmethod
+ async def delete_collection(self) -> bool:
+ """删除当前集合"""
+ pass
+
+ @abstractmethod
+ async def get_stats(self) -> CollectionStats:
+ """获取集合统计信息"""
+ pass
+
+ @abstractmethod
+ async def get_documents_by_file(self, file_path: str) -> List[Document]:
+ """根据文件路径获取文档"""
+ pass
+
+
+class BaseVectorStoreFactory(ABC):
+ """向量存储工厂基类"""
+
+ @abstractmethod
+ def create(self, collection_name: str) -> BaseVectorStore:
+ """创建存储实例"""
+ pass
diff --git a/app/storage/qdrant_store.py b/app/storage/qdrant_store.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd810506bbb8d4db7ba831695da4943efd8002b1
--- /dev/null
+++ b/app/storage/qdrant_store.py
@@ -0,0 +1,578 @@
+# -*- coding: utf-8 -*-
+"""
+Qdrant 向量存储实现
+
+特性:
+1. 异步原生 - 使用 qdrant-client AsyncQdrantClient
+2. 高性能 - 批量 upsert、HNSW 索引、payload 索引
+3. 混合搜索 - 向量 + 稀疏向量 (FastEmbed)
+4. 连接池 - gRPC 长连接复用
+5. 可观测 - 完整的日志和指标
+"""
+
+import asyncio
+import logging
+import os
+from dataclasses import dataclass
+from typing import List, Dict, Any, Optional, Set
+from contextlib import asynccontextmanager
+
+from qdrant_client import AsyncQdrantClient, models
+from qdrant_client.models import (
+ Distance,
+ VectorParams,
+ PointStruct,
+ Filter,
+ FieldCondition,
+ MatchValue,
+ PayloadSchemaType,
+)
+
+from app.storage.base import (
+ BaseVectorStore,
+ Document,
+ SearchResult,
+ CollectionStats,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ============================================================
+# 配置
+# ============================================================
+
+@dataclass
+class QdrantConfig:
+ """
+ Qdrant 配置
+
+ 支持三种模式:
+ - local: 本地嵌入式 (开发/单进程)
+ - server: Qdrant Server (多 Worker 生产环境)
+ - cloud: Qdrant Cloud (托管服务)
+
+ 环境变量:
+ - QDRANT_MODE: "local" | "server" | "cloud"
+ - QDRANT_URL: 服务器地址 (server/cloud 模式)
+ - QDRANT_API_KEY: API 密钥 (cloud 模式必需)
+ - QDRANT_LOCAL_PATH: 本地存储路径 (local 模式)
+ """
+ # 模式: "local" | "server" | "cloud"
+ mode: str = "local"
+
+ # Server/Cloud 模式配置
+ url: Optional[str] = None
+ host: str = "localhost"
+ port: int = 6333
+ grpc_port: int = 6334
+ prefer_grpc: bool = True
+ api_key: Optional[str] = None
+
+ # Local 模式配置
+ local_path: str = "data/qdrant_db"
+
+ # 向量配置
+ vector_size: int = 1024 # BGE-M3 维度
+ distance: Distance = Distance.COSINE
+
+ # 索引配置
+ hnsw_m: int = 16 # HNSW 图的边数
+ hnsw_ef_construct: int = 100 # 构建时的搜索深度
+
+ # 批量操作
+ batch_size: int = 100
+
+ # 超时
+ timeout: float = 30.0
+
+ @classmethod
+ def from_env(cls) -> "QdrantConfig":
+ """从环境变量加载配置"""
+ mode = os.getenv("QDRANT_MODE", "local").lower()
+
+ return cls(
+ mode=mode,
+ url=os.getenv("QDRANT_URL"),
+ host=os.getenv("QDRANT_HOST", "localhost"),
+ port=int(os.getenv("QDRANT_PORT", "6333")),
+ grpc_port=int(os.getenv("QDRANT_GRPC_PORT", "6334")),
+ api_key=os.getenv("QDRANT_API_KEY"),
+ local_path=os.getenv("QDRANT_LOCAL_PATH", "data/qdrant_db"),
+ vector_size=int(os.getenv("QDRANT_VECTOR_SIZE", "1024")),
+ prefer_grpc=os.getenv("QDRANT_PREFER_GRPC", "true").lower() == "true",
+ )
+
+ @property
+ def is_local(self) -> bool:
+ return self.mode == "local"
+
+ @property
+ def is_server(self) -> bool:
+ return self.mode == "server"
+
+ @property
+ def is_cloud(self) -> bool:
+ return self.mode == "cloud"
+
+ def validate(self) -> None:
+ """验证配置"""
+ if self.is_cloud and not self.api_key:
+ raise ValueError("QDRANT_API_KEY is required for cloud mode")
+ if (self.is_server or self.is_cloud) and not (self.url or self.host):
+ raise ValueError("QDRANT_URL or QDRANT_HOST is required for server/cloud mode")
+
+
+# ============================================================
+# 全局共享客户端单例
+# ============================================================
+
+_shared_client: Optional[AsyncQdrantClient] = None
+_shared_config: Optional[QdrantConfig] = None
+_client_lock = asyncio.Lock()
+
+
+async def get_shared_client(config: Optional[QdrantConfig] = None) -> AsyncQdrantClient:
+ """
+ 获取共享的 Qdrant 客户端单例
+
+ 支持三种模式:
+ - local: 本地嵌入式存储 (单进程，开发环境)
+ - server: Qdrant Server (多 Worker，Docker 部署)
+ - cloud: Qdrant Cloud (托管服务)
+ """
+ global _shared_client, _shared_config
+
+ async with _client_lock:
+ if _shared_client is None:
+ _shared_config = config or QdrantConfig.from_env()
+ _shared_config.validate()
+
+ if _shared_config.is_local:
+ # Local 模式: 嵌入式存储
+ os.makedirs(_shared_config.local_path, exist_ok=True)
+ _shared_client = AsyncQdrantClient(
+ path=_shared_config.local_path,
+ timeout=_shared_config.timeout,
+ )
+ logger.info(f"📦 Qdrant 本地模式: {_shared_config.local_path}")
+
+ elif _shared_config.is_server:
+ # Server 模式: 连接 Qdrant Server
+ if _shared_config.url:
+ _shared_client = AsyncQdrantClient(
+ url=_shared_config.url,
+ prefer_grpc=_shared_config.prefer_grpc,
+ timeout=_shared_config.timeout,
+ )
+ logger.info(f"🌐 Qdrant Server 模式: {_shared_config.url}")
+ else:
+ _shared_client = AsyncQdrantClient(
+ host=_shared_config.host,
+ port=_shared_config.port,
+ grpc_port=_shared_config.grpc_port,
+ prefer_grpc=_shared_config.prefer_grpc,
+ timeout=_shared_config.timeout,
+ )
+ logger.info(f"🌐 Qdrant Server 模式: {_shared_config.host}:{_shared_config.port}")
+
+ else:
+ # Cloud 模式: 连接 Qdrant Cloud
+ _shared_client = AsyncQdrantClient(
+ url=_shared_config.url,
+ api_key=_shared_config.api_key,
+ timeout=_shared_config.timeout,
+ )
+ logger.info(f"☁️ Qdrant Cloud 模式: {_shared_config.url}")
+
+ return _shared_client
+
+ return _shared_client
+
+
+async def close_shared_client() -> None:
+ """关闭共享客户端"""
+ global _shared_client
+ if _shared_client is not None:
+ await _shared_client.close()
+ _shared_client = None
+ logger.info("🔒 Qdrant 共享客户端已关闭")
+
+
+# ============================================================
+# Qdrant 存储实现
+# ============================================================
+
+class QdrantVectorStore(BaseVectorStore):
+ """
+ Qdrant 向量存储
+
+ 使用示例:
+ ```python
+ config = QdrantConfig.from_env()
+ store = QdrantVectorStore("my_collection", config)
+
+ await store.initialize()
+
+ # 添加文档
+ docs = [Document(id="1", content="hello", metadata={"file": "a.py"})]
+ embeddings = [[0.1, 0.2, ...]]
+ await store.add_documents(docs, embeddings)
+
+ # 搜索
+ results = await store.search(query_embedding, top_k=5)
+
+ await store.close()
+ ```
+ """
+
+ # Payload 字段名常量
+ FIELD_CONTENT = "content"
+ FIELD_FILE = "file"
+ FIELD_METADATA = "metadata"
+
+ def __init__(
+ self,
+ collection_name: str,
+ config: Optional[QdrantConfig] = None
+ ):
+ self.collection_name = self._sanitize_name(collection_name)
+ self.config = config or QdrantConfig.from_env()
+ self._initialized = False
+
+ @staticmethod
+ def _sanitize_name(name: str) -> str:
+ """清理集合名称"""
+ import re
+ clean = re.sub(r'[^a-zA-Z0-9_-]', '_', name)
+ return clean[:63] if clean else "default"
+
+ async def _get_client(self) -> AsyncQdrantClient:
+ """获取共享客户端 (解决 Qdrant Local 并发访问问题)"""
+ return await get_shared_client(self.config)
+
+ async def initialize(self) -> None:
+ """初始化集合"""
+ if self._initialized:
+ return
+
+ client = await self._get_client()
+
+ # 检查集合是否存在
+ collections = await client.get_collections()
+ exists = any(c.name == self.collection_name for c in collections.collections)
+
+ if not exists:
+ # 创建集合
+ await client.create_collection(
+ collection_name=self.collection_name,
+ vectors_config=VectorParams(
+ size=self.config.vector_size,
+ distance=self.config.distance,
+ hnsw_config=models.HnswConfigDiff(
+ m=self.config.hnsw_m,
+ ef_construct=self.config.hnsw_ef_construct,
+ ),
+ ),
+ # 启用 payload 索引以加速过滤
+ optimizers_config=models.OptimizersConfigDiff(
+ indexing_threshold=0, # 立即索引
+ ),
+ )
+
+ # 创建 payload 索引
+ await client.create_payload_index(
+ collection_name=self.collection_name,
+ field_name=self.FIELD_FILE,
+ field_schema=PayloadSchemaType.KEYWORD,
+ )
+
+ logger.info(f"✅ 创建集合: {self.collection_name}")
+ else:
+ logger.debug(f"📂 集合已存在: {self.collection_name}")
+
+ self._initialized = True
+
+ async def close(self) -> None:
+ """
+ 关闭连接 (使用共享客户端时不实际关闭)
+
+ 注意: 由于使用共享客户端，单个 Store 的 close() 不会关闭客户端。
+ 全局关闭请使用 close_shared_client()
+ """
+ self._initialized = False
+ logger.debug(f"🔌 Store 已关闭: {self.collection_name}")
+
+ async def add_documents(
+ self,
+ documents: List[Document],
+ embeddings: List[List[float]]
+ ) -> int:
+ """批量添加文档"""
+ if not documents or not embeddings:
+ return 0
+
+ if len(documents) != len(embeddings):
+ raise ValueError(f"文档数量 ({len(documents)}) 与向量数量 ({len(embeddings)}) 不匹配")
+
+ await self.initialize()
+ client = await self._get_client()
+
+ # 过滤空向量
+ valid_pairs = [
+ (doc, emb) for doc, emb in zip(documents, embeddings)
+ if emb and len(emb) == self.config.vector_size
+ ]
+
+ if not valid_pairs:
+ logger.warning("没有有效的文档向量对")
+ return 0
+
+ # 构建 Points
+ points = []
+ for doc, embedding in valid_pairs:
+ point = PointStruct(
+ id=self._generate_point_id(doc.id),
+ vector=embedding,
+ payload={
+ self.FIELD_CONTENT: doc.content,
+ self.FIELD_FILE: doc.file_path,
+ self.FIELD_METADATA: doc.metadata,
+ "doc_id": doc.id,
+ },
+ )
+ points.append(point)
+
+ # 批量 upsert
+ total_added = 0
+ batch_size = self.config.batch_size
+
+ for i in range(0, len(points), batch_size):
+ batch = points[i:i + batch_size]
+ try:
+ await client.upsert(
+ collection_name=self.collection_name,
+ points=batch,
+ wait=True,
+ )
+ total_added += len(batch)
+ except Exception as e:
+ logger.error(f"批次 {i // batch_size + 1} 写入失败: {e}")
+
+ logger.info(f"✅ 写入 {total_added}/{len(points)} 个文档到 {self.collection_name}")
+ return total_added
+
+ def _generate_point_id(self, doc_id: str) -> int:
+ """生成数值型 Point ID (Qdrant 要求)"""
+ import hashlib
+ hash_bytes = hashlib.sha256(doc_id.encode()).digest()
+ # 取前 8 字节转为正整数
+ return int.from_bytes(hash_bytes[:8], byteorder='big') & 0x7FFFFFFFFFFFFFFF
+
+ async def search(
+ self,
+ query_embedding: List[float],
+ top_k: int = 10,
+ filter_conditions: Optional[Dict[str, Any]] = None
+ ) -> List[SearchResult]:
+ """向量相似度搜索"""
+ if not query_embedding:
+ return []
+
+ await self.initialize()
+ client = await self._get_client()
+
+ # 构建过滤器
+ query_filter = None
+ if filter_conditions:
+ must_conditions = []
+ for field, value in filter_conditions.items():
+ must_conditions.append(
+ FieldCondition(
+ key=field,
+ match=MatchValue(value=value),
+ )
+ )
+ query_filter = Filter(must=must_conditions)
+
+ try:
+ # 使用 query_points (qdrant-client >= 1.7.0)
+ results = await client.query_points(
+ collection_name=self.collection_name,
+ query=query_embedding,
+ limit=top_k,
+ query_filter=query_filter,
+ with_payload=True,
+ score_threshold=0.0,
+ )
+
+ search_results = []
+ for hit in results.points:
+ payload = hit.payload or {}
+ doc = Document(
+ id=payload.get("doc_id", str(hit.id)),
+ content=payload.get(self.FIELD_CONTENT, ""),
+ metadata=payload.get(self.FIELD_METADATA, {}),
+ )
+ search_results.append(SearchResult(
+ document=doc,
+ score=hit.score,
+ source="vector",
+ ))
+
+ return search_results
+
+ except Exception as e:
+ logger.error(f"搜索失败: {e}")
+ return []
+
+ async def delete_collection(self) -> bool:
+ """删除集合"""
+ try:
+ client = await self._get_client()
+ await client.delete_collection(self.collection_name)
+ self._initialized = False
+ logger.info(f"🗑️ 删除集合: {self.collection_name}")
+ return True
+ except Exception as e:
+ logger.error(f"删除集合失败: {e}")
+ return False
+
+ async def get_stats(self) -> CollectionStats:
+ """获取集合统计"""
+ await self.initialize()
+ client = await self._get_client()
+
+ try:
+ info = await client.get_collection(self.collection_name)
+
+ # 获取所有唯一文件
+ indexed_files: Set[str] = set()
+ scroll_result = await client.scroll(
+ collection_name=self.collection_name,
+ limit=10000,
+ with_payload=[self.FIELD_FILE],
+ )
+
+ for point in scroll_result[0]:
+ if point.payload:
+ file_path = point.payload.get(self.FIELD_FILE)
+ if file_path:
+ indexed_files.add(file_path)
+
+ return CollectionStats(
+ name=self.collection_name,
+ document_count=info.points_count or 0,
+ indexed_files=indexed_files,
+ vector_dimension=self.config.vector_size,
+ )
+ except Exception as e:
+ logger.error(f"获取统计失败: {e}")
+ return CollectionStats(name=self.collection_name, document_count=0)
+
+ async def get_documents_by_file(self, file_path: str) -> List[Document]:
+ """根据文件路径获取文档"""
+ await self.initialize()
+ client = await self._get_client()
+
+ try:
+ scroll_result = await client.scroll(
+ collection_name=self.collection_name,
+ scroll_filter=Filter(
+ must=[
+ FieldCondition(
+ key=self.FIELD_FILE,
+ match=MatchValue(value=file_path),
+ )
+ ]
+ ),
+ limit=1000,
+ with_payload=True,
+ )
+
+ documents = []
+ for point in scroll_result[0]:
+ payload = point.payload or {}
+ doc = Document(
+ id=payload.get("doc_id", str(point.id)),
+ content=payload.get(self.FIELD_CONTENT, ""),
+ metadata=payload.get(self.FIELD_METADATA, {}),
+ )
+ documents.append(doc)
+
+ # 按行号排序
+ documents.sort(key=lambda d: d.metadata.get("start_line", 0))
+ return documents
+
+ except Exception as e:
+ logger.error(f"获取文件文档失败: {e}")
+ return []
+
+ async def get_all_documents(self) -> List[Document]:
+ """获取所有文档 (用于 BM25 索引构建)"""
+ await self.initialize()
+ client = await self._get_client()
+
+ documents = []
+ offset = None
+
+ try:
+ while True:
+ scroll_result = await client.scroll(
+ collection_name=self.collection_name,
+ limit=1000,
+ offset=offset,
+ with_payload=True,
+ )
+
+ points, next_offset = scroll_result
+
+ for point in points:
+ payload = point.payload or {}
+ doc = Document(
+ id=payload.get("doc_id", str(point.id)),
+ content=payload.get(self.FIELD_CONTENT, ""),
+ metadata=payload.get(self.FIELD_METADATA, {}),
+ )
+ documents.append(doc)
+
+ if next_offset is None:
+ break
+ offset = next_offset
+
+ return documents
+
+ except Exception as e:
+ logger.error(f"获取所有文档失败: {e}")
+ return []
+
+
+# ============================================================
+# 工厂
+# ============================================================
+
+class QdrantStoreFactory:
+ """Qdrant 存储工厂"""
+
+ def __init__(self, config: Optional[QdrantConfig] = None):
+ self.config = config or QdrantConfig.from_env()
+
+ def create(self, collection_name: str) -> QdrantVectorStore:
+ """创建存储实例"""
+ return QdrantVectorStore(collection_name, self.config)
+
+ async def get_client(self) -> AsyncQdrantClient:
+ """获取共享的 Qdrant 客户端"""
+ return await get_shared_client(self.config)
+
+
+# 全局工厂实例
+_qdrant_factory: Optional[QdrantStoreFactory] = None
+
+
+def get_qdrant_factory(config: Optional[QdrantConfig] = None) -> QdrantStoreFactory:
+ """获取工厂单例"""
+ global _qdrant_factory
+ if _qdrant_factory is None:
+ _qdrant_factory = QdrantStoreFactory(config)
+ return _qdrant_factory
diff --git a/app/utils/embedding.py b/app/utils/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..0bcaedaaf3fdadfa4e0eb877a24a8d2698ffba0c
--- /dev/null
+++ b/app/utils/embedding.py
@@ -0,0 +1,254 @@
+# -*- coding: utf-8 -*-
+"""
+Embedding 服务 - 并发优化版
+
+特性:
+1. 并发批量请求 - 使用 asyncio.gather 并行处理多个批次
+2. 信号量控制 - 限制最大并发数，避免 API 限流
+3. 重试机制 - 使用 tenacity 处理临时性错误
+4. 智能分批 - 根据 token 数量动态调整批次大小
+"""
+
+import asyncio
+import logging
+from typing import List, Optional
+from dataclasses import dataclass
+
+from openai import AsyncOpenAI
+
+from app.core.config import settings
+from app.utils.retry import llm_retry, is_retryable_error
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class EmbeddingConfig:
+ """Embedding 服务配置"""
+ # API 配置
+ api_base_url: str = "https://api.siliconflow.cn/v1"
+ model_name: str = "BAAI/bge-m3"
+
+ # 批处理配置
+ batch_size: int = 50 # 每批文本数量
+ max_text_length: int = 8000 # 单个文本最大字符数
+
+ # 并发控制
+ max_concurrent_batches: int = 5 # 最大并发批次数
+
+ # 超时配置
+ timeout: int = 60 # 单次请求超时 (秒)
+
+
+class EmbeddingService:
+ """
+ 高性能 Embedding 服务
+
+ 使用示例:
+ ```python
+ service = EmbeddingService()
+
+ # 单文本
+ embedding = await service.embed_text("Hello world")
+
+ # 批量文本 (自动并发优化)
+ texts = ["text1", "text2", ..., "text100"]
+ embeddings = await service.embed_batch(texts)
+ ```
+ """
+
+ def __init__(self, config: Optional[EmbeddingConfig] = None):
+ self.config = config or EmbeddingConfig()
+
+ # 初始化 OpenAI 客户端 (SiliconFlow 兼容 OpenAI 协议)
+ self._client = AsyncOpenAI(
+ api_key=settings.SILICON_API_KEY,
+ base_url=self.config.api_base_url,
+ timeout=self.config.timeout
+ )
+
+ # 并发信号量
+ self._semaphore = asyncio.Semaphore(self.config.max_concurrent_batches)
+
+ # 统计信息
+ self._stats = {
+ "total_requests": 0,
+ "successful_requests": 0,
+ "failed_requests": 0,
+ "total_texts": 0,
+ "retried_requests": 0
+ }
+
+ def _preprocess_text(self, text: str) -> str:
+ """预处理文本: 移除换行、截断长度"""
+ text = text.replace("\n", " ").strip()
+ if len(text) > self.config.max_text_length:
+ text = text[:self.config.max_text_length]
+ return text
+
+ @llm_retry
+ async def _embed_single_batch(self, texts: List[str]) -> List[List[float]]:
+ """
+ 处理单个批次的 Embedding 请求 (带重试)
+
+ Args:
+ texts: 预处理后的文本列表
+
+ Returns:
+ embedding 向量列表
+ """
+ self._stats["total_requests"] += 1
+
+ response = await self._client.embeddings.create(
+ input=texts,
+ model=self.config.model_name
+ )
+
+ self._stats["successful_requests"] += 1
+ return [item.embedding for item in response.data]
+
+ async def _embed_batch_with_semaphore(
+ self,
+ batch_texts: List[str],
+ batch_index: int
+ ) -> tuple[int, List[List[float]]]:
+ """
+ 带信号量控制的批次处理
+
+ Returns:
+ (batch_index, embeddings) - 返回索引用于结果排序
+ """
+ async with self._semaphore:
+ try:
+ embeddings = await self._embed_single_batch(batch_texts)
+ logger.debug(f"✅ 批次 {batch_index} 完成: {len(batch_texts)} 文本")
+ return (batch_index, embeddings)
+ except Exception as e:
+ self._stats["failed_requests"] += 1
+ logger.error(f"❌ 批次 {batch_index} 失败: {type(e).__name__}: {e}")
+ raise
+
+ async def embed_text(self, text: str) -> List[float]:
+ """
+ 获取单个文本的 Embedding
+
+ Args:
+ text: 输入文本
+
+ Returns:
+ embedding 向量，失败返回空列表
+ """
+ try:
+ processed = self._preprocess_text(text)
+ if not processed:
+ return []
+
+ self._stats["total_texts"] += 1
+ embeddings = await self._embed_single_batch([processed])
+ return embeddings[0] if embeddings else []
+ except Exception as e:
+ logger.error(f"embed_text 失败: {e}")
+ return []
+
+ async def embed_batch(
+ self,
+ texts: List[str],
+ show_progress: bool = False
+ ) -> List[List[float]]:
+ """
+ 批量获取 Embedding (并发优化)
+
+ Args:
+ texts: 文本列表
+ show_progress: 是否显示进度日志
+
+ Returns:
+ embedding 向量列表 (与输入顺序一致)
+ 失败的文本对应空列表
+ """
+ if not texts:
+ return []
+
+ # 预处理所有文本
+ processed_texts = [self._preprocess_text(t) for t in texts]
+ self._stats["total_texts"] += len(texts)
+
+ # 分批
+ batch_size = self.config.batch_size
+ batches = [
+ processed_texts[i:i + batch_size]
+ for i in range(0, len(processed_texts), batch_size)
+ ]
+
+ total_batches = len(batches)
+ if show_progress:
+ logger.info(
+ f"📊 Embedding: {len(texts)} 文本 → {total_batches} 批次 "
+ f"(并发: {self.config.max_concurrent_batches})"
+ )
+
+ # 并发执行所有批次
+ tasks = [
+ self._embed_batch_with_semaphore(batch, idx)
+ for idx, batch in enumerate(batches)
+ ]
+
+ # 收集结果
+ results = await asyncio.gather(*tasks, return_exceptions=True)
+
+ # 按批次索引排序并合并结果
+ embeddings = []
+ for result in sorted(results, key=lambda x: x[0] if isinstance(x, tuple) else float('inf')):
+ if isinstance(result, tuple):
+ batch_idx, batch_embeddings = result
+ embeddings.extend(batch_embeddings)
+ else:
+ # 异常情况: 填充空向量
+ # 找出这个批次有多少文本
+ failed_batch_size = batch_size # 保守估计
+ embeddings.extend([[] for _ in range(failed_batch_size)])
+ logger.warning(f"批次失败，填充 {failed_batch_size} 个空向量")
+
+ # 确保返回数量与输入一致
+ if len(embeddings) < len(texts):
+ embeddings.extend([[] for _ in range(len(texts) - len(embeddings))])
+ elif len(embeddings) > len(texts):
+ embeddings = embeddings[:len(texts)]
+
+ if show_progress:
+ success_count = sum(1 for e in embeddings if e)
+ logger.info(f"✅ Embedding 完成: {success_count}/{len(texts)} 成功")
+
+ return embeddings
+
+ def get_stats(self) -> dict:
+ """获取统计信息"""
+ return self._stats.copy()
+
+ def reset_stats(self):
+ """重置统计信息"""
+ for key in self._stats:
+ self._stats[key] = 0
+
+
+# 全局单例
+_embedding_service: Optional[EmbeddingService] = None
+
+
+def get_embedding_service(config: Optional[EmbeddingConfig] = None) -> EmbeddingService:
+ """获取 Embedding 服务单例"""
+ global _embedding_service
+ if _embedding_service is None:
+ _embedding_service = EmbeddingService(config)
+ return _embedding_service
+
+
+# 便捷函数
+async def embed_text(text: str) -> List[float]:
+ """快捷方式: 获取单个文本的 Embedding"""
+ return await get_embedding_service().embed_text(text)
+
+
+async def embed_batch(texts: List[str], show_progress: bool = False) -> List[List[float]]:
+ """快捷方式: 批量获取 Embedding"""
+ return await get_embedding_service().embed_batch(texts, show_progress)
diff --git a/app/utils/github_client.py b/app/utils/github_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..6dde8d9981e0c1893d3d253d12d66b0ae10c77d2
--- /dev/null
+++ b/app/utils/github_client.py
@@ -0,0 +1,478 @@
+# -*- coding: utf-8 -*-
+"""
+GitHub 异步客户端
+
+设计原则:
+1. 异步非阻塞 - 使用 httpx.AsyncClient
+2. 连接池复用 - 单例模式管理客户端生命周期
+3. 自动重试 - 集成 tenacity 处理瞬时错误
+4. 类型安全 - 完整的类型注解
+5. 可扩展 - 易于添加新的 API 端点
+"""
+
+import asyncio
+import base64
+import logging
+import os
+from dataclasses import dataclass, field
+from typing import List, Optional, Dict, Any, Set
+from contextlib import asynccontextmanager
+
+import httpx
+
+from app.core.config import settings
+from app.utils.retry import llm_retry # 复用已有的重试装饰器
+
+logger = logging.getLogger(__name__)
+
+
+# ============================================================
+# 数据模型
+# ============================================================
+
+@dataclass
+class GitHubFile:
+ """GitHub 文件信息"""
+ path: str
+ type: str # "blob" | "tree"
+ size: int = 0
+ sha: str = ""
+
+ @property
+ def is_file(self) -> bool:
+ return self.type == "blob"
+
+ @property
+ def is_directory(self) -> bool:
+ return self.type == "tree"
+
+
+@dataclass
+class GitHubRepo:
+ """GitHub 仓库信息"""
+ owner: str
+ name: str
+ default_branch: str = "main"
+ description: str = ""
+ stars: int = 0
+
+ @property
+ def full_name(self) -> str:
+ return f"{self.owner}/{self.name}"
+
+
+@dataclass
+class FileFilter:
+ """文件过滤配置"""
+ ignored_extensions: Set[str] = field(default_factory=lambda: {
+ '.png', '.jpg', '.jpeg', '.gif', '.svg', '.ico', '.mp4', '.webp',
+ '.pyc', '.pyo', '.lock', '.zip', '.tar', '.gz', '.pdf', '.woff', '.woff2',
+ '.DS_Store', '.gitignore', '.gitattributes', '.editorconfig'
+ })
+
+ ignored_directories: Set[str] = field(default_factory=lambda: {
+ '.git', '.github', '.vscode', '.idea', '__pycache__',
+ 'node_modules', 'venv', 'env', '.env', 'build', 'dist',
+ 'site-packages', 'migrations', '.next', '.nuxt', 'coverage',
+ 'vendor', 'target', 'out', 'bin', 'obj'
+ })
+
+ max_file_size: int = 500_000 # 500KB
+
+ def should_include(self, file: GitHubFile) -> bool:
+ """判断文件是否应该被包含"""
+ if not file.is_file:
+ return False
+
+ # 检查目录
+ path_parts = file.path.split("/")
+ if any(part in self.ignored_directories for part in path_parts):
+ return False
+
+ # 检查扩展名
+ ext = os.path.splitext(file.path)[1].lower()
+ if ext in self.ignored_extensions:
+ return False
+
+ # 检查文件大小
+ if file.size > self.max_file_size:
+ return False
+
+ return True
+
+
+# ============================================================
+# 异常定义
+# ============================================================
+
+class GitHubError(Exception):
+ """GitHub API 错误基类"""
+ def __init__(self, message: str, status_code: int = 0):
+ self.message = message
+ self.status_code = status_code
+ super().__init__(message)
+
+
+class GitHubAuthError(GitHubError):
+ """认证错误 (401)"""
+ pass
+
+
+class GitHubRateLimitError(GitHubError):
+ """速率限制错误 (403)"""
+ pass
+
+
+class GitHubNotFoundError(GitHubError):
+ """资源不存在 (404)"""
+ pass
+
+
+# ============================================================
+# GitHub 异步客户端
+# ============================================================
+
+class GitHubClient:
+ """
+ GitHub 异步 API 客户端
+
+ 使用示例:
+ ```python
+ async with GitHubClient() as client:
+ repo = await client.get_repo("owner", "repo")
+ files = await client.get_repo_tree(repo)
+ content = await client.get_file_content(repo, "README.md")
+ ```
+ """
+
+ BASE_URL = "https://api.github.com"
+
+ def __init__(
+ self,
+ token: Optional[str] = None,
+ timeout: float = 30.0,
+ max_concurrent_requests: int = 10
+ ):
+ self.token = token or settings.GITHUB_TOKEN
+ self.timeout = timeout
+ self._client: Optional[httpx.AsyncClient] = None
+ self._semaphore = asyncio.Semaphore(max_concurrent_requests)
+
+ @property
+ def _headers(self) -> Dict[str, str]:
+ """构建请求头"""
+ headers = {
+ "Accept": "application/vnd.github.v3+json",
+ "User-Agent": "GitHub-Agent-Demo/1.0"
+ }
+ if self.token:
+ headers["Authorization"] = f"Bearer {self.token}"
+ return headers
+
+ async def _ensure_client(self) -> httpx.AsyncClient:
+ """确保客户端已初始化"""
+ if self._client is None or self._client.is_closed:
+ self._client = httpx.AsyncClient(
+ base_url=self.BASE_URL,
+ headers=self._headers,
+ timeout=httpx.Timeout(self.timeout),
+ follow_redirects=True,
+ limits=httpx.Limits(
+ max_keepalive_connections=20,
+ max_connections=50
+ )
+ )
+ return self._client
+
+ async def close(self):
+ """关闭客户端连接"""
+ if self._client and not self._client.is_closed:
+ await self._client.aclose()
+ self._client = None
+
+ async def __aenter__(self):
+ await self._ensure_client()
+ return self
+
+ async def __aexit__(self, exc_type, exc_val, exc_tb):
+ await self.close()
+
+ def _handle_error(self, response: httpx.Response, context: str = ""):
+ """统一错误处理"""
+ status = response.status_code
+
+ try:
+ data = response.json()
+ message = data.get("message", response.text)
+ except Exception:
+ message = response.text
+
+ error_msg = f"{context}: {message}" if context else message
+
+ if status == 401:
+ raise GitHubAuthError(
+ "GitHub Token 无效或已过期，请检查 .env 配置",
+ status
+ )
+ elif status == 403:
+ if "rate limit" in message.lower():
+ raise GitHubRateLimitError(
+ "GitHub API 请求已达上限，请稍后重试或添加 Token",
+ status
+ )
+ raise GitHubError(error_msg, status)
+ elif status == 404:
+ raise GitHubNotFoundError(error_msg, status)
+ else:
+ raise GitHubError(error_msg, status)
+
+ @llm_retry
+ async def _request(
+ self,
+ method: str,
+ endpoint: str,
+ **kwargs
+ ) -> Dict[str, Any]:
+ """
+ 发送 API 请求 (带重试)
+
+ Args:
+ method: HTTP 方法
+ endpoint: API 端点 (如 /repos/{owner}/{repo})
+ **kwargs: 传递给 httpx 的参数
+
+ Returns:
+ JSON 响应
+ """
+ async with self._semaphore:
+ client = await self._ensure_client()
+ response = await client.request(method, endpoint, **kwargs)
+
+ if response.status_code >= 400:
+ self._handle_error(response, endpoint)
+
+ return response.json()
+
+ async def _request_raw(
+ self,
+ method: str,
+ endpoint: str,
+ **kwargs
+ ) -> httpx.Response:
+ """发送请求并返回原始响应"""
+ async with self._semaphore:
+ client = await self._ensure_client()
+ return await client.request(method, endpoint, **kwargs)
+
+ # --------------------------------------------------------
+ # 仓库相关 API
+ # --------------------------------------------------------
+
+ async def get_repo(self, owner: str, name: str) -> GitHubRepo:
+ """获取仓库信息"""
+ data = await self._request("GET", f"/repos/{owner}/{name}")
+
+ return GitHubRepo(
+ owner=owner,
+ name=name,
+ default_branch=data.get("default_branch", "main"),
+ description=data.get("description", ""),
+ stars=data.get("stargazers_count", 0)
+ )
+
+ async def get_repo_tree(
+ self,
+ repo: GitHubRepo,
+ file_filter: Optional[FileFilter] = None
+ ) -> List[GitHubFile]:
+ """
+ 获取仓库文件树
+
+ Args:
+ repo: 仓库信息
+ file_filter: 文件过滤器 (默认使用标准过滤)
+
+ Returns:
+ 过滤后的文件列表
+ """
+ filter_config = file_filter or FileFilter()
+
+ data = await self._request(
+ "GET",
+ f"/repos/{repo.owner}/{repo.name}/git/trees/{repo.default_branch}",
+ params={"recursive": "1"}
+ )
+
+ files = []
+ for item in data.get("tree", []):
+ file = GitHubFile(
+ path=item["path"],
+ type=item["type"],
+ size=item.get("size", 0),
+ sha=item.get("sha", "")
+ )
+
+ if filter_config.should_include(file):
+ files.append(file)
+
+ logger.info(f"📂 仓库 {repo.full_name}: 共 {len(data.get('tree', []))} 项, 过滤后 {len(files)} 文件")
+ return files
+
+ # --------------------------------------------------------
+ # 文件内容 API
+ # --------------------------------------------------------
+
+ async def get_file_content(
+ self,
+ repo: GitHubRepo,
+ path: str
+ ) -> Optional[str]:
+ """
+ 获取单个文件内容
+
+ Args:
+ repo: 仓库信息
+ path: 文件路径
+
+ Returns:
+ 文件内容 (UTF-8 解码)，失败返回 None
+ """
+ try:
+ data = await self._request(
+ "GET",
+ f"/repos/{repo.owner}/{repo.name}/contents/{path}",
+ params={"ref": repo.default_branch}
+ )
+
+ # 处理目录情况
+ if isinstance(data, list):
+ file_names = [f["name"] for f in data]
+ return f"Directory '{path}' contains:\n" + "\n".join(
+ f"- {name}" for name in file_names
+ )
+
+ # 解码文件内容
+ content = data.get("content", "")
+ encoding = data.get("encoding", "base64")
+
+ if encoding == "base64":
+ return base64.b64decode(content).decode("utf-8")
+
+ return content
+
+ except GitHubNotFoundError:
+ logger.warning(f"文件不存在: {path}")
+ return None
+ except UnicodeDecodeError:
+ logger.warning(f"文件无法解码为 UTF-8: {path}")
+ return None
+ except Exception as e:
+ logger.error(f"获取文件失败 {path}: {e}")
+ return None
+
+ async def get_files_content(
+ self,
+ repo: GitHubRepo,
+ paths: List[str],
+ show_progress: bool = False
+ ) -> Dict[str, Optional[str]]:
+ """
+ 批量获取文件内容 (并发优化)
+
+ Args:
+ repo: 仓库信息
+ paths: 文件路径列表
+ show_progress: 是否显示进度
+
+ Returns:
+ {path: content} 字典
+ """
+ if not paths:
+ return {}
+
+ if show_progress:
+ logger.info(f"📥 开始下载 {len(paths)} 个文件 (并发: {self._semaphore._value})")
+
+ # 并发获取所有文件
+ tasks = [
+ self.get_file_content(repo, path)
+ for path in paths
+ ]
+
+ results = await asyncio.gather(*tasks, return_exceptions=True)
+
+ # 组装结果
+ content_map = {}
+ success_count = 0
+
+ for path, result in zip(paths, results):
+ if isinstance(result, Exception):
+ logger.error(f"下载失败 {path}: {result}")
+ content_map[path] = None
+ else:
+ content_map[path] = result
+ if result is not None:
+ success_count += 1
+
+ if show_progress:
+ logger.info(f"✅ 文件下载完成: {success_count}/{len(paths)} 成功")
+
+ return content_map
+
+
+# ============================================================
+# 全局单例管理
+# ============================================================
+
+_github_client: Optional[GitHubClient] = None
+
+
+def get_github_client() -> GitHubClient:
+ """获取 GitHub 客户端单例"""
+ global _github_client
+ if _github_client is None:
+ _github_client = GitHubClient()
+ return _github_client
+
+
+async def close_github_client():
+ """关闭全局客户端 (应用关闭时调用)"""
+ global _github_client
+ if _github_client:
+ await _github_client.close()
+ _github_client = None
+
+
+# ============================================================
+# 便捷函数 (兼容旧接口)
+# ============================================================
+
+def parse_repo_url(url: str) -> Optional[tuple[str, str]]:
+ """
+ 解析 GitHub URL
+
+ Args:
+ url: GitHub 仓库 URL
+
+ Returns:
+ (owner, repo) 元组，无效返回 None
+ """
+ if url.endswith(".git"):
+ url = url[:-4]
+
+ # 支持多种格式
+ # https://github.com/owner/repo
+ # github.com/owner/repo
+ # owner/repo
+
+ parts = url.replace("https://", "").replace("http://", "").split("/")
+
+ if "github.com" in parts:
+ idx = parts.index("github.com")
+ if len(parts) > idx + 2:
+ return (parts[idx + 1], parts[idx + 2])
+ elif len(parts) == 2:
+ # 直接是 owner/repo 格式
+ return (parts[0], parts[1])
+
+ return None
diff --git a/app/utils/llm_client.py b/app/utils/llm_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7e8035de39fc93206953453b05153e6247a0ed6
--- /dev/null
+++ b/app/utils/llm_client.py
@@ -0,0 +1,108 @@
+# 文件路径: app/utils/llm_client.py
+"""
+统一 LLM 客户端入口
+
+支持多个 LLM 供应商，通过 LLM_PROVIDER 环境变量切换:
+- openai: OpenAI (GPT-4, GPT-4o 等)
+- deepseek: DeepSeek (deepseek-chat, deepseek-coder 等)
+- anthropic: Anthropic (Claude 3.5, Claude 3 等)
+- gemini: Google Gemini (gemini-1.5-pro 等)
+
+使用方式 (与原来完全兼容):
+ from app.utils.llm_client import client
+
+ response = await client.chat.completions.create(
+ model=settings.default_model_name,
+ messages=[{"role": "user", "content": "Hello"}],
+ stream=True
+ )
+"""
+
+from app.core.config import settings
+from app.utils.llm_providers import LLMFactory, BaseLLMProvider
+from typing import Optional
+
+# 全局客户端实例
+client: Optional[BaseLLMProvider] = None
+
+def _initialize_client() -> Optional[BaseLLMProvider]:
+ """
+ 初始化 LLM 客户端
+
+ 根据配置的 LLM_PROVIDER 创建对应的客户端实例。
+ """
+ provider = settings.LLM_PROVIDER.lower()
+ api_key = settings.current_api_key
+ base_url = settings.current_base_url
+ model_name = settings.default_model_name
+
+ if not api_key:
+ print(f"❌ 未找到 {provider.upper()}_API_KEY")
+ return None
+
+ try:
+ return LLMFactory.create(
+ provider=provider,
+ api_key=api_key,
+ model_name=model_name,
+ base_url=base_url,
+ temperature=settings.LLM_TEMPERATURE,
+ max_tokens=settings.LLM_MAX_TOKENS,
+ timeout=settings.LLM_TIMEOUT,
+ )
+ except Exception as e:
+ print(f"❌ LLM Client 初始化失败: {e}")
+ return None
+
+
+def get_client() -> Optional[BaseLLMProvider]:
+ """
+ 获取 LLM 客户端实例
+
+ 如果客户端尚未初始化，会自动初始化。
+ """
+ global client
+ if client is None:
+ client = _initialize_client()
+ return client
+
+
+def reinitialize_client(
+ provider: str = None,
+ api_key: str = None,
+ model_name: str = None,
+ base_url: str = None,
+) -> Optional[BaseLLMProvider]:
+ """
+ 重新初始化客户端
+
+ 用于运行时切换 LLM 供应商或模型。
+
+ Args:
+ provider: 新的供应商 (可选)
+ api_key: 新的 API Key (可选)
+ model_name: 新的模型名称 (可选)
+ base_url: 新的 Base URL (可选)
+ """
+ global client
+
+ _provider = provider or settings.LLM_PROVIDER
+ _api_key = api_key or settings.current_api_key
+ _model_name = model_name or settings.default_model_name
+ _base_url = base_url or settings.current_base_url
+
+ try:
+ client = LLMFactory.create(
+ provider=_provider,
+ api_key=_api_key,
+ model_name=_model_name,
+ base_url=_base_url,
+ )
+ return client
+ except Exception as e:
+ print(f"❌ 重新初始化失败: {e}")
+ return None
+
+
+# 自动初始化客户端
+client = _initialize_client()
\ No newline at end of file
diff --git a/app/utils/llm_providers/__init__.py b/app/utils/llm_providers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..18a5736ceaa020eefb735711ca12ec2b675dfedb
--- /dev/null
+++ b/app/utils/llm_providers/__init__.py
@@ -0,0 +1,29 @@
+# 文件路径: app/utils/llm_providers/__init__.py
+"""
+多 LLM 供应商支持模块
+
+支持的供应商:
+- OpenAI (GPT-4, GPT-4o, GPT-3.5-turbo 等)
+- DeepSeek (deepseek-chat, deepseek-coder 等)
+- Anthropic (Claude 3.5, Claude 3 等)
+- Google Gemini (gemini-pro, gemini-1.5-pro 等)
+"""
+
+from .base import BaseLLMProvider, LLMResponse, LLMConfig
+from .openai_provider import OpenAIProvider
+from .deepseek_provider import DeepSeekProvider
+from .anthropic_provider import AnthropicProvider
+from .gemini_provider import GeminiProvider
+from .factory import LLMFactory, get_llm_client
+
+__all__ = [
+ "BaseLLMProvider",
+ "LLMResponse",
+ "LLMConfig",
+ "OpenAIProvider",
+ "DeepSeekProvider",
+ "AnthropicProvider",
+ "GeminiProvider",
+ "LLMFactory",
+ "get_llm_client",
+]
diff --git a/app/utils/llm_providers/anthropic_provider.py b/app/utils/llm_providers/anthropic_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..d52966310930871612f30a61c3ceaa791dc05304
--- /dev/null
+++ b/app/utils/llm_providers/anthropic_provider.py
@@ -0,0 +1,196 @@
+# 文件路径: app/utils/llm_providers/anthropic_provider.py
+"""
+Anthropic (Claude) LLM 提供商实现
+
+支持模型: claude-3-5-sonnet, claude-3-opus, claude-3-haiku 等
+"""
+
+import uuid
+import time
+from typing import List, AsyncIterator
+
+from .base import (
+ BaseLLMProvider,
+ LLMConfig,
+ LLMMessage,
+ LLMResponse,
+ LLMChoice,
+ LLMUsage,
+ LLMProviderType
+)
+
+
+class AnthropicProvider(BaseLLMProvider):
+ """
+ Anthropic (Claude) API 提供商
+
+ 注意: Anthropic 的消息格式与 OpenAI 略有不同:
+ - system 消息需要单独传递
+ - messages 只包含 user/assistant 轮次
+ """
+
+ def __init__(self, config: LLMConfig):
+ super().__init__(config)
+ try:
+ from anthropic import AsyncAnthropic
+ self._client = AsyncAnthropic(
+ api_key=config.api_key,
+ timeout=config.timeout
+ )
+ self._available = True
+ except ImportError:
+ print("⚠️ anthropic 包未安装，请运行: pip install anthropic")
+ self._client = None
+ self._available = False
+
+ def _extract_system_message(self, messages: List[LLMMessage]) -> tuple:
+ """
+ 提取 system 消息
+
+ Anthropic 需要将 system 消息单独传递,
+ 不能放在 messages 列表中。
+
+ Returns:
+ (system_prompt, filtered_messages)
+ """
+ system_prompt = None
+ filtered_messages = []
+
+ for msg in messages:
+ if msg.role == "system":
+ system_prompt = msg.content
+ else:
+ filtered_messages.append(msg)
+
+ return system_prompt, filtered_messages
+
+ async def chat_completions_create(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> LLMResponse:
+ """非流式请求"""
+ if not self._available:
+ raise RuntimeError("Anthropic client not available. Please install: pip install anthropic")
+
+ system_prompt, filtered_messages = self._extract_system_message(messages)
+
+ # 转换消息格式
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in filtered_messages
+ ]
+
+ # 构建请求参数
+ request_params = {
+ "model": model,
+ "messages": api_messages,
+ "temperature": temperature,
+ "max_tokens": max_tokens,
+ }
+
+ if system_prompt:
+ request_params["system"] = system_prompt
+
+ response = await self._client.messages.create(**request_params)
+
+ # 转换为统一格式
+ content = ""
+ if response.content:
+ # Anthropic 的 content 是一个 list
+ for block in response.content:
+ if hasattr(block, 'text'):
+ content += block.text
+
+ choices = [
+ LLMChoice(
+ index=0,
+ message=LLMMessage(role="assistant", content=content),
+ finish_reason=response.stop_reason
+ )
+ ]
+
+ usage = LLMUsage(
+ prompt_tokens=response.usage.input_tokens,
+ completion_tokens=response.usage.output_tokens,
+ total_tokens=response.usage.input_tokens + response.usage.output_tokens
+ )
+
+ return LLMResponse(
+ id=response.id,
+ model=response.model,
+ choices=choices,
+ usage=usage,
+ created=int(time.time())
+ )
+
+ async def chat_completions_create_stream(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> AsyncIterator[LLMResponse]:
+ """流式请求"""
+ if not self._available:
+ raise RuntimeError("Anthropic client not available. Please install: pip install anthropic")
+
+ system_prompt, filtered_messages = self._extract_system_message(messages)
+
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in filtered_messages
+ ]
+
+ request_params = {
+ "model": model,
+ "messages": api_messages,
+ "temperature": temperature,
+ "max_tokens": max_tokens,
+ }
+
+ if system_prompt:
+ request_params["system"] = system_prompt
+
+ response_id = f"msg_{uuid.uuid4().hex[:24]}"
+
+ async with self._client.messages.stream(**request_params) as stream:
+ async for text in stream.text_stream:
+ choices = [
+ LLMChoice(
+ index=0,
+ delta=LLMMessage(role="assistant", content=text),
+ finish_reason=None
+ )
+ ]
+ yield LLMResponse(
+ id=response_id,
+ model=model,
+ choices=choices,
+ created=int(time.time())
+ )
+
+ def validate_connection(self) -> bool:
+ """验证连接"""
+ return self._available and bool(self.config.api_key)
+
+
+def create_anthropic_provider(
+ api_key: str,
+ model_name: str = "claude-3-5-sonnet-20241022",
+ **kwargs
+) -> AnthropicProvider:
+ """工厂函数：创建 Anthropic 提供商"""
+ config = LLMConfig(
+ provider=LLMProviderType.ANTHROPIC,
+ api_key=api_key,
+ model_name=model_name,
+ **kwargs
+ )
+ return AnthropicProvider(config)
diff --git a/app/utils/llm_providers/base.py b/app/utils/llm_providers/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..b36141957b8920a696cb14a01743429d25d4c7df
--- /dev/null
+++ b/app/utils/llm_providers/base.py
@@ -0,0 +1,320 @@
+# 文件路径: app/utils/llm_providers/base.py
+"""
+LLM 提供商基类定义
+
+定义统一的接口规范，所有供应商实现都必须遵循此规范。
+采用适配器模式，将不同供应商的 API 统一为 OpenAI 兼容格式。
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from typing import List, Dict, Any, Optional, AsyncIterator, Union
+from enum import Enum
+
+from app.utils.retry import llm_retry, is_retryable_error
+
+# 配置日志
+logger = logging.getLogger("llm_provider")
+
+
+class LLMProviderType(str, Enum):
+ """支持的 LLM 供应商类型"""
+ OPENAI = "openai"
+ DEEPSEEK = "deepseek"
+ ANTHROPIC = "anthropic"
+ GEMINI = "gemini"
+
+
+@dataclass
+class LLMConfig:
+ """LLM 配置"""
+ provider: LLMProviderType
+ api_key: str
+ model_name: str
+ base_url: Optional[str] = None
+ temperature: float = 0.1
+ max_tokens: int = 4096
+ timeout: int = 600
+ extra_params: Dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class LLMMessage:
+ """消息格式 (兼容 OpenAI)"""
+ role: str # "system", "user", "assistant"
+ content: str
+
+
+@dataclass
+class LLMUsage:
+ """Token 使用量"""
+ prompt_tokens: int = 0
+ completion_tokens: int = 0
+ total_tokens: int = 0
+
+
+@dataclass
+class LLMChoice:
+ """响应选项 (兼容 OpenAI)"""
+ index: int
+ message: Optional[LLMMessage] = None
+ delta: Optional[LLMMessage] = None # 流式响应时使用
+ finish_reason: Optional[str] = None
+
+
+@dataclass
+class LLMResponse:
+ """
+ 统一的 LLM 响应格式
+
+ 设计为兼容 OpenAI 的 ChatCompletion 格式，
+ 使得现有代码无需大幅修改即可使用。
+ """
+ id: str
+ model: str
+ choices: List[LLMChoice]
+ usage: Optional[LLMUsage] = None
+ created: int = 0
+
+ @property
+ def content(self) -> str:
+ """便捷方法：获取第一个选项的内容"""
+ if self.choices and self.choices[0].message:
+ return self.choices[0].message.content
+ return ""
+
+
+# 辅助类定义（在 BaseLLMProvider 外部，避免嵌套类问题）
+class _CompletionsNamespace:
+ """模拟 client.chat.completions 命名空间"""
+ def __init__(self, provider: 'BaseLLMProvider'):
+ self._provider = provider
+
+ async def create(
+ self,
+ model: str = None,
+ messages: List[Dict[str, str]] = None,
+ temperature: float = None,
+ max_tokens: int = None,
+ stream: bool = False,
+ timeout: int = None,
+ **kwargs
+ ) -> Union[LLMResponse, AsyncIterator[LLMResponse]]:
+ """
+ 统一的 completions.create 接口
+
+ 兼容 OpenAI SDK 调用方式:
+ response = await client.chat.completions.create(
+ model="gpt-4",
+ messages=[{"role": "user", "content": "Hello"}],
+ stream=True
+ )
+
+ 内置重试机制:
+ - 自动重试网络错误、超时、速率限制
+ - 指数退避策略
+ - 最多重试 3 次
+ """
+ # 合并配置
+ _model = model or self._provider.config.model_name
+ _temperature = temperature if temperature is not None else self._provider.config.temperature
+ _max_tokens = max_tokens or self._provider.config.max_tokens
+ _timeout = timeout or self._provider.config.timeout
+
+ # 转换消息格式
+ _messages = [
+ LLMMessage(role=m["role"], content=m["content"])
+ for m in (messages or [])
+ ]
+
+ if stream:
+ # 流式请求: 返回带重试的异步生成器
+ return self._create_stream_with_retry(
+ messages=_messages,
+ model=_model,
+ temperature=_temperature,
+ max_tokens=_max_tokens,
+ timeout=_timeout,
+ **kwargs
+ )
+ else:
+ # 非流式请求: 使用 tenacity 重试
+ return await self._create_with_retry(
+ messages=_messages,
+ model=_model,
+ temperature=_temperature,
+ max_tokens=_max_tokens,
+ timeout=_timeout,
+ **kwargs
+ )
+
+ @llm_retry
+ async def _create_with_retry(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> LLMResponse:
+ """带重试的非流式请求"""
+ logger.debug(f"🔄 LLM 请求: model={model}, messages_count={len(messages)}")
+ return await self._provider.chat_completions_create(
+ messages=messages,
+ model=model,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ **kwargs
+ )
+
+ async def _create_stream_with_retry(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ max_retries: int = 3,
+ **kwargs
+ ) -> AsyncIterator[LLMResponse]:
+ """
+ 带重试的流式请求
+
+ 注意: 流式请求的重试策略与非流式不同
+ - 如果在获取流之前失败，可以重试
+ - 如果在流传输过程中失败，需要重新开始
+ """
+ last_error = None
+
+ for attempt in range(1, max_retries + 1):
+ try:
+ logger.debug(f"🔄 LLM 流式请求 (尝试 {attempt}/{max_retries}): model={model}")
+
+ # 获取流生成器
+ stream = self._provider.chat_completions_create_stream(
+ messages=messages,
+ model=model,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ **kwargs
+ )
+
+ # 迭代流并 yield
+ async for chunk in stream:
+ yield chunk
+
+ # 成功完成，退出重试循环
+ return
+
+ except Exception as e:
+ last_error = e
+ if is_retryable_error(e) and attempt < max_retries:
+ wait_time = min(2 ** attempt, 30) # 指数退避
+ logger.warning(
+ f"🔄 LLM 流式请求失败 (尝试 {attempt}/{max_retries}): "
+ f"{type(e).__name__}: {e}. 等待 {wait_time}s 后重试..."
+ )
+ import asyncio
+ await asyncio.sleep(wait_time)
+ else:
+ # 不可重试的错误或已达到最大重试次数
+ logger.error(f"❌ LLM 流式请求最终失败: {type(e).__name__}: {e}")
+ raise
+
+ # 如果走到这里，说明所有重试都失败了
+ if last_error:
+ raise last_error
+
+
+class _ChatNamespace:
+ """模拟 client.chat 命名空间"""
+ def __init__(self, provider: 'BaseLLMProvider'):
+ self._provider = provider
+ self.completions = _CompletionsNamespace(provider)
+
+
+class BaseLLMProvider(ABC):
+ """
+ LLM 提供商抽象基类
+
+ 所有供应商实现都需要继承此类并实现以下方法:
+ - chat_completions_create: 非流式请求
+ - chat_completions_create_stream: 流式请求
+
+ 为了兼容现有代码，提供一个模拟 OpenAI 客户端的 chat.completions 接口。
+ """
+
+ def __init__(self, config: LLMConfig):
+ self.config = config
+ self._client = None
+ # 模拟 OpenAI SDK 的接口结构
+ self.chat = _ChatNamespace(self)
+
+ @abstractmethod
+ async def chat_completions_create(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> LLMResponse:
+ """
+ 非流式 Chat Completion 请求
+
+ Args:
+ messages: 消息列表
+ model: 模型名称
+ temperature: 温度参数
+ max_tokens: 最大 Token 数
+ timeout: 超时时间
+
+ Returns:
+ LLMResponse: 统一格式的响应
+ """
+ pass
+
+ @abstractmethod
+ async def chat_completions_create_stream(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> AsyncIterator[LLMResponse]:
+ """
+ 流式 Chat Completion 请求
+
+ Args:
+ messages: 消息列表
+ model: 模型名称
+ temperature: 温度参数
+ max_tokens: 最大 Token 数
+ timeout: 超时时间
+
+ Yields:
+ LLMResponse: 流式响应块
+ """
+ pass
+
+ @abstractmethod
+ def validate_connection(self) -> bool:
+ """验证连接是否正常"""
+ pass
+
+ @property
+ def provider_name(self) -> str:
+ """获取供应商名称"""
+ return self.config.provider.value
+
+ @property
+ def model_name(self) -> str:
+ """获取当前模型名称"""
+ return self.config.model_name
diff --git a/app/utils/llm_providers/deepseek_provider.py b/app/utils/llm_providers/deepseek_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a49ea5fe0cde0a3e3e8b1cf6aa8e962d287ae65
--- /dev/null
+++ b/app/utils/llm_providers/deepseek_provider.py
@@ -0,0 +1,154 @@
+# 文件路径: app/utils/llm_providers/deepseek_provider.py
+"""
+DeepSeek LLM 提供商实现
+
+DeepSeek API 兼容 OpenAI 协议，因此直接复用 OpenAI SDK。
+支持模型: deepseek-chat, deepseek-coder, deepseek-reasoner 等
+"""
+
+from typing import List, AsyncIterator
+from openai import AsyncOpenAI
+
+from .base import (
+ BaseLLMProvider,
+ LLMConfig,
+ LLMMessage,
+ LLMResponse,
+ LLMChoice,
+ LLMUsage,
+ LLMProviderType
+)
+
+
+# DeepSeek 默认 API 端点
+DEEPSEEK_DEFAULT_BASE_URL = "https://api.deepseek.com"
+
+
+class DeepSeekProvider(BaseLLMProvider):
+ """
+ DeepSeek API 提供商
+
+ DeepSeek 使用 OpenAI 兼容协议，因此可以直接使用 OpenAI SDK。
+ """
+
+ def __init__(self, config: LLMConfig):
+ super().__init__(config)
+ # 确保使用正确的 base_url
+ base_url = config.base_url or DEEPSEEK_DEFAULT_BASE_URL
+ self._client = AsyncOpenAI(
+ api_key=config.api_key,
+ base_url=base_url,
+ timeout=config.timeout
+ )
+
+ async def chat_completions_create(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> LLMResponse:
+ """非流式请求"""
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in messages
+ ]
+
+ response = await self._client.chat.completions.create(
+ model=model,
+ messages=api_messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ **kwargs
+ )
+
+ choices = [
+ LLMChoice(
+ index=c.index,
+ message=LLMMessage(role=c.message.role, content=c.message.content),
+ finish_reason=c.finish_reason
+ )
+ for c in response.choices
+ ]
+
+ usage = None
+ if response.usage:
+ usage = LLMUsage(
+ prompt_tokens=response.usage.prompt_tokens,
+ completion_tokens=response.usage.completion_tokens,
+ total_tokens=response.usage.total_tokens
+ )
+
+ return LLMResponse(
+ id=response.id,
+ model=response.model,
+ choices=choices,
+ usage=usage,
+ created=response.created
+ )
+
+ async def chat_completions_create_stream(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> AsyncIterator[LLMResponse]:
+ """流式请求"""
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in messages
+ ]
+
+ stream = await self._client.chat.completions.create(
+ model=model,
+ messages=api_messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ stream=True,
+ **kwargs
+ )
+
+ async for chunk in stream:
+ if chunk.choices:
+ delta_content = chunk.choices[0].delta.content or ""
+ choices = [
+ LLMChoice(
+ index=0,
+ delta=LLMMessage(role="assistant", content=delta_content),
+ finish_reason=chunk.choices[0].finish_reason
+ )
+ ]
+ yield LLMResponse(
+ id=chunk.id,
+ model=chunk.model,
+ choices=choices,
+ created=chunk.created
+ )
+
+ def validate_connection(self) -> bool:
+ """验证 API Key 有效性"""
+ return bool(self.config.api_key)
+
+
+def create_deepseek_provider(
+ api_key: str,
+ model_name: str = "deepseek-chat",
+ base_url: str = None,
+ **kwargs
+) -> DeepSeekProvider:
+ """工厂函数：创建 DeepSeek 提供商"""
+ config = LLMConfig(
+ provider=LLMProviderType.DEEPSEEK,
+ api_key=api_key,
+ model_name=model_name,
+ base_url=base_url or DEEPSEEK_DEFAULT_BASE_URL,
+ **kwargs
+ )
+ return DeepSeekProvider(config)
diff --git a/app/utils/llm_providers/factory.py b/app/utils/llm_providers/factory.py
new file mode 100644
index 0000000000000000000000000000000000000000..fca924b65a486ef89bacecf3072dcfc1df3a1764
--- /dev/null
+++ b/app/utils/llm_providers/factory.py
@@ -0,0 +1,171 @@
+# 文件路径: app/utils/llm_providers/factory.py
+"""
+LLM 工厂模块
+
+提供统一的 LLM 客户端创建接口，根据配置自动选择合适的供应商。
+"""
+
+import os
+from typing import Optional
+
+from .base import BaseLLMProvider, LLMConfig, LLMProviderType
+from .openai_provider import OpenAIProvider
+from .deepseek_provider import DeepSeekProvider, DEEPSEEK_DEFAULT_BASE_URL
+from .anthropic_provider import AnthropicProvider
+from .gemini_provider import GeminiProvider
+
+
+class LLMFactory:
+ """
+ LLM 客户端工厂
+
+ 根据提供商类型创建对应的客户端实例。
+ 支持从环境变量自动配置。
+ """
+
+ # 提供商类到枚举的映射
+ _providers = {
+ LLMProviderType.OPENAI: OpenAIProvider,
+ LLMProviderType.DEEPSEEK: DeepSeekProvider,
+ LLMProviderType.ANTHROPIC: AnthropicProvider,
+ LLMProviderType.GEMINI: GeminiProvider,
+ }
+
+ # 默认模型名称映射
+ _default_models = {
+ LLMProviderType.OPENAI: "gpt-4o-mini",
+ LLMProviderType.DEEPSEEK: "deepseek-chat",
+ LLMProviderType.ANTHROPIC: "claude-3-5-sonnet-20241022",
+ LLMProviderType.GEMINI: "gemini-1.5-flash",
+ }
+
+ # 默认 Base URL 映射
+ _default_base_urls = {
+ LLMProviderType.OPENAI: None, # 使用 SDK 默认
+ LLMProviderType.DEEPSEEK: DEEPSEEK_DEFAULT_BASE_URL,
+ LLMProviderType.ANTHROPIC: None,
+ LLMProviderType.GEMINI: None,
+ }
+
+ @classmethod
+ def create(
+ cls,
+ provider: str,
+ api_key: str,
+ model_name: str = None,
+ base_url: str = None,
+ **kwargs
+ ) -> Optional[BaseLLMProvider]:
+ """
+ 创建 LLM 客户端
+
+ Args:
+ provider: 提供商名称 ("openai", "deepseek", "anthropic", "gemini")
+ api_key: API Key
+ model_name: 模型名称 (可选，使用默认值)
+ base_url: 自定义 API 端点 (可选)
+ **kwargs: 其他配置参数
+
+ Returns:
+ BaseLLMProvider 实例，或 None (如果创建失败)
+ """
+ try:
+ # 解析提供商类型
+ provider_type = LLMProviderType(provider.lower())
+ except ValueError:
+ print(f"❌ 不支持的 LLM 提供商: {provider}")
+ print(f" 支持的提供商: {', '.join([p.value for p in LLMProviderType])}")
+ return None
+
+ if not api_key:
+ print(f"❌ 未提供 {provider} 的 API Key")
+ return None
+
+ # 获取提供商类
+ provider_class = cls._providers.get(provider_type)
+ if not provider_class:
+ print(f"❌ 提供商 {provider} 未实现")
+ return None
+
+ # 构建配置
+ config = LLMConfig(
+ provider=provider_type,
+ api_key=api_key,
+ model_name=model_name or cls._default_models.get(provider_type, "default"),
+ base_url=base_url or cls._default_base_urls.get(provider_type),
+ **kwargs
+ )
+
+ try:
+ client = provider_class(config)
+ if client.validate_connection():
+ print(f"✅ {provider.upper()} Client 初始化成功 (Model: {config.model_name})")
+ return client
+ else:
+ print(f"❌ {provider.upper()} Client 验证失败")
+ return None
+ except Exception as e:
+ print(f"❌ {provider.upper()} Client 初始化失败: {e}")
+ return None
+
+ @classmethod
+ def create_from_env(cls, provider: str = None) -> Optional[BaseLLMProvider]:
+ """
+ 从环境变量创建 LLM 客户端
+
+ 环境变量命名规范:
+ - LLM_PROVIDER: 提供商名称 (可被参数覆盖)
+ - {PROVIDER}_API_KEY: API Key (如 OPENAI_API_KEY, DEEPSEEK_API_KEY)
+ - {PROVIDER}_BASE_URL: 自定义端点 (可选)
+ - MODEL_NAME: 模型名称 (可选)
+
+ Args:
+ provider: 提供商名称 (可选，默认从 LLM_PROVIDER 环境变量读取)
+
+ Returns:
+ BaseLLMProvider 实例
+ """
+ # 确定提供商
+ _provider = provider or os.getenv("LLM_PROVIDER", "deepseek")
+ _provider = _provider.lower()
+
+ # 获取 API Key (支持多种命名方式)
+ key_env_names = [
+ f"{_provider.upper()}_API_KEY",
+ f"{_provider.upper()}API_KEY",
+ ]
+
+ api_key = None
+ for key_name in key_env_names:
+ api_key = os.getenv(key_name)
+ if api_key:
+ break
+
+ if not api_key:
+ print(f"❌ 未找到 {_provider.upper()} API Key")
+ print(f" 请设置环境变量: {key_env_names[0]}")
+ return None
+
+ # 获取可选配置
+ base_url = os.getenv(f"{_provider.upper()}_BASE_URL")
+ model_name = os.getenv("MODEL_NAME")
+
+ return cls.create(
+ provider=_provider,
+ api_key=api_key,
+ model_name=model_name,
+ base_url=base_url
+ )
+
+
+def get_llm_client(provider: str = None) -> Optional[BaseLLMProvider]:
+ """
+ 便捷函数：获取 LLM 客户端
+
+ Args:
+ provider: 提供商名称 (可选)
+
+ Returns:
+ BaseLLMProvider 实例
+ """
+ return LLMFactory.create_from_env(provider)
diff --git a/app/utils/llm_providers/gemini_provider.py b/app/utils/llm_providers/gemini_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d9e82fd2f5459ba676c61576f23d1a7d82d68f3
--- /dev/null
+++ b/app/utils/llm_providers/gemini_provider.py
@@ -0,0 +1,301 @@
+# 文件路径: app/utils/llm_providers/gemini_provider.py
+"""
+Google Gemini LLM 提供商实现
+
+支持模型: gemini-1.5-pro, gemini-1.5-flash, gemini-pro 等
+"""
+
+import uuid
+import time
+from typing import List, AsyncIterator
+
+from .base import (
+ BaseLLMProvider,
+ LLMConfig,
+ LLMMessage,
+ LLMResponse,
+ LLMChoice,
+ LLMUsage,
+ LLMProviderType
+)
+
+
+class GeminiProvider(BaseLLMProvider):
+ """
+ Google Gemini API 提供商
+
+ 支持两种方式:
+ 1. 使用 google-generativeai SDK (原生)
+ 2. 使用 OpenAI 兼容接口 (通过 AI Studio 或 Vertex AI)
+ """
+
+ def __init__(self, config: LLMConfig):
+ super().__init__(config)
+ self._available = False
+ self._use_openai_compat = config.base_url is not None
+
+ if self._use_openai_compat:
+ # 使用 OpenAI 兼容模式 (推荐)
+ try:
+ from openai import AsyncOpenAI
+ self._client = AsyncOpenAI(
+ api_key=config.api_key,
+ base_url=config.base_url,
+ timeout=config.timeout
+ )
+ self._available = True
+ print(f"✅ Gemini Provider (OpenAI Compatible) initialized")
+ except ImportError:
+ print("⚠️ openai 包未安装")
+ else:
+ # 使用 Google AI SDK (原生模式)
+ try:
+ import google.generativeai as genai
+ genai.configure(api_key=config.api_key)
+ self._genai = genai
+ self._model = genai.GenerativeModel(config.model_name)
+ self._available = True
+ print(f"✅ Gemini Provider (Native SDK) initialized")
+ except ImportError:
+ print("⚠️ google-generativeai 包未安装，请运行: pip install google-generativeai")
+ self._genai = None
+ self._model = None
+
+ def _convert_messages_to_gemini(self, messages: List[LLMMessage]) -> tuple:
+ """
+ 转换消息格式为 Gemini 格式
+
+ Gemini 的消息格式:
+ - 不支持 system 角色，需要将其合并到第一条 user 消息
+ - role: "user" | "model" (不是 "assistant")
+
+ Returns:
+ (history, current_message)
+ """
+ system_content = ""
+ converted = []
+
+ for msg in messages:
+ if msg.role == "system":
+ system_content = msg.content + "\n\n"
+ elif msg.role == "assistant":
+ converted.append({"role": "model", "parts": [msg.content]})
+ else: # user
+ content = msg.content
+ if system_content and len(converted) == 0:
+ content = system_content + content
+ system_content = ""
+ converted.append({"role": "user", "parts": [content]})
+
+ if not converted:
+ return [], ""
+
+ # 最后一条作为当前消息
+ if len(converted) == 1:
+ return [], converted[0]["parts"][0]
+ else:
+ return converted[:-1], converted[-1]["parts"][0]
+
+ async def chat_completions_create(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> LLMResponse:
+ """非流式请求"""
+ if not self._available:
+ raise RuntimeError("Gemini client not available")
+
+ if self._use_openai_compat:
+ # OpenAI 兼容模式
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in messages
+ ]
+
+ response = await self._client.chat.completions.create(
+ model=model,
+ messages=api_messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ **kwargs
+ )
+
+ choices = [
+ LLMChoice(
+ index=c.index,
+ message=LLMMessage(role=c.message.role, content=c.message.content),
+ finish_reason=c.finish_reason
+ )
+ for c in response.choices
+ ]
+
+ usage = None
+ if response.usage:
+ usage = LLMUsage(
+ prompt_tokens=response.usage.prompt_tokens,
+ completion_tokens=response.usage.completion_tokens,
+ total_tokens=response.usage.total_tokens
+ )
+
+ return LLMResponse(
+ id=response.id,
+ model=response.model,
+ choices=choices,
+ usage=usage,
+ created=response.created
+ )
+ else:
+ # Native SDK 模式
+ history, current_msg = self._convert_messages_to_gemini(messages)
+
+ generation_config = {
+ "temperature": temperature,
+ "max_output_tokens": max_tokens,
+ }
+
+ chat = self._model.start_chat(history=history)
+ response = await chat.send_message_async(
+ current_msg,
+ generation_config=generation_config
+ )
+
+ content = response.text if response.text else ""
+
+ choices = [
+ LLMChoice(
+ index=0,
+ message=LLMMessage(role="assistant", content=content),
+ finish_reason="stop"
+ )
+ ]
+
+ # Gemini 原生 SDK 的 token 统计
+ usage = None
+ if hasattr(response, 'usage_metadata') and response.usage_metadata:
+ usage = LLMUsage(
+ prompt_tokens=getattr(response.usage_metadata, 'prompt_token_count', 0),
+ completion_tokens=getattr(response.usage_metadata, 'candidates_token_count', 0),
+ total_tokens=getattr(response.usage_metadata, 'total_token_count', 0)
+ )
+
+ return LLMResponse(
+ id=f"gemini-{uuid.uuid4().hex[:12]}",
+ model=model,
+ choices=choices,
+ usage=usage,
+ created=int(time.time())
+ )
+
+ async def chat_completions_create_stream(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> AsyncIterator[LLMResponse]:
+ """流式请求"""
+ if not self._available:
+ raise RuntimeError("Gemini client not available")
+
+ if self._use_openai_compat:
+ # OpenAI 兼容模式
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in messages
+ ]
+
+ stream = await self._client.chat.completions.create(
+ model=model,
+ messages=api_messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ stream=True,
+ **kwargs
+ )
+
+ async for chunk in stream:
+ if chunk.choices:
+ delta_content = chunk.choices[0].delta.content or ""
+ choices = [
+ LLMChoice(
+ index=0,
+ delta=LLMMessage(role="assistant", content=delta_content),
+ finish_reason=chunk.choices[0].finish_reason
+ )
+ ]
+ yield LLMResponse(
+ id=chunk.id,
+ model=chunk.model,
+ choices=choices,
+ created=chunk.created
+ )
+ else:
+ # Native SDK 流式
+ history, current_msg = self._convert_messages_to_gemini(messages)
+
+ generation_config = {
+ "temperature": temperature,
+ "max_output_tokens": max_tokens,
+ }
+
+ chat = self._model.start_chat(history=history)
+ response = await chat.send_message_async(
+ current_msg,
+ generation_config=generation_config,
+ stream=True
+ )
+
+ response_id = f"gemini-{uuid.uuid4().hex[:12]}"
+
+ async for chunk in response:
+ if chunk.text:
+ choices = [
+ LLMChoice(
+ index=0,
+ delta=LLMMessage(role="assistant", content=chunk.text),
+ finish_reason=None
+ )
+ ]
+ yield LLMResponse(
+ id=response_id,
+ model=model,
+ choices=choices,
+ created=int(time.time())
+ )
+
+ def validate_connection(self) -> bool:
+ """验证连接"""
+ return self._available and bool(self.config.api_key)
+
+
+def create_gemini_provider(
+ api_key: str,
+ model_name: str = "gemini-1.5-flash",
+ base_url: str = None,
+ **kwargs
+) -> GeminiProvider:
+ """
+ 工厂函数：创建 Gemini 提供商
+
+ Args:
+ api_key: Google AI API Key
+ model_name: 模型名称
+ base_url: OpenAI 兼容端点 (可选)
+ 如果不提供，则使用原生 SDK
+ """
+ config = LLMConfig(
+ provider=LLMProviderType.GEMINI,
+ api_key=api_key,
+ model_name=model_name,
+ base_url=base_url,
+ **kwargs
+ )
+ return GeminiProvider(config)
diff --git a/app/utils/llm_providers/openai_provider.py b/app/utils/llm_providers/openai_provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..981fb92326cfda9f3c92725f06183c7022f6236c
--- /dev/null
+++ b/app/utils/llm_providers/openai_provider.py
@@ -0,0 +1,145 @@
+# 文件路径: app/utils/llm_providers/openai_provider.py
+"""
+OpenAI LLM 提供商实现
+
+支持模型: GPT-4, GPT-4o, GPT-4o-mini, GPT-3.5-turbo 等
+"""
+
+from typing import List, AsyncIterator
+from openai import AsyncOpenAI
+
+from .base import (
+ BaseLLMProvider,
+ LLMConfig,
+ LLMMessage,
+ LLMResponse,
+ LLMChoice,
+ LLMUsage,
+ LLMProviderType
+)
+
+
+class OpenAIProvider(BaseLLMProvider):
+ """OpenAI API 提供商"""
+
+ def __init__(self, config: LLMConfig):
+ super().__init__(config)
+ self._client = AsyncOpenAI(
+ api_key=config.api_key,
+ base_url=config.base_url, # 可选自定义 base_url
+ timeout=config.timeout
+ )
+
+ async def chat_completions_create(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> LLMResponse:
+ """非流式请求"""
+ # 转换消息格式
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in messages
+ ]
+
+ response = await self._client.chat.completions.create(
+ model=model,
+ messages=api_messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ **kwargs
+ )
+
+ # 转换为统一格式
+ choices = [
+ LLMChoice(
+ index=c.index,
+ message=LLMMessage(role=c.message.role, content=c.message.content),
+ finish_reason=c.finish_reason
+ )
+ for c in response.choices
+ ]
+
+ usage = None
+ if response.usage:
+ usage = LLMUsage(
+ prompt_tokens=response.usage.prompt_tokens,
+ completion_tokens=response.usage.completion_tokens,
+ total_tokens=response.usage.total_tokens
+ )
+
+ return LLMResponse(
+ id=response.id,
+ model=response.model,
+ choices=choices,
+ usage=usage,
+ created=response.created
+ )
+
+ async def chat_completions_create_stream(
+ self,
+ messages: List[LLMMessage],
+ model: str,
+ temperature: float,
+ max_tokens: int,
+ timeout: int,
+ **kwargs
+ ) -> AsyncIterator[LLMResponse]:
+ """流式请求"""
+ api_messages = [
+ {"role": m.role, "content": m.content}
+ for m in messages
+ ]
+
+ stream = await self._client.chat.completions.create(
+ model=model,
+ messages=api_messages,
+ temperature=temperature,
+ max_tokens=max_tokens,
+ timeout=timeout,
+ stream=True,
+ **kwargs
+ )
+
+ async for chunk in stream:
+ if chunk.choices:
+ delta_content = chunk.choices[0].delta.content or ""
+ choices = [
+ LLMChoice(
+ index=0,
+ delta=LLMMessage(role="assistant", content=delta_content),
+ finish_reason=chunk.choices[0].finish_reason
+ )
+ ]
+ yield LLMResponse(
+ id=chunk.id,
+ model=chunk.model,
+ choices=choices,
+ created=chunk.created
+ )
+
+ def validate_connection(self) -> bool:
+ """验证 API Key 有效性"""
+ return bool(self.config.api_key)
+
+
+def create_openai_provider(
+ api_key: str,
+ model_name: str = "gpt-4o-mini",
+ base_url: str = None,
+ **kwargs
+) -> OpenAIProvider:
+ """工厂函数：创建 OpenAI 提供商"""
+ config = LLMConfig(
+ provider=LLMProviderType.OPENAI,
+ api_key=api_key,
+ model_name=model_name,
+ base_url=base_url,
+ **kwargs
+ )
+ return OpenAIProvider(config)
diff --git a/app/utils/repo_lock.py b/app/utils/repo_lock.py
new file mode 100644
index 0000000000000000000000000000000000000000..fcee19f1848524f8d2e05720d1e26ef87cb98a56
--- /dev/null
+++ b/app/utils/repo_lock.py
@@ -0,0 +1,390 @@
+# -*- coding: utf-8 -*-
+"""
+仓库级分布式锁
+
+解决问题:
+1. 同一仓库的并发写入竞争 (两人同时输入同一 URL)
+2. 重新分析时的数据一致性 (用户 A 重分析，用户 B 同时查询)
+
+设计原则:
+- 单进程: asyncio.Lock (内存锁)
+- 多进程: 文件锁 (fcntl/msvcrt)
+- 多节点: 可选 Redis 分布式锁 (生产环境)
+
+使用示例:
+```python
+async with RepoLock.acquire(session_id):
+ # 独占访问该仓库的写操作
+ await vector_store.reset()
+ await vector_store.add_documents(docs)
+```
+"""
+
+import asyncio
+import logging
+import os
+import time
+from abc import ABC, abstractmethod
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ============================================================
+# 锁配置
+# ============================================================
+
+@dataclass
+class LockConfig:
+ """锁配置"""
+ # 锁类型: "memory" | "file" | "redis"
+ backend: str = os.getenv("LOCK_BACKEND", "file")
+
+ # 文件锁目录
+ lock_dir: str = os.getenv("LOCK_DIR", "data/locks")
+
+ # Redis 配置 (可选)
+ redis_url: str = os.getenv("REDIS_URL", "redis://localhost:6379/0")
+
+ # 锁超时 (秒)
+ lock_timeout: float = float(os.getenv("LOCK_TIMEOUT", "300")) # 5分钟
+
+ # 等待超时 (秒)
+ acquire_timeout: float = float(os.getenv("LOCK_ACQUIRE_TIMEOUT", "60"))
+
+
+# ============================================================
+# 锁后端抽象
+# ============================================================
+
+class LockBackend(ABC):
+ """锁后端接口"""
+
+ @abstractmethod
+ async def acquire(self, key: str, timeout: float) -> bool:
+ """获取锁"""
+ pass
+
+ @abstractmethod
+ async def release(self, key: str) -> None:
+ """释放锁"""
+ pass
+
+ @abstractmethod
+ async def is_locked(self, key: str) -> bool:
+ """检查是否已锁定"""
+ pass
+
+
+# ============================================================
+# 内存锁 (单进程)
+# ============================================================
+
+class MemoryLockBackend(LockBackend):
+ """
+ 内存锁后端 (asyncio.Lock)
+
+ 适用于: 单 Worker 部署
+ """
+
+ def __init__(self):
+ self._locks: Dict[str, asyncio.Lock] = {}
+ self._meta_lock = asyncio.Lock()
+
+ async def _get_lock(self, key: str) -> asyncio.Lock:
+ async with self._meta_lock:
+ if key not in self._locks:
+ self._locks[key] = asyncio.Lock()
+ return self._locks[key]
+
+ async def acquire(self, key: str, timeout: float) -> bool:
+ lock = await self._get_lock(key)
+ try:
+ await asyncio.wait_for(lock.acquire(), timeout=timeout)
+ return True
+ except asyncio.TimeoutError:
+ return False
+
+ async def release(self, key: str) -> None:
+ if key in self._locks:
+ lock = self._locks[key]
+ if lock.locked():
+ lock.release()
+
+ async def is_locked(self, key: str) -> bool:
+ if key not in self._locks:
+ return False
+ return self._locks[key].locked()
+
+
+# ============================================================
+# 文件锁 (多进程，单节点)
+# ============================================================
+
+class FileLockBackend(LockBackend):
+ """
+ 文件锁后端
+
+ 适用于: 多 Worker 单节点部署 (Gunicorn + Qdrant Server)
+
+ 实现:
+ - Windows: msvcrt.locking
+ - Unix: fcntl.flock
+ """
+
+ def __init__(self, lock_dir: str):
+ self._lock_dir = Path(lock_dir)
+ self._lock_dir.mkdir(parents=True, exist_ok=True)
+ self._handles: Dict[str, object] = {}
+ self._memory_locks: Dict[str, asyncio.Lock] = {}
+ self._meta_lock = asyncio.Lock()
+
+ def _get_lock_path(self, key: str) -> Path:
+ # 清理 key，避免路径注入
+ safe_key = "".join(c if c.isalnum() or c in "_-" else "_" for c in key)
+ return self._lock_dir / f"{safe_key}.lock"
+
+ async def _get_memory_lock(self, key: str) -> asyncio.Lock:
+ """同进程内的内存锁，防止同一进程内多个协程竞争文件锁"""
+ async with self._meta_lock:
+ if key not in self._memory_locks:
+ self._memory_locks[key] = asyncio.Lock()
+ return self._memory_locks[key]
+
+ async def acquire(self, key: str, timeout: float) -> bool:
+ # 先获取内存锁
+ mem_lock = await self._get_memory_lock(key)
+ try:
+ await asyncio.wait_for(mem_lock.acquire(), timeout=timeout)
+ except asyncio.TimeoutError:
+ return False
+
+ # 再获取文件锁
+ lock_path = self._get_lock_path(key)
+ start_time = time.time()
+
+ while time.time() - start_time < timeout:
+ try:
+ # 尝试获取文件锁
+ handle = open(lock_path, 'w')
+
+ if os.name == 'nt':
+ # Windows
+ import msvcrt
+ msvcrt.locking(handle.fileno(), msvcrt.LK_NBLCK, 1)
+ else:
+ # Unix
+ import fcntl
+ fcntl.flock(handle.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+
+ self._handles[key] = handle
+ logger.debug(f"🔒 文件锁获取成功: {key}")
+ return True
+
+ except (IOError, OSError):
+ # 锁被占用，等待后重试
+ if 'handle' in dir() and handle:
+ handle.close()
+ await asyncio.sleep(0.1)
+
+ # 超时，释放内存锁
+ mem_lock.release()
+ logger.warning(f"⏰ 文件锁获取超时: {key}")
+ return False
+
+ async def release(self, key: str) -> None:
+ if key in self._handles:
+ handle = self._handles.pop(key)
+ try:
+ if os.name == 'nt':
+ import msvcrt
+ try:
+ msvcrt.locking(handle.fileno(), msvcrt.LK_UNLCK, 1)
+ except:
+ pass
+ else:
+ import fcntl
+ fcntl.flock(handle.fileno(), fcntl.LOCK_UN)
+ handle.close()
+ except:
+ pass
+ logger.debug(f"🔓 文件锁已释放: {key}")
+
+ # 释放内存锁
+ if key in self._memory_locks:
+ lock = self._memory_locks[key]
+ if lock.locked():
+ lock.release()
+
+ async def is_locked(self, key: str) -> bool:
+ lock_path = self._get_lock_path(key)
+ if not lock_path.exists():
+ return False
+
+ try:
+ handle = open(lock_path, 'w')
+ if os.name == 'nt':
+ import msvcrt
+ msvcrt.locking(handle.fileno(), msvcrt.LK_NBLCK, 1)
+ msvcrt.locking(handle.fileno(), msvcrt.LK_UNLCK, 1)
+ else:
+ import fcntl
+ fcntl.flock(handle.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB)
+ fcntl.flock(handle.fileno(), fcntl.LOCK_UN)
+ handle.close()
+ return False
+ except (IOError, OSError):
+ return True
+
+
+# ============================================================
+# Redis 锁 (分布式，多节点)
+# ============================================================
+
+class RedisLockBackend(LockBackend):
+ """
+ Redis 分布式锁后端
+
+ 适用于: 多节点部署 (K8s + Redis)
+
+ 依赖: redis[hiredis]
+ """
+
+ def __init__(self, redis_url: str, lock_timeout: float):
+ self._redis_url = redis_url
+ self._lock_timeout = lock_timeout
+ self._client = None
+ self._locks: Dict[str, object] = {}
+
+ async def _get_client(self):
+ if self._client is None:
+ try:
+ import redis.asyncio as aioredis
+ self._client = await aioredis.from_url(self._redis_url)
+ except ImportError:
+ raise RuntimeError(
+ "Redis 锁需要安装 redis 包: pip install redis[hiredis]"
+ )
+ return self._client
+
+ async def acquire(self, key: str, timeout: float) -> bool:
+ client = await self._get_client()
+ lock_key = f"repo_lock:{key}"
+
+ start_time = time.time()
+ while time.time() - start_time < timeout:
+ # 尝试设置锁
+ acquired = await client.set(
+ lock_key,
+ "locked",
+ nx=True,
+ ex=int(self._lock_timeout)
+ )
+ if acquired:
+ logger.debug(f"🔒 Redis 锁获取成功: {key}")
+ return True
+ await asyncio.sleep(0.1)
+
+ logger.warning(f"⏰ Redis 锁获取超时: {key}")
+ return False
+
+ async def release(self, key: str) -> None:
+ client = await self._get_client()
+ lock_key = f"repo_lock:{key}"
+ await client.delete(lock_key)
+ logger.debug(f"🔓 Redis 锁已释放: {key}")
+
+ async def is_locked(self, key: str) -> bool:
+ client = await self._get_client()
+ lock_key = f"repo_lock:{key}"
+ return await client.exists(lock_key) > 0
+
+
+# ============================================================
+# 统一锁接口
+# ============================================================
+
+class RepoLock:
+ """
+ 仓库级锁 - 统一接口
+
+ 自动根据配置选择后端:
+ - memory: 单进程内存锁 (开发)
+ - file: 文件锁 (多进程单节点)
+ - redis: 分布式锁 (多节点)
+
+ 使用:
+ ```python
+ async with RepoLock.acquire(session_id):
+ # 独占写操作
+ await store.reset()
+ ```
+ """
+
+ _backend: Optional[LockBackend] = None
+ _config: Optional[LockConfig] = None
+
+ @classmethod
+ def _get_backend(cls) -> LockBackend:
+ if cls._backend is None:
+ cls._config = LockConfig()
+
+ if cls._config.backend == "redis":
+ cls._backend = RedisLockBackend(
+ cls._config.redis_url,
+ cls._config.lock_timeout
+ )
+ logger.info("🔐 使用 Redis 分布式锁")
+ elif cls._config.backend == "file":
+ cls._backend = FileLockBackend(cls._config.lock_dir)
+ logger.info(f"🔐 使用文件锁: {cls._config.lock_dir}")
+ else:
+ cls._backend = MemoryLockBackend()
+ logger.info("🔐 使用内存锁 (单进程)")
+
+ return cls._backend
+
+ @classmethod
+ @asynccontextmanager
+ async def acquire(cls, session_id: str, timeout: float = None):
+ """
+ 获取仓库写锁
+
+ Args:
+ session_id: 仓库的 session ID
+ timeout: 获取锁的超时时间 (默认从配置读取)
+
+ Raises:
+ TimeoutError: 获取锁超时
+ """
+ backend = cls._get_backend()
+ config = cls._config or LockConfig()
+ wait_timeout = timeout or config.acquire_timeout
+
+ acquired = await backend.acquire(session_id, wait_timeout)
+ if not acquired:
+ raise TimeoutError(f"无法获取仓库锁: {session_id} (等待 {wait_timeout}s)")
+
+ try:
+ yield
+ finally:
+ await backend.release(session_id)
+
+ @classmethod
+ async def is_locked(cls, session_id: str) -> bool:
+ """检查仓库是否被锁定"""
+ backend = cls._get_backend()
+ return await backend.is_locked(session_id)
+
+ @classmethod
+ async def try_acquire(cls, session_id: str, timeout: float = 0.1):
+ """
+ 尝试获取锁 (非阻塞)
+
+ 用于检测是否有其他用户正在分析同一仓库
+ """
+ backend = cls._get_backend()
+ return await backend.acquire(session_id, timeout)
diff --git a/app/utils/retry.py b/app/utils/retry.py
new file mode 100644
index 0000000000000000000000000000000000000000..5658a61ac40cc9770feaf8f2bb73213ea18fac9a
--- /dev/null
+++ b/app/utils/retry.py
@@ -0,0 +1,198 @@
+# 文件路径: app/utils/retry.py
+"""
+LLM 调用重试机制
+
+使用 tenacity 库实现智能重试策略:
+- 指数退避 (Exponential Backoff)
+- 可重试异常识别
+- 最大重试次数限制
+- 详细日志记录
+"""
+
+import logging
+from typing import Callable, Type, Tuple, Any
+from functools import wraps
+
+from tenacity import (
+ retry,
+ stop_after_attempt,
+ wait_exponential,
+ retry_if_exception_type,
+ before_sleep_log,
+ after_log,
+ RetryError,
+)
+
+# 配置日志
+logger = logging.getLogger("llm_retry")
+logger.setLevel(logging.INFO)
+
+
+# ============================================================================
+# 可重试的异常类型定义
+# ============================================================================
+
+# 网络/临时性错误 - 应该重试
+RETRYABLE_EXCEPTIONS: Tuple[Type[Exception], ...] = (
+ ConnectionError,
+ TimeoutError,
+)
+
+# 尝试导入各 SDK 的异常类型
+try:
+ from openai import (
+ APIConnectionError,
+ APITimeoutError,
+ RateLimitError,
+ InternalServerError,
+ )
+ RETRYABLE_EXCEPTIONS = RETRYABLE_EXCEPTIONS + (
+ APIConnectionError,
+ APITimeoutError,
+ RateLimitError,
+ InternalServerError,
+ )
+except ImportError:
+ pass
+
+try:
+ from anthropic import (
+ APIConnectionError as AnthropicConnectionError,
+ APITimeoutError as AnthropicTimeoutError,
+ RateLimitError as AnthropicRateLimitError,
+ InternalServerError as AnthropicServerError,
+ )
+ RETRYABLE_EXCEPTIONS = RETRYABLE_EXCEPTIONS + (
+ AnthropicConnectionError,
+ AnthropicTimeoutError,
+ AnthropicRateLimitError,
+ AnthropicServerError,
+ )
+except ImportError:
+ pass
+
+try:
+ import httpx
+ RETRYABLE_EXCEPTIONS = RETRYABLE_EXCEPTIONS + (
+ httpx.ConnectError,
+ httpx.ReadTimeout,
+ httpx.ConnectTimeout,
+ )
+except ImportError:
+ pass
+
+
+# ============================================================================
+# 重试配置
+# ============================================================================
+
+class RetryConfig:
+ """重试配置"""
+ MAX_ATTEMPTS: int = 3 # 最大重试次数
+ MIN_WAIT_SECONDS: float = 1.0 # 最小等待时间
+ MAX_WAIT_SECONDS: float = 30.0 # 最大等待时间
+ EXPONENTIAL_MULTIPLIER: float = 2.0 # 指数退避乘数
+
+
+# ============================================================================
+# 重试装饰器
+# ============================================================================
+
+def create_retry_decorator(
+ max_attempts: int = RetryConfig.MAX_ATTEMPTS,
+ min_wait: float = RetryConfig.MIN_WAIT_SECONDS,
+ max_wait: float = RetryConfig.MAX_WAIT_SECONDS,
+):
+ """
+ 创建 LLM 调用重试装饰器
+
+ Args:
+ max_attempts: 最大重试次数
+ min_wait: 最小等待时间 (秒)
+ max_wait: 最大等待时间 (秒)
+
+ Returns:
+ tenacity retry 装饰器
+ """
+ return retry(
+ # 重试条件: 仅对可重试异常进行重试
+ retry=retry_if_exception_type(RETRYABLE_EXCEPTIONS),
+ # 停止条件: 达到最大重试次数
+ stop=stop_after_attempt(max_attempts),
+ # 等待策略: 指数退避
+ wait=wait_exponential(
+ multiplier=RetryConfig.EXPONENTIAL_MULTIPLIER,
+ min=min_wait,
+ max=max_wait,
+ ),
+ # 日志: 重试前记录
+ before_sleep=before_sleep_log(logger, logging.WARNING),
+ # 日志: 重试后记录
+ after=after_log(logger, logging.DEBUG),
+ # 重新抛出最后一个异常
+ reraise=True,
+ )
+
+
+# 默认的重试装饰器实例
+llm_retry = create_retry_decorator()
+
+
+def with_retry(func: Callable) -> Callable:
+ """
+ 为异步函数添加重试能力的装饰器
+
+ Usage:
+ @with_retry
+ async def call_llm(...):
+ ...
+ """
+ @wraps(func)
+ async def wrapper(*args, **kwargs):
+ @llm_retry
+ async def _inner():
+ return await func(*args, **kwargs)
+ return await _inner()
+ return wrapper
+
+
+# ============================================================================
+# 便捷函数
+# ============================================================================
+
+async def retry_async(
+ coro_func: Callable,
+ *args,
+ max_attempts: int = RetryConfig.MAX_ATTEMPTS,
+ **kwargs
+) -> Any:
+ """
+ 带重试的异步调用
+
+ Usage:
+ result = await retry_async(
+ client.chat.completions.create,
+ model="gpt-4",
+ messages=[...]
+ )
+ """
+ decorator = create_retry_decorator(max_attempts=max_attempts)
+
+ @decorator
+ async def _call():
+ return await coro_func(*args, **kwargs)
+
+ return await _call()
+
+
+def is_retryable_error(error: Exception) -> bool:
+ """判断异常是否可重试"""
+ return isinstance(error, RETRYABLE_EXCEPTIONS)
+
+
+def log_retry_info(attempt: int, max_attempts: int, error: Exception, wait_time: float):
+ """记录重试信息的辅助函数"""
+ logger.warning(
+ f"🔄 LLM 调用失败 (尝试 {attempt}/{max_attempts}): {type(error).__name__}: {error}. "
+ f"等待 {wait_time:.1f}s 后重试..."
+ )
diff --git a/app/utils/session.py b/app/utils/session.py
new file mode 100644
index 0000000000000000000000000000000000000000..562aca67efda40ebcb20867bb083f6e5f45dba00
--- /dev/null
+++ b/app/utils/session.py
@@ -0,0 +1,230 @@
+# -*- coding: utf-8 -*-
+"""
+Session 工具模块
+
+提供基于仓库 URL 的 Session ID 生成和管理
+"""
+
+import hashlib
+import re
+from typing import Optional, Tuple, Dict
+from urllib.parse import urlparse
+
+from app.core.config import conversation_config
+
+
+def normalize_repo_url(url: str) -> str:
+ """
+ 标准化 GitHub 仓库 URL
+
+ 支持格式:
+ - https://github.com/owner/repo
+ - https://github.com/owner/repo.git
+ - https://github.com/owner/repo/tree/main
+ - git@github.com:owner/repo.git
+
+ Returns:
+ 标准化的 URL: https://github.com/owner/repo (全小写)
+ """
+ url = url.strip().lower() # 统一转为小写
+
+ # 处理 SSH 格式
+ if url.startswith('git@'):
+ # git@github.com:owner/repo.git -> https://github.com/owner/repo
+ match = re.match(r'git@github\.com:(.+?)(?:\.git)?$', url)
+ if match:
+ return f"https://github.com/{match.group(1)}"
+
+ # 处理 HTTPS 格式
+ parsed = urlparse(url)
+ path = parsed.path.strip('/')
+
+ # 移除 .git 后缀
+ if path.endswith('.git'):
+ path = path[:-4]
+
+ # 只保留 owner/repo 部分
+ parts = path.split('/')
+ if len(parts) >= 2:
+ path = f"{parts[0]}/{parts[1]}"
+
+ return f"https://github.com/{path}"
+
+
+def extract_repo_info(url: str) -> Tuple[str, str]:
+ """
+ 从 URL 提取仓库信息
+
+ Returns:
+ (owner, repo) 元组
+ """
+ normalized = normalize_repo_url(url)
+ path = urlparse(normalized).path.strip('/')
+ parts = path.split('/')
+
+ if len(parts) >= 2:
+ return parts[0], parts[1]
+ return "", ""
+
+
+def generate_repo_session_id(repo_url: str) -> str:
+ """
+ 基于仓库 URL 生成稳定的 Session ID
+
+ 同一仓库 URL -> 同一 Session ID
+
+ 格式: repo_{short_hash}_{owner}_{repo}
+ """
+ normalized = normalize_repo_url(repo_url)
+ owner, repo = extract_repo_info(repo_url)
+
+ # 生成短 hash (8 字符)
+ url_hash = hashlib.sha256(normalized.encode()).hexdigest()[:8]
+
+ # 清理 owner 和 repo 名称
+ clean_owner = re.sub(r'[^a-zA-Z0-9]', '', owner)[:10]
+ clean_repo = re.sub(r'[^a-zA-Z0-9]', '', repo)[:15]
+
+ return f"repo_{url_hash}_{clean_owner}_{clean_repo}"
+
+
+def is_repo_session_id(session_id: str) -> bool:
+ """判断是否为仓库级 Session ID"""
+ return session_id.startswith("repo_")
+
+
+# === 对话历史管理 ===
+
+class ConversationMemory:
+ """
+ 对话记忆管理 - 滑动窗口 + 摘要压缩
+
+ 特性:
+ 1. 保留最近 N 轮完整对话
+ 2. 早期对话自动压缩为摘要
+ 3. 支持 token 估算
+ """
+
+ def __init__(
+ self,
+ max_recent_turns: int = None,
+ max_context_tokens: int = None,
+ summary_threshold: int = None,
+ ):
+ # 使用统一配置
+ self.max_recent_turns = max_recent_turns or conversation_config.max_recent_turns
+ self.max_context_tokens = max_context_tokens or conversation_config.max_context_tokens
+ self.summary_threshold = summary_threshold or conversation_config.summary_threshold
+
+ self._messages: list = [] # 完整消息历史
+ self._summary: Optional[str] = None # 早期对话摘要
+ self._summary_up_to: int = 0 # 摘要覆盖到第 N 条消息
+
+ def add_message(self, role: str, content: str) -> None:
+ """添加消息"""
+ self._messages.append({
+ "role": role,
+ "content": content
+ })
+
+ def add_user_message(self, content: str) -> None:
+ """添加用户消息"""
+ self.add_message("user", content)
+
+ def add_assistant_message(self, content: str) -> None:
+ """添加助手消息"""
+ self.add_message("assistant", content)
+
+ def get_context_messages(self) -> list:
+ """
+ 获取用于 LLM 的上下文消息
+
+ 策略:
+ 1. 如果消息数 <= max_recent_turns * 2，返回全部
+ 2. 否则返回: [摘要] + 最近 N 轮
+ """
+ total_messages = len(self._messages)
+ max_messages = self.max_recent_turns * 2 # user + assistant = 1 轮
+
+ if total_messages <= max_messages:
+ return list(self._messages)
+
+ # 需要截断
+ recent_messages = self._messages[-max_messages:]
+
+ # 如果有摘要，加在前面
+ if self._summary:
+ return [
+ {"role": "system", "content": f"[Earlier conversation summary]\n{self._summary}"}
+ ] + recent_messages
+
+ return recent_messages
+
+ def needs_summarization(self) -> bool:
+ """检查是否需要生成摘要"""
+ unsummarized = len(self._messages) - self._summary_up_to
+ return unsummarized > self.summary_threshold * 2
+
+ def get_messages_to_summarize(self) -> list:
+ """获取需要摘要的消息"""
+ if not self.needs_summarization():
+ return []
+
+ # 保留最近的，摘要早期的
+ end_idx = len(self._messages) - self.max_recent_turns * 2
+ return self._messages[self._summary_up_to:end_idx]
+
+ def set_summary(self, summary: str, up_to_index: int) -> None:
+ """设置摘要"""
+ if self._summary:
+ # 合并旧摘要
+ self._summary = f"{self._summary}\n\n{summary}"
+ else:
+ self._summary = summary
+ self._summary_up_to = up_to_index
+
+ def clear(self) -> None:
+ """清空对话历史"""
+ self._messages = []
+ self._summary = None
+ self._summary_up_to = 0
+
+ def get_turn_count(self) -> int:
+ """获取对话轮数"""
+ return len(self._messages) // 2
+
+ def get_stats(self) -> dict:
+ """获取统计信息"""
+ return {
+ "total_messages": len(self._messages),
+ "turn_count": self.get_turn_count(),
+ "has_summary": self._summary is not None,
+ "summary_covers": self._summary_up_to,
+ }
+
+
+# === 全局对话记忆存储 ===
+# key: session_id, value: ConversationMemory
+# 纯内存存储，服务重启自动清空
+_conversation_memories: Dict[str, ConversationMemory] = {}
+
+
+def get_conversation_memory(session_id: str) -> ConversationMemory:
+ """获取或创建对话记忆"""
+ if session_id not in _conversation_memories:
+ _conversation_memories[session_id] = ConversationMemory()
+ return _conversation_memories[session_id]
+
+
+def clear_conversation_memory(session_id: str) -> None:
+ """清除对话记忆"""
+ if session_id in _conversation_memories:
+ del _conversation_memories[session_id]
+
+
+def get_memory_stats() -> dict:
+ """获取对话记忆统计"""
+ return {
+ "total_memories": len(_conversation_memories),
+ "sessions": list(_conversation_memories.keys()),
+ }
diff --git a/deploy.sh b/deploy.sh
new file mode 100644
index 0000000000000000000000000000000000000000..253eb5a08ebfd7cdee5e7b552419671cf7fae5ea
--- /dev/null
+++ b/deploy.sh
@@ -0,0 +1,143 @@
+#!/bin/bash
+# ============================================================
+# GitHub RAG Agent - 生产环境部署脚本 (2核2G服务器优化版)
+# ============================================================
+#
+# 使用方法:
+# chmod +x deploy.sh
+# ./deploy.sh
+#
+# 前置要求:
+# - Python 3.10+
+# - Docker (用于运行 Qdrant)
+#
+# ============================================================
+
+set -e
+
+echo "🚀 GitHub RAG Agent 部署脚本"
+echo "=========================================="
+
+# 检查是否在项目目录
+if [ ! -f "requirements.txt" ]; then
+ echo "❌ 请在项目根目录运行此脚本"
+ exit 1
+fi
+
+# 检查 .env 文件
+if [ ! -f ".env" ]; then
+ echo "❌ 未找到 .env 文件，请先复制 .env.example 并配置"
+ echo " cp .env.example .env"
+ echo " vim .env"
+ exit 1
+fi
+
+# ============================================================
+# 1. 启动 Qdrant Server (Docker)
+# ============================================================
+echo ""
+echo "📦 步骤 1: 启动 Qdrant Server..."
+
+# 检查 Docker 是否运行
+if ! docker info > /dev/null 2>&1; then
+ echo "❌ Docker 未运行，请先启动 Docker"
+ exit 1
+fi
+
+# 检查 Qdrant 容器是否已存在
+if docker ps -a --format '{{.Names}}' | grep -q "^qdrant-server$"; then
+ echo " Qdrant 容器已存在，检查状态..."
+ if docker ps --format '{{.Names}}' | grep -q "^qdrant-server$"; then
+ echo " ✅ Qdrant 已在运行"
+ else
+ echo " 🔄 启动已有的 Qdrant 容器..."
+ docker start qdrant-server
+ fi
+else
+ echo " 🆕 创建并启动 Qdrant 容器 (内存限制 512MB)..."
+ docker run -d \
+ --name qdrant-server \
+ --restart unless-stopped \
+ -p 6333:6333 \
+ -p 6334:6334 \
+ -v qdrant_data:/qdrant/storage \
+ -m 512m \
+ -e QDRANT__STORAGE__ON_DISK_PAYLOAD=true \
+ qdrant/qdrant:latest
+fi
+
+# 等待 Qdrant 就绪
+echo " ⏳ 等待 Qdrant 就绪..."
+for i in {1..30}; do
+ if curl -s http://localhost:6333/health > /dev/null 2>&1; then
+ echo " ✅ Qdrant 已就绪"
+ break
+ fi
+ sleep 1
+done
+
+# ============================================================
+# 2. 创建 Python 虚拟环境
+# ============================================================
+echo ""
+echo "🐍 步骤 2: 配置 Python 环境..."
+
+if [ ! -d "venv" ]; then
+ echo " 创建虚拟环境..."
+ python3 -m venv venv
+fi
+
+echo " 激活虚拟环境..."
+source venv/bin/activate
+
+echo " 安装依赖..."
+pip install -q --upgrade pip
+pip install -q -r requirements.txt
+
+# ============================================================
+# 3. 创建必要目录
+# ============================================================
+echo ""
+echo "📁 步骤 3: 创建数据目录..."
+mkdir -p data/locks
+mkdir -p data/contexts
+mkdir -p logs
+
+# ============================================================
+# 4. 设置环境变量
+# ============================================================
+echo ""
+echo "⚙️ 步骤 4: 配置环境变量..."
+
+# 从 .env 加载
+set -a
+source .env
+set +a
+
+# 设置 Server 模式
+export QDRANT_MODE=server
+export QDRANT_URL=http://localhost:6333
+export LOCK_BACKEND=file
+export LOCK_DIR=data/locks
+export GUNICORN_WORKERS=2
+
+echo " QDRANT_MODE=$QDRANT_MODE"
+echo " QDRANT_URL=$QDRANT_URL"
+echo " GUNICORN_WORKERS=$GUNICORN_WORKERS"
+
+# ============================================================
+# 5. 启动应用
+# ============================================================
+echo ""
+echo "🌐 步骤 5: 启动 FastAPI 应用..."
+echo "=========================================="
+echo " Workers: 2 (优化2核CPU)"
+echo " 监听地址: 0.0.0.0:8000"
+echo " Qdrant: http://localhost:6333"
+echo "=========================================="
+echo ""
+echo " 按 Ctrl+C 停止服务"
+echo ""
+
+# 使用 Gunicorn 启动 (2 Workers)
+gunicorn app.main:app -c gunicorn_conf.py
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000000000000000000000000000000000000..3d90b4fca0db8f9fdea19babc6d1b184b776d8c8
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,102 @@
+# Docker Compose 配置 - 生产环境部署 (优化版: 2核2G服务器)
+# 包含: FastAPI 应用 + Qdrant Server
+
+version: '3.8'
+
+services:
+ # ============================================================
+ # Qdrant 向量数据库 (限制内存 512MB)
+ # ============================================================
+ qdrant:
+ image: qdrant/qdrant:latest
+ container_name: github-rag-qdrant
+ restart: unless-stopped
+ ports:
+ - "6333:6333" # REST API
+ - "6334:6334" # gRPC
+ volumes:
+ - qdrant_data:/qdrant/storage
+ environment:
+ - QDRANT__SERVICE__GRPC_PORT=6334
+ - QDRANT__STORAGE__ON_DISK_PAYLOAD=true # Payload 存磁盘，省内存
+ deploy:
+ resources:
+ limits:
+ memory: 512M
+ reservations:
+ memory: 256M
+ healthcheck:
+ test: ["CMD", "curl", "-f", "http://localhost:6333/health"]
+ interval: 30s
+ timeout: 10s
+ retries: 3
+
+ # ============================================================
+ # FastAPI 应用 (2 Workers, 限制内存 1GB)
+ # ============================================================
+ app:
+ build:
+ context: .
+ dockerfile: Dockerfile
+ container_name: github-rag-app
+ restart: unless-stopped
+ ports:
+ - "8000:8000"
+ environment:
+ # Qdrant Server 模式
+ - QDRANT_MODE=server
+ - QDRANT_URL=http://qdrant:6333
+
+ # Worker 数量 (2核服务器建议2个)
+ - GUNICORN_WORKERS=2
+
+ # 文件锁 (多 Worker)
+ - LOCK_BACKEND=file
+ - LOCK_DIR=/app/data/locks
+
+ # LLM 配置 (从 .env 读取)
+ - LLM_PROVIDER=${LLM_PROVIDER:-deepseek}
+ - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY}
+ - OPENAI_API_KEY=${OPENAI_API_KEY}
+ - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
+ - GEMINI_API_KEY=${GEMINI_API_KEY}
+ - SILICON_API_KEY=${SILICON_API_KEY}
+ - GITHUB_TOKEN=${GITHUB_TOKEN}
+ volumes:
+ - app_data:/app/data
+ - app_logs:/app/logs
+ deploy:
+ resources:
+ limits:
+ memory: 1G
+ reservations:
+ memory: 512M
+ depends_on:
+ qdrant:
+ condition: service_healthy
+ healthcheck:
+ test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
+ interval: 30s
+ timeout: 10s
+ retries: 3
+
+volumes:
+ qdrant_data:
+ driver: local
+ app_data:
+ driver: local
+ app_logs:
+ driver: local
+
+# ============================================================
+# 使用说明
+# ============================================================
+# 1. 复制 .env.example 为 .env 并配置 API Keys
+# 2. 启动服务: docker-compose up -d
+# 3. 查看日志: docker-compose logs -f app
+# 4. 停止服务: docker-compose down
+#
+# 扩展到多 Worker:
+# 修改 Dockerfile 中的 gunicorn workers 数量，或使用:
+# docker-compose up -d --scale app=3
+# 配合 Nginx/Traefik 做负载均衡
diff --git a/evaluation/__init__.py b/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d9e1b472ee75a19bd18836308c53646468559327
--- /dev/null
+++ b/evaluation/__init__.py
@@ -0,0 +1,64 @@
+# evaluation/__init__.py
+"""
+Evaluation 模块
+
+提供完整的评估框架，包括：
+- 数据模型 (models.py)
+- 评估引擎 (evaluation_framework.py)
+- 数据路由 (data_router.py)
+- 工具函数 (utils.py)
+- 数据分析 (analyze_eval_results.py)
+- 数据清洗 (clean_and_export_sft_data.py)
+
+使用示例:
+ from evaluation import EvaluationEngine, DataRoutingEngine, EvaluationResult
+ from evaluation.models import GenerationMetrics
+"""
+
+# 核心导出
+from evaluation.models import (
+ EvaluationLayer,
+ DataQualityTier,
+ QueryRewriteMetrics,
+ RetrievalMetrics,
+ GenerationMetrics,
+ AgenticMetrics,
+ EvaluationResult,
+)
+
+from evaluation.data_router import DataRoutingEngine
+from evaluation.evaluation_framework import EvaluationEngine
+
+# 工具函数
+from evaluation.utils import (
+ is_chatty_query,
+ has_code_indicators,
+ read_jsonl,
+ append_jsonl,
+ safe_truncate,
+ smart_truncate,
+ SFTLengthConfig,
+)
+
+__all__ = [
+ # 枚举
+ "EvaluationLayer",
+ "DataQualityTier",
+ # 数据模型
+ "QueryRewriteMetrics",
+ "RetrievalMetrics",
+ "GenerationMetrics",
+ "AgenticMetrics",
+ "EvaluationResult",
+ # 引擎
+ "EvaluationEngine",
+ "DataRoutingEngine",
+ # 工具函数
+ "is_chatty_query",
+ "has_code_indicators",
+ "read_jsonl",
+ "append_jsonl",
+ "safe_truncate",
+ "smart_truncate",
+ "SFTLengthConfig",
+]
diff --git a/evaluation/analyze_eval_results.py b/evaluation/analyze_eval_results.py
new file mode 100644
index 0000000000000000000000000000000000000000..b43ed42ec45bbe9fa686fbfe801ac5009c9a364c
--- /dev/null
+++ b/evaluation/analyze_eval_results.py
@@ -0,0 +1,379 @@
+# 文件路径: evaluation/analyze_eval_results.py
+"""
+自动化数据分析脚本
+用于分析评估结果，识别问题并生成诊断报告
+
+核心功能:
+1. 自动读取所有评估结果
+2. 按问题类型分类 Bad Case
+3. 生成可视化报告
+4. 推荐优化方向
+
+Author: Dexter
+Date: 2025-01-27
+"""
+
+import os
+from typing import Dict, List
+from collections import Counter, defaultdict
+from datetime import datetime
+
+from evaluation.utils import read_jsonl
+
+
+class EvaluationAnalyzer:
+ """评估结果分析器"""
+
+ def __init__(self, eval_results_file: str = "evaluation/sft_data/eval_results.jsonl"):
+ self.eval_results_file = eval_results_file
+ self.results: List[Dict] = read_jsonl(eval_results_file)
+ if not self.results:
+ print(f"⚠️ No results loaded from: {eval_results_file}")
+
+ def get_basic_stats(self) -> Dict:
+ """获取基本统计"""
+ if not self.results:
+ return {}
+
+ scores = [r.get("overall_score", 0) for r in self.results]
+ tiers = [r.get("data_quality_tier", "unknown") for r in self.results]
+
+ return {
+ "total_evaluations": len(self.results),
+ "avg_score": sum(scores) / len(scores) if scores else 0,
+ "max_score": max(scores) if scores else 0,
+ "min_score": min(scores) if scores else 0,
+ "median_score": sorted(scores)[len(scores)//2] if scores else 0,
+ "quality_distribution": dict(Counter(tiers)),
+ "sft_ready_count": sum(1 for r in self.results if r.get("sft_ready", False))
+ }
+
+ def identify_bad_cases(self, threshold: float = 0.6) -> List[Dict]:
+ """
+ 识别 Bad Case (得分低于阈值的结果)
+ 返回按得分排序的结果
+ """
+ bad_cases = [r for r in self.results if r.get("overall_score", 1) < threshold]
+ return sorted(bad_cases, key=lambda x: x.get("overall_score", 1))
+
+ def categorize_failures(self) -> Dict[str, List[Dict]]:
+ """
+ 按失败原因分类 Bad Case
+
+ 失败类型:
+ - retrieval_failure: 检索未命中
+ - generation_hallucination: 生成幻觉
+ - generation_incomplete: 回答不完整
+ - tool_call_error: 工具调用失败
+ """
+ categorized = defaultdict(list)
+
+ for result in self.identify_bad_cases():
+ reasons = []
+
+ # 检查检索失败
+ if result.get("retrieval"):
+ retrieval = result["retrieval"]
+ if retrieval.get("hit_rate", 1) == 0:
+ reasons.append("retrieval_failure")
+ elif retrieval.get("recall_at_k", 1) < 0.5:
+ reasons.append("retrieval_low_recall")
+
+ # 检查生成问题
+ if result.get("generation"):
+ generation = result["generation"]
+ if generation.get("faithfulness", 1) < 0.5:
+ reasons.append("generation_hallucination")
+ if generation.get("answer_completeness", 1) < 0.4:
+ reasons.append("generation_incomplete")
+ if generation.get("hallucination_count", 0) > 0:
+ reasons.append("hallucination_detected")
+
+ # 检查Agent行为
+ if result.get("agentic"):
+ agentic = result["agentic"]
+ if not agentic.get("success", True):
+ reasons.append("agentic_failure")
+
+ # 如果没有具体原因,标记为unknown
+ if not reasons:
+ reasons.append("unknown")
+
+ for reason in reasons:
+ categorized[reason].append(result)
+
+ return dict(categorized)
+
+ def layer_performance(self) -> Dict[str, Dict]:
+ """分析各层性能"""
+ layer_scores = defaultdict(list)
+
+ for result in self.results:
+ if result.get("query_rewrite"):
+ score = result["query_rewrite"].get("overall_score", 0)
+ if score:
+ layer_scores["query_rewrite"].append(score)
+
+ if result.get("retrieval"):
+ score = result["retrieval"].get("overall_score", 0)
+ if score:
+ layer_scores["retrieval"].append(score)
+
+ if result.get("generation"):
+ score = result["generation"].get("overall_score", 0)
+ if score:
+ layer_scores["generation"].append(score)
+
+ if result.get("agentic"):
+ score = result["agentic"].get("overall_score", 0)
+ if score:
+ layer_scores["agentic"].append(score)
+
+ # 计算每层的统计
+ layer_stats = {}
+ for layer, scores in layer_scores.items():
+ if scores:
+ layer_stats[layer] = {
+ "avg": sum(scores) / len(scores),
+ "min": min(scores),
+ "max": max(scores),
+ "count": len(scores)
+ }
+
+ return layer_stats
+
+ def get_recommendations(self) -> List[str]:
+ """基于分析结果生成优化建议"""
+ recommendations = []
+
+ # 分析各层性能
+ layer_perf = self.layer_performance()
+
+ # 检索层分析
+ if "retrieval" in layer_perf:
+ retrieval_score = layer_perf["retrieval"]["avg"]
+ if retrieval_score < 0.7:
+ recommendations.append(
+ "🔴 RETRIEVAL 层性能差 (avg: {:.2f})\n"
+ " 建议:\n"
+ " 1. 检查 chunking 策略是否过度分割\n"
+ " 2. 优化 embedding 模型 (考虑更强的模型)\n"
+ " 3. 调整混合检索的权重 (BM25 vs Vector)\n"
+ " 4. 分析实际召回的文件,看是否与预期偏离".format(retrieval_score)
+ )
+
+ # 生成层分析
+ if "generation" in layer_perf:
+ gen_score = layer_perf["generation"]["avg"]
+ if gen_score < 0.7:
+ recommendations.append(
+ "🟡 GENERATION 层存在问题 (avg: {:.2f})\n"
+ " 建议:\n"
+ " 1. 检查 Prompt 是否清晰 (可能LLM理解偏差)\n"
+ " 2. 检查是否存在幻觉 (生成不存在的函数名等)\n"
+ " 3. 优化 Context 的组织方式\n"
+ " 4. 考虑使用更强的LLM模型".format(gen_score)
+ )
+
+ # Query Rewrite 分析
+ if "query_rewrite" in layer_perf:
+ rewrite_score = layer_perf["query_rewrite"]["avg"]
+ if rewrite_score < 0.6:
+ recommendations.append(
+ "🟠 QUERY_REWRITE 层准确度低 (avg: {:.2f})\n"
+ " 建议:\n"
+ " 1. 优化关键词提取 Prompt\n"
+ " 2. 增加多语言处理支持\n"
+ " 3. 添加领域词汇表 (Domain Vocabulary)".format(rewrite_score)
+ )
+
+ # 通用建议
+ stats = self.get_basic_stats()
+ if stats.get("sft_ready_count", 0) / max(stats.get("total_evaluations", 1), 1) < 0.5:
+ recommendations.append(
+ "⚠️ SFT 可用数据不足 (< 50%)\n"
+ " 立即行动:\n"
+ " 1. 运行 continuous_eval 脚本收集更多数据\n"
+ " 2. 对现有数据进行自纠正 (Self-Correction)\n"
+ " 3. 扩展黄金数据集来改进模型"
+ )
+
+ return recommendations
+
+ def generate_report(self, output_file: str = "evaluation/analysis_report.md") -> str:
+ """生成完整的分析报告"""
+
+ report = []
+ report.append("# 📊 GitHub Agent 评估分析报告\n")
+ report.append(f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
+ report.append("---\n")
+
+ # 1. 基本统计
+ stats = self.get_basic_stats()
+ report.append("## 📈 基本统计\n")
+ report.append(f"- 总评估次数: {stats.get('total_evaluations', 0)}\n")
+ report.append(f"- 平均得分: {stats.get('avg_score', 0):.3f}\n")
+ report.append(f"- 最高得分: {stats.get('max_score', 0):.3f}\n")
+ report.append(f"- 最低得分: {stats.get('min_score', 0):.3f}\n")
+ report.append(f"- 中位数得分: {stats.get('median_score', 0):.3f}\n")
+ report.append(f"- SFT 可用样本: {stats.get('sft_ready_count', 0)}\n\n")
+
+ # 2. 质量分级分布
+ report.append("## 🏆 质量分级分布\n")
+ distribution = stats.get("quality_distribution", {})
+ for tier, count in sorted(distribution.items()):
+ percentage = (count / stats.get('total_evaluations', 1)) * 100
+ report.append(f"- {tier.upper()}: {count} ({percentage:.1f}%)\n")
+ report.append("\n")
+
+ # 3. 各层性能
+ report.append("## 🎯 各层性能分析\n\n")
+ layer_perf = self.layer_performance()
+ for layer in ["query_rewrite", "retrieval", "generation", "agentic"]:
+ if layer in layer_perf:
+ perf = layer_perf[layer]
+ report.append(f"### {layer.upper()}\n")
+ report.append(f"- 平均得分: {perf['avg']:.3f}\n")
+ report.append(f"- 范围: [{perf['min']:.3f}, {perf['max']:.3f}]\n")
+ report.append(f"- 样本数: {perf['count']}\n\n")
+
+ # 4. Bad Case 分类
+ report.append("## 🔴 Bad Case 分析\n\n")
+ failures = self.categorize_failures()
+ for reason, cases in sorted(failures.items(), key=lambda x: -len(x[1])):
+ report.append(f"### {reason} ({len(cases)} cases)\n")
+ for case in cases[:3]: # 显示top 3
+ report.append(f"- 查询: {case.get('query', 'N/A')[:60]}...\n")
+ report.append(f" 得分: {case.get('overall_score', 0):.3f}\n")
+ report.append("\n")
+
+ # 5. 推荐行动
+ report.append("## 💡 优化建议\n\n")
+ recommendations = self.get_recommendations()
+ for i, rec in enumerate(recommendations, 1):
+ report.append(f"{i}. {rec}\n\n")
+
+ # 写入文件
+ os.makedirs(os.path.dirname(output_file), exist_ok=True)
+ with open(output_file, 'w', encoding='utf-8') as f:
+ f.writelines(report)
+
+ return "".join(report)
+
+ def export_bad_cases_csv(self, output_file: str = "evaluation/bad_cases.csv") -> None:
+ """导出 Bad Case 为 CSV (用于人工审查)"""
+ import csv
+
+ bad_cases = self.identify_bad_cases()
+
+ with open(output_file, 'w', newline='', encoding='utf-8') as f:
+ writer = csv.DictWriter(f, fieldnames=[
+ "query", "overall_score", "tier",
+ "retrieval_score", "generation_score", "agentic_score",
+ "error_message", "timestamp"
+ ])
+
+ writer.writeheader()
+ for case in bad_cases:
+ writer.writerow({
+ "query": case.get("query", ""),
+ "overall_score": case.get("overall_score", 0),
+ "tier": case.get("data_quality_tier", "unknown"),
+ "retrieval_score": case.get("retrieval", {}).get("overall_score", 0),
+ "generation_score": case.get("generation", {}).get("overall_score", 0),
+ "agentic_score": case.get("agentic", {}).get("overall_score", 0),
+ "error_message": case.get("error_message", ""),
+ "timestamp": case.get("timestamp", "")
+ })
+
+ print(f"✅ Exported {len(bad_cases)} bad cases to {output_file}")
+
+
+# ============================================================================
+# 命令行工具
+# ============================================================================
+
+def print_summary(analyzer: EvaluationAnalyzer):
+ """打印摘要"""
+ print("\n" + "=" * 70)
+ print("📊 评估结果摘要")
+ print("=" * 70)
+
+ stats = analyzer.get_basic_stats()
+
+ print(f"\n📈 基本统计:")
+ print(f" 总评估: {stats.get('total_evaluations', 0)}")
+ print(f" 平均分: {stats.get('avg_score', 0):.3f}")
+ print(f" 分布: {stats.get('quality_distribution', {})}")
+ print(f" SFT可用: {stats.get('sft_ready_count', 0)}")
+
+ print(f"\n🎯 各层性能:")
+ layer_perf = analyzer.layer_performance()
+ for layer, perf in layer_perf.items():
+ print(f" {layer:.<30} {perf['avg']:.3f} (avg)")
+
+ print(f"\n🔴 Bad Case Top 5:")
+ bad_cases = analyzer.identify_bad_cases()[:5]
+ for i, case in enumerate(bad_cases, 1):
+ print(f" {i}. {case.get('query', 'N/A')[:40]:<40} Score: {case.get('overall_score', 0):.3f}")
+
+ print(f"\n💡 优化建议:")
+ recommendations = analyzer.get_recommendations()
+ for rec in recommendations[:3]:
+ print(f" - {rec.split(chr(10))[0]}")
+
+ print("\n" + "=" * 70)
+
+
+def main():
+ import sys
+
+ analyzer = EvaluationAnalyzer()
+
+ if len(sys.argv) > 1:
+ command = sys.argv[1]
+
+ if command == "summary":
+ print_summary(analyzer)
+
+ elif command == "report":
+ report = analyzer.generate_report()
+ print(report)
+
+ elif command == "bad-cases":
+ analyzer.export_bad_cases_csv()
+ bad_cases = analyzer.identify_bad_cases()
+ print(f"\n✅ Found {len(bad_cases)} bad cases")
+ print("详见 evaluation/bad_cases.csv")
+
+ elif command == "layer-perf":
+ layer_perf = analyzer.layer_performance()
+ print("\n🎯 各层性能:")
+ for layer, perf in layer_perf.items():
+ print(f"\n{layer.upper()}:")
+ print(f" Average: {perf['avg']:.3f}")
+ print(f" Range: [{perf['min']:.3f}, {perf['max']:.3f}]")
+ print(f" Samples: {perf['count']}")
+
+ elif command == "recommendations":
+ recs = analyzer.get_recommendations()
+ print("\n💡 优化建议:\n")
+ for i, rec in enumerate(recs, 1):
+ print(f"{i}.\n{rec}\n")
+
+ else:
+ print(f"Unknown command: {command}")
+
+ else:
+ print("自动化评估数据分析工具")
+ print()
+ print("用法:")
+ print(" python analyze_eval_results.py summary # 快速摘要")
+ print(" python analyze_eval_results.py report # 生成完整报告")
+ print(" python analyze_eval_results.py bad-cases # 导出Bad Case")
+ print(" python analyze_eval_results.py layer-perf # 各层性能分析")
+ print(" python analyze_eval_results.py recommendations # 优化建议")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/evaluation/clean_and_export_sft_data.py b/evaluation/clean_and_export_sft_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac194aa642270e93c1d4bcb16dcfa5b8df05b015
--- /dev/null
+++ b/evaluation/clean_and_export_sft_data.py
@@ -0,0 +1,369 @@
+#!/usr/bin/env python3
+"""
+SFT 数据清洗与导出脚本
+
+功能:
+1. 从 eval_results.jsonl 读取原始评估数据
+2. 应用严格的质量过滤规则
+3. 转换为标准 SFT 训练格式
+4. 导出为可直接用于训练的数据集
+
+Author: Dexter
+Date: 2026-01-28
+"""
+
+import json
+import os
+from datetime import datetime
+from typing import Dict, List, Tuple
+from pathlib import Path
+
+from evaluation.utils import is_chatty_query, has_code_indicators
+
+
+# ============================================================================
+# 配置
+# ============================================================================
+
+class CleaningConfig:
+ """数据清洗配置"""
+ # 质量阈值
+ MIN_OVERALL_SCORE = 0.7 # 最低综合分
+ MIN_FAITHFULNESS = 0.6 # 最低 faithfulness
+ MIN_ANSWER_RELEVANCE = 0.6 # 最低 answer_relevance
+
+ # 长度阈值
+ MIN_QUERY_LENGTH = 10 # 最短 query
+ MIN_ANSWER_LENGTH = 100 # 最短 answer
+ MIN_CONTEXT_LENGTH = 50 # 最短 context
+ MAX_CONTEXT_LENGTH = 4000 # 最长 context（截断）
+
+ # 必须条件
+ REQUIRE_REPO_URL = True # 必须有仓库 URL
+ REQUIRE_CODE_IN_CONTEXT = True # 上下文必须包含代码
+
+ # 输出配置
+ OUTPUT_DIR = "evaluation/sft_data/cleaned"
+
+
+# ============================================================================
+# 数据清洗逻辑
+# ============================================================================
+
+def validate_sample(sample: Dict, config: CleaningConfig) -> Tuple[bool, str]:
+ """
+ 验证单个样本是否符合质量标准
+
+ Returns:
+ (is_valid, rejection_reason)
+ """
+ # 1. 检查基本字段存在
+ if not sample.get("query"):
+ return False, "missing_query"
+
+ if not sample.get("generation"):
+ return False, "missing_generation"
+
+ gen = sample["generation"]
+
+ # 2. 检查 repo_url
+ if config.REQUIRE_REPO_URL and not sample.get("repo_url"):
+ return False, "missing_repo_url"
+
+ # 3. 检查质量分数
+ overall_score = sample.get("overall_score", 0)
+ if overall_score < config.MIN_OVERALL_SCORE:
+ return False, f"low_score:{overall_score:.2f}"
+
+ faithfulness = gen.get("faithfulness", 0)
+ if faithfulness < config.MIN_FAITHFULNESS:
+ return False, f"low_faithfulness:{faithfulness:.2f}"
+
+ answer_relevance = gen.get("answer_relevance", 0)
+ if answer_relevance < config.MIN_ANSWER_RELEVANCE:
+ return False, f"low_relevance:{answer_relevance:.2f}"
+
+ # 4. 检查长度
+ query = sample.get("query", "")
+ if len(query) < config.MIN_QUERY_LENGTH:
+ return False, f"short_query:{len(query)}"
+
+ answer = gen.get("generated_answer", "")
+ if len(answer) < config.MIN_ANSWER_LENGTH:
+ return False, f"short_answer:{len(answer)}"
+
+ context = gen.get("retrieved_context", "")
+ if len(context) < config.MIN_CONTEXT_LENGTH:
+ return False, f"short_context:{len(context)}"
+
+ # 5. 检查闲聊
+ if is_chatty_query(query):
+ return False, "chatty_query"
+
+ # 6. 检查代码存在
+ if config.REQUIRE_CODE_IN_CONTEXT and not has_code_indicators(context):
+ return False, "no_code_in_context"
+
+ return True, "passed"
+
+
+def transform_to_sft_format(sample: Dict, config: CleaningConfig) -> Dict:
+ """
+ 将原始评估数据转换为标准 SFT 格式
+ """
+ gen = sample["generation"]
+
+ # 清理和截断 context
+ context = gen.get("retrieved_context", "")
+ if len(context) > config.MAX_CONTEXT_LENGTH:
+ context = context[:config.MAX_CONTEXT_LENGTH] + "\n... [truncated]"
+
+ # 构建标准 SFT 格式
+ sft_sample = {
+ # === 核心训练字段 ===
+ "instruction": "你是一个专业的GitHub代码仓库分析助手。根据提供的代码上下文，准确回答用户关于代码实现、架构设计、功能逻辑等问题。回答时应该：1) 直接引用相关代码 2) 解释代码的工作原理 3) 如有必要，提供代码示例。",
+ "input": f"[用户问题]\n{sample['query']}\n\n[代码上下文]\n{context}",
+ "output": gen.get("generated_answer", ""),
+
+ # === 元数据 ===
+ "metadata": {
+ "query": sample["query"],
+ "repo_url": sample.get("repo_url", ""),
+ "language": sample.get("language", "en"),
+ "session_id": sample.get("session_id", ""),
+ "timestamp": sample.get("timestamp", ""),
+ "quality_tier": sample.get("data_quality_tier", ""),
+ "overall_score": sample.get("overall_score", 0),
+ "faithfulness": gen.get("faithfulness", 0),
+ "answer_relevance": gen.get("answer_relevance", 0),
+ "answer_completeness": gen.get("answer_completeness", 0),
+ "code_correctness": gen.get("code_correctness", 0),
+ }
+ }
+
+ return sft_sample
+
+
+def clean_and_export(
+ input_file: str = "evaluation/sft_data/eval_results.jsonl",
+ config: CleaningConfig = None
+) -> Dict:
+ """
+ 清洗数据并导出
+
+ Returns:
+ 统计信息
+ """
+ config = config or CleaningConfig()
+
+ # 创建输出目录
+ output_dir = Path(config.OUTPUT_DIR)
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ # 统计
+ stats = {
+ "total_read": 0,
+ "passed": 0,
+ "rejected": 0,
+ "rejection_reasons": {},
+ "quality_distribution": {"gold": 0, "silver": 0, "bronze": 0}
+ }
+
+ # 输出文件
+ output_file = output_dir / f"sft_train_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl"
+ rejected_file = output_dir / f"rejected_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl"
+
+ print("=" * 60)
+ print("🧹 SFT 数据清洗与导出")
+ print("=" * 60)
+ print(f"输入文件: {input_file}")
+ print(f"输出目录: {output_dir}")
+ print(f"质量阈值: score >= {config.MIN_OVERALL_SCORE}")
+ print()
+
+ if not os.path.exists(input_file):
+ print(f"❌ 输入文件不存在: {input_file}")
+ return stats
+
+ passed_samples = []
+ rejected_samples = []
+
+ # 读取并处理
+ with open(input_file, 'r', encoding='utf-8') as f:
+ for line_num, line in enumerate(f, 1):
+ try:
+ sample = json.loads(line)
+ stats["total_read"] += 1
+
+ # 验证
+ is_valid, reason = validate_sample(sample, config)
+
+ if is_valid:
+ # 转换格式
+ sft_sample = transform_to_sft_format(sample, config)
+ passed_samples.append(sft_sample)
+ stats["passed"] += 1
+
+ # 统计质量分布
+ score = sample.get("overall_score", 0)
+ if score > 0.9:
+ stats["quality_distribution"]["gold"] += 1
+ elif score > 0.7:
+ stats["quality_distribution"]["silver"] += 1
+ else:
+ stats["quality_distribution"]["bronze"] += 1
+ else:
+ rejected_samples.append({
+ "reason": reason,
+ "query": sample.get("query", "")[:50],
+ "score": sample.get("overall_score", 0)
+ })
+ stats["rejected"] += 1
+ stats["rejection_reasons"][reason] = stats["rejection_reasons"].get(reason, 0) + 1
+
+ except json.JSONDecodeError as e:
+ print(f" ⚠️ 第 {line_num} 行 JSON 解析错误: {e}")
+ continue
+
+ # 写入通过的样本
+ if passed_samples:
+ with open(output_file, 'w', encoding='utf-8') as f:
+ for sample in passed_samples:
+ f.write(json.dumps(sample, ensure_ascii=False) + '\n')
+ print(f"✅ 已导出 {len(passed_samples)} 条高质量样本到: {output_file}")
+
+ # 写入拒绝的样本（用于分析）
+ if rejected_samples:
+ with open(rejected_file, 'w', encoding='utf-8') as f:
+ for sample in rejected_samples:
+ f.write(json.dumps(sample, ensure_ascii=False) + '\n')
+ print(f"📝 已记录 {len(rejected_samples)} 条被拒绝样本到: {rejected_file}")
+
+ # 打印统计
+ print()
+ print("=" * 60)
+ print("📊 统计报告")
+ print("=" * 60)
+ print(f"总读取: {stats['total_read']}")
+ print(f"通过: {stats['passed']} ({stats['passed']/max(stats['total_read'],1)*100:.1f}%)")
+ print(f"拒绝: {stats['rejected']} ({stats['rejected']/max(stats['total_read'],1)*100:.1f}%)")
+ print()
+ print("质量分布:")
+ print(f" 🥇 Gold (>0.9): {stats['quality_distribution']['gold']}")
+ print(f" 🥈 Silver (>0.7): {stats['quality_distribution']['silver']}")
+ print(f" 🥉 Bronze (>0.5): {stats['quality_distribution']['bronze']}")
+ print()
+
+ if stats["rejection_reasons"]:
+ print("拒绝原因分布:")
+ for reason, count in sorted(stats["rejection_reasons"].items(), key=lambda x: -x[1]):
+ print(f" - {reason}: {count}")
+
+ print()
+ print("=" * 60)
+
+ return stats
+
+
+def export_for_training(
+ input_file: str,
+ output_file: str,
+ format_type: str = "alpaca"
+) -> int:
+ """
+ 将清洗后的数据导出为特定训练格式
+
+ Args:
+ input_file: 清洗后的 JSONL 文件
+ output_file: 输出文件
+ format_type: 格式类型 (alpaca, sharegpt, messages)
+
+ Returns:
+ 导出的样本数量
+ """
+ samples = []
+
+ with open(input_file, 'r', encoding='utf-8') as f:
+ for line in f:
+ sample = json.loads(line)
+
+ if format_type == "alpaca":
+ # Alpaca 格式（适用于 LLaMA-Factory 等）
+ formatted = {
+ "instruction": sample["instruction"],
+ "input": sample["input"],
+ "output": sample["output"]
+ }
+
+ elif format_type == "sharegpt":
+ # ShareGPT 格式
+ formatted = {
+ "conversations": [
+ {"from": "system", "value": sample["instruction"]},
+ {"from": "human", "value": sample["input"]},
+ {"from": "gpt", "value": sample["output"]}
+ ]
+ }
+
+ elif format_type == "messages":
+ # OpenAI messages 格式
+ formatted = {
+ "messages": [
+ {"role": "system", "content": sample["instruction"]},
+ {"role": "user", "content": sample["input"]},
+ {"role": "assistant", "content": sample["output"]}
+ ]
+ }
+
+ else:
+ formatted = sample
+
+ samples.append(formatted)
+
+ # 写入
+ with open(output_file, 'w', encoding='utf-8') as f:
+ if output_file.endswith('.json'):
+ json.dump(samples, f, ensure_ascii=False, indent=2)
+ else:
+ for sample in samples:
+ f.write(json.dumps(sample, ensure_ascii=False) + '\n')
+
+ print(f"✅ 已导出 {len(samples)} 条样本为 {format_type} 格式: {output_file}")
+ return len(samples)
+
+
+# ============================================================================
+# 主函数
+# ============================================================================
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser(description="SFT 数据清洗与导出工具")
+ parser.add_argument("--input", "-i", default="evaluation/sft_data/eval_results.jsonl",
+ help="输入文件路径")
+ parser.add_argument("--min-score", "-s", type=float, default=0.7,
+ help="最低质量分数 (默认: 0.7)")
+ parser.add_argument("--format", "-f", choices=["alpaca", "sharegpt", "messages"],
+ default="alpaca", help="导出格式 (默认: alpaca)")
+ parser.add_argument("--export", "-e", action="store_true",
+ help="同时导出为训练格式")
+
+ args = parser.parse_args()
+
+ # 配置
+ config = CleaningConfig()
+ config.MIN_OVERALL_SCORE = args.min_score
+
+ # 清洗
+ stats = clean_and_export(args.input, config)
+
+ # 导出为训练格式
+ if args.export and stats["passed"] > 0:
+ # 找到最新的清洗文件
+ output_dir = Path(config.OUTPUT_DIR)
+ cleaned_files = sorted(output_dir.glob("sft_train_*.jsonl"), reverse=True)
+ if cleaned_files:
+ latest_file = cleaned_files[0]
+ export_file = output_dir / f"train_{args.format}.jsonl"
+ export_for_training(str(latest_file), str(export_file), args.format)
diff --git a/evaluation/data_router.py b/evaluation/data_router.py
new file mode 100644
index 0000000000000000000000000000000000000000..c82e91f0f4923780da3cfb43ff53ecedbfb2b47c
--- /dev/null
+++ b/evaluation/data_router.py
@@ -0,0 +1,222 @@
+# 文件路径: evaluation/data_router.py
+"""
+数据路由引擎 - 负责 SFT 数据管理和路由
+
+根据评估结果将样本路由到不同的数据集
+"""
+
+import json
+import os
+from typing import Dict, List, Any
+
+from evaluation.models import EvaluationResult, DataQualityTier
+from evaluation.utils import smart_truncate, SFTLengthConfig
+
+
+class DataRoutingEngine:
+ """评估驱动的数据路由引擎"""
+
+ # SFT 训练提示词
+ SFT_INSTRUCTION = (
+ "你是一个专业的GitHub代码仓库分析助手。根据提供的代码上下文，"
+ "准确回答用户关于代码实现、架构设计、功能逻辑等问题。"
+ "回答时应该：1) 直接引用相关代码 2) 解释代码的工作原理 3) 如有必要，提供代码示例。"
+ )
+
+ def __init__(self, output_dir: str = "evaluation/sft_data"):
+ self.output_dir = output_dir
+ os.makedirs(output_dir, exist_ok=True)
+
+ self.positive_samples_file = os.path.join(output_dir, "positive_samples.jsonl")
+ self.negative_samples_file = os.path.join(output_dir, "negative_samples.jsonl")
+ self.dpo_pairs_file = os.path.join(output_dir, "dpo_pairs.jsonl")
+ self.eval_results_file = os.path.join(output_dir, "eval_results.jsonl")
+
+ def route_sample(self, eval_result: EvaluationResult) -> str:
+ """路由单个样本，返回数据质量等级"""
+ if eval_result.overall_score == 0.0:
+ eval_result.compute_overall_score()
+
+ self.route_data(eval_result)
+ return eval_result.data_quality_tier.value
+
+ def route_data(self, eval_result: EvaluationResult) -> None:
+ """
+ 根据评估结果路由数据
+
+ 路由规则:
+ - score > 0.9 → Gold → positive_samples.jsonl
+ - score > 0.6 → Silver → positive_samples.jsonl
+ - score > 0.4 → Bronze → negative_samples.jsonl
+ - score <= 0.4 → Rejected (不应到达此处，在 auto_eval 中已过滤)
+
+ 注意: eval_results.jsonl 记录所有通过验证的样本，用于分析和审计
+ """
+ # 记录所有评估结果（完整审计日志）
+ self._append_jsonl(self.eval_results_file, eval_result.to_dict())
+
+ # 根据质量分级路由到不同的 SFT 数据文件
+ if eval_result.overall_score > 0.9:
+ # Gold: 高质量正样本
+ sft_sample = self._build_sft_sample(eval_result)
+ self._append_jsonl(self.positive_samples_file, sft_sample)
+
+ elif eval_result.overall_score > 0.6:
+ # Silver: 可用正样本
+ sft_sample = self._build_sft_sample(eval_result)
+ self._append_jsonl(self.positive_samples_file, sft_sample)
+
+ elif eval_result.overall_score > 0.4:
+ # Bronze: 负样本，可用于 DPO 或人工修正
+ sft_sample = self._build_sft_sample(eval_result, negative=True)
+ self._append_jsonl(self.negative_samples_file, sft_sample)
+
+ # <= 0.4: 不写入任何 SFT 文件（已在 auto_eval 中被拒绝）
+
+ def _build_sft_sample(self, eval_result: EvaluationResult, negative: bool = False) -> Dict:
+ """
+ 构建 SFT 训练样本
+
+ 长度限制（基于 SFTLengthConfig）:
+ - Context: 最大 2500 字符 (~800 tokens)
+ - Answer: 最大 3000 字符 (~1000 tokens)
+ - 总计: ~2000 tokens，适合 4096 max_length 训练
+ """
+ if eval_result.generation_metrics is None:
+ return {}
+
+ cfg = SFTLengthConfig
+
+ # 1. 截断 Query
+ query = eval_result.query
+ if len(query) > cfg.MAX_QUERY_CHARS:
+ query = query[:cfg.MAX_QUERY_CHARS] + "..."
+
+ # 2. 智能截断 Context（保留开头 70% + 结尾 30%）
+ context = eval_result.generation_metrics.retrieved_context
+ context = smart_truncate(context, cfg.MAX_CONTEXT_CHARS, keep_ratio=0.7)
+
+ # 3. 截断 Answer（保留开头，通常结论在开头）
+ answer = eval_result.generation_metrics.generated_answer
+ if len(answer) > cfg.MAX_ANSWER_CHARS:
+ answer = answer[:cfg.MAX_ANSWER_CHARS] + "\n\n... [回答过长，已截断]"
+
+ # 4. 构建 input 并检查总长度
+ input_text = f"[用户问题]\n{query}\n\n[代码上下文]\n{context}"
+
+ # 如果总长度仍超限，进一步压缩 context
+ total_len = len(self.SFT_INSTRUCTION) + len(input_text) + len(answer)
+ if total_len > cfg.MAX_TOTAL_CHARS:
+ excess = total_len - cfg.MAX_TOTAL_CHARS
+ new_context_len = max(500, len(context) - excess) # 至少保留 500 字符
+ context = smart_truncate(
+ eval_result.generation_metrics.retrieved_context,
+ new_context_len,
+ keep_ratio=0.7
+ )
+ input_text = f"[用户问题]\n{query}\n\n[代码上下文]\n{context}"
+
+ return {
+ "instruction": self.SFT_INSTRUCTION,
+ "input": input_text,
+ "output": answer,
+ "metadata": {
+ "query": eval_result.query[:200], # metadata 中也截断，节省空间
+ "repo_url": eval_result.repo_url,
+ "language": eval_result.language,
+ "session_id": eval_result.session_id,
+ "timestamp": eval_result.timestamp.isoformat(),
+ "quality_tier": eval_result.data_quality_tier.value,
+ "overall_score": eval_result.overall_score,
+ "faithfulness": eval_result.generation_metrics.faithfulness,
+ "answer_relevance": eval_result.generation_metrics.answer_relevance,
+ "answer_completeness": eval_result.generation_metrics.answer_completeness,
+ "code_correctness": eval_result.generation_metrics.code_correctness,
+ "is_negative": negative,
+ "sft_ready": eval_result.sft_ready,
+ # 记录原始长度，便于分析
+ "original_context_len": len(eval_result.generation_metrics.retrieved_context),
+ "original_answer_len": len(eval_result.generation_metrics.generated_answer),
+ "truncated": len(eval_result.generation_metrics.retrieved_context) > cfg.MAX_CONTEXT_CHARS
+ or len(eval_result.generation_metrics.generated_answer) > cfg.MAX_ANSWER_CHARS,
+ }
+ }
+
+ def _append_jsonl(self, filepath: str, data: Dict) -> None:
+ """追加数据到 JSONL 文件"""
+ with open(filepath, 'a', encoding='utf-8') as f:
+ f.write(json.dumps(data, ensure_ascii=False) + '\n')
+
+ def get_statistics(self) -> Dict[str, int]:
+ """获取当前数据统计"""
+ stats = {}
+ for name, filepath in [
+ ("positive", self.positive_samples_file),
+ ("negative", self.negative_samples_file),
+ ("dpo_pairs", self.dpo_pairs_file),
+ ]:
+ if os.path.exists(filepath):
+ with open(filepath, 'r', encoding='utf-8') as f:
+ stats[name] = sum(1 for _ in f)
+ else:
+ stats[name] = 0
+ return stats
+
+ def get_distribution(self) -> Dict[str, int]:
+ """获取评估结果的质量分布"""
+ distribution = {"gold": 0, "silver": 0, "bronze": 0, "rejected": 0, "corrected": 0}
+
+ if not os.path.exists(self.eval_results_file):
+ return distribution
+
+ try:
+ with open(self.eval_results_file, 'r', encoding='utf-8') as f:
+ for line in f:
+ try:
+ result = json.loads(line)
+ tier = result.get("data_quality_tier", "bronze")
+ if tier in distribution:
+ distribution[tier] += 1
+ except json.JSONDecodeError:
+ continue
+ except Exception as e:
+ print(f"⚠️ Error reading eval results: {e}")
+
+ return distribution
+
+ def get_bad_samples(self, limit: int = 10) -> List[Dict[str, Any]]:
+ """获取低质量样本用于人工审核"""
+ bad_samples = []
+
+ if not os.path.exists(self.eval_results_file):
+ return bad_samples
+
+ try:
+ with open(self.eval_results_file, 'r', encoding='utf-8') as f:
+ for line in f:
+ try:
+ result = json.loads(line)
+ if result.get("overall_score", 0) < 0.5:
+ sample = {
+ "query": result.get("query", ""),
+ "score": result.get("overall_score", 0),
+ "issue": result.get("error_message", "Low quality"),
+ "quality_tier": result.get("data_quality_tier", "rejected"),
+ "timestamp": result.get("timestamp", "")
+ }
+ if result.get("generation"):
+ gen = result["generation"]
+ sample.update({
+ "faithfulness": gen.get("faithfulness", 0),
+ "answer_relevance": gen.get("answer_relevance", 0),
+ "answer_completeness": gen.get("answer_completeness", 0),
+ })
+ bad_samples.append(sample)
+ if len(bad_samples) >= limit:
+ break
+ except json.JSONDecodeError:
+ continue
+ except Exception as e:
+ print(f"⚠️ Error reading bad samples: {e}")
+
+ return sorted(bad_samples, key=lambda x: x["score"])[:limit]
diff --git a/evaluation/evaluation_framework.py b/evaluation/evaluation_framework.py
new file mode 100644
index 0000000000000000000000000000000000000000..b4794842987cedf6971fd8a14e251f58428aef4e
--- /dev/null
+++ b/evaluation/evaluation_framework.py
@@ -0,0 +1,512 @@
+# 文件路径: evaluation/evaluation_framework.py
+"""
+GitHub Agent 完整评估框架
+四层评估架构 + 数据路由引擎
+
+Author: Dexter
+Date: 2025-01-27
+
+注意: 数据模型已拆分到 models.py，数据路由已拆分到 data_router.py
+ 此文件保留核心评估引擎逻辑，并重新导出所有符号保持向后兼容
+"""
+
+import json
+import os
+import re
+from typing import List, Dict, Any
+from datetime import datetime
+
+# 重新导出所有模型（保持向后兼容）
+from evaluation.models import (
+ EvaluationLayer,
+ DataQualityTier,
+ QueryRewriteMetrics,
+ RetrievalMetrics,
+ GenerationMetrics,
+ AgenticMetrics,
+ EvaluationResult,
+)
+from evaluation.data_router import DataRoutingEngine
+
+
+# ============================================================================
+# 评估引擎核心逻辑
+# ============================================================================
+
+class EvaluationEngine:
+ """评估引擎 - 负责多层面打分"""
+
+ def __init__(
+ self,
+ llm_client=None,
+ golden_dataset_path: str = "evaluation/golden_dataset.json",
+ model_name: str = None
+ ):
+ self.llm_client = llm_client
+ self.model_name = model_name or "gpt-4o-mini" # 默认使用轻量模型
+ self.golden_dataset = self._load_golden_dataset(golden_dataset_path)
+
+ def _load_golden_dataset(self, path: str) -> List[Dict]:
+ """加载黄金数据集"""
+ if not os.path.exists(path):
+ print(f"⚠️ Golden dataset not found at {path}")
+ return []
+
+ with open(path, 'r', encoding='utf-8') as f:
+ return json.load(f)
+
+ async def evaluate_query_rewrite(
+ self,
+ original_query: str,
+ rewritten_query: str,
+ language_detected: str
+ ) -> QueryRewriteMetrics:
+ """
+ 评估查询重写质量
+
+ 指标:
+ - keyword_coverage: 重写后的关键词是否覆盖了原Query的核心概念?
+ - semantic_preservation: 语义是否保留?
+ - diversity_score: 关键词多样性
+ """
+
+ # 简化版: 使用关键词匹配
+ original_tokens = set(original_query.lower().split())
+ rewritten_tokens = set(rewritten_query.lower().split())
+
+ # 关键词覆盖度: 原Query的关键词有多少在重写中保留
+ if original_tokens:
+ coverage = len(original_tokens & rewritten_tokens) / len(original_tokens)
+ else:
+ coverage = 0.0
+
+ # 多样性: 重写后的关键词数量越多、越不重复，分数越高
+ unique_ratio = len(rewritten_tokens) / max(len(original_tokens), 1)
+ diversity = min(1.0, unique_ratio)
+
+ # 语义保留度 (简化版本: 假设如果覆盖度高就认为语义保留良好)
+ semantic_preservation = min(1.0, coverage + 0.2) # 基础分+覆盖度加分
+
+ return QueryRewriteMetrics(
+ original_query=original_query,
+ rewritten_query=rewritten_query,
+ language_detected=language_detected,
+ keyword_coverage=coverage,
+ semantic_preservation=semantic_preservation,
+ diversity_score=diversity
+ )
+
+ async def evaluate_retrieval(
+ self,
+ query: str,
+ retrieved_files: List[str],
+ ground_truth_files: List[str],
+ top_k: int = 5,
+ retrieval_latency_ms: float = 0,
+ vector_scores: List[float] = None,
+ bm25_scores: List[float] = None
+ ) -> RetrievalMetrics:
+ """
+ 评估检索层质量
+
+ 指标:
+ - hit_rate: 是否找到了任何正确的文件?
+ - recall_at_k: 前K个中有多少是正确的?
+ - precision_at_k: 返回的文件中有多少是正确的?
+ - mrr: 第一个正确结果的排名倒数
+ """
+
+ retrieved_set = set(retrieved_files[:top_k])
+ ground_truth_set = set(ground_truth_files)
+
+ # Hit rate: 是否有交集
+ hit_rate = 1.0 if retrieved_set & ground_truth_set else 0.0
+
+ # Recall@K: 找到的正确结果数 / 正确结果总数
+ correct_count = len(retrieved_set & ground_truth_set)
+ recall = correct_count / len(ground_truth_set) if ground_truth_set else 0.0
+
+ # Precision@K: 找到的正确结果数 / 返回的结果总数
+ precision = correct_count / len(retrieved_set) if retrieved_set else 0.0
+
+ # MRR: 第一个正确结果的倒数排名
+ mrr = 0.0
+ for i, file in enumerate(retrieved_files[:top_k], 1):
+ if file in ground_truth_set:
+ mrr = 1.0 / i
+ break
+
+ # Context Relevance: 简化版 - 假设Precision反映了相关性
+ context_relevance = precision
+
+ # Chunk Integrity: 简化版 - 假设没有太多文件就认为完整度高
+ chunk_integrity = min(1.0, 1.0 / len(retrieved_set)) if retrieved_set else 0.0
+
+ vector_avg = sum(vector_scores) / len(vector_scores) if vector_scores else 0.0
+ bm25_avg = sum(bm25_scores) / len(bm25_scores) if bm25_scores else 0.0
+
+ return RetrievalMetrics(
+ query=query,
+ top_k=top_k,
+ hit_rate=hit_rate,
+ recall_at_k=recall,
+ precision_at_k=precision,
+ mrr=mrr,
+ context_relevance=context_relevance,
+ chunk_integrity=chunk_integrity,
+ retrieval_latency_ms=retrieval_latency_ms,
+ vector_score_avg=vector_avg,
+ bm25_score_avg=bm25_avg,
+ retrieved_files=retrieved_files,
+ ground_truth_files=ground_truth_files
+ )
+
+ async def evaluate_generation(
+ self,
+ query: str,
+ retrieved_context: str,
+ generated_answer: str,
+ ground_truth_answer: str = "",
+ generation_latency_ms: float = 0,
+ token_usage: Dict[str, int] = None
+ ) -> GenerationMetrics:
+ """
+ 评估生成层质量
+
+ 指标:
+ - faithfulness: 回答是否严格基于Context?
+ - answer_relevance: 回答是否回答了问题?
+ - answer_completeness: 回答是否足够完整?
+ - code_correctness: 生成的代码是否正确?
+ """
+
+ # 1. Faithfulness: 使用LLM-as-Judge进行幻觉检测
+ faithfulness = await self._judge_faithfulness(
+ retrieved_context,
+ generated_answer
+ )
+
+ # 2. Answer Relevance: 回答和问题的相似度
+ answer_relevance = await self._judge_answer_relevance(
+ query,
+ generated_answer
+ )
+
+ # 3. Answer Completeness: 简化版 - 通过长度和结构判断
+ completeness = self._judge_completeness(
+ generated_answer,
+ ground_truth_answer
+ )
+
+ # 4. Code Correctness: 使用AST检查代码块
+ code_samples = self._extract_code_blocks(generated_answer)
+ code_correctness = self._check_code_correctness(code_samples)
+
+ metrics = GenerationMetrics(
+ query=query,
+ retrieved_context=retrieved_context,
+ generated_answer=generated_answer,
+ ground_truth_answer=ground_truth_answer,
+ faithfulness=faithfulness,
+ answer_relevance=answer_relevance,
+ answer_completeness=completeness,
+ code_correctness=code_correctness,
+ generated_code_samples=code_samples,
+ generation_latency_ms=generation_latency_ms,
+ token_usage=token_usage or {"input": 0, "output": 0}
+ )
+
+ return metrics
+
+ async def _judge_faithfulness(self, context: str, answer: str) -> float:
+ """
+ LLM-as-Judge: 判断回答是否由Context支撑
+ 返回 0-1 的分数
+
+ 注意：Faithfulness 判断的是"回答中的信息是否能从 Context 中找到依据"
+ 而不是"回答是否完全复制 Context 内容"
+ """
+ if not self.llm_client:
+ # 简化版: 如果没有LLM客户端，使用启发式方法
+ # 统计Answer中的关键词有多少出现在Context中
+ context_lower = context.lower()
+ answer_words = set(answer.lower().split())
+ # 过滤掉常见停用词
+ stop_words = {'the', 'a', 'an', 'is', 'are', 'was', 'were', 'be', 'been',
+ 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will',
+ 'would', 'could', 'should', 'may', 'might', 'must', 'shall',
+ 'can', 'need', 'dare', 'ought', 'used', 'to', 'of', 'in',
+ 'for', 'on', 'with', 'at', 'by', 'from', 'as', 'into', 'that',
+ 'which', 'who', 'whom', 'this', 'these', 'those', 'it', 'its'}
+ meaningful_words = answer_words - stop_words
+ if not meaningful_words:
+ return 0.7 # 没有有意义的词，给默认分
+ # 计算答案中有多少有意义的词出现在Context中
+ found_count = sum(1 for word in meaningful_words if word in context_lower)
+ overlap = found_count / len(meaningful_words)
+ return min(1.0, overlap + 0.2) # 给一定的基础分
+
+ # 智能截取 Context：提取与 Answer 相关的部分
+ # 如果 Context 太长，优先包含 Answer 中提到的关键词附近的内容
+ max_context_len = 6000 # 增加到 6000 字符
+ if len(context) > max_context_len:
+ # 尝试找到 Answer 中提到的关键文件/函数名
+ import re
+ # 提取 Answer 中可能的文件路径或函数名
+ patterns = re.findall(r'[a-zA-Z_][a-zA-Z0-9_]*(?:\.[a-zA-Z_][a-zA-Z0-9_]*)*', answer[:500])
+ important_terms = [p for p in patterns if len(p) > 3][:5] # 取前5个重要词
+
+ # 优先截取包含这些词的部分
+ context_parts = []
+ remaining = max_context_len
+ for term in important_terms:
+ idx = context.find(term)
+ if idx != -1 and remaining > 0:
+ start = max(0, idx - 300)
+ end = min(len(context), idx + 700)
+ snippet = context[start:end]
+ if snippet not in ''.join(context_parts):
+ context_parts.append(snippet)
+ remaining -= len(snippet)
+
+ # 如果没找到相关部分，还是用前 6000 字符
+ if context_parts:
+ truncated_context = "\n...\n".join(context_parts)
+ else:
+ truncated_context = context[:max_context_len]
+ else:
+ truncated_context = context
+
+ # 改进的 Prompt：更明确定义 Faithfulness
+ prompt = f"""Evaluate the FAITHFULNESS of the answer to the given context.
+
+FAITHFULNESS means: The claims and information in the answer can be verified from or are consistent with the context.
+- Score HIGH (0.7-1.0) if the answer correctly identifies or explains concepts that ARE in the context
+- Score MEDIUM (0.4-0.7) if the answer is partially supported but makes some unsupported claims
+- Score LOW (0.0-0.4) if the answer contradicts the context or makes completely unsupported claims
+
+NOTE: If the answer says "X is not in the context" and X is indeed not shown, that's a FAITHFUL statement (score 0.7+)
+NOTE: If the answer correctly identifies WHERE something is defined based on imports/references in context, that's FAITHFUL
+
+[Context]
+{truncated_context}
+
+[Answer]
+{answer[:1500]}
+
+SCORE (0.0-1.0):"""
+
+ try:
+ response = await self.llm_client.chat.completions.create(
+ model=self.model_name,
+ messages=[{"role": "user", "content": prompt}],
+ temperature=0.1,
+ max_tokens=10
+ )
+ score_str = response.choices[0].message.content.strip()
+ # 提取数字（处理可能的额外文本）
+ import re
+ match = re.search(r'(\d+\.?\d*)', score_str)
+ if match:
+ score = float(match.group(1))
+ else:
+ score = float(score_str)
+ return min(1.0, max(0.0, score))
+ except Exception as e:
+ print(f"⚠️ Faithfulness judgment failed: {e}")
+ return 0.5
+
+ async def _judge_answer_relevance(self, query: str, answer: str) -> float:
+ """判断回答与问题的相关性"""
+ if not self.llm_client:
+ # 简化版: 使用关键词重叠度
+ query_words = set(query.lower().split())
+ answer_words = set(answer.lower().split())
+ overlap = len(query_words & answer_words) / max(len(query_words), 1)
+ return min(1.0, overlap + 0.3) # 基础分0.3+重叠度
+
+ prompt = f"""
+ Does the answer address the query?
+
+ [Query]
+ {query}
+
+ [Answer]
+ {answer[:1000]}
+
+ Score (0.0-1.0):
+ """
+
+ try:
+ response = await self.llm_client.chat.completions.create(
+ model=self.model_name,
+ messages=[{"role": "user", "content": prompt}],
+ temperature=0.1,
+ max_tokens=10
+ )
+ score = float(response.choices[0].message.content.strip())
+ return min(1.0, max(0.0, score))
+ except:
+ return 0.5
+
+ def _judge_completeness(self, generated_answer: str, ground_truth: str = "") -> float:
+ """判断回答的完整性"""
+ # 简化版: 根据长度和结构
+ if len(generated_answer) < 50:
+ return 0.3
+ elif len(generated_answer) < 200:
+ return 0.6
+ else:
+ return 0.9
+
+ def _extract_code_blocks(self, text: str) -> List[str]:
+ """从文本中提取代码块"""
+ import re
+ code_pattern = r'```[\w]*\n(.*?)\n```'
+ matches = re.findall(code_pattern, text, re.DOTALL)
+ return matches
+
+ def _check_code_correctness(self, code_samples: List[str]) -> float:
+ """检查代码是否有语法错误"""
+ if not code_samples:
+ return 1.0 # 没有代码就认为正确
+
+ import ast
+ correct_count = 0
+ for code in code_samples:
+ try:
+ ast.parse(code)
+ correct_count += 1
+ except SyntaxError:
+ pass
+
+ return correct_count / len(code_samples)
+
+ async def evaluate_agentic(
+ self,
+ query: str,
+ tool_calls: List[Dict[str, Any]],
+ success: bool,
+ steps_taken: int = 0,
+ end_to_end_latency_ms: float = 0
+ ) -> AgenticMetrics:
+ """
+ 评估Agent的决策和行为
+ """
+
+ # Tool Selection Accuracy: 工具选择是否正确?
+ tool_selection_accuracy = 1.0 if success else 0.5
+
+ # Tool Parameter Correctness: 参数是否正确传递?
+ tool_param_correctness = 1.0 if all(
+ tc.get("success", False) for tc in tool_calls
+ ) else 0.5
+
+ # 计算冗余步骤
+ unnecessary_steps = 0
+ backtrack_count = 0
+
+ # 简化版: 如果有重复的工具调用则视为冗余
+ tool_call_signatures = [tc.get("name", "") for tc in tool_calls]
+ for i, sig in enumerate(tool_call_signatures):
+ if i > 0 and sig == tool_call_signatures[i-1]:
+ unnecessary_steps += 1
+
+ return AgenticMetrics(
+ query=query,
+ tool_calls=tool_calls,
+ tool_selection_accuracy=tool_selection_accuracy,
+ tool_parameter_correctness=tool_param_correctness,
+ steps_taken=steps_taken,
+ unnecessary_steps=unnecessary_steps,
+ backtrack_count=backtrack_count,
+ success=success,
+ end_to_end_latency_ms=end_to_end_latency_ms
+ )
+
+ def get_statistics(self) -> Dict[str, Any]:
+ """
+ 获取评估统计信息
+
+ Returns:
+ 包含 total_evaluations, average_score, quality_distribution, top_issues 的字典
+ """
+ # 从 eval_results.jsonl 读取评估结果
+ eval_results_path = "evaluation/sft_data/eval_results.jsonl"
+
+ stats = {
+ "total_evaluations": 0,
+ "average_score": 0.0,
+ "quality_distribution": {
+ "gold": 0,
+ "silver": 0,
+ "bronze": 0,
+ "rejected": 0
+ },
+ "top_issues": []
+ }
+
+ if not os.path.exists(eval_results_path):
+ return stats
+
+ # 读取和分析评估结果
+ scores = []
+ issues = {}
+
+ try:
+ with open(eval_results_path, 'r', encoding='utf-8') as f:
+ for line in f:
+ try:
+ result = json.loads(line)
+ stats["total_evaluations"] += 1
+
+ # 收集得分
+ score = result.get("overall_score", 0)
+ scores.append(score)
+
+ # 统计质量分布
+ tier = result.get("data_quality_tier", "bronze")
+ if tier in stats["quality_distribution"]:
+ stats["quality_distribution"][tier] += 1
+
+ # 收集常见问题 (假设记录在 notes 或 error_message 中)
+ note = result.get("notes", "") or result.get("error_message", "")
+ if note:
+ issues[note] = issues.get(note, 0) + 1
+ except json.JSONDecodeError:
+ continue
+ except Exception as e:
+ print(f"⚠️ Error reading eval results: {e}")
+
+ # 计算平均分
+ if scores:
+ stats["average_score"] = sum(scores) / len(scores)
+
+ # 获取前5个常见问题
+ if issues:
+ stats["top_issues"] = [
+ {"issue": issue, "count": count}
+ for issue, count in sorted(issues.items(), key=lambda x: x[1], reverse=True)[:5]
+ ]
+
+ return stats
+
+
+# ============================================================================
+# __all__ 导出列表（保持向后兼容）
+# ============================================================================
+
+__all__ = [
+ # 枚举
+ "EvaluationLayer",
+ "DataQualityTier",
+ # 数据模型
+ "QueryRewriteMetrics",
+ "RetrievalMetrics",
+ "GenerationMetrics",
+ "AgenticMetrics",
+ "EvaluationResult",
+ # 引擎
+ "EvaluationEngine",
+ "DataRoutingEngine",
+]
diff --git a/evaluation/golden_dataset_builder.py b/evaluation/golden_dataset_builder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8251b0a4cdd5c4f6a67c90a6211d297c6660f21
--- /dev/null
+++ b/evaluation/golden_dataset_builder.py
@@ -0,0 +1,414 @@
+# 文件路径: evaluation/golden_dataset_builder.py
+"""
+黄金数据集构建工具
+用于快速构建评估所需的标注数据集
+
+使用场景:
+1. 初始化: 为新项目快速创建 50 条测试用例
+2. 扩展: 定期添加新的问题和标注
+3. 验证: 自动验证数据集的完整性
+
+Author: Dexter
+Date: 2025-01-27
+"""
+
+import json
+import os
+from typing import List, Dict, Optional
+from dataclasses import dataclass, asdict
+from datetime import datetime
+
+
+@dataclass
+class GoldenSample:
+ """黄金数据集样本"""
+ id: str # 唯一ID
+ description: str # 问题描述 (用于标注人员理解问题类型)
+ query: str # 用户查询
+ expected_files: List[str] # 标准答案: 应该返回的文件列表
+ expected_answer: str = "" # 标准答案: 预期回答 (可选)
+ difficulty: str = "medium" # 难度: easy/medium/hard
+ category: str = "general" # 类别: general/code_finding/architecture/workflow
+ language: str = "en" # 语言: en/zh
+ created_at: str = ""
+
+ def __post_init__(self):
+ if not self.created_at:
+ self.created_at = datetime.now().isoformat()
+
+
+class GoldenDatasetBuilder:
+ """黄金数据集构建器"""
+
+ def __init__(self, filepath: str = "evaluation/golden_dataset.json"):
+ self.filepath = filepath
+ self.samples: List[GoldenSample] = []
+ self.load()
+
+ def load(self):
+ """加载现有数据集"""
+ if os.path.exists(self.filepath):
+ with open(self.filepath, 'r', encoding='utf-8') as f:
+ try:
+ raw_data = json.load(f)
+ # 兼容旧格式 (直接是字典列表)
+ if isinstance(raw_data, list):
+ self.samples = [
+ GoldenSample(**item) if isinstance(item, dict) and "id" in item
+ else GoldenSample(
+ id=str(len(self.samples)),
+ description=item.get("description", ""),
+ query=item.get("query", ""),
+ expected_files=[item.get("answer_file", "")] if item.get("answer_file") else []
+ )
+ for item in raw_data
+ ]
+ except:
+ self.samples = []
+
+ def save(self):
+ """保存数据集"""
+ os.makedirs(os.path.dirname(self.filepath), exist_ok=True)
+ data = [asdict(s) for s in self.samples]
+ with open(self.filepath, 'w', encoding='utf-8') as f:
+ json.dump(data, f, ensure_ascii=False, indent=2)
+
+ def add_sample(self, sample: GoldenSample):
+ """添加样本"""
+ sample.id = f"sample_{len(self.samples):04d}"
+ self.samples.append(sample)
+
+ def add_samples_batch(self, samples: List[GoldenSample]):
+ """批量添加样本"""
+ for sample in samples:
+ self.add_sample(sample)
+
+ def get_samples_by_category(self, category: str) -> List[GoldenSample]:
+ """按类别筛选"""
+ return [s for s in self.samples if s.category == category]
+
+ def get_samples_by_difficulty(self, difficulty: str) -> List[GoldenSample]:
+ """按难度筛选"""
+ return [s for s in self.samples if s.difficulty == difficulty]
+
+ def get_statistics(self) -> Dict:
+ """获取统计信息"""
+ stats = {
+ "total": len(self.samples),
+ "by_category": {},
+ "by_difficulty": {},
+ "by_language": {}
+ }
+
+ for s in self.samples:
+ stats["by_category"][s.category] = stats["by_category"].get(s.category, 0) + 1
+ stats["by_difficulty"][s.difficulty] = stats["by_difficulty"].get(s.difficulty, 0) + 1
+ stats["by_language"][s.language] = stats["by_language"].get(s.language, 0) + 1
+
+ return stats
+
+
+# ============================================================================
+# 预定义的通用问题模板
+# ============================================================================
+
+# 针对 FastAPI 项目的初始数据集 (参考你现有的 golden_dataset.json)
+FASTAPI_GOLDEN_SAMPLES = [
+ # Easy: 代码位置查找
+ GoldenSample(
+ id="",
+ description="简单函数查找",
+ query="Where is the 'serialize_response' function?",
+ expected_files=["fastapi/routing.py"],
+ difficulty="easy",
+ category="code_finding"
+ ),
+
+ # Medium: 理解数据流
+ GoldenSample(
+ id="",
+ description="理解核心模块职责",
+ query="How does dependency injection work in FastAPI?",
+ expected_files=["fastapi/dependencies/utils.py", "fastapi/depends.py"],
+ difficulty="medium",
+ category="architecture"
+ ),
+
+ # Hard: 跨文件理解工作流
+ GoldenSample(
+ id="",
+ description="完整工作流理解",
+ query="Show me the complete flow from request to response in FastAPI",
+ expected_files=["fastapi/routing.py", "fastapi/applications.py", "fastapi/dependencies/utils.py"],
+ difficulty="hard",
+ category="workflow"
+ ),
+]
+
+# GitHub Agent 项目的初始数据集
+GITHUB_AGENT_GOLDEN_SAMPLES = [
+ GoldenSample(
+ id="",
+ description="检索核心逻辑",
+ query="How is chunk_file method implemented?",
+ expected_files=["app/services/chunking_service.py"],
+ expected_answer="The chunk_file method is implemented in chunking_service.py. It takes content and file_path as parameters and uses AST parsing for Python files to intelligently chunk the code.",
+ difficulty="easy",
+ category="code_finding",
+ language="en"
+ ),
+
+ GoldenSample(
+ id="",
+ description="向量搜索机制",
+ query="What vector database is used for retrieval?",
+ expected_files=["app/services/vector_service.py"],
+ difficulty="medium",
+ category="architecture",
+ language="en"
+ ),
+
+ GoldenSample(
+ id="",
+ description="完整分析流程",
+ query="How does the agent analyze a GitHub repository?",
+ expected_files=["app/services/agent_service.py", "app/services/chunking_service.py", "app/services/vector_service.py"],
+ difficulty="hard",
+ category="workflow",
+ language="en"
+ ),
+]
+
+
+# ============================================================================
+# 交互式数据集构建工具
+# ============================================================================
+
+def interactive_builder():
+ """交互式构建黄金数据集"""
+ builder = GoldenDatasetBuilder()
+
+ print("=" * 60)
+ print("🛠️ 黄金数据集构建工具")
+ print("=" * 60)
+
+ while True:
+ print("\n请选择操作:")
+ print("1. 添加新样本")
+ print("2. 查看现有样本")
+ print("3. 按类别筛选")
+ print("4. 统计信息")
+ print("5. 保存并退出")
+ print("0. 退出(不保存)")
+
+ choice = input("请输入选项 (0-5): ").strip()
+
+ if choice == "1":
+ sample = GoldenSample(
+ id="",
+ description=input("📝 描述 (问题类型): "),
+ query=input("❓ 查询/问题: "),
+ expected_files=input("📁 预期文件 (逗号分隔): ").split(","),
+ expected_answer=input("📄 标准答案 (可选): "),
+ difficulty=input("⭐ 难度 (easy/medium/hard) [medium]: ") or "medium",
+ category=input("🏷️ 类别 (code_finding/architecture/workflow/general) [general]: ") or "general",
+ language=input("🌍 语言 (en/zh) [en]: ") or "en"
+ )
+ builder.add_sample(sample)
+ print("✅ 样本已添加")
+
+ elif choice == "2":
+ print(f"\n总共 {len(builder.samples)} 个样本:")
+ for s in builder.samples[-10:]: # 显示最后10个
+ print(f" - [{s.difficulty}] {s.query[:50]}")
+
+ elif choice == "3":
+ category = input("输入类别: ")
+ samples = builder.get_samples_by_category(category)
+ print(f"\n找到 {len(samples)} 个 '{category}' 类别的样本:")
+ for s in samples:
+ print(f" - {s.query}")
+
+ elif choice == "4":
+ stats = builder.get_statistics()
+ print(f"\n📊 数据集统计:")
+ print(f" 总样本数: {stats['total']}")
+ print(f" 按类别: {stats['by_category']}")
+ print(f" 按难度: {stats['by_difficulty']}")
+ print(f" 按语言: {stats['by_language']}")
+
+ elif choice == "5":
+ builder.save()
+ print("✅ 数据集已保存")
+ break
+
+ elif choice == "0":
+ print("⚠️ 未保存,退出")
+ break
+
+
+# ============================================================================
+# 自动评估数据集的完整性
+# ============================================================================
+
+def validate_golden_dataset(filepath: str = "evaluation/golden_dataset.json") -> Dict:
+ """验证黄金数据集的完整性"""
+
+ builder = GoldenDatasetBuilder(filepath)
+ issues = {
+ "missing_fields": [],
+ "empty_queries": [],
+ "empty_files": [],
+ "duplicates": []
+ }
+
+ seen_queries = set()
+
+ for i, sample in enumerate(builder.samples):
+ # 检查必填字段
+ if not sample.query:
+ issues["empty_queries"].append(f"Sample {i}: query is empty")
+
+ if not sample.expected_files or all(not f for f in sample.expected_files):
+ issues["empty_files"].append(f"Sample {i}: expected_files is empty")
+
+ # 检查重复
+ if sample.query in seen_queries:
+ issues["duplicates"].append(f"Sample {i}: duplicate query")
+ seen_queries.add(sample.query)
+
+ return {
+ "valid": len(issues) == 0 or not any(issues.values()),
+ "total_samples": len(builder.samples),
+ "issues": issues,
+ "stats": builder.get_statistics()
+ }
+
+
+# ============================================================================
+# 快速初始化脚本
+# ============================================================================
+
+def init_github_agent_dataset():
+ """快速初始化 GitHub Agent 项目的数据集"""
+ builder = GoldenDatasetBuilder("evaluation/golden_dataset.json")
+
+ # 清空现有 (可选)
+ # builder.samples = []
+
+ # 添加初始样本
+ builder.add_samples_batch(GITHUB_AGENT_GOLDEN_SAMPLES)
+
+ # 额外添加更多样本 (扩展到30+)
+ extra_samples = [
+ GoldenSample(
+ id="",
+ description="向量检索质量",
+ query="What retrieval metrics are tracked?",
+ expected_files=["evaluation/evaluation_framework.py"],
+ difficulty="medium",
+ category="architecture"
+ ),
+ GoldenSample(
+ id="",
+ description="Agent决策过程",
+ query="How does the agent decide which files to read?",
+ expected_files=["app/services/agent_service.py"],
+ difficulty="hard",
+ category="workflow"
+ ),
+ GoldenSample(
+ id="",
+ description="错误处理",
+ query="Where are network timeout errors handled?",
+ expected_files=["app/services/agent_service.py", "app/services/chat_service.py"],
+ difficulty="medium",
+ category="code_finding"
+ ),
+ ]
+ builder.add_samples_batch(extra_samples)
+ builder.save()
+
+ print(f"✅ 初始化完成: {len(builder.samples)} 个样本")
+ print(f"📊 {builder.get_statistics()}")
+
+
+# ============================================================================
+# 导出为 Ragas 格式
+# ============================================================================
+
+def export_to_ragas_format(golden_filepath: str, output_filepath: str = "evaluation/ragas_eval_dataset.json"):
+ """
+ 将黄金数据集导出为 Ragas 评估框架所需的格式
+
+ Ragas 格式:
+ {
+ "questions": [...],
+ "contexts": [...],
+ "ground_truths": [...]
+ }
+ """
+ builder = GoldenDatasetBuilder(golden_filepath)
+
+ ragas_data = {
+ "questions": [],
+ "contexts": [],
+ "ground_truths": [],
+ "metadata": []
+ }
+
+ for sample in builder.samples:
+ ragas_data["questions"].append(sample.query)
+ ragas_data["ground_truths"].append({
+ "answer": sample.expected_answer,
+ "files": sample.expected_files
+ })
+ ragas_data["contexts"].append("\n".join(sample.expected_files))
+ ragas_data["metadata"].append({
+ "difficulty": sample.difficulty,
+ "category": sample.category,
+ "description": sample.description
+ })
+
+ os.makedirs(os.path.dirname(output_filepath), exist_ok=True)
+ with open(output_filepath, 'w', encoding='utf-8') as f:
+ json.dump(ragas_data, f, ensure_ascii=False, indent=2)
+
+ print(f"✅ Exported to {output_filepath}")
+ print(f" Questions: {len(ragas_data['questions'])}")
+
+
+# ============================================================================
+# 命令行接口
+# ============================================================================
+
+if __name__ == "__main__":
+ import sys
+
+ if len(sys.argv) > 1:
+ command = sys.argv[1]
+
+ if command == "init":
+ init_github_agent_dataset()
+
+ elif command == "validate":
+ result = validate_golden_dataset()
+ print(json.dumps(result, indent=2, ensure_ascii=False))
+
+ elif command == "export-ragas":
+ export_to_ragas_format("evaluation/golden_dataset.json")
+
+ elif command == "interactive":
+ interactive_builder()
+
+ else:
+ print(f"Unknown command: {command}")
+
+ else:
+ print("黄金数据集构建工具")
+ print()
+ print("用法:")
+ print(" python golden_dataset_builder.py init # 快速初始化")
+ print(" python golden_dataset_builder.py validate # 验证数据集")
+ print(" python golden_dataset_builder.py export-ragas # 导出为Ragas格式")
+ print(" python golden_dataset_builder.py interactive # 交互式构建")
diff --git a/evaluation/models.py b/evaluation/models.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d3242b3635ea6632f2102ea13a9f61ea87b71c2
--- /dev/null
+++ b/evaluation/models.py
@@ -0,0 +1,244 @@
+# 文件路径: evaluation/models.py
+"""
+评估数据模型定义
+
+将所有数据类和枚举集中管理，保持代码职责清晰
+"""
+
+from dataclasses import dataclass, field, asdict
+from typing import List, Dict, Optional, Any
+from enum import Enum
+from datetime import datetime
+
+
+class EvaluationLayer(Enum):
+ """评估层次分类"""
+ QUERY_REWRITE = "query_rewrite"
+ RETRIEVAL = "retrieval"
+ GENERATION = "generation"
+ AGENTIC = "agentic"
+
+
+class DataQualityTier(Enum):
+ """数据质量分级 (用于SFT数据路由)"""
+ GOLD = "gold" # 完美样本 (score > 0.9)
+ SILVER = "silver" # 优质样本 (score 0.7-0.9)
+ BRONZE = "bronze" # 可用样本 (score 0.5-0.7)
+ REJECTED = "rejected" # 拒绝 (score < 0.5)
+ CORRECTED = "corrected" # 自纠正后的样本 (用于DPO)
+
+
+# ============================================================================
+# 各层评估指标
+# ============================================================================
+
+@dataclass
+class QueryRewriteMetrics:
+ """查询重写评估指标"""
+ original_query: str
+ rewritten_query: str
+ language_detected: str
+ keyword_coverage: float # 0-1
+ semantic_preservation: float # 0-1
+ diversity_score: float # 0-1
+
+ def overall_score(self) -> float:
+ return (
+ self.keyword_coverage * 0.4 +
+ self.semantic_preservation * 0.4 +
+ self.diversity_score * 0.2
+ )
+
+
+@dataclass
+class RetrievalMetrics:
+ """检索层评估指标"""
+ query: str
+ top_k: int
+
+ # 核心指标
+ hit_rate: float
+ recall_at_k: float
+ precision_at_k: float
+ mrr: float # Mean Reciprocal Rank
+
+ # 高级指标
+ context_relevance: float
+ chunk_integrity: float
+ retrieval_latency_ms: float
+
+ # 混合检索
+ vector_score_avg: float
+ bm25_score_avg: float
+
+ retrieved_files: List[str] = field(default_factory=list)
+ ground_truth_files: List[str] = field(default_factory=list)
+
+ def overall_score(self) -> float:
+ return (
+ self.recall_at_k * 0.3 +
+ self.precision_at_k * 0.3 +
+ self.context_relevance * 0.25 +
+ self.chunk_integrity * 0.15
+ )
+
+
+@dataclass
+class GenerationMetrics:
+ """生成层评估指标"""
+ query: str
+ retrieved_context: str
+ generated_answer: str
+
+ # 核心指标
+ faithfulness: float
+ answer_relevance: float
+ answer_completeness: float
+ code_correctness: float
+
+ # 可选
+ ground_truth_answer: str = ""
+ hallucination_count: int = 0
+ unsupported_claims: List[str] = field(default_factory=list)
+ generated_code_samples: List[str] = field(default_factory=list)
+ generation_latency_ms: float = 0
+ token_usage: Dict[str, int] = field(default_factory=lambda: {"input": 0, "output": 0})
+
+ def overall_score(self) -> float:
+ base_score = (
+ self.faithfulness * 0.35 +
+ self.answer_relevance * 0.35 +
+ self.answer_completeness * 0.2 +
+ self.code_correctness * 0.1
+ )
+ penalty = self.hallucination_count * 0.1
+ return max(0, base_score - penalty)
+
+
+@dataclass
+class AgenticMetrics:
+ """Agent行为评估指标"""
+ query: str
+ tool_selection_accuracy: float
+ tool_parameter_correctness: float
+
+ tool_calls: List[Dict[str, Any]] = field(default_factory=list)
+ steps_taken: int = 0
+ unnecessary_steps: int = 0
+ backtrack_count: int = 0
+ success: bool = True
+ early_termination: bool = False
+ end_to_end_latency_ms: float = 0
+
+ def efficiency_score(self) -> float:
+ if self.steps_taken == 0:
+ return 0
+ redundancy_ratio = self.unnecessary_steps / self.steps_taken
+ return max(0, min(1, 1 - redundancy_ratio - self.backtrack_count * 0.1))
+
+ def overall_score(self) -> float:
+ return (
+ self.tool_selection_accuracy * 0.4 +
+ self.tool_parameter_correctness * 0.3 +
+ self.efficiency_score() * 0.2 +
+ (1.0 if self.success else 0.0) * 0.1
+ )
+
+
+# ============================================================================
+# 综合评估结果
+# ============================================================================
+
+@dataclass
+class EvaluationResult:
+ """单次评估完整结果"""
+ session_id: str
+ query: str
+ repo_url: str
+ timestamp: datetime
+ language: str = "en"
+
+ # 各层评估结果
+ query_rewrite_metrics: Optional[QueryRewriteMetrics] = None
+ retrieval_metrics: Optional[RetrievalMetrics] = None
+ generation_metrics: Optional[GenerationMetrics] = None
+ agentic_metrics: Optional[AgenticMetrics] = None
+
+ # 综合评分
+ overall_score: float = 0.0
+ data_quality_tier: DataQualityTier = DataQualityTier.BRONZE
+
+ # SFT标注
+ sft_ready: bool = False
+ dpo_candidate: bool = False
+
+ # 元数据
+ error_message: Optional[str] = None
+ notes: str = ""
+
+ def compute_overall_score(self) -> float:
+ """计算加权综合得分"""
+ scores, weights = [], []
+
+ if self.query_rewrite_metrics:
+ scores.append(self.query_rewrite_metrics.overall_score())
+ weights.append(0.15)
+
+ if self.retrieval_metrics:
+ scores.append(self.retrieval_metrics.overall_score())
+ weights.append(0.35)
+
+ if self.generation_metrics:
+ scores.append(self.generation_metrics.overall_score())
+ weights.append(0.4)
+
+ if self.agentic_metrics:
+ scores.append(self.agentic_metrics.overall_score())
+ weights.append(0.1)
+
+ if not scores:
+ return 0.0
+
+ total_weight = sum(weights)
+ self.overall_score = sum(s * w for s, w in zip(scores, weights)) / total_weight
+
+ # 分级
+ if self.overall_score > 0.9:
+ self.data_quality_tier = DataQualityTier.GOLD
+ self.sft_ready = True
+ elif self.overall_score > 0.7:
+ self.data_quality_tier = DataQualityTier.SILVER
+ self.sft_ready = True
+ elif self.overall_score > 0.5:
+ self.data_quality_tier = DataQualityTier.BRONZE
+ else:
+ self.data_quality_tier = DataQualityTier.REJECTED
+
+ return self.overall_score
+
+ def to_dict(self) -> Dict:
+ """转换为字典供存储"""
+ result = {
+ "session_id": self.session_id,
+ "query": self.query,
+ "repo_url": self.repo_url,
+ "timestamp": self.timestamp.isoformat(),
+ "language": self.language,
+ "overall_score": self.overall_score,
+ "data_quality_tier": self.data_quality_tier.value,
+ "sft_ready": self.sft_ready,
+ "dpo_candidate": self.dpo_candidate,
+ "error_message": self.error_message,
+ "notes": self.notes,
+ }
+
+ if self.query_rewrite_metrics:
+ result["query_rewrite"] = asdict(self.query_rewrite_metrics)
+ if self.retrieval_metrics:
+ result["retrieval"] = asdict(self.retrieval_metrics)
+ if self.generation_metrics:
+ result["generation"] = asdict(self.generation_metrics)
+ if self.agentic_metrics:
+ result["agentic"] = asdict(self.agentic_metrics)
+
+ return result
diff --git a/evaluation/test_retrieval.py b/evaluation/test_retrieval.py
new file mode 100644
index 0000000000000000000000000000000000000000..24629ecfa0edf8461c8a23bac0f816c5a7e6144c
--- /dev/null
+++ b/evaluation/test_retrieval.py
@@ -0,0 +1,330 @@
+#!/usr/bin/env python3
+"""
+检索系统离线评估脚本
+
+用于测试 chunking 和检索策略的准确率。
+使用 golden_dataset.json 中的标注数据作为 ground truth。
+
+使用方法:
+ python evaluation/test_retrieval.py --repo https://github.com/tiangolo/fastapi
+ python evaluation/test_retrieval.py --repo https://github.com/tiangolo/fastapi --top-k 5
+ python evaluation/test_retrieval.py --repo https://github.com/tiangolo/fastapi --verbose
+
+Author: Dexter
+Date: 2026-01-28
+"""
+
+import json
+import os
+import sys
+import asyncio
+import argparse
+from typing import List, Dict, Tuple
+from dataclasses import dataclass, field
+from datetime import datetime
+
+# 添加项目根目录到 path
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from app.services.vector_service import store_manager
+from app.services.github_service import get_repo_structure
+
+
+@dataclass
+class RetrievalTestResult:
+ """单个测试用例的结果"""
+ query: str
+ expected_files: List[str]
+ retrieved_files: List[str]
+ hit: bool # 是否命中任意一个预期文件
+ recall: float # 召回率: 命中的预期文件 / 总预期文件
+ precision: float # 精确率: 命中的预期文件 / 检索结果数
+ reciprocal_rank: float # 倒数排名: 1 / 第一个命中的位置
+ difficulty: str = ""
+ category: str = ""
+
+
+@dataclass
+class EvaluationReport:
+ """完整评估报告"""
+ repo_url: str
+ top_k: int
+ total_queries: int
+ timestamp: str = field(default_factory=lambda: datetime.now().isoformat())
+
+ # 聚合指标
+ hit_rate: float = 0.0 # 命中率: 至少命中一个的查询比例
+ mean_recall: float = 0.0 # 平均召回率
+ mean_precision: float = 0.0 # 平均精确率
+ mrr: float = 0.0 # Mean Reciprocal Rank
+
+ # 按难度分组
+ by_difficulty: Dict[str, Dict] = field(default_factory=dict)
+
+ # 详细结果
+ results: List[RetrievalTestResult] = field(default_factory=list)
+ failed_cases: List[Dict] = field(default_factory=list)
+
+
+class RetrievalEvaluator:
+ """检索系统评估器"""
+
+ def __init__(self, golden_dataset_path: str = "evaluation/golden_dataset.json"):
+ self.golden_dataset = self._load_golden_dataset(golden_dataset_path)
+ print(f"📊 Loaded {len(self.golden_dataset)} test cases from golden dataset")
+
+ def _load_golden_dataset(self, path: str) -> List[Dict]:
+ """加载黄金数据集"""
+ if not os.path.exists(path):
+ raise FileNotFoundError(f"Golden dataset not found: {path}")
+
+ with open(path, 'r', encoding='utf-8') as f:
+ return json.load(f)
+
+ async def evaluate(
+ self,
+ repo_url: str,
+ session_id: str = "eval_test",
+ top_k: int = 5,
+ verbose: bool = False
+ ) -> EvaluationReport:
+ """
+ 运行完整的检索评估
+
+ Args:
+ repo_url: 要评估的仓库 URL
+ session_id: 会话 ID
+ top_k: 每次检索返回的文件数
+ verbose: 是否打印详细信息
+ """
+ print(f"\n{'='*60}")
+ print(f"🔍 Retrieval Evaluation")
+ print(f"{'='*60}")
+ print(f"Repository: {repo_url}")
+ print(f"Top-K: {top_k}")
+ print(f"Test Cases: {len(self.golden_dataset)}")
+ print(f"{'='*60}\n")
+
+ # 获取仓库文件列表
+ print("📂 Fetching repository structure...")
+ file_list = get_repo_structure(repo_url) # 同步函数，不需要 await
+ print(f" Found {len(file_list)} files")
+
+ # 获取向量存储
+ store = store_manager.get_store(session_id)
+ chunk_count = store.collection.count() # 使用 collection.count()
+ if chunk_count == 0:
+ print("\n⚠️ Vector store is empty!")
+ print(" Please run the agent first to index the repository.")
+ print(" Example: Access http://localhost:8000 and analyze the repo first.")
+ return None
+ print(f" Vector store has {chunk_count} chunks")
+
+ # 运行评估
+ report = EvaluationReport(
+ repo_url=repo_url,
+ top_k=top_k,
+ total_queries=len(self.golden_dataset)
+ )
+
+ hits = 0
+ recalls = []
+ precisions = []
+ reciprocal_ranks = []
+
+ difficulty_stats = {}
+
+ for i, sample in enumerate(self.golden_dataset):
+ query = sample.get("query", "")
+ expected_files = sample.get("expected_files", [])
+ difficulty = sample.get("difficulty", "medium")
+ category = sample.get("category", "general")
+
+ if not query or not expected_files:
+ continue
+
+ # 执行检索 (使用 hybrid search)
+ try:
+ results = await store.search_hybrid(query, top_k=top_k)
+ except Exception as e:
+ if verbose:
+ print(f" [ERR] Search failed: {e}")
+ continue
+
+ # 提取检索到的文件路径
+ retrieved_files = []
+ for doc in results:
+ if isinstance(doc, dict):
+ file_path = doc.get("file", "")
+ if file_path and file_path not in retrieved_files:
+ retrieved_files.append(file_path)
+
+ # 计算指标
+ expected_set = set(expected_files)
+ retrieved_set = set(retrieved_files[:top_k])
+
+ # 命中的文件
+ hits_set = expected_set & retrieved_set
+
+ # Hit: 是否命中任意一个
+ hit = len(hits_set) > 0
+ if hit:
+ hits += 1
+
+ # Recall: 命中的 / 期望的
+ recall = len(hits_set) / len(expected_set) if expected_set else 0
+ recalls.append(recall)
+
+ # Precision: 命中的 / 检索的
+ precision = len(hits_set) / min(len(retrieved_files), top_k) if retrieved_files else 0
+ precisions.append(precision)
+
+ # Reciprocal Rank: 1 / 第一个命中的位置
+ rr = 0.0
+ for rank, file in enumerate(retrieved_files[:top_k], 1):
+ if file in expected_set:
+ rr = 1.0 / rank
+ break
+ reciprocal_ranks.append(rr)
+
+ # 记录结果
+ result = RetrievalTestResult(
+ query=query,
+ expected_files=expected_files,
+ retrieved_files=retrieved_files[:top_k],
+ hit=hit,
+ recall=recall,
+ precision=precision,
+ reciprocal_rank=rr,
+ difficulty=difficulty,
+ category=category
+ )
+ report.results.append(result)
+
+ # 按难度统计
+ if difficulty not in difficulty_stats:
+ difficulty_stats[difficulty] = {"hits": 0, "total": 0, "recalls": [], "precisions": []}
+ difficulty_stats[difficulty]["total"] += 1
+ if hit:
+ difficulty_stats[difficulty]["hits"] += 1
+ difficulty_stats[difficulty]["recalls"].append(recall)
+ difficulty_stats[difficulty]["precisions"].append(precision)
+
+ # 记录失败案例
+ if not hit:
+ report.failed_cases.append({
+ "query": query,
+ "expected": expected_files,
+ "retrieved": retrieved_files[:top_k],
+ "difficulty": difficulty
+ })
+
+ # 打印进度
+ if verbose:
+ status = "✅" if hit else "❌"
+ print(f" [{i+1:3d}] {status} Recall={recall:.2f} | {query[:50]}...")
+ else:
+ print(f"\r Progress: {i+1}/{len(self.golden_dataset)}", end="")
+
+ print("\n")
+
+ # 计算聚合指标
+ report.hit_rate = hits / len(self.golden_dataset) if self.golden_dataset else 0
+ report.mean_recall = sum(recalls) / len(recalls) if recalls else 0
+ report.mean_precision = sum(precisions) / len(precisions) if precisions else 0
+ report.mrr = sum(reciprocal_ranks) / len(reciprocal_ranks) if reciprocal_ranks else 0
+
+ # 按难度汇总
+ for diff, stats in difficulty_stats.items():
+ report.by_difficulty[diff] = {
+ "hit_rate": stats["hits"] / stats["total"] if stats["total"] else 0,
+ "mean_recall": sum(stats["recalls"]) / len(stats["recalls"]) if stats["recalls"] else 0,
+ "mean_precision": sum(stats["precisions"]) / len(stats["precisions"]) if stats["precisions"] else 0,
+ "total": stats["total"]
+ }
+
+ return report
+
+ def print_report(self, report: EvaluationReport):
+ """打印评估报告"""
+ print(f"\n{'='*60}")
+ print(f"📊 RETRIEVAL EVALUATION REPORT")
+ print(f"{'='*60}")
+ print(f"Repository: {report.repo_url}")
+ print(f"Top-K: {report.top_k}")
+ print(f"Total Queries: {report.total_queries}")
+ print(f"Timestamp: {report.timestamp}")
+ print(f"{'='*60}\n")
+
+ print("📈 OVERALL METRICS")
+ print(f" Hit Rate: {report.hit_rate:.1%}")
+ print(f" Mean Recall: {report.mean_recall:.1%}")
+ print(f" Mean Precision: {report.mean_precision:.1%}")
+ print(f" MRR: {report.mrr:.3f}")
+
+ print(f"\n📊 BY DIFFICULTY")
+ for diff, stats in sorted(report.by_difficulty.items()):
+ print(f" {diff.upper():8s} | Hit: {stats['hit_rate']:.1%} | Recall: {stats['mean_recall']:.1%} | n={stats['total']}")
+
+ if report.failed_cases:
+ print(f"\n❌ FAILED CASES ({len(report.failed_cases)} total)")
+ for case in report.failed_cases[:5]: # 只显示前5个
+ print(f" Query: {case['query'][:60]}...")
+ print(f" Expected: {case['expected']}")
+ print(f" Got: {case['retrieved'][:3]}...")
+ print()
+
+ print(f"{'='*60}")
+
+ def save_report(self, report: EvaluationReport, output_path: str = "evaluation/retrieval_report.json"):
+ """保存报告到文件"""
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+ # 转换为可序列化格式
+ data = {
+ "repo_url": report.repo_url,
+ "top_k": report.top_k,
+ "total_queries": report.total_queries,
+ "timestamp": report.timestamp,
+ "metrics": {
+ "hit_rate": report.hit_rate,
+ "mean_recall": report.mean_recall,
+ "mean_precision": report.mean_precision,
+ "mrr": report.mrr
+ },
+ "by_difficulty": report.by_difficulty,
+ "failed_cases": report.failed_cases
+ }
+
+ with open(output_path, 'w', encoding='utf-8') as f:
+ json.dump(data, f, ensure_ascii=False, indent=2)
+
+ print(f"\n💾 Report saved to: {output_path}")
+
+
+async def main():
+ parser = argparse.ArgumentParser(description="Evaluate retrieval system using golden dataset")
+ parser.add_argument("--repo", required=True, help="GitHub repository URL to evaluate")
+ parser.add_argument("--top-k", type=int, default=5, help="Number of results to retrieve (default: 5)")
+ parser.add_argument("--session", default="eval_test", help="Session ID for vector store")
+ parser.add_argument("--verbose", "-v", action="store_true", help="Print detailed results")
+ parser.add_argument("--save", action="store_true", help="Save report to file")
+
+ args = parser.parse_args()
+
+ evaluator = RetrievalEvaluator()
+ report = await evaluator.evaluate(
+ repo_url=args.repo,
+ session_id=args.session,
+ top_k=args.top_k,
+ verbose=args.verbose
+ )
+
+ if report:
+ evaluator.print_report(report)
+ if args.save:
+ evaluator.save_report(report)
+
+
+if __name__ == "__main__":
+ asyncio.run(main())
diff --git a/evaluation/utils.py b/evaluation/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9914280a4cf976d3792357408062de4a6d8da505
--- /dev/null
+++ b/evaluation/utils.py
@@ -0,0 +1,196 @@
+# 文件路径: evaluation/utils.py
+"""
+评估模块公共工具函数和常量
+
+将重复的逻辑抽取到这里，保持代码 DRY (Don't Repeat Yourself)
+"""
+
+from typing import List
+
+
+# ============================================================================
+# 闲聊/无效 Query 检测
+# ============================================================================
+
+CHATTY_PATTERNS: List[str] = [
+ # 中文闲聊
+ "你好", "您好", "嗨", "在吗", "在不在", "谢谢", "多谢", "再见", "拜拜",
+ "什么是", "你是谁", "你叫什么", "帮帮我", "教教我",
+ # 英文闲聊
+ "hello", "hi", "hey", "thanks", "thank you", "bye", "goodbye",
+ "what is", "who are you", "help me", "can you",
+ # 单词/简短
+ "test", "测试", "ok", "yes", "no",
+]
+
+# 代码语言指示符
+CODE_INDICATORS: List[str] = [
+ # Python
+ "def ", "class ", "import ", "from ",
+ # JavaScript/TypeScript
+ "function ", "const ", "let ", "var ",
+ # Java/C#
+ "public ", "private ", "void ",
+ # Go
+ "func ", "package ",
+ # 通用
+ "```", # Markdown 代码块
+]
+
+
+def is_chatty_query(query: str, min_length: int = 5) -> bool:
+ """
+ 检测是否为闲聊/无效 query
+
+ Args:
+ query: 用户查询
+ min_length: 最小有效长度，低于此值视为无效
+
+ Returns:
+ True 如果是闲聊/无效查询
+ """
+ if not query:
+ return True
+
+ query_lower = query.lower().strip()
+
+ # 长度检查
+ if len(query_lower) < min_length:
+ return True
+
+ # 模式匹配
+ for pattern in CHATTY_PATTERNS:
+ if query_lower == pattern or query_lower.startswith(pattern + " "):
+ return True
+
+ return False
+
+
+def has_code_indicators(text: str) -> bool:
+ """
+ 检查文本是否包含代码指示符
+
+ Args:
+ text: 要检查的文本
+
+ Returns:
+ True 如果包含代码特征
+ """
+ if not text:
+ return False
+
+ for indicator in CODE_INDICATORS:
+ if indicator in text:
+ return True
+
+ return False
+
+
+# ============================================================================
+# 文件操作工具
+# ============================================================================
+
+def append_jsonl(filepath: str, data: dict) -> None:
+ """
+ 追加一行 JSON 到 JSONL 文件
+
+ Args:
+ filepath: 文件路径
+ data: 要追加的数据字典
+ """
+ import json
+ with open(filepath, 'a', encoding='utf-8') as f:
+ f.write(json.dumps(data, ensure_ascii=False) + '\n')
+
+
+def read_jsonl(filepath: str) -> list:
+ """
+ 读取 JSONL 文件
+
+ Args:
+ filepath: 文件路径
+
+ Returns:
+ 数据列表
+ """
+ import json
+ import os
+
+ if not os.path.exists(filepath):
+ return []
+
+ results = []
+ with open(filepath, 'r', encoding='utf-8') as f:
+ for line in f:
+ try:
+ results.append(json.loads(line))
+ except json.JSONDecodeError:
+ continue
+ return results
+
+
+def safe_truncate(text: str, max_length: int, suffix: str = "\n... [truncated]") -> str:
+ """
+ 安全截断文本
+
+ Args:
+ text: 原始文本
+ max_length: 最大长度
+ suffix: 截断后缀
+
+ Returns:
+ 截断后的文本
+ """
+ if not text or len(text) <= max_length:
+ return text
+ return text[:max_length] + suffix
+
+
+def smart_truncate(text: str, max_length: int, keep_ratio: float = 0.7) -> str:
+ """
+ 智能截断：保留开头大部分 + 结尾小部分，适合代码上下文
+
+ Args:
+ text: 原始文本
+ max_length: 最大长度
+ keep_ratio: 开头保留比例（默认 70% 开头，30% 结尾）
+
+ Returns:
+ 截断后的文本，保留首尾关键内容
+ """
+ if not text or len(text) <= max_length:
+ return text
+
+ separator = "\n\n... [中间内容已省略] ...\n\n"
+ available = max_length - len(separator)
+
+ if available <= 0:
+ return text[:max_length]
+
+ head_len = int(available * keep_ratio)
+ tail_len = available - head_len
+
+ return text[:head_len] + separator + text[-tail_len:]
+
+
+# ============================================================================
+# SFT 数据长度配置
+# ============================================================================
+
+class SFTLengthConfig:
+ """SFT 训练数据长度配置"""
+
+ # Context 限制（检索到的代码上下文）
+ MAX_CONTEXT_CHARS = 2500 # 最大字符数 (~800 tokens)
+
+ # Answer 限制（模型生成的回答）
+ MAX_ANSWER_CHARS = 3000 # 最大字符数 (~1000 tokens)
+
+ # Query 限制
+ MAX_QUERY_CHARS = 500 # 最大字符数
+
+ # 总体限制
+ MAX_TOTAL_CHARS = 6000 # 总字符数上限 (~2000 tokens)
+
+ # Token 估算（中英文混合，保守估计）
+ CHARS_PER_TOKEN = 3 # 平均每 token 的字符数
diff --git a/frontend-dist/assets/Tableau10-B-NsZVaP.js b/frontend-dist/assets/Tableau10-B-NsZVaP.js
new file mode 100644
index 0000000000000000000000000000000000000000..4223ec34a3bdc31019c4e2b5589b485c14449370
--- /dev/null
+++ b/frontend-dist/assets/Tableau10-B-NsZVaP.js
@@ -0,0 +1 @@
+function o(e){for(var c=e.length/6|0,n=new Array(c),a=0;au*u+V*V&&(F=P,G=x),{cx:F,cy:G,x01:-n,y01:-m,x11:F*(v/T-1),y11:G*(v/T-1)}}function vn(){var l=cn,h=yn,z=I(0),E=null,v=gn,A=mn,O=pn,a=null,B=ln(i);function i(){var n,m,r=+l.apply(this,arguments),s=+h.apply(this,arguments),f=v.apply(this,arguments)-rn,c=A.apply(this,arguments)-rn,S=un(c-f),o=c>f;if(a||(a=n=B()),sy))a.moveTo(0,0);else if(S>on-y)a.moveTo(s*j(f),s*D(f)),a.arc(0,0,s,f,c,!o),r>y&&(a.moveTo(r*j(c),r*D(c)),a.arc(0,0,r,c,f,o));else{var p=f,g=c,R=f,T=c,w=S,C=S,F=O.apply(this,arguments)/2,G=F>y&&(E?+E.apply(this,arguments):K(r*r+s*s)),P=_(un(s-r)/2,+z.apply(this,arguments)),x=P,d=P,e,u;if(G>y){var V=sn(G/r*D(F)),L=sn(G/s*D(F));(w-=V*2)>y?(V*=o?1:-1,R+=V,T-=V):(w=0,R=T=(f+c)/2),(C-=L*2)>y?(L*=o?1:-1,p+=L,g-=L):(C=0,p=g=(f+c)/2)}var H=s*j(p),J=s*D(p),M=r*j(T),N=r*D(T);if(P>y){var Q=s*j(g),U=s*D(g),X=r*j(R),Y=r*D(R),q;if(Sy?d>y?(e=W(X,Y,H,J,s,d,o),u=W(Q,U,M,N,s,d,o),a.moveTo(e.cx+e.x01,e.cy+e.y01),dy)||!(w>y)?a.lineTo(M,N):x>y?(e=W(M,N,Q,U,r,-x,o),u=W(H,J,X,Y,r,-x,o),a.lineTo(e.cx+e.x01,e.cy+e.y01),x"u"&&(w.yylloc={});var J=w.yylloc;t.push(J);var me=w.options&&w.options.ranges;typeof K.yy.parseError=="function"?this.parseError=K.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function _e(){var P;return P=u.pop()||w.lex()||C,typeof P!="number"&&(P instanceof Array&&(u=P,P=u.pop()),P=s.symbols_[P]||P),P}for(var I,M,z,Q,W={},X,B,ae,G;;){if(M=i[i.length-1],this.defaultActions[M]?z=this.defaultActions[M]:((I===null||typeof I>"u")&&(I=_e()),z=m[M]&&m[M][I]),typeof z>"u"||!z.length||!z[0]){var $="";G=[];for(X in m[M])this.terminals_[X]&&X>F&&G.push("'"+this.terminals_[X]+"'");w.showPosition?$="Parse error on line "+(R+1)+`:
+`+w.showPosition()+`
+Expecting `+G.join(", ")+", got '"+(this.terminals_[I]||I)+"'":$="Parse error on line "+(R+1)+": Unexpected "+(I==C?"end of input":"'"+(this.terminals_[I]||I)+"'"),this.parseError($,{text:w.match,token:this.terminals_[I]||I,line:w.yylineno,loc:J,expected:G})}if(z[0]instanceof Array&&z.length>1)throw new Error("Parse Error: multiple actions possible at state: "+M+", token: "+I);switch(z[0]){case 1:i.push(I),h.push(w.yytext),t.push(w.yylloc),i.push(z[1]),I=null,Y=w.yyleng,r=w.yytext,R=w.yylineno,J=w.yylloc;break;case 2:if(B=this.productions_[z[1]][1],W.$=h[h.length-B],W._$={first_line:t[t.length-(B||1)].first_line,last_line:t[t.length-1].last_line,first_column:t[t.length-(B||1)].first_column,last_column:t[t.length-1].last_column},me&&(W._$.range=[t[t.length-(B||1)].range[0],t[t.length-1].range[1]]),Q=this.performAction.apply(W,[r,Y,R,K.yy,z[1],h,t].concat(Le)),typeof Q<"u")return Q;B&&(i=i.slice(0,-1*B*2),h=h.slice(0,-1*B),t=t.slice(0,-1*B)),i.push(this.productions_[z[1]][0]),h.push(W.$),t.push(W._$),ae=m[i[i.length-2]][i[i.length-1]],i.push(ae);break;case 3:return!0}}return!0}},A=function(){var D={EOF:1,parseError:function(s,i){if(this.yy.parser)this.yy.parser.parseError(s,i);else throw new Error(s)},setInput:function(o,s){return this.yy=s||this.yy||{},this._input=o,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var o=this._input[0];this.yytext+=o,this.yyleng++,this.offset++,this.match+=o,this.matched+=o;var s=o.match(/(?:\r\n?|\n).*/g);return s?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),o},unput:function(o){var s=o.length,i=o.split(/(?:\r\n?|\n)/g);this._input=o+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-s),this.offset-=s;var u=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),i.length-1&&(this.yylineno-=i.length-1);var h=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:i?(i.length===u.length?this.yylloc.first_column:0)+u[u.length-i.length].length-i[0].length:this.yylloc.first_column-s},this.options.ranges&&(this.yylloc.range=[h[0],h[0]+this.yyleng-s]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(o){this.unput(this.match.slice(o))},pastInput:function(){var o=this.matched.substr(0,this.matched.length-this.match.length);return(o.length>20?"...":"")+o.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var o=this.match;return o.length<20&&(o+=this._input.substr(0,20-o.length)),(o.substr(0,20)+(o.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var o=this.pastInput(),s=new Array(o.length+1).join("-");return o+this.upcomingInput()+`
+`+s+"^"},test_match:function(o,s){var i,u,h;if(this.options.backtrack_lexer&&(h={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(h.yylloc.range=this.yylloc.range.slice(0))),u=o[0].match(/(?:\r\n?|\n).*/g),u&&(this.yylineno+=u.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:u?u[u.length-1].length-u[u.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+o[0].length},this.yytext+=o[0],this.match+=o[0],this.matches=o,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(o[0].length),this.matched+=o[0],i=this.performAction.call(this,this.yy,this,s,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),i)return i;if(this._backtrack){for(var t in h)this[t]=h[t];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var o,s,i,u;this._more||(this.yytext="",this.match="");for(var h=this._currentRules(),t=0;ts[0].length)){if(s=i,u=t,this.options.backtrack_lexer){if(o=this.test_match(i,h[t]),o!==!1)return o;if(this._backtrack){s=!1;continue}else return!1}else if(!this.options.flex)break}return s?(o=this.test_match(s,h[u]),o!==!1?o:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var s=this.next();return s||this.lex()},begin:function(s){this.conditionStack.push(s)},popState:function(){var s=this.conditionStack.length-1;return s>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(s){return s=this.conditionStack.length-1-Math.abs(s||0),s>=0?this.conditionStack[s]:"INITIAL"},pushState:function(s){this.begin(s)},stateStackSize:function(){return this.conditionStack.length},options:{},performAction:function(s,i,u,h){switch(u){case 0:return 10;case 1:return s.getLogger().debug("Found space-block"),31;case 2:return s.getLogger().debug("Found nl-block"),31;case 3:return s.getLogger().debug("Found space-block"),29;case 4:s.getLogger().debug(".",i.yytext);break;case 5:s.getLogger().debug("_",i.yytext);break;case 6:return 5;case 7:return i.yytext=-1,28;case 8:return i.yytext=i.yytext.replace(/columns\s+/,""),s.getLogger().debug("COLUMNS (LEX)",i.yytext),28;case 9:this.pushState("md_string");break;case 10:return"MD_STR";case 11:this.popState();break;case 12:this.pushState("string");break;case 13:s.getLogger().debug("LEX: POPPING STR:",i.yytext),this.popState();break;case 14:return s.getLogger().debug("LEX: STR end:",i.yytext),"STR";case 15:return i.yytext=i.yytext.replace(/space\:/,""),s.getLogger().debug("SPACE NUM (LEX)",i.yytext),21;case 16:return i.yytext="1",s.getLogger().debug("COLUMNS (LEX)",i.yytext),21;case 17:return 43;case 18:return"LINKSTYLE";case 19:return"INTERPOLATE";case 20:return this.pushState("CLASSDEF"),40;case 21:return this.popState(),this.pushState("CLASSDEFID"),"DEFAULT_CLASSDEF_ID";case 22:return this.popState(),this.pushState("CLASSDEFID"),41;case 23:return this.popState(),42;case 24:return this.pushState("CLASS"),44;case 25:return this.popState(),this.pushState("CLASS_STYLE"),45;case 26:return this.popState(),46;case 27:return this.pushState("STYLE_STMNT"),47;case 28:return this.popState(),this.pushState("STYLE_DEFINITION"),48;case 29:return this.popState(),49;case 30:return this.pushState("acc_title"),"acc_title";case 31:return this.popState(),"acc_title_value";case 32:return this.pushState("acc_descr"),"acc_descr";case 33:return this.popState(),"acc_descr_value";case 34:this.pushState("acc_descr_multiline");break;case 35:this.popState();break;case 36:return"acc_descr_multiline_value";case 37:return 30;case 38:return this.popState(),s.getLogger().debug("Lex: (("),"NODE_DEND";case 39:return this.popState(),s.getLogger().debug("Lex: (("),"NODE_DEND";case 40:return this.popState(),s.getLogger().debug("Lex: ))"),"NODE_DEND";case 41:return this.popState(),s.getLogger().debug("Lex: (("),"NODE_DEND";case 42:return this.popState(),s.getLogger().debug("Lex: (("),"NODE_DEND";case 43:return this.popState(),s.getLogger().debug("Lex: (-"),"NODE_DEND";case 44:return this.popState(),s.getLogger().debug("Lex: -)"),"NODE_DEND";case 45:return this.popState(),s.getLogger().debug("Lex: (("),"NODE_DEND";case 46:return this.popState(),s.getLogger().debug("Lex: ]]"),"NODE_DEND";case 47:return this.popState(),s.getLogger().debug("Lex: ("),"NODE_DEND";case 48:return this.popState(),s.getLogger().debug("Lex: ])"),"NODE_DEND";case 49:return this.popState(),s.getLogger().debug("Lex: /]"),"NODE_DEND";case 50:return this.popState(),s.getLogger().debug("Lex: /]"),"NODE_DEND";case 51:return this.popState(),s.getLogger().debug("Lex: )]"),"NODE_DEND";case 52:return this.popState(),s.getLogger().debug("Lex: )"),"NODE_DEND";case 53:return this.popState(),s.getLogger().debug("Lex: ]>"),"NODE_DEND";case 54:return this.popState(),s.getLogger().debug("Lex: ]"),"NODE_DEND";case 55:return s.getLogger().debug("Lexa: -)"),this.pushState("NODE"),36;case 56:return s.getLogger().debug("Lexa: (-"),this.pushState("NODE"),36;case 57:return s.getLogger().debug("Lexa: ))"),this.pushState("NODE"),36;case 58:return s.getLogger().debug("Lexa: )"),this.pushState("NODE"),36;case 59:return s.getLogger().debug("Lex: ((("),this.pushState("NODE"),36;case 60:return s.getLogger().debug("Lexa: )"),this.pushState("NODE"),36;case 61:return s.getLogger().debug("Lexa: )"),this.pushState("NODE"),36;case 62:return s.getLogger().debug("Lexa: )"),this.pushState("NODE"),36;case 63:return s.getLogger().debug("Lexc: >"),this.pushState("NODE"),36;case 64:return s.getLogger().debug("Lexa: (["),this.pushState("NODE"),36;case 65:return s.getLogger().debug("Lexa: )"),this.pushState("NODE"),36;case 66:return this.pushState("NODE"),36;case 67:return this.pushState("NODE"),36;case 68:return this.pushState("NODE"),36;case 69:return this.pushState("NODE"),36;case 70:return this.pushState("NODE"),36;case 71:return this.pushState("NODE"),36;case 72:return this.pushState("NODE"),36;case 73:return s.getLogger().debug("Lexa: ["),this.pushState("NODE"),36;case 74:return this.pushState("BLOCK_ARROW"),s.getLogger().debug("LEX ARR START"),38;case 75:return s.getLogger().debug("Lex: NODE_ID",i.yytext),32;case 76:return s.getLogger().debug("Lex: EOF",i.yytext),8;case 77:this.pushState("md_string");break;case 78:this.pushState("md_string");break;case 79:return"NODE_DESCR";case 80:this.popState();break;case 81:s.getLogger().debug("Lex: Starting string"),this.pushState("string");break;case 82:s.getLogger().debug("LEX ARR: Starting string"),this.pushState("string");break;case 83:return s.getLogger().debug("LEX: NODE_DESCR:",i.yytext),"NODE_DESCR";case 84:s.getLogger().debug("LEX POPPING"),this.popState();break;case 85:s.getLogger().debug("Lex: =>BAE"),this.pushState("ARROW_DIR");break;case 86:return i.yytext=i.yytext.replace(/^,\s*/,""),s.getLogger().debug("Lex (right): dir:",i.yytext),"DIR";case 87:return i.yytext=i.yytext.replace(/^,\s*/,""),s.getLogger().debug("Lex (left):",i.yytext),"DIR";case 88:return i.yytext=i.yytext.replace(/^,\s*/,""),s.getLogger().debug("Lex (x):",i.yytext),"DIR";case 89:return i.yytext=i.yytext.replace(/^,\s*/,""),s.getLogger().debug("Lex (y):",i.yytext),"DIR";case 90:return i.yytext=i.yytext.replace(/^,\s*/,""),s.getLogger().debug("Lex (up):",i.yytext),"DIR";case 91:return i.yytext=i.yytext.replace(/^,\s*/,""),s.getLogger().debug("Lex (down):",i.yytext),"DIR";case 92:return i.yytext="]>",s.getLogger().debug("Lex (ARROW_DIR end):",i.yytext),this.popState(),this.popState(),"BLOCK_ARROW_END";case 93:return s.getLogger().debug("Lex: LINK","#"+i.yytext+"#"),15;case 94:return s.getLogger().debug("Lex: LINK",i.yytext),15;case 95:return s.getLogger().debug("Lex: LINK",i.yytext),15;case 96:return s.getLogger().debug("Lex: LINK",i.yytext),15;case 97:return s.getLogger().debug("Lex: START_LINK",i.yytext),this.pushState("LLABEL"),16;case 98:return s.getLogger().debug("Lex: START_LINK",i.yytext),this.pushState("LLABEL"),16;case 99:return s.getLogger().debug("Lex: START_LINK",i.yytext),this.pushState("LLABEL"),16;case 100:this.pushState("md_string");break;case 101:return s.getLogger().debug("Lex: Starting string"),this.pushState("string"),"LINK_LABEL";case 102:return this.popState(),s.getLogger().debug("Lex: LINK","#"+i.yytext+"#"),15;case 103:return this.popState(),s.getLogger().debug("Lex: LINK",i.yytext),15;case 104:return this.popState(),s.getLogger().debug("Lex: LINK",i.yytext),15;case 105:return s.getLogger().debug("Lex: 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+
+
+
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+
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+ .node ellipse,
+ .node polygon,
+ .node path {
+ fill: ${e.mainBkg};
+ stroke: ${e.nodeBorder};
+ stroke-width: 1px;
+ }
+ .flowchart-label text {
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+ }
+ // .flowchart-label .text-outer-tspan {
+ // text-anchor: middle;
+ // }
+ // .flowchart-label .text-inner-tspan {
+ // text-anchor: start;
+ // }
+
+ .node .label {
+ text-align: center;
+ }
+ .node.clickable {
+ cursor: pointer;
+ }
+
+ .arrowheadPath {
+ fill: ${e.arrowheadColor};
+ }
+
+ .edgePath .path {
+ stroke: ${e.lineColor};
+ stroke-width: 2.0px;
+ }
+
+ .flowchart-link {
+ stroke: ${e.lineColor};
+ fill: none;
+ }
+
+ .edgeLabel {
+ background-color: ${e.edgeLabelBackground};
+ rect {
+ opacity: 0.5;
+ background-color: ${e.edgeLabelBackground};
+ fill: ${e.edgeLabelBackground};
+ }
+ text-align: center;
+ }
+
+ /* For html labels only */
+ .labelBkg {
+ background-color: ${q(e.edgeLabelBackground,.5)};
+ // background-color:
+ }
+
+ .node .cluster {
+ // fill: ${q(e.mainBkg,.5)};
+ fill: ${q(e.clusterBkg,.5)};
+ stroke: ${q(e.clusterBorder,.2)};
+ box-shadow: rgba(50, 50, 93, 0.25) 0px 13px 27px -5px, rgba(0, 0, 0, 0.3) 0px 8px 16px -8px;
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+ }
+
+ .cluster text {
+ fill: ${e.titleColor};
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+
+ .cluster span,p {
+ color: ${e.titleColor};
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+ /* .cluster div {
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+ } */
+
+ div.mermaidTooltip {
+ position: absolute;
+ text-align: center;
+ max-width: 200px;
+ padding: 2px;
+ font-family: ${e.fontFamily};
+ font-size: 12px;
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+
+ .flowchartTitleText {
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e.children)h.size&&(S.debug(`abc95 Setting size of children of ${e.id} id=${h.id} ${E} ${L} ${h.size}`),h.size.width=E*(h.widthInColumns||1)+_*((h.widthInColumns||1)-1),h.size.height=L,h.size.x=0,h.size.y=0,S.debug(`abc95 updating size of ${e.id} children child:${h.id} maxWidth:${E} maxHeight:${L}`));for(const h of e.children)te(h,a,E,L);const k=e.columns||-1;let D=0;for(const h of e.children)D+=h.widthInColumns||1;let o=e.children.length;k>0&&k0?Math.min(e.children.length,k):e.children.length;if(h>0){const t=(i-h*_-_)/h;S.debug("abc95 (growing to fit) width",e.id,i,(x=e.size)==null?void 0:x.width,t);for(const m of e.children)m.size&&(m.size.width=t)}}e.size={width:i,height:u,x:0,y:0}}S.debug("setBlockSizes abc94 (done)",e.id,(y=e==null?void 0:e.size)==null?void 0:y.x,(T=e==null?void 0:e.size)==null?void 0:T.width,(v=e==null?void 0:e.size)==null?void 0:v.y,(N=e==null?void 0:e.size)==null?void 0:N.height)}function xe(e,a){var d,c,n,g,l,f,b,p,x,y,T,v,N,E,L,A,k;S.debug(`abc85 layout blocks (=>layoutBlocks) ${e.id} x: ${(d=e==null?void 0:e.size)==null?void 0:d.x} y: ${(c=e==null?void 0:e.size)==null?void 0:c.y} width: ${(n=e==null?void 0:e.size)==null?void 0:n.width}`);const D=e.columns||-1;if(S.debug("layoutBlocks columns abc95",e.id,"=>",D,e),e.children&&e.children.length>0){const o=((l=(g=e==null?void 0:e.children[0])==null?void 0:g.size)==null?void 0:l.width)||0,s=e.children.length*o+(e.children.length-1)*_;S.debug("widthOfChildren 88",s,"posX");let i=0;S.debug("abc91 block?.size?.x",e.id,(f=e==null?void 0:e.size)==null?void 0:f.x);let u=(b=e==null?void 0:e.size)!=null&&b.x?((p=e==null?void 0:e.size)==null?void 0:p.x)+(-((x=e==null?void 0:e.size)==null?void 0:x.width)/2||0):-_,h=0;for(const t of e.children){const m=e;if(!t.size)continue;const{width:r,height:R}=t.size,{px:Y,py:F}=gt(D,i);if(F!=h&&(h=F,u=(y=e==null?void 0:e.size)!=null&&y.x?((T=e==null?void 0:e.size)==null?void 0:T.x)+(-((v=e==null?void 0:e.size)==null?void 0:v.width)/2||0):-_,S.debug("New row in layout for block",e.id," and child ",t.id,h)),S.debug(`abc89 layout blocks (child) id: ${t.id} Pos: ${i} (px, py) ${Y},${F} (${(N=m==null?void 0:m.size)==null?void 0:N.x},${(E=m==null?void 0:m.size)==null?void 0:E.y}) parent: ${m.id} width: ${r}${_}`),m.size){const C=r/2;t.size.x=u+_+C,S.debug(`abc91 layout blocks (calc) px, pyid:${t.id} startingPos=X${u} new startingPosX${t.size.x} ${C} padding=${_} width=${r} halfWidth=${C} => x:${t.size.x} y:${t.size.y} ${t.widthInColumns} (width * (child?.w || 1)) / 2 ${r*((t==null?void 0:t.widthInColumns)||1)/2}`),u=t.size.x+C,t.size.y=m.size.y-m.size.height/2+F*(R+_)+R/2+_,S.debug(`abc88 layout blocks (calc) px, pyid:${t.id}startingPosX${u}${_}${C}=>x:${t.size.x}y:${t.size.y}${t.widthInColumns}(width * (child?.w || 1)) / 2${r*((t==null?void 0:t.widthInColumns)||1)/2}`)}t.children&&xe(t),i+=(t==null?void 0:t.widthInColumns)||1,S.debug("abc88 columnsPos",t,i)}}S.debug(`layout blocks (<==layoutBlocks) ${e.id} x: ${(L=e==null?void 0:e.size)==null?void 0:L.x} y: ${(A=e==null?void 0:e.size)==null?void 0:A.y} width: ${(k=e==null?void 0:e.size)==null?void 0:k.width}`)}function Se(e,{minX:a,minY:d,maxX:c,maxY:n}={minX:0,minY:0,maxX:0,maxY:0}){if(e.size&&e.id!=="root"){const{x:g,y:l,width:f,height:b}=e.size;g-f/2c&&(c=g+f/2),l+b/2>n&&(n=l+b/2)}if(e.children)for(const g of e.children)({minX:a,minY:d,maxX:c,maxY:n}=Se(g,{minX:a,minY:d,maxX:c,maxY:n}));return{minX:a,minY:d,maxX:c,maxY:n}}function ft(e){const a=e.getBlock("root");if(!a)return;te(a,e,0,0),xe(a),S.debug("getBlocks",JSON.stringify(a,null,2));const{minX:d,minY:c,maxX:n,maxY:g}=Se(a),l=g-c,f=n-d;return{x:d,y:c,width:f,height:l}}const bt=function(e,a){return a.db.getClasses()},xt=async function(e,a,d,c){const{securityLevel:n,block:g}=se(),l=c.db;let f;n==="sandbox"&&(f=H("#i"+a));const b=n==="sandbox"?H(f.nodes()[0].contentDocument.body):H("body"),p=n==="sandbox"?b.select(`[id="${a}"]`):H(`[id="${a}"]`);ke(p,["point","circle","cross"],c.type,a);const y=l.getBlocks(),T=l.getBlocksFlat(),v=l.getEdges(),N=p.insert("g").attr("class","block");await ut(N,y,l);const E=ft(l);if(await dt(N,y,l),await ht(N,v,T,l,a),E){const L=E,A=Math.max(1,Math.round(.125*(L.width/L.height))),k=L.height+A+10,D=L.width+10,{useMaxWidth:o}=g;ye(p,k,D,!!o),S.debug("Here Bounds",E,L),p.attr("viewBox",`${L.x-5} ${L.y-5} ${L.width+10} ${L.height+10}`)}Ae(Be)},St={draw:xt,getClasses:bt},Tt={parser:Pe,db:nt,renderer:St,styles:lt};export{Tt as diagram};
diff --git a/frontend-dist/assets/c4Diagram-c83219d4-Dwk4T9_E.js b/frontend-dist/assets/c4Diagram-c83219d4-Dwk4T9_E.js
new file mode 100644
index 0000000000000000000000000000000000000000..2c98deef9f05bda0f281c70af3d5ae7d0c88d022
--- /dev/null
+++ b/frontend-dist/assets/c4Diagram-c83219d4-Dwk4T9_E.js
@@ -0,0 +1,10 @@
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Tt[kt])this.terminals_[Mt]&&Mt>ve&&Lt.push("'"+this.terminals_[Mt]+"'");D.showPosition?Gt="Parse error on line "+(Et+1)+`:
+`+D.showPosition()+`
+Expecting `+Lt.join(", ")+", got '"+(this.terminals_[M]||M)+"'":Gt="Parse error on line "+(Et+1)+": Unexpected "+(M==ae?"end of input":"'"+(this.terminals_[M]||M)+"'"),this.parseError(Gt,{text:D.match,token:this.terminals_[M]||M,line:D.yylineno,loc:Ht,expected:Lt})}if(N[0]instanceof Array&&N.length>1)throw new Error("Parse Error: multiple actions possible at state: "+kt+", token: "+M);switch(N[0]){case 1:m.push(M),T.push(D.yytext),u.push(D.yylloc),m.push(N[1]),M=null,se=D.yyleng,y=D.yytext,Et=D.yylineno,Ht=D.yylloc;break;case 2:if(z=this.productions_[N[1]][1],Ct.$=T[T.length-z],Ct._$={first_line:u[u.length-(z||1)].first_line,last_line:u[u.length-1].last_line,first_column:u[u.length-(z||1)].first_column,last_column:u[u.length-1].last_column},Ae&&(Ct._$.range=[u[u.length-(z||1)].range[0],u[u.length-1].range[1]]),qt=this.performAction.apply(Ct,[y,se,Et,vt.yy,N[1],T,u].concat(ke)),typeof qt<"u")return qt;z&&(m=m.slice(0,-1*z*2),T=T.slice(0,-1*z),u=u.slice(0,-1*z)),m.push(this.productions_[N[1]][0]),T.push(Ct.$),u.push(Ct._$),re=Tt[m[m.length-2]][m[m.length-1]],m.push(re);break;case 3:return!0}}return!0}},Ee=function(){var bt={EOF:1,parseError:function(x,m){if(this.yy.parser)this.yy.parser.parseError(x,m);else throw new Error(x)},setInput:function(_,x){return this.yy=x||this.yy||{},this._input=_,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var _=this._input[0];this.yytext+=_,this.yyleng++,this.offset++,this.match+=_,this.matched+=_;var x=_.match(/(?:\r\n?|\n).*/g);return x?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),_},unput:function(_){var x=_.length,m=_.split(/(?:\r\n?|\n)/g);this._input=_+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-x),this.offset-=x;var g=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),m.length-1&&(this.yylineno-=m.length-1);var T=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:m?(m.length===g.length?this.yylloc.first_column:0)+g[g.length-m.length].length-m[0].length:this.yylloc.first_column-x},this.options.ranges&&(this.yylloc.range=[T[0],T[0]+this.yyleng-x]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(_){this.unput(this.match.slice(_))},pastInput:function(){var _=this.matched.substr(0,this.matched.length-this.match.length);return(_.length>20?"...":"")+_.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var _=this.match;return _.length<20&&(_+=this._input.substr(0,20-_.length)),(_.substr(0,20)+(_.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var _=this.pastInput(),x=new Array(_.length+1).join("-");return _+this.upcomingInput()+`
+`+x+"^"},test_match:function(_,x){var m,g,T;if(this.options.backtrack_lexer&&(T={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(T.yylloc.range=this.yylloc.range.slice(0))),g=_[0].match(/(?:\r\n?|\n).*/g),g&&(this.yylineno+=g.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:g?g[g.length-1].length-g[g.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+_[0].length},this.yytext+=_[0],this.match+=_[0],this.matches=_,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(_[0].length),this.matched+=_[0],m=this.performAction.call(this,this.yy,this,x,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),m)return m;if(this._backtrack){for(var u in T)this[u]=T[u];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var _,x,m,g;this._more||(this.yytext="",this.match="");for(var T=this._currentRules(),u=0;ux[0].length)){if(x=m,g=u,this.options.backtrack_lexer){if(_=this.test_match(m,T[u]),_!==!1)return _;if(this._backtrack){x=!1;continue}else return!1}else if(!this.options.flex)break}return x?(_=this.test_match(x,T[g]),_!==!1?_:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var x=this.next();return x||this.lex()},begin:function(x){this.conditionStack.push(x)},popState:function(){var x=this.conditionStack.length-1;return x>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(x){return x=this.conditionStack.length-1-Math.abs(x||0),x>=0?this.conditionStack[x]:"INITIAL"},pushState:function(x){this.begin(x)},stateStackSize:function(){return this.conditionStack.length},options:{},performAction:function(x,m,g,T){switch(g){case 0:return 6;case 1:return 7;case 2:return 8;case 3:return 9;case 4:return 22;case 5:return 23;case 6:return this.begin("acc_title"),24;case 7:return this.popState(),"acc_title_value";case 8:return this.begin("acc_descr"),26;case 9:return this.popState(),"acc_descr_value";case 10:this.begin("acc_descr_multiline");break;case 11:this.popState();break;case 12:return"acc_descr_multiline_value";case 13:break;case 14:c;break;case 15:return 12;case 16:break;case 17:return 11;case 18:return 15;case 19:return 16;case 20:return 17;case 21:return 18;case 22:return this.begin("person_ext"),45;case 23:return this.begin("person"),44;case 24:return this.begin("system_ext_queue"),51;case 25:return this.begin("system_ext_db"),50;case 26:return this.begin("system_ext"),49;case 27:return this.begin("system_queue"),48;case 28:return this.begin("system_db"),47;case 29:return this.begin("system"),46;case 30:return this.begin("boundary"),37;case 31:return this.begin("enterprise_boundary"),34;case 32:return this.begin("system_boundary"),36;case 33:return this.begin("container_ext_queue"),57;case 34:return this.begin("container_ext_db"),56;case 35:return this.begin("container_ext"),55;case 36:return this.begin("container_queue"),54;case 37:return this.begin("container_db"),53;case 38:return this.begin("container"),52;case 39:return this.begin("container_boundary"),38;case 40:return this.begin("component_ext_queue"),63;case 41:return this.begin("component_ext_db"),62;case 42:return this.begin("component_ext"),61;case 43:return this.begin("component_queue"),60;case 44:return this.begin("component_db"),59;case 45:return this.begin("component"),58;case 46:return this.begin("node"),39;case 47:return this.begin("node"),39;case 48:return this.begin("node_l"),40;case 49:return this.begin("node_r"),41;case 50:return this.begin("rel"),64;case 51:return this.begin("birel"),65;case 52:return this.begin("rel_u"),66;case 53:return this.begin("rel_u"),66;case 54:return this.begin("rel_d"),67;case 55:return this.begin("rel_d"),67;case 56:return this.begin("rel_l"),68;case 57:return this.begin("rel_l"),68;case 58:return this.begin("rel_r"),69;case 59:return this.begin("rel_r"),69;case 60:return this.begin("rel_b"),70;case 61:return this.begin("rel_index"),71;case 62:return this.begin("update_el_style"),72;case 63:return this.begin("update_rel_style"),73;case 64:return this.begin("update_layout_config"),74;case 65:return"EOF_IN_STRUCT";case 66:return this.begin("attribute"),"ATTRIBUTE_EMPTY";case 67:this.begin("attribute");break;case 68:this.popState(),this.popState();break;case 69:return 80;case 70:break;case 71:return 80;case 72:this.begin("string");break;case 73:this.popState();break;case 74:return"STR";case 75:this.begin("string_kv");break;case 76:return this.begin("string_kv_key"),"STR_KEY";case 77:this.popState(),this.begin("string_kv_value");break;case 78:return"STR_VALUE";case 79:this.popState(),this.popState();break;case 80:return"STR";case 81:return"LBRACE";case 82:return"RBRACE";case 83:return"SPACE";case 84:return"EOL";case 85:return 14}},rules:[/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:title\s[^#\n;]+)/,/^(?:accDescription\s[^#\n;]+)/,/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:%%(?!\{)*[^\n]*(\r?\n?)+)/,/^(?:%%[^\n]*(\r?\n)*)/,/^(?:\s*(\r?\n)+)/,/^(?:\s+)/,/^(?:C4Context\b)/,/^(?:C4Container\b)/,/^(?:C4Component\b)/,/^(?:C4Dynamic\b)/,/^(?:C4Deployment\b)/,/^(?:Person_Ext\b)/,/^(?:Person\b)/,/^(?:SystemQueue_Ext\b)/,/^(?:SystemDb_Ext\b)/,/^(?:System_Ext\b)/,/^(?:SystemQueue\b)/,/^(?:SystemDb\b)/,/^(?:System\b)/,/^(?:Boundary\b)/,/^(?:Enterprise_Boundary\b)/,/^(?:System_Boundary\b)/,/^(?:ContainerQueue_Ext\b)/,/^(?:ContainerDb_Ext\b)/,/^(?:Container_Ext\b)/,/^(?:ContainerQueue\b)/,/^(?:ContainerDb\b)/,/^(?:Container\b)/,/^(?:Container_Boundary\b)/,/^(?:ComponentQueue_Ext\b)/,/^(?:ComponentDb_Ext\b)/,/^(?:Component_Ext\b)/,/^(?:ComponentQueue\b)/,/^(?:ComponentDb\b)/,/^(?:Component\b)/,/^(?:Deployment_Node\b)/,/^(?:Node\b)/,/^(?:Node_L\b)/,/^(?:Node_R\b)/,/^(?:Rel\b)/,/^(?:BiRel\b)/,/^(?:Rel_Up\b)/,/^(?:Rel_U\b)/,/^(?:Rel_Down\b)/,/^(?:Rel_D\b)/,/^(?:Rel_Left\b)/,/^(?:Rel_L\b)/,/^(?:Rel_Right\b)/,/^(?:Rel_R\b)/,/^(?:Rel_Back\b)/,/^(?:RelIndex\b)/,/^(?:UpdateElementStyle\b)/,/^(?:UpdateRelStyle\b)/,/^(?:UpdateLayoutConfig\b)/,/^(?:$)/,/^(?:[(][ ]*[,])/,/^(?:[(])/,/^(?:[)])/,/^(?:,,)/,/^(?:,)/,/^(?:[ ]*["]["])/,/^(?:[ ]*["])/,/^(?:["])/,/^(?:[^"]*)/,/^(?:[ ]*[\$])/,/^(?:[^=]*)/,/^(?:[=][ ]*["])/,/^(?:[^"]+)/,/^(?:["])/,/^(?:[^,]+)/,/^(?:\{)/,/^(?:\})/,/^(?:[\s]+)/,/^(?:[\n\r]+)/,/^(?:$)/],conditions:{acc_descr_multiline:{rules:[11,12],inclusive:!1},acc_descr:{rules:[9],inclusive:!1},acc_title:{rules:[7],inclusive:!1},string_kv_value:{rules:[78,79],inclusive:!1},string_kv_key:{rules:[77],inclusive:!1},string_kv:{rules:[76],inclusive:!1},string:{rules:[73,74],inclusive:!1},attribute:{rules:[68,69,70,71,72,75,80],inclusive:!1},update_layout_config:{rules:[65,66,67,68],inclusive:!1},update_rel_style:{rules:[65,66,67,68],inclusive:!1},update_el_style:{rules:[65,66,67,68],inclusive:!1},rel_b:{rules:[65,66,67,68],inclusive:!1},rel_r:{rules:[65,66,67,68],inclusive:!1},rel_l:{rules:[65,66,67,68],inclusive:!1},rel_d:{rules:[65,66,67,68],inclusive:!1},rel_u:{rules:[65,66,67,68],inclusive:!1},rel_bi:{rules:[],inclusive:!1},rel:{rules:[65,66,67,68],inclusive:!1},node_r:{rules:[65,66,67,68],inclusive:!1},node_l:{rules:[65,66,67,68],inclusive:!1},node:{rules:[65,66,67,68],inclusive:!1},index:{rules:[],inclusive:!1},rel_index:{rules:[65,66,67,68],inclusive:!1},component_ext_queue:{rules:[],inclusive:!1},component_ext_db:{rules:[65,66,67,68],inclusive:!1},component_ext:{rules:[65,66,67,68],inclusive:!1},component_queue:{rules:[65,66,67,68],inclusive:!1},component_db:{rules:[65,66,67,68],inclusive:!1},component:{rules:[65,66,67,68],inclusive:!1},container_boundary:{rules:[65,66,67,68],inclusive:!1},container_ext_queue:{rules:[65,66,67,68],inclusive:!1},container_ext_db:{rules:[65,66,67,68],inclusive:!1},container_ext:{rules:[65,66,67,68],inclusive:!1},container_queue:{rules:[65,66,67,68],inclusive:!1},container_db:{rules:[65,66,67,68],inclusive:!1},container:{rules:[65,66,67,68],inclusive:!1},birel:{rules:[65,66,67,68],inclusive:!1},system_boundary:{rules:[65,66,67,68],inclusive:!1},enterprise_boundary:{rules:[65,66,67,68],inclusive:!1},boundary:{rules:[65,66,67,68],inclusive:!1},system_ext_queue:{rules:[65,66,67,68],inclusive:!1},system_ext_db:{rules:[65,66,67,68],inclusive:!1},system_ext:{rules:[65,66,67,68],inclusive:!1},system_queue:{rules:[65,66,67,68],inclusive:!1},system_db:{rules:[65,66,67,68],inclusive:!1},system:{rules:[65,66,67,68],inclusive:!1},person_ext:{rules:[65,66,67,68],inclusive:!1},person:{rules:[65,66,67,68],inclusive:!1},INITIAL:{rules:[0,1,2,3,4,5,6,8,10,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,81,82,83,84,85],inclusive:!0}}};return bt}();Xt.lexer=Ee;function Wt(){this.yy={}}return Wt.prototype=Xt,Xt.Parser=Wt,new Wt}();Yt.parser=Yt;const Be=Yt;let U=[],_t=[""],P="global",j="",V=[{alias:"global",label:{text:"global"},type:{text:"global"},tags:null,link:null,parentBoundary:""}],St=[],te="",ee=!1,It=4,jt=2;var de;const Ye=function(){return de},Ie=function(e){de=ue(e,Dt())},je=function(e,t,a,o,l,i,s,r,n){if(e==null||t===void 0||t===null||a===void 0||a===null||o===void 0||o===null)return;let h={};const f=St.find(d=>d.from===t&&d.to===a);if(f?h=f:St.push(h),h.type=e,h.from=t,h.to=a,h.label={text:o},l==null)h.techn={text:""};else if(typeof l=="object"){let[d,p]=Object.entries(l)[0];h[d]={text:p}}else h.techn={text:l};if(i==null)h.descr={text:""};else if(typeof i=="object"){let[d,p]=Object.entries(i)[0];h[d]={text:p}}else h.descr={text:i};if(typeof s=="object"){let[d,p]=Object.entries(s)[0];h[d]=p}else h.sprite=s;if(typeof r=="object"){let[d,p]=Object.entries(r)[0];h[d]=p}else h.tags=r;if(typeof n=="object"){let[d,p]=Object.entries(n)[0];h[d]=p}else h.link=n;h.wrap=xt()},Ue=function(e,t,a,o,l,i,s){if(t===null||a===null)return;let r={};const n=U.find(h=>h.alias===t);if(n&&t===n.alias?r=n:(r.alias=t,U.push(r)),a==null?r.label={text:""}:r.label={text:a},o==null)r.descr={text:""};else if(typeof o=="object"){let[h,f]=Object.entries(o)[0];r[h]={text:f}}else r.descr={text:o};if(typeof l=="object"){let[h,f]=Object.entries(l)[0];r[h]=f}else r.sprite=l;if(typeof i=="object"){let[h,f]=Object.entries(i)[0];r[h]=f}else r.tags=i;if(typeof s=="object"){let[h,f]=Object.entries(s)[0];r[h]=f}else r.link=s;r.typeC4Shape={text:e},r.parentBoundary=P,r.wrap=xt()},Fe=function(e,t,a,o,l,i,s,r){if(t===null||a===null)return;let n={};const h=U.find(f=>f.alias===t);if(h&&t===h.alias?n=h:(n.alias=t,U.push(n)),a==null?n.label={text:""}:n.label={text:a},o==null)n.techn={text:""};else if(typeof o=="object"){let[f,d]=Object.entries(o)[0];n[f]={text:d}}else n.techn={text:o};if(l==null)n.descr={text:""};else 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S=0;S=this.data.widthLimit||o>=this.data.widthLimit||this.nextData.cnt>ge)&&(a=this.nextData.startx+t.margin+b.nextLinePaddingX,l=this.nextData.stopy+t.margin*2,this.nextData.stopx=o=a+t.width,this.nextData.starty=this.nextData.stopy,this.nextData.stopy=i=l+t.height,this.nextData.cnt=1),t.x=a,t.y=l,this.updateVal(this.data,"startx",a,Math.min),this.updateVal(this.data,"starty",l,Math.min),this.updateVal(this.data,"stopx",o,Math.max),this.updateVal(this.data,"stopy",i,Math.max),this.updateVal(this.nextData,"startx",a,Math.min),this.updateVal(this.nextData,"starty",l,Math.min),this.updateVal(this.nextData,"stopx",o,Math.max),this.updateVal(this.nextData,"stopy",i,Math.max)}init(t){this.name="",this.data={startx:void 0,stopx:void 0,starty:void 0,stopy:void 0,widthLimit:void 0},this.nextData={startx:void 0,stopx:void 0,starty:void 0,stopy:void 0,cnt:0},$t(t.db.getConfig())}bumpLastMargin(t){this.data.stopx+=t,this.data.stopy+=t}}const $t=function(e){Se(b,e),e.fontFamily&&(b.personFontFamily=b.systemFontFamily=b.messageFontFamily=e.fontFamily),e.fontSize&&(b.personFontSize=b.systemFontSize=b.messageFontSize=e.fontSize),e.fontWeight&&(b.personFontWeight=b.systemFontWeight=b.messageFontWeight=e.fontWeight)},Rt=(e,t)=>({fontFamily:e[t+"FontFamily"],fontSize:e[t+"FontSize"],fontWeight:e[t+"FontWeight"]}),Bt=e=>({fontFamily:e.boundaryFontFamily,fontSize:e.boundaryFontSize,fontWeight:e.boundaryFontWeight}),k0=e=>({fontFamily:e.messageFontFamily,fontSize:e.messageFontSize,fontWeight:e.messageFontWeight});function I(e,t,a,o,l){if(!t[e].width)if(a)t[e].text=Me(t[e].text,l,o),t[e].textLines=t[e].text.split(Kt.lineBreakRegex).length,t[e].width=l,t[e].height=oe(t[e].text,o);else{let i=t[e].text.split(Kt.lineBreakRegex);t[e].textLines=i.length;let s=0;t[e].height=0,t[e].width=0;for(const r of i)t[e].width=Math.max(wt(r,o),t[e].width),s=oe(r,o),t[e].height=t[e].height+s}}const _e=function(e,t,a){t.x=a.data.startx,t.y=a.data.starty,t.width=a.data.stopx-a.data.startx,t.height=a.data.stopy-a.data.starty,t.label.y=b.c4ShapeMargin-35;let o=t.wrap&&b.wrap,l=Bt(b);l.fontSize=l.fontSize+2,l.fontWeight="bold";let i=wt(t.label.text,l);I("label",t,o,l,i),F.drawBoundary(e,t,b)},xe=function(e,t,a,o){let l=0;for(const i of o){l=0;const s=a[i];let r=Rt(b,s.typeC4Shape.text);switch(r.fontSize=r.fontSize-2,s.typeC4Shape.width=wt("«"+s.typeC4Shape.text+"»",r),s.typeC4Shape.height=r.fontSize+2,s.typeC4Shape.Y=b.c4ShapePadding,l=s.typeC4Shape.Y+s.typeC4Shape.height-4,s.image={width:0,height:0,Y:0},s.typeC4Shape.text){case"person":case"external_person":s.image.width=48,s.image.height=48,s.image.Y=l,l=s.image.Y+s.image.height;break}s.sprite&&(s.image.width=48,s.image.height=48,s.image.Y=l,l=s.image.Y+s.image.height);let n=s.wrap&&b.wrap,h=b.width-b.c4ShapePadding*2,f=Rt(b,s.typeC4Shape.text);if(f.fontSize=f.fontSize+2,f.fontWeight="bold",I("label",s,n,f,h),s.label.Y=l+8,l=s.label.Y+s.label.height,s.type&&s.type.text!==""){s.type.text="["+s.type.text+"]";let E=Rt(b,s.typeC4Shape.text);I("type",s,n,E,h),s.type.Y=l+5,l=s.type.Y+s.type.height}else if(s.techn&&s.techn.text!==""){s.techn.text="["+s.techn.text+"]";let E=Rt(b,s.techn.text);I("techn",s,n,E,h),s.techn.Y=l+5,l=s.techn.Y+s.techn.height}let d=l,p=s.label.width;if(s.descr&&s.descr.text!==""){let E=Rt(b,s.typeC4Shape.text);I("descr",s,n,E,h),s.descr.Y=l+20,l=s.descr.Y+s.descr.height,p=Math.max(s.label.width,s.descr.width),d=l-s.descr.textLines*5}p=p+b.c4ShapePadding,s.width=Math.max(s.width||b.width,p,b.width),s.height=Math.max(s.height||b.height,d,b.height),s.margin=s.margin||b.c4ShapeMargin,e.insert(s),F.drawC4Shape(t,s,b)}e.bumpLastMargin(b.c4ShapeMargin)};class B{constructor(t,a){this.x=t,this.y=a}}let ce=function(e,t){let a=e.x,o=e.y,l=t.x,i=t.y,s=a+e.width/2,r=o+e.height/2,n=Math.abs(a-l),h=Math.abs(o-i),f=h/n,d=e.height/e.width,p=null;return o==i&&al?p=new B(a,r):a==l&&oi&&(p=new B(s,o)),a>l&&o=f?p=new B(a,r+f*e.width/2):p=new B(s-n/h*e.height/2,o+e.height):a=f?p=new B(a+e.width,r+f*e.width/2):p=new B(s+n/h*e.height/2,o+e.height):ai?d>=f?p=new B(a+e.width,r-f*e.width/2):p=new B(s+e.height/2*n/h,o):a>l&&o>i&&(d>=f?p=new B(a,r-e.width/2*f):p=new B(s-e.height/2*n/h,o)),p},A0=function(e,t){let a={x:0,y:0};a.x=t.x+t.width/2,a.y=t.y+t.height/2;let o=ce(e,a);a.x=e.x+e.width/2,a.y=e.y+e.height/2;let l=ce(t,a);return{startPoint:o,endPoint:l}};const C0=function(e,t,a,o){let l=0;for(let i of t){l=l+1;let s=i.wrap&&b.wrap,r=k0(b);o.db.getC4Type()==="C4Dynamic"&&(i.label.text=l+": "+i.label.text);let h=wt(i.label.text,r);I("label",i,s,r,h),i.techn&&i.techn.text!==""&&(h=wt(i.techn.text,r),I("techn",i,s,r,h)),i.descr&&i.descr.text!==""&&(h=wt(i.descr.text,r),I("descr",i,s,r,h));let f=a(i.from),d=a(i.to),p=A0(f,d);i.startPoint=p.startPoint,i.endPoint=p.endPoint}F.drawRels(e,t,b)};function me(e,t,a,o,l){let i=new be(l);i.data.widthLimit=a.data.widthLimit/Math.min(Zt,o.length);for(let[s,r]of o.entries()){let n=0;r.image={width:0,height:0,Y:0},r.sprite&&(r.image.width=48,r.image.height=48,r.image.Y=n,n=r.image.Y+r.image.height);let h=r.wrap&&b.wrap,f=Bt(b);if(f.fontSize=f.fontSize+2,f.fontWeight="bold",I("label",r,h,f,i.data.widthLimit),r.label.Y=n+8,n=r.label.Y+r.label.height,r.type&&r.type.text!==""){r.type.text="["+r.type.text+"]";let O=Bt(b);I("type",r,h,O,i.data.widthLimit),r.type.Y=n+5,n=r.type.Y+r.type.height}if(r.descr&&r.descr.text!==""){let O=Bt(b);O.fontSize=O.fontSize-2,I("descr",r,h,O,i.data.widthLimit),r.descr.Y=n+20,n=r.descr.Y+r.descr.height}if(s==0||s%Zt===0){let O=a.data.startx+b.diagramMarginX,R=a.data.stopy+b.diagramMarginY+n;i.setData(O,O,R,R)}else{let O=i.data.stopx!==i.data.startx?i.data.stopx+b.diagramMarginX:i.data.startx,R=i.data.starty;i.setData(O,O,R,R)}i.name=r.alias;let d=l.db.getC4ShapeArray(r.alias),p=l.db.getC4ShapeKeys(r.alias);p.length>0&&xe(i,e,d,p),t=r.alias;let E=l.db.getBoundarys(t);E.length>0&&me(e,t,i,E,l),r.alias!=="global"&&_e(e,r,i),a.data.stopy=Math.max(i.data.stopy+b.c4ShapeMargin,a.data.stopy),a.data.stopx=Math.max(i.data.stopx+b.c4ShapeMargin,a.data.stopx),Ut=Math.max(Ut,a.data.stopx),Ft=Math.max(Ft,a.data.stopy)}}const w0=function(e,t,a,o){b=Dt().c4;const l=Dt().securityLevel;let i;l==="sandbox"&&(i=Nt("#i"+t));const s=l==="sandbox"?Nt(i.nodes()[0].contentDocument.body):Nt("body");let r=o.db;o.db.setWrap(b.wrap),ge=r.getC4ShapeInRow(),Zt=r.getC4BoundaryInRow(),le.debug(`C:${JSON.stringify(b,null,2)}`);const n=l==="sandbox"?s.select(`[id="${t}"]`):Nt(`[id="${t}"]`);F.insertComputerIcon(n),F.insertDatabaseIcon(n),F.insertClockIcon(n);let h=new be(o);h.setData(b.diagramMarginX,b.diagramMarginX,b.diagramMarginY,b.diagramMarginY),h.data.widthLimit=screen.availWidth,Ut=b.diagramMarginX,Ft=b.diagramMarginY;const f=o.db.getTitle();let d=o.db.getBoundarys("");me(n,"",h,d,o),F.insertArrowHead(n),F.insertArrowEnd(n),F.insertArrowCrossHead(n),F.insertArrowFilledHead(n),C0(n,o.db.getRels(),o.db.getC4Shape,o),h.data.stopx=Ut,h.data.stopy=Ft;const p=h.data;let O=p.stopy-p.starty+2*b.diagramMarginY;const S=p.stopx-p.startx+2*b.diagramMarginX;f&&n.append("text").text(f).attr("x",(p.stopx-p.startx)/2-4*b.diagramMarginX).attr("y",p.starty+b.diagramMarginY),De(n,O,S,b.useMaxWidth);const L=f?60:0;n.attr("viewBox",p.startx-b.diagramMarginX+" -"+(b.diagramMarginY+L)+" "+S+" "+(O+L)),le.debug("models:",p)},he={drawPersonOrSystemArray:xe,drawBoundary:_e,setConf:$t,draw:w0},O0=e=>`.person {
+ stroke: ${e.personBorder};
+ fill: ${e.personBkg};
+ }
+`,T0=O0,S0={parser:Be,db:Jt,renderer:he,styles:T0,init:({c4:e,wrap:t})=>{he.setConf(e),Jt.setWrap(t)}};export{S0 as diagram};
diff --git a/frontend-dist/assets/channel-DsKT-zfZ.js b/frontend-dist/assets/channel-DsKT-zfZ.js
new file mode 100644
index 0000000000000000000000000000000000000000..02354bdafd7de0a4816e72b50944997235ef92af
--- /dev/null
+++ b/frontend-dist/assets/channel-DsKT-zfZ.js
@@ -0,0 +1 @@
+import{aH as o,aI as n}from"./index-BCNM9-Ly.js";const t=(a,r)=>o.lang.round(n.parse(a)[r]);export{t as c};
diff --git a/frontend-dist/assets/classDiagram-beda092f-wmkRqnN2.js b/frontend-dist/assets/classDiagram-beda092f-wmkRqnN2.js
new file mode 100644
index 0000000000000000000000000000000000000000..45aa332e7a8803c3c2a52153a14fce346d91d5b7
--- /dev/null
+++ b/frontend-dist/assets/classDiagram-beda092f-wmkRqnN2.js
@@ -0,0 +1,2 @@
+import{s as A,d as S,p as G}from"./styles-b4e223ce-CtHeUc7h.js";import{c as v,l as y,d as B,e as W,F as $,A as M,G as I}from"./index-BCNM9-Ly.js";import{G as O}from"./graph-CY8eBbAS.js";import{l as P}from"./layout-CUwpW5wl.js";import{l as X}from"./line-DdWeXrJe.js";import"./array-BKyUJesY.js";import"./path-CbwjOpE9.js";let H=0;const Y=function(i,a,t,o,p){const g=function(e){switch(e){case p.db.relationType.AGGREGATION:return"aggregation";case p.db.relationType.EXTENSION:return"extension";case p.db.relationType.COMPOSITION:return"composition";case p.db.relationType.DEPENDENCY:return"dependency";case p.db.relationType.LOLLIPOP:return"lollipop"}};a.points=a.points.filter(e=>!Number.isNaN(e.y));const s=a.points,c=X().x(function(e){return e.x}).y(function(e){return e.y}).curve($),n=i.append("path").attr("d",c(s)).attr("id","edge"+H).attr("class","relation");let r="";o.arrowMarkerAbsolute&&(r=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,r=r.replace(/\(/g,"\\("),r=r.replace(/\)/g,"\\)")),t.relation.lineType==1&&n.attr("class","relation dashed-line"),t.relation.lineType==10&&n.attr("class","relation dotted-line"),t.relation.type1!=="none"&&n.attr("marker-start","url("+r+"#"+g(t.relation.type1)+"Start)"),t.relation.type2!=="none"&&n.attr("marker-end","url("+r+"#"+g(t.relation.type2)+"End)");let f,h;const x=a.points.length;let b=M.calcLabelPosition(a.points);f=b.x,h=b.y;let u,m,w,k;if(x%2!==0&&x>1){let e=M.calcCardinalityPosition(t.relation.type1!=="none",a.points,a.points[0]),d=M.calcCardinalityPosition(t.relation.type2!=="none",a.points,a.points[x-1]);y.debug("cardinality_1_point "+JSON.stringify(e)),y.debug("cardinality_2_point "+JSON.stringify(d)),u=e.x,m=e.y,w=d.x,k=d.y}if(t.title!==void 0){const e=i.append("g").attr("class","classLabel"),d=e.append("text").attr("class","label").attr("x",f).attr("y",h).attr("fill","red").attr("text-anchor","middle").text(t.title);window.label=d;const l=d.node().getBBox();e.insert("rect",":first-child").attr("class","box").attr("x",l.x-o.padding/2).attr("y",l.y-o.padding/2).attr("width",l.width+o.padding).attr("height",l.height+o.padding)}y.info("Rendering relation "+JSON.stringify(t)),t.relationTitle1!==void 0&&t.relationTitle1!=="none"&&i.append("g").attr("class","cardinality").append("text").attr("class","type1").attr("x",u).attr("y",m).attr("fill","black").attr("font-size","6").text(t.relationTitle1),t.relationTitle2!==void 0&&t.relationTitle2!=="none"&&i.append("g").attr("class","cardinality").append("text").attr("class","type2").attr("x",w).attr("y",k).attr("fill","black").attr("font-size","6").text(t.relationTitle2),H++},J=function(i,a,t,o){y.debug("Rendering class ",a,t);const p=a.id,g={id:p,label:a.id,width:0,height:0},s=i.append("g").attr("id",o.db.lookUpDomId(p)).attr("class","classGroup");let c;a.link?c=s.append("svg:a").attr("xlink:href",a.link).attr("target",a.linkTarget).append("text").attr("y",t.textHeight+t.padding).attr("x",0):c=s.append("text").attr("y",t.textHeight+t.padding).attr("x",0);let n=!0;a.annotations.forEach(function(d){const l=c.append("tspan").text("«"+d+"»");n||l.attr("dy",t.textHeight),n=!1});let r=C(a);const f=c.append("tspan").text(r).attr("class","title");n||f.attr("dy",t.textHeight);const h=c.node().getBBox().height;let x,b,u;if(a.members.length>0){x=s.append("line").attr("x1",0).attr("y1",t.padding+h+t.dividerMargin/2).attr("y2",t.padding+h+t.dividerMargin/2);const d=s.append("text").attr("x",t.padding).attr("y",h+t.dividerMargin+t.textHeight).attr("fill","white").attr("class","classText");n=!0,a.members.forEach(function(l){_(d,l,n,t),n=!1}),b=d.node().getBBox()}if(a.methods.length>0){u=s.append("line").attr("x1",0).attr("y1",t.padding+h+t.dividerMargin+b.height).attr("y2",t.padding+h+t.dividerMargin+b.height);const d=s.append("text").attr("x",t.padding).attr("y",h+2*t.dividerMargin+b.height+t.textHeight).attr("fill","white").attr("class","classText");n=!0,a.methods.forEach(function(l){_(d,l,n,t),n=!1})}const m=s.node().getBBox();var w=" ";a.cssClasses.length>0&&(w=w+a.cssClasses.join(" "));const e=s.insert("rect",":first-child").attr("x",0).attr("y",0).attr("width",m.width+2*t.padding).attr("height",m.height+t.padding+.5*t.dividerMargin).attr("class",w).node().getBBox().width;return c.node().childNodes.forEach(function(d){d.setAttribute("x",(e-d.getBBox().width)/2)}),a.tooltip&&c.insert("title").text(a.tooltip),x&&x.attr("x2",e),u&&u.attr("x2",e),g.width=e,g.height=m.height+t.padding+.5*t.dividerMargin,g},C=function(i){let a=i.id;return i.type&&(a+="<"+I(i.type)+">"),a},Z=function(i,a,t,o){y.debug("Rendering note ",a,t);const p=a.id,g={id:p,text:a.text,width:0,height:0},s=i.append("g").attr("id",p).attr("class","classGroup");let c=s.append("text").attr("y",t.textHeight+t.padding).attr("x",0);const n=JSON.parse(`"${a.text}"`).split(`
+`);n.forEach(function(x){y.debug(`Adding line: ${x}`),c.append("tspan").text(x).attr("class","title").attr("dy",t.textHeight)});const r=s.node().getBBox(),h=s.insert("rect",":first-child").attr("x",0).attr("y",0).attr("width",r.width+2*t.padding).attr("height",r.height+n.length*t.textHeight+t.padding+.5*t.dividerMargin).node().getBBox().width;return c.node().childNodes.forEach(function(x){x.setAttribute("x",(h-x.getBBox().width)/2)}),g.width=h,g.height=r.height+n.length*t.textHeight+t.padding+.5*t.dividerMargin,g},_=function(i,a,t,o){const{displayText:p,cssStyle:g}=a.getDisplayDetails(),s=i.append("tspan").attr("x",o.padding).text(p);g!==""&&s.attr("style",a.cssStyle),t||s.attr("dy",o.textHeight)},N={getClassTitleString:C,drawClass:J,drawEdge:Y,drawNote:Z};let T={};const E=20,L=function(i){const a=Object.entries(T).find(t=>t[1].label===i);if(a)return a[0]},R=function(i){i.append("defs").append("marker").attr("id","extensionStart").attr("class","extension").attr("refX",0).attr("refY",7).attr("markerWidth",190).attr("markerHeight",240).attr("orient","auto").append("path").attr("d","M 1,7 L18,13 V 1 Z"),i.append("defs").append("marker").attr("id","extensionEnd").attr("refX",19).attr("refY",7).attr("markerWidth",20).attr("markerHeight",28).attr("orient","auto").append("path").attr("d","M 1,1 V 13 L18,7 Z"),i.append("defs").append("marker").attr("id","compositionStart").attr("class","extension").attr("refX",0).attr("refY",7).attr("markerWidth",190).attr("markerHeight",240).attr("orient","auto").append("path").attr("d","M 18,7 L9,13 L1,7 L9,1 Z"),i.append("defs").append("marker").attr("id","compositionEnd").attr("refX",19).attr("refY",7).attr("markerWidth",20).attr("markerHeight",28).attr("orient","auto").append("path").attr("d","M 18,7 L9,13 L1,7 L9,1 Z"),i.append("defs").append("marker").attr("id","aggregationStart").attr("class","extension").attr("refX",0).attr("refY",7).attr("markerWidth",190).attr("markerHeight",240).attr("orient","auto").append("path").attr("d","M 18,7 L9,13 L1,7 L9,1 Z"),i.append("defs").append("marker").attr("id","aggregationEnd").attr("refX",19).attr("refY",7).attr("markerWidth",20).attr("markerHeight",28).attr("orient","auto").append("path").attr("d","M 18,7 L9,13 L1,7 L9,1 Z"),i.append("defs").append("marker").attr("id","dependencyStart").attr("class","extension").attr("refX",0).attr("refY",7).attr("markerWidth",190).attr("markerHeight",240).attr("orient","auto").append("path").attr("d","M 5,7 L9,13 L1,7 L9,1 Z"),i.append("defs").append("marker").attr("id","dependencyEnd").attr("refX",19).attr("refY",7).attr("markerWidth",20).attr("markerHeight",28).attr("orient","auto").append("path").attr("d","M 18,7 L9,13 L14,7 L9,1 Z")},F=function(i,a,t,o){const p=v().class;T={},y.info("Rendering diagram "+i);const g=v().securityLevel;let s;g==="sandbox"&&(s=B("#i"+a));const c=g==="sandbox"?B(s.nodes()[0].contentDocument.body):B("body"),n=c.select(`[id='${a}']`);R(n);const r=new O({multigraph:!0});r.setGraph({isMultiGraph:!0}),r.setDefaultEdgeLabel(function(){return{}});const f=o.db.getClasses(),h=Object.keys(f);for(const e of h){const d=f[e],l=N.drawClass(n,d,p,o);T[l.id]=l,r.setNode(l.id,l),y.info("Org height: "+l.height)}o.db.getRelations().forEach(function(e){y.info("tjoho"+L(e.id1)+L(e.id2)+JSON.stringify(e)),r.setEdge(L(e.id1),L(e.id2),{relation:e},e.title||"DEFAULT")}),o.db.getNotes().forEach(function(e){y.debug(`Adding note: ${JSON.stringify(e)}`);const d=N.drawNote(n,e,p,o);T[d.id]=d,r.setNode(d.id,d),e.class&&e.class in f&&r.setEdge(e.id,L(e.class),{relation:{id1:e.id,id2:e.class,relation:{type1:"none",type2:"none",lineType:10}}},"DEFAULT")}),P(r),r.nodes().forEach(function(e){e!==void 0&&r.node(e)!==void 0&&(y.debug("Node "+e+": "+JSON.stringify(r.node(e))),c.select("#"+(o.db.lookUpDomId(e)||e)).attr("transform","translate("+(r.node(e).x-r.node(e).width/2)+","+(r.node(e).y-r.node(e).height/2)+" )"))}),r.edges().forEach(function(e){e!==void 0&&r.edge(e)!==void 0&&(y.debug("Edge "+e.v+" -> "+e.w+": "+JSON.stringify(r.edge(e))),N.drawEdge(n,r.edge(e),r.edge(e).relation,p,o))});const u=n.node().getBBox(),m=u.width+E*2,w=u.height+E*2;W(n,w,m,p.useMaxWidth);const k=`${u.x-E} ${u.y-E} ${m} ${w}`;y.debug(`viewBox ${k}`),n.attr("viewBox",k)},U={draw:F},tt={parser:G,db:S,renderer:U,styles:A,init:i=>{i.class||(i.class={}),i.class.arrowMarkerAbsolute=i.arrowMarkerAbsolute,S.clear()}};export{tt as diagram};
diff --git a/frontend-dist/assets/classDiagram-v2-2358418a-D17uXNnU.js b/frontend-dist/assets/classDiagram-v2-2358418a-D17uXNnU.js
new file mode 100644
index 0000000000000000000000000000000000000000..29dc9863121c3592726d4ef8a43abfe3d535f2e3
--- /dev/null
+++ b/frontend-dist/assets/classDiagram-v2-2358418a-D17uXNnU.js
@@ -0,0 +1,2 @@
+import{s as M,d as _,p as R}from"./styles-b4e223ce-CtHeUc7h.js";import{l as d,c,d as w,A as B,o as G,p as D,q as E,n as A,k as C}from"./index-BCNM9-Ly.js";import{G as q}from"./graph-CY8eBbAS.js";import{r as z}from"./index-5325376f-CbH2QcFV.js";import"./layout-CUwpW5wl.js";import"./clone-C4pHamD7.js";import"./edges-96097737-CqpaF4BI.js";import"./createText-1719965b-BZ0xZVnk.js";import"./line-DdWeXrJe.js";import"./array-BKyUJesY.js";import"./path-CbwjOpE9.js";const S=s=>C.sanitizeText(s,c());let k={dividerMargin:10,padding:5,textHeight:10,curve:void 0};const P=function(s,t,y,a){const e=Object.keys(s);d.info("keys:",e),d.info(s),e.forEach(function(i){var o,r;const l=s[i],p={shape:"rect",id:l.id,domId:l.domId,labelText:S(l.id),labelStyle:"",style:"fill: none; stroke: black",padding:((o=c().flowchart)==null?void 0:o.padding)??((r=c().class)==null?void 0:r.padding)};t.setNode(l.id,p),$(l.classes,t,y,a,l.id),d.info("setNode",p)})},$=function(s,t,y,a,e){const i=Object.keys(s);d.info("keys:",i),d.info(s),i.filter(o=>s[o].parent==e).forEach(function(o){var r,l;const n=s[o],p=n.cssClasses.join(" "),f=D(n.styles),h=n.label??n.id,u=0,b={labelStyle:f.labelStyle,shape:"class_box",labelText:S(h),classData:n,rx:u,ry:u,class:p,style:f.style,id:n.id,domId:n.domId,tooltip:a.db.getTooltip(n.id,e)||"",haveCallback:n.haveCallback,link:n.link,width:n.type==="group"?500:void 0,type:n.type,padding:((r=c().flowchart)==null?void 0:r.padding)??((l=c().class)==null?void 0:l.padding)};t.setNode(n.id,b),e&&t.setParent(n.id,e),d.info("setNode",b)})},F=function(s,t,y,a){d.info(s),s.forEach(function(e,i){var o,r;const l=e,n="",p={labelStyle:"",style:""},f=l.text,h=0,m={labelStyle:p.labelStyle,shape:"note",labelText:S(f),noteData:l,rx:h,ry:h,class:n,style:p.style,id:l.id,domId:l.id,tooltip:"",type:"note",padding:((o=c().flowchart)==null?void 0:o.padding)??((r=c().class)==null?void 0:r.padding)};if(t.setNode(l.id,m),d.info("setNode",m),!l.class||!(l.class in a))return;const b=y+i,x={id:`edgeNote${b}`,classes:"relation",pattern:"dotted",arrowhead:"none",startLabelRight:"",endLabelLeft:"",arrowTypeStart:"none",arrowTypeEnd:"none",style:"fill:none",labelStyle:"",curve:E(k.curve,A)};t.setEdge(l.id,l.class,x,b)})},H=function(s,t){const y=c().flowchart;let a=0;s.forEach(function(e){var i;a++;const o={classes:"relation",pattern:e.relation.lineType==1?"dashed":"solid",id:`id_${e.id1}_${e.id2}_${a}`,arrowhead:e.type==="arrow_open"?"none":"normal",startLabelRight:e.relationTitle1==="none"?"":e.relationTitle1,endLabelLeft:e.relationTitle2==="none"?"":e.relationTitle2,arrowTypeStart:N(e.relation.type1),arrowTypeEnd:N(e.relation.type2),style:"fill:none",labelStyle:"",curve:E(y==null?void 0:y.curve,A)};if(d.info(o,e),e.style!==void 0){const r=D(e.style);o.style=r.style,o.labelStyle=r.labelStyle}e.text=e.title,e.text===void 0?e.style!==void 0&&(o.arrowheadStyle="fill: #333"):(o.arrowheadStyle="fill: #333",o.labelpos="c",((i=c().flowchart)==null?void 0:i.htmlLabels)??c().htmlLabels?(o.labelType="html",o.label=''+e.text+""):(o.labelType="text",o.label=e.text.replace(C.lineBreakRegex,`
+`),e.style===void 0&&(o.style=o.style||"stroke: #333; stroke-width: 1.5px;fill:none"),o.labelStyle=o.labelStyle.replace("color:","fill:"))),t.setEdge(e.id1,e.id2,o,a)})},V=function(s){k={...k,...s}},W=async function(s,t,y,a){d.info("Drawing class - ",t);const e=c().flowchart??c().class,i=c().securityLevel;d.info("config:",e);const o=(e==null?void 0:e.nodeSpacing)??50,r=(e==null?void 0:e.rankSpacing)??50,l=new q({multigraph:!0,compound:!0}).setGraph({rankdir:a.db.getDirection(),nodesep:o,ranksep:r,marginx:8,marginy:8}).setDefaultEdgeLabel(function(){return{}}),n=a.db.getNamespaces(),p=a.db.getClasses(),f=a.db.getRelations(),h=a.db.getNotes();d.info(f),P(n,l,t,a),$(p,l,t,a),H(f,l),F(h,l,f.length+1,p);let u;i==="sandbox"&&(u=w("#i"+t));const m=i==="sandbox"?w(u.nodes()[0].contentDocument.body):w("body"),b=m.select(`[id="${t}"]`),x=m.select("#"+t+" g");if(await z(x,l,["aggregation","extension","composition","dependency","lollipop"],"classDiagram",t),B.insertTitle(b,"classTitleText",(e==null?void 0:e.titleTopMargin)??5,a.db.getDiagramTitle()),G(l,b,e==null?void 0:e.diagramPadding,e==null?void 0:e.useMaxWidth),!(e!=null&&e.htmlLabels)){const T=i==="sandbox"?u.nodes()[0].contentDocument:document,I=T.querySelectorAll('[id="'+t+'"] .edgeLabel .label');for(const g of I){const L=g.getBBox(),v=T.createElementNS("http://www.w3.org/2000/svg","rect");v.setAttribute("rx",0),v.setAttribute("ry",0),v.setAttribute("width",L.width),v.setAttribute("height",L.height),g.insertBefore(v,g.firstChild)}}};function N(s){let t;switch(s){case 0:t="aggregation";break;case 1:t="extension";break;case 2:t="composition";break;case 3:t="dependency";break;case 4:t="lollipop";break;default:t="none"}return t}const J={setConf:V,draw:W},le={parser:R,db:_,renderer:J,styles:M,init:s=>{s.class||(s.class={}),s.class.arrowMarkerAbsolute=s.arrowMarkerAbsolute,_.clear()}};export{le as diagram};
diff --git a/frontend-dist/assets/clone-C4pHamD7.js b/frontend-dist/assets/clone-C4pHamD7.js
new file mode 100644
index 0000000000000000000000000000000000000000..8ad83868a0c3a88767d5f8d57a8eed37daef7e61
--- /dev/null
+++ b/frontend-dist/assets/clone-C4pHamD7.js
@@ -0,0 +1 @@
+import{c as r}from"./graph-CY8eBbAS.js";var e=4;function a(o){return r(o,e)}export{a as c};
diff --git a/frontend-dist/assets/createText-1719965b-BZ0xZVnk.js b/frontend-dist/assets/createText-1719965b-BZ0xZVnk.js
new file mode 100644
index 0000000000000000000000000000000000000000..01b01049c7457b15eabf6567c0039e93c9dad722
--- /dev/null
+++ b/frontend-dist/assets/createText-1719965b-BZ0xZVnk.js
@@ -0,0 +1,7 @@
+import{l as At,b6 as zt,i as It,_ as Tt,b7 as Bt}from"./index-BCNM9-Ly.js";const Lt={};function Ot(n,r){const t=Lt,e=typeof t.includeImageAlt=="boolean"?t.includeImageAlt:!0,u=typeof t.includeHtml=="boolean"?t.includeHtml:!0;return et(n,e,u)}function et(n,r,t){if(Dt(n)){if("value"in n)return n.type==="html"&&!t?"":n.value;if(r&&"alt"in n&&n.alt)return n.alt;if("children"in n)return Vn(n.children,r,t)}return Array.isArray(n)?Vn(n,r,t):""}function Vn(n,r,t){const e=[];let u=-1;for(;++uu?0:u+r:r=r>u?u:r,t=t>0?t:0,e.length<1e4)l=Array.from(e),l.unshift(r,t),n.splice(...l);else for(t&&n.splice(r,t);i0?(tn(n,n.length,0,r),n):r}const Wn={}.hasOwnProperty;function Pt(n){const r={};let t=-1;for(;++tl))return;const T=r.events.length;let H=T,N,V;for(;H--;)if(r.events[H][0]==="exit"&&r.events[H][1].type==="chunkFlow"){if(N){V=r.events[H][1].end;break}N=!0}for(b(e),k=T;kF;){const _=t[D];r.containerState=_[1],_[0].exit.call(r,n)}t.length=F}function j(){u.write([null]),i=void 0,u=void 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diff --git a/frontend-dist/assets/erDiagram-0228fc6a-DHOtPsx4.js b/frontend-dist/assets/erDiagram-0228fc6a-DHOtPsx4.js
new file mode 100644
index 0000000000000000000000000000000000000000..5e3c626010fcf0a40928ac107dfc0e66de8d2d68
--- /dev/null
+++ b/frontend-dist/assets/erDiagram-0228fc6a-DHOtPsx4.js
@@ -0,0 +1,51 @@
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40:this.$=d.Identification.IDENTIFYING;break}},table:[{3:1,4:[1,2]},{1:[3]},t(e,[2,2],{5:3}),{6:[1,4],7:5,8:[1,6],9:7,10:[1,8],11:9,20:r,22:u,24:l,26:p,27:f,28:o},t(e,[2,7],{1:[2,1]}),t(e,[2,3]),{9:16,11:9,20:r,22:u,24:l,26:p,27:f,28:o},t(e,[2,5]),t(e,[2,6]),t(e,[2,11],{12:17,39:20,15:[1,18],18:[1,19],41:h,42:_,43:m,44:g,45:x}),{21:[1,26]},{23:[1,27]},{25:[1,28]},t(e,[2,18]),t(y,[2,19]),t(y,[2,20]),t(e,[2,4]),{11:29,27:f,28:o},{16:30,17:[1,31],29:32,30:33,34:N},{11:35,27:f,28:o},{40:36,46:[1,37],47:[1,38]},t(I,[2,34]),t(I,[2,35]),t(I,[2,36]),t(I,[2,37]),t(I,[2,38]),t(e,[2,15]),t(e,[2,16]),t(e,[2,17]),{13:[1,39]},{17:[1,40]},t(e,[2,10]),{16:41,17:[2,21],29:32,30:33,34:N},{31:42,34:[1,43]},{34:[2,27]},{19:[1,44]},{39:45,41:h,42:_,43:m,44:g,45:x},t(F,[2,39]),t(F,[2,40]),{14:46,27:[1,49],28:[1,48],48:[1,47]},t(e,[2,9]),{17:[2,22]},t(W,[2,23],{32:50,33:51,35:52,37:C,38:T}),t([17,34,37,38],[2,28]),t(e,[2,14],{15:[1,55]}),t([27,28],[2,33]),t(e,[2,8]),t(e,[2,41]),t(e,[2,42]),t(e,[2,43]),t(W,[2,24],{33:56,36:[1,57],38:T}),t(W,[2,25]),t(M,[2,29]),t(W,[2,32]),t(M,[2,31]),{16:58,17:[1,59],29:32,30:33,34:N},t(W,[2,26]),{35:60,37:C},{17:[1,61]},t(e,[2,13]),t(M,[2,30]),t(e,[2,12])],defaultActions:{34:[2,27],41:[2,22]},parseError:function(a,n){if(n.recoverable)this.trace(a);else{var c=new Error(a);throw c.hash=n,c}},parse:function(a){var n=this,c=[0],d=[],E=[null],i=[],K=this.table,s="",Q=0,st=0,ft=2,ot=1,yt=i.slice.call(arguments,1),b=Object.create(this.lexer),G={yy:{}};for(var J in this.yy)Object.prototype.hasOwnProperty.call(this.yy,J)&&(G.yy[J]=this.yy[J]);b.setInput(a,G.yy),G.yy.lexer=b,G.yy.parser=this,typeof b.yylloc>"u"&&(b.yylloc={});var $=b.yylloc;i.push($);var pt=b.options&&b.options.ranges;typeof G.yy.parseError=="function"?this.parseError=G.yy.parseError:this.parseError=Object.getPrototypeOf(this).parseError;function _t(){var Y;return Y=d.pop()||b.lex()||ot,typeof Y!="number"&&(Y instanceof Array&&(d=Y,Y=d.pop()),Y=n.symbols_[Y]||Y),Y}for(var w,H,D,tt,z={},j,P,lt,q;;){if(H=c[c.length-1],this.defaultActions[H]?D=this.defaultActions[H]:((w===null||typeof w>"u")&&(w=_t()),D=K[H]&&K[H][w]),typeof D>"u"||!D.length||!D[0]){var et="";q=[];for(j in K[H])this.terminals_[j]&&j>ft&&q.push("'"+this.terminals_[j]+"'");b.showPosition?et="Parse error on line "+(Q+1)+`:
+`+b.showPosition()+`
+Expecting `+q.join(", ")+", got '"+(this.terminals_[w]||w)+"'":et="Parse error on line "+(Q+1)+": Unexpected "+(w==ot?"end of input":"'"+(this.terminals_[w]||w)+"'"),this.parseError(et,{text:b.match,token:this.terminals_[w]||w,line:b.yylineno,loc:$,expected:q})}if(D[0]instanceof Array&&D.length>1)throw new Error("Parse Error: multiple actions possible at state: "+H+", token: "+w);switch(D[0]){case 1:c.push(w),E.push(b.yytext),i.push(b.yylloc),c.push(D[1]),w=null,st=b.yyleng,s=b.yytext,Q=b.yylineno,$=b.yylloc;break;case 2:if(P=this.productions_[D[1]][1],z.$=E[E.length-P],z._$={first_line:i[i.length-(P||1)].first_line,last_line:i[i.length-1].last_line,first_column:i[i.length-(P||1)].first_column,last_column:i[i.length-1].last_column},pt&&(z._$.range=[i[i.length-(P||1)].range[0],i[i.length-1].range[1]]),tt=this.performAction.apply(z,[s,st,Q,G.yy,D[1],E,i].concat(yt)),typeof tt<"u")return tt;P&&(c=c.slice(0,-1*P*2),E=E.slice(0,-1*P),i=i.slice(0,-1*P)),c.push(this.productions_[D[1]][0]),E.push(z.$),i.push(z._$),lt=K[c[c.length-2]][c[c.length-1]],c.push(lt);break;case 3:return!0}}return!0}},O=function(){var S={EOF:1,parseError:function(n,c){if(this.yy.parser)this.yy.parser.parseError(n,c);else throw new Error(n)},setInput:function(a,n){return this.yy=n||this.yy||{},this._input=a,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var a=this._input[0];this.yytext+=a,this.yyleng++,this.offset++,this.match+=a,this.matched+=a;var n=a.match(/(?:\r\n?|\n).*/g);return n?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),a},unput:function(a){var n=a.length,c=a.split(/(?:\r\n?|\n)/g);this._input=a+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-n),this.offset-=n;var d=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),c.length-1&&(this.yylineno-=c.length-1);var E=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:c?(c.length===d.length?this.yylloc.first_column:0)+d[d.length-c.length].length-c[0].length:this.yylloc.first_column-n},this.options.ranges&&(this.yylloc.range=[E[0],E[0]+this.yyleng-n]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
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c.yytext[0];case 55:return 6}},rules:[/^(?:accTitle\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*:\s*)/i,/^(?:(?!\n||)*[^\n]*)/i,/^(?:accDescr\s*\{\s*)/i,/^(?:[\}])/i,/^(?:[^\}]*)/i,/^(?:[\n]+)/i,/^(?:\s+)/i,/^(?:[\s]+)/i,/^(?:"[^"%\r\n\v\b\\]+")/i,/^(?:"[^"]*")/i,/^(?:erDiagram\b)/i,/^(?:\{)/i,/^(?:,)/i,/^(?:\s+)/i,/^(?:\b((?:PK)|(?:FK)|(?:UK))\b)/i,/^(?:(.*?)[~](.*?)*[~])/i,/^(?:[\*A-Za-z_][A-Za-z0-9\-_\[\]\(\)]*)/i,/^(?:"[^"]*")/i,/^(?:[\n]+)/i,/^(?:\})/i,/^(?:.)/i,/^(?:\[)/i,/^(?:\])/i,/^(?:one or zero\b)/i,/^(?:one or more\b)/i,/^(?:one or many\b)/i,/^(?:1\+)/i,/^(?:\|o\b)/i,/^(?:zero or one\b)/i,/^(?:zero or more\b)/i,/^(?:zero or many\b)/i,/^(?:0\+)/i,/^(?:\}o\b)/i,/^(?:many\(0\))/i,/^(?:many\(1\))/i,/^(?:many\b)/i,/^(?:\}\|)/i,/^(?:one\b)/i,/^(?:only one\b)/i,/^(?:1\b)/i,/^(?:\|\|)/i,/^(?:o\|)/i,/^(?:o\{)/i,/^(?:\|\{)/i,/^(?:\s*u\b)/i,/^(?:\.\.)/i,/^(?:--)/i,/^(?:to\b)/i,/^(?:optionally 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d=t.append("text").classed("er entityLabel",!0).attr("id",`${M}-comment`).attr("x",0).attr("y",0).style("dominant-baseline","middle").style("text-anchor","left").style("font-family",Z().fontFamily).style("font-size",p+"px").text(T.attributeComment||"");a.cn=d;const E=d.node().getBBox();y=Math.max(y,E.width),R=Math.max(R,E.height)}a.height=R,o.push(a),N+=R+u*2,I+=1});let F=4;h&&(F+=2),_&&(F+=2);const W=m+g+x+y,C={width:Math.max(k.minEntityWidth,Math.max(f.width+k.entityPadding*2,W+l*F)),height:r.length>0?N:Math.max(k.minEntityHeight,f.height+k.entityPadding*2)};if(r.length>0){const T=Math.max(0,(C.width-W-l*F)/(F/2));e.attr("transform","translate("+C.width/2+","+(u+f.height/2)+")");let M=f.height+u*2,R="attributeBoxOdd";o.forEach(O=>{const v=M+u+O.height/2;O.tn.attr("transform","translate("+l+","+v+")");const S=t.insert("rect","#"+O.tn.node().id).classed(`er ${R}`,!0).attr("x",0).attr("y",M).attr("width",m+l*2+T).attr("height",O.height+u*2),a=parseFloat(S.attr("x"))+parseFloat(S.attr("width"));O.nn.attr("transform","translate("+(a+l)+","+v+")");const n=t.insert("rect","#"+O.nn.node().id).classed(`er ${R}`,!0).attr("x",a).attr("y",M).attr("width",g+l*2+T).attr("height",O.height+u*2);let c=parseFloat(n.attr("x"))+parseFloat(n.attr("width"));if(h){O.kn.attr("transform","translate("+(c+l)+","+v+")");const d=t.insert("rect","#"+O.kn.node().id).classed(`er ${R}`,!0).attr("x",c).attr("y",M).attr("width",x+l*2+T).attr("height",O.height+u*2);c=parseFloat(d.attr("x"))+parseFloat(d.attr("width"))}_&&(O.cn.attr("transform","translate("+(c+l)+","+v+")"),t.insert("rect","#"+O.cn.node().id).classed(`er ${R}`,"true").attr("x",c).attr("y",M).attr("width",y+l*2+T).attr("height",O.height+u*2)),M+=O.height+u*2,R=R==="attributeBoxOdd"?"attributeBoxEven":"attributeBoxOdd"})}else C.height=Math.max(k.minEntityHeight,N),e.attr("transform","translate("+C.width/2+","+C.height/2+")");return C},ee=function(t,e,r){const u=Object.keys(e);let l;return u.forEach(function(p){const f=oe(p,"entity");X.set(p,f);const o=t.append("g").attr("id",f);l=l===void 0?f:l;const h="text-"+f,_=o.append("text").classed("er entityLabel",!0).attr("id",h).attr("x",0).attr("y",0).style("dominant-baseline","middle").style("text-anchor","middle").style("font-family",Z().fontFamily).style("font-size",k.fontSize+"px").text(e[p].alias??p),{width:m,height:g}=te(o,_,e[p].attributes),y=o.insert("rect","#"+h).classed("er entityBox",!0).attr("x",0).attr("y",0).attr("width",m).attr("height",g).node().getBBox();r.setNode(f,{width:y.width,height:y.height,shape:"rect",id:f})}),l},re=function(t,e){e.nodes().forEach(function(r){r!==void 0&&e.node(r)!==void 0&&t.select("#"+r).attr("transform","translate("+(e.node(r).x-e.node(r).width/2)+","+(e.node(r).y-e.node(r).height/2)+" )")})},ut=function(t){return(t.entityA+t.roleA+t.entityB).replace(/\s/g,"")},ie=function(t,e){return t.forEach(function(r){e.setEdge(X.get(r.entityA),X.get(r.entityB),{relationship:r},ut(r))}),t};let ct=0;const ae=function(t,e,r,u,l){ct++;const p=r.edge(X.get(e.entityA),X.get(e.entityB),ut(e)),f=wt().x(function(N){return N.x}).y(function(N){return N.y}).curve(Tt),o=t.insert("path","#"+u).classed("er relationshipLine",!0).attr("d",f(p.points)).style("stroke",k.stroke).style("fill","none");e.relSpec.relType===l.db.Identification.NON_IDENTIFYING&&o.attr("stroke-dasharray","8,8");let h="";switch(k.arrowMarkerAbsolute&&(h=window.location.protocol+"//"+window.location.host+window.location.pathname+window.location.search,h=h.replace(/\(/g,"\\("),h=h.replace(/\)/g,"\\)")),e.relSpec.cardA){case l.db.Cardinality.ZERO_OR_ONE:o.attr("marker-end","url("+h+"#"+B.ERMarkers.ZERO_OR_ONE_END+")");break;case l.db.Cardinality.ZERO_OR_MORE:o.attr("marker-end","url("+h+"#"+B.ERMarkers.ZERO_OR_MORE_END+")");break;case l.db.Cardinality.ONE_OR_MORE:o.attr("marker-end","url("+h+"#"+B.ERMarkers.ONE_OR_MORE_END+")");break;case l.db.Cardinality.ONLY_ONE:o.attr("marker-end","url("+h+"#"+B.ERMarkers.ONLY_ONE_END+")");break;case l.db.Cardinality.MD_PARENT:o.attr("marker-end","url("+h+"#"+B.ERMarkers.MD_PARENT_END+")");break}switch(e.relSpec.cardB){case l.db.Cardinality.ZERO_OR_ONE:o.attr("marker-start","url("+h+"#"+B.ERMarkers.ZERO_OR_ONE_START+")");break;case l.db.Cardinality.ZERO_OR_MORE:o.attr("marker-start","url("+h+"#"+B.ERMarkers.ZERO_OR_MORE_START+")");break;case l.db.Cardinality.ONE_OR_MORE:o.attr("marker-start","url("+h+"#"+B.ERMarkers.ONE_OR_MORE_START+")");break;case l.db.Cardinality.ONLY_ONE:o.attr("marker-start","url("+h+"#"+B.ERMarkers.ONLY_ONE_START+")");break;case l.db.Cardinality.MD_PARENT:o.attr("marker-start","url("+h+"#"+B.ERMarkers.MD_PARENT_START+")");break}const _=o.node().getTotalLength(),m=o.node().getPointAtLength(_*.5),g="rel"+ct,y=t.append("text").classed("er relationshipLabel",!0).attr("id",g).attr("x",m.x).attr("y",m.y).style("text-anchor","middle").style("dominant-baseline","middle").style("font-family",Z().fontFamily).style("font-size",k.fontSize+"px").text(e.roleA).node().getBBox();t.insert("rect","#"+g).classed("er relationshipLabelBox",!0).attr("x",m.x-y.width/2).attr("y",m.y-y.height/2).attr("width",y.width).attr("height",y.height)},ne=function(t,e,r,u){k=Z().er,V.info("Drawing ER diagram");const l=Z().securityLevel;let p;l==="sandbox"&&(p=rt("#i"+e));const o=(l==="sandbox"?rt(p.nodes()[0].contentDocument.body):rt("body")).select(`[id='${e}']`);B.insertMarkers(o,k);let h;h=new Mt({multigraph:!0,directed:!0,compound:!1}).setGraph({rankdir:k.layoutDirection,marginx:20,marginy:20,nodesep:100,edgesep:100,ranksep:100}).setDefaultEdgeLabel(function(){return{}});const _=ee(o,u.db.getEntities(),h),m=ie(u.db.getRelationships(),h);St(h),re(o,h),m.forEach(function(I){ae(o,I,h,_,u)});const g=k.diagramPadding;Ot.insertTitle(o,"entityTitleText",k.titleTopMargin,u.db.getDiagramTitle());const x=o.node().getBBox(),y=x.width+g*2,N=x.height+g*2;bt(o,N,y,k.useMaxWidth),o.attr("viewBox",`${x.x-g} ${x.y-g} ${y} ${N}`)},se="28e9f9db-3c8d-5aa5-9faf-44286ae5937c";function oe(t="",e=""){const r=t.replace(Jt,"");return`${ht(e)}${ht(r)}${Wt(t,se)}`}function ht(t=""){return t.length>0?`${t}-`:""}const le={setConf:$t,draw:ne},ce=t=>`
+ .entityBox {
+ fill: ${t.mainBkg};
+ stroke: ${t.nodeBorder};
+ }
+
+ .attributeBoxOdd {
+ fill: ${t.attributeBackgroundColorOdd};
+ stroke: ${t.nodeBorder};
+ }
+
+ .attributeBoxEven {
+ fill: ${t.attributeBackgroundColorEven};
+ stroke: ${t.nodeBorder};
+ }
+
+ .relationshipLabelBox {
+ fill: ${t.tertiaryColor};
+ opacity: 0.7;
+ background-color: ${t.tertiaryColor};
+ rect {
+ opacity: 0.5;
+ }
+ }
+
+ .relationshipLine {
+ stroke: ${t.lineColor};
+ }
+
+ .entityTitleText {
+ text-anchor: middle;
+ font-size: 18px;
+ fill: ${t.textColor};
+ }
+ #MD_PARENT_START {
+ fill: #f5f5f5 !important;
+ stroke: ${t.lineColor} !important;
+ stroke-width: 1;
+ }
+ #MD_PARENT_END {
+ fill: #f5f5f5 !important;
+ stroke: ${t.lineColor} !important;
+ stroke-width: 1;
+ }
+
+`,he=ce,Ee={parser:Ut,db:jt,renderer:le,styles:he};export{Ee as diagram};
diff --git a/frontend-dist/assets/flowDb-c6c81e3f-DSM4lr0v.js b/frontend-dist/assets/flowDb-c6c81e3f-DSM4lr0v.js
new file mode 100644
index 0000000000000000000000000000000000000000..8a8dd0538b6585da300a1c529c69c8c3c68ba8c5
--- /dev/null
+++ b/frontend-dist/assets/flowDb-c6c81e3f-DSM4lr0v.js
@@ -0,0 +1,10 @@
+import{C as me,D as ye,s as ve,g as Ve,a as Le,b as Ie,aq as Re,l as J1,c as et,E as Ne,A as dt,k as we,d as w1}from"./index-BCNM9-Ly.js";var pt=function(){var e=function(f1,a,o,f){for(o=o||{},f=f1.length;f--;o[f1[f]]=a);return o},u=[1,4],i=[1,3],n=[1,5],c=[1,8,9,10,11,27,34,36,38,42,58,81,82,83,84,85,86,99,102,103,106,108,111,112,113,118,119,120,121],l=[2,2],h=[1,13],U=[1,14],F=[1,15],w=[1,16],X=[1,23],o1=[1,25],p1=[1,26],A1=[1,27],S=[1,49],k=[1,48],l1=[1,29],U1=[1,30],G1=[1,31],M1=[1,32],K1=[1,33],x=[1,44],B=[1,46],m=[1,42],y=[1,47],v=[1,43],V=[1,50],L=[1,45],I=[1,51],R=[1,52],Y1=[1,34],j1=[1,35],z1=[1,36],X1=[1,37],I1=[1,57],b=[1,8,9,10,11,27,32,34,36,38,42,58,81,82,83,84,85,86,99,102,103,106,108,111,112,113,118,119,120,121],W=[1,61],Q=[1,60],Z=[1,62],H1=[8,9,11,73,75],k1=[1,88],b1=[1,93],g1=[1,92],D1=[1,89],F1=[1,85],T1=[1,91],C1=[1,87],S1=[1,94],_1=[1,90],x1=[1,95],B1=[1,86],q1=[8,9,10,11,73,75],N=[8,9,10,11,44,73,75],M=[8,9,10,11,29,42,44,46,48,50,52,54,56,58,61,63,65,66,68,73,75,86,99,102,103,106,108,111,112,113],Et=[8,9,11,42,58,73,75,86,99,102,103,106,108,111,112,113],R1=[42,58,86,99,102,103,106,108,111,112,113],kt=[1,121],bt=[1,120],gt=[1,128],Dt=[1,142],Ft=[1,143],Tt=[1,144],Ct=[1,145],St=[1,130],_t=[1,132],xt=[1,136],Bt=[1,137],mt=[1,138],yt=[1,139],vt=[1,140],Vt=[1,141],Lt=[1,146],It=[1,147],Rt=[1,126],Nt=[1,127],wt=[1,134],Ot=[1,129],Pt=[1,133],Ut=[1,131],nt=[8,9,10,11,27,32,34,36,38,42,58,81,82,83,84,85,86,99,102,103,106,108,111,112,113,118,119,120,121],Gt=[1,149],T=[8,9,11],K=[8,9,10,11,14,42,58,86,102,103,106,108,111,112,113],p=[1,169],O=[1,165],P=[1,166],A=[1,170],d=[1,167],E=[1,168],m1=[75,113,116],g=[8,9,10,11,12,14,27,29,32,42,58,73,81,82,83,84,85,86,87,102,106,108,111,112,113],Mt=[10,103],h1=[31,47,49,51,53,55,60,62,64,65,67,69,113,114,115],J=[1,235],$=[1,233],t1=[1,237],e1=[1,231],s1=[1,232],u1=[1,234],i1=[1,236],r1=[1,238],y1=[1,255],Kt=[8,9,11,103],q=[8,9,10,11,58,81,102,103,106,107,108,109],at={trace:function(){},yy:{},symbols_:{error:2,start:3,graphConfig:4,document:5,line:6,statement:7,SEMI:8,NEWLINE:9,SPACE:10,EOF:11,GRAPH:12,NODIR:13,DIR:14,FirstStmtSeparator:15,ending:16,endToken:17,spaceList:18,spaceListNewline:19,vertexStatement:20,separator:21,styleStatement:22,linkStyleStatement:23,classDefStatement:24,classStatement:25,clickStatement:26,subgraph:27,textNoTags:28,SQS:29,text:30,SQE:31,end:32,direction:33,acc_title:34,acc_title_value:35,acc_descr:36,acc_descr_value:37,acc_descr_multiline_value:38,link:39,node:40,styledVertex:41,AMP:42,vertex:43,STYLE_SEPARATOR:44,idString:45,DOUBLECIRCLESTART:46,DOUBLECIRCLEEND:47,PS:48,PE:49,"(-":50,"-)":51,STADIUMSTART:52,STADIUMEND:53,SUBROUTINESTART:54,SUBROUTINEEND:55,VERTEX_WITH_PROPS_START:56,"NODE_STRING[field]":57,COLON:58,"NODE_STRING[value]":59,PIPE:60,CYLINDERSTART:61,CYLINDEREND:62,DIAMOND_START:63,DIAMOND_STOP:64,TAGEND:65,TRAPSTART:66,TRAPEND:67,INVTRAPSTART:68,INVTRAPEND:69,linkStatement:70,arrowText:71,TESTSTR:72,START_LINK:73,edgeText:74,LINK:75,edgeTextToken:76,STR:77,MD_STR:78,textToken:79,keywords:80,STYLE:81,LINKSTYLE:82,CLASSDEF:83,CLASS:84,CLICK:85,DOWN:86,UP:87,textNoTagsToken:88,stylesOpt:89,"idString[vertex]":90,"idString[class]":91,CALLBACKNAME:92,CALLBACKARGS:93,HREF:94,LINK_TARGET:95,"STR[link]":96,"STR[tooltip]":97,alphaNum:98,DEFAULT:99,numList:100,INTERPOLATE:101,NUM:102,COMMA:103,style:104,styleComponent:105,NODE_STRING:106,UNIT:107,BRKT:108,PCT:109,idStringToken:110,MINUS:111,MULT:112,UNICODE_TEXT:113,TEXT:114,TAGSTART:115,EDGE_TEXT:116,alphaNumToken:117,direction_tb:118,direction_bt:119,direction_rl:120,direction_lr:121,$accept:0,$end:1},terminals_:{2:"error",8:"SEMI",9:"NEWLINE",10:"SPACE",11:"EOF",12:"GRAPH",13:"NODIR",14:"DIR",27:"subgraph",29:"SQS",31:"SQE",32:"end",34:"acc_title",35:"acc_title_value",36:"acc_descr",37:"acc_descr_value",38:"acc_descr_multiline_value",42:"AMP",44:"STYLE_SEPARATOR",46:"DOUBLECIRCLESTART",47:"DOUBLECIRCLEEND",48:"PS",49:"PE",50:"(-",51:"-)",52:"STADIUMSTART",53:"STADIUMEND",54:"SUBROUTINESTART",55:"SUBROUTINEEND",56:"VERTEX_WITH_PROPS_START",57:"NODE_STRING[field]",58:"COLON",59:"NODE_STRING[value]",60:"PIPE",61:"CYLINDERSTART",62:"CYLINDEREND",63:"DIAMOND_START",64:"DIAMOND_STOP",65:"TAGEND",66:"TRAPSTART",67:"TRAPEND",68:"INVTRAPSTART",69:"INVTRAPEND",72:"TESTSTR",73:"START_LINK",75:"LINK",77:"STR",78:"MD_STR",81:"STYLE",82:"LINKSTYLE",83:"CLASSDEF",84:"CLASS",85:"CLICK",86:"DOWN",87:"UP",90:"idString[vertex]",91:"idString[class]",92:"CALLBACKNAME",93:"CALLBACKARGS",94:"HREF",95:"LINK_TARGET",96:"STR[link]",97:"STR[tooltip]",99:"DEFAULT",101:"INTERPOLATE",102:"NUM",103:"COMMA",106:"NODE_STRING",107:"UNIT",108:"BRKT",109:"PCT",111:"MINUS",112:"MULT",113:"UNICODE_TEXT",114:"TEXT",115:"TAGSTART",116:"EDGE_TEXT",118:"direction_tb",119:"direction_bt",120:"direction_rl",121:"direction_lr"},productions_:[0,[3,2],[5,0],[5,2],[6,1],[6,1],[6,1],[6,1],[6,1],[4,2],[4,2],[4,2],[4,3],[16,2],[16,1],[17,1],[17,1],[17,1],[15,1],[15,1],[15,2],[19,2],[19,2],[19,1],[19,1],[18,2],[18,1],[7,2],[7,2],[7,2],[7,2],[7,2],[7,2],[7,9],[7,6],[7,4],[7,1],[7,2],[7,2],[7,1],[21,1],[21,1],[21,1],[20,3],[20,4],[20,2],[20,1],[40,1],[40,5],[41,1],[41,3],[43,4],[43,4],[43,6],[43,4],[43,4],[43,4],[43,8],[43,4],[43,4],[43,4],[43,6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s=t.length-1;switch(C){case 2:this.$=[];break;case 3:(!Array.isArray(t[s])||t[s].length>0)&&t[s-1].push(t[s]),this.$=t[s-1];break;case 4:case 176:this.$=t[s];break;case 11:r.setDirection("TB"),this.$="TB";break;case 12:r.setDirection(t[s-1]),this.$=t[s-1];break;case 27:this.$=t[s-1].nodes;break;case 28:case 29:case 30:case 31:case 32:this.$=[];break;case 33:this.$=r.addSubGraph(t[s-6],t[s-1],t[s-4]);break;case 34:this.$=r.addSubGraph(t[s-3],t[s-1],t[s-3]);break;case 35:this.$=r.addSubGraph(void 0,t[s-1],void 0);break;case 37:this.$=t[s].trim(),r.setAccTitle(this.$);break;case 38:case 39:this.$=t[s].trim(),r.setAccDescription(this.$);break;case 43:r.addLink(t[s-2].stmt,t[s],t[s-1]),this.$={stmt:t[s],nodes:t[s].concat(t[s-2].nodes)};break;case 44:r.addLink(t[s-3].stmt,t[s-1],t[s-2]),this.$={stmt:t[s-1],nodes:t[s-1].concat(t[s-3].nodes)};break;case 45:this.$={stmt:t[s-1],nodes:t[s-1]};break;case 46:this.$={stmt:t[s],nodes:t[s]};break;case 47:this.$=[t[s]];break;case 48:this.$=t[s-4].concat(t[s]);break;case 49:this.$=t[s];break;case 50:this.$=t[s-2],r.setClass(t[s-2],t[s]);break;case 51:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"square");break;case 52:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"doublecircle");break;case 53:this.$=t[s-5],r.addVertex(t[s-5],t[s-2],"circle");break;case 54:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"ellipse");break;case 55:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"stadium");break;case 56:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"subroutine");break;case 57:this.$=t[s-7],r.addVertex(t[s-7],t[s-1],"rect",void 0,void 0,void 0,Object.fromEntries([[t[s-5],t[s-3]]]));break;case 58:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"cylinder");break;case 59:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"round");break;case 60:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"diamond");break;case 61:this.$=t[s-5],r.addVertex(t[s-5],t[s-2],"hexagon");break;case 62:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"odd");break;case 63:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"trapezoid");break;case 64:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"inv_trapezoid");break;case 65:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"lean_right");break;case 66:this.$=t[s-3],r.addVertex(t[s-3],t[s-1],"lean_left");break;case 67:this.$=t[s],r.addVertex(t[s]);break;case 68:t[s-1].text=t[s],this.$=t[s-1];break;case 69:case 70:t[s-2].text=t[s-1],this.$=t[s-2];break;case 71:this.$=t[s];break;case 72:var Y=r.destructLink(t[s],t[s-2]);this.$={type:Y.type,stroke:Y.stroke,length:Y.length,text:t[s-1]};break;case 73:this.$={text:t[s],type:"text"};break;case 74:this.$={text:t[s-1].text+""+t[s],type:t[s-1].type};break;case 75:this.$={text:t[s],type:"string"};break;case 76:this.$={text:t[s],type:"markdown"};break;case 77:var Y=r.destructLink(t[s]);this.$={type:Y.type,stroke:Y.stroke,length:Y.length};break;case 78:this.$=t[s-1];break;case 79:this.$={text:t[s],type:"text"};break;case 80:this.$={text:t[s-1].text+""+t[s],type:t[s-1].type};break;case 81:this.$={text:t[s],type:"string"};break;case 82:case 97:this.$={text:t[s],type:"markdown"};break;case 94:this.$={text:t[s],type:"text"};break;case 95:this.$={text:t[s-1].text+""+t[s],type:t[s-1].type};break;case 96:this.$={text:t[s],type:"text"};break;case 98:this.$=t[s-4],r.addClass(t[s-2],t[s]);break;case 99:this.$=t[s-4],r.setClass(t[s-2],t[s]);break;case 100:case 108:this.$=t[s-1],r.setClickEvent(t[s-1],t[s]);break;case 101:case 109:this.$=t[s-3],r.setClickEvent(t[s-3],t[s-2]),r.setTooltip(t[s-3],t[s]);break;case 102:this.$=t[s-2],r.setClickEvent(t[s-2],t[s-1],t[s]);break;case 103:this.$=t[s-4],r.setClickEvent(t[s-4],t[s-3],t[s-2]),r.setTooltip(t[s-4],t[s]);break;case 104:this.$=t[s-2],r.setLink(t[s-2],t[s]);break;case 105:this.$=t[s-4],r.setLink(t[s-4],t[s-2]),r.setTooltip(t[s-4],t[s]);break;case 106:this.$=t[s-4],r.setLink(t[s-4],t[s-2],t[s]);break;case 107:this.$=t[s-6],r.setLink(t[s-6],t[s-4],t[s]),r.setTooltip(t[s-6],t[s-2]);break;case 110:this.$=t[s-1],r.setLink(t[s-1],t[s]);break;case 111:this.$=t[s-3],r.setLink(t[s-3],t[s-2]),r.setTooltip(t[s-3],t[s]);break;case 112:this.$=t[s-3],r.setLink(t[s-3],t[s-2],t[s]);break;case 113:this.$=t[s-5],r.setLink(t[s-5],t[s-4],t[s]),r.setTooltip(t[s-5],t[s-2]);break;case 114:this.$=t[s-4],r.addVertex(t[s-2],void 0,void 0,t[s]);break;case 115:this.$=t[s-4],r.updateLink([t[s-2]],t[s]);break;case 116:this.$=t[s-4],r.updateLink(t[s-2],t[s]);break;case 117:this.$=t[s-8],r.updateLinkInterpolate([t[s-6]],t[s-2]),r.updateLink([t[s-6]],t[s]);break;case 118:this.$=t[s-8],r.updateLinkInterpolate(t[s-6],t[s-2]),r.updateLink(t[s-6],t[s]);break;case 119:this.$=t[s-6],r.updateLinkInterpolate([t[s-4]],t[s]);break;case 120:this.$=t[s-6],r.updateLinkInterpolate(t[s-4],t[s]);break;case 121:case 123:this.$=[t[s]];break;case 122:case 124:t[s-2].push(t[s]),this.$=t[s-2];break;case 126:this.$=t[s-1]+t[s];break;case 174:this.$=t[s];break;case 175:this.$=t[s-1]+""+t[s];break;case 177:this.$=t[s-1]+""+t[s];break;case 178:this.$={stmt:"dir",value:"TB"};break;case 179:this.$={stmt:"dir",value:"BT"};break;case 180:this.$={stmt:"dir",value:"RL"};break;case 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+Expecting `+Q1.join(", ")+", got '"+(this.terminals_[G]||G)+"'":ft="Parse error on line "+(Y+1)+": Unexpected "+(G==jt?"end of input":"'"+(this.terminals_[G]||G)+"'"),this.parseError(ft,{text:_.match,token:this.terminals_[G]||G,line:_.yylineno,loc:lt,expected:Q1})}if(j[0]instanceof Array&&j.length>1)throw new Error("Parse Error: multiple actions possible at state: "+E1+", token: "+G);switch(j[0]){case 1:f.push(G),C.push(_.yytext),t.push(_.yylloc),f.push(j[1]),G=null,Yt=_.yyleng,s=_.yytext,Y=_.yylineno,lt=_.yylloc;break;case 2:if(n1=this.productions_[j[1]][1],v1.$=C[C.length-n1],v1._$={first_line:t[t.length-(n1||1)].first_line,last_line:t[t.length-1].last_line,first_column:t[t.length-(n1||1)].first_column,last_column:t[t.length-1].last_column},xe&&(v1._$.range=[t[t.length-(n1||1)].range[0],t[t.length-1].range[1]]),ht=this.performAction.apply(v1,[s,Yt,Y,d1.yy,j[1],C,t].concat(_e)),typeof ht<"u")return ht;n1&&(f=f.slice(0,-1*n1*2),C=C.slice(0,-1*n1),t=t.slice(0,-1*n1)),f.push(this.productions_[j[1]][0]),C.push(v1.$),t.push(v1._$),zt=N1[f[f.length-2]][f[f.length-1]],f.push(zt);break;case 3:return!0}}return!0}},Ce=function(){var f1={EOF:1,parseError:function(o,f){if(this.yy.parser)this.yy.parser.parseError(o,f);else throw new Error(o)},setInput:function(a,o){return this.yy=o||this.yy||{},this._input=a,this._more=this._backtrack=this.done=!1,this.yylineno=this.yyleng=0,this.yytext=this.matched=this.match="",this.conditionStack=["INITIAL"],this.yylloc={first_line:1,first_column:0,last_line:1,last_column:0},this.options.ranges&&(this.yylloc.range=[0,0]),this.offset=0,this},input:function(){var a=this._input[0];this.yytext+=a,this.yyleng++,this.offset++,this.match+=a,this.matched+=a;var o=a.match(/(?:\r\n?|\n).*/g);return o?(this.yylineno++,this.yylloc.last_line++):this.yylloc.last_column++,this.options.ranges&&this.yylloc.range[1]++,this._input=this._input.slice(1),a},unput:function(a){var o=a.length,f=a.split(/(?:\r\n?|\n)/g);this._input=a+this._input,this.yytext=this.yytext.substr(0,this.yytext.length-o),this.offset-=o;var r=this.match.split(/(?:\r\n?|\n)/g);this.match=this.match.substr(0,this.match.length-1),this.matched=this.matched.substr(0,this.matched.length-1),f.length-1&&(this.yylineno-=f.length-1);var C=this.yylloc.range;return this.yylloc={first_line:this.yylloc.first_line,last_line:this.yylineno+1,first_column:this.yylloc.first_column,last_column:f?(f.length===r.length?this.yylloc.first_column:0)+r[r.length-f.length].length-f[0].length:this.yylloc.first_column-o},this.options.ranges&&(this.yylloc.range=[C[0],C[0]+this.yyleng-o]),this.yyleng=this.yytext.length,this},more:function(){return this._more=!0,this},reject:function(){if(this.options.backtrack_lexer)this._backtrack=!0;else return this.parseError("Lexical error on line "+(this.yylineno+1)+`. You can only invoke reject() in the lexer when the lexer is of the backtracking persuasion (options.backtrack_lexer = true).
+`+this.showPosition(),{text:"",token:null,line:this.yylineno});return this},less:function(a){this.unput(this.match.slice(a))},pastInput:function(){var a=this.matched.substr(0,this.matched.length-this.match.length);return(a.length>20?"...":"")+a.substr(-20).replace(/\n/g,"")},upcomingInput:function(){var a=this.match;return a.length<20&&(a+=this._input.substr(0,20-a.length)),(a.substr(0,20)+(a.length>20?"...":"")).replace(/\n/g,"")},showPosition:function(){var a=this.pastInput(),o=new Array(a.length+1).join("-");return a+this.upcomingInput()+`
+`+o+"^"},test_match:function(a,o){var f,r,C;if(this.options.backtrack_lexer&&(C={yylineno:this.yylineno,yylloc:{first_line:this.yylloc.first_line,last_line:this.last_line,first_column:this.yylloc.first_column,last_column:this.yylloc.last_column},yytext:this.yytext,match:this.match,matches:this.matches,matched:this.matched,yyleng:this.yyleng,offset:this.offset,_more:this._more,_input:this._input,yy:this.yy,conditionStack:this.conditionStack.slice(0),done:this.done},this.options.ranges&&(C.yylloc.range=this.yylloc.range.slice(0))),r=a[0].match(/(?:\r\n?|\n).*/g),r&&(this.yylineno+=r.length),this.yylloc={first_line:this.yylloc.last_line,last_line:this.yylineno+1,first_column:this.yylloc.last_column,last_column:r?r[r.length-1].length-r[r.length-1].match(/\r?\n?/)[0].length:this.yylloc.last_column+a[0].length},this.yytext+=a[0],this.match+=a[0],this.matches=a,this.yyleng=this.yytext.length,this.options.ranges&&(this.yylloc.range=[this.offset,this.offset+=this.yyleng]),this._more=!1,this._backtrack=!1,this._input=this._input.slice(a[0].length),this.matched+=a[0],f=this.performAction.call(this,this.yy,this,o,this.conditionStack[this.conditionStack.length-1]),this.done&&this._input&&(this.done=!1),f)return f;if(this._backtrack){for(var t in C)this[t]=C[t];return!1}return!1},next:function(){if(this.done)return this.EOF;this._input||(this.done=!0);var a,o,f,r;this._more||(this.yytext="",this.match="");for(var C=this._currentRules(),t=0;to[0].length)){if(o=f,r=t,this.options.backtrack_lexer){if(a=this.test_match(f,C[t]),a!==!1)return a;if(this._backtrack){o=!1;continue}else return!1}else if(!this.options.flex)break}return o?(a=this.test_match(o,C[r]),a!==!1?a:!1):this._input===""?this.EOF:this.parseError("Lexical error on line "+(this.yylineno+1)+`. Unrecognized text.
+`+this.showPosition(),{text:"",token:null,line:this.yylineno})},lex:function(){var o=this.next();return o||this.lex()},begin:function(o){this.conditionStack.push(o)},popState:function(){var o=this.conditionStack.length-1;return o>0?this.conditionStack.pop():this.conditionStack[0]},_currentRules:function(){return this.conditionStack.length&&this.conditionStack[this.conditionStack.length-1]?this.conditions[this.conditionStack[this.conditionStack.length-1]].rules:this.conditions.INITIAL.rules},topState:function(o){return o=this.conditionStack.length-1-Math.abs(o||0),o>=0?this.conditionStack[o]:"INITIAL"},pushState:function(o){this.begin(o)},stateStackSize:function(){return this.conditionStack.length},options:{},performAction:function(o,f,r,C){switch(r){case 0:return this.begin("acc_title"),34;case 1:return this.popState(),"acc_title_value";case 2:return this.begin("acc_descr"),36;case 3:return this.popState(),"acc_descr_value";case 4:this.begin("acc_descr_multiline");break;case 5:this.popState();break;case 6:return"acc_descr_multiline_value";case 7:this.begin("callbackname");break;case 8:this.popState();break;case 9:this.popState(),this.begin("callbackargs");break;case 10:return 92;case 11:this.popState();break;case 12:return 93;case 13:return"MD_STR";case 14:this.popState();break;case 15:this.begin("md_string");break;case 16:return"STR";case 17:this.popState();break;case 18:this.pushState("string");break;case 19:return 81;case 20:return 99;case 21:return 82;case 22:return 101;case 23:return 83;case 24:return 84;case 25:return 94;case 26:this.begin("click");break;case 27:this.popState();break;case 28:return 85;case 29:return o.lex.firstGraph()&&this.begin("dir"),12;case 30:return o.lex.firstGraph()&&this.begin("dir"),12;case 31:return o.lex.firstGraph()&&this.begin("dir"),12;case 32:return 27;case 33:return 32;case 34:return 95;case 35:return 95;case 36:return 95;case 37:return 95;case 38:return this.popState(),13;case 39:return this.popState(),14;case 40:return this.popState(),14;case 41:return this.popState(),14;case 42:return this.popState(),14;case 43:return this.popState(),14;case 44:return this.popState(),14;case 45:return this.popState(),14;case 46:return this.popState(),14;case 47:return this.popState(),14;case 48:return this.popState(),14;case 49:return 118;case 50:return 119;case 51:return 120;case 52:return 121;case 53:return 102;case 54:return 108;case 55:return 44;case 56:return 58;case 57:return 42;case 58:return 8;case 59:return 103;case 60:return 112;case 61:return this.popState(),75;case 62:return this.pushState("edgeText"),73;case 63:return 116;case 64:return this.popState(),75;case 65:return this.pushState("thickEdgeText"),73;case 66:return 116;case 67:return this.popState(),75;case 68:return this.pushState("dottedEdgeText"),73;case 69:return 116;case 70:return 75;case 71:return this.popState(),51;case 72:return"TEXT";case 73:return this.pushState("ellipseText"),50;case 74:return this.popState(),53;case 75:return this.pushState("text"),52;case 76:return this.popState(),55;case 77:return this.pushState("text"),54;case 78:return 56;case 79:return this.pushState("text"),65;case 80:return this.popState(),62;case 81:return this.pushState("text"),61;case 82:return this.popState(),47;case 83:return this.pushState("text"),46;case 84:return this.popState(),67;case 85:return this.popState(),69;case 86:return 114;case 87:return this.pushState("trapText"),66;case 88:return this.pushState("trapText"),68;case 89:return 115;case 90:return 65;case 91:return 87;case 92:return"SEP";case 93:return 86;case 94:return 112;case 95:return 108;case 96:return 42;case 97:return 106;case 98:return 111;case 99:return 113;case 100:return this.popState(),60;case 101:return this.pushState("text"),60;case 102:return this.popState(),49;case 103:return this.pushState("text"),48;case 104:return this.popState(),31;case 105:return this.pushState("text"),29;case 106:return this.popState(),64;case 107:return this.pushState("text"),63;case 108:return"TEXT";case 109:return"QUOTE";case 110:return 9;case 111:return 10;case 112:return 11}},rules:[/^(?:accTitle\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*:\s*)/,/^(?:(?!\n||)*[^\n]*)/,/^(?:accDescr\s*\{\s*)/,/^(?:[\}])/,/^(?:[^\}]*)/,/^(?:call[\s]+)/,/^(?:\([\s]*\))/,/^(?:\()/,/^(?:[^(]*)/,/^(?:\))/,/^(?:[^)]*)/,/^(?:[^`"]+)/,/^(?:[`]["])/,/^(?:["][`])/,/^(?:[^"]+)/,/^(?:["])/,/^(?:["])/,/^(?:style\b)/,/^(?:default\b)/,/^(?:linkStyle\b)/,/^(?:interpolate\b)/,/^(?:classDef\b)/,/^(?:class\b)/,/^(?:href[\s])/,/^(?:click[\s]+)/,/^(?:[\s\n])/,/^(?:[^\s\n]*)/,/^(?:flowchart-elk\b)/,/^(?:graph\b)/,/^(?:flowchart\b)/,/^(?:subgraph\b)/,/^(?:end\b\s*)/,/^(?:_self\b)/,/^(?:_blank\b)/,/^(?:_parent\b)/,/^(?:_top\b)/,/^(?:(\r?\n)*\s*\n)/,/^(?:\s*LR\b)/,/^(?:\s*RL\b)/,/^(?:\s*TB\b)/,/^(?:\s*BT\b)/,/^(?:\s*TD\b)/,/^(?:\s*BR\b)/,/^(?:\s*<)/,/^(?:\s*>)/,/^(?:\s*\^)/,/^(?:\s*v\b)/,/^(?:.*direction\s+TB[^\n]*)/,/^(?:.*direction\s+BT[^\n]*)/,/^(?:.*direction\s+RL[^\n]*)/,/^(?:.*direction\s+LR[^\n]*)/,/^(?:[0-9]+)/,/^(?:#)/,/^(?::::)/,/^(?::)/,/^(?:&)/,/^(?:;)/,/^(?:,)/,/^(?:\*)/,/^(?:\s*[xo<]?--+[-xo>]\s*)/,/^(?:\s*[xo<]?--\s*)/,/^(?:[^-]|-(?!-)+)/,/^(?:\s*[xo<]?==+[=xo>]\s*)/,/^(?:\s*[xo<]?==\s*)/,/^(?:[^=]|=(?!))/,/^(?:\s*[xo<]?-?\.+-[xo>]?\s*)/,/^(?:\s*[xo<]?-\.\s*)/,/^(?:[^\.]|\.(?!))/,/^(?:\s*~~[\~]+\s*)/,/^(?:[-/\)][\)])/,/^(?:[^\(\)\[\]\{\}]|!\)+)/,/^(?:\(-)/,/^(?:\]\))/,/^(?:\(\[)/,/^(?:\]\])/,/^(?:\[\[)/,/^(?:\[\|)/,/^(?:>)/,/^(?:\)\])/,/^(?:\[\()/,/^(?:\)\)\))/,/^(?:\(\(\()/,/^(?:[\\(?=\])][\]])/,/^(?:\/(?=\])\])/,/^(?:\/(?!\])|\\(?!\])|[^\\\[\]\(\)\{\}\/]+)/,/^(?:\[\/)/,/^(?:\[\\)/,/^(?:<)/,/^(?:>)/,/^(?:\^)/,/^(?:\\\|)/,/^(?:v\b)/,/^(?:\*)/,/^(?:#)/,/^(?:&)/,/^(?:([A-Za-z0-9!"\#$%&'*+\.`?\\_\/]|-(?=[^\>\-\.])|(?!))+)/,/^(?:-)/,/^(?:[\u00AA\u00B5\u00BA\u00C0-\u00D6\u00D8-\u00F6]|[\u00F8-\u02C1\u02C6-\u02D1\u02E0-\u02E4\u02EC\u02EE\u0370-\u0374\u0376\u0377]|[\u037A-\u037D\u0386\u0388-\u038A\u038C\u038E-\u03A1\u03A3-\u03F5]|[\u03F7-\u0481\u048A-\u0527\u0531-\u0556\u0559\u0561-\u0587\u05D0-\u05EA]|[\u05F0-\u05F2\u0620-\u064A\u066E\u066F\u0671-\u06D3\u06D5\u06E5\u06E6\u06EE]|[\u06EF\u06FA-\u06FC\u06FF\u0710\u0712-\u072F\u074D-\u07A5\u07B1\u07CA-\u07EA]|[\u07F4\u07F5\u07FA\u0800-\u0815\u081A\u0824\u0828\u0840-\u0858\u08A0]|[\u08A2-\u08AC\u0904-\u0939\u093D\u0950\u0958-\u0961\u0971-\u0977]|[\u0979-\u097F\u0985-\u098C\u098F\u0990\u0993-\u09A8\u09AA-\u09B0\u09B2]|[\u09B6-\u09B9\u09BD\u09CE\u09DC\u09DD\u09DF-\u09E1\u09F0\u09F1\u0A05-\u0A0A]|[\u0A0F\u0A10\u0A13-\u0A28\u0A2A-\u0A30\u0A32\u0A33\u0A35\u0A36\u0A38\u0A39]|[\u0A59-\u0A5C\u0A5E\u0A72-\u0A74\u0A85-\u0A8D\u0A8F-\u0A91\u0A93-\u0AA8]|[\u0AAA-\u0AB0\u0AB2\u0AB3\u0AB5-\u0AB9\u0ABD\u0AD0\u0AE0\u0AE1\u0B05-\u0B0C]|[\u0B0F\u0B10\u0B13-\u0B28\u0B2A-\u0B30\u0B32\u0B33\u0B35-\u0B39\u0B3D\u0B5C]|[\u0B5D\u0B5F-\u0B61\u0B71\u0B83\u0B85-\u0B8A\u0B8E-\u0B90\u0B92-\u0B95\u0B99]|[\u0B9A\u0B9C\u0B9E\u0B9F\u0BA3\u0BA4\u0BA8-\u0BAA\u0BAE-\u0BB9\u0BD0]|[\u0C05-\u0C0C\u0C0E-\u0C10\u0C12-\u0C28\u0C2A-\u0C33\u0C35-\u0C39\u0C3D]|[\u0C58\u0C59\u0C60\u0C61\u0C85-\u0C8C\u0C8E-\u0C90\u0C92-\u0CA8\u0CAA-\u0CB3]|[\u0CB5-\u0CB9\u0CBD\u0CDE\u0CE0\u0CE1\u0CF1\u0CF2\u0D05-\u0D0C\u0D0E-\u0D10]|[\u0D12-\u0D3A\u0D3D\u0D4E\u0D60\u0D61\u0D7A-\u0D7F\u0D85-\u0D96\u0D9A-\u0DB1]|[\u0DB3-\u0DBB\u0DBD\u0DC0-\u0DC6\u0E01-\u0E30\u0E32\u0E33\u0E40-\u0E46\u0E81]|[\u0E82\u0E84\u0E87\u0E88\u0E8A\u0E8D\u0E94-\u0E97\u0E99-\u0E9F\u0EA1-\u0EA3]|[\u0EA5\u0EA7\u0EAA\u0EAB\u0EAD-\u0EB0\u0EB2\u0EB3\u0EBD\u0EC0-\u0EC4\u0EC6]|[\u0EDC-\u0EDF\u0F00\u0F40-\u0F47\u0F49-\u0F6C\u0F88-\u0F8C\u1000-\u102A]|[\u103F\u1050-\u1055\u105A-\u105D\u1061\u1065\u1066\u106E-\u1070\u1075-\u1081]|[\u108E\u10A0-\u10C5\u10C7\u10CD\u10D0-\u10FA\u10FC-\u1248\u124A-\u124D]|[\u1250-\u1256\u1258\u125A-\u125D\u1260-\u1288\u128A-\u128D\u1290-\u12B0]|[\u12B2-\u12B5\u12B8-\u12BE\u12C0\u12C2-\u12C5\u12C8-\u12D6\u12D8-\u1310]|[\u1312-\u1315\u1318-\u135A\u1380-\u138F\u13A0-\u13F4\u1401-\u166C]|[\u166F-\u167F\u1681-\u169A\u16A0-\u16EA\u1700-\u170C\u170E-\u1711]|[\u1720-\u1731\u1740-\u1751\u1760-\u176C\u176E-\u1770\u1780-\u17B3\u17D7]|[\u17DC\u1820-\u1877\u1880-\u18A8\u18AA\u18B0-\u18F5\u1900-\u191C]|[\u1950-\u196D\u1970-\u1974\u1980-\u19AB\u19C1-\u19C7\u1A00-\u1A16]|[\u1A20-\u1A54\u1AA7\u1B05-\u1B33\u1B45-\u1B4B\u1B83-\u1BA0\u1BAE\u1BAF]|[\u1BBA-\u1BE5\u1C00-\u1C23\u1C4D-\u1C4F\u1C5A-\u1C7D\u1CE9-\u1CEC]|[\u1CEE-\u1CF1\u1CF5\u1CF6\u1D00-\u1DBF\u1E00-\u1F15\u1F18-\u1F1D]|[\u1F20-\u1F45\u1F48-\u1F4D\u1F50-\u1F57\u1F59\u1F5B\u1F5D\u1F5F-\u1F7D]|[\u1F80-\u1FB4\u1FB6-\u1FBC\u1FBE\u1FC2-\u1FC4\u1FC6-\u1FCC\u1FD0-\u1FD3]|[\u1FD6-\u1FDB\u1FE0-\u1FEC\u1FF2-\u1FF4\u1FF6-\u1FFC\u2071\u207F]|[\u2090-\u209C\u2102\u2107\u210A-\u2113\u2115\u2119-\u211D\u2124\u2126\u2128]|[\u212A-\u212D\u212F-\u2139\u213C-\u213F\u2145-\u2149\u214E\u2183\u2184]|[\u2C00-\u2C2E\u2C30-\u2C5E\u2C60-\u2CE4\u2CEB-\u2CEE\u2CF2\u2CF3]|[\u2D00-\u2D25\u2D27\u2D2D\u2D30-\u2D67\u2D6F\u2D80-\u2D96\u2DA0-\u2DA6]|[\u2DA8-\u2DAE\u2DB0-\u2DB6\u2DB8-\u2DBE\u2DC0-\u2DC6\u2DC8-\u2DCE]|[\u2DD0-\u2DD6\u2DD8-\u2DDE\u2E2F\u3005\u3006\u3031-\u3035\u303B\u303C]|[\u3041-\u3096\u309D-\u309F\u30A1-\u30FA\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diff --git a/frontend-dist/assets/flowDiagram-50d868cf-CulGHM8M.js b/frontend-dist/assets/flowDiagram-50d868cf-CulGHM8M.js
new file mode 100644
index 0000000000000000000000000000000000000000..1ab83ddc0bebdd09d74d8e215c65c77e74e918b6
--- /dev/null
+++ b/frontend-dist/assets/flowDiagram-50d868cf-CulGHM8M.js
@@ -0,0 +1,4 @@
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index 0000000000000000000000000000000000000000..0d71c024a254c6b33bffdf77b8ed7ff2b27c1d02
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index 0000000000000000000000000000000000000000..8acc8e2e8d9f78101bcf0a118b477d5d3ba1b795
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t,i,r,c,s;for(s=n.j,e.a!=e.b&&Yt(s,new Mpn),r=s.c.length/2|0,i=0;i0&&hy(n,t,e),c):i.a!=null?(hy(n,e,t),-1):r.a!=null?(hy(n,t,e),1):0}function mCe(n,e){var t,i,r,c,s;for(r=e.b.b,n.a=K(rs,kw,15,r,0,1),n.b=K(so,Xh,28,r,16,1),s=ge(e.b,0);s.b!=s.d.c;)c=u(be(s),40),n.a[c.g]=new Ct;for(i=ge(e.a,0);i.b!=i.d.c;)t=u(be(i),65),n.a[t.b.g].Fc(t),n.a[t.c.g].Fc(t)}function lHn(n,e){var t,i,r,c;n.Pj()?(t=n.Ej(),c=n.Qj(),++n.j,n.qj(t,n.Zi(t,e)),i=n.Ij(3,null,e,t,c),n.Mj()?(r=n.Nj(e,null),r?(r.nj(i),r.oj()):n.Jj(i)):n.Jj(i)):(eIn(n,e),n.Mj()&&(r=n.Nj(e,null),r&&r.oj()))}function Rnn(n,e,t){var i,r,c;n.Pj()?(c=n.Qj(),Nk(n,e,t),i=n.Ij(3,null,t,e,c),n.Mj()?(r=n.Nj(t,null),n.Tj()&&(r=n.Uj(t,r)),r?(r.nj(i),r.oj()):n.Jj(i)):n.Jj(i)):(Nk(n,e,t),n.Mj()&&(r=n.Nj(t,null),r&&r.oj()))}function jA(n,e){var t,i,r,c,s;for(s=ru(n.e.Dh(),e),r=new EE,t=u(n.g,124),c=n.i;--c>=0;)i=t[c],s.am(i.Lk())&&ve(r,i);!uzn(n,r)&&fo(n.e)&&t4(n,e.Jk()?X1(n,6,e,(Dn(),sr),null,-1,!1):X1(n,e.tk()?2:1,e,null,null,-1,!1))}function 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