134 lines
4.2 KiB
Markdown
134 lines
4.2 KiB
Markdown
# ai-service - Progress
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---
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## 📋 Context
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- module: `ai-service`
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- feature: `AISVC` (Python AI 中台)
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- status: 🔄 进行中
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---
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## 🔗 Spec References (SSOT)
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- agents: `agents.md`
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- contracting: `spec/contracting.md`
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- requirements: `spec/ai-service/requirements.md`
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- openapi_provider: `spec/ai-service/openapi.provider.yaml`
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- design: `spec/ai-service/design.md`
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- tasks: `spec/ai-service/tasks.md`
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---
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## 📊 Overall Progress (Phases)
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- [x] Phase 1: 基础设施（FastAPI 框架与多租户基础） (100%) ✅
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- [x] Phase 2: 存储与检索实现（Memory & Retrieval） (100%) ✅
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- [ ] Phase 3: 核心编排（Orchestrator & LLM Adapter） (40%) 🔄
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- [ ] Phase 4: 流式响应（SSE 实现与状态机） (0%) ⏳
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- [ ] Phase 5: 集成与冒烟测试（Quality Assurance） (0%) ⏳
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---
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## 🔄 Current Phase
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### Goal
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实现核心编排层，包括 LLM Adapter 和 Orchestrator 的完整功能。
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### Sub Tasks
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#### Phase 3: 核心编排（Orchestrator & LLM Adapter）
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- [x] T3.1 实现 LLM Adapter：封装 `langchain-openai` 或官方 SDK，支持 `generate` 与 `stream_generate` `[AC-AISVC-02, AC-AISVC-06]` ✅
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- [x] T3.2 实现 Orchestrator：实现上下文合并逻辑（H_local + H_ext 的去重与截断策略） `[AC-AISVC-14, AC-AISVC-15]` ✅
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- [ ] T3.3 实现 Orchestrator：实现 RAG 检索不足时的置信度下调与 `shouldTransfer` 逻辑 `[AC-AISVC-17, AC-AISVC-18, AC-AISVC-19]` ⏳
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- [ ] T3.4 实现 Orchestrator：整合 Memory、Retrieval 与 LLM 完成 non-streaming 生成闭环 `[AC-AISVC-01, AC-AISVC-02]` ⏳
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- [ ] T3.5 验证 non-streaming 响应字段完全符合 `openapi.provider.yaml` 契约 `[AC-AISVC-02]` ⏳
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### Next Action (Must be Specific)
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**Immediate**: 执行 T3.3 - 实现 RAG 检索不足时的置信度下调与 `shouldTransfer` 逻辑。
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**Details**:
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1. 定义"检索不足"的判定条件：
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- `hits.size < minHits`
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- `max(score) < scoreThreshold`
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- evidence token 超限
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2. 实现置信度计算策略：
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- 基于检索分数计算 confidence
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- 检索不足时下调 confidence
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3. 实现 `shouldTransfer` 逻辑：
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- 当 `confidence < T_low` 时 `shouldTransfer=true`
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- 添加 `transferReason` 说明
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4. reference:
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- `spec/ai-service/design.md` Section 4.3 - 检索不中兜底与置信度策略
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- `spec/ai-service/requirements.md` AC-AISVC-17, AC-AISVC-18, AC-AISVC-19
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---
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## 🏗️ Technical Context
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### Module Structure
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- `ai-service/`
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- `app/`
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- `api/` - FastAPI 路由层
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- `core/` - 配置、异常、中间件、SSE
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- `models/` - Pydantic 模型和 SQLModel 实体
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- `services/`
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- `llm/` - LLM Adapter 实现 ✅
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- `base.py` - LLMClient 抽象接口
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- `openai_client.py` - OpenAI 兼容客户端
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- `memory.py` - Memory 服务
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- `orchestrator.py` - 编排服务
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- `retrieval/` - 检索层
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- `tests/` - 单元测试
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### Key Decisions (Why / Impact)
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- decision: LLM Adapter 使用 httpx 而非 langchain-openai
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reason: 更轻量、更可控、减少依赖
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impact: 需要手动处理 OpenAI API 响应解析
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- decision: 使用 tenacity 实现重试逻辑
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reason: 简单可靠的重试机制
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impact: 提高服务稳定性
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### Code Snippets
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```python
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# [AC-AISVC-02] LLM Response generation
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response = await llm_client.generate(messages, config=LLMConfig(...))
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# [AC-AISVC-06] Streaming generation
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async for chunk in llm_client.stream_generate(messages):
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yield create_message_event(delta=chunk.delta)
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```
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---
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## 🧾 Session History
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### Session #1 (2026-02-24)
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- completed:
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- T3.1 实现 LLM Adapter
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- 创建 LLMClient 抽象接口 (base.py)
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- 实现 OpenAIClient (openai_client.py)
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- 编写单元测试 (test_llm_adapter.py)
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- 修复 entities.py JSON 类型问题
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- changes:
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- 新增 `app/services/llm/__init__.py`
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- 新增 `app/services/llm/base.py`
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- 新增 `app/services/llm/openai_client.py`
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- 新增 `tests/test_llm_adapter.py`
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- 更新 `app/core/config.py` 添加 LLM 配置
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- 修复 `app/models/entities.py` JSON 列类型
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---
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## 🚀 Startup Guide
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1. 读取本进度文档，定位当前 Phase 与 Next Action。
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2. 打开并阅读 Spec References 指向的模块规范。
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3. 直接执行 Next Action；遇到缺口先更新 spec 再编码。
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