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MerCry c005066162 feat(v0.7.0-window2): implement flow simulation and guardrail testing/monitoring 
Refs: AC-AISVC-101, AC-AISVC-102, AC-AISVC-103, AC-AISVC-104, AC-AISVC-105, AC-AISVC-106, AC-AISVC-107
Refs: AC-ASA-59, AC-ASA-60, AC-ASA-61, AC-ASA-62, AC-ASA-63, AC-ASA-64

Backend changes:
- New: ai-service/app/services/flow/tester.py (ScriptFlowTester)
- New: ai-service/app/services/guardrail/tester.py (GuardrailTester)
- New: ai-service/app/services/monitoring/flow_monitor.py (FlowMonitor)
- New: ai-service/app/services/monitoring/guardrail_monitor.py (GuardrailMonitor)
- Modified: ai-service/app/api/admin/script_flows.py (add POST /{flowId}/simulate)
- Modified: ai-service/app/api/admin/guardrails.py (add POST /test)
- Modified: ai-service/app/api/admin/monitoring.py (add flow/guardrail stats endpoints)

Frontend changes:
- New: SimulateDialog.vue (flow simulation dialog)
- New: TestDialog.vue (guardrail test dialog)
- New: ScriptFlows.vue (flow monitoring page)
- New: Guardrails.vue (guardrail monitoring page)
- Extended: API services (monitoring.ts, script-flow.ts, guardrail.ts)
- Updated: Router with new monitoring routes
 		2026-02-27 23:13:45 +08:00
..
app feat(v0.7.0-window2): implement flow simulation and guardrail testing/monitoring 2026-02-27 23:13:45 +08:00
scripts fix(AISVC): 修复 knowledge-bases 接口 500 错误 [AC-AISVC-60] 2026-02-27 21:37:48 +08:00
tests feat(ai-service): add Phase 5 integration and contract tests [AC-AISVC-10,11,17,18] 2026-02-24 13:53:55 +08:00
.dockerignore feat: 添加Docker容器部署配置 [AC-AISVC-01] 2026-02-26 01:22:30 +08:00
Dockerfile fix: Docker构建时复制README.md文件 [AC-AISVC-01] 2026-02-26 02:13:26 +08:00
README.md feat(AISVC-T7): 嵌入模型可插拔设计与文档解析支持 [AC-AISVC-29, AC-AISVC-30, AC-AISVC-31, AC-AISVC-32, AC-AISVC-33, AC-AISVC-34, AC-AISVC-35, AC-AISVC-36, AC-AISVC-37, AC-AISVC-38, AC-AISVC-39, AC-AISVC-40, AC-AISVC-41] 2026-02-24 23:08:08 +08:00
pyproject.toml fix: 适配qdrant-client 1.17.0 API变更，search方法改为query_points [AC-AISVC-50] 2026-02-26 19:07:04 +08:00

README.md

AI Service

Python AI Service for intelligent chat with RAG support.

Features

  • Multi-tenant isolation via X-Tenant-Id header
  • SSE streaming support via Accept: text/event-stream
  • RAG-powered responses with confidence scoring

Prerequisites

  • PostgreSQL 12+
  • Qdrant vector database
  • Python 3.10+

Installation

pip install -e ".[dev]"

Database Initialization

Option 1: Using Python script (Recommended)

# Create database and tables
python scripts/init_db.py --create-db

# Or just create tables (database must exist)
python scripts/init_db.py

Option 2: Using SQL script

# Connect to PostgreSQL and run
psql -U postgres -f scripts/init_db.sql

Configuration

Create a .env file in the project root:

AI_SERVICE_DATABASE_URL=postgresql+asyncpg://postgres:password@localhost:5432/ai_service
AI_SERVICE_QDRANT_URL=http://localhost:6333
AI_SERVICE_LLM_API_KEY=your-api-key
AI_SERVICE_LLM_BASE_URL=https://api.openai.com/v1
AI_SERVICE_LLM_MODEL=gpt-4o-mini
AI_SERVICE_DEBUG=true

Running

uvicorn app.main:app --host 0.0.0.0 --port 8000

API Endpoints

Chat API

  • POST /ai/chat - Generate AI reply (supports SSE streaming)
  • GET /ai/health - Health check

Admin API

  • GET /admin/kb/documents - List documents
  • POST /admin/kb/documents - Upload document
  • GET /admin/kb/index/jobs/{jobId} - Get indexing job status
  • DELETE /admin/kb/documents/{docId} - Delete document
  • POST /admin/rag/experiments/run - Run RAG experiment
  • GET /admin/sessions - List chat sessions
  • GET /admin/sessions/{sessionId} - Get session details