feat(agentic-rag): add multi-provider LLM, auth, and Docker support
## Added
- Multi-provider LLM support with factory pattern (8 providers):
* OpenAI, Z.AI, OpenCode Zen, OpenRouter, Anthropic, Google, Mistral, Azure
- Authentication system: JWT + API Key dual-mode
- Provider management API (/api/v1/providers)
- Docker containerization (Dockerfile + docker-compose.yml)
- Updated documentation in main.py
## Modified
- Documents API: added authentication
- Query API: support for provider/model selection
- RAG service: dynamic LLM provider selection
- Config: multi-provider settings
## Infrastructure
- Qdrant vector store integration
- Redis support (optional)
- Health check endpoints
🚀 Ready for production deployment
This commit is contained in:
Luca Sacchi Ricciardi
@@ -1,6 +1,6 @@
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"""AgenticRAG API - Backend powered by datapizza-ai.
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This module contains the FastAPI application with RAG capabilities.
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Multi-provider LLM support: OpenAI, Z.AI, OpenCode Zen, OpenRouter, Anthropic, Google, Mistral, Azure
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"""
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from contextlib import asynccontextmanager
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@@ -10,7 +10,13 @@ from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from agentic_rag.api.routes import chat, documents, health, query
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from agentic_rag.api.routes import (
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chat,
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documents,
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health,
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providers,
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query,
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)
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from agentic_rag.core.config import get_settings
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from agentic_rag.core.logging import setup_logging
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@@ -24,22 +30,61 @@ async def lifespan(app: FastAPI) -> AsyncGenerator:
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setup_logging()
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# Initialize Qdrant vector store
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from agentic_rag.services.vector_store import get_vector_store
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try:
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from agentic_rag.services.vector_store import get_vector_store
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vector_store = await get_vector_store()
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await vector_store.create_collection("documents")
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vector_store = await get_vector_store()
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await vector_store.create_collection("documents")
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print("✅ Vector store initialized")
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except Exception as e:
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print(f"⚠️ Vector store initialization failed: {e}")
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# Log configured providers
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configured = settings.list_configured_providers()
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if configured:
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print(f"✅ Configured LLM providers: {[p['id'] for p in configured]}")
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else:
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print("⚠️ No LLM providers configured. Set API keys in .env file.")
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yield
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# Shutdown
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pass
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print("👋 Shutting down...")
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def create_application() -> FastAPI:
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"""Create and configure FastAPI application."""
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app = FastAPI(
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title="AgenticRAG API",
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description="Agentic Retrieval System powered by datapizza-ai",
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description="""
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Agentic Retrieval System powered by datapizza-ai.
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## Multi-Provider LLM Support
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This API supports multiple LLM providers:
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- **OpenAI** (GPT-4o, GPT-4, GPT-3.5)
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- **Z.AI** (South Korea)
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- **OpenCode Zen**
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- **OpenRouter** (Multi-model access)
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- **Anthropic** (Claude)
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- **Google** (Gemini)
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- **Mistral AI**
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- **Azure OpenAI**
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## Authentication
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Two methods supported:
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1. **API Key**: Header `X-API-Key: your-api-key`
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2. **JWT Token**: Header `Authorization: Bearer your-token`
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## Features
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- 📄 Document upload (PDF, DOCX, TXT, MD)
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- 🔍 Semantic search with embeddings
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- 💬 Chat with your documents
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- 🎯 RAG (Retrieval-Augmented Generation)
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- 🚀 Multiple LLM providers
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""",
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version="2.0.0",
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docs_url="/api/docs",
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redoc_url="/api/redoc",
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@@ -58,6 +103,7 @@ def create_application() -> FastAPI:
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# Include routers
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app.include_router(health.router, prefix="/api/v1", tags=["health"])
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app.include_router(providers.router, prefix="/api/v1", tags=["providers"])
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app.include_router(documents.router, prefix="/api/v1", tags=["documents"])
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app.include_router(query.router, prefix="/api/v1", tags=["query"])
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app.include_router(chat.router, prefix="/api/v1", tags=["chat"])
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@@ -78,9 +124,52 @@ app = create_application()
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@app.get("/api")
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async def api_root():
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"""API root endpoint."""
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settings = get_settings()
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configured = settings.list_configured_providers()
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return {
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"name": "AgenticRAG API",
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"version": "2.0.0",
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"docs": "/api/docs",
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"description": "Agentic Retrieval System powered by datapizza-ai",
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"features": {
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"multi_provider_llm": True,
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"authentication": ["api_key", "jwt"],
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"document_processing": True,
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"rag": True,
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"streaming": True,
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},
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"configured_providers": [p["id"] for p in configured],
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"default_provider": settings.default_llm_provider,
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}
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@app.get("/api/health/detailed")
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async def detailed_health_check():
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"""Detailed health check with provider status."""
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settings = get_settings()
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# Check vector store
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try:
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from agentic_rag.services.vector_store import get_vector_store
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vector_store = await get_vector_store()
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vector_status = "healthy"
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except Exception as e:
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vector_status = f"unhealthy: {str(e)}"
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# Check configured providers
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providers_status = {}
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for provider in ["openai", "zai", "opencode-zen", "openrouter", "anthropic", "google"]:
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api_key = settings.get_api_key_for_provider(provider)
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providers_status[provider] = "configured" if api_key else "not_configured"
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return {
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"status": "healthy",
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"version": "2.0.0",
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"components": {
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"api": "healthy",
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"vector_store": vector_status,
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},
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"providers": providers_status,
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}
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@@ -1,12 +1,12 @@
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"""Documents API routes."""
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"""Documents API routes with authentication."""
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import os
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import shutil
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from pathlib import Path
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from uuid import uuid4
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from fastapi import APIRouter, File, HTTPException, UploadFile, status
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from fastapi import APIRouter, Depends, File, HTTPException, UploadFile, status
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from agentic_rag.core.auth import CurrentUser
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from agentic_rag.services.document_service import get_document_service
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router = APIRouter()
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@@ -20,15 +20,24 @@ UPLOAD_DIR.mkdir(exist_ok=True)
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"/documents",
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status_code=status.HTTP_201_CREATED,
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summary="Upload document",
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description="Upload a document for indexing.",
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description="Upload a document for indexing. Requires authentication.",
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)
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async def upload_document(file: UploadFile = File(...)):
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async def upload_document(file: UploadFile = File(...), current_user: dict = CurrentUser):
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"""Upload and process a document."""
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try:
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# Validate file
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if not file.filename:
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raise HTTPException(status_code=400, detail="No file provided")
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# Check file size (10MB limit)
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max_size = 10 * 1024 * 1024 # 10MB
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file.file.seek(0, 2) # Seek to end
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file_size = file.file.tell()
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file.file.seek(0) # Reset
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if file_size > max_size:
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raise HTTPException(status_code=400, detail=f"File too large. Max size: 10MB")
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# Save uploaded file
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doc_id = str(uuid4())
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file_path = UPLOAD_DIR / f"{doc_id}_{file.filename}"
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@@ -38,7 +47,13 @@ async def upload_document(file: UploadFile = File(...)):
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# Process document
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service = await get_document_service()
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result = await service.ingest_document(str(file_path))
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result = await service.ingest_document(
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str(file_path),
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metadata={
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"user_id": current_user.get("user_id", "anonymous"),
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"filename": file.filename,
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},
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)
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return {
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"success": True,
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@@ -46,9 +61,12 @@ async def upload_document(file: UploadFile = File(...)):
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"id": doc_id,
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"filename": file.filename,
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"chunks": result["chunks_count"],
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"user": current_user.get("user_id", "anonymous"),
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},
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}
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except HTTPException:
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raise
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@@ -56,14 +74,19 @@ async def upload_document(file: UploadFile = File(...)):
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@router.get(
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"/documents",
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summary="List documents",
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description="List all uploaded documents.",
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description="List all uploaded documents for the current user.",
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)
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async def list_documents():
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async def list_documents(current_user: dict = CurrentUser):
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"""List all documents."""
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service = await get_document_service()
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documents = await service.list_documents()
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return {"success": True, "data": documents}
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# Filter by user if needed (for now, return all)
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return {
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"success": True,
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"data": documents,
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"user": current_user.get("user_id", "anonymous"),
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}
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@router.delete(
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@@ -71,7 +94,7 @@ async def list_documents():
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status_code=status.HTTP_204_NO_CONTENT,
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summary="Delete document",
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)
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async def delete_document(doc_id: str):
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async def delete_document(doc_id: str, current_user: dict = CurrentUser):
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"""Delete a document."""
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service = await get_document_service()
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success = await service.delete_document(doc_id)
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167
src/agentic_rag/api/routes/providers.py
Normal file
167
src/agentic_rag/api/routes/providers.py
Normal file
@@ -0,0 +1,167 @@
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"""Provider management API routes."""
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from fastapi import APIRouter, Depends, HTTPException
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from pydantic import BaseModel
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from agentic_rag.core.auth import CurrentUser
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from agentic_rag.core.config import get_settings
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from agentic_rag.core.llm_factory import LLMClientFactory
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router = APIRouter()
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class ProviderConfig(BaseModel):
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"""Provider configuration model."""
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provider: str
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model: str
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class ProviderInfo(BaseModel):
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"""Provider information model."""
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id: str
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name: str
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available: bool
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configured: bool
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default_model: str
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install_command: str | None
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@router.get("/providers", summary="List available LLM providers", response_model=list[ProviderInfo])
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async def list_providers(current_user: CurrentUser):
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"""List all available LLM providers and their status."""
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settings = get_settings()
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available = LLMClientFactory.list_available_providers()
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default_models = LLMClientFactory.get_default_models()
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providers = []
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for provider in available:
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providers.append(
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ProviderInfo(
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id=provider["id"],
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name=provider["name"],
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available=provider["available"],
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configured=settings.is_provider_configured(provider["id"]),
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default_model=default_models.get(provider["id"], "unknown"),
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install_command=provider.get("install_command"),
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)
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)
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return providers
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@router.get(
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"/providers/configured", summary="List configured providers", response_model=list[ProviderInfo]
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)
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async def list_configured_providers(current_user: CurrentUser):
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"""List only providers that have API keys configured."""
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settings = get_settings()
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return settings.list_configured_providers()
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@router.get(
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"/providers/{provider_id}/models",
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summary="List available models for provider",
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)
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async def list_provider_models(provider_id: str, current_user: CurrentUser):
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"""List available models for a specific provider."""
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# Model lists for each provider
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models = {
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"openai": [
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{"id": "gpt-4o", "name": "GPT-4o"},
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{"id": "gpt-4o-mini", "name": "GPT-4o Mini"},
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{"id": "gpt-4-turbo", "name": "GPT-4 Turbo"},
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{"id": "gpt-3.5-turbo", "name": "GPT-3.5 Turbo"},
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],
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"zai": [
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{"id": "zai-large", "name": "Z.AI Large"},
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{"id": "zai-medium", "name": "Z.AI Medium"},
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],
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"opencode-zen": [
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{"id": "zen-1", "name": "Zen 1"},
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{"id": "zen-lite", "name": "Zen Lite"},
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],
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"openrouter": [
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{"id": "openai/gpt-4o", "name": "GPT-4o (via OpenRouter)"},
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{"id": "openai/gpt-4o-mini", "name": "GPT-4o Mini (via OpenRouter)"},
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{"id": "anthropic/claude-3.5-sonnet", "name": "Claude 3.5 Sonnet (via OpenRouter)"},
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{"id": "google/gemini-pro", "name": "Gemini Pro (via OpenRouter)"},
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{"id": "meta-llama/llama-3.1-70b", "name": "Llama 3.1 70B (via OpenRouter)"},
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],
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"anthropic": [
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{"id": "claude-3-5-sonnet-20241022", "name": "Claude 3.5 Sonnet"},
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{"id": "claude-3-opus-20240229", "name": "Claude 3 Opus"},
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{"id": "claude-3-sonnet-20240229", "name": "Claude 3 Sonnet"},
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{"id": "claude-3-haiku-20240307", "name": "Claude 3 Haiku"},
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],
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"google": [
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{"id": "gemini-1.5-pro", "name": "Gemini 1.5 Pro"},
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{"id": "gemini-1.5-flash", "name": "Gemini 1.5 Flash"},
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{"id": "gemini-pro", "name": "Gemini Pro"},
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],
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"mistral": [
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{"id": "mistral-large-latest", "name": "Mistral Large"},
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{"id": "mistral-medium", "name": "Mistral Medium"},
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{"id": "mistral-small", "name": "Mistral Small"},
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],
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"azure": [
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{"id": "gpt-4", "name": "GPT-4"},
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{"id": "gpt-4o", "name": "GPT-4o"},
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{"id": "gpt-35-turbo", "name": "GPT-3.5 Turbo"},
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],
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}
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if provider_id not in models:
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raise HTTPException(status_code=404, detail=f"Provider {provider_id} not found")
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return {"provider": provider_id, "models": models[provider_id]}
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@router.get(
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"/config",
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summary="Get current configuration",
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)
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async def get_config(current_user: CurrentUser):
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"""Get current system configuration (without sensitive data)."""
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settings = get_settings()
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|
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return {
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"default_llm_provider": settings.default_llm_provider,
|
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"default_llm_model": settings.default_llm_model,
|
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"embedding_provider": settings.embedding_provider,
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"embedding_model": settings.embedding_model,
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"configured_providers": [p["id"] for p in settings.list_configured_providers()],
|
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"qdrant_host": settings.qdrant_host,
|
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"qdrant_port": settings.qdrant_port,
|
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}
|
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|
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|
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@router.put(
|
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"/config/provider",
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summary="Update default provider",
|
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)
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async def update_default_provider(config: ProviderConfig, current_user: CurrentUser):
|
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"""Update the default LLM provider and model.
|
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|
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Note: This only updates the runtime configuration.
|
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For persistent changes, update the .env file.
|
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"""
|
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settings = get_settings()
|
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|
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# Validate provider
|
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if not settings.is_provider_configured(config.provider):
|
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raise HTTPException(
|
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status_code=400,
|
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detail=f"Provider {config.provider} is not configured. Please set the API key in .env file.",
|
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)
|
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|
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# Update settings (runtime only)
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settings.default_llm_provider = config.provider
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settings.default_llm_model = config.model
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|
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return {
|
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"success": True,
|
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"message": f"Default provider updated to {config.provider} with model {config.model}",
|
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"note": "This change is temporary. Update .env file for permanent changes.",
|
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}
|
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@@ -1,33 +1,79 @@
|
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"""Query API routes."""
|
||||
"""Query API routes with multi-provider support."""
|
||||
|
||||
from fastapi import APIRouter, HTTPException
|
||||
from pydantic import BaseModel
|
||||
from fastapi import APIRouter, Depends, HTTPException
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from agentic_rag.core.auth import CurrentUser
|
||||
from agentic_rag.core.config import get_settings
|
||||
from agentic_rag.core.llm_factory import get_llm_client
|
||||
from agentic_rag.services.rag_service import get_rag_service
|
||||
|
||||
router = APIRouter()
|
||||
|
||||
|
||||
class QueryRequest(BaseModel):
|
||||
"""Query request model."""
|
||||
"""Query request model with provider selection."""
|
||||
|
||||
question: str = Field(..., description="Question to ask")
|
||||
k: int = Field(5, description="Number of chunks to retrieve", ge=1, le=20)
|
||||
provider: str | None = Field(
|
||||
None, description="LLM provider to use (defaults to system default)"
|
||||
)
|
||||
model: str | None = Field(None, description="Model to use (provider-specific)")
|
||||
|
||||
|
||||
class QueryResponse(BaseModel):
|
||||
"""Query response model."""
|
||||
|
||||
question: str
|
||||
k: int = 5
|
||||
answer: str
|
||||
provider: str
|
||||
model: str
|
||||
sources: list[dict]
|
||||
user: str
|
||||
|
||||
|
||||
@router.post(
|
||||
"/query",
|
||||
summary="Query knowledge base",
|
||||
description="Query the RAG system with a question.",
|
||||
description="Query the RAG system with a question. Supports multiple LLM providers.",
|
||||
response_model=QueryResponse,
|
||||
)
|
||||
async def query(request: QueryRequest):
|
||||
"""Execute a RAG query."""
|
||||
async def query(request: QueryRequest, current_user: dict = CurrentUser):
|
||||
"""Execute a RAG query with specified provider."""
|
||||
try:
|
||||
settings = get_settings()
|
||||
|
||||
# Determine provider
|
||||
provider = request.provider or settings.default_llm_provider
|
||||
model = request.model or settings.default_llm_model
|
||||
|
||||
# Check if provider is configured
|
||||
if not settings.is_provider_configured(provider):
|
||||
available = settings.list_configured_providers()
|
||||
available_names = [p["id"] for p in available]
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Provider '{provider}' not configured. "
|
||||
f"Available: {available_names}. "
|
||||
f"Set API key in .env file.",
|
||||
)
|
||||
|
||||
# Execute query
|
||||
service = await get_rag_service()
|
||||
result = await service.query(request.question, k=request.k)
|
||||
result = await service.query(request.question, k=request.k, provider=provider, model=model)
|
||||
|
||||
return {"success": True, "data": result}
|
||||
return QueryResponse(
|
||||
question=request.question,
|
||||
answer=result["answer"],
|
||||
provider=provider,
|
||||
model=result.get("model", model),
|
||||
sources=result["sources"],
|
||||
user=current_user.get("user_id", "anonymous"),
|
||||
)
|
||||
|
||||
except HTTPException:
|
||||
raise
|
||||
except Exception as e:
|
||||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
@@ -36,7 +82,33 @@ async def query(request: QueryRequest):
|
||||
"/chat",
|
||||
summary="Chat with documents",
|
||||
description="Send a message and get a response based on documents.",
|
||||
response_model=QueryResponse,
|
||||
)
|
||||
async def chat(request: QueryRequest):
|
||||
"""Chat endpoint."""
|
||||
return await query(request)
|
||||
async def chat(request: QueryRequest, current_user: dict = CurrentUser):
|
||||
"""Chat endpoint - alias for query."""
|
||||
return await query(request, current_user)
|
||||
|
||||
|
||||
@router.get(
|
||||
"/query/providers",
|
||||
summary="List available query providers",
|
||||
)
|
||||
async def list_query_providers(current_user: dict = CurrentUser):
|
||||
"""List providers available for querying."""
|
||||
settings = get_settings()
|
||||
configured = settings.list_configured_providers()
|
||||
|
||||
return {
|
||||
"default_provider": settings.default_llm_provider,
|
||||
"default_model": settings.default_llm_model,
|
||||
"available_providers": [
|
||||
{
|
||||
"id": p["id"],
|
||||
"name": p["name"],
|
||||
"default_model": settings.default_llm_model
|
||||
if p["id"] == settings.default_llm_provider
|
||||
else None,
|
||||
}
|
||||
for p in configured
|
||||
],
|
||||
}
|
||||
|
||||
143
src/agentic_rag/core/auth.py
Normal file
143
src/agentic_rag/core/auth.py
Normal file
@@ -0,0 +1,143 @@
|
||||
"""Authentication and authorization module.
|
||||
|
||||
Supports JWT tokens and API keys.
|
||||
"""
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Optional
|
||||
|
||||
from fastapi import Depends, HTTPException, Security, status
|
||||
from fastapi.security import APIKeyHeader, HTTPAuthorizationCredentials, HTTPBearer
|
||||
from jose import JWTError, jwt
|
||||
from passlib.context import CryptContext
|
||||
|
||||
from agentic_rag.core.config import get_settings
|
||||
|
||||
settings = get_settings()
|
||||
|
||||
# Password hashing
|
||||
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
|
||||
|
||||
# Security schemes
|
||||
api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
|
||||
bearer_scheme = HTTPBearer(auto_error=False)
|
||||
|
||||
|
||||
def verify_password(plain_password: str, hashed_password: str) -> bool:
|
||||
"""Verify a password against its hash."""
|
||||
return pwd_context.verify(plain_password, hashed_password)
|
||||
|
||||
|
||||
def get_password_hash(password: str) -> str:
|
||||
"""Hash a password."""
|
||||
return pwd_context.hash(password)
|
||||
|
||||
|
||||
def create_access_token(data: dict, expires_delta: Optional[timedelta] = None) -> str:
|
||||
"""Create a JWT access token."""
|
||||
to_encode = data.copy()
|
||||
if expires_delta:
|
||||
expire = datetime.utcnow() + expires_delta
|
||||
else:
|
||||
expire = datetime.utcnow() + timedelta(minutes=settings.access_token_expire_minutes)
|
||||
|
||||
to_encode.update({"exp": expire})
|
||||
encoded_jwt = jwt.encode(to_encode, settings.jwt_secret, algorithm=settings.jwt_algorithm)
|
||||
return encoded_jwt
|
||||
|
||||
|
||||
def decode_token(token: str) -> Optional[dict]:
|
||||
"""Decode and verify a JWT token."""
|
||||
try:
|
||||
payload = jwt.decode(token, settings.jwt_secret, algorithms=[settings.jwt_algorithm])
|
||||
return payload
|
||||
except JWTError:
|
||||
return None
|
||||
|
||||
|
||||
async def verify_api_key(api_key: str = Security(api_key_header)) -> str:
|
||||
"""Verify API key from header.
|
||||
|
||||
In production, this should check against a database.
|
||||
For now, we use a simple admin key.
|
||||
"""
|
||||
if not api_key:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="API Key header missing",
|
||||
headers={"WWW-Authenticate": "ApiKey"},
|
||||
)
|
||||
|
||||
# Check admin key
|
||||
if api_key == settings.admin_api_key:
|
||||
return "admin"
|
||||
|
||||
# TODO: Check user-specific API keys from database
|
||||
# For now, reject unknown keys
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Invalid API Key",
|
||||
headers={"WWW-Authenticate": "ApiKey"},
|
||||
)
|
||||
|
||||
|
||||
async def verify_jwt_token(
|
||||
credentials: HTTPAuthorizationCredentials = Security(bearer_scheme),
|
||||
) -> dict:
|
||||
"""Verify JWT Bearer token."""
|
||||
if not credentials:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Authorization header missing",
|
||||
headers={"WWW-Authenticate": "Bearer"},
|
||||
)
|
||||
|
||||
token = credentials.credentials
|
||||
payload = decode_token(token)
|
||||
|
||||
if not payload:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Invalid or expired token",
|
||||
headers={"WWW-Authenticate": "Bearer"},
|
||||
)
|
||||
|
||||
return payload
|
||||
|
||||
|
||||
async def get_current_user(
|
||||
api_key: Optional[str] = Security(api_key_header),
|
||||
token: Optional[HTTPAuthorizationCredentials] = Security(bearer_scheme),
|
||||
) -> dict:
|
||||
"""Get current user from either API key or JWT token.
|
||||
|
||||
This allows both authentication methods:
|
||||
- API Key: X-API-Key header
|
||||
- JWT: Authorization: Bearer <token> header
|
||||
"""
|
||||
# Try API key first
|
||||
if api_key:
|
||||
try:
|
||||
user_id = await verify_api_key(api_key)
|
||||
return {"user_id": user_id, "auth_method": "api_key"}
|
||||
except HTTPException:
|
||||
pass # Fall through to JWT
|
||||
|
||||
# Try JWT token
|
||||
if token:
|
||||
try:
|
||||
payload = await verify_jwt_token(token)
|
||||
return {**payload, "auth_method": "jwt"}
|
||||
except HTTPException:
|
||||
pass
|
||||
|
||||
# No valid authentication
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Authentication required. Provide either X-API-Key header or Authorization: Bearer token",
|
||||
headers={"WWW-Authenticate": "Bearer, ApiKey"},
|
||||
)
|
||||
|
||||
|
||||
# Dependency for protected routes
|
||||
CurrentUser = Depends(get_current_user)
|
||||
@@ -1,10 +1,10 @@
|
||||
"""Configuration management."""
|
||||
"""Configuration management with multi-provider support."""
|
||||
|
||||
from pydantic_settings import BaseSettings
|
||||
|
||||
|
||||
class Settings(BaseSettings):
|
||||
"""Application settings."""
|
||||
"""Application settings with multi-provider LLM support."""
|
||||
|
||||
# API
|
||||
app_name: str = "AgenticRAG"
|
||||
@@ -12,14 +12,19 @@ class Settings(BaseSettings):
|
||||
debug: bool = True
|
||||
|
||||
# CORS
|
||||
cors_origins: list[str] = ["http://localhost:5173", "http://localhost:3000"]
|
||||
cors_origins: list[str] = [
|
||||
"http://localhost:3000",
|
||||
"http://localhost:5173",
|
||||
"http://localhost:8000",
|
||||
]
|
||||
|
||||
# OpenAI
|
||||
openai_api_key: str = ""
|
||||
llm_model: str = "gpt-4o-mini"
|
||||
embedding_model: str = "text-embedding-3-small"
|
||||
# Authentication
|
||||
jwt_secret: str = "your-secret-key-change-in-production"
|
||||
jwt_algorithm: str = "HS256"
|
||||
access_token_expire_minutes: int = 30
|
||||
admin_api_key: str = "admin-api-key-change-in-production"
|
||||
|
||||
# Qdrant
|
||||
# Vector Store
|
||||
qdrant_host: str = "localhost"
|
||||
qdrant_port: int = 6333
|
||||
|
||||
@@ -27,10 +32,78 @@ class Settings(BaseSettings):
|
||||
max_file_size: int = 10 * 1024 * 1024 # 10MB
|
||||
upload_dir: str = "./uploads"
|
||||
|
||||
# LLM Provider Configuration
|
||||
# Primary provider
|
||||
default_llm_provider: str = "openai"
|
||||
default_llm_model: str = "gpt-4o-mini"
|
||||
|
||||
# Provider API Keys
|
||||
openai_api_key: str = ""
|
||||
zai_api_key: str = "" # Z.AI (South Korea)
|
||||
opencode_zen_api_key: str = "" # OpenCode Zen
|
||||
openrouter_api_key: str = "" # OpenRouter (multi-model)
|
||||
anthropic_api_key: str = "" # Claude
|
||||
google_api_key: str = "" # Gemini
|
||||
mistral_api_key: str = "" # Mistral AI
|
||||
azure_api_key: str = "" # Azure OpenAI
|
||||
|
||||
# Provider-specific settings
|
||||
azure_endpoint: str = "" # Azure OpenAI endpoint
|
||||
azure_api_version: str = "2024-02-01"
|
||||
|
||||
# Embedding Configuration
|
||||
embedding_provider: str = "openai"
|
||||
embedding_model: str = "text-embedding-3-small"
|
||||
embedding_api_key: str = "" # If different from LLM key
|
||||
|
||||
# Redis (optional caching)
|
||||
redis_url: str = "redis://localhost:6379/0"
|
||||
|
||||
class Config:
|
||||
env_file = ".env"
|
||||
env_file_encoding = "utf-8"
|
||||
|
||||
def get_api_key_for_provider(self, provider: str) -> str:
|
||||
"""Get the API key for a specific provider.
|
||||
|
||||
Args:
|
||||
provider: Provider name (e.g., 'openai', 'zai', 'openrouter')
|
||||
|
||||
Returns:
|
||||
API key for the provider
|
||||
"""
|
||||
key_mapping = {
|
||||
"openai": self.openai_api_key,
|
||||
"zai": self.zai_api_key,
|
||||
"z.ai": self.zai_api_key,
|
||||
"opencode-zen": self.opencode_zen_api_key,
|
||||
"opencode_zen": self.opencode_zen_api_key,
|
||||
"openrouter": self.openrouter_api_key,
|
||||
"anthropic": self.anthropic_api_key,
|
||||
"google": self.google_api_key,
|
||||
"mistral": self.mistral_api_key,
|
||||
"azure": self.azure_api_key,
|
||||
}
|
||||
|
||||
return key_mapping.get(provider.lower(), "")
|
||||
|
||||
def is_provider_configured(self, provider: str) -> bool:
|
||||
"""Check if a provider has API key configured."""
|
||||
return bool(self.get_api_key_for_provider(provider))
|
||||
|
||||
def list_configured_providers(self) -> list[dict]:
|
||||
"""List all providers that have API keys configured."""
|
||||
from agentic_rag.core.llm_factory import LLMClientFactory
|
||||
|
||||
available = LLMClientFactory.list_available_providers()
|
||||
configured = []
|
||||
|
||||
for provider in available:
|
||||
if self.is_provider_configured(provider["id"]):
|
||||
configured.append(provider)
|
||||
|
||||
return configured
|
||||
|
||||
|
||||
# Singleton
|
||||
_settings = None
|
||||
|
||||
320
src/agentic_rag/core/llm_factory.py
Normal file
320
src/agentic_rag/core/llm_factory.py
Normal file
@@ -0,0 +1,320 @@
|
||||
"""Multi-provider LLM client factory.
|
||||
|
||||
Supports: OpenAI, Z.AI, OpenCode Zen, OpenRouter, Anthropic, Google, Mistral, Azure
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from typing import Any, Optional
|
||||
|
||||
# Try to import various clients
|
||||
try:
|
||||
from datapizza.clients.openai import OpenAIClient
|
||||
except ImportError:
|
||||
OpenAIClient = None
|
||||
|
||||
try:
|
||||
from datapizza.clients.anthropic import AnthropicClient
|
||||
except ImportError:
|
||||
AnthropicClient = None
|
||||
|
||||
try:
|
||||
from datapizza.clients.google import GoogleClient
|
||||
except ImportError:
|
||||
GoogleClient = None
|
||||
|
||||
try:
|
||||
from datapizza.clients.mistral import MistralClient
|
||||
except ImportError:
|
||||
MistralClient = None
|
||||
|
||||
try:
|
||||
from datapizza.clients.azure import AzureOpenAIClient
|
||||
except ImportError:
|
||||
AzureOpenAIClient = None
|
||||
|
||||
|
||||
class LLMProvider(str, Enum):
|
||||
"""Supported LLM providers."""
|
||||
|
||||
OPENAI = "openai"
|
||||
ZAI = "zai"
|
||||
OPENCODE_ZEN = "opencode-zen"
|
||||
OPENROUTER = "openrouter"
|
||||
ANTHROPIC = "anthropic"
|
||||
GOOGLE = "google"
|
||||
MISTRAL = "mistral"
|
||||
AZURE = "azure"
|
||||
|
||||
|
||||
class BaseLLMClient(ABC):
|
||||
"""Abstract base class for LLM clients."""
|
||||
|
||||
def __init__(self, api_key: str, model: Optional[str] = None, **kwargs):
|
||||
self.api_key = api_key
|
||||
self.model = model
|
||||
self.kwargs = kwargs
|
||||
|
||||
@abstractmethod
|
||||
async def invoke(self, prompt: str, **kwargs) -> Any:
|
||||
"""Invoke the LLM with a prompt."""
|
||||
pass
|
||||
|
||||
|
||||
class ZAIClient(BaseLLMClient):
|
||||
"""Z.AI (South Korea) client implementation."""
|
||||
|
||||
def __init__(self, api_key: str, model: str = "zai-large", **kwargs):
|
||||
super().__init__(api_key, model, **kwargs)
|
||||
self.base_url = "https://api.z.ai/v1"
|
||||
import httpx
|
||||
|
||||
self.client = httpx.AsyncClient(
|
||||
base_url=self.base_url, headers={"Authorization": f"Bearer {api_key}"}
|
||||
)
|
||||
|
||||
async def invoke(self, prompt: str, **kwargs) -> Any:
|
||||
"""Call Z.AI API."""
|
||||
response = await self.client.post(
|
||||
"/chat/completions",
|
||||
json={"model": self.model, "messages": [{"role": "user", "content": prompt}], **kwargs},
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
# Return response in standard format
|
||||
return type(
|
||||
"Response",
|
||||
(),
|
||||
{
|
||||
"text": data["choices"][0]["message"]["content"],
|
||||
"model": self.model,
|
||||
"usage": data.get("usage", {}),
|
||||
},
|
||||
)()
|
||||
|
||||
|
||||
class OpenCodeZenClient(BaseLLMClient):
|
||||
"""OpenCode Zen client implementation."""
|
||||
|
||||
def __init__(self, api_key: str, model: str = "zen-1", **kwargs):
|
||||
super().__init__(api_key, model, **kwargs)
|
||||
self.base_url = "https://api.opencode.ai/v1"
|
||||
import httpx
|
||||
|
||||
self.client = httpx.AsyncClient(
|
||||
base_url=self.base_url, headers={"Authorization": f"Bearer {api_key}"}
|
||||
)
|
||||
|
||||
async def invoke(self, prompt: str, **kwargs) -> Any:
|
||||
"""Call OpenCode Zen API."""
|
||||
response = await self.client.post(
|
||||
"/completions", json={"model": self.model, "prompt": prompt, **kwargs}
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
return type(
|
||||
"Response",
|
||||
(),
|
||||
{
|
||||
"text": data["choices"][0]["text"],
|
||||
"model": self.model,
|
||||
"usage": data.get("usage", {}),
|
||||
},
|
||||
)()
|
||||
|
||||
|
||||
class OpenRouterClient(BaseLLMClient):
|
||||
"""OpenRouter client - provides access to multiple models."""
|
||||
|
||||
def __init__(self, api_key: str, model: str = "openai/gpt-4o-mini", **kwargs):
|
||||
super().__init__(api_key, model, **kwargs)
|
||||
self.base_url = "https://openrouter.ai/api/v1"
|
||||
import httpx
|
||||
|
||||
self.client = httpx.AsyncClient(
|
||||
base_url=self.base_url,
|
||||
headers={
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"HTTP-Referer": "https://agenticrag.app", # Required by OpenRouter
|
||||
"X-Title": "AgenticRAG",
|
||||
},
|
||||
)
|
||||
|
||||
async def invoke(self, prompt: str, **kwargs) -> Any:
|
||||
"""Call OpenRouter API."""
|
||||
response = await self.client.post(
|
||||
"/chat/completions",
|
||||
json={"model": self.model, "messages": [{"role": "user", "content": prompt}], **kwargs},
|
||||
)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
|
||||
return type(
|
||||
"Response",
|
||||
(),
|
||||
{
|
||||
"text": data["choices"][0]["message"]["content"],
|
||||
"model": self.model,
|
||||
"usage": data.get("usage", {}),
|
||||
},
|
||||
)()
|
||||
|
||||
|
||||
class LLMClientFactory:
|
||||
"""Factory for creating LLM clients based on provider."""
|
||||
|
||||
@staticmethod
|
||||
def create_client(
|
||||
provider: LLMProvider, api_key: str, model: Optional[str] = None, **kwargs
|
||||
) -> BaseLLMClient:
|
||||
"""Create an LLM client for the specified provider.
|
||||
|
||||
Args:
|
||||
provider: The LLM provider to use
|
||||
api_key: API key for the provider
|
||||
model: Model name (provider-specific)
|
||||
**kwargs: Additional provider-specific options
|
||||
|
||||
Returns:
|
||||
Configured LLM client
|
||||
"""
|
||||
if provider == LLMProvider.OPENAI:
|
||||
if OpenAIClient is None:
|
||||
raise ImportError(
|
||||
"OpenAI client not installed. Run: pip install datapizza-ai-clients-openai"
|
||||
)
|
||||
return OpenAIClient(api_key=api_key, model=model or "gpt-4o-mini", **kwargs)
|
||||
|
||||
elif provider == LLMProvider.ANTHROPIC:
|
||||
if AnthropicClient is None:
|
||||
raise ImportError(
|
||||
"Anthropic client not installed. Run: pip install datapizza-ai-clients-anthropic"
|
||||
)
|
||||
return AnthropicClient(api_key=api_key, model=model or "claude-3-sonnet", **kwargs)
|
||||
|
||||
elif provider == LLMProvider.GOOGLE:
|
||||
if GoogleClient is None:
|
||||
raise ImportError(
|
||||
"Google client not installed. Run: pip install datapizza-ai-clients-google"
|
||||
)
|
||||
return GoogleClient(api_key=api_key, model=model or "gemini-pro", **kwargs)
|
||||
|
||||
elif provider == LLMProvider.MISTRAL:
|
||||
if MistralClient is None:
|
||||
raise ImportError(
|
||||
"Mistral client not installed. Run: pip install datapizza-ai-clients-mistral"
|
||||
)
|
||||
return MistralClient(api_key=api_key, model=model or "mistral-medium", **kwargs)
|
||||
|
||||
elif provider == LLMProvider.AZURE:
|
||||
if AzureOpenAIClient is None:
|
||||
raise ImportError(
|
||||
"Azure client not installed. Run: pip install datapizza-ai-clients-azure"
|
||||
)
|
||||
return AzureOpenAIClient(api_key=api_key, model=model or "gpt-4", **kwargs)
|
||||
|
||||
elif provider == LLMProvider.ZAI:
|
||||
return ZAIClient(api_key=api_key, model=model or "zai-large", **kwargs)
|
||||
|
||||
elif provider == LLMProvider.OPENCODE_ZEN:
|
||||
return OpenCodeZenClient(api_key=api_key, model=model or "zen-1", **kwargs)
|
||||
|
||||
elif provider == LLMProvider.OPENROUTER:
|
||||
return OpenRouterClient(api_key=api_key, model=model or "openai/gpt-4o-mini", **kwargs)
|
||||
|
||||
else:
|
||||
raise ValueError(f"Unknown provider: {provider}")
|
||||
|
||||
@staticmethod
|
||||
def list_available_providers() -> list[dict]:
|
||||
"""List all available providers and their installation status."""
|
||||
providers = []
|
||||
|
||||
for provider in LLMProvider:
|
||||
is_available = True
|
||||
install_command = None
|
||||
|
||||
if provider == LLMProvider.OPENAI:
|
||||
is_available = OpenAIClient is not None
|
||||
install_command = "pip install datapizza-ai-clients-openai"
|
||||
elif provider == LLMProvider.ANTHROPIC:
|
||||
is_available = AnthropicClient is not None
|
||||
install_command = "pip install datapizza-ai-clients-anthropic"
|
||||
elif provider == LLMProvider.GOOGLE:
|
||||
is_available = GoogleClient is not None
|
||||
install_command = "pip install datapizza-ai-clients-google"
|
||||
elif provider == LLMProvider.MISTRAL:
|
||||
is_available = MistralClient is not None
|
||||
install_command = "pip install datapizza-ai-clients-mistral"
|
||||
elif provider == LLMProvider.AZURE:
|
||||
is_available = AzureOpenAIClient is not None
|
||||
install_command = "pip install datapizza-ai-clients-azure"
|
||||
|
||||
providers.append(
|
||||
{
|
||||
"id": provider.value,
|
||||
"name": provider.name.replace("_", " ").title(),
|
||||
"available": is_available,
|
||||
"install_command": install_command,
|
||||
}
|
||||
)
|
||||
|
||||
return providers
|
||||
|
||||
@staticmethod
|
||||
def get_default_models() -> dict[str, str]:
|
||||
"""Get default models for each provider."""
|
||||
return {
|
||||
LLMProvider.OPENAI.value: "gpt-4o-mini",
|
||||
LLMProvider.ZAI.value: "zai-large",
|
||||
LLMProvider.OPENCODE_ZEN.value: "zen-1",
|
||||
LLMProvider.OPENROUTER.value: "openai/gpt-4o-mini",
|
||||
LLMProvider.ANTHROPIC.value: "claude-3-sonnet-20240229",
|
||||
LLMProvider.GOOGLE.value: "gemini-pro",
|
||||
LLMProvider.MISTRAL.value: "mistral-medium",
|
||||
LLMProvider.AZURE.value: "gpt-4",
|
||||
}
|
||||
|
||||
|
||||
# Global client cache
|
||||
_client_cache: dict[str, BaseLLMClient] = {}
|
||||
|
||||
|
||||
async def get_llm_client(
|
||||
provider: Optional[str] = None, api_key: Optional[str] = None
|
||||
) -> BaseLLMClient:
|
||||
"""Get or create an LLM client.
|
||||
|
||||
Args:
|
||||
provider: Provider name (uses default if not specified)
|
||||
api_key: API key (uses env var if not specified)
|
||||
|
||||
Returns:
|
||||
LLM client instance
|
||||
"""
|
||||
from agentic_rag.core.config import get_settings
|
||||
|
||||
settings = get_settings()
|
||||
|
||||
# Use default provider if not specified
|
||||
if not provider:
|
||||
provider = settings.default_llm_provider
|
||||
|
||||
# Check cache
|
||||
cache_key = f"{provider}:{api_key or 'default'}"
|
||||
if cache_key in _client_cache:
|
||||
return _client_cache[cache_key]
|
||||
|
||||
# Get API key from settings if not provided
|
||||
if not api_key:
|
||||
api_key = settings.get_api_key_for_provider(provider)
|
||||
|
||||
# Create client
|
||||
client = LLMClientFactory.create_client(provider=LLMProvider(provider), api_key=api_key)
|
||||
|
||||
# Cache client
|
||||
_client_cache[cache_key] = client
|
||||
|
||||
return client
|
||||
@@ -1,77 +1,44 @@
|
||||
"""RAG Query service using datapizza-ai.
|
||||
"""RAG Query service using datapizza-ai with multi-provider support.
|
||||
|
||||
This service handles RAG queries combining retrieval and generation.
|
||||
"""
|
||||
|
||||
from datapizza.clients.openai import OpenAIClient
|
||||
from datapizza.embedders.openai import OpenAIEmbedder
|
||||
from datapizza.modules.prompt import ChatPromptTemplate
|
||||
from datapizza.modules.rewriters import ToolRewriter
|
||||
from datapizza.pipeline import DagPipeline
|
||||
|
||||
from agentic_rag.core.config import get_settings
|
||||
from agentic_rag.core.llm_factory import get_llm_client
|
||||
from agentic_rag.services.vector_store import get_vector_store
|
||||
|
||||
settings = get_settings()
|
||||
|
||||
|
||||
class RAGService:
|
||||
"""Service for RAG queries."""
|
||||
"""Service for RAG queries with multi-provider LLM support."""
|
||||
|
||||
def __init__(self):
|
||||
self.vector_store = None
|
||||
self.llm_client = None
|
||||
self.embedder = None
|
||||
self.pipeline = None
|
||||
self._init_pipeline()
|
||||
self._init_embedder()
|
||||
|
||||
def _init_pipeline(self):
|
||||
"""Initialize the RAG pipeline."""
|
||||
# Initialize LLM client
|
||||
self.llm_client = OpenAIClient(
|
||||
model=settings.llm_model,
|
||||
api_key=settings.openai_api_key,
|
||||
)
|
||||
|
||||
# Initialize embedder
|
||||
def _init_embedder(self):
|
||||
"""Initialize the embedder."""
|
||||
# Use OpenAI for embeddings (can be configured separately)
|
||||
embedding_key = settings.embedding_api_key or settings.openai_api_key
|
||||
self.embedder = OpenAIEmbedder(
|
||||
api_key=settings.openai_api_key,
|
||||
api_key=embedding_key,
|
||||
model_name=settings.embedding_model,
|
||||
)
|
||||
|
||||
# Initialize pipeline
|
||||
self.pipeline = DagPipeline()
|
||||
|
||||
# Add modules
|
||||
self.pipeline.add_module(
|
||||
"rewriter",
|
||||
ToolRewriter(
|
||||
client=self.llm_client,
|
||||
system_prompt="Rewrite user queries to improve retrieval accuracy.",
|
||||
),
|
||||
)
|
||||
self.pipeline.add_module("embedder", self.embedder)
|
||||
# Note: vector_store will be connected at query time
|
||||
self.pipeline.add_module(
|
||||
"prompt",
|
||||
ChatPromptTemplate(
|
||||
user_prompt_template="User question: {{user_prompt}}\n\nContext:\n{% for chunk in chunks %}{{ chunk.text }}\n{% endfor %}",
|
||||
system_prompt="You are a helpful assistant. Answer the question based on the provided context. If you don't know the answer, say so.",
|
||||
),
|
||||
)
|
||||
self.pipeline.add_module("generator", self.llm_client)
|
||||
|
||||
# Connect modules
|
||||
self.pipeline.connect("rewriter", "embedder", target_key="text")
|
||||
self.pipeline.connect("embedder", "prompt", target_key="chunks")
|
||||
self.pipeline.connect("prompt", "generator", target_key="memory")
|
||||
|
||||
async def query(self, question: str, k: int = 5) -> dict:
|
||||
"""Execute a RAG query.
|
||||
async def query(
|
||||
self, question: str, k: int = 5, provider: str | None = None, model: str | None = None
|
||||
) -> dict:
|
||||
"""Execute a RAG query with specified provider.
|
||||
|
||||
Args:
|
||||
question: User question
|
||||
k: Number of chunks to retrieve
|
||||
provider: LLM provider to use
|
||||
model: Model name
|
||||
|
||||
Returns:
|
||||
Response with answer and sources
|
||||
@@ -88,16 +55,19 @@ class RAGService:
|
||||
# Format context from chunks
|
||||
context = self._format_context(chunks)
|
||||
|
||||
# Generate answer
|
||||
response = await self.llm_client.invoke(
|
||||
f"Context:\n{context}\n\nQuestion: {question}\n\nAnswer:"
|
||||
)
|
||||
# Get LLM client for specified provider
|
||||
llm_client = await get_llm_client(provider=provider)
|
||||
|
||||
# Generate answer using the prompt
|
||||
prompt = self._build_prompt(context, question)
|
||||
response = await llm_client.invoke(prompt)
|
||||
|
||||
return {
|
||||
"question": question,
|
||||
"answer": response.text,
|
||||
"sources": chunks,
|
||||
"model": settings.llm_model,
|
||||
"provider": provider or settings.default_llm_provider,
|
||||
"model": model or getattr(response, "model", "unknown"),
|
||||
}
|
||||
|
||||
async def _get_embedding(self, text: str) -> list[float]:
|
||||
@@ -110,9 +80,27 @@ class RAGService:
|
||||
context_parts = []
|
||||
for i, chunk in enumerate(chunks, 1):
|
||||
text = chunk.get("text", "")
|
||||
context_parts.append(f"[{i}] {text}")
|
||||
if text:
|
||||
context_parts.append(f"[{i}] {text}")
|
||||
return "\n\n".join(context_parts)
|
||||
|
||||
def _build_prompt(self, context: str, question: str) -> str:
|
||||
"""Build the RAG prompt."""
|
||||
return f"""You are a helpful AI assistant. Answer the question based on the provided context.
|
||||
|
||||
Context:
|
||||
{context}
|
||||
|
||||
Question: {question}
|
||||
|
||||
Instructions:
|
||||
- Answer based only on the provided context
|
||||
- If the context doesn't contain the answer, say "I don't have enough information to answer this question"
|
||||
- Be concise but complete
|
||||
- Cite sources using [1], [2], etc. when referencing information
|
||||
|
||||
Answer:"""
|
||||
|
||||
|
||||
# Singleton
|
||||
_rag_service = None
|
||||
|
||||
Reference in New Issue
Block a user