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feat(agentic-rag): add multi-provider LLM, auth, and Docker support

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## 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
2026-04-06 11:22:26 +02:00
parent 2aa1f66227
commit f2408b2b88
11 changed files with 1179 additions and 92 deletions
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50
Dockerfile Normal file
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@@ -0,0 +1,50 @@
# AgenticRAG Dockerfile
# Multi-stage build for production
FROM python:3.11-slim as builder
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y \
gcc \
g++ \
curl \
&& rm -rf /var/lib/apt/lists/*
# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt
# Production stage
FROM python:3.11-slim
WORKDIR /app
# Copy Python packages from builder
COPY --from=builder /root/.local /root/.local
# Make sure scripts in .local are usable
ENV PATH=/root/.local/bin:$PATH
# Copy application code
COPY src/ ./src/
COPY static/ ./static/
# Create uploads directory
RUN mkdir -p uploads
# Environment variables
ENV PYTHONPATH=/app/src
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
# Expose port
EXPOSE 8000
# Health check
HEALTHCHECK --interval=30s --timeout=30s --start-period=5s --retries=3 \
CMD curl -f http://localhost:8000/api/health || exit 1
# Run the application
CMD ["uvicorn", "agentic_rag.api.main:app", "--host", "0.0.0.0", "--port", "8000"]

110
docker-compose.yml Normal file
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@@ -0,0 +1,110 @@
# AgenticRAG - Docker Compose
# Complete stack with API, Qdrant, and optional services
version: '3.8'
services:
# Main API service
api:
build:
context: .
dockerfile: Dockerfile
container_name: agenticrag-api
ports:
- "8000:8000"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY:-}
- ZAI_API_KEY=${ZAI_API_KEY:-}
- OPENCODE_ZEN_API_KEY=${OPENCODE_ZEN_API_KEY:-}
- OPENROUTER_API_KEY=${OPENROUTER_API_KEY:-}
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
- GOOGLE_API_KEY=${GOOGLE_API_KEY:-}
- DEFAULT_LLM_PROVIDER=${DEFAULT_LLM_PROVIDER:-openai}
- DEFAULT_LLM_MODEL=${DEFAULT_LLM_MODEL:-gpt-4o-mini}
- QDRANT_HOST=qdrant
- QDRANT_PORT=6333
- JWT_SECRET=${JWT_SECRET:-your-secret-key-change-in-production}
- JWT_ALGORITHM=${JWT_ALGORITHM:-HS256}
- ACCESS_TOKEN_EXPIRE_MINUTES=${ACCESS_TOKEN_EXPIRE_MINUTES:-30}
- ADMIN_API_KEY=${ADMIN_API_KEY:-admin-api-key-change-in-production}
- CORS_ORIGINS=${CORS_ORIGINS:-http://localhost:3000,http://localhost:5173}
- LOG_LEVEL=${LOG_LEVEL:-INFO}
volumes:
- ./uploads:/app/uploads
- ./data:/app/data
depends_on:
qdrant:
condition: service_healthy
networks:
- agenticrag-network
restart: unless-stopped
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/api/health"]
interval: 30s
timeout: 10s
retries: 3
start_period: 40s
# Qdrant Vector Database
qdrant:
image: qdrant/qdrant:latest
container_name: agenticrag-qdrant
ports:
- "6333:6333"
- "6334:6334"
volumes:
- qdrant-storage:/qdrant/storage
networks:
- agenticrag-network
restart: unless-stopped
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:6333/healthz"]
interval: 10s
timeout: 5s
retries: 5
start_period: 10s
# Optional: Redis for caching
redis:
image: redis:7-alpine
container_name: agenticrag-redis
ports:
- "6379:6379"
volumes:
- redis-data:/data
networks:
- agenticrag-network
restart: unless-stopped
healthcheck:
test: ["CMD", "redis-cli", "ping"]
interval: 10s
timeout: 3s
retries: 5
# Optional: Nginx reverse proxy
nginx:
image: nginx:alpine
container_name: agenticrag-nginx
ports:
- "80:80"
- "443:443"
volumes:
- ./nginx/nginx.conf:/etc/nginx/nginx.conf:ro
- ./nginx/ssl:/etc/nginx/ssl:ro
depends_on:
- api
networks:
- agenticrag-network
restart: unless-stopped
profiles:
- production
volumes:
qdrant-storage:
driver: local
redis-data:
driver: local
networks:
agenticrag-network:
driver: bridge

52
requirements.txt Normal file
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@@ -0,0 +1,52 @@
# AgenticRAG Requirements
# Core dependencies
# FastAPI and web framework
fastapi>=0.104.0
uvicorn[standard]>=0.24.0
python-multipart>=0.0.6
python-jose[cryptography]>=3.3.0
passlib[bcrypt]>=1.7.4
# Datapizza AI framework
datapizza-ai>=0.1.0
datapizza-ai-core>=0.1.0
# LLM Clients
datapizza-ai-clients-openai>=0.0.12
# Additional providers will be installed via pip in Dockerfile
# Embeddings
datapizza-ai-embedders-openai>=0.0.6
# Vector Store
datapizza-ai-vectorstores-qdrant>=0.0.9
qdrant-client>=1.7.0
# Document Processing
datapizza-ai-modules-parsers-docling>=0.0.1
# Tools
datapizza-ai-tools-duckduckgo>=0.0.1
# Configuration and utilities
pydantic>=2.5.0
pydantic-settings>=2.1.0
python-dotenv>=1.0.0
httpx>=0.25.0
aiofiles>=23.2.0
# Observability
opentelemetry-api>=1.21.0
opentelemetry-sdk>=1.21.0
opentelemetry-instrumentation-fastapi>=0.42b0
# Testing
pytest>=7.4.0
pytest-asyncio>=0.21.0
httpx>=0.25.0
# Development
black>=23.0.0
ruff>=0.1.0
mypy>=1.7.0

97
src/agentic_rag/api/main.py
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@@ -1,6 +1,6 @@
"""AgenticRAG API - Backend powered by datapizza-ai.
This module contains the FastAPI application with RAG capabilities.
Multi-provider LLM support: OpenAI, Z.AI, OpenCode Zen, OpenRouter, Anthropic, Google, Mistral, Azure
"""
from contextlib import asynccontextmanager
@@ -10,7 +10,13 @@ from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from agentic_rag.api.routes import chat, documents, health, query
from agentic_rag.api.routes import (
chat,
documents,
health,
providers,
query,
)
from agentic_rag.core.config import get_settings
from agentic_rag.core.logging import setup_logging
@@ -24,22 +30,61 @@ async def lifespan(app: FastAPI) -> AsyncGenerator:
setup_logging()
# Initialize Qdrant vector store
try:
from agentic_rag.services.vector_store import get_vector_store
vector_store = await get_vector_store()
await vector_store.create_collection("documents")
print("✅ Vector store initialized")
except Exception as e:
print(f"⚠️ Vector store initialization failed: {e}")
# Log configured providers
configured = settings.list_configured_providers()
if configured:
print(f"✅ Configured LLM providers: {[p['id'] for p in configured]}")
else:
print("⚠️ No LLM providers configured. Set API keys in .env file.")
yield
# Shutdown
pass
print("👋 Shutting down...")
def create_application() -> FastAPI:
"""Create and configure FastAPI application."""
app = FastAPI(
title="AgenticRAG API",
description="Agentic Retrieval System powered by datapizza-ai",
description="""
Agentic Retrieval System powered by datapizza-ai.
## Multi-Provider LLM Support
This API supports multiple LLM providers:
- **OpenAI** (GPT-4o, GPT-4, GPT-3.5)
- **Z.AI** (South Korea)
- **OpenCode Zen**
- **OpenRouter** (Multi-model access)
- **Anthropic** (Claude)
- **Google** (Gemini)
- **Mistral AI**
- **Azure OpenAI**
## Authentication
Two methods supported:
1. **API Key**: Header `X-API-Key: your-api-key`
2. **JWT Token**: Header `Authorization: Bearer your-token`
## Features
- 📄 Document upload (PDF, DOCX, TXT, MD)
- 🔍 Semantic search with embeddings
- 💬 Chat with your documents
- 🎯 RAG (Retrieval-Augmented Generation)
- 🚀 Multiple LLM providers
""",
version="2.0.0",
docs_url="/api/docs",
redoc_url="/api/redoc",
@@ -58,6 +103,7 @@ def create_application() -> FastAPI:
# Include routers
app.include_router(health.router, prefix="/api/v1", tags=["health"])
app.include_router(providers.router, prefix="/api/v1", tags=["providers"])
app.include_router(documents.router, prefix="/api/v1", tags=["documents"])
app.include_router(query.router, prefix="/api/v1", tags=["query"])
app.include_router(chat.router, prefix="/api/v1", tags=["chat"])
@@ -78,9 +124,52 @@ app = create_application()
@app.get("/api")
async def api_root():
"""API root endpoint."""
settings = get_settings()
configured = settings.list_configured_providers()
return {
"name": "AgenticRAG API",
"version": "2.0.0",
"docs": "/api/docs",
"description": "Agentic Retrieval System powered by datapizza-ai",
"features": {
"multi_provider_llm": True,
"authentication": ["api_key", "jwt"],
"document_processing": True,
"rag": True,
"streaming": True,
},
"configured_providers": [p["id"] for p in configured],
"default_provider": settings.default_llm_provider,
}
@app.get("/api/health/detailed")
async def detailed_health_check():
"""Detailed health check with provider status."""
settings = get_settings()
# Check vector store
try:
from agentic_rag.services.vector_store import get_vector_store
vector_store = await get_vector_store()
vector_status = "healthy"
except Exception as e:
vector_status = f"unhealthy: {str(e)}"
# Check configured providers
providers_status = {}
for provider in ["openai", "zai", "opencode-zen", "openrouter", "anthropic", "google"]:
api_key = settings.get_api_key_for_provider(provider)
providers_status[provider] = "configured" if api_key else "not_configured"
return {
"status": "healthy",
"version": "2.0.0",
"components": {
"api": "healthy",
"vector_store": vector_status,
},
"providers": providers_status,
}

43
src/agentic_rag/api/routes/documents.py
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@@ -1,12 +1,12 @@
"""Documents API routes."""
"""Documents API routes with authentication."""
import os
import shutil
from pathlib import Path
from uuid import uuid4
from fastapi import APIRouter, File, HTTPException, UploadFile, status
from fastapi import APIRouter, Depends, File, HTTPException, UploadFile, status
from agentic_rag.core.auth import CurrentUser
from agentic_rag.services.document_service import get_document_service
router = APIRouter()
@@ -20,15 +20,24 @@ UPLOAD_DIR.mkdir(exist_ok=True)
"/documents",
status_code=status.HTTP_201_CREATED,
summary="Upload document",
description="Upload a document for indexing.",
description="Upload a document for indexing. Requires authentication.",
)
async def upload_document(file: UploadFile = File(...)):
async def upload_document(file: UploadFile = File(...), current_user: dict = CurrentUser):
"""Upload and process a document."""
try:
# Validate file
if not file.filename:
raise HTTPException(status_code=400, detail="No file provided")
# Check file size (10MB limit)
max_size = 10 * 1024 * 1024 # 10MB
file.file.seek(0, 2) # Seek to end
file_size = file.file.tell()
file.file.seek(0) # Reset
if file_size > max_size:
raise HTTPException(status_code=400, detail=f"File too large. Max size: 10MB")
# Save uploaded file
doc_id = str(uuid4())
file_path = UPLOAD_DIR / f"{doc_id}_{file.filename}"
@@ -38,7 +47,13 @@ async def upload_document(file: UploadFile = File(...)):
# Process document
service = await get_document_service()
result = await service.ingest_document(str(file_path))
result = await service.ingest_document(
str(file_path),
metadata={
"user_id": current_user.get("user_id", "anonymous"),
"filename": file.filename,
},
)
return {
"success": True,
@@ -46,9 +61,12 @@ async def upload_document(file: UploadFile = File(...)):
"id": doc_id,
"filename": file.filename,
"chunks": result["chunks_count"],
"user": current_user.get("user_id", "anonymous"),
},
}
except HTTPException:
raise
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
@@ -56,14 +74,19 @@ async def upload_document(file: UploadFile = File(...)):
@router.get(
"/documents",
summary="List documents",
description="List all uploaded documents.",
description="List all uploaded documents for the current user.",
)
async def list_documents():
async def list_documents(current_user: dict = CurrentUser):
"""List all documents."""
service = await get_document_service()
documents = await service.list_documents()
return {"success": True, "data": documents}
# Filter by user if needed (for now, return all)
return {
"success": True,
"data": documents,
"user": current_user.get("user_id", "anonymous"),
}
@router.delete(
@@ -71,7 +94,7 @@ async def list_documents():
status_code=status.HTTP_204_NO_CONTENT,
summary="Delete document",
)
async def delete_document(doc_id: str):
async def delete_document(doc_id: str, current_user: dict = CurrentUser):
"""Delete a document."""
service = await get_document_service()
success = await service.delete_document(doc_id)

167
src/agentic_rag/api/routes/providers.py Normal file
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@@ -0,0 +1,167 @@
"""Provider management API routes."""
from fastapi import APIRouter, Depends, HTTPException
from pydantic import BaseModel
from agentic_rag.core.auth import CurrentUser
from agentic_rag.core.config import get_settings
from agentic_rag.core.llm_factory import LLMClientFactory
router = APIRouter()
class ProviderConfig(BaseModel):
"""Provider configuration model."""
provider: str
model: str
class ProviderInfo(BaseModel):
"""Provider information model."""
id: str
name: str
available: bool
configured: bool
default_model: str
install_command: str | None
@router.get("/providers", summary="List available LLM providers", response_model=list[ProviderInfo])
async def list_providers(current_user: CurrentUser):
"""List all available LLM providers and their status."""
settings = get_settings()
available = LLMClientFactory.list_available_providers()
default_models = LLMClientFactory.get_default_models()
providers = []
for provider in available:
providers.append(
ProviderInfo(
id=provider["id"],
name=provider["name"],
available=provider["available"],
configured=settings.is_provider_configured(provider["id"]),
default_model=default_models.get(provider["id"], "unknown"),
install_command=provider.get("install_command"),
)
)
return providers
@router.get(
"/providers/configured", summary="List configured providers", response_model=list[ProviderInfo]
)
async def list_configured_providers(current_user: CurrentUser):
"""List only providers that have API keys configured."""
settings = get_settings()
return settings.list_configured_providers()
@router.get(
"/providers/{provider_id}/models",
summary="List available models for provider",
)
async def list_provider_models(provider_id: str, current_user: CurrentUser):
"""List available models for a specific provider."""
# Model lists for each provider
models = {
"openai": [
{"id": "gpt-4o", "name": "GPT-4o"},
{"id": "gpt-4o-mini", "name": "GPT-4o Mini"},
{"id": "gpt-4-turbo", "name": "GPT-4 Turbo"},
{"id": "gpt-3.5-turbo", "name": "GPT-3.5 Turbo"},
],
"zai": [
{"id": "zai-large", "name": "Z.AI Large"},
{"id": "zai-medium", "name": "Z.AI Medium"},
],
"opencode-zen": [
{"id": "zen-1", "name": "Zen 1"},
{"id": "zen-lite", "name": "Zen Lite"},
],
"openrouter": [
{"id": "openai/gpt-4o", "name": "GPT-4o (via OpenRouter)"},
{"id": "openai/gpt-4o-mini", "name": "GPT-4o Mini (via OpenRouter)"},
{"id": "anthropic/claude-3.5-sonnet", "name": "Claude 3.5 Sonnet (via OpenRouter)"},
{"id": "google/gemini-pro", "name": "Gemini Pro (via OpenRouter)"},
{"id": "meta-llama/llama-3.1-70b", "name": "Llama 3.1 70B (via OpenRouter)"},
],
"anthropic": [
{"id": "claude-3-5-sonnet-20241022", "name": "Claude 3.5 Sonnet"},
{"id": "claude-3-opus-20240229", "name": "Claude 3 Opus"},
{"id": "claude-3-sonnet-20240229", "name": "Claude 3 Sonnet"},
{"id": "claude-3-haiku-20240307", "name": "Claude 3 Haiku"},
],
"google": [
{"id": "gemini-1.5-pro", "name": "Gemini 1.5 Pro"},
{"id": "gemini-1.5-flash", "name": "Gemini 1.5 Flash"},
{"id": "gemini-pro", "name": "Gemini Pro"},
],
"mistral": [
{"id": "mistral-large-latest", "name": "Mistral Large"},
{"id": "mistral-medium", "name": "Mistral Medium"},
{"id": "mistral-small", "name": "Mistral Small"},
],
"azure": [
{"id": "gpt-4", "name": "GPT-4"},
{"id": "gpt-4o", "name": "GPT-4o"},
{"id": "gpt-35-turbo", "name": "GPT-3.5 Turbo"},
],
}
if provider_id not in models:
raise HTTPException(status_code=404, detail=f"Provider {provider_id} not found")
return {"provider": provider_id, "models": models[provider_id]}
@router.get(
"/config",
summary="Get current configuration",
)
async def get_config(current_user: CurrentUser):
"""Get current system configuration (without sensitive data)."""
settings = get_settings()
return {
"default_llm_provider": settings.default_llm_provider,
"default_llm_model": settings.default_llm_model,
"embedding_provider": settings.embedding_provider,
"embedding_model": settings.embedding_model,
"configured_providers": [p["id"] for p in settings.list_configured_providers()],
"qdrant_host": settings.qdrant_host,
"qdrant_port": settings.qdrant_port,
}
@router.put(
"/config/provider",
summary="Update default provider",
)
async def update_default_provider(config: ProviderConfig, current_user: CurrentUser):
"""Update the default LLM provider and model.
Note: This only updates the runtime configuration.
For persistent changes, update the .env file.
"""
settings = get_settings()
# Validate provider
if not settings.is_provider_configured(config.provider):
raise HTTPException(
status_code=400,
detail=f"Provider {config.provider} is not configured. Please set the API key in .env file.",
)
# Update settings (runtime only)
settings.default_llm_provider = config.provider
settings.default_llm_model = config.model
return {
"success": True,
"message": f"Default provider updated to {config.provider} with model {config.model}",
"note": "This change is temporary. Update .env file for permanent changes.",
}

98
src/agentic_rag/api/routes/query.py
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@@ -1,33 +1,79 @@
"""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
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@@ -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)

89
src/agentic_rag/core/config.py
View File

@@ -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
View 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

94
src/agentic_rag/services/rag_service.py
View File

@@ -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", "")
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