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"""
FastAPI REST API Service
Exposes the multi-agent knowledge system
"""
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from typing import List, Dict
import time
from dotenv import load_dotenv
import os
from .rag_system import RAGSystem
from agent_orchestrator import AgentOrchestrator
load_dotenv()
# Initialize FastAPI
app = FastAPI(
title="Multi-Agent Knowledge System",
description="RAG system with query understanding, retrieval, synthesis, and validation",
version="1.0.0"
)
# Initialize RAG system
api_key = os.getenv("GROQ_API_KEY")
rag_system = RAGSystem(groq_api_key=api_key)
# Request/Response Models
class QueryRequest(BaseModel):
query: str
top_k: int = 5
class SourceDocument(BaseModel):
source: str
relevance: float
class ValidationInfo(BaseModel):
status: str
confidence: int
class QueryResponse(BaseModel):
query: str
reformulated_query: str
answer: str
validation: ValidationInfo
sources: List[SourceDocument]
processing_time: float
class HealthResponse(BaseModel):
status: str
model_loaded: bool
db_connected: bool
timestamp: str
class MetricsResponse(BaseModel):
total_queries: int
avg_latency: float
avg_confidence: float
# Global metrics
metrics = {
"total_queries": 0,
"latencies": [],
"confidences": []
}
# Health check endpoint
@app.get("/health", response_model=HealthResponse)
async def health_check():
"""Check system health"""
from datetime import datetime
return HealthResponse(
status="healthy",
model_loaded=True,
db_connected=True,
timestamp=datetime.now().isoformat()
)
# Main query endpoint
@app.post("/query", response_model=QueryResponse)
async def query(request: QueryRequest):
"""
Process a query through the multi-agent system
Args:
query: User query
top_k: Number of documents to retrieve
Returns:
QueryResponse with answer, sources, and validation
"""
try:
start_time = time.time()
# Store top_k in rag_system temporarily
original_top_k = 5
# Run orchestrator
result = rag_system.answer_question(request.query)
# Extract data
processing_time = time.time() - start_time
# Format sources
sources = []
for doc in result.get("retrieved_documents", []):
sources.append(SourceDocument(
source=doc["source"],
relevance=doc["score"]
))
# Format validation
validation_info = result.get("validation_result", {})
validation = ValidationInfo(
status="✅ VALID" if validation_info.get("is_valid") else "⚠️ NEEDS REVIEW",
confidence=validation_info.get("confidence", 0)
)
# Update metrics
metrics["total_queries"] += 1
metrics["latencies"].append(processing_time)
metrics["confidences"].append(validation.confidence)
# Build response
response = QueryResponse(
query=result.get("original_query", ""),
reformulated_query=result.get("reformulated_query", ""),
answer=result.get("final_answer", ""),
validation=validation,
sources=sources,
processing_time=processing_time
)
return response
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
# Metrics endpoint
@app.get("/metrics", response_model=MetricsResponse)
async def get_metrics():
"""Get system metrics"""
avg_latency = sum(metrics["latencies"]) / len(metrics["latencies"]) if metrics["latencies"] else 0
avg_confidence = sum(metrics["confidences"]) / len(metrics["confidences"]) if metrics["confidences"] else 0
return MetricsResponse(
total_queries=metrics["total_queries"],
avg_latency=avg_latency,
avg_confidence=avg_confidence
)
# Root endpoint
@app.get("/")
async def root():
"""Root endpoint"""
return {
"message": "Multi-Agent Knowledge System API",
"version": "1.0.0",
"endpoints": {
"health": "/health",
"query": "/query (POST)",
"metrics": "/metrics",
"docs": "/docs"
}
}
if __name__ == "__main__":
import uvicorn
print("\n" + "=" * 70)
print("🚀 Starting Multi-Agent Knowledge System API")
print("=" * 70)
print("📍 API running at: http://localhost:8000")
print("📚 Documentation at: http://localhost:8000/docs")
print("=" * 70 + "\n")
uvicorn.run(app, host="0.0.0.0", port=8000)
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