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from fastapi import FastAPI, HTTPException
from fastapi.responses import HTMLResponse
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel
from typing import List, Optional, Dict, Any
import uvicorn
import logging
import time
import os
import asyncio
from contextlib import asynccontextmanager
from pathlib import Path
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Global RAG system instance
rag_system = None
system_loading = False
system_load_error = None
@asynccontextmanager
async def lifespan(app: FastAPI):
# Startup
global rag_system, system_loading, system_load_error
logger.info("Starting Text-to-SQL RAG API with CodeLlama for HF Spaces...")
# Start system loading in background
system_loading = True
system_load_error = None
try:
# Import here to avoid startup delays
from rag_system import VectorStore, SQLRetriever, PromptEngine, SQLGenerator, DataProcessor
# Initialize RAG system components
logger.info("Initializing RAG system components...")
# Initialize vector store
logger.info("Initializing vector store...")
vector_store = VectorStore()
# Initialize SQL retriever
logger.info("Initializing SQL retriever...")
sql_retriever = SQLRetriever(vector_store)
# Initialize prompt engine
logger.info("Initializing prompt engine...")
prompt_engine = PromptEngine()
# Initialize SQL generator (with CodeLlama as primary)
logger.info("Initializing SQL generator with CodeLlama...")
sql_generator = SQLGenerator(sql_retriever, prompt_engine)
# Initialize data processor
logger.info("Initializing data processor...")
data_processor = DataProcessor()
# Create RAG system object
rag_system = {
"vector_store": vector_store,
"sql_retriever": sql_retriever,
"prompt_engine": prompt_engine,
"sql_generator": sql_generator,
"data_processor": data_processor
}
# Load or create sample data
logger.info("Loading sample data...")
await load_or_create_sample_data(data_processor, vector_store)
logger.info("All RAG system components initialized successfully!")
except Exception as e:
logger.error(f"Failed to initialize RAG system: {str(e)}")
system_load_error = str(e)
finally:
system_loading = False
yield
# Shutdown
logger.info("Shutting down Text-to-SQL RAG API...")
async def load_or_create_sample_data(data_processor, vector_store):
"""Load existing data or create sample dataset."""
try:
# Try to load existing processed data
examples = data_processor.load_processed_data()
if examples:
logger.info(f"Loaded {len(examples)} existing examples")
# Add to vector store
vector_store.add_examples(examples)
else:
# Create sample dataset
logger.info("Creating sample dataset...")
sample_data = data_processor.create_sample_dataset()
vector_store.add_examples(sample_data)
logger.info(f"Added {len(sample_data)} sample examples to vector store")
except Exception as e:
logger.warning(f"Could not load sample data: {e}")
# Create minimal sample data
try:
sample_data = data_processor.create_sample_dataset()
vector_store.add_examples(sample_data)
logger.info(f"Added {len(sample_data)} sample examples to vector store")
except Exception as e2:
logger.error(f"Failed to create sample data: {e2}")
# Create FastAPI app
app = FastAPI(
title="Text-to-SQL RAG API with CodeLlama",
description="Advanced API for converting natural language questions to SQL queries using RAG and CodeLlama",
version="2.0.0",
lifespan=lifespan
)
# Pydantic models for request/response
class SQLRequest(BaseModel):
question: str
table_headers: List[str]
class SQLResponse(BaseModel):
question: str
table_headers: List[str]
sql_query: str
model_used: str
processing_time: float
retrieved_examples: List[Dict[str, Any]]
status: str
class BatchRequest(BaseModel):
queries: List[SQLRequest]
class BatchResponse(BaseModel):
results: List[SQLResponse]
total_queries: int
successful_queries: int
class HealthResponse(BaseModel):
status: str
system_loaded: bool
system_loading: bool
system_error: Optional[str] = None
model_info: Optional[Dict[str, Any]] = None
timestamp: float
@app.get("/", response_class=HTMLResponse)
async def root():
"""Serve the main HTML interface"""
try:
with open("index.html", "r", encoding="utf-8") as f:
return HTMLResponse(content=f.read())
except FileNotFoundError:
return HTMLResponse(content="""
<html>
<body>
<h1>Text-to-SQL RAG API with CodeLlama</h1>
<p>Advanced SQL generation using RAG and CodeLlama models</p>
<p>index.html not found. Please ensure the file exists in the same directory.</p>
</body>
</html>
""")
@app.get("/api", response_model=dict)
async def api_info():
"""API information endpoint"""
return {
"message": "Text-to-SQL RAG API with CodeLlama",
"version": "2.0.0",
"features": [
"RAG-enhanced SQL generation",
"CodeLlama as primary model",
"Vector-based example retrieval",
"Advanced prompt engineering"
],
"endpoints": {
"/": "GET - Web interface",
"/api": "GET - API information",
"/predict": "POST - Generate SQL from single question",
"/batch": "POST - Generate SQL from multiple questions",
"/health": "GET - Health check",
"/docs": "GET - API documentation"
}
}
@app.get("/health", response_model=HealthResponse)
async def health_check():
"""Health check endpoint"""
global rag_system, system_loading, system_load_error
model_info = None
if rag_system and "sql_generator" in rag_system:
try:
model_info = rag_system["sql_generator"].get_model_info()
except Exception as e:
logger.warning(f"Could not get model info: {e}")
return HealthResponse(
status="healthy" if rag_system and not system_loading else "unhealthy",
system_loaded=rag_system is not None,
system_loading=system_loading,
system_error=system_load_error,
model_info=model_info,
timestamp=time.time()
)
@app.post("/predict", response_model=SQLResponse)
async def predict_sql(request: SQLRequest):
"""
Generate SQL query from a natural language question using RAG and CodeLlama
Args:
request: SQLRequest containing question and table headers
Returns:
SQLResponse with generated SQL query and metadata
"""
global rag_system, system_loading, system_load_error
if system_loading:
raise HTTPException(status_code=503, detail="System is still loading, please try again in a few minutes")
if rag_system is None:
error_msg = system_load_error or "RAG system not loaded"
raise HTTPException(status_code=503, detail=f"System not available: {error_msg}")
start_time = time.time()
try:
# Generate SQL using RAG system
result = rag_system["sql_generator"].generate_sql(
question=request.question,
table_headers=request.table_headers
)
processing_time = time.time() - start_time
return SQLResponse(
question=request.question,
table_headers=request.table_headers,
sql_query=result["sql_query"],
model_used=result["model_used"],
processing_time=processing_time,
retrieved_examples=result["retrieved_examples"],
status=result["status"]
)
except Exception as e:
logger.error(f"Error generating SQL: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error generating SQL: {str(e)}")
@app.post("/batch", response_model=BatchResponse)
async def batch_predict(request: BatchRequest):
"""
Generate SQL queries from multiple questions using RAG and CodeLlama
Args:
request: BatchRequest containing list of questions and table headers
Returns:
BatchResponse with generated SQL queries
"""
global rag_system, system_loading, system_load_error
if system_loading:
raise HTTPException(status_code=503, detail="System is still loading, please try again in a few minutes")
if rag_system is None:
error_msg = system_load_error or "RAG system not loaded"
raise HTTPException(status_code=503, detail=f"System not available: {error_msg}")
start_time = time.time()
try:
results = []
successful_count = 0
for query in request.queries:
try:
result = rag_system["sql_generator"].generate_sql(
question=query.question,
table_headers=query.table_headers
)
sql_response = SQLResponse(
question=query.question,
table_headers=query.table_headers,
sql_query=result["sql_query"],
model_used=result["model_used"],
processing_time=result["processing_time"],
retrieved_examples=result["retrieved_examples"],
status=result["status"]
)
results.append(sql_response)
if result["status"] == "success":
successful_count += 1
except Exception as e:
logger.error(f"Error processing query '{query.question}': {str(e)}")
# Add error response
error_response = SQLResponse(
question=query.question,
table_headers=query.table_headers,
sql_query="",
model_used="none",
processing_time=0.0,
retrieved_examples=[],
status="error"
)
results.append(error_response)
total_time = time.time() - start_time
return BatchResponse(
results=results,
total_queries=len(request.queries),
successful_queries=successful_count
)
except Exception as e:
logger.error(f"Error in batch processing: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error in batch processing: {str(e)}")
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=8000)
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