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""" |
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FastAPI backend for Bhagwad Gita RAG Chatbot |
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""" |
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from fastapi import FastAPI, HTTPException |
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from fastapi.middleware.cors import CORSMiddleware |
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from fastapi.staticfiles import StaticFiles |
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from fastapi.responses import HTMLResponse |
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from pydantic import BaseModel |
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from typing import List, Optional |
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import uvicorn |
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import os |
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from pathlib import Path |
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from backend.services.rag_service import RAGService |
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from backend.models.schemas import ( |
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QueryRequest, QueryResponse, HealthResponse, |
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TranslationRequest, TranslationResponse, SupportedLanguagesResponse |
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) |
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app = FastAPI( |
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title="Bhagwad Gita RAG Chatbot API", |
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description="API for querying Bhagwad Gita and Patanjali Yoga Sutras using RAG (Retrieval Augmented Generation)", |
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version="1.0.0", |
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docs_url="/docs", |
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redoc_url="/redoc" |
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) |
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cors_origins = os.getenv( |
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"CORS_ORIGINS", |
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"http://localhost:3000,http://localhost:8000,https://spiritual-rag-chatbot.netlify.app" |
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).split(",") |
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cors_regex = os.getenv( |
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"CORS_ORIGIN_REGEX", |
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r"^https?:\/\/(localhost(:\d+)?|.*\.netlify\.app|.*\.onrender\.com)$" |
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) |
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app.add_middleware( |
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CORSMiddleware, |
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allow_origins=cors_origins, |
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allow_origin_regex=cors_regex, |
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allow_credentials=True, |
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allow_methods=["*"], |
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allow_headers=["*"], |
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) |
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rag_service = None |
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@app.on_event("startup") |
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async def startup_event(): |
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"""Initialize the RAG service on startup""" |
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global rag_service |
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try: |
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data_dir = os.getenv("DATA_DIR", str(Path(__file__).parent.parent / "dataset")) |
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data_dir = Path(data_dir) |
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print(f"Initializing RAG Service with data directory: {data_dir}") |
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if not data_dir.exists(): |
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print(f"Warning: Data directory {data_dir} does not exist") |
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alt_paths = [ |
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Path("/app/dataset"), |
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Path("/opt/render/project/src/dataset"), |
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Path("./dataset") |
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] |
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for alt_path in alt_paths: |
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if alt_path.exists(): |
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data_dir = alt_path |
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print(f"Using alternative data directory: {data_dir}") |
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break |
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rag_service = RAGService(data_dir) |
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await rag_service.initialize() |
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print("RAG Service initialized successfully") |
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except Exception as e: |
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print(f"Error initializing RAG service: {e}") |
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if os.getenv("ENVIRONMENT") != "production": |
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raise |
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@app.get("/", response_class=HTMLResponse) |
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async def read_root(): |
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"""Serve the main page""" |
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return """ |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>Bhagwad Gita RAG Chatbot</title> |
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<meta charset="utf-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1"> |
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<style> |
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body { font-family: Arial, sans-serif; margin: 40px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; } |
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.container { max-width: 800px; margin: 0 auto; background: rgba(255,255,255,0.1); padding: 30px; border-radius: 15px; backdrop-filter: blur(10px); } |
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h1 { text-align: center; margin-bottom: 30px; } |
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.api-info { background: rgba(255,255,255,0.2); padding: 20px; border-radius: 10px; margin: 20px 0; } |
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a { color: #FFD700; text-decoration: none; } |
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a:hover { text-decoration: underline; } |
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</style> |
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</head> |
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<body> |
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<div class="container"> |
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<h1>🕉️ Bhagwad Gita RAG Chatbot API</h1> |
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<div class="api-info"> |
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<h3>Welcome to the Spiritual Wisdom API</h3> |
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<p>This API provides access to verses and wisdom from the Bhagavad Gita and Patanjali Yoga Sutras using advanced RAG (Retrieval Augmented Generation) technology.</p> |
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<h4>📚 Features:</h4> |
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<ul> |
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<li>Query-based verse retrieval from sacred texts</li> |
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<li>Intelligent matching using TF-IDF and cosine similarity</li> |
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<li>Automatic summary generation</li> |
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<li>Bilingual support (Sanskrit and English)</li> |
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</ul> |
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<h4>🔗 Quick Links:</h4> |
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<ul> |
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<li><a href="/docs">📖 Interactive API Documentation (Swagger)</a></li> |
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<li><a href="/redoc">📋 Alternative API Documentation (ReDoc)</a></li> |
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<li><a href="/health">💚 Health Check</a></li> |
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<li><a href="/frontend">🌐 Frontend Application</a></li> |
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</ul> |
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<h4>🚀 Quick Test:</h4> |
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<p>Try a sample query: <code>POST /query</code> with body: <code>{"query": "How to control the mind?"}</code></p> |
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</div> |
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</div> |
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</body> |
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</html> |
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""" |
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@app.get("/health", response_model=HealthResponse) |
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async def health_check(): |
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"""Health check endpoint""" |
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return HealthResponse( |
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status="healthy", |
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message="Bhagwad Gita RAG Chatbot API is running", |
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version="1.0.0" |
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) |
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@app.get("/ready") |
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async def readiness_check(): |
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"""Readiness endpoint that verifies the RAG service is initialized.""" |
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return {"ready": rag_service is not None} |
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@app.post("/query", response_model=QueryResponse) |
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async def process_query(request: QueryRequest): |
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""" |
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Process a spiritual query and return relevant verses with summary |
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""" |
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if not rag_service: |
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raise HTTPException(status_code=503, detail="RAG service not initialized") |
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try: |
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if not request.query.strip(): |
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raise HTTPException(status_code=400, detail="Query cannot be empty") |
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result = await rag_service.process_query( |
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query=request.query, |
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top_n=request.top_n, |
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include_summary=request.include_summary |
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) |
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return result |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}") |
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@app.get("/sources") |
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async def get_available_sources(): |
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"""Get information about available text sources""" |
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if not rag_service: |
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raise HTTPException(status_code=503, detail="RAG service not initialized") |
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return await rag_service.get_sources_info() |
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@app.post("/translate", response_model=TranslationResponse) |
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async def translate_text(request: TranslationRequest): |
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"""Translate text to supported languages""" |
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if rag_service is None: |
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raise HTTPException(status_code=503, detail="RAG service not initialized") |
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try: |
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translated_text = await rag_service.translate_text(request.text, request.target_language) |
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if translated_text.startswith("Language") or translated_text.startswith("Translation failed"): |
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raise HTTPException(status_code=400, detail=translated_text) |
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language_name = rag_service.supported_languages.get(request.target_language, "Unknown") |
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return TranslationResponse( |
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original_text=request.text, |
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translated_text=translated_text, |
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target_language=request.target_language, |
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language_name=language_name |
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) |
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except Exception as e: |
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raise HTTPException(status_code=500, detail=f"Translation failed: {str(e)}") |
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@app.get("/supported-languages", response_model=SupportedLanguagesResponse) |
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async def get_supported_languages(): |
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"""Get list of supported languages for translation""" |
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if rag_service is None: |
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raise HTTPException(status_code=503, detail="RAG service not initialized") |
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return SupportedLanguagesResponse(languages=rag_service.supported_languages) |
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static_path = Path(__file__).parent.parent / "frontend" / "build" |
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if static_path.exists(): |
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app.mount("/frontend", StaticFiles(directory=static_path, html=True), name="frontend") |
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if __name__ == "__main__": |
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host = os.getenv("API_HOST", "0.0.0.0") |
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port = int(os.getenv("API_PORT", "8000")) |
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print(f"Starting Bhagwad Gita RAG Chatbot API on {host}:{port}") |
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print(f"CORS Origins: {cors_origins}") |
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uvicorn.run( |
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"main:app", |
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host=host, |
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port=port, |
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reload=os.getenv("ENVIRONMENT") != "production", |
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log_level=os.getenv("LOG_LEVEL", "info") |
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) |
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