| from flask import Flask, render_template, request, jsonify
|
| from rag_model import get_qa_chain, build_rag_system, PERSIST_DIRECTORY, HuggingFaceEmbeddings, Chroma
|
| from dotenv import load_dotenv
|
| import os
|
| load_dotenv(override=True)
|
| print(f"App: API Key loaded (starting with {os.getenv('GOOGLE_API_KEY', 'None')[:5]}...)")
|
|
|
| app = Flask(__name__)
|
|
|
|
|
| if not os.path.exists(PERSIST_DIRECTORY):
|
| print("Vector store not found. Building...")
|
| vectorstore = build_rag_system()
|
| else:
|
| print("Loading existing vector store...")
|
| embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
|
| vectorstore = Chroma(persist_directory=PERSIST_DIRECTORY, embedding_function=embeddings)
|
|
|
| qa_chain = get_qa_chain(vectorstore)
|
|
|
| @app.route('/')
|
| def index():
|
| return render_template('index.html')
|
|
|
| @app.route('/ask', methods=['POST'])
|
| def ask():
|
| data = request.json
|
| query = data.get('query')
|
| print(f"Processing query: {query}")
|
|
|
| if not query:
|
| return jsonify({"error": "No query provided"}), 400
|
|
|
| if not qa_chain:
|
| return jsonify({"error": "QA chain not initialized. Check GOOGLE_API_KEY."}), 500
|
|
|
| try:
|
| result = qa_chain({"query": query})
|
| answer = result['result']
|
| sources = []
|
| seen = set()
|
| for doc in result['source_documents']:
|
| source_name = doc.metadata.get('name', 'Unknown')
|
| if source_name not in seen:
|
| sources.append(source_name)
|
| seen.add(source_name)
|
|
|
| return jsonify({
|
| "answer": answer,
|
| "sources": sources[:3]
|
| })
|
| except Exception as e:
|
| return jsonify({"error": str(e)}), 500
|
|
|
| if __name__ == '__main__':
|
| app.run(debug=True, port=5000)
|
|
|
