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sandipto729

Jun 12th, 2025

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from flask import Flask, request, jsonify
from flask_cors import CORS
from dotenv import load_dotenv
import os
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferWindowMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain_groq import ChatGroq
# Load environment
load_dotenv()
groq_api_key = os.getenv("GROQ_API_KEY")
FRONTEND_URL = os.getenv("FRONTEND_URL")
if not groq_api_key:
raise ValueError("GROQ_API_KEY is not set in your .env file.")
os.environ["GROQ_API_KEY"] = groq_api_key
# Flask setup
app = Flask(__name__)
CORS(app, supports_credentials=True, resources={r"/*": {"origins": FRONTEND_URL}})
# Load existing vectorstore (Chroma) from disk
embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = Chroma(persist_directory="./chroma_doc_db", embedding_function=embedding)
@app.route('/')
def index():
return "Backend is running!"
@app.route('/api/process-url', methods=['POST'])
def process_url():
data = request.json
query = data.get('query')
if not query:
return jsonify({"error": "Query is required"}), 400
try:
# Initialize LLM
model = ChatGroq(
model_name="llama3-8b-8192",
api_key=groq_api_key,
temperature=0.6,
max_tokens=512
)
# Prompt template
prompt_template = PromptTemplate(
input_variables=["context", "question"],
template="""
You are a helpful assistant that provides concise answers based on the provided documents.
Context: {context}
Question: {question}
Answer:"""
)
# Memory
memory = ConversationBufferWindowMemory(
memory_key="chat_history",
return_messages=True,
k=5,
output_key="result"
)
# Retriever and QA chain
retriever = vectorstore.as_retriever(search_kwargs={"k": 2})
qa = RetrievalQA.from_chain_type(
llm=model,
chain_type="stuff",
retriever=retriever,
return_source_documents=True,
memory=memory,
chain_type_kwargs={"prompt": prompt_template},
output_key="result"
)
# Run query
result = qa({"query": query})
return jsonify({
"answer": result["result"],
})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
port = int(os.environ.get("PORT", 8000))
app.run(host="0.0.0.0", port=port)

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