from flask import Flask, request, jsonify from langchain_community.document_loaders import TextLoader from langchain.text_splitter import CharacterTextSplitter from langchain_community.embeddings import HuggingFaceEmbeddings from langchain_community.vectorstores import FAISS from langchain.chains.question_answering import load_qa_chain from langchain_community.llms.huggingface_endpoint import HuggingFaceEndpoint # To comment in production usage # import Constants # import os # os.environ["HUGGINGFACEHUB_API_TOKEN"] = Constants.TOKEN app = Flask(__name__) try: loader = TextLoader("./data/app.txt") document = loader.load() # Split the document into chunks text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) docs = text_splitter.split_documents(document) # Create embeddings embedding = HuggingFaceEmbeddings(model_name = "sentence-transformers/all-mpnet-base-v2") db = FAISS.from_documents(docs, embedding) # Load the Question-Answering chain llm = HuggingFaceEndpoint(repo_id="google/flan-t5-xxl", temperature=0.8, model_kwargs={"max_length": 512}) chain = load_qa_chain(llm, chain_type="stuff") except Exception as e: print("Recived Setup error: ", e) def process_query(query): # os.system("cls") try: querySimilarDocs = db.similarity_search(query) res = chain.run(input_documents = querySimilarDocs, question = query) return res except Exception as e: print("Received process error: ", e) return "An Error occurred!!" @app.route('/query', methods=['POST']) def process_request(): try: data = request.get_json() user_input = data['query'] response = process_query(user_input) return jsonify({"response": response}) except Exception as e: print("Received Process request error: ", e) return jsonify({"response": e}) ## Development phase use case only # if __name__ == '__main__': # app.run(debug=True)