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from flask import Flask, request, jsonify from transformers import pipeline from langchain.embeddings import HuggingFaceEmbeddings from langchain.vectorstores import FAISS from langchain.chains import RetrievalQAChain app = Flask(__name__) # Load AI models nlp_model = pipeline("text2text-generation", model="gpt-3.5-turbo") embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2") vectorstore = FAISS.from_embeddings(documents, embeddings) qa_chain = RetrievalQAChain( vectorstore=vectorstore, combine_docs=True, question_generator=nlp_model, llm=nlp_model, ) @app.route('/ask', methods=['POST']) def ask_question(): query = request.json['query'] response = qa_chain.run(query) return jsonify({'response': response}) if __name__ == '__main__': app.run()
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