Resp = n(input_documents=similar_docs, question=q) Similar_docs = vector_store.similarity_search(q) Vector_store = om_documents(chunked_docs, embedder) # Create embeddings and store them in a FAISS vector store Splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=10)Ĭhunked_docs = splitter.split_documents(loaded_docs) With open(output_file, "w", encoding='utf-8') as file:
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