isayahc commited on
Commit
a2ede9f
·
unverified ·
1 Parent(s): 0e17a46

removing commented out cod

Browse files
rag_app/knowledge_base/build_vector_store.py CHANGED
@@ -60,26 +60,3 @@ def build_vector_store(
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  result = f"built vectore store at {FAISS_INDEX_PATH}"
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  return result
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-
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- # # Path for saving the FAISS index
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- # FAISS_INDEX_PATH = "./vectorstore/lc-faiss-multi-mpnet-500"
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-
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- # try:
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- # # Stage two: Vectorization of the document chunks
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- # model_name = "sentence-transformers/multi-qa-mpnet-base-dot-v1" # Model used for embedding
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-
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- # # Initialize HuggingFace embeddings with the specified model
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- # embeddings = HuggingFaceEmbeddings(model_name=model_name)
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-
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- # print(f'Loading chunks into vector store ...')
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- # st = time.time() # Start time for performance measurement
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- # # Create a FAISS vector store from the document chunks and save it locally
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- # db = FAISS.from_documents(filter_complex_metadata(chunks), embeddings)
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- # db.save_local(FAISS_INDEX_PATH)
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- # et = time.time() - st # Calculate time taken for vectorization
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- # print(f'Time taken for vectorization and saving: {et} seconds.')
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- # except Exception as e:
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- # print(f"Error during vectorization or FAISS index saving: {e}", file=sys.stderr)
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-
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- # alternatively download a preparaed vectorized index from S3 and load the index into vectorstore
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- # Import necessary libraries for AWS S3 interaction, file handling, and FAISS vector stores
 
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  result = f"built vectore store at {FAISS_INDEX_PATH}"
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  return result
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