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How can use Haystack to identify the top k sentences that are the closest match to a user query, and then returns the docs containing these sentences?

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In GPytorch is there any way to initialize a VNNGP model with faiss-cpu and then set it to cuda?

About faiss's c_api, can indexpq support uint8 data?

How can I install faiss-gpu?

Store FAISS vector store in some database like SQlite3

Facing an issue with FAISS in langchain

AttributeError: 'function' object has no attribute 'embed_query' using Langchain FAISS vectorstore

paddleOCR Installation error, Failed building wheel for faiss-cpu

Connection error when using langchain_community.vectorstores.faiss.FAISS

The de-serialization relies loading a pickle file

faiss.reconstruct_n() as numpy array on MacOS causes system error

IndexError: list index out of range in FAISS.from_documents

Cannot use Hugginfaceembedding in the process, need helps on FAISS

How to fix, cannot find -lfaiss_c: No such file or directory

Building Local LLM for document Q&A using LangChain, Sentence Transformers, and FAISS but I keep getting Type Error while processing the query

Finding embedding dimentions of the HuggingFace model

Issue On Writing Faiss Index to a File when using through Docker Image

Cannot import langchain.vectorstores.FAISS, only langchain_community.vectorstores.faiss

How to load precomputed embeddings to FAISS from langchain

create llamaindex servicecontext without embedding model?

How can use Haystack to identify the top k sentences that are the closest match to a user query, and then returns the docs containing these sentences?

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