Update vector_store_component.py to add keyword based search alongside semantic search #1603
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I want to highlight this PR, I think it is really important |
jaluma
requested changes
Jul 8, 2024
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First of all, I would like to thank you for your PR but, currently, much of this code is unnecessary. The llama-index framework itself takes care of hybrid search if the provider supports it. I recommend looking at the VectorStoreQueryMode enum in the llama-index code.
If you can try, one first possible option to implement it would be to pass the VectorStoreQueryMode parameter to the Retriever. You would define a new setting to define which will be the selected query mode.
| from private_gpt.components.vector_store.batched_chroma import BatchedChromaVectorStore | ||
| from private_gpt.open_ai.extensions.context_filter import ContextFilter | ||
| from private_gpt.paths import local_data_path | ||
| from private_gpt.settings.settings import Settings | ||
| from langchain.retrievers import BM25Retriever, EnsembleRetriever |
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You don't should use langchain.retrievers
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hi,i tried to make a PR in order to increase the accuracy of search results ,im not sure if i did it in the right way but i tried
the idea is to have both semantic search and traditional keyword based search in order to get more relevant results
i hope this feature be implemented in the project in future, maybe more modifications are needed