community: update VDMS vectorstore with latest VDMS release #28443
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| embedding: Optional[Embeddings] = None, | ||
| collection_name: str = DEFAULT_COLLECTION_NAME, # DescriptorSet name | ||
| distance_strategy: DISTANCE_METRICS = "L2", | ||
| embedding: Embeddings, |
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@baskaryan, this change wasn't necessary. I reverted it
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Deployment failed with the following error: |
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@baskaryan could you please review this? |
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@cwlacewe I'm going to close this for now with the recommendation that you / Intel implement the VDMS vector store in a new package (e.g., langchain-vdms or langchain-intel).
We've written a walkthrough on this process here and are happy to answer your questions as you go through it:
https://python.langchain.com/docs/contributing/how_to/integrations/
We are encouraging contributors of LangChain integrations to go this route. This way we don't have to be in the loop for reviews, you're able to properly integration test the code, and you have control over versioning.
I think this route is preferable to reviewing a large code change (thousands of lines), only to have the implementation deprecated if/when the standalone package is released.
Docs would continue to be maintained in the langchain repo.
Let me know what you think!
| "FindDescriptor", {"entities": list(), "returned": 0, "status": 0} | ||
| ) | ||
| new_response = [ | ||
| # {"FindDescriptor": {"returned": 0, "status": 0, "entities": []}} |
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| # {"FindDescriptor": {"returned": 0, "status": 0, "entities": []}} |
| # else: | ||
| # kwargs["normalize_distance"] = True |
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Should we handle the else condition here?
| ids=ids, | ||
| metadatas=metadatas, | ||
| batch_size=batch_size, | ||
| # Remove IDs if exist-TEST_REMOVAL |
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What is meant by -TEST_REMOVAL?
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@ccurme is there a way to get this one integrated as we have customers waiting and we can work on the package for new year? |
Description:
Issue: N/A
Dependencies: N/A