Upgrade the AwaDB from 0.3.5 to 0.3.6 #7363
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1. support get, delete and update batch documents 2. add the implementation of max_marginal_relevance_search 3. change the vector's default distance from L2 to InnerProduct
Explicitly list requires_reference in function (langchain-ai#7357)
delete unnecessary annotation
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adjust the code format of awadb.py for the awadb vectorstore
efriis
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efriis
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Great changes! Main requested change is to align docstring on similarity_search_with_relevance_scores with the referenced types.
Looks like you removed the reference to relevance scores even though they're still present.
langchain/vectorstores/awadb.py
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| **kwargs: Any, | ||
| ) -> List[Document]: | ||
| """Return docs most similar to query.""" | ||
| """The most k similar documents of the specified text query. |
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| """The most k similar documents of the specified text query. | |
| """Returns the k most similar documents to the specified text query. |
nit
langchain/vectorstores/awadb.py
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| Args: | ||
| query: Text query | ||
| k: The most k similar documents of the text query |
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| k: The most k similar documents of the text query | |
| k: The maximum number of documents to return |
langchain/vectorstores/awadb.py
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| kwargs: Any possible extend parameters in the future. | ||
| Returns: | ||
| The most k similar documents of the specified text query |
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| The most k similar documents of the specified text query | |
| The k most similar documents to the specified text query |
langchain/vectorstores/awadb.py
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| 0 is dissimilar, 1 is most similar. | ||
| Returns: | ||
| List of Documents the most similar to the text query. |
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| List of Documents the most similar to the text query. | |
| List of (Document, relevance_score) tuples the most similar to the text query. |
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@efriis I have added and changed the notes about relevance scores. please check, thanks! |
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@ljeagle looks like the formatter/lint check failed - could you update that and I can merge it in? |
ok, thanks |
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Thanks @ljeagle ! |
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Upgrade the AwaDB from 0.3.5 to 0.3.6
Please review @rlancemartin, @eyurtsev @hwchase17. Thanks!