Improve AnalyticDB Vector Store implementation without affecting user #6086
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… behavior: 1) Streamline the __post_init__ behavior by creating a single table with automatic indexing. 2) Update the add_texts API for document insertion. 3) Optimize similarity_search_with_score_by_vector to retrieve results directly from the table. 4) Implement _similarity_search_with_relevance_scores. 5) Add an embedding_dimension parameter to support different dimension embedding functions. Users can continue using the API as before.
bradcrossen
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Support for SQLAlchemy 1.3 was removed in version 0.0.203 by change langchain-ai#6086. Re-adding support.
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Support for SQLAlchemy 1.3 was removed in version 0.0.203 by change #6086. Re-adding support. - Description: Imports SQLAlchemy Row at class creation time instead of at init to support SQLAlchemy <1.4. This is the only breaking change and was introduced in version 0.0.203 #6086. A similar change was merged before: #4647 - Dependencies: Reduces SQLAlchemy dependency to > 1.3 - Tag maintainer: @rlancemartin, @eyurtsev, @hwchase17, @wangxuqi --------- Co-authored-by: rlm <pexpresss31@gmail.com>
vowelparrot
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Support for SQLAlchemy 1.3 was removed in version 0.0.203 by change #6086. Re-adding support. - Description: Imports SQLAlchemy Row at class creation time instead of at init to support SQLAlchemy <1.4. This is the only breaking change and was introduced in version 0.0.203 #6086. A similar change was merged before: #4647 - Dependencies: Reduces SQLAlchemy dependency to > 1.3 - Tag maintainer: @rlancemartin, @eyurtsev, @hwchase17, @wangxuqi --------- Co-authored-by: rlm <pexpresss31@gmail.com>
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Hi there:
As I implement the AnalyticDB VectorStore use two table to store the document before. It seems just use one table is a better way. So this commit is try to improve AnalyticDB VectorStore implementation without affecting user behavior:
1. Streamline the
post_initbehavior by creating a single table with vector indexing.2. Update the
add_textsAPI for document insertion.3. Optimize
similarity_search_with_score_by_vectorto retrieve results directly from the table.4. Implement
_similarity_search_with_relevance_scores.5. Add
embedding_dimensionparameter to support different dimension embedding functions.Users can continue using the API as before.
Test cases added before is enough to meet this commit.