Description: This pull request aims to support generating the correct
generic relevancy scores for different vector stores by refactoring the
relevance score functions and their selection in the base class and
subclasses of VectorStore. This is especially relevant with VectorStores
that require a distance metric upon initialization. Note many of the
current implenetations of `_similarity_search_with_relevance_scores` are
not technically correct, as they just return
`self.similarity_search_with_score(query, k, **kwargs)` without applying
the relevant score function
Also includes changes associated with:
https://github.com/hwchase17/langchain/pull/6564 and
https://github.com/hwchase17/langchain/pull/6494
See more indepth discussion in thread in #6494
Issue:
https://github.com/hwchase17/langchain/issues/6526https://github.com/hwchase17/langchain/issues/6481https://github.com/hwchase17/langchain/issues/6346
Dependencies: None
The changes include:
- Properly handling score thresholding in FAISS
`similarity_search_with_score_by_vector` for the corresponding distance
metric.
- Refactoring the `_similarity_search_with_relevance_scores` method in
the base class and removing it from the subclasses for incorrectly
implemented subclasses.
- Adding a `_select_relevance_score_fn` method in the base class and
implementing it in the subclasses to select the appropriate relevance
score function based on the distance strategy.
- Updating the `__init__` methods of the subclasses to set the
`relevance_score_fn` attribute.
- Removing the `_default_relevance_score_fn` function from the FAISS
class and using the base class's `_euclidean_relevance_score_fn`
instead.
- Adding the `DistanceStrategy` enum to the `utils.py` file and updating
the imports in the vector store classes.
- Updating the tests to import the `DistanceStrategy` enum from the
`utils.py` file.
---------
Co-authored-by: Hanit <37485638+hanit-com@users.noreply.github.com>
1. Introduced new distance strategies support: **DOT_PRODUCT** and
**EUCLIDEAN_DISTANCE** for enhanced flexibility.
2. Implemented a feature to filter results based on metadata fields.
3. Incorporated connection attributes specifying "langchain python sdk"
usage for enhanced traceability and debugging.
4. Expanded the suite of integration tests for improved code
reliability.
5. Updated the existing notebook with the usage example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Added `SingleStoreDB` vector store, which is a wrapper over the
SingleStore DB database, that can be used as a vector storage and has an
efficient similarity search.
- Added integration tests for the vector store
- Added jupyter notebook with the example
@dev2049
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
