PR community:Removing knn beta content in mongodb atlas vectorstore (#15865)

<!-- Thank you for contributing to LangChain!

Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.

Replace this entire comment with:
  - **Description:** a description of the change, 
  - **Issue:** the issue # it fixes if applicable,
  - **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!

Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.

See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/

If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.

If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
 -->
pull/15952/head
manishsahni2000 9 months ago committed by GitHub
parent bdd90ae2ee
commit 74d9fc2f9e
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23

@ -221,12 +221,11 @@ class MongoDBAtlasVectorSearch(VectorStore):
) -> List[Tuple[Document, float]]:
"""Return MongoDB documents most similar to the given query and their scores.
Uses the knnBeta Operator available in MongoDB Atlas Search.
This feature is in early access and available only for evaluation purposes, to
validate functionality, and to gather feedback from a small closed group of
early access users. It is not recommended for production deployments as we
may introduce breaking changes.
For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta
Uses the $vectorSearch stage
performs aNN search on a vector in the specified field.
Index the field as "vector" using Atlas Vector Search "vectorSearch" index type
For more info : https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
Args:
query: Text to look up documents similar to.
@ -234,7 +233,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
pre_filter: (Optional) dictionary of argument(s) to prefilter document
fields on.
post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
following the knnBeta vector search.
following the vector Search.
Returns:
List of documents most similar to the query and their scores.
@ -258,12 +257,11 @@ class MongoDBAtlasVectorSearch(VectorStore):
) -> List[Document]:
"""Return MongoDB documents most similar to the given query.
Uses the knnBeta Operator available in MongoDB Atlas Search.
This feature is in early access and available only for evaluation purposes, to
validate functionality, and to gather feedback from a small closed group of
early access users. It is not recommended for production deployments as we
may introduce breaking changes.
For more: https://www.mongodb.com/docs/atlas/atlas-search/knn-beta
Uses the $vectorSearch stage
performs aNN search on a vector in the specified field.
Index the field as "vector" using Atlas Vector Search "vectorSearch" index type
For more info : https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
Args:
query: Text to look up documents similar to.
@ -271,7 +269,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
pre_filter: (Optional) dictionary of argument(s) to prefilter document
fields on.
post_filter_pipeline: (Optional) Pipeline of MongoDB aggregation stages
following the knnBeta vector search.
following the vector search.
Returns:
List of documents most similar to the query and their scores.
@ -311,7 +309,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
pre_filter: (Optional) dictionary of argument(s) to prefilter on document
fields.
post_filter_pipeline: (Optional) pipeline of MongoDB aggregation stages
following the knnBeta vector search.
following the vector search.
Returns:
List of documents selected by maximal marginal relevance.
"""

Loading…
Cancel
Save