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## Description This pull-request extends the existing vector search strategies of MongoDBAtlasVectorSearch to include Hybrid (Reciprocal Rank Fusion) and Full-text via new Retrievers. There is a small breaking change in the form of the `prefilter` kwarg to search. For this, and because we have now added a great deal of features, including programmatic Index creation/deletion since 0.1.0, we plan to bump the version to 0.2.0. ### Checklist * Unit tests have been extended * formatting has been applied * One mypy error remains which will either go away in CI or be simplified. --------- Signed-off-by: Casey Clements <casey.clements@mongodb.com> Co-authored-by: Erick Friis <erick@langchain.dev>
21 lines
695 B
Python
21 lines
695 B
Python
"""
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Integrate your operational database and vector search in a single, unified,
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fully managed platform with full vector database capabilities on MongoDB Atlas.
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Store your operational data, metadata, and vector embeddings in oue VectorStore,
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MongoDBAtlasVectorSearch.
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Insert into a Chain via a Vector, FullText, or Hybrid Retriever.
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"""
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from langchain_mongodb.cache import MongoDBAtlasSemanticCache, MongoDBCache
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from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
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from langchain_mongodb.vectorstores import MongoDBAtlasVectorSearch
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__all__ = [
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"MongoDBAtlasVectorSearch",
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"MongoDBChatMessageHistory",
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"MongoDBCache",
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"MongoDBAtlasSemanticCache",
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]
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