Integrate your operational database and vector search in a single, unified, fully managed platform with full vector database capabilities on MongoDB Atlas.
Store your operational data, metadata, and vector embeddings in oue VectorStore, MongoDBAtlasVectorSearch. Insert into a Chain via a Vector, FullText, or Hybrid Retriever.
MongoDB Atlas Semantic cache.
A Cache backed by a MongoDB Atlas server with vector-store support
MongoDB Atlas cache
A cache that uses MongoDB Atlas as a backend
Chat message history that stores history in MongoDB.
MongoDB Atlas vector store integration.
MongoDBAtlasVectorSearch performs data operations on text, embeddings and arbitrary data. In addition to CRUD operations, the VectorStore provides Vector Search based on similarity of embedding vectors following the Hierarchical Navigable Small Worlds (HNSW) algorithm.
This supports a number of models to ascertain scores, "similarity" (default), "MMR", and "similarity_score_threshold". These are described in the search_type argument to as_retriever, which provides the Runnable.invoke(query) API, allowing MongoDBAtlasVectorSearch to be used within a chain.
Various Utility Functions
The help IDs live as ObjectId in MongoDB and str in Langchain and JSON.
These are duplicated from langchain_community to avoid cross-dependencies.
Functions "maximal_marginal_relevance" and "cosine_similarity" are duplicated in this utility respectively from modules:
- "libs/community/langchain_community/vectorstores/utils.py"
- "libs/community/langchain_community/utils/math.py"
LangChain MongoDB Caches.
Aggregation pipeline components used in Atlas Full-Text, Vector, and Hybrid Search
Search Index Commands
Search Retrievers of various types.
Use MongoDBAtlasVectorSearch.as_retriever(**)
to create MongoDB's core Vector Search Retriever.