A path to the module that contains the class, eg. ["langchain", "llms"] Usually should be the same as the entrypoint the class is exported from.
A map of aliases for constructor args. Keys are the attribute names, e.g. "foo". Values are the alias that will replace the key in serialization. This is used to eg. make argument names match Python.
A map of additional attributes to merge with constructor args. Keys are the attribute names, e.g. "foo". Values are the attribute values, which will be serialized. These attributes need to be accepted by the constructor as arguments.
The final serialized identifier for the module.
A map of secrets, which will be omitted from serialization. Keys are paths to the secret in constructor args, e.g. "foo.bar.baz". Values are the secret ids, which will be used when deserializing.
Method to add documents to the MongoDB collection. It first converts the documents to vectors using the embeddings and then calls the addVectors method.
Documents to be added.
Promise that resolves when the documents have been added.
Method to add vectors and their corresponding documents to the MongoDB collection.
Vectors to be added.
Corresponding documents to be added.
Promise that resolves when the vectors and documents have been added.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<MongoDBAtlasVectorSearch>>Optional filter: MongoDBAtlasFilterOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanReturn documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.
Text to look up documents similar to.
Method that performs a similarity search on the vectors stored in the MongoDB collection. It returns a list of documents and their corresponding similarity scores.
Query vector for the similarity search.
Number of nearest neighbors to return.
Optional filter: MongoDBAtlasFilterOptional filter to be applied.
Promise that resolves to a list of documents and their corresponding similarity scores.
Static fromStatic method to create an instance of MongoDBAtlasVectorSearch from a list of documents. It first converts the documents to vectors and then adds them to the MongoDB collection.
List of documents to be converted to vectors.
Embeddings to be used for conversion.
Database configuration for MongoDB Atlas.
Promise that resolves to a new instance of MongoDBAtlasVectorSearch.
Static fromStatic method to create an instance of MongoDBAtlasVectorSearch from a list of texts. It first converts the texts to vectors and then adds them to the MongoDB collection.
List of texts to be converted to vectors.
Metadata for the texts.
Embeddings to be used for conversion.
Database configuration for MongoDB Atlas.
Promise that resolves to a new instance of MongoDBAtlasVectorSearch.
Static lc_Generated using TypeDoc
Class that is a wrapper around MongoDB Atlas Vector Search. It is used to store embeddings in MongoDB documents, create a vector search index, and perform K-Nearest Neighbors (KNN) search with an approximate nearest neighbor algorithm.