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.
Optional k: numberOptional filter: MongoDBAtlasFilterOptional _callbacks: CallbacksMethod 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.
Optional k: numberOptional filter: MongoDBAtlasFilterOptional _callbacks: CallbacksStatic fixStatic method to fix the precision of the array that ensures that every number in this array is always float when casted to other types. This is needed since MongoDB Atlas Vector Search does not cast integer inside vector search to float automatically. This method shall introduce a hint of error but should be safe to use since introduced error is very small, only applies to integer numbers returned by embeddings, and most embeddings shall not have precision as high as 15 decimal places.
Array of number to be fixed.
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.
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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.