Creates a new MomentoVectorIndex instance.
The embeddings instance to use to generate embeddings from documents.
The arguments to use to configure the vector store.
Adds vectors to the index. Generates embeddings from the documents
using the Embeddings instance passed to the constructor.
Array of Document instances to be added to the index.
Optional documentProps: DocumentPropsPromise that resolves when the documents have been added to the index.
Adds vectors to the index.
The vectors to add to the index.
The documents to add to the index.
Optional documentProps: DocumentPropsThe properties of the documents to add to the index, specifically the ids.
Promise that resolves when the vectors have been added to the index. Also returns the ids of the documents that were added.
If the index does not already exist, it will be created if ensureIndexExists is true.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<MomentoVectorIndex>>Optional filter: string | objectOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanDeletes vectors from the index by id.
The parameters to use to delete the vectors, specifically the ids.
Return 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.
Searches the index for the most similar vectors to the query vector.
The query vector.
The number of results to return.
Promise that resolves to the documents of the most similar vectors to the query vector.
Static fromStores the documents in the index.
The documents to store in the index.
The embeddings instance to use to generate embeddings from the documents.
The configuration to use to instantiate the vector store.
Optional documentProps: DocumentPropsThe properties of the documents to add to the index, specifically the ids.
Promise that resolves to the vector store.
Static fromStores the documents in the index.
Converts the documents to vectors using the Embeddings instance passed.
The texts to store in the index.
The metadata to store in the index.
The embeddings instance to use to generate embeddings from the documents.
The configuration to use to instantiate the vector store.
Optional documentProps: DocumentPropsThe properties of the documents to add to the index, specifically the ids.
Promise that resolves to the vector store.
Generated using TypeDoc
A vector store that uses the Momento Vector Index.
Remarks
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