Creates a new MomentoVectorIndex instance.
The embeddings instance to use to generate embeddings from documents.
The arguments to use to configure the vector store.
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.
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.
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.
Optional maxReturn 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.
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.
Static lc_Generated using TypeDoc
A vector store that uses the Momento Vector Index.
Remarks
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