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 OpenSearch index. It first converts the documents to vectors using the embeddings, then adds the vectors to the index.
The documents to be added to the OpenSearch index.
Promise resolving to void.
Method to add vectors to the OpenSearch index. It ensures the index exists, then adds the vectors and associated documents to the index.
The vectors to be added to the OpenSearch index.
The documents associated with the vectors.
Optional options: { Optional parameter that can contain the IDs for the documents.
Optional ids?: string[]Promise resolving to void.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<OpenSearchVectorStore>>Optional filter: objectOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanMethod to perform a similarity search on the OpenSearch index using a query vector. It returns the k most similar documents and their scores.
The query vector.
The number of similar documents to return.
Optional filter: objectOptional filter for the OpenSearch query.
Promise resolving to an array of tuples, each containing a Document and its score.
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 fromStatic method to create a new OpenSearchVectorStore from an array of Documents, embeddings, and OpenSearch client arguments.
The documents to be added to the OpenSearch index.
The embeddings used to convert the documents into vectors.
The OpenSearch client arguments.
Promise resolving to a new instance of OpenSearchVectorStore.
Static fromStatic method to create a new OpenSearchVectorStore from an existing OpenSearch index, embeddings, and OpenSearch client arguments.
The embeddings used to convert the documents into vectors.
The OpenSearch client arguments.
Promise resolving to a new instance of OpenSearchVectorStore.
Static fromStatic method to create a new OpenSearchVectorStore from an array of texts, their metadata, embeddings, and OpenSearch client arguments.
The texts to be converted into documents and added to the OpenSearch index.
The metadata associated with the texts. Can be an array of objects or a single object.
The embeddings used to convert the texts into vectors.
The OpenSearch client arguments.
Promise resolving to a new instance of OpenSearchVectorStore.
Static lc_Generated using TypeDoc
Class that provides a wrapper around the OpenSearch service for vector search. It provides methods for adding documents and vectors to the OpenSearch index, searching for similar vectors, and managing the OpenSearch index.