VectorStoreInterface

Instance of EmbeddingsInterface used to generate vector embeddings for documents, enabling vector-based search operations.

Defines the filter type used in search and delete operations. Can be an object for structured conditions or a string for simpler filtering.

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.

Returns a string identifying the type of vector store implementation, useful for distinguishing between different vector storage backends.

Adds an array of documents to the vector store.

Adds precomputed vectors and their corresponding documents to the vector store.

Converts the vector store into a retriever, making it suitable for use in retrieval-based workflows and allowing additional configuration.

Deletes documents from the vector store based on the specified parameters.

Return documents selected using the maximal marginal relevance. Maximal marginal relevance optimizes for similarity to the query AND diversity among selected documents.

Searches for documents similar to a text query, embedding the query and retrieving documents based on vector similarity.

Searches for documents similar to a given vector query and returns them with similarity scores.

Searches for documents similar to a text query and includes similarity scores in the result.