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 the average of cosine distances between vectors a and b
first vector
second vector
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 memory vector store. It extracts the text from each document, generates embeddings for them, and adds the resulting vectors to the store.
Array of Document instances to be added to the store.
Promise that resolves when all documents have been added.
Method to add vectors to the memory vector store. It creates
MemoryVector instances for each vector and document pair and adds
them to the store.
Array of vectors to be added to the store.
Array of Document instances corresponding to the vectors.
Promise that resolves when all vectors have been added.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<MemoryVectorStore>>Optional filter: ((doc) => boolean)Optional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanMethod to perform a similarity search in the memory vector store. It
calculates the similarity between the query vector and each vector in
the store, sorts the results by similarity, and returns the top k
results along with their scores.
Query vector to compare against the vectors in the store.
Number of top results to return.
Optional filter: ((doc) => boolean)Optional filter function to apply to the vectors before performing the search.
Promise that resolves with an array of tuples, each containing a Document and its similarity 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 MemoryVectorStore instance from an array of
Document instances. It adds the documents to the store.
Array of Document instances to be added to the store.
Embeddings instance used to generate embeddings for the documents.
Optional dbConfig: MemoryVectorStoreArgsOptional MemoryVectorStoreArgs to configure the MemoryVectorStore instance.
Promise that resolves with a new MemoryVectorStore instance.
Static fromStatic method to create a MemoryVectorStore instance from an existing
index. It creates a new MemoryVectorStore instance without adding any
documents or vectors.
Embeddings instance used to generate embeddings for the documents.
Optional dbConfig: MemoryVectorStoreArgsOptional MemoryVectorStoreArgs to configure the MemoryVectorStore instance.
Promise that resolves with a new MemoryVectorStore instance.
Static fromStatic method to create a MemoryVectorStore instance from an array of
texts. It creates a Document for each text and metadata pair, and
adds them to the store.
Array of texts to be added to the store.
Array or single object of metadata corresponding to the texts.
Embeddings instance used to generate embeddings for the texts.
Optional dbConfig: MemoryVectorStoreArgsOptional MemoryVectorStoreArgs to configure the MemoryVectorStore instance.
Promise that resolves with a new MemoryVectorStore instance.
Static lc_Generated using TypeDoc
Class that extends
VectorStoreto store vectors in memory. Provides methods for adding documents, performing similarity searches, and creating instances from texts, documents, or an existing index.