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.
Optional _verboseOptional filterA 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 vector store. It ensures the existence of the table in the database, converts the documents into vectors, and adds them to the store.
Array of Document instances.
Promise that resolves when the documents have been added.
Method to add vectors to the vector store. It converts the vectors into rows and inserts them into the database.
Array of vectors.
Array of Document instances.
Promise that resolves when the vectors have been added.
Optional kOrFields: number | Partial<VectorStoreRetrieverInput<TypeORMVectorStore>>Optional filter: MetadataOptional callbacks: CallbacksOptional tags: string[]Optional metadata: Record<string, unknown>Optional verbose: booleanMethod to perform a similarity search in the vector store. It returns
the k most similar documents to the query vector, along with their
similarity scores.
Query vector.
Number of most similar documents to return.
Optional filter: MetadataOptional filter to apply to the search.
Promise that resolves with an array of tuples, each containing a TypeORMVectorStoreDocument 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 new TypeORMVectorStore instance from a
DataSource. It initializes the DataSource if it is not already
initialized.
Embeddings instance.
TypeORMVectorStoreArgs instance.
A new instance of TypeORMVectorStore.
Static fromStatic method to create a new TypeORMVectorStore instance from an
array of Document instances. It adds the documents to the store.
Array of Document instances.
Embeddings instance.
TypeORMVectorStoreArgs instance.
Promise that resolves with a new instance of TypeORMVectorStore.
Static fromStatic method to create a new TypeORMVectorStore instance from an
existing index.
Embeddings instance.
TypeORMVectorStoreArgs instance.
Promise that resolves with a new instance of TypeORMVectorStore.
Static fromStatic method to create a new TypeORMVectorStore instance from an
array of texts and their metadata. It converts the texts into
Document instances and adds them to the store.
Array of texts.
Array of metadata objects or a single metadata object.
Embeddings instance.
TypeORMVectorStoreArgs instance.
Promise that resolves with a new instance of TypeORMVectorStore.
Static lc_Generated using TypeDoc
Class that provides an interface to a Postgres vector database. It extends the
VectorStorebase class and implements methods for adding documents and vectors, performing similarity searches, and ensuring the existence of a table in the database.