langchain.vectorstores.zep.ZepVectorStore¶
class langchain.vectorstores.zep.ZepVectorStore(collection_name: str, api_url: str, *, api_key: Optional[str] = None, config: Optional[CollectionConfig] = None, embedding: Optional[Embeddings] = None)[source]¶
Zep vector store.
It provides methods for adding texts or documents to the store,
searching for similar documents, and deleting documents.
Search scores are calculated using cosine similarity normalized to [0, 1].
Parameters
api_url (str) – The URL of the Zep API.
collection_name (str) – The name of the collection in the Zep store.
api_key (Optional[str]) – The API key for the Zep API.
config (Optional[CollectionConfig]) – The configuration for the collection.
Required if the collection does not already exist.
embedding (Optional[Embeddings]) – Optional embedding function to use to
embed the texts. Required if the collection is not auto-embedded.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(collection_name, api_url, *[, ...])
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas, document_ids])
Run more texts through the embeddings and add to the vectorstore.
add_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
add_texts(texts[, metadatas, document_ids])
Run more texts through the embeddings and add to the vectorstore.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])