langchain.vectorstores.vectara.Vectara¶
class langchain.vectorstores.vectara.Vectara(vectara_customer_id: Optional[str] = None, vectara_corpus_id: Optional[str] = None, vectara_api_key: Optional[str] = None, vectara_api_timeout: int = 60)[source]¶
Vectara API vector store.
See (https://vectara.com).
Example
from langchain.vectorstores import Vectara
vectorstore = Vectara(
    vectara_customer_id=vectara_customer_id,
    vectara_corpus_id=vectara_corpus_id,
    vectara_api_key=vectara_api_key
)
Initialize with Vectara API.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([vectara_customer_id, ...])
Initialize with Vectara API.
aadd_documents(documents, **kwargs)
Run more documents through the embeddings and add to the vectorstore.
aadd_texts(texts[, metadatas])
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_files(files_list[, metadatas])
Vectara provides a way to add documents directly via our API where pre-processing and chunking occurs internally in an optimal way This method provides a way to use that API in LangChain
add_texts(texts[, metadatas, doc_metadata])
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])
Return VectorStore initialized from texts and embeddings.