langchain.vectorstores.tencentvectordb.TencentVectorDB¶
class langchain.vectorstores.tencentvectordb.TencentVectorDB(embedding: ~langchain.schema.embeddings.Embeddings, connection_params: ~langchain.vectorstores.tencentvectordb.ConnectionParams, index_params: ~langchain.vectorstores.tencentvectordb.IndexParams = <langchain.vectorstores.tencentvectordb.IndexParams object>, database_name: str = 'LangChainDatabase', collection_name: str = 'LangChainCollection', drop_old: ~typing.Optional[bool] = False)[source]¶
Initialize wrapper around the tencent vector database.
In order to use this you need to have a database instance.
See the following documentation for details:
https://cloud.tencent.com/document/product/1709/94951
Attributes
embeddings
Access the query embedding object if available.
field_id
field_metadata
field_text
field_vector
Methods
__init__(embedding, connection_params[, ...])
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_texts(texts[, metadatas, timeout, ...])
Insert text data into TencentVectorDB.
afrom_documents(documents, embedding, **kwargs)
Return VectorStore initialized from documents and embeddings.
afrom_texts(texts, embedding[, metadatas])
Return VectorStore initialized from texts and embeddings.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)