langchain.vectorstores.dingo.Dingo¶
class langchain.vectorstores.dingo.Dingo(embedding: Embeddings, text_key: str, *, client: Any = None, index_name: Optional[str] = None, host: Optional[List[str]] = None, user: str = 'root', password: str = '123123', self_id: bool = False)[source]¶
Dingo vector store.
To use, you should have the dingodb python package installed.
Example
from langchain.vectorstores import Dingo
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
dingo = Dingo(embeddings, "text")
Initialize with Dingo client.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__(embedding, text_key, *[, client, ...])
Initialize with Dingo client.
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, ids, text_key, ...])
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.
amax_marginal_relevance_search(query[, k, ...])
Return docs selected using the maximal marginal relevance.
amax_marginal_relevance_search_by_vector(...)
Return docs selected using the maximal marginal relevance.