langchain.vectorstores.clarifai.Clarifai¶
class langchain.vectorstores.clarifai.Clarifai(user_id: Optional[str] = None, app_id: Optional[str] = None, pat: Optional[str] = None, number_of_docs: Optional[int] = None, api_base: Optional[str] = None)[source]¶
Clarifai AI vector store.
To use, you should have the clarifai python package installed.
Example
from langchain.vectorstores import Clarifai
from langchain.embeddings.openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
vectorstore = Clarifai("langchain_store", embeddings.embed_query)
Initialize with Clarifai client.
Parameters
user_id (Optional[str], optional) – User ID. Defaults to None.
app_id (Optional[str], optional) – App ID. Defaults to None.
pat (Optional[str], optional) – Personal access token. Defaults to None.
number_of_docs (Optional[int], optional) – Number of documents to return
None. (during vector search. Defaults to) – 
api_base (Optional[str], optional) – API base. Defaults to None.
Raises
ValueError – If user ID, app ID or personal access token is not provided.
Attributes
embeddings
Access the query embedding object if available.
Methods
__init__([user_id, app_id, pat, ...])
Initialize with Clarifai 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.