Implementation of Vector Store using LLMRails.
Example:
.. code-block:: python
from langchain_community.vectorstores import LLMRails
vectorstore = LLMRails( api_key=llm_rails_api_key, datastore_id=datastore_id )
Run more texts through the embeddings and add to the vectorstore.
LLMRails 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
Return LLMRails documents most similar to query, along with scores.
Return LLMRails documents most similar to query, along with scores.
Construct LLMRails wrapper from raw documents. This is intended to be a quick way to get started. Example: .. code-block:: python
from langchain_community.vectorstores import LLMRails
llm_rails = LLMRails.from_texts(
texts,
datastore_id=datastore_id,
api_key=llm_rails_api_key
)