From 8a507154ca68207326db0274d60452fd400e3cd9 Mon Sep 17 00:00:00 2001 From: Aashish Saini <141953346+ShorthillsAI@users.noreply.github.com> Date: Tue, 3 Oct 2023 10:46:00 +0530 Subject: [PATCH] Update clarifai.mdx (#11318) @baskaryan , Small typo fix --- docs/extras/integrations/providers/clarifai.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/extras/integrations/providers/clarifai.mdx b/docs/extras/integrations/providers/clarifai.mdx index 58d266ba49..5d0ba1c99c 100644 --- a/docs/extras/integrations/providers/clarifai.mdx +++ b/docs/extras/integrations/providers/clarifai.mdx @@ -43,7 +43,7 @@ For more details, the docs on the Clarifai Embeddings wrapper provide a [detaile Clarifai's vector DB was launched in 2016 and has been optimized to support live search queries. With workflows in the Clarifai platform, you data is automatically indexed by am embedding model and optionally other models as well to index that information in the DB for search. You can query the DB not only via the vectors but also filter by metadata matches, other AI predicted concepts, and even do geo-coordinate search. Simply create an application, select the appropriate base workflow for your type of data, and upload it (through the API as [documented here](https://docs.clarifai.com/api-guide/data/create-get-update-delete) or the UIs at clarifai.com). -You an also add data directly from LangChain as well, and the auto-indexing will take place for you. You'll notice this is a little different than other vectorstores where you need to provide an embedding model in their constructor and have LangChain coordinate getting the embeddings from text and writing those to the index. Not only is it more convenient, but it's much more scalable to use Clarifai's distributed cloud to do all the index in the background. +You can also add data directly from LangChain as well, and the auto-indexing will take place for you. You'll notice this is a little different than other vectorstores where you need to provide an embedding model in their constructor and have LangChain coordinate getting the embeddings from text and writing those to the index. Not only is it more convenient, but it's much more scalable to use Clarifai's distributed cloud to do all the index in the background. ```python from langchain.vectorstores import Clarifai