Note that unlike many other integrations in this category, Vectara provides an end-to-end managed service for Grounded Generation (aka retrieval augmented generation), which includes:A way to extract text from document files and chunk them into sentences.Its own embeddings model and vector store - each text segment is encoded into a vector embedding and stored in the Vectara internal vector storeA query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for Hybrid Search)All of these are supported in this LangChain integration.SetupYou will need a Vectara account to use Vectara with LangChain. To get started, use the following steps (see our quickstart guide):Sign up for a Vectara account if you don't already have one. Once you have completed your sign up you will have a Vectara customer ID. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window.Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the "Create Corpus" button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.Next you'll need to create API keys to access the corpus. Click on the "Authorization" tab in the corpus view and then the "Create API Key" button. Give your key a name, and choose whether you want query only or query+index for your key. Click "Create" and you now have an active API key. Keep this key confidential. To use LangChain with Vectara, you'll need to have these three values: customer ID, corpus ID and api_key.
Vectara is a API platform for building GenAI applications. It provides an easy-to-use API for document indexing and querying that is managed by Vectara and is optimized for performance and accuracy.
Vectara is a API platform for building GenAI applications. It provides an easy-to-use API for document indexing and querying that is managed by Vectara and is optimized for performance and accuracy. ->: Note that unlike many other integrations in this category, Vectara provides an end-to-end managed service for Grounded Generation (aka retrieval augmented generation), which includes:A way to extract text from document files and chunk them into sentences.Its own embeddings model and vector store - each text segment is encoded into a vector embedding and stored in the Vectara internal vector storeA query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for Hybrid Search)All of these are supported in this LangChain integration.SetupYou will need a Vectara account to use Vectara with LangChain. To get started, use the following steps (see our quickstart guide):Sign up for a Vectara account if you don't already have one. Once you have completed your sign up you will have a Vectara customer ID. You can find your customer ID by clicking on your name, on the top-right of the Vectara console window.Within your account you can create one or more corpora. Each corpus represents an area that stores text data upon ingest from input documents. To create a corpus, use the "Create Corpus" button. You then provide a name to your corpus as well as a description. Optionally you can define filtering attributes and apply some advanced options. If you click on your created corpus, you can see its name and corpus ID right on the top.Next you'll need to create API keys to access the corpus. Click on the "Authorization" tab in the corpus view and then the "Create API Key" button. Give your key a name, and choose whether you want query only or query+index for your key. Click "Create" and you now have an active API key. Keep this key confidential. To use LangChain with Vectara, you'll need to have these three values: customer ID, corpus ID and api_key.