- Description: updates to Vectara documentation with more details on how
to get started.
- Issue: NA
- Dependencies: NA
- Tag maintainer: @rlancemartin, @eyurtsev
- Twitter handle: @vectara, @ofermend
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
">[Vectara](https://vectara.com/) is a API platform for building LLM-powered applications. It provides a simple to use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy. \n",
">[Vectara](https://vectara.com/) is a API platform for building GenAI applications. It provides an easy-to-use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy. \n",
"See the [Vectara API documentation ](https://docs.vectara.com/docs/) for more information on how to use the API.\n",
"\n",
"This notebook shows how to use functionality related to the `Vectara`'s integration with langchain.\n",
"Note that unlike many other integrations in this category, Vectara provides an end-to-end managed service for [Grounded Generation](https://vectara.com/grounded-generation/) (aka retrieval agumented generation), which includes:\n",
"1. A way to extract text from document files and chunk them into sentences.\n",
"2. Its own embeddings model and vector store - each text segment is encoded into a vector embedding and stored in the Vectara internal vector store\n",
"3. A query service that automatically encodes the query into embedding, and retrieves the most relevant text segments (including support for [Hybrid Search](https://docs.vectara.com/docs/api-reference/search-apis/lexical-matching))\n",
"\n",
"This notebook shows how to use functionality related to the `Vectara` vector database or the `Vectara` retriever. \n",
"\n",
"See the [Vectara API documentation ](https://docs.vectara.com/docs/) for more information on how to use the API."
"All of these are supported in this LangChain integration."
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"# Setup\n",
"\n",
"You will need a Vectara account to use Vectara with LangChain. To get started, use the following steps:\n",
"1. [Sign up](https://console.vectara.com/signup) 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.\n",
"2. 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.\n",
"3. 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. \n",
"\n",
"To use LangChain with Vectara, you'll need to have these three values: customer ID, corpus ID and api_key.\n",
"You can provide those to LangChain in two ways:\n",
"\n",
"1. Include in your environment these three variables: `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`.\n",
"\n",
"> For example, you can set these variables using os.environ and getpass as follows:\n",
"Vectara's indexing API provides a file upload API where the file is handled directly by Vectara - pre-processed, chunked optimally and added to the Vectara vector store.\n",
"To use this, we added the add_files() method (and from_files()). \n",
"To use this, we added the add_files() method (as well as from_files()). \n",
"\n",
"Let's see this in action. We pick two PDF documents to upload: \n",
"1. The \"I have a dream\" speech by Dr. King\n",
@ -296,7 +332,7 @@
"source": [
"## Vectara as a Retriever\n",
"\n",
"Vectara, as all the other vector stores, can be used also as a LangChain Retriever:"
"Vectara, as all the other LangChain vectorstores, is most often used as a LangChain Retriever:"
Ofer Mendelevitch
1 year ago
committed by
GitHub