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Updates to Vectara documentation (#8699)
- 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>
This commit is contained in:
Ofer Mendelevitch
GitHub
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@ -8,32 +8,55 @@
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"source": [
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"# Vectara\n",
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"\n",
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">[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",
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">[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",
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"See the [Vectara API documentation ](https://docs.vectara.com/docs/) for more information on how to use the API.\n",
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"\n",
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"This notebook shows how to use functionality related to the `Vectara`'s integration with langchain.\n",
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"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",
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"1. A way to extract text from document files and chunk them into sentences.\n",
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"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",
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"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",
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"\n",
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"This notebook shows how to use functionality related to the `Vectara` vector database or the `Vectara` retriever. \n",
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"\n",
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"See the [Vectara API documentation ](https://docs.vectara.com/docs/) for more information on how to use the API."
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"All of these are supported in this LangChain integration."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "aac9563e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.282884Z",
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"start_time": "2023-04-04T10:51:21.408077Z"
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},
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"tags": []
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},
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"outputs": [],
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"cell_type": "markdown",
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"id": "dc0f4344",
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"metadata": {},
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"source": [
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"# Setup\n",
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"\n",
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"You will need a Vectara account to use Vectara with LangChain. To get started, use the following steps:\n",
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"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",
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"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",
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"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",
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"\n",
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"To use LangChain with Vectara, you'll need to have these three values: customer ID, corpus ID and api_key.\n",
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"You can provide those to LangChain in two ways:\n",
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"\n",
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"1. Include in your environment these three variables: `VECTARA_CUSTOMER_ID`, `VECTARA_CORPUS_ID` and `VECTARA_API_KEY`.\n",
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"\n",
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"> For example, you can set these variables using os.environ and getpass as follows:\n",
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"\n",
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"```python\n",
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"import os\n",
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"from langchain.embeddings import FakeEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Vectara\n",
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"from langchain.document_loaders import TextLoader"
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"import getpass\n",
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"\n",
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"os.environ[\"VECTARA_CUSTOMER_ID\"] = getpass.getpass(\"Vectara Customer ID:\")\n",
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"os.environ[\"VECTARA_CORPUS_ID\"] = getpass.getpass(\"Vectara Corpus ID:\")\n",
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"os.environ[\"VECTARA_API_KEY\"] = getpass.getpass(\"Vectara API Key:\")\n",
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"```\n",
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"\n",
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"2. Add them to the Vectara vectorstore constructor:\n",
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"\n",
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"```python\n",
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"vectorstore = Vectara(\n",
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" vectara_customer_id=vectara_customer_id,\n",
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" vectara_corpus_id=vectara_corpus_id,\n",
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" vectara_api_key=vectara_api_key\n",
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" )\n",
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"```"
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]
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},
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{
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@ -44,8 +67,21 @@
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"source": [
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"## Connecting to Vectara from LangChain\n",
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"\n",
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"The Vectara API provides simple API endpoints for indexing and querying, which is encapsulated in the Vectara integration.\n",
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"First let's ingest the documents using the from_documents() method:"
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"To get started, let's ingest the documents using the from_documents() method.\n",
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"We assume here that you've added your VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and query+indexing VECTARA_API_KEY as environment variables."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "04a1f1a0",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.embeddings import FakeEmbeddings\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.vectorstores import Vectara\n",
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"from langchain.document_loaders import TextLoader"
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]
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},
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{
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@ -88,7 +124,7 @@
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"metadata": {},
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"source": [
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"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",
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"To use this, we added the add_files() method (and from_files()). \n",
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"To use this, we added the add_files() method (as well as from_files()). \n",
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"\n",
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"Let's see this in action. We pick two PDF documents to upload: \n",
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"1. The \"I have a dream\" speech by Dr. King\n",
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@ -296,7 +332,7 @@
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"source": [
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"## Vectara as a Retriever\n",
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"\n",
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"Vectara, as all the other vector stores, can be used also as a LangChain Retriever:"
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"Vectara, as all the other LangChain vectorstores, is most often used as a LangChain Retriever:"
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]
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},
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{
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@ -378,7 +414,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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