mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
426 lines
14 KiB
Plaintext
426 lines
14 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "683953b3",
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"metadata": {},
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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 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. \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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"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": "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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"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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"cell_type": "markdown",
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"id": "eeead681",
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"metadata": {},
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"source": [
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"## Connecting to Vectara from LangChain\n",
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"\n",
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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": 1,
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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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"cell_type": "code",
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"execution_count": 2,
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"id": "be0a4973",
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"metadata": {},
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"outputs": [],
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"source": [
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"loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n",
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"documents = loader.load()\n",
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"text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
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"docs = text_splitter.split_documents(documents)"
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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": 3,
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"id": "8429667e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:22.525091Z",
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"start_time": "2023-04-04T10:51:22.522015Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"vectara = Vectara.from_documents(\n",
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" docs,\n",
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" embedding=FakeEmbeddings(size=768),\n",
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" doc_metadata={\"speech\": \"state-of-the-union\"},\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "90dbf3e7",
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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 (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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"2. Churchill's \"We Shall Fight on the Beaches\" speech"
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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": 4,
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"id": "85ef3468",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tempfile\n",
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"import urllib.request\n",
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"\n",
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"urls = [\n",
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" [\n",
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" \"https://www.gilderlehrman.org/sites/default/files/inline-pdfs/king.dreamspeech.excerpts.pdf\",\n",
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" \"I-have-a-dream\",\n",
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" ],\n",
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" [\n",
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" \"https://www.parkwayschools.net/cms/lib/MO01931486/Centricity/Domain/1578/Churchill_Beaches_Speech.pdf\",\n",
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" \"we shall fight on the beaches\",\n",
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" ],\n",
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"]\n",
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"files_list = []\n",
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"for url, _ in urls:\n",
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" name = tempfile.NamedTemporaryFile().name\n",
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" urllib.request.urlretrieve(url, name)\n",
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" files_list.append(name)\n",
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"\n",
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"docsearch: Vectara = Vectara.from_files(\n",
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" files=files_list,\n",
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" embedding=FakeEmbeddings(size=768),\n",
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" metadatas=[{\"url\": url, \"speech\": title} for url, title in urls],\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f9215c8",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T09:27:29.920258Z",
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"start_time": "2023-04-04T09:27:29.913714Z"
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}
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},
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"source": [
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"## Similarity search\n",
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"\n",
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"The simplest scenario for using Vectara is to perform a similarity search. "
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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": 5,
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"id": "a8c513ab",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.204469Z",
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"start_time": "2023-04-04T10:51:24.855618Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = vectara.similarity_search(\n",
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" query, n_sentence_context=0, filter=\"doc.speech = 'state-of-the-union'\"\n",
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")"
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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": 6,
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"id": "fc516993",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.220984Z",
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"start_time": "2023-04-04T10:51:25.213943Z"
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson.\n"
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]
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}
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],
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"source": [
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"print(found_docs[0].page_content)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1bda9bf5",
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"metadata": {},
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"source": [
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"## Similarity search with score\n",
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"\n",
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"Sometimes we might want to perform the search, but also obtain a relevancy score to know how good is a particular result."
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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": 7,
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"id": "8804a21d",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.631585Z",
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"start_time": "2023-04-04T10:51:25.227384Z"
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}
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},
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"outputs": [],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"found_docs = vectara.similarity_search_with_score(\n",
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" query, filter=\"doc.speech = 'state-of-the-union'\", score_threshold=0.2,\n",
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")"
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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": 8,
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"id": "756a6887",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:25.642282Z",
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"start_time": "2023-04-04T10:51:25.635947Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice.\n",
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"\n",
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"Score: 0.786569\n"
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]
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}
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],
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"source": [
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"document, score = found_docs[0]\n",
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"print(document.page_content)\n",
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"print(f\"\\nScore: {score}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1f9876a8",
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"metadata": {},
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"source": [
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"Now let's do similar search for content in the files we uploaded"
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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": 9,
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"id": "47784de5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"With this threshold of 1.2 we have 0 documents\n"
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]
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}
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],
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"source": [
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"query = \"We must forever conduct our struggle\"\n",
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"min_score = 1.2\n",
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"found_docs = vectara.similarity_search_with_score(\n",
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" query, filter=\"doc.speech = 'I-have-a-dream'\", score_threshold=min_score,\n",
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")\n",
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"print(f\"With this threshold of {min_score} we have {len(found_docs)} documents\")"
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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": 10,
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"id": "3e22949f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"With this threshold of 0.2 we have 3 documents\n"
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]
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}
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],
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"source": [
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"query = \"We must forever conduct our struggle\"\n",
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"min_score = 0.2\n",
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"found_docs = vectara.similarity_search_with_score(\n",
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" query, filter=\"doc.speech = 'I-have-a-dream'\", score_threshold=min_score,\n",
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")\n",
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"print(f\"With this threshold of {min_score} we have {len(found_docs)} documents\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "691a82d6",
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"metadata": {},
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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 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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"cell_type": "code",
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"execution_count": 11,
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"id": "9427195f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2023-04-04T10:51:26.031451Z",
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"start_time": "2023-04-04T10:51:26.018763Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"VectaraRetriever(tags=['Vectara'], metadata=None, vectorstore=<langchain.vectorstores.vectara.Vectara object at 0x1586bd330>, search_type='similarity', search_kwargs={'lambda_val': 0.025, 'k': 5, 'filter': '', 'n_sentence_context': '2'})"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"retriever = vectara.as_retriever()\n",
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"retriever"
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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": 12,
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"id": "f3c70c31",
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"metadata": {
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"ExecuteTime": {
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||
"end_time": "2023-04-04T10:51:26.495652Z",
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"start_time": "2023-04-04T10:51:26.046407Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Document(page_content='Justice Breyer, thank you for your service. One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence. A former top litigator in private practice.', metadata={'source': 'langchain', 'lang': 'eng', 'offset': '596', 'len': '97', 'speech': 'state-of-the-union'})"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"query = \"What did the president say about Ketanji Brown Jackson\"\n",
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"retriever.get_relevant_documents(query)[0]"
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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": "2300e785",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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