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Add support for Xata as a vector store (#2125)
* Added Xata as vector store + integration tests * Added examples and docs * Declare xata in peerDependencies as per the contributing guide * Removed any for the client type * review comments
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docs/extras/modules/data_connection/vectorstores/integrations/xata.mdx
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| # Xata | ||
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| [Xata](https://xata.io) is a serverless data platform, based on PostgreSQL. It provides a type-safe TypeScript/JavaScript SDK for interacting with your database, and a UI for managing your data. | ||
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| Xata has a native vector type, which can be added to any table, and supports similarity search. LangChain inserts vectors directly to Xata, and queries it for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Xata. | ||
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| ## Setup | ||
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| ### Install the Xata CLI | ||
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| ```bash | ||
| npm install @xata.io/cli -g | ||
| ``` | ||
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| ### Create a database to be used as a vector store | ||
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| In the [Xata UI](https://app.xata.io) create a new database. You can name it whatever you want, but for this example we'll use `langchain`. | ||
| Create a table, again you can name it anything, but we will use `vectors`. Add the following columns via the UI: | ||
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| * `content` of type "Long text". This is used to store the `Document.pageContent` values. | ||
| * `embedding` of type "Vector". Use the dimension used by the model you plan to use (1536 for OpenAI). | ||
| * any other columns you want to use as metadata. They are populated from the `Document.metadata` object. For example, if in the `Document.metadata` object you have a `title` property, you can create a `title` column in the table and it will be populated. | ||
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| ### Initialize the project | ||
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| In your project, run: | ||
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| ```bash | ||
| xata init | ||
| ``` | ||
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| and then choose the database you created above. This will also generate a `xata.ts` or `xata.js` file that defines the client you can use to interact with the database. See the [Xata getting started docs](https://xata.io/docs/getting-started/installation) for more details on using the Xata JavaScript/TypeScript SDK. | ||
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| ## Usage | ||
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| import CodeBlock from "@theme/CodeBlock"; | ||
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| ### Example: Q&A chatbot using OpenAI and Xata as vector store | ||
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| This example uses the `VectorDBQAChain` to search the documents stored in Xata and then pass them as context to the OpenAI model, in order to answer the question asked by the user. | ||
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| import FromDocs from "@examples/indexes/vector_stores/xata.ts"; | ||
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| <CodeBlock language="typescript">{FromDocs}</CodeBlock> | ||
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| ### Example: Similarity search with a metadata filter | ||
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| This example shows how to implement semantic search using LangChain.js and Xata. Before running it, make sure to add an `author` column of type String to the `vectors` table in Xata. | ||
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| import SimSearch from "@examples/indexes/vector_stores/xata_metadata.ts"; | ||
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| <CodeBlock language="typescript">{SimSearch}</CodeBlock> |
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| import { XataVectorSearch } from "langchain/vectorstores/xata"; | ||
| import { OpenAIEmbeddings } from "langchain/embeddings/openai"; | ||
| import { BaseClient } from "@xata.io/client"; | ||
| import { Document } from "langchain/document"; | ||
| import { VectorDBQAChain } from "langchain/chains"; | ||
| import { OpenAI } from "langchain/llms/openai"; | ||
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| // First, follow set-up instructions at | ||
| // https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/xata | ||
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| // if you use the generated client, you don't need this function. | ||
| // Just import getXataClient from the generated xata.ts instead. | ||
| const getXataClient = () => { | ||
| if (!process.env.XATA_API_KEY) { | ||
| throw new Error("XATA_API_KEY not set"); | ||
| } | ||
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| if (!process.env.XATA_DB_URL) { | ||
| throw new Error("XATA_DB_URL not set"); | ||
| } | ||
| const xata = new BaseClient({ | ||
| databaseURL: process.env.XATA_DB_URL, | ||
| apiKey: process.env.XATA_API_KEY, | ||
| branch: process.env.XATA_BRANCH || "main", | ||
| }); | ||
| return xata; | ||
| }; | ||
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| export async function run() { | ||
| const client = getXataClient(); | ||
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| const table = "vectors"; | ||
| const embeddings = new OpenAIEmbeddings(); | ||
| const store = new XataVectorSearch(embeddings, { client, table }); | ||
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| // Add documents | ||
| const docs = [ | ||
| new Document({ | ||
| pageContent: "Xata is a Serverless Data platform based on PostgreSQL", | ||
| }), | ||
| new Document({ | ||
| pageContent: | ||
| "Xata offers a built-in vector type that can be used to store and query vectors", | ||
| }), | ||
| new Document({ | ||
| pageContent: "Xata includes similarity search", | ||
| }), | ||
| ]; | ||
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| const ids = await store.addDocuments(docs); | ||
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| // eslint-disable-next-line no-promise-executor-return | ||
| await new Promise((r) => setTimeout(r, 2000)); | ||
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| const model = new OpenAI(); | ||
| const chain = VectorDBQAChain.fromLLM(model, store, { | ||
| k: 1, | ||
| returnSourceDocuments: true, | ||
| }); | ||
| const response = await chain.call({ query: "What is Xata?" }); | ||
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| console.log(JSON.stringify(response, null, 2)); | ||
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| await store.delete({ ids }); | ||
| } |
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| import { XataVectorSearch } from "langchain/vectorstores/xata"; | ||
| import { OpenAIEmbeddings } from "langchain/embeddings/openai"; | ||
| import { BaseClient } from "@xata.io/client"; | ||
| import { Document } from "langchain/document"; | ||
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| // First, follow set-up instructions at | ||
| // https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/xata | ||
| // Also, add a column named "author" to the "vectors" table. | ||
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| // if you use the generated client, you don't need this function. | ||
| // Just import getXataClient from the generated xata.ts instead. | ||
| const getXataClient = () => { | ||
| if (!process.env.XATA_API_KEY) { | ||
| throw new Error("XATA_API_KEY not set"); | ||
| } | ||
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| if (!process.env.XATA_DB_URL) { | ||
| throw new Error("XATA_DB_URL not set"); | ||
| } | ||
| const xata = new BaseClient({ | ||
| databaseURL: process.env.XATA_DB_URL, | ||
| apiKey: process.env.XATA_API_KEY, | ||
| branch: process.env.XATA_BRANCH || "main", | ||
| }); | ||
| return xata; | ||
| }; | ||
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| export async function run() { | ||
| const client = getXataClient(); | ||
| const table = "vectors"; | ||
| const embeddings = new OpenAIEmbeddings(); | ||
| const store = new XataVectorSearch(embeddings, { client, table }); | ||
| // Add documents | ||
| const docs = [ | ||
| new Document({ | ||
| pageContent: "Xata works great with Langchain.js", | ||
| metadata: { author: "Xata" }, | ||
| }), | ||
| new Document({ | ||
| pageContent: "Xata works great with Langchain", | ||
| metadata: { author: "Langchain" }, | ||
| }), | ||
| new Document({ | ||
| pageContent: "Xata includes similarity search", | ||
| metadata: { author: "Xata" }, | ||
| }), | ||
| ]; | ||
| const ids = await store.addDocuments(docs); | ||
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| // eslint-disable-next-line no-promise-executor-return | ||
| await new Promise((r) => setTimeout(r, 2000)); | ||
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| // author is applied as pre-filter to the similarity search | ||
| const results = await store.similaritySearchWithScore("xata works great", 6, { | ||
| author: "Langchain", | ||
| }); | ||
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| console.log(JSON.stringify(results, null, 2)); | ||
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| await store.delete({ ids }); | ||
| } |
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