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WeaviateStore

Weaviate is an open source vector database that stores both objects and vectors, allowing for combining vector search with structured filtering. LangChain connects to Weaviate via the weaviate-ts-client package, the official Typescript client for Weaviate.

This guide provides a quick overview for getting started with Weaviate vector stores. For detailed documentation of all WeaviateStore features and configurations head to the API reference.

Overview​

Integration details​

ClassPackagePY supportPackage latest
WeaviateStore@langchain/weaviate✅NPM - Version

Setup​

To use Weaviate vector stores, you’ll need to set up a Weaviate instance and install the @langchain/weaviate integration package. You should also install the weaviate-ts-client package to initialize a client to connect to your instance with, and the uuid package if you want to assign indexed documents ids.

This guide will also use OpenAI embeddings, which require you to install the @langchain/openai integration package. You can also use other supported embeddings models if you wish.

tip

See this section for general instructions on installing integration packages.

yarn add @langchain/weaviate @langchain/core weaviate-ts-client uuid @langchain/openai

You’ll need to run Weaviate either locally or on a server. See the Weaviate documentation for more information.

Credentials​

Once you’ve set up your instance, set the following environment variables:

// http or https
process.env.WEAVIATE_SCHEME = "";
// If running locally, include port e.g. "localhost:8080"
process.env.WEAVIATE_HOST = "YOUR_HOSTNAME";
// Optional, for cloud deployments
process.env.WEAVIATE_API_KEY = "YOUR_API_KEY";

If you are using OpenAI embeddings for this guide, you’ll need to set your OpenAI key as well:

process.env.OPENAI_API_KEY = "YOUR_API_KEY";

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

// process.env.LANGCHAIN_TRACING_V2="true"
// process.env.LANGCHAIN_API_KEY="your-api-key"

Instantiation​

import { WeaviateStore } from "@langchain/weaviate";
import { OpenAIEmbeddings } from "@langchain/openai";

import weaviate from "weaviate-ts-client";
// import { ApiKey } from "weaviate-ts-client"

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

// The Weaviate SDK has an issue with types
const weaviateClient = (weaviate as any).client({
  scheme: process.env.WEAVIATE_SCHEME ?? "http",
  host: process.env.WEAVIATE_HOST ?? "localhost",
  // If necessary
  // apiKey: new ApiKey(process.env.WEAVIATE_API_KEY ?? "default"),
});

const vectorStore = new WeaviateStore(embeddings, {
  client: weaviateClient,
  // Must start with a capital letter
  indexName: "Langchainjs_test",
  // Default value
  textKey: "text",
  // Any keys you intend to set as metadata
  metadataKeys: ["source"],
});

Manage vector store​

Add items to vector store​

Note: If you want to associate ids with your indexed documents, they must be UUIDs.

import type { Document } from "@langchain/core/documents";
import { v4 as uuidv4 } from "uuid";

const document1: Document = {
  pageContent: "The powerhouse of the cell is the mitochondria",
  metadata: { source: "https://example.com" },
};

const document2: Document = {
  pageContent: "Buildings are made out of brick",
  metadata: { source: "https://example.com" },
};

const document3: Document = {
  pageContent: "Mitochondria are made out of lipids",
  metadata: { source: "https://example.com" },
};

const document4: Document = {
  pageContent: "The 2024 Olympics are in Paris",
  metadata: { source: "https://example.com" },
};

const documents = [document1, document2, document3, document4];
const uuids = [uuidv4(), uuidv4(), uuidv4(), uuidv4()];

await vectorStore.addDocuments(documents, { ids: uuids });
[
  '610f9b92-9bee-473f-a4db-8f2ca6e3442d',
  '995160fa-441e-41a0-b476-cf3785518a0d',
  '0cdbe6d4-0df8-4f99-9b67-184009fee9a2',
  '18a8211c-0649-467b-a7c5-50ebb4b9ca9d'
]

Delete items from vector store​

You can delete by id as by passing a filter param:

await vectorStore.delete({ ids: [uuids[3]] });

Query vector store​

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Query directly​

Performing a simple similarity search can be done as follows:

const filter = {
  where: {
    operator: "Equal" as const,
    path: ["source"],
    valueText: "https://example.com",
  },
};

const similaritySearchResults = await vectorStore.similaritySearch(
  "biology",
  2,
  filter
);

for (const doc of similaritySearchResults) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
* The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* Mitochondria are made out of lipids [{"source":"https://example.com"}]

See this page for more on Weaviat filter syntax.

If you want to execute a similarity search and receive the corresponding scores you can run:

const similaritySearchWithScoreResults =
  await vectorStore.similaritySearchWithScore("biology", 2, filter);

for (const [doc, score] of similaritySearchWithScoreResults) {
  console.log(
    `* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(
      doc.metadata
    )}]`
  );
}
* [SIM=0.835] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.852] Mitochondria are made out of lipids [{"source":"https://example.com"}]

Query by turning into retriever​

You can also transform the vector store into a retriever for easier usage in your chains.

const retriever = vectorStore.asRetriever({
  // Optional filter
  filter: filter,
  k: 2,
});
await retriever.invoke("biology");
[
  Document {
    pageContent: 'The powerhouse of the cell is the mitochondria',
    metadata: { source: 'https://example.com' },
    id: undefined
  },
  Document {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { source: 'https://example.com' },
    id: undefined
  }
]

Usage for retrieval-augmented generation​

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

API reference​

For detailed documentation of all WeaviateStore features and configurations head to the API reference.

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