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Weaviate

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.

LangChain inserts vectors directly to Weaviate, and queries Weaviate for the nearest neighbors of a given vector, so that you can use all the LangChain Embeddings integrations with Weaviate.

Setup​

npm install weaviate-ts-client graphql

You'll need to run Weaviate either locally or on a server, see the Weaviate documentation for more information.

Usage, insert documents​

/* eslint-disable @typescript-eslint/no-explicit-any */
import weaviate from "weaviate-ts-client";
import { WeaviateStore } from "langchain/vectorstores/weaviate";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";

export async function run() {
  // Something wrong with the weaviate-ts-client types, so we need to disable
  const client = (weaviate as any).client({
    scheme: process.env.WEAVIATE_SCHEME || "https",
    host: process.env.WEAVIATE_HOST || "localhost",
    apiKey: new (weaviate as any).ApiKey(
      process.env.WEAVIATE_API_KEY || "default"
    ),
  });

  // Create a store and fill it with some texts + metadata
  await WeaviateStore.fromTexts(
    ["hello world", "hi there", "how are you", "bye now"],
    [{ foo: "bar" }, { foo: "baz" }, { foo: "qux" }, { foo: "bar" }],
    new OpenAIEmbeddings(),
    {
      client,
      indexName: "Test",
      textKey: "text",
      metadataKeys: ["foo"],
    }
  );
}

API Reference:

Usage, query documents​

/* eslint-disable @typescript-eslint/no-explicit-any */
import weaviate from "weaviate-ts-client";
import { WeaviateStore } from "langchain/vectorstores/weaviate";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";

export async function run() {
  // Something wrong with the weaviate-ts-client types, so we need to disable
  const client = (weaviate as any).client({
    scheme: process.env.WEAVIATE_SCHEME || "https",
    host: process.env.WEAVIATE_HOST || "localhost",
    apiKey: new (weaviate as any).ApiKey(
      process.env.WEAVIATE_API_KEY || "default"
    ),
  });

  // Create a store for an existing index
  const store = await WeaviateStore.fromExistingIndex(new OpenAIEmbeddings(), {
    client,
    indexName: "Test",
    metadataKeys: ["foo"],
  });

  // Search the index without any filters
  const results = await store.similaritySearch("hello world", 1);
  console.log(results);
  /*
  [ Document { pageContent: 'hello world', metadata: { foo: 'bar' } } ]
  */

  // Search the index with a filter, in this case, only return results where
  // the "foo" metadata key is equal to "baz", see the Weaviate docs for more
  // https://weaviate.io/developers/weaviate/api/graphql/filters
  const results2 = await store.similaritySearch("hello world", 1, {
    where: {
      operator: "Equal",
      path: ["foo"],
      valueText: "baz",
    },
  });
  console.log(results2);
  /*
  [ Document { pageContent: 'hi there', metadata: { foo: 'baz' } } ]
  */
}

API Reference: