@langchain/weaviate

This package contains the LangChain.js integrations for Weaviate with the weaviate-ts-client SDK.

Installation

npm install @langchain/weaviate
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Vectorstore

This package adds support for Weaviate vectorstore.

To follow along with this example install the @langchain/openai package for their Embeddings model.

npm install @langchain/openai
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Now set the necessary environment variables (or pass them in via the client object):

export WEAVIATE_SCHEME=
export WEAVIATE_HOST=
export WEAVIATE_API_KEY=
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import weaviate, { ApiKey } from 'weaviate-ts-client';
import { WeaviateStore } from "@langchain/weaviate";

// Weaviate SDK has a TypeScript issue so we must do this.
const client = (weaviate as any).client({
  scheme: process.env.WEAVIATE_SCHEME || "https",
  host: process.env.WEAVIATE_HOST || "localhost",
  apiKey: new 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"],
  }
);
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Development

To develop the @langchain/weaviate package, you'll need to follow these instructions:

Install dependencies

yarn install
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Build the package

yarn build
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Or from the repo root:

yarn build --filter=@langchain/weaviate
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Run tests

Test files should live within a tests/ file in the src/ folder. Unit tests should end in .test.ts and integration tests should end in .int.test.ts:

$ yarn test
$ yarn test:int
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Lint & Format

Run the linter & formatter to ensure your code is up to standard:

yarn lint && yarn format
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Adding new entrypoints

If you add a new file to be exported, either import & re-export from src/index.ts, or add it to the entrypoints field in the config variable located inside langchain.config.js and run yarn build to generate the new entrypoint.