@langchain/turbopuffer

This package contains the LangChain.js integration for the turbopuffer vector database.

Installation

npm install @langchain/turbopuffer @turbopuffer/turbopuffer

Usage

import { Turbopuffer } from "@turbopuffer/turbopuffer";
import { TurbopufferVectorStore } from "@langchain/turbopuffer";
import { OpenAIEmbeddings } from "@langchain/openai";

const client = new Turbopuffer({ apiKey: process.env.TURBOPUFFER_API_KEY });

const vectorStore = new TurbopufferVectorStore(new OpenAIEmbeddings(), {
  namespace: client.namespace("my-namespace"),
});

const ids = await vectorStore.addDocuments([
  { pageContent: "Hello world", metadata: { source: "greeting" } },
]);

const results = await vectorStore.similaritySearch("hello", 1);

await vectorStore.delete({ ids });

Configuration

| Option | Description | Default | | ---------------- | --------------------------------------------- | ------------------- | | namespace | A configured turbopuffer Namespace instance | Required | | distanceMetric | "cosine_distance" or "euclidean_squared" | "cosine_distance" |

Add Options

| Option | Description | Default | | ----------- | ------------------------ | -------------------- | | ids | Custom IDs for documents | Auto-generated UUIDs | | batchSize | Batch size for upserts | 3000 |

Filtering

const results = await vectorStore.similaritySearch("query", 10, [
  "category",
  "Eq",
  "books",
]);

Development

pnpm install
pnpm build
pnpm test
pnpm test:int
pnpm lint && pnpm format