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UpstashVectorStore

Upstash Vector is a REST based serverless vector database, designed for working with vector embeddings.

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

Overview​

Integration details​

ClassPackagePY supportPackage latest
UpstashVectorStore@langchain/community✅NPM - Version

Setup​

To use Upstash vector stores, you’ll need to create an Upstash account, create an index, and install the @langchain/community integration package. You’ll also need to install the @upstash/vector package as a peer dependency.

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/community @langchain/core @upstash/vector @langchain/openai

You can create an index from the Upstash Console. For further reference, see the official docs.

Credentials​

Once you’ve set up an index, set the following environment variables:

process.env.UPSTASH_VECTOR_REST_URL = "your-rest-url";
process.env.UPSTASH_VECTOR_REST_TOKEN = "your-rest-token";

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​

Make sure your index has the same dimension count as your embeddings. The default for OpenAI text-embedding-3-small is 1536.

import { UpstashVectorStore } from "@langchain/community/vectorstores/upstash";
import { OpenAIEmbeddings } from "@langchain/openai";

import { Index } from "@upstash/vector";

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

const indexWithCredentials = new Index({
  url: process.env.UPSTASH_VECTOR_REST_URL,
  token: process.env.UPSTASH_VECTOR_REST_TOKEN,
});

const vectorStore = new UpstashVectorStore(embeddings, {
  index: indexWithCredentials,
  // You can use namespaces to partition your data in an index
  // namespace: "test-namespace",
});

Manage vector store​

Add items to vector store​

import type { Document } from "@langchain/core/documents";

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];

await vectorStore.addDocuments(documents, { ids: ["1", "2", "3", "4"] });
[ '1', '2', '3', '4' ]

Note: After adding documents, there may be a slight delay before they become queryable.

Delete items from vector store​

await vectorStore.delete({ ids: ["4"] });

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 = "source = '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 Upstash Vector 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=0.576] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.557] 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 UpstashVectorStore features and configurations head to the API reference.

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