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Pinecone

You can use Pinecone vectorstores with LangChain. To get started, install the integration package and the official Pinecone SDK with:

tip

See this section for general instructions on installing integration packages.

npm install -S @langchain/pinecone @pinecone-database/pinecone

The below examples use OpenAI embeddings, but you can swap in whichever provider you'd like. Keep in mind different embeddings models may have a different number of dimensions:

npm install -S @langchain/openai

Index docs​

/* eslint-disable @typescript-eslint/no-non-null-assertion */
import { Pinecone } from "@pinecone-database/pinecone";
import { Document } from "@langchain/core/documents";
import { OpenAIEmbeddings } from "@langchain/openai";
import { PineconeStore } from "@langchain/pinecone";

// Instantiate a new Pinecone client, which will automatically read the
// env vars: PINECONE_API_KEY and PINECONE_ENVIRONMENT which come from
// the Pinecone dashboard at https://app.pinecone.io

const pinecone = new Pinecone();

const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);

const docs = [
  new Document({
    metadata: { foo: "bar" },
    pageContent: "pinecone is a vector db",
  }),
  new Document({
    metadata: { foo: "bar" },
    pageContent: "the quick brown fox jumped over the lazy dog",
  }),
  new Document({
    metadata: { baz: "qux" },
    pageContent: "lorem ipsum dolor sit amet",
  }),
  new Document({
    metadata: { baz: "qux" },
    pageContent: "pinecones are the woody fruiting body and of a pine tree",
  }),
];

await PineconeStore.fromDocuments(docs, new OpenAIEmbeddings(), {
  pineconeIndex,
  maxConcurrency: 5, // Maximum number of batch requests to allow at once. Each batch is 1000 vectors.
});

API Reference:

Query docs​

/* eslint-disable @typescript-eslint/no-non-null-assertion */
import { Pinecone } from "@pinecone-database/pinecone";
import { OpenAIEmbeddings } from "@langchain/openai";
import { PineconeStore } from "@langchain/pinecone";

// Instantiate a new Pinecone client, which will automatically read the
// env vars: PINECONE_API_KEY and PINECONE_ENVIRONMENT which come from
// the Pinecone dashboard at https://app.pinecone.io

const pinecone = new Pinecone();

const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);

const vectorStore = await PineconeStore.fromExistingIndex(
  new OpenAIEmbeddings(),
  { pineconeIndex }
);

/* Search the vector DB independently with metadata filters */
const results = await vectorStore.similaritySearch("pinecone", 1, {
  foo: "bar",
});
console.log(results);
/*
  [
    Document {
      pageContent: 'pinecone is a vector db',
      metadata: { foo: 'bar' }
    }
  ]
*/

API Reference:

Delete docs​

/* eslint-disable @typescript-eslint/no-non-null-assertion */
import { Pinecone } from "@pinecone-database/pinecone";
import { Document } from "@langchain/core/documents";
import { OpenAIEmbeddings } from "@langchain/openai";
import { PineconeStore } from "@langchain/pinecone";

// Instantiate a new Pinecone client, which will automatically read the
// env vars: PINECONE_API_KEY and PINECONE_ENVIRONMENT which come from
// the Pinecone dashboard at https://app.pinecone.io

const pinecone = new Pinecone();

const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);
const embeddings = new OpenAIEmbeddings();
const pineconeStore = new PineconeStore(embeddings, { pineconeIndex });

const docs = [
  new Document({
    metadata: { foo: "bar" },
    pageContent: "pinecone is a vector db",
  }),
  new Document({
    metadata: { foo: "bar" },
    pageContent: "the quick brown fox jumped over the lazy dog",
  }),
  new Document({
    metadata: { baz: "qux" },
    pageContent: "lorem ipsum dolor sit amet",
  }),
  new Document({
    metadata: { baz: "qux" },
    pageContent: "pinecones are the woody fruiting body and of a pine tree",
  }),
];

const pageContent = "some arbitrary content";

// Also takes an additional {ids: []} parameter for upsertion
const ids = await pineconeStore.addDocuments(docs);

const results = await pineconeStore.similaritySearch(pageContent, 2, {
  foo: "bar",
});

console.log(results);
/*
[
  Document {
    pageContent: 'pinecone is a vector db',
    metadata: { foo: 'bar' },
  },
  Document {
    pageContent: "the quick brown fox jumped over the lazy dog",
    metadata: { foo: "bar" },
  }
]
*/

await pineconeStore.delete({
  ids: [ids[0], ids[1]],
});

const results2 = await pineconeStore.similaritySearch(pageContent, 2, {
  foo: "bar",
});

console.log(results2);
/*
  []
*/

API Reference:

Pinecone supports maximal marginal relevance search, which takes a combination of documents that are most similar to the inputs, then reranks and optimizes for diversity.

/* eslint-disable @typescript-eslint/no-non-null-assertion */
import { Pinecone } from "@pinecone-database/pinecone";
import { OpenAIEmbeddings } from "@langchain/openai";
import { PineconeStore } from "@langchain/pinecone";

// Instantiate a new Pinecone client, which will automatically read the
// env vars: PINECONE_API_KEY and PINECONE_ENVIRONMENT which come from
// the Pinecone dashboard at https://app.pinecone.io

const pinecone = new Pinecone();

const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);

const vectorStore = await PineconeStore.fromExistingIndex(
  new OpenAIEmbeddings(),
  { pineconeIndex }
);

/* Search the vector DB independently with meta filters */
const results = await vectorStore.maxMarginalRelevanceSearch("pinecone", {
  k: 5,
  fetchK: 20, // Default value for the number of initial documents to fetch for reranking.
  // You can pass a filter as well
  // filter: {},
});
console.log(results);

API Reference:

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