> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-docsdy-1782656769-5d7a089.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# PineconeStore integration
> Integrate with the PineconeStore using LangChain JavaScript.
[Pinecone](https://www.pinecone.io/) is a vector database that helps power AI for some of the world’s best companies.
This guide provides a quick overview for getting started with Pinecone [vector stores](/oss/javascript/integrations/vectorstores). For detailed documentation of all `PineconeStore` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-pinecone/PineconeStore).
## Overview
### Integration details
| Class | Package | [PY support](https://python.langchain.com/docs/integrations/vectorstores/pinecone/) | Downloads | Version |
| :--------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------- | :---------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: |
| [`PineconeStore`](https://reference.langchain.com/javascript/langchain-pinecone/PineconeStore) | [`@langchain/pinecone`](https://www.npmjs.com/package/@langchain/pinecone) | ✅ |  |  |
## Setup
To use Pinecone vector stores, create a Pinecone account, initialize an index, and install `@langchain/pinecone`, `@langchain/core`, and the [official Pinecone SDK](https://www.npmjs.com/package/@pinecone-database/pinecone) (`@pinecone-database/pinecone` v5.x) to initialize a client for `PineconeStore`.
This guide uses [OpenAI embeddings](/oss/javascript/integrations/embeddings/openai) as an example. You can use [other supported embeddings models](/oss/javascript/integrations/embeddings) instead.
```bash npm theme={null}
npm install @langchain/pinecone @langchain/openai @langchain/core @pinecone-database/pinecone@5
```
```bash yarn theme={null}
yarn add @langchain/pinecone @langchain/openai @langchain/core @pinecone-database/pinecone@5
```
```bash pnpm theme={null}
pnpm add @langchain/pinecone @langchain/openai @langchain/core @pinecone-database/pinecone@5
```
### Credentials
Sign up for a [Pinecone](https://www.pinecone.io/) account and create an index. Make sure the dimensions match those of the embeddings you want to use (the default is 1536 for OpenAI's `text-embedding-3-small`). Once you've done this set the `PINECONE_INDEX`, `PINECONE_API_KEY`, and (optionally) `PINECONE_ENVIRONMENT` environment variables:
```typescript theme={null}
process.env.PINECONE_API_KEY = "your-pinecone-api-key";
process.env.PINECONE_INDEX = "your-pinecone-index";
// Optional
process.env.PINECONE_ENVIRONMENT = "your-pinecone-environment";
```
If you are using OpenAI embeddings for this guide, set your OpenAI key as well:
```typescript theme={null}
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](/langsmith/observability) API key by uncommenting below:
```typescript theme={null}
// process.env.LANGSMITH_TRACING="true"
// process.env.LANGSMITH_API_KEY="your-api-key"
```
## Instantiation
```typescript theme={null}
import { PineconeStore } from "@langchain/pinecone";
import { OpenAIEmbeddings } from "@langchain/openai";
import { Pinecone as PineconeClient } from "@pinecone-database/pinecone";
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small",
});
const pinecone = new PineconeClient();
// Will automatically read the PINECONE_API_KEY and PINECONE_ENVIRONMENT env vars
const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);
const vectorStore = await PineconeStore.fromExistingIndex(
embeddings,
{
pineconeIndex,
// Maximum number of batch requests to allow at once. Each batch is 1000 vectors.
maxConcurrency: 5,
// You can pass a namespace here too
// namespace: "foo",
}
);
```
## Manage vector store
### Add items to vector store
```typescript theme={null}
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"] });
```
```text theme={null}
[ '1', '2', '3', '4' ]
```
**Note:** After adding documents, there is a slight delay before they become queryable.
### Delete items from vector store
```typescript theme={null}
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:
```typescript theme={null}
// Optional filter
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)}]`);
}
```
```text theme={null}
* The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* Mitochondria are made out of lipids [{"source":"https://example.com"}]
```
If you want to execute a similarity search and receive the corresponding scores you can run:
```typescript theme={null}
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)}]`);
}
```
```text theme={null}
* [SIM=0.165] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.148] 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](/oss/javascript/langchain/retrieval) for easier usage in your chains.
```typescript theme={null}
const retriever = vectorStore.asRetriever({
// Optional filter
filter: filter,
k: 2,
});
await retriever.invoke("biology");
```
```javascript theme={null}
[
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:
* [Build a RAG app with LangChain](/oss/javascript/langchain/rag).
* [Agentic RAG](/oss/javascript/langgraph/agentic-rag)
* [Retrieval docs](/oss/javascript/langchain/retrieval)
***
## API reference
For detailed documentation of all `PineconeStore` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-pinecone/PineconeStore).
***
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[Edit this page on GitHub](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/vectorstores/pinecone.mdx) or [file an issue](https://github.com/langchain-ai/docs/issues/new/choose).