> ## Documentation Index
> Fetch the complete documentation index at: https://langchain-5e9cc07a-preview-devupd-1765394015-eccef47.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# MongoDB Atlas
**Compatibility**: Only available on Node.js.
You can still create API routes that use MongoDB with Next.js by setting the `runtime` variable to `nodejs` like so:
`export const runtime = "nodejs";`
You can read more about Edge runtimes in the Next.js documentation [here](https://nextjs.org/docs/app/building-your-application/rendering/edge-and-nodejs-runtimes).
````
This guide provides a quick overview for getting started with MongoDB Atlas [vector stores](/oss/javascript/integrations/vectorstores). For detailed documentation of all `MongoDBAtlasVectorSearch` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_mongodb.MongoDBAtlasVectorSearch.html).
## Overview
### Integration details
| Class | Package | [PY support](https://python.langchain.com/docs/integrations/vectorstores/mongodb_atlas/) | Version |
| :--- | :--- | :---: | :---: |
| [`MongoDBAtlasVectorSearch`](https://api.js.langchain.com/classes/langchain_mongodb.MongoDBAtlasVectorSearch.html) | [`@langchain/mongodb`](https://www.npmjs.com/package/@langchain/mongodb) | ✅ |  |
## Setup
To use MongoDB Atlas vector stores, you'll need to configure a MongoDB Atlas cluster and install the `@langchain/mongodb` integration package.
### Initial Cluster Configuration
To create a MongoDB Atlas cluster, navigate to the [MongoDB Atlas website](https://www.mongodb.com/products/platform/atlas-database) and create an account if you don't already have one.
Create and name a cluster when prompted, then find it under `Database`. Select `Browse Collections` and create either a blank collection or one from the provided sample data.
**Note:** The cluster created must be MongoDB 7.0 or higher.
### Creating an Index
After configuring your cluster, you'll need to create an index on the collection field you want to search over.
Switch to the `Atlas Search` tab and click `Create Search Index`. From there, make sure you select `Atlas Vector Search - JSON Editor`, then select the appropriate database and collection and paste the following into the textbox:
```json
{
"fields": [
{
"numDimensions": 1536,
"path": "embedding",
"similarity": "euclidean",
"type": "vector"
}
]
}
````
Note that the dimensions property should match the dimensionality of the embeddings you are using. For example, Cohere embeddings have 1024 dimensions, and by default OpenAI embeddings have 1536:
Note: By default the vector store expects an index name of default, an indexed collection field name of embedding, and a raw text field name of text. You should initialize the vector store with field names matching your index name collection schema as shown below.
Finally, proceed to build the index.
### Embeddings
This guide will also use [OpenAI embeddings](/oss/javascript/integrations/text_embedding/openai), which require you to install the `@langchain/openai` integration package. You can also use [other supported embeddings models](/oss/javascript/integrations/text_embedding) if you wish.
### Installation
Install the following packages:
```bash npm theme={null}
npm install @langchain/mongodb mongodb @langchain/openai @langchain/core
```
```bash yarn theme={null}
yarn add @langchain/mongodb mongodb @langchain/openai @langchain/core
```
```bash pnpm theme={null}
pnpm add @langchain/mongodb mongodb @langchain/openai @langchain/core
```
### Credentials
Once you've done the above, set the `MONGODB_ATLAS_URI` environment variable from the `Connect` button in Mongo's dashboard. You'll also need your DB name and collection name:
```typescript theme={null}
process.env.MONGODB_ATLAS_URI = "your-atlas-url";
process.env.MONGODB_ATLAS_COLLECTION_NAME = "your-atlas-db-name";
process.env.MONGODB_ATLAS_DB_NAME = "your-atlas-db-name";
```
If you are using OpenAI embeddings for this guide, you'll need to 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](https://docs.smith.langchain.com/) API key by uncommenting below:
```typescript theme={null}
// process.env.LANGSMITH_TRACING="true"
// process.env.LANGSMITH_API_KEY="your-api-key"
```
## Instantiation
Once you've set up your cluster as shown above, you can initialize your vector store as follows:
```typescript theme={null}
import { MongoDBAtlasVectorSearch } from "@langchain/mongodb";
import { OpenAIEmbeddings } from "@langchain/openai";
import { MongoClient } from "mongodb";
const client = new MongoClient(process.env.MONGODB_ATLAS_URI || "");
const collection = client.db(process.env.MONGODB_ATLAS_DB_NAME)
.collection(process.env.MONGODB_ATLAS_COLLECTION_NAME);
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-small",
});
const vectorStore = new MongoDBAtlasVectorSearch(embeddings, {
collection: collection,
indexName: "vector_index", // The name of the Atlas search index. Defaults to "default"
textKey: "text", // The name of the collection field containing the raw content. Defaults to "text"
embeddingKey: "embedding", // The name of the collection field containing the embedded text. Defaults to "embedding"
});
```
## Manage vector store
### Add items to vector store
You can now add documents to your 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"] });
```
```output theme={null}
[ '1', '2', '3', '4' ]
```
**Note:** After adding documents, there is a slight delay before they become queryable.
Adding a document with the same `id` as an existing document will update the existing one.
### 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}
const similaritySearchResults = await vectorStore.similaritySearch("biology", 2);
for (const doc of similaritySearchResults) {
console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
```
```output theme={null}
* The powerhouse of the cell is the mitochondria [{"_id":"1","source":"https://example.com"}]
* Mitochondria are made out of lipids [{"_id":"3","source":"https://example.com"}]
```
### Filtering
MongoDB Atlas supports pre-filtering of results on other fields. They require you to define which metadata fields you plan to filter on by updating the index you created initially. Here's an example:
```json theme={null}
{
"fields": [
{
"numDimensions": 1024,
"path": "embedding",
"similarity": "euclidean",
"type": "vector"
},
{
"path": "source",
"type": "filter"
}
]
}
```
Above, the first item in `fields` is the vector index, and the second item is the metadata property you want to filter on. The name of the property is the value of the `path` key. So the above index would allow us to search on a metadata field named `source`.
Then, in your code you can use [MQL Query Operators](https://www.mongodb.com/docs/manual/reference/operator/query/) for filtering.
The below example illustrates this:
```typescript theme={null}
const filter = {
preFilter: {
source: {
$eq: "https://example.com",
},
},
}
const filteredResults = await vectorStore.similaritySearch("biology", 2, filter);
for (const doc of filteredResults) {
console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}
```
```output theme={null}
* The powerhouse of the cell is the mitochondria [{"_id":"1","source":"https://example.com"}]
* Mitochondria are made out of lipids [{"_id":"3","source":"https://example.com"}]
```
### Returning scores
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)}]`);
}
```
```output theme={null}
* [SIM=0.374] The powerhouse of the cell is the mitochondria [{"_id":"1","source":"https://example.com"}]
* [SIM=0.370] Mitochondria are made out of lipids [{"_id":"3","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");
```
```output theme={null}
[
Document {
pageContent: 'The powerhouse of the cell is the mitochondria',
metadata: { _id: '1', source: 'https://example.com' },
id: undefined
},
Document {
pageContent: 'Mitochondria are made out of lipids',
metadata: { _id: '3', 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)
## Closing connections
Make sure you close the client instance when you are finished to avoid excessive resource consumption:
```typescript theme={null}
await client.close();
```
***
## API reference
For detailed documentation of all `MongoDBAtlasVectorSearch` features and configurations head to the [API reference](https://api.js.langchain.com/classes/langchain_mongodb.MongoDBAtlasVectorSearch.html).
***
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