> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# MongoDB

> Use MongoDB as a vector database in Mem0 with built-in vector search for high-dimensional similarity queries.

# MongoDB

[MongoDB](https://www.mongodb.com/) is a versatile document database that supports vector search capabilities, allowing for efficient high-dimensional similarity searches over large datasets with robust scalability and performance.

## Usage

<CodeGroup>
  ```python Python theme={null}
  import os
  from mem0 import Memory

  os.environ["OPENAI_API_KEY"] = "sk-xx"

  config = {
      "vector_store": {
          "provider": "mongodb",
          "config": {
              "db_name": "mem0-db",
              "collection_name": "mem0-collection",
              "mongo_uri": "mongodb://username:password@localhost:27017"
          }
      }
  }

  m = Memory.from_config(config)
  messages = [
      {
          "role": "user",
          "content": "I'm planning to watch a movie tonight. Any recommendations?",
      },
      {
          "role": "assistant",
          "content": "How about thriller movies? They can be quite engaging.",
      },
      {
          "role": "user",
          "content": "I’m not a big fan of thriller movies but I love sci-fi movies.",
      },
      {
          "role": "assistant",
          "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.",
      },
  ]

  m.add(messages, user_id="alice", metadata={"category": "movies"})
  ```

  ```typescript TypeScript theme={null}
  import { Memory } from "mem0ai/oss";

  const config = {
    vectorStore: {
      provider: "mongodb",
      config: {
        dbName: "mem0-db",
        collectionName: "mem0-collection",
        url: "mongodb://username:password@localhost:27017",
      },
    },
  };

  const memory = new Memory(config);

  const messages = [
    {
      role: "user",
      content: "I'm planning to watch a movie tonight. Any recommendations?",
    },
    {
      role: "assistant",
      content: "How about thriller movies? They can be quite engaging.",
    },
    {
      role: "user",
      content: "I’m not a big fan of thriller movies but I love sci-fi movies.",
    },
    {
      role: "assistant",
      content:
        "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future.",
    },
  ];

  await memory.add(messages, {
    userId: "alice",
    metadata: {
      category: "movies",
    },
  });
  ```
</CodeGroup>

## Config

Here are the parameters available for configuring MongoDB:

| Python                 | TypeScript         | Description                         | Default Value             |
| ---------------------- | ------------------ | ----------------------------------- | ------------------------- |
| db\_name               | dbName             | Name of the MongoDB database        | "mem0\_db"                |
| collection\_name       | collectionName     | Name of the MongoDB collection      | "mem0"                    |
| embedding\_model\_dims | embeddingModelDims | Dimensions of the embedding vectors | 1536                      |
| mongo\_uri             | url                | The MongoDB URI connection string   | mongodb://localhost:27017 |

> **Note**: If `mongo_uri` (Python) or `url` (TypeScript) is not provided, it defaults to `mongodb://localhost:27017`. A local instance must be running MongoDB v8.2+ for vector search to work.

> **Note**: The vector search index builds asynchronously after the first write. A search issued right after the first `add()` may return no results (and log an "index not initialized" message) until the index finishes building. This takes a few seconds on a local deployment and up to about a minute on Atlas. This is expected; the search returns results once the index is ready.