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docs/docs/providers/vector_io/remote_mongodb.mdx
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docs/docs/providers/vector_io/remote_mongodb.mdx
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---
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description: |
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[MongoDB Atlas](https://www.mongodb.com/products/platform/atlas-vector-search) is a remote vector database provider for Llama Stack. It
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uses MongoDB Atlas Vector Search to store and query vectors in the cloud.
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That means you get enterprise-grade vector search with MongoDB's scalability and reliability.
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## Features
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- Cloud-native vector search with MongoDB Atlas
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- Fully integrated with Llama Stack
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- Enterprise-grade security and scalability
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- Supports multiple search modes: vector, keyword, and hybrid search
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- Built-in metadata filtering and text search capabilities
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- Automatic index management
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## Search Modes
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MongoDB Atlas Vector Search supports three different search modes:
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### Vector Search
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Vector search uses MongoDB's `$vectorSearch` aggregation stage to perform semantic similarity search using embedding vectors.
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```python
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# Vector search example
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="What is machine learning?",
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search_mode="vector",
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max_num_results=5,
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)
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```
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### Keyword Search
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Keyword search uses MongoDB's text search capabilities with full-text indexes to find chunks containing specific terms.
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```python
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# Keyword search example
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="Python programming language",
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search_mode="keyword",
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max_num_results=5,
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)
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```
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### Hybrid Search
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Hybrid search combines both vector and keyword search methods using configurable reranking algorithms.
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```python
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# Hybrid search with RRF ranker (default)
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="neural networks in Python",
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search_mode="hybrid",
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max_num_results=5,
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)
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# Hybrid search with weighted ranker
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="neural networks in Python",
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search_mode="hybrid",
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max_num_results=5,
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ranking_options={
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"ranker": {
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"type": "weighted",
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"alpha": 0.7, # 70% vector search, 30% keyword search
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}
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},
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)
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```
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## Usage
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To use MongoDB Atlas in your Llama Stack project, follow these steps:
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1. Create a MongoDB Atlas cluster with Vector Search enabled
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2. Install the necessary dependencies
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3. Configure your Llama Stack project to use MongoDB
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4. Start storing and querying vectors
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## Configuration
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### Environment Variables
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Set up the following environment variable for your MongoDB Atlas connection:
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```bash
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export MONGODB_CONNECTION_STRING="mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority&appName=llama-stack"
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```
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### Configuration Example
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```yaml
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vector_io:
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- provider_id: mongodb_atlas
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provider_type: remote::mongodb
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config:
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connection_string: "${env.MONGODB_CONNECTION_STRING}"
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database_name: "llama_stack"
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index_name: "vector_index"
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similarity_metric: "cosine"
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```
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## Installation
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You can install the MongoDB Python driver using pip:
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```bash
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pip install pymongo
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```
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## Documentation
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See [MongoDB Atlas Vector Search documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/) for more details about MongoDB Atlas Vector Search.
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For general MongoDB documentation, visit [MongoDB Documentation](https://docs.mongodb.com/).
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sidebar_label: Remote - Mongodb
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title: remote::mongodb
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---
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# remote::mongodb
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## Description
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[MongoDB Atlas](https://www.mongodb.com/products/platform/atlas-vector-search) is a remote vector database provider for Llama Stack. It
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uses MongoDB Atlas Vector Search to store and query vectors in the cloud.
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That means you get enterprise-grade vector search with MongoDB's scalability and reliability.
|
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|
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## Features
|
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|
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- Cloud-native vector search with MongoDB Atlas
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- Fully integrated with Llama Stack
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- Enterprise-grade security and scalability
|
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- Supports multiple search modes: vector, keyword, and hybrid search
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- Built-in metadata filtering and text search capabilities
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- Automatic index management
|
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|
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## Search Modes
|
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|
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MongoDB Atlas Vector Search supports three different search modes:
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|
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### Vector Search
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Vector search uses MongoDB's `$vectorSearch` aggregation stage to perform semantic similarity search using embedding vectors.
|
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|
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```python
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# Vector search example
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="What is machine learning?",
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search_mode="vector",
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max_num_results=5,
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)
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```
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### Keyword Search
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Keyword search uses MongoDB's text search capabilities with full-text indexes to find chunks containing specific terms.
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|
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```python
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# Keyword search example
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="Python programming language",
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search_mode="keyword",
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max_num_results=5,
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)
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```
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|
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### Hybrid Search
|
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Hybrid search combines both vector and keyword search methods using configurable reranking algorithms.
|
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|
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```python
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# Hybrid search with RRF ranker (default)
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="neural networks in Python",
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search_mode="hybrid",
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max_num_results=5,
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)
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|
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# Hybrid search with weighted ranker
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search_response = client.vector_stores.search(
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vector_store_id=vector_store.id,
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query="neural networks in Python",
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search_mode="hybrid",
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max_num_results=5,
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ranking_options={
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"ranker": {
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"type": "weighted",
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"alpha": 0.7, # 70% vector search, 30% keyword search
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}
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},
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)
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```
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## Usage
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|
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To use MongoDB Atlas in your Llama Stack project, follow these steps:
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|
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1. Create a MongoDB Atlas cluster with Vector Search enabled
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2. Install the necessary dependencies
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3. Configure your Llama Stack project to use MongoDB
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4. Start storing and querying vectors
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## Configuration
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### Environment Variables
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Set up the following environment variable for your MongoDB Atlas connection:
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```bash
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export MONGODB_CONNECTION_STRING="mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority&appName=llama-stack"
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```
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### Configuration Example
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```yaml
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vector_io:
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- provider_id: mongodb_atlas
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provider_type: remote::mongodb
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config:
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connection_string: "${env.MONGODB_CONNECTION_STRING}"
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database_name: "llama_stack"
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index_name: "vector_index"
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similarity_metric: "cosine"
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```
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## Installation
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You can install the MongoDB Python driver using pip:
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```bash
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pip install pymongo
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```
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## Documentation
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See [MongoDB Atlas Vector Search documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/) for more details about MongoDB Atlas Vector Search.
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For general MongoDB documentation, visit [MongoDB Documentation](https://docs.mongodb.com/).
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `connection_string` | `str \| None` | No | | MongoDB connection string (e.g., mongodb://user:pass@localhost:27017/ or mongodb+srv://user:pass@cluster.mongodb.net/) |
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| `host` | `str \| None` | No | | MongoDB host (used if connection_string is not provided) |
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| `port` | `int \| None` | No | | MongoDB port (used if connection_string is not provided) |
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| `username` | `str \| None` | No | | MongoDB username (used if connection_string is not provided) |
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| `password` | `str \| None` | No | | MongoDB password (used if connection_string is not provided) |
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| `database_name` | `<class 'str'>` | No | llama_stack | Database name to use for vector collections |
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| `index_name` | `<class 'str'>` | No | vector_index | Name of the vector search index |
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| `path_field` | `<class 'str'>` | No | embedding | Field name for storing embeddings |
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| `similarity_metric` | `<class 'str'>` | No | cosine | Similarity metric: cosine, euclidean, or dotProduct |
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| `max_pool_size` | `<class 'int'>` | No | 100 | Maximum connection pool size |
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| `timeout_ms` | `<class 'int'>` | No | 30000 | Connection timeout in milliseconds |
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| `persistence` | `llama_stack.core.storage.datatypes.KVStoreReference \| None` | No | | Config for KV store backend for metadata storage |
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## Sample Configuration
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```yaml
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connection_string: ${env.MONGODB_CONNECTION_STRING:=}
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host: ${env.MONGODB_HOST:=localhost}
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port: ${env.MONGODB_PORT:=27017}
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username: ${env.MONGODB_USERNAME:=}
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password: ${env.MONGODB_PASSWORD:=}
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database_name: ${env.MONGODB_DATABASE_NAME:=llama_stack}
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index_name: ${env.MONGODB_INDEX_NAME:=vector_index}
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path_field: ${env.MONGODB_PATH_FIELD:=embedding}
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similarity_metric: ${env.MONGODB_SIMILARITY_METRIC:=cosine}
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max_pool_size: ${env.MONGODB_MAX_POOL_SIZE:=100}
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timeout_ms: ${env.MONGODB_TIMEOUT_MS:=30000}
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persistence:
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namespace: vector_io::mongodb_atlas
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backend: kv_default
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```
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