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feat: mongodb vector io #3772
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@@ -0,0 +1,268 @@ | ||
--- | ||
description: | | ||
[MongoDB Atlas](https://www.mongodb.com/products/platform/atlas-vector-search) is a remote vector database provider for Llama Stack. It | ||
uses MongoDB Atlas Vector Search to store and query vectors in the cloud. | ||
That means you get enterprise-grade vector search with MongoDB's scalability and reliability. | ||
## Features | ||
- Cloud-native vector search with MongoDB Atlas | ||
- Fully integrated with Llama Stack | ||
- Enterprise-grade security and scalability | ||
- Supports multiple search modes: vector, keyword, and hybrid search | ||
- Built-in metadata filtering and text search capabilities | ||
- Automatic index management | ||
## Search Modes | ||
MongoDB Atlas Vector Search supports three different search modes: | ||
### Vector Search | ||
Vector search uses MongoDB's `$vectorSearch` aggregation stage to perform semantic similarity search using embedding vectors. | ||
```python | ||
# Vector search example | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="What is machine learning?", | ||
search_mode="vector", | ||
max_num_results=5, | ||
) | ||
``` | ||
### Keyword Search | ||
Keyword search uses MongoDB's text search capabilities with full-text indexes to find chunks containing specific terms. | ||
```python | ||
# Keyword search example | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="Python programming language", | ||
search_mode="keyword", | ||
max_num_results=5, | ||
) | ||
``` | ||
### Hybrid Search | ||
Hybrid search combines both vector and keyword search methods using configurable reranking algorithms. | ||
```python | ||
# Hybrid search with RRF ranker (default) | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="neural networks in Python", | ||
search_mode="hybrid", | ||
max_num_results=5, | ||
) | ||
# Hybrid search with weighted ranker | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="neural networks in Python", | ||
search_mode="hybrid", | ||
max_num_results=5, | ||
ranking_options={ | ||
"ranker": { | ||
"type": "weighted", | ||
"alpha": 0.7, # 70% vector search, 30% keyword search | ||
} | ||
}, | ||
) | ||
``` | ||
## Usage | ||
To use MongoDB Atlas in your Llama Stack project, follow these steps: | ||
1. Create a MongoDB Atlas cluster with Vector Search enabled | ||
2. Install the necessary dependencies | ||
3. Configure your Llama Stack project to use MongoDB | ||
4. Start storing and querying vectors | ||
## Configuration | ||
### Environment Variables | ||
Set up the following environment variable for your MongoDB Atlas connection: | ||
```bash | ||
export MONGODB_CONNECTION_STRING="mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority&appName=llama-stack" | ||
``` | ||
### Configuration Example | ||
```yaml | ||
vector_io: | ||
- provider_id: mongodb_atlas | ||
provider_type: remote::mongodb | ||
config: | ||
connection_string: "${env.MONGODB_CONNECTION_STRING}" | ||
database_name: "llama_stack" | ||
index_name: "vector_index" | ||
similarity_metric: "cosine" | ||
``` | ||
## Installation | ||
You can install the MongoDB Python driver using pip: | ||
```bash | ||
pip install pymongo | ||
``` | ||
## Documentation | ||
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. | ||
For general MongoDB documentation, visit [MongoDB Documentation](https://docs.mongodb.com/). | ||
sidebar_label: Remote - Mongodb | ||
title: remote::mongodb | ||
--- | ||
|
||
# remote::mongodb | ||
|
||
## Description | ||
|
||
|
||
[MongoDB Atlas](https://www.mongodb.com/products/platform/atlas-vector-search) is a remote vector database provider for Llama Stack. It | ||
uses MongoDB Atlas Vector Search to store and query vectors in the cloud. | ||
That means you get enterprise-grade vector search with MongoDB's scalability and reliability. | ||
|
||
## Features | ||
|
||
- Cloud-native vector search with MongoDB Atlas | ||
- Fully integrated with Llama Stack | ||
- Enterprise-grade security and scalability | ||
- Supports multiple search modes: vector, keyword, and hybrid search | ||
- Built-in metadata filtering and text search capabilities | ||
- Automatic index management | ||
|
||
## Search Modes | ||
|
||
MongoDB Atlas Vector Search supports three different search modes: | ||
|
||
### Vector Search | ||
Vector search uses MongoDB's `$vectorSearch` aggregation stage to perform semantic similarity search using embedding vectors. | ||
|
||
```python | ||
# Vector search example | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="What is machine learning?", | ||
search_mode="vector", | ||
max_num_results=5, | ||
) | ||
``` | ||
|
||
### Keyword Search | ||
Keyword search uses MongoDB's text search capabilities with full-text indexes to find chunks containing specific terms. | ||
|
||
```python | ||
# Keyword search example | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="Python programming language", | ||
search_mode="keyword", | ||
max_num_results=5, | ||
) | ||
``` | ||
|
||
### Hybrid Search | ||
Hybrid search combines both vector and keyword search methods using configurable reranking algorithms. | ||
|
||
```python | ||
# Hybrid search with RRF ranker (default) | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="neural networks in Python", | ||
search_mode="hybrid", | ||
max_num_results=5, | ||
) | ||
|
||
# Hybrid search with weighted ranker | ||
search_response = client.vector_stores.search( | ||
vector_store_id=vector_store.id, | ||
query="neural networks in Python", | ||
search_mode="hybrid", | ||
max_num_results=5, | ||
ranking_options={ | ||
"ranker": { | ||
"type": "weighted", | ||
"alpha": 0.7, # 70% vector search, 30% keyword search | ||
} | ||
}, | ||
) | ||
``` | ||
|
||
## Usage | ||
|
||
To use MongoDB Atlas in your Llama Stack project, follow these steps: | ||
|
||
1. Create a MongoDB Atlas cluster with Vector Search enabled | ||
2. Install the necessary dependencies | ||
3. Configure your Llama Stack project to use MongoDB | ||
4. Start storing and querying vectors | ||
|
||
## Configuration | ||
|
||
### Environment Variables | ||
Set up the following environment variable for your MongoDB Atlas connection: | ||
|
||
```bash | ||
export MONGODB_CONNECTION_STRING="mongodb+srv://username:password@cluster.mongodb.net/?retryWrites=true&w=majority&appName=llama-stack" | ||
``` | ||
|
||
### Configuration Example | ||
|
||
```yaml | ||
vector_io: | ||
- provider_id: mongodb_atlas | ||
provider_type: remote::mongodb | ||
config: | ||
connection_string: "${env.MONGODB_CONNECTION_STRING}" | ||
database_name: "llama_stack" | ||
index_name: "vector_index" | ||
similarity_metric: "cosine" | ||
``` | ||
## Installation | ||
You can install the MongoDB Python driver using pip: | ||
```bash | ||
pip install pymongo | ||
``` | ||
|
||
## Documentation | ||
|
||
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. | ||
|
||
For general MongoDB documentation, visit [MongoDB Documentation](https://docs.mongodb.com/). | ||
|
||
|
||
## Configuration | ||
|
||
| Field | Type | Required | Default | Description | | ||
|-------|------|----------|---------|-------------| | ||
| `connection_string` | `<class 'str'>` | No | | MongoDB Atlas connection string (e.g., mongodb+srv://user:pass@cluster.mongodb.net/) | | ||
| `database_name` | `<class 'str'>` | No | llama_stack | Database name to use for vector collections | | ||
| `index_name` | `<class 'str'>` | No | vector_index | Name of the vector search index | | ||
| `path_field` | `<class 'str'>` | No | embedding | Field name for storing embeddings | | ||
| `similarity_metric` | `<class 'str'>` | No | cosine | Similarity metric: cosine, euclidean, or dotProduct | | ||
| `max_pool_size` | `<class 'int'>` | No | 100 | Maximum connection pool size | | ||
| `timeout_ms` | `<class 'int'>` | No | 30000 | Connection timeout in milliseconds | | ||
| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | Config for KV store backend for metadata storage | | ||
|
||
## Sample Configuration | ||
|
||
```yaml | ||
connection_string: ${env.MONGODB_CONNECTION_STRING:=} | ||
database_name: ${env.MONGODB_DATABASE_NAME:=llama_stack} | ||
index_name: ${env.MONGODB_INDEX_NAME:=vector_index} | ||
path_field: ${env.MONGODB_PATH_FIELD:=embedding} | ||
similarity_metric: ${env.MONGODB_SIMILARITY_METRIC:=cosine} | ||
max_pool_size: ${env.MONGODB_MAX_POOL_SIZE:=100} | ||
timeout_ms: ${env.MONGODB_TIMEOUT_MS:=30000} | ||
kvstore: | ||
type: sqlite | ||
db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/mongodb_registry.db | ||
``` |
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|
@@ -25,6 +25,7 @@ distribution_spec: | |
- provider_type: inline::milvus | ||
- provider_type: remote::chromadb | ||
- provider_type: remote::pgvector | ||
- provider_type: remote::mongodb | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. you need to also add an image that'll run MongoDB and expose the vector search functionality. There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. you'll need to add this to the |
||
files: | ||
- provider_type: inline::localfs | ||
safety: | ||
|
Original file line number | Diff line number | Diff line change |
---|---|---|
|
@@ -28,6 +28,9 @@ | |
) | ||
from llama_stack.providers.registry.inference import available_providers | ||
from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig | ||
from llama_stack.providers.remote.vector_io.mongodb.config import ( | ||
MongoDBVectorIOConfig, | ||
) | ||
from llama_stack.providers.remote.vector_io.pgvector.config import ( | ||
PGVectorVectorIOConfig, | ||
) | ||
|
@@ -113,6 +116,7 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate: | |
BuildProvider(provider_type="inline::milvus"), | ||
BuildProvider(provider_type="remote::chromadb"), | ||
BuildProvider(provider_type="remote::pgvector"), | ||
BuildProvider(provider_type="remote::mongodb"), | ||
], | ||
"files": [BuildProvider(provider_type="inline::localfs")], | ||
"safety": [ | ||
|
@@ -222,6 +226,13 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate: | |
password="${env.PGVECTOR_PASSWORD:=}", | ||
), | ||
), | ||
Provider( | ||
provider_id="${env.MONGODB_CONNECTION_STRING:+mongodb_atlas}", | ||
provider_type="remote::mongodb", | ||
config=MongoDBVectorIOConfig.sample_run_config( | ||
f"~/.llama/distributions/{name}", | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. you probably want to add the other configuration parameters. |
||
), | ||
), | ||
], | ||
"files": [files_provider], | ||
}, | ||
|
@@ -295,5 +306,13 @@ def get_distribution_template(name: str = "starter") -> DistributionTemplate: | |
"azure", | ||
"Azure API Type", | ||
), | ||
"MONGODB_CONNECTION_STRING": ( | ||
"", | ||
"MongoDB Atlas connection string (e.g., mongodb+srv://user:pass@cluster.mongodb.net/)", | ||
), | ||
"MONGODB_DATABASE_NAME": ( | ||
"llama_stack", | ||
"MongoDB database name", | ||
), | ||
}, | ||
) |
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i think part of this readme is duplicated.
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I believe the
# Usage
and# Configuration
sections are duplicated.