# MongoDBAtlasDocumentStore
Use the MongoDB database as the document store for storing data your pipelines can query.
***
## Basic Information
- Used with `MongoDBAtlasEmbeddingRetriever`
- Type: `haystack_integrations.document_stores.mongodb_atlas.document_store.MongoDBAtlasDocumentStore`
## Overview
For details, see [MongoDB documentation](https://www.mongodb.com/docs/atlas/) and [Haystack documentation](https://docs.haystack.deepset.ai/docs/mongodbatlasdocumentstore).
## Authorization
You must have an Atlas account. For details on setting it up, see [MongoDB documentation](https://www.mongodb.com/docs/atlas/getting-started/?tck=atlas_learn).
To connect to the MongoDB database, you must provide a connection string in the format `"mongodb+srv://{mongo_atlas_username}:{mongo_atlas_password}@{mongo_atlas_host}/?{mongo_atlas_params_string}"`. For detailed instructions on how to obtain it, see [Create a Connection String](https://www.mongodb.com/docs/mongoid/current/quick-start-sinatra/create-a-connection-string/) in MongoDB documentation.
Once you have your connection string, connect MongoDB to on the Integrations page:
1. Log in to .
2. Click your profile icon in the top right corner and choose *Settings*.
3. Go to *Workspace>Integrations*, find MongoDB, and click **Connect** next to it.
4. Paste your MongoDB connection string and click **Connect**.
## Usage
To configure MongoDB as the document store, you need:
- The name of the database to use.
- The name of the collection to use. This collection must have a vector search index set up on the `embedding` field and a full text search index.
- The name of the vector search index to use for vector search. You can create a vector search index on your collection in the Atlas web user interface.
**Important**: Your MongoDB vector search index must have an `embedding` field of type `knnVector` defined, for example:
```python
const index = {
name: "vector_index",
type: "vectorSearch",
definition: {
"fields": [
{
"type": "knnVector",
"path": "embedding",
"similarity": "cosine",
"numDimensions": 768
}
]
}
}
```
This allows you to use embedding retrieval with the MongoDB document store.
- The name of the full text search index. You create a full text search index on your collection in the Altas user interface.
You must create both a vector search index and a full text search index on your Atlas collection, one for embeddings and one for text. Vector search index is used for embedding-based similarity queries, while the full-text index is used for keyword queries. This way, you can use hybrid search out of the box.
For details, see [MongoDB documentation](https://www.mongodb.com/docs/compass/current/indexes/create-vector-search-index/).
### Writing Data to MongoDB
To write the preprocessed files into the MongoDB document store:
1. Add `DocumentWriter` to your index.
2. Add `MongoDBAtlasDocumentStore` and configure the required parameters on the component card. You must provide:
- `database_name`: The name of your MongoDB database.
- `collection_name`: The name of your collection.
- `vector_search_index`: The name of the vector search index created on your collection. Make sure the index has an `embedding` field of type `knnVector` defined.
3. Connect `MongoDBAtlasDocumentStore` to `DocumentWriter`.
### Retrieving Files From MongoDB
To retrieve files from the MongoDB document store and use them for search:
1. Add a MongoDB Atlas Retriever your query pipeline.
2. Add `MongoDBAtlasDocumentStore` and configure the required parameters on the component card. You must provide:
- `database_name`: The name of your MongoDB database.
- `collection_name`: The name of your collection.
- `vector_search_index`: The name of the vector search index created on your collection. Make sure the index has an `embedding` field of type `knnVector` defined.
3. Connect `MongoDBAtlasDocumentStore` to the Retriever.
### Examples
This is how you connect the document store to writer:
When you switch to YAML, you can see that the document store is a parameter of `DocumentWriter` and that's where you can configure it as well:
```yaml
writer:
type: haystack.components.writers.document_writer.DocumentWriter
init_parameters:
document_store:
type: haystack_integrations.document_stores.mongodb_atlas.document_store.MongoDBAtlasDocumentStore #document store configuration
init_parameters:
mongo_connection_string:
type: env_var
env_vars:
- MONGO_CONNECTION_STRING
strict: false
database_name: myDatabase
collection_name: myCollection
vector_search_index: vectorIndex
full_text_search_index: fullTextIndex
policy: OVERWRITE
```
## Init Parameters
| Parameter | Type | Possible values | Description |
|--------------------------|---------|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `mongo_connection_string` | Secret | | The connection string to connect with MongoDB Atlas. You can obtain it by clicking the **CONNECT** button on the MongoDB Atlas Dashboard. For details, see the _Authorization_ section above.
Required. |
| `database_name` | String | | The name of your MongoDB Atlas database.
Required. |
| `collection_name` | String | | The name of the MongoDB Atlas collection you want to use. To use a collection for vector search, it must have a vector search index configured on the `embedding` field. For details, see the _Usage_ section above.
Required. |
| `vector_search_index` | String | | The name of the MongoDB Atlas vector search index to use for embedding retrieval. Create a vector search index in the Atlas dashboard. For details, see the _Usage_ section above.
Required. |
| `full_text_search_index` | String | | The name of the text search index to use for keyword retrieval. Create the index in your MongoDB Atlas dashboard.
Required. |