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t6_mem0/docs/components/vectordbs/dbs/mongodb.mdx
Fabian Valle a0cd4065d9 +MongoDB Vector Support (#2367)
Co-authored-by: Divya Gupta <divya.gupta@mongodb.com>
2025-06-14 17:57:06 +05:30

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# 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
```python
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",
"user": "my-user",
"password": "my-password",
}
}
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "user", "content": "Im 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"})
```
## Config
Here are the parameters available for configuring MongoDB:
| Parameter | Description | Default Value |
| --- | --- | --- |
| db_name | Name of the MongoDB database | `"mem0_db"` |
| collection_name | Name of the MongoDB collection | `"mem0_collection"` |
| embedding_model_dims | Dimensions of the embedding vectors | `1536` |
| user | MongoDB user for authentication | `None` |
| password | Password for the MongoDB user | `None` |
| host | MongoDB host | `"localhost"` |
| port | MongoDB port | `27017` |
> **Note**: `user` and `password` must either be provided together or omitted together.