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my-project-public — gitlawb

#1	# MongoDB
#2
#3	[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.
#4
#5	## Usage
#6
#7	```python
#8	import os
#9	from mem0 import Memory
#10
#11	os.environ["OPENAI_API_KEY"] = "sk-xx"
#12
#13	config = {
#14	"vector_store": {
#15	"provider": "mongodb",
#16	"config": {
#17	"db_name": "mem0-db",
#18	"collection_name": "mem0-collection",
#19	"mongo_uri":"mongodb://username:password@localhost:27017"
#20	}
#21	}
#22	}
#23
#24	m = Memory.from_config(config)
#25	messages = [
#26	{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
#27	{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
#28	{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
#29	{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
#30	]
#31	m.add(messages, user_id="alice", metadata={"category": "movies"})
#32	```
#33
#34	## Config
#35
#36	Here are the parameters available for configuring MongoDB:
#37
#38	\| Parameter \| Description \| Default Value \|
#39	\| --- \| --- \| --- \|
#40	\| db_name \| Name of the MongoDB database \| `"mem0_db"` \|
#41	\| collection_name \| Name of the MongoDB collection \| `"mem0_collection"` \|
#42	\| embedding_model_dims \| Dimensions of the embedding vectors \| `1536` \|
#43	\| mongo_uri \| The MongoDB URI connection string \| `mongodb://username:password@localhost:27017` \|
#44
#45	> Note: If `mongo_uri` is not provided, it will default to `mongodb://username:password@localhost:27017`.
#46

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