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my-project-public — gitlawb

#1	[Weaviate](https://weaviate.io/) is an open-source vector search engine. It allows efficient storage and retrieval of high-dimensional vector embeddings, enabling powerful search and retrieval capabilities.
#2
#3
#4	### Installation
#5	```bash
#6	pip install weaviate weaviate-client
#7	```
#8
#9	### Usage
#10
#11	```python Python
#12	import os
#13	from mem0 import Memory
#14
#15	os.environ["OPENAI_API_KEY"] = "sk-xx"
#16
#17	config = {
#18	"vector_store": {
#19	"provider": "weaviate",
#20	"config": {
#21	"collection_name": "test",
#22	"cluster_url": "http://localhost:8080",
#23	"auth_client_secret": None,
#24	}
#25	}
#26	}
#27
#28	m = Memory.from_config(config)
#29	messages = [
#30	{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
#31	{"role": "assistant", "content": "How about a thriller movie? They can be quite engaging."},
#32	{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
#33	{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
#34	]
#35	m.add(messages, user_id="alice", metadata={"category": "movies"})
#36	```
#37
#38	### Config
#39
#40	Here are the parameters available for configuring Weaviate:
#41
#42	\| Parameter \| Description \| Default Value \|
#43	\| --- \| --- \| --- \|
#44	\| `collection_name` \| The name of the collection to store the vectors \| `mem0` \|
#45	\| `embedding_model_dims` \| Dimensions of the embedding model \| `1536` \|
#46	\| `cluster_url` \| URL for the Weaviate server \| `None` \|
#47	\| `auth_client_secret` \| API key for Weaviate authentication \| `None` \|

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