You can use embedding models from Huggingface to run Mem0 locally.
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
config = {
"embedder": {
"provider": "huggingface",
"config": {
"model": "multi-qa-MiniLM-L6-cos-v1"
}
}
}
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": "I'm 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="john")
Using Text Embeddings Inference (TEI)
You can also use Hugging Face’s Text Embeddings Inference service for faster and more efficient embeddings:
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM
# Using HuggingFace Text Embeddings Inference API
config = {
"embedder": {
"provider": "huggingface",
"config": {
"huggingface_base_url": "http://localhost:3000/v1"
}
}
}
m = Memory.from_config(config)
m.add("This text will be embedded using the TEI service.", user_id="john")
To run the TEI service, you can use Docker:
docker run -d -p 3000:80 -v huggingfacetei:/data --platform linux/amd64 \
ghcr.io/huggingface/text-embeddings-inference:cpu-1.6 \
--model-id BAAI/bge-small-en-v1.5
Here are the parameters available for configuring Huggingface embedder:
| Parameter | Description | Default Value |
|---|
model | The name of the model to use | multi-qa-MiniLM-L6-cos-v1 |
embedding_dims | Dimensions of the embedding model | selected_model_dimensions |
model_kwargs | Additional arguments for the model | None |
huggingface_base_url | URL to connect to Text Embeddings Inference (TEI) API | None |
