Documentation Index
Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt
Use this file to discover all available pages before exploring further.
You can use embedding models from Huggingface to run Mem0 locally.
Usage
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 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")
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
Config
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 |
