LM Studio - Mem0

Documentation Index

Fetch the complete documentation index at: /llms.txt

Use this file to discover all available pages before exploring further.

You can use embedding models from LM Studio to run Mem0 locally.

Usage

import os
from mem0 import Memory

os.environ["OPENAI_API_KEY"] = "your_api_key" # For LLM

config = {
    "embedder": {
        "provider": "lmstudio",
        "config": {
            "model": "nomic-ai/nomic-embed-text-v1.5-GGUF"
        }
    }
}

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")

Config

Here are the parameters available for configuring LM Studio embedder:

Parameter Description Default Value
model The name of the LM Studio model to use nomic-ai/nomic-embed-text-v1.5-GGUF
embedding_dims Dimensions of the embedding model 1536
lmstudio_base_url Base URL for LM Studio connection http://localhost:1234/v1