Mem0 supports LangChain as a provider to access a wide range of embedding models. LangChain is a framework for developing applications powered by language models, making it easy to integrate various embedding providers through a consistent interface.
For a complete list of available embedding models supported by LangChain, refer to the LangChain Text Embedding documentation.
Usage
import os
from mem0 import Memory
from langchain_openai import OpenAIEmbeddings
# Set necessary environment variables for your chosen LangChain provider
os.environ["OPENAI_API_KEY"] = "your-api-key"
# Initialize a LangChain embeddings model directly
openai_embeddings = OpenAIEmbeddings(
model="text-embedding-3-small",
dimensions=1536
)
# Pass the initialized model to the config
config = {
"embedder": {
"provider": "langchain",
"config": {
"model": openai_embeddings
}
}
}
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="alice", metadata={"category": "movies"})
Supported LangChain Embedding Providers
LangChain supports a wide range of embedding providers, including:
- OpenAI (
OpenAIEmbeddings
)
- Cohere (
CohereEmbeddings
)
- Google (
VertexAIEmbeddings
)
- Hugging Face (
HuggingFaceEmbeddings
)
- Sentence Transformers (
HuggingFaceEmbeddings
)
- Azure OpenAI (
AzureOpenAIEmbeddings
)
- Ollama (
OllamaEmbeddings
)
- Together (
TogetherEmbeddings
)
- And many more
You can use any of these model instances directly in your configuration. For a complete and up-to-date list of available embedding providers, refer to the LangChain Text Embedding documentation.
Provider-Specific Configuration
When using LangChain as an embedder provider, you’ll need to:
- Set the appropriate environment variables for your chosen embedding provider
- Import and initialize the specific model class you want to use
- Pass the initialized model instance to the config
Examples with Different Providers
Make sure to install the necessary LangChain packages and any provider-specific dependencies.
Config
All available parameters for the langchain
embedder config are present in Master List of All Params in Config.