Overview

Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.

Supported Embedders

See the list of supported embedders below.

<Note> All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **FastEmbed**, **Google AI**, **Langchain**, **LM Studio**, **Ollama**, and **Together**. </Note> <CardGroup cols={4}> <Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/embedders/models/openai"></Card> <Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure_openai"></Card> <Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/embedders/models/ollama"></Card> <Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/embedders/models/huggingface"></Card> <Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google_AI"></Card> <Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/embedders/models/vertexai"></Card> <Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/embedders/models/together"></Card> <Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/embedders/models/lmstudio"></Card> <Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/embedders/models/langchain"></Card> <Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws_bedrock"></Card> <Card title="FastEmbed" icon="/images/provider-icons/qdrant.svg" href="/components/embedders/models/fastembed"></Card> </CardGroup>

Usage

To utilize an embedding model, you must provide a configuration to customize its usage. If no configuration is supplied, a default configuration will be applied, and OpenAI will be used as the embedding model.

For a comprehensive list of available parameters for embedding model configuration, please refer to Config.