chore: merge origin/main into mintlify/af82bda4 and resolve conflicts
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mintlify[bot]
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---
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title: Configurations
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description: "Reference for Mem0 embedder configuration in Python and TypeScript, covering provider selection, model names, API keys, and dimension settings."
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title: "Embedder Configuration Reference"
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sidebarTitle: "Configurations"
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description: "Reference for embedder configuration options in Mem0, including provider selection and model settings."
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---
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@@ -97,4 +98,4 @@ Here's a comprehensive list of all parameters that can be used across different
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## Supported Embedding Models
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For detailed information on configuring specific embedders, please visit the [Embedding Models](./models) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
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For detailed information on configuring specific embedders, please visit the [Embedding Models](./overview) section. There you'll find information for each supported embedder with provider-specific usage examples and configuration details.
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---
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title: AWS Bedrock
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description: "Use AWS Bedrock as a Mem0 embedder with Amazon Titan or Cohere models, boto3 or aws-sdk auth, and configurable vector output dimensions."
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title: "AWS Bedrock as Embedding Provider"
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sidebarTitle: "AWS Bedrock"
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description: "Configure AWS Bedrock as an embedding provider in Mem0 with IAM credentials and boto3 authentication."
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---
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To use AWS Bedrock embedding models, you need the appropriate AWS credentials and permissions. Python uses `boto3`, and TypeScript uses `@aws-sdk/client-bedrock-runtime`.
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---
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title: Azure OpenAI
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description: "Set up Azure OpenAI embeddings in Mem0 with deployment name, endpoint, API version, and optional Azure Identity role-based authentication."
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title: "Azure OpenAI as Embedding Provider"
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sidebarTitle: "Azure OpenAI"
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description: "Configure Azure OpenAI as an embedding provider in Mem0 with API key, deployment, and endpoint settings."
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---
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To use Azure OpenAI embedding models, set the `EMBEDDING_AZURE_OPENAI_API_KEY`, `EMBEDDING_AZURE_DEPLOYMENT`, `EMBEDDING_AZURE_ENDPOINT` and `EMBEDDING_AZURE_API_VERSION` environment variables. You can obtain the Azure OpenAI API key from the Azure Portal.
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---
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title: Google AI
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description: "Use Google AI Gemini embedding models in Mem0 with the GOOGLE_API_KEY, including gemini-embedding-001 and configurable output dimensionality."
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title: "Google AI as Embedding Provider"
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sidebarTitle: "Google AI"
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description: "Configure Google AI as an embedding provider in Mem0 using Gemini models and the GOOGLE_API_KEY variable."
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---
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To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
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@@ -99,6 +99,7 @@ Here are the parameters available for configuring the Hugging Face embedder:
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| `embedding_dims` | Dimensions of the embedding model | `selected_model_dimensions` |
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| `model_kwargs` | Additional arguments for the model | `None` |
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| `huggingface_base_url` | URL to connect to Text Embeddings Inference (TEI) API | `None` |
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| `api_key` | API key for the endpoint; falls back to the `HUGGINGFACE_API_KEY` env var. Only used on the `huggingface_base_url` path | `"hf"` |
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</Tab>
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<Tab title="TypeScript">
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| Parameter | Description | Default Value |
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---
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title: LangChain
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description: "Plug LangChain embedding classes into Mem0 to reuse OpenAI, Cohere, HuggingFace, or Ollama embedding models through a single unified interface."
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title: "LangChain as Embedding Provider"
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sidebarTitle: "LangChain"
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description: "Use LangChain as an embedding provider in Mem0 to access a wide range of models through a unified interface."
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---
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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.
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---
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title: "LM Studio"
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description: "Run LM Studio locally as a Mem0 embedder using GGUF models like nomic-embed-text through the built-in OpenAI-compatible HTTP server."
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title: "LM Studio as Embedding Provider"
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sidebarTitle: "LM Studio"
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description: "Configure LM Studio as an embedding provider in Mem0 for local embedding generation with models like nomic-embed-text."
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---
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You can use embedding models from LM Studio to run Mem0 locally.
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---
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title: "Ollama"
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description: "Serve Ollama embedding models like nomic-embed-text or mxbai-embed-large inside Mem0 for private, offline vector generation on your own machine."
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title: "Ollama as Embedding Provider"
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sidebarTitle: "Ollama"
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description: "Configure Ollama as an embedding provider in Mem0 to generate embeddings locally using open-source models."
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---
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You can use embedding models from Ollama to run Mem0 locally.
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---
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title: OpenAI
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description: "Use OpenAI embedding models such as text-embedding-3-small and text-embedding-3-large inside Mem0 with the OPENAI_API_KEY environment variable."
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title: "OpenAI as Embedding Provider"
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sidebarTitle: "OpenAI"
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description: "Configure OpenAI as an embedding provider in Mem0 using models like text-embedding-3-large for vector generation."
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---
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To use OpenAI embedding models, set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
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---
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title: Together
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description: "Use Together AI in Mem0 with the multilingual-e5-large-instruct embedding model at 1024 dimensions for cross-language semantic search."
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title: "Together AI as Embedding Provider"
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sidebarTitle: "Together"
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description: "Configure Together AI as an embedding provider in Mem0 with support for 1024-dimensional embedding models."
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---
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To use Together embedding models, set the `TOGETHER_API_KEY` environment variable. You can obtain the Together API key from the [Together Platform](https://api.together.ai/settings/projects/~current/api-keys).
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---
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title: Overview
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description: "Browse Mem0 embedder providers including OpenAI, Azure, Ollama, Hugging Face, Vertex AI, Bedrock, Together, LM Studio, FastEmbed, and LangChain."
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title: "Embedding Providers Overview"
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sidebarTitle: Overview
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description: "Overview of all supported embedding model providers in Mem0, including OpenAI, Azure, Ollama, and more."
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---
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Mem0 offers support for various embedding models, allowing users to choose the one that best suits their needs.
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@@ -15,15 +16,15 @@ See the list of supported embedders below.
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<CardGroup cols={4}>
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<Card title="OpenAI" icon="/images/provider-icons/openai.svg" href="/components/embedders/models/openai"></Card>
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<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure_openai"></Card>
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<Card title="Azure OpenAI" icon="/images/provider-icons/azure-color.svg" href="/components/embedders/models/azure-openai"></Card>
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<Card title="Ollama" icon="/images/provider-icons/ollama.svg" href="/components/embedders/models/ollama"></Card>
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<Card title="Hugging Face" icon="/images/provider-icons/huggingface.svg" href="/components/embedders/models/huggingface"></Card>
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<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google_AI"></Card>
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<Card title="Google AI" icon="/images/provider-icons/google-color.svg" href="/components/embedders/models/google-ai"></Card>
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<Card title="Vertex AI" icon="/images/provider-icons/vertexai.svg" href="/components/embedders/models/vertexai"></Card>
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<Card title="Together" icon="/images/provider-icons/together-color.svg" href="/components/embedders/models/together"></Card>
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<Card title="LM Studio" icon="/images/provider-icons/lmstudio.svg" href="/components/embedders/models/lmstudio"></Card>
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<Card title="Langchain" icon="/images/provider-icons/langchain-color.svg" href="/components/embedders/models/langchain"></Card>
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<Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws_bedrock"></Card>
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<Card title="AWS Bedrock" icon="/images/provider-icons/bedrock-color.svg" href="/components/embedders/models/aws-bedrock"></Card>
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<Card title="FastEmbed" icon="/images/provider-icons/qdrant.svg" href="/components/embedders/models/fastembed"></Card>
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</CardGroup>
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