cognee/docs/ko/setup-configuration/embedding-providers.md

Embedding Providers

Configure embedding providers for semantic search in Cognee

Embedding providers convert text into vector representations that enable semantic search. These vectors capture the meaning of text, allowing Cognee to find conceptually related content even when the wording is different.

**New to configuration?**

See the Setup Configuration Overview for the complete workflow:

install extras → create .env → choose providers → handle pruning.

Supported Providers

Cognee supports multiple embedding providers:

• OpenAI — Text embedding models via OpenAI API (default)
• Azure OpenAI — Text embedding models via Azure OpenAI Service
• Google Gemini — Embedding models via Google AI
• Mistral — Embedding models via Mistral AI
• AWS Bedrock — Embedding models via AWS Bedrock
• Ollama — Local embedding models via Ollama
• LM Studio — Local embedding models via LM Studio
• Fastembed — CPU-friendly local embeddings
• Custom — OpenAI-compatible embedding endpoints

**LLM/Embedding Configuration**: If you configure only LLM or only embeddings, the other defaults to OpenAI. Ensure you have a working OpenAI API key, or configure both LLM and embeddings to avoid unexpected defaults.

Configuration

Set these environment variables in your `.env` file:

• EMBEDDING_PROVIDER — The provider to use (openai, gemini, mistral, ollama, fastembed, custom)
• EMBEDDING_MODEL — The specific embedding model to use
• EMBEDDING_DIMENSIONS — The vector dimension size (must match your vector store)
• EMBEDDING_API_KEY — Your API key (falls back to LLM_API_KEY if not set)
• EMBEDDING_ENDPOINT — Custom endpoint URL (for Azure, Ollama, or custom providers)
• EMBEDDING_API_VERSION — API version (for Azure OpenAI)
• EMBEDDING_MAX_TOKENS — Maximum tokens per request (optional)

Provider Setup Guides

OpenAI provides high-quality embeddings with good performance.

```dotenv  theme={null}
EMBEDDING_PROVIDER="openai"
EMBEDDING_MODEL="openai/text-embedding-3-large"
EMBEDDING_DIMENSIONS="3072"
# Optional
# EMBEDDING_API_KEY=sk-...   # falls back to LLM_API_KEY if omitted
# EMBEDDING_ENDPOINT=https://api.openai.com/v1
# EMBEDDING_API_VERSION=
# EMBEDDING_MAX_TOKENS=8191
```

Use Azure OpenAI Service for embeddings with your own deployment.

```dotenv  theme={null}
EMBEDDING_PROVIDER="openai"
EMBEDDING_MODEL="azure/text-embedding-3-large"
EMBEDDING_ENDPOINT="https://<your-az>.cognitiveservices.azure.com/openai/deployments/text-embedding-3-large"
EMBEDDING_API_KEY="az-..."
EMBEDDING_API_VERSION="2023-05-15"
EMBEDDING_DIMENSIONS="3072"
```

Use Google's embedding models for semantic search.

```dotenv  theme={null}
EMBEDDING_PROVIDER="gemini"
EMBEDDING_MODEL="gemini/text-embedding-004"
EMBEDDING_API_KEY="AIza..."
EMBEDDING_DIMENSIONS="768"
```

Use Mistral's embedding models for high-quality vector representations.

```dotenv  theme={null}
EMBEDDING_PROVIDER="mistral"
EMBEDDING_MODEL="mistral/mistral-embed"
EMBEDDING_API_KEY="sk-mis-..."
EMBEDDING_DIMENSIONS="1024"
```

**Installation**: Install the required dependency:

```bash  theme={null}
pip install mistral-common[sentencepiece]
```

Use embedding models provided by the AWS Bedrock service.

```dotenv  theme={null}
EMBEDDING_PROVIDER="bedrock"
EMBEDDING_MODEL="<your_model_name>"
EMBEDDING_DIMENSIONS="<dimensions_of_the_model>"
EMBEDDING_API_KEY="<your_api_key>"
EMBEDDING_MAX_TOKENS="<max_tokens_of_your_model>"
```

Run embedding models locally with Ollama for privacy and cost control.

```dotenv  theme={null}
EMBEDDING_PROVIDER="ollama"
EMBEDDING_MODEL="nomic-embed-text:latest"
EMBEDDING_ENDPOINT="http://localhost:11434/api/embed"
EMBEDDING_DIMENSIONS="768"
HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5"
```

**Installation**: Install Ollama from [ollama.ai](https://ollama.ai) and pull your desired embedding model:

```bash  theme={null}
ollama pull nomic-embed-text:latest
```

Run embedding models locally with LM Studio for privacy and cost control.

```dotenv  theme={null}
EMBEDDING_PROVIDER="custom"
EMBEDDING_MODEL="lm_studio/text-embedding-nomic-embed-text-1.5"
EMBEDDING_ENDPOINT="http://127.0.0.1:1234/v1"
EMBEDDING_API_KEY="."
EMBEDDING_DIMENSIONS="768"
```

**Installation**: Install LM Studio from [lmstudio.ai](https://lmstudio.ai/) and download your desired model from
LM Studio's interface.
Load your model, start the LM Studio server, and Cognee will be able to connect to it.

Use Fastembed for CPU-friendly local embeddings without GPU requirements.

```dotenv  theme={null}
EMBEDDING_PROVIDER="fastembed"
EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2"
EMBEDDING_DIMENSIONS="384"
```

**Installation**: Fastembed is included by default with Cognee.

**Known Issues**:

* As of September 2025, Fastembed requires Python \< 3.13 (not compatible with Python 3.13+)

Use OpenAI-compatible embedding endpoints from other providers.

```dotenv  theme={null}
EMBEDDING_PROVIDER="custom"
EMBEDDING_MODEL="provider/your-embedding-model"
EMBEDDING_ENDPOINT="https://your-endpoint.example.com/v1"
EMBEDDING_API_KEY="provider-..."
EMBEDDING_DIMENSIONS="<match-your-model>"
```

Advanced Options

```dotenv theme={null} EMBEDDING_RATE_LIMIT_ENABLED="true" EMBEDDING_RATE_LIMIT_REQUESTS="10" EMBEDDING_RATE_LIMIT_INTERVAL="5" ``` ```dotenv theme={null} # Mock embeddings for testing (returns zero vectors) MOCK_EMBEDDING="true" ```

Important Notes

• Dimension Consistency: EMBEDDING_DIMENSIONS must match your vector store collection schema
• API Key Fallback: If EMBEDDING_API_KEY is not set, Cognee uses LLM_API_KEY (except for custom providers)
• Tokenization: For Ollama and Hugging Face models, set HUGGINGFACE_TOKENIZER for proper token counting
• Performance: Local providers (Ollama, Fastembed) are slower but offer privacy and cost benefits

Configure LLM providers for text generation Set up vector databases for embedding storage Return to setup configuration overview

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To find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt