> ## Documentation Index
> Fetch the complete documentation index at: https://docs.negentro.tech/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

> Configure dense vector representations for text, code, and multimodal entities across local and cloud models.

## Supported Embedding Models

| Provider | Model | Dimensions | Cost / Deployment |
| :- | :- | :- | :- |
| OpenAI | text-embedding-3-small | 1536 | Cloud API (Fast & cost-efficient) |
| OpenAI | text-embedding-3-large | 3072 | Cloud API (Maximum accuracy) |
| FastEmbed | BAAI/bge-small-en-v1.5 | 384 | Local ONNX runtime (Zero API cost) |
| Cohere | embed-english-v3.0 | 1024 | Cloud API (High quality search) |
| Ollama | nomic-embed-text | 768 | Local Ollama instance |

## Zero-Cost Local FastEmbed

```python fastembed_config.py theme={null}
from negentro import Memory

m = Memory.from_config({
    "embedder": {
        "provider": "fastembed",
        "config": {
            "model": "BAAI/bge-small-en-v1.5"
        }
    }
})
```


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