> For the complete documentation index, see [llms.txt](https://docs.parasail.io/parasail-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.parasail.io/parasail-docs/use-cases/rag-embeddings.md).

# RAG and Embeddings

Build retrieval-augmented generation (RAG) pipelines and vector search systems with Parasail embeddings.

Build retrieval-augmented generation (RAG) pipelines, semantic search, and document similarity systems. Parasail supports leading open-source embedding models that rival proprietary alternatives.

## Embedding models

| Model                 | HuggingFace ID                      | Notes                                              |
| --------------------- | ----------------------------------- | -------------------------------------------------- |
| GritLM                | `parasail-ai/GritLM-7B-vllm`        | Developed by Contextual, rivals proprietary models |
| GTE-Qwen2-7B-Instruct | `Alibaba-NLP/gte-Qwen2-7B-instruct` | From Alibaba, strong general-purpose embeddings    |

Any embedding model on Hugging Face can be used with a Dedicated Instance.

## Generate embeddings

### Serverless

```python
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>"
)

response = client.embeddings.create(
    model="parasail-ai/GritLM-7B-vllm",
    input="Parasail provides affordable cloud GPUs for AI inference.",
    encoding_format="base64"
)

print(response.data[0].embedding[:5])
```

Use `encoding_format="base64"` to reduce output sizes when you don't need floating-point arrays directly.

## Relevant guides

* [RAG guide](/parasail-docs/guides/rag.md)—step-by-step RAG implementation
* [Model selection](/parasail-docs/guides/model-recommendations.md#evaluate-candidates)—includes embedding model evaluation

## API reference

* [Embeddings API](/parasail-docs/api-reference/embeddings.md)