RAG and 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

Any embedding model on HuggingFace can be used with Parasail's batch and dedicated APIs.

Generate embeddings

Serverless (low volume)

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])

Batch (large scale—50% off)

from openai_batch import Batch

with Batch() as batch:
    for i in range(100):
        batch.add_to_batch(
            model="parasail-ai/GritLM-7B-vllm",
            encoding_format="base64",
            input=f"This is input #{i}"
        )
    result, output_path, error_path = batch.submit_wait_download()

Parasail strongly recommends using encoding_format="base64" to reduce output file sizes.

Relevant guides

• RAG guide—step-by-step RAG implementation
• Model selection—includes embedding model evaluation

API reference

• Embeddings API
• Batch API—for large-scale embedding generation
• Batch embedding models

Next steps

• Batch quickstart—submit your first batch embedding job
• Batch full guide—comprehensive batch processing documentation