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 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