Embeddings

Embeddings API for generating vector representations of text using open-source models.

Generate vector embeddings for text using open-source embedding models. The API is OpenAI-compatible—use the same client.embeddings.create interface.

Endpoint

POST https://api.parasail.io/v1/embeddings

Authentication

Pass your API key in the Authorization header:

Authorization: Bearer <PARASAIL_API_KEY>

The OpenAI SDK reads this from the api_key argument. See Authentication for how to create and store a key.

Request

Python (OpenAI SDK)

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key=os.environ["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])

cURL

curl https://api.parasail.io/v1/embeddings \
  -H "Authorization: Bearer $PARASAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "parasail-ai/GritLM-7B-vllm",
    "input": "Parasail provides affordable cloud GPUs for AI inference.",
    "encoding_format": "float"
  }'

Parameters

Parameter	Type	Default	Description
model	string	required	Embedding model ID (for example, parasail-ai/GritLM-7B-vllm)
input	string or array	required	Text to embed. Pass an array to embed multiple inputs in one request
encoding_format	string	"float"	"float" or "base64" (recommended for smaller payloads)

These fields follow the OpenAI schema. For exhaustive field semantics, see the OpenAI embeddings reference.

Response

A successful request returns a list of embedding objects. With one input string, data contains a single embedding:

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0091, 0.0145, "..."]
    }
  ],
  "model": "parasail-ai/GritLM-7B-vllm",
  "usage": {
    "prompt_tokens": 12,
    "total_tokens": 12
  }
}

Field	Description
data[].embedding	The vector (array of floats, or a base64 string when encoding_format="base64")
data[].index	Position of this embedding in the input array
usage	Prompt and total token counts

When input is an array, data contains one object per input, ordered by index.

Available embedding models

Model	HuggingFace ID
GritLM	parasail-ai/GritLM-7B-vllm
GTE-Qwen2-7B-Instruct	Alibaba-NLP/gte-Qwen2-7B-instruct

Any embedding model on HuggingFace can be used with dedicated or batch endpoints.

Error responses

Errors use OpenAI-compatible status codes and a JSON body with an error object (message, type, code).

Status Code	Description
400	Malformed request—invalid JSON, missing input, or unsupported parameter value
401	Missing or invalid API key
403	API key lacks access to the requested model
404	Model or endpoint not found
429	Rate limit or quota exceeded—back off and retry
500	Internal server error—retry with exponential backoff

Tips

• Use encoding_format="base64" to reduce output file sizes, especially for batch processing.

• For large-scale embedding generation, use batch processing at 50% off serverless pricing.

Next steps

• RAG and embeddings use case—RAG pipeline guide

• Batch embedding models—batch embedding generation

• Model selection—embedding model evaluation

• Chat completions API—endpoint compatibility matrix and base URL