Create embeddings

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High-throughput, OpenAI-compatible text embedding & reranker powered by Infinity

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1. Quickstart
2. Endpoint Configuration
3. API Specification
   1. List Models
   2. Create Embeddings
   3. Rerank Documents
4. Usage
5. Further Documentation
6. Acknowledgements

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Quickstart

1. 🐳 Pull an image – use the tag shown on the latest GitHub release page (e.g. runpod/worker-infinity-embedding:<version>)
2. 🔧 Configure – set at least MODEL_NAMES (see Endpoint Configuration)
3. 🚀 Deploy – create a RunPod Serverless endpoint
4. 🧪 Call the API – follow the example in the Usage section

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Endpoint Configuration

All behaviour is controlled through environment variables:

Variable	Required	Default	Description
MODEL_NAMES	Yes	—	One or more Hugging-Face model IDs. Separate multiple IDs with a semicolon. Example: BAAI/bge-small-en-v1.5
BATCH_SIZES	No	32	Per-model batch size; semicolon-separated list matching MODEL_NAMES.
BACKEND	No	torch	Inference engine for all models: torch, optimum, or ctranslate2.
DTYPES	No	auto	Precision per model (auto, fp16, fp8). Semicolon-separated, must match MODEL_NAMES.
INFINITY_QUEUE_SIZE	No	48000	Max items queueable inside the Infinity engine.
RUNPOD_MAX_CONCURRENCY	No	300	Max concurrent requests the RunPod wrapper will accept.

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API Specification

Two flavours, one schema.

• OpenAI-compatible – drop-in replacement for /v1/models, /v1/embeddings, so you can use this endpoint instead of the API from OpenAI by replacing the base url with the URL of your endpoint: https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1 and use your API key from RunPod instead of the one from OpenAI
• Standard RunPod – call /run or /runsync with a JSON body under the input key.
  Base URL: https://api.runpod.ai/v2/<ENDPOINT_ID>

Except for transport (path + wrapper object) the JSON you send/receive is identical. The tables below describe the shared payload.

List Models

Method	Path	Body
GET	/openai/v1/models	–
POST	/runsync	{ "input": { "openai_route": "/v1/models" } }

Response

{
  "data": [
    { "id": "BAAI/bge-small-en-v1.5", "stats": {} },
    { "id": "intfloat/e5-large-v2", "stats": {} }
  ]
}

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Create Embeddings

Request Fields (shared)

Field	Type	Required	Description
model	string	Yes	One of the IDs supplied via MODEL_NAMES.
input	string | array	Yes	A single text string or list of texts to embed.

OpenAI route vs. Standard:

Flavour	Method	Path	Body
OpenAI	POST	/v1/embeddings	{ "model": "…", "input": "…" }
Standard	POST	/runsync	{ "input": { "model": "…", "input": "…" } }

Response (both flavours)

{
  "object": "list",
  "model": "BAAI/bge-small-en-v1.5",
  "data": [
    { "object": "embedding", "embedding": [0.01, -0.02 /* … */], "index": 0 }
  ],
  "usage": { "prompt_tokens": 2, "total_tokens": 2 }
}

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Rerank Documents (Standard only)

Field	Type	Required	Description
model	string	Yes	Any deployed reranker model
query	string	Yes	The search/query text
docs	array	Yes	List of documents to rerank
return_docs	bool	No	If true, return the documents in ranked order (default false)

Call pattern

POST /runsync
Content-Type: application/json

{
  "input": {
    "model": "BAAI/bge-reranker-large",
    "query": "Which product has warranty coverage?",
    "docs": [
      "Product A comes with a 2-year warranty",
      "Product B is available in red and blue colors",
      "All electronics include a standard 1-year warranty"
    ],
    "return_docs": true
  }
}

Response contains either scores or the full docs list, depending on return_docs.

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Usage

Below are minimal curl snippets so you can copy-paste from any machine.

Replace <ENDPOINT_ID> with your endpoint ID and <API_KEY> with a RunPod API key.

OpenAI-Compatible Calls

# List models
curl -H "Authorization: Bearer <API_KEY>" \
     https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1/models

# Create embeddings
curl -X POST \
  -H "Authorization: Bearer <API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{"model":"BAAI/bge-small-en-v1.5","input":"Hello world"}' \
  https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1/embeddings

Standard RunPod Calls

# Create embeddings (wait for result)
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{"input":{"model":"BAAI/bge-small-en-v1.5","input":"Hello world"}}' \
  https://api.runpod.ai/v2/<ENDPOINT_ID>/runsync

# Rerank
curl -X POST \
  -H "Content-Type: application/json" \
  -d '{"input":{"model":"BAAI/bge-reranker-large","query":"Which product has warranty coverage?","docs":["Product A comes with a 2-year warranty","Product B is available in red and blue colors","All electronics include a standard 1-year warranty"],"return_docs":true}}' \
  https://api.runpod.ai/v2/<ENDPOINT_ID>/runsync

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Further Documentation

• Infinity Engine – how the ultra-fast backend works.
• RunPod Docs – serverless concepts, limits, and API reference.

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Acknowledgements

Special thanks to Michael Feil for creating the Infinity engine and for his ongoing support of this project.