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Create embeddings ​

Text embedding vectors, OpenAI-compatible.

POST/v1/embeddings
Request
bash
curl http://localhost:8080/v1/embeddings \
  -H "Authorization: Bearer $LLM_PROXY_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "text-embedding-model", "input": ["hello", "world"]}'
Request — OpenAI SDK
python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="sk-your-proxy-key")
emb = client.embeddings.create(model="text-embedding-model", input=["hello", "world"])
print(len(emb.data[0].embedding))
Response
json
{
  "object": "list",
  "data": [
    {"object": "embedding", "index": 0, "embedding": [0.012, -0.031, "…"]},
    {"object": "embedding", "index": 1, "embedding": [0.008, 0.044, "…"]}
  ],
  "model": "text-embedding-model",
  "usage": {"prompt_tokens": 4, "total_tokens": 4}
}

Request fields ​

model (required), input (string, list of strings, or token-id forms), encoding_format (float default, or base64), dimensions, user.

  • Upstream, only model, input, dimensions, and encoding_format are forwarded (the user field is parsed but not forwarded by the default builder).
  • Gemini sends multi-item input as a single batchEmbedContents call, falling back to per-item embedContent calls only on HTTP 400/401/403/404/405.

Response ​

{"object":"list","data":[{"object":"embedding","embedding":…,"index":…}], "model": <alias>} plus usage when the provider reports it. base64 passes through as returned. No streaming variant.

Provider support ​

openai, the openai-compatible family (including DeepSeek, Kimi, MiniMax, Moonshot, Qwen, xAI, vLLM, SGLang, Chutes, Mistral, NanoGPT, OpenRouter), gemini, and ollama — see the capability matrix.

Request
bash
curl http://localhost:8080/v1/embeddings \
  -H "Authorization: Bearer $LLM_PROXY_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "text-embedding-model", "input": ["hello", "world"]}'
Request — OpenAI SDK
python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8080/v1", api_key="sk-your-proxy-key")
emb = client.embeddings.create(model="text-embedding-model", input=["hello", "world"])
print(len(emb.data[0].embedding))
Response
json
{
  "object": "list",
  "data": [
    {"object": "embedding", "index": 0, "embedding": [0.012, -0.031, "…"]},
    {"object": "embedding", "index": 1, "embedding": [0.008, 0.044, "…"]}
  ],
  "model": "text-embedding-model",
  "usage": {"prompt_tokens": 4, "total_tokens": 4}
}

MIT licensed

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