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Documentation Index

Fetch the complete documentation index at: https://docs.lemondata.cc/llms.txt

Use this file to discover all available pages before exploring further.

For coding agents, discover the current recommended embedding shortlist first with GET /v1/models?recommended_for=embedding, then send the selected model explicitly to this endpoint.

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Request Body

Synchronous request timeout: This non-chat endpoint waits for the routed model to finish. Large inputs, long audio, or large batches can exceed common 30s client defaults, so set your HTTP client timeout to at least 120s.
​
model
string
required
ID of the embedding model to use (e.g., text-embedding-3-small).
​
input
string | array
required
Input text to embed. Can be a string or array of strings.
​
encoding_format
string
default:"float"
Format for the embeddings: float or base64.
​
dimensions
integer
Number of dimensions for the output (model-specific).
​
user
string
A unique identifier representing your end-user for abuse monitoring.

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Available Models

ModelDimensionsDescription
text-embedding-3-large3072Best quality
text-embedding-3-small1536Balanced
text-embedding-ada-0021536Legacy

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Response

​
object
string
Always list.
​
data
array
Array of embedding objects.Each object contains:
  • object (string): embedding
  • index (integer): Index in the input array
  • embedding (array): The embedding vector
​
model
string
Model used.
​
usage
object
Token usage with prompt_tokens and total_tokens.
curl -X POST "https://api.lemondata.cc/v1/embeddings" \
  -H "Authorization: Bearer sk-your-api-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog"
  }'

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0194, 0.0081, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}

​
Batch Embeddings

# Embed multiple texts at once
response = client.embeddings.create(
    model="text-embedding-3-small",
    input=[
        "First document text",
        "Second document text",
        "Third document text"
    ]
)

for i, data in enumerate(response.data):
    print(f"Document {i}: {len(data.embedding)} dimensions")