Convert text into numerical vectors for semantic search, similarity comparison, and retrieval-augmented generation (RAG).

Embeddings

POST /v1/embeddings

Request body

Parameter	Type	Description
`model`	string	Embedding model. Use `bge-m3`. Required
`input`	string or array	Text to embed. String or array of strings. Required
`encoding_format`	string	Output format: `float` (default) or `base64`.

Example request

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://router.appelon.ai/v1",
    api_key=os.environ["APPELON_API_KEY"]
)

response = client.embeddings.create(
    model="bge-m3",
    input="This is a sample text to embed."
)

embedding = response.data[0].embedding
print(f"Dimensions: {len(embedding)}")
# → Dimensions: 1024

Response

Returns an array of embedding objects, one for each input text.

{
  "object": "list",
  "model": "bge-m3",
  "data": [{
    "object": "embedding",
    "index": 0,
    "embedding": [-0.023, 0.017, 0.042, ...]
  }],
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Batch embeddings

Embed multiple texts in a single request for better efficiency.

response = client.embeddings.create(
    model="bge-m3",
    input=[
        "First document to embed",
        "Second document to embed",
        "Third document to embed"
    ]
)

# Returns 3 embeddings in response.data

Common use cases

Semantic search: Embed documents and queries, then find similar documents using cosine similarity.
RAG: Retrieve relevant context before generating responses with chat models.
Clustering & classification: Group similar documents or classify content based on embedding similarity.

BGE-M3 produces 1024-dimensional vectors optimized for multilingual retrieval. It supports 100+ languages including Dutch, English, German, and French.