Paired with bge-m3 for two-stage retrieval: recall with embeddings, precision with this reranker.
POST /v1/chat/completions model: "baai/bge-reranker-base"
{
"model": "baai/bge-reranker-base",
"input": "Text to embed, or an array of strings for batch."
}{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0123, -0.0456, 0.0789, /* ... */]
}
],
"model": "baai/bge-reranker-base",
"usage": { "prompt_tokens": 5, "total_tokens": 5 }
}from openai import OpenAI client = OpenAI(base_url="https://api.aigateway.sh/v1", api_key="sk-aig-...") r = client.embeddings.create(model="baai/bge-reranker-base", input="hello world") print(r.data[0].embedding[:5])