bge-reranker-base

Text Classification • BAAI

@cf/baai/bge-reranker-base

  • Cloudflare-hosted

Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.

Model Info
Unit Pricing $0.00311 per M input tokens

Usage

export interface Env {
    AI: Ai;
}

export default {
    async fetch(request, env): Promise<Response> {
        const query = 'Which one is cooler?'
        const contexts = [
            {
                text: 'a cyberpunk lizzard'
            },
            {
                text: 'a cyberpunk cat'
            }
        ];

        const response = await env.AI.run('@cf/baai/bge-reranker-base', { query, contexts });

        return Response.json(response);
    },
} satisfies ExportedHandler<Env>;

import os
import requests

ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")

response = requests.post(
  f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/baai/bge-reranker-base",
    headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
    json={
      "query": "Which one is better?",
      "contexts": [
        {"text": "a cyberpunk lizzard"},
            {"text": "a cyberpunk car"},
      ]
    }
)
result = response.json()
print(result)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/baai/bge-reranker-base \
  -X POST \
  -H "Authorization: Bearer $CLOUDFLARE_AUTH_TOKEN" \
  -d '{ "query": "Which one is better?", "contexts": [{ "text": "a cyberpunk lizzard" }, {"text": "a cyberpunk cat"}]}'

Parameters

query

stringrequiredminLength: 1A query you wish to perform against the provided contexts.

top_k

integerminimum: 1Number of returned results starting with the best score.

▶contexts[]

arrayrequiredList of provided contexts. Note that the index in this array is important, as the response will refer to it.

▶response[]

array

API Schemas (Raw)

Input Open Download

Output Open Download

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description: BAAI general embedding (Small) model that transforms any given text into a 384-dimensional vector title: bge-small-en-v1.5 image: https://developers.cloudflare.com/og-docs.png

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