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Prerequisites

1

Prepare Your Query and Codebase

Define your user request and collect the files from your codebase that need to be ranked for relevance.
const userQuery = "Add a user profile component with avatar and edit functionality";
const codebaseFiles = [
  { filename: "src/components/UserProfile.tsx", content: "..." },
  { filename: "src/types/user.ts", content: "..." },
  { filename: "src/components/Header.tsx", content: "..." },
  // ... more files
];
2

Call the Code Reranker API

Send your query and codebase to the reranker to get relevance scores for each file.
const url = "https://ranker.endpoint.relace.run/v2/code/rank";
const apiKey = "[YOUR_API_KEY]";

const headers = {
  "Authorization": `Bearer ${apiKey}`,
  "Content-Type": "application/json"
};

const data = {
  query: userQuery,
  codebase: codebaseFiles,
  token_limit: 150000,  // Set to your model's context limit with buffer room for system prompt
};

const response = await fetch(url, {
  method: "POST",
  headers: headers,
  body: JSON.stringify(data)
});

const rankedFiles = await response.json();
3

Parse Ranked Results from Response

The API returns files ranked by relevance with scores between 0 and 1 up to the token limit you set.
[
  {
    "filename": "src/components/UserProfile.tsx",
    "score": 0.9598
  },
  {
    "filename": "src/types/user.ts",
    "score": 0.0321
  },
  {
    "filename": "src/components/Header.tsx",
    "score": 0.0014
  }
]
We recommend additionally filtering out results with low relevance scores. See the agent tool definition or workflow guide for specific recommendations based on your system.
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