NousResearch/atropos
1
0
Fork 0
mirror of https://github.com/NousResearch/atropos.git synced 2026-04-24 17:04:55 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Move to subfolder

Browse source
This commit is contained in:
Eric Liu 2025-05-21 16:18:01 -07:00
parent a88e3afddf
commit 7eae51cc5c
23 changed files with 0 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

76
environments/hack0/server/llm/llm_feedback.ts
View file

@ -1,76 +0,0 @@
/**
* Placeholder for LLM-based move aggression/brilliance scoring.
* Will use OpenAI GPT-4o-mini via callOpenAI in the future.
*/
import { callOpenAI } from "./openai_client";
export async function scoreMoveAggression(
fenHistory: string[],
moveIdx: number,
moveSAN: string,
): Promise<string> {
const prompt = `Given the following chess game FEN history (one FEN per move):\n${fenHistory.map((f, i) => `${i + 1}: ${f}`).join("\n")}\nEvaluate the aggression or brilliance of move #${moveIdx + 1} (${moveSAN}). Respond ONLY with a single digit from 1 (not aggressive) to 10 (extremely aggressive/brilliant). Be fast and concise.`;
const response = await callOpenAI(
[
{
role: "system",
content:
"You are a chess grandmaster evaluating move aggression and brilliance. Respond as quickly and concisely as possible.",
},
{ role: "user", content: prompt },
],
16,
);
return response;
}
/**
* Placeholder for LLM-based sacrifice justification.
* Will use OpenAI GPT-4o-mini via callOpenAI in the future.
*/
export async function justifySacrifice(
fenHistory: string[],
moveIdx: number,
moveSAN: string,
): Promise<string> {
const prompt = `Given the following chess game FEN history (one FEN per move):\n${fenHistory.map((f, i) => `${i + 1}: ${f}`).join("\n")}\nWas the sacrifice in move #${moveIdx + 1} (${moveSAN}) justified? Reply in 1 short sentence: is the sacrifice justified or not, and why. Be fast and concise.`;
const response = await callOpenAI(
[
{
role: "system",
content:
"You are a chess grandmaster evaluating sacrifices. Respond as quickly and concisely as possible.",
},
{ role: "user", content: prompt },
],
16,
);
return response;
}
export async function scoreAndJustifyGame(
fenHistory: string[],
moveSANs: string[],
): Promise<{ score: string; justification: string }[]> {
const prompt = `Given the following chess game FEN history (one FEN per move) and the corresponding SAN moves, evaluate each move for aggression/brilliance and sacrifice justification.\n\nFEN history (one per move):\n${fenHistory.map((f, i) => `${i + 1}: ${f}`).join("\n")}\n\nSAN moves (one per move):\n${moveSANs.map((san, i) => `${i + 1}: ${san}`).join("\n")}\n\nFor each move, respond with a JSON array of objects, each with:\n- score: a single digit from 1 (not aggressive) to 10 (extremely aggressive/brilliant)\n- justification: 1 short sentence on whether the move is a justified sacrifice or not, and why.\n\nExample:\n[{"score": "7", "justification": "The sacrifice is risky but justified."}, ...]\n\nRespond ONLY with the JSON array, nothing else.`;
const response = await callOpenAI(
[
{
role: "system",
content:
"You are a chess grandmaster evaluating a full game for aggression and sacrifice justification. Respond as quickly and concisely as possible.",
},
{ role: "user", content: prompt },
],
512,
);
try {
const parsed = JSON.parse(response);
if (Array.isArray(parsed)) {
return parsed;
}
throw new Error("Response is not an array");
} catch (e) {
throw new Error("Failed to parse LLM response as JSON: " + response);
}
}