diff --git a/.github/workflows/detect-duplicate.yml b/.github/workflows/detect-duplicate.yml new file mode 100644 index 00000000..bf8756a3 --- /dev/null +++ b/.github/workflows/detect-duplicate.yml @@ -0,0 +1,72 @@ +# yaml-language-server: $schema=https://json.schemastore.org/github-workflow.json +name: Duplicate Issue Detector + +on: + issues: + types: [opened] + +permissions: {} + +env: + EMBEDDING_MODEL: ${{ vars.EMBEDDING_MODEL }} + GROQ_MODEL: ${{ vars.GROQ_MODEL }} + GROQ_API_KEY: ${{ secrets.GROQ_API_KEY }} + +jobs: + detect-duplicate: + runs-on: ubuntu-24.04 + if: ${{ !github.event.issue.pull_request }} + permissions: + issues: write + actions: read + contents: read + + steps: + - name: Checkout repository + uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1 + + - name: Set up Node.js + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0 + with: + node-version-file: 'package.json' + + - name: Cache embedding model + uses: actions/cache@cdf6c1fa76f9f475f3d7449005a359c84ca0f306 # v5.0.3 + with: + path: ~/.cache/huggingface + key: hf-model-${{ vars.EMBEDDING_MODEL }} + + - name: Install dependencies + working-directory: bin/duplicate-detector + run: npm ci + + - name: Download issue index + uses: dawidd6/action-download-artifact@5c98f0b039f36ef966fdb7dfa9779262785ecb05 # v14 + with: + name: issue-index + workflow: rebuild-issue-index.yml + path: bin/duplicate-detector + search_artifacts: true + if_no_artifact_found: warn + + - name: Build index if missing + working-directory: bin/duplicate-detector + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_REPOSITORY: ${{ github.repository }} + INDEX_PATH: issue_index.json + run: | + if [ ! -f issue_index.json ]; then + echo "No index found — building from scratch..." + node build-index.mjs + fi + + - name: Detect duplicates + working-directory: bin/duplicate-detector + continue-on-error: true + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_REPOSITORY: ${{ github.repository }} + ISSUE_NUMBER: ${{ github.event.issue.number }} + INDEX_PATH: issue_index.json + run: node detect.mjs \ No newline at end of file diff --git a/.github/workflows/rebuild-issue-index.yml b/.github/workflows/rebuild-issue-index.yml new file mode 100644 index 00000000..5872fedf --- /dev/null +++ b/.github/workflows/rebuild-issue-index.yml @@ -0,0 +1,54 @@ +# yaml-language-server: $schema=https://json.schemastore.org/github-workflow.json +name: Rebuild Issue Index + +on: + schedule: + - cron: "0 3 * * *" + workflow_dispatch: + +permissions: {} + +env: + EMBEDDING_MODEL: ${{ vars.EMBEDDING_MODEL }} + +jobs: + build-index: + runs-on: ubuntu-24.04 + permissions: + issues: read + actions: write + contents: read + + steps: + - name: Checkout repository + uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1 + + - name: Set up Node.js + uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5 + with: + node-version-file: 'package.json' + + - name: Cache embedding model + uses: actions/cache@cdf6c1fa76f9f475f3d7449005a359c84ca0f306 # v5.0.3 + with: + path: ~/.cache/huggingface + key: hf-model-${{ vars.EMBEDDING_MODEL }} + + - name: Install dependencies + working-directory: bin/duplicate-detector + run: npm ci + + - name: Build issue index + working-directory: bin/duplicate-detector + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_REPOSITORY: ${{ github.repository }} + INDEX_PATH: issue_index.json + run: node build-index.mjs + + - name: Upload index artifact + uses: actions/upload-artifact@b7c566a772e6b6bfb58ed0dc250532a479d7789f # v6.0.0 + with: + name: issue-index + path: bin/duplicate-detector/issue_index.json + retention-days: 7 \ No newline at end of file diff --git a/bin/duplicate-detector/build-index.mjs b/bin/duplicate-detector/build-index.mjs new file mode 100644 index 00000000..6fb5c809 --- /dev/null +++ b/bin/duplicate-detector/build-index.mjs @@ -0,0 +1,120 @@ +#!/usr/bin/env node +/** + * Build Issue Embedding Index + * + * Fetches all open issues and recently closed ones, + * generates embeddings using a local ONNX transformer model, + * and saves them as a JSON artifact for the duplicate detector. + */ + +import { pipeline } from '@huggingface/transformers'; +import { mkdirSync, writeFileSync } from 'node:fs'; +import { dirname } from 'node:path'; +import { fetchIssues, issueText } from './utils.mjs'; + +const MODEL_NAME = process.env.EMBEDDING_MODEL || 'Xenova/all-MiniLM-L6-v2'; +const OUTPUT_PATH = 'issue_index.json'; +const INCLUDE_CLOSED_DAYS = 90; +const MAX_ISSUES = 5000; +const BATCH_SIZE = 64; + +async function main() { + console.log('Fetching open issues...'); + const openIssues = await fetchIssues({ + state: 'open', + maxIssues: MAX_ISSUES, + }); + console.log(`Fetched ${openIssues.length} open issues`); + + const since = new Date( + Date.now() - INCLUDE_CLOSED_DAYS * 24 * 60 * 60 * 1000 + ).toISOString(); + console.log( + `Fetching closed issues from last ${INCLUDE_CLOSED_DAYS} days...` + ); + + const closedIssues = await fetchIssues({ + state: 'closed', + since, + maxIssues: MAX_ISSUES, + }); + console.log(`Fetched ${closedIssues.length} closed issues`); + let allIssues = [...openIssues, ...closedIssues]; + + const seen = new Set(); + allIssues = allIssues.filter((issue) => { + if (seen.has(issue.number)) return false; + seen.add(issue.number); + return true; + }); + + console.log(`Total unique issues to index: ${allIssues.length}`); + + if (allIssues.length === 0) { + console.warn('No issues found - writing empty index'); + writeFileSync(OUTPUT_PATH, JSON.stringify({ issues: [], embeddings: [] })); + return; + } + + console.log(`Loading model: ${MODEL_NAME}`); + const extractor = await pipeline('feature-extraction', MODEL_NAME, { + dtype: 'fp32', + }); + + const texts = allIssues.map((issue) => issueText(issue.title, issue.body)); + const allEmbeddings = []; + + console.log(`Generating embeddings for ${texts.length} issues...`); + for (let i = 0; i < texts.length; i += BATCH_SIZE) { + const batch = texts.slice(i, i + BATCH_SIZE); + const output = await extractor(batch, { + pooling: 'mean', + normalize: true, + }); + + const vectors = output.tolist(); + allEmbeddings.push(...vectors); + + const progress = Math.min(i + BATCH_SIZE, texts.length); + console.log(` ${progress}/${texts.length}`); + } + + const issueMetadata = allIssues.map((issue) => { + const body = (issue.body || '').trim(); + return { + number: issue.number, + title: issue.title, + state: issue.state, + url: issue.html_url, + body_preview: body.slice(0, 500) || '', + labels: (issue.labels || []).map((l) => l.name), + created_at: issue.created_at, + updated_at: issue.updated_at, + }; + }); + + const indexData = { + issues: issueMetadata, + embeddings: allEmbeddings, + model: MODEL_NAME, + issue_count: issueMetadata.length, + built_at: new Date().toISOString(), + }; + + const dir = dirname(OUTPUT_PATH); + if (dir && dir !== '.') mkdirSync(dir, { recursive: true }); + writeFileSync(OUTPUT_PATH, JSON.stringify(indexData)); + + const sizeMb = ( + Buffer.byteLength(JSON.stringify(indexData)) / + (1024 * 1024) + ).toFixed(1); + console.log( + `Index saved to ${OUTPUT_PATH} (${sizeMb} MB, ${issueMetadata.length} issues)` + ); +} + +main().catch((err) => { + console.error(err); + process.exit(1); +}); diff --git a/bin/duplicate-detector/detect.mjs b/bin/duplicate-detector/detect.mjs new file mode 100644 index 00000000..1b002b4a --- /dev/null +++ b/bin/duplicate-detector/detect.mjs @@ -0,0 +1,274 @@ +#!/usr/bin/env node +/** + * Duplicate Issue Detector + * + * Triggered on new issue creation. Compares the new issue against an + * existing embedding index, then uses an LLM to + * confirm duplicates before posting a comment for maintainer review. + */ + +import { pipeline } from '@huggingface/transformers'; +import { existsSync, readFileSync } from 'node:fs'; +import { + addLabel, + dotProduct, + fetchIssues, + getIssue, + issueText, + postComment, +} from './utils.mjs'; + +const SIMILARITY_THRESHOLD = 0.55; +const TOP_K = 5; +const MAX_COMMENT_CANDIDATES = 3; +const MODEL_NAME = process.env.EMBEDDING_MODEL || 'Xenova/all-MiniLM-L6-v2'; +const GROQ_MODEL = process.env.GROQ_MODEL || 'llama-3.3-70b-versatile'; +const INDEX_PATH = 'issue_index.json'; +const LABEL_NAME = 'possible-duplicate'; + +const GROQ_API_KEY = process.env.GROQ_API_KEY || ''; +const ISSUE_NUMBER = parseInt(process.env.ISSUE_NUMBER, 10); + +function loadIndex(path) { + if (!existsSync(path)) { + console.error( + `Index file not found at ${path}. Run build-index.mjs first.` + ); + process.exit(1); + } + + const data = JSON.parse(readFileSync(path, 'utf-8')); + console.log(`Loaded index with ${data.issues.length} issues`); + return data; +} + +function findSimilar( + queryEmbedding, + index, + { topK = TOP_K, threshold = SIMILARITY_THRESHOLD, excludeNumber } = {} +) { + const { issues, embeddings } = index; + if (!issues.length) return []; + + const scored = issues.map((issue, i) => ({ + ...issue, + score: dotProduct(queryEmbedding, embeddings[i]), + })); + + return scored + .sort((a, b) => b.score - a.score) + .filter( + (c) => + c.score >= threshold && (!excludeNumber || c.number !== excludeNumber) + ) + .slice(0, topK); +} + +const CONFIRM_SYSTEM_PROMPT = `You are a GitHub issue triage assistant. You will be given a NEW issue and one \ +or more CANDIDATE issues that may be duplicates. + +For each candidate, determine if the new issue is truly a duplicate (same root \ +problem/request) or merely related (similar area but different issue). + +Respond ONLY with a JSON array of objects, each with: +- "number": the candidate issue number +- "duplicate": true or false +- "reason": one-sentence explanation + +Example: +[{"number": 123, "duplicate": true, "reason": "Both report the same crash when ..."}]`; + +async function confirmWithLlm(newIssue, candidates) { + if (!GROQ_API_KEY) { + console.warn('GROQ_API_KEY not set — skipping LLM confirmation'); + return candidates; + } + + const candidateText = candidates + .map( + (c) => + `### Candidate #${c.number} (similarity: ${c.score.toFixed(2)})\n` + + `**Title:** ${c.title}\n` + + `**State:** ${c.state}\n` + + `**Body preview:** ${(c.body_preview || 'N/A').slice(0, 500)}` + ) + .join('\n\n'); + + const userPrompt = + `## NEW ISSUE #${newIssue.number}\n` + + `**Title:** ${newIssue.title}\n` + + `**Body:**\n${(newIssue.body || 'No body').slice(0, 1500)}\n\n` + + `---\n\n` + + `## CANDIDATES\n${candidateText}`; + + try { + const resp = await fetch( + 'https://api.groq.com/openai/v1/chat/completions', + { + method: 'POST', + headers: { + Authorization: `Bearer ${GROQ_API_KEY}`, + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + model: GROQ_MODEL, + messages: [ + { role: 'system', content: CONFIRM_SYSTEM_PROMPT }, + { role: 'user', content: userPrompt }, + ], + temperature: 0.1, + max_tokens: 1024, + }), + signal: AbortSignal.timeout(30_000), + } + ); + + if (!resp.ok) { + const text = await resp.text(); + throw new Error(`Groq API error ${resp.status}: ${text}`); + } + + let content = (await resp.json()).choices[0].message.content.trim(); + + if (content.startsWith('```')) { + content = content + .split('\n') + .slice(1) + .join('\n') + .replace(/```\s*$/, '') + .trim(); + } + + const verdicts = JSON.parse(content); + if (!Array.isArray(verdicts)) { + throw new Error('Invalid LLM response format - expected array'); + } + + const verdictMap = new Map(verdicts.map((v) => [v.number, v])); + + const confirmed = []; + for (const c of candidates) { + const verdict = verdictMap.get(c.number); + if (verdict?.duplicate) { + c.llm_reason = verdict.reason || ''; + confirmed.push(c); + } else { + const reason = verdict?.reason || 'not evaluated'; + console.log(` #${c.number} ruled out by LLM: ${reason}`); + } + } + + return confirmed; + } catch (err) { + console.warn( + `LLM confirmation failed: ${err.message} - falling back to all candidates` + ); + return candidates; + } +} + +function formatComment(candidates) { + const lines = [ + '**Possible duplicate detected**', + '', + 'This issue may be a duplicate of the following (detected via semantic similarity + LLM review):', + '', + ]; + + for (const c of candidates.slice(0, MAX_COMMENT_CANDIDATES)) { + const confidence = `${(c.score * 100).toFixed(0)}%`; + let line = `- #${c.number} (${confidence} match) — ${c.title}`; + if (c.llm_reason) { + line += `\n > *${c.llm_reason}*`; + } + lines.push(line); + } + + lines.push( + '', + 'A maintainer will review this. If this is **not** a duplicate, no action is needed.', + '', + `` + ); + + return lines.join('\n'); +} + +async function main() { + if (!ISSUE_NUMBER) { + console.error('ISSUE_NUMBER not set'); + process.exit(1); + } + + console.log(`Processing issue #${ISSUE_NUMBER}`); + const issue = await getIssue(ISSUE_NUMBER); + + const oneHourAgo = new Date(Date.now() - 60 * 60 * 1000).toISOString(); + const recentIssues = await fetchIssues({ + creator: issue.user.login, + since: oneHourAgo, + state: 'all', + }); + + if (recentIssues.length > 10) { + console.log( + `User ${issue.user.login} created ${recentIssues.length} issues in the last hour - skipping to prevent spam` + ); + return; + } + + if (issue.pull_request) { + console.log('Skipping - this is a pull request'); + return; + } + + if (issue.user.type === 'Bot') { + console.log('Skipping - issue created by bot'); + return; + } + + console.log(`Loading model: ${MODEL_NAME}`); + const extractor = await pipeline('feature-extraction', MODEL_NAME, { + dtype: 'fp32', + }); + const index = loadIndex(INDEX_PATH); + + const text = issueText(issue.title, issue.body); + const output = await extractor(text, { pooling: 'mean', normalize: true }); + const queryEmbedding = output.tolist()[0]; + + let candidates = findSimilar(queryEmbedding, index, { + topK: TOP_K, + threshold: SIMILARITY_THRESHOLD, + excludeNumber: issue.number, + }); + + if (!candidates.length) { + console.log('No similar issues found above threshold - done'); + return; + } + + console.log(`Found ${candidates.length} candidates above threshold:`); + for (const c of candidates) { + console.log(` #${c.number} (${c.score.toFixed(3)}) - ${c.title}`); + } + + console.log('Running LLM confirmation via Groq...'); + candidates = await confirmWithLlm(issue, candidates); + + if (!candidates.length) { + console.log('LLM ruled out all candidates - done'); + return; + } + + const comment = formatComment(candidates); + await postComment(ISSUE_NUMBER, comment); + await addLabel(ISSUE_NUMBER, LABEL_NAME); + + console.log('Done!'); +} + +main().catch((err) => { + console.error(err); + process.exit(1); +}); diff --git a/bin/duplicate-detector/package-lock.json b/bin/duplicate-detector/package-lock.json new file mode 100644 index 00000000..536b387a --- /dev/null +++ b/bin/duplicate-detector/package-lock.json @@ -0,0 +1,1043 @@ +{ + "name": "duplicate-detector", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "duplicate-detector", + "version": "1.0.0", + "dependencies": { + "@huggingface/transformers": "^3.8.1" + } + }, + "node_modules/@emnapi/runtime": { + "version": "1.8.1", + "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.8.1.tgz", + "integrity": "sha512-mehfKSMWjjNol8659Z8KxEMrdSJDDot5SXMq00dM8BN4o+CLNXQ0xH2V7EchNHV4RmbZLmmPdEaXZc5H2FXmDg==", + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, + "node_modules/@huggingface/jinja": { + 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true, + "type": "module", + "scripts": { + "build-index": "node build-index.mjs", + "detect": "node detect.mjs" + }, + "dependencies": { + "@huggingface/transformers": "^3.8.1" + } +} diff --git a/bin/duplicate-detector/utils.mjs b/bin/duplicate-detector/utils.mjs new file mode 100644 index 00000000..4f20b85f --- /dev/null +++ b/bin/duplicate-detector/utils.mjs @@ -0,0 +1,116 @@ +const GITHUB_API = 'https://api.github.com'; +const GITHUB_TOKEN = process.env.GITHUB_TOKEN; +const GITHUB_REPOSITORY = process.env.GITHUB_REPOSITORY; + +function ghHeaders() { + return { + Authorization: `token ${GITHUB_TOKEN}`, + Accept: 'application/vnd.github+json', + }; +} + +export async function fetchIssues({ + state = 'open', + since, + maxIssues = 5000, +} = {}) { + const issues = []; + let page = 1; + const perPage = 100; + + while (issues.length < maxIssues) { + const params = new URLSearchParams({ + state, + per_page: String(perPage), + page: String(page), + sort: 'updated', + direction: 'desc', + }); + if (since) params.set('since', since); + + const url = `${GITHUB_API}/repos/${GITHUB_REPOSITORY}/issues?${params}`; + const resp = await fetch(url, { headers: ghHeaders() }); + + if (!resp.ok) { + throw new Error(`GitHub API error: ${resp.status} ${resp.statusText}`); + } + + const batch = await resp.json(); + if (!batch.length) break; + + for (const item of batch) { + if (!item.pull_request) { + issues.push(item); + } + } + + page++; + if (batch.length < perPage) break; + } + + return issues.slice(0, maxIssues); +} + +export async function getIssue(issueNumber) { + const url = `${GITHUB_API}/repos/${GITHUB_REPOSITORY}/issues/${issueNumber}`; + const resp = await fetch(url, { headers: ghHeaders() }); + + if (!resp.ok) { + throw new Error(`GitHub API error: ${resp.status} ${resp.statusText}`); + } + + return resp.json(); +} + +export async function postComment(issueNumber, body) { + const url = `${GITHUB_API}/repos/${GITHUB_REPOSITORY}/issues/${issueNumber}/comments`; + const resp = await fetch(url, { + method: 'POST', + headers: { ...ghHeaders(), 'Content-Type': 'application/json' }, + body: JSON.stringify({ body }), + }); + + if (!resp.ok) { + throw new Error( + `Failed to post comment: ${resp.status} ${resp.statusText}` + ); + } + + console.log(`Posted comment on #${issueNumber}`); +} + +export async function addLabel(issueNumber, label) { + const url = `${GITHUB_API}/repos/${GITHUB_REPOSITORY}/issues/${issueNumber}/labels`; + const resp = await fetch(url, { + method: 'POST', + headers: { ...ghHeaders(), 'Content-Type': 'application/json' }, + body: JSON.stringify({ labels: [label] }), + }); + + if (resp.status === 404) { + console.warn( + `Label '${label}' does not exist - skipping. Create it manually.` + ); + return; + } + + if (!resp.ok) { + throw new Error(`Failed to add label: ${resp.status} ${resp.statusText}`); + } + + console.log(`Added label '${label}' to #${issueNumber}`); +} + +export function issueText(title, body) { + body = (body || '').trim(); + if (body.length > 2000) body = body.slice(0, 2000) + '...'; + return body ? `${title}\n\n${body}` : title; +} + +export function dotProduct(a, b) { + let sum = 0; + for (let i = 0; i < a.length; i++) { + sum += a[i] * b[i]; + } + return sum; +}