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Create discussion-assistant.yml (#2130)
* Create discussion-assistant.yml * Update discussion-assistant.yml * Update .github/workflows/discussion-assistant.yml Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
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‎.github/workflows/discussion-assistant.yml‎

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name: Discussion Assistant
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on:
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discussion:
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types: [created]
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permissions:
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discussions: write
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contents: read
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jobs:
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find-similar-discussions:
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runs-on: ubuntu-latest
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steps:
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- name: Find and comment on similar discussions
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uses: actions/github-script@v8
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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with:
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github-token: ${{ secrets.GITHUB_TOKEN }}
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script: |
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// Configuration
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const MAX_RESULTS = 5;
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const MIN_SIMILARITY_TO_COMMENT = 0.65;
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const MIN_KEYWORD_SIMILARITY = 0.20;
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const IGNORED_CATEGORIES = ['Announcement', 'Announcements', 'Polls', 'Poll']; // Categories to ignore
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// Get the new discussion details
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const discussionNumber = context.payload.discussion.number;
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const discussionTitle = context.payload.discussion.title;
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const discussionBody = context.payload.discussion.body || '';
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const discussionUrl = context.payload.discussion.html_url;
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const discussionCategory = context.payload.discussion.category?.name || '';
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console.log(`Processing new discussion #${discussionNumber}: "${discussionTitle}"`);
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console.log(`Category: ${discussionCategory}`);
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// Check if discussion is in an ignored category
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if (IGNORED_CATEGORIES.some(cat => discussionCategory.toLowerCase().includes(cat.toLowerCase()))) {
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console.log(`Skipping discussion in category "${discussionCategory}" - category is in ignore list`);
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return;
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}
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// Function to fetch all discussions using GraphQL
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async function fetchAllDiscussions() {
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const query = `
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query($owner: String!, $repo: String!, $cursor: String) {
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repository(owner: $owner, name: $repo) {
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discussions(first: 100, after: $cursor, orderBy: {field: CREATED_AT, direction: DESC}) {
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pageInfo {
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hasNextPage
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endCursor
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}
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nodes {
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number
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title
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body
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url
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createdAt
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category {
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name
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}
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answer {
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id
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}
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}
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}
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}
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}
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`;
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let allDiscussions = [];
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let hasNextPage = true;
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let cursor = null;
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while (hasNextPage) {
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const result = await github.graphql(query, {
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owner: context.repo.owner,
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repo: context.repo.repo,
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cursor: cursor
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});
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const discussions = result.repository.discussions.nodes;
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// Filter out the current discussion and ignored categories
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const filtered = discussions.filter(d => {
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if (d.number === discussionNumber) return false;
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const category = d.category?.name || '';
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if (IGNORED_CATEGORIES.some(cat => category.toLowerCase().includes(cat.toLowerCase()))) {
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return false;
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}
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return true;
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});
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allDiscussions = allDiscussions.concat(filtered);
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hasNextPage = result.repository.discussions.pageInfo.hasNextPage;
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cursor = result.repository.discussions.pageInfo.endCursor;
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// Limit to prevent excessive API calls (adjust as needed)
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if (allDiscussions.length >= 500) break;
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}
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console.log(`Fetched ${allDiscussions.length} existing discussions (excluding ignored categories)`);
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return allDiscussions;
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}
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// Function to calculate similarity using OpenAI embeddings
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async function calculateSimilarityOpenAI(newText, existingDiscussions) {
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const apiKey = process.env.OPENAI_API_KEY;
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if (!apiKey) {
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console.log('OPENAI_API_KEY not found, skipping AI analysis');
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return [];
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}
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try {
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// Get embedding for new discussion
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const newEmbedding = await getEmbedding(newText, apiKey);
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// Prepare texts for batch embedding with indices
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const existingTexts = existingDiscussions.map(d => `${d.title}\n${d.body || ''}`);
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// Process in batches of 100
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const BATCH_SIZE = 100;
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const discussionEmbeddings = []; // Array of {discussion, embedding} pairs
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for (let i = 0; i < existingTexts.length; i += BATCH_SIZE) {
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const batchStart = i;
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const batchEnd = Math.min(i + BATCH_SIZE, existingTexts.length);
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const batch = existingTexts.slice(batchStart, batchEnd);
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try {
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const batchEmbeddings = await getEmbedding(batch, apiKey);
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if (!Array.isArray(batchEmbeddings) || batchEmbeddings.length !== batch.length) {
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throw new Error(`Expected ${batch.length} embeddings, got ${batchEmbeddings?.length || 0}`);
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}
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// Map embeddings back to their discussions
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for (let j = 0; j < batchEmbeddings.length; j++) {
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discussionEmbeddings.push({
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discussion: existingDiscussions[batchStart + j],
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embedding: batchEmbeddings[j]
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});
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}
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// Small delay between batches to avoid rate limits
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if (i + BATCH_SIZE < existingTexts.length) {
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await new Promise(resolve => setTimeout(resolve, 500));
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}
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} catch (batchError) {
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console.error(`Error processing batch ${Math.floor(i / BATCH_SIZE) + 1} (discussions ${batchStart}-${batchEnd - 1}):`, batchError);
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// Skip this batch and continue with next one
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}
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}
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if (discussionEmbeddings.length === 0) {
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console.log('Failed to retrieve embeddings for all discussion batches. Check API key, quota, and network connectivity.');
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throw new Error('Failed to retrieve embeddings for all discussion batches');
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}
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if (discussionEmbeddings.length < existingDiscussions.length) {
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console.log(`Warning: Only got ${discussionEmbeddings.length} embeddings for ${existingDiscussions.length} discussions. Some batches may have failed.`);
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}
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// Calculate similarity scores
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const similarities = [];
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for (const item of discussionEmbeddings) {
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const similarity = cosineSimilarity(newEmbedding, item.embedding);
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if (similarity >= MIN_SIMILARITY_TO_COMMENT) {
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similarities.push({
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discussion: item.discussion,
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similarity
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});
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}
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}
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// Sort by similarity and return top results
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similarities.sort((a, b) => b.similarity - a.similarity);
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return similarities.slice(0, MAX_RESULTS);
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} catch (error) {
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console.error('Error calculating similarity:', error);
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return [];
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}
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}
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// Function to get embedding from OpenAI
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async function getEmbedding(text, apiKey) {
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const response = await fetch('https://api.openai.com/v1/embeddings', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'Authorization': `Bearer ${apiKey}`
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},
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body: JSON.stringify({
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model: 'text-embedding-3-small',
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input: Array.isArray(text)
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? text.map(t => t.substring(0, 8000))
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: text.substring(0, 8000)
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}),
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signal: AbortSignal.timeout(30000) // 30s timeout
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});
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if (!response.ok) {
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const errorBody = await response.text();
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throw new Error(`OpenAI API error: ${response.status} ${response.statusText} - ${errorBody}`);
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}
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const data = await response.json();
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// Return single embedding or array of embeddings
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return Array.isArray(text)
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? data.data.sort((a, b) => a.index - b.index).map(item => item.embedding)
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: data.data[0].embedding;
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}
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// Function to calculate cosine similarity
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function cosineSimilarity(vecA, vecB) {
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let dotProduct = 0;
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let normA = 0;
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let normB = 0;
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for (let i = 0; i < vecA.length; i++) {
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dotProduct += vecA[i] * vecB[i];
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normA += vecA[i] * vecA[i];
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normB += vecB[i] * vecB[i];
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}
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const denominator = Math.sqrt(normA) * Math.sqrt(normB);
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return denominator === 0 ? 0 : dotProduct / denominator;
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}
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// Function to use simple keyword matching as fallback
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function calculateSimilarityKeywords(newText, existingDiscussions) {
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const newKeywords = extractKeywords(newText);
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const similarities = [];
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for (const discussion of existingDiscussions) {
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const existingText = `${discussion.title}\n${discussion.body || ''}`;
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const existingKeywords = extractKeywords(existingText);
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const similarity = keywordSimilarity(newKeywords, existingKeywords);
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if (similarity >= MIN_KEYWORD_SIMILARITY) {
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similarities.push({
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discussion,
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similarity
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});
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}
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}
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similarities.sort((a, b) => b.similarity - a.similarity);
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return similarities.slice(0, MAX_RESULTS);
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}
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// Extract keywords (simple implementation)
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function extractKeywords(text) {
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const stopWords = new Set(['the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by', 'from', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'do', 'does', 'did', 'will', 'would', 'could', 'should', 'may', 'might', 'can', 'this', 'that', 'these', 'those', 'i', 'you', 'he', 'she', 'it', 'we', 'they', 'what', 'which', 'who', 'when', 'where', 'why', 'how']);
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const words = text.toLowerCase()
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.replace(/[^\w\s]/g, ' ')
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.split(/\s+/)
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.filter(word => word.length > 3 && !stopWords.has(word));
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return words;
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}
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// Calculate keyword similarity using Jaccard index
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function keywordSimilarity(keywords1, keywords2) {
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const set1 = new Set(keywords1);
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const set2 = new Set(keywords2);
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const intersection = new Set([...set1].filter(x => set2.has(x)));
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const union = new Set([...set1, ...set2]);
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return intersection.size / union.size;
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}
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// Main execution
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try {
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// Fetch all existing discussions
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const existingDiscussions = await fetchAllDiscussions();
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if (existingDiscussions.length === 0) {
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console.log('No existing discussions found');
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return;
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}
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// Prepare text for comparison
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const newText = `${discussionTitle}\n${discussionBody}`;
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// Try AI-based similarity first, fallback to keyword matching
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let similarDiscussions;
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if (process.env.OPENAI_API_KEY) {
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console.log('Using OpenAI for similarity analysis');
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similarDiscussions = await calculateSimilarityOpenAI(newText, existingDiscussions);
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}
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if (!similarDiscussions || similarDiscussions.length === 0) {
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console.log('Using keyword matching for similarity analysis');
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similarDiscussions = calculateSimilarityKeywords(newText, existingDiscussions);
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}
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// Post comment if similar discussions found
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if (similarDiscussions.length > 0) {
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console.log(`Found ${similarDiscussions.length} similar discussions`);
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for (const item of similarDiscussions) {
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const { discussion, similarity } = item;
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const percentage = Math.round(similarity * 100);
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const answeredTag = discussion.answer ? ' ✅ (Answered)' : '';
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const safeTitle = discussion.title.replace(/\[/g, '\\[').replace(/\]/g, '\\]');
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commentBody += `- [${safeTitle}](${discussion.url}) (${percentage}% similar)${answeredTag}\n`;
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}
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// Post the comment using GraphQL
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const discussionId = context.payload.discussion.node_id;
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const mutation = `
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mutation($discussionId: ID!, $body: String!) {
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addDiscussionComment(input: {discussionId: $discussionId, body: $body}) {
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comment {
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id
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}
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}
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}
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`;
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await github.graphql(mutation, {
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discussionId: discussionId,
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body: commentBody
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});
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console.log('Comment posted successfully');
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} else {
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console.log('No similar discussions found above threshold');
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}
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} catch (error) {
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console.error('Error in discussion assistant:', error);
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core.setFailed(error.message);
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}

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