From 76a3f7f41105587f54c0a5c3af4a457bcc2962b9 Mon Sep 17 00:00:00 2001 From: Thomas Davis Date: Fri, 20 Dec 2024 00:12:32 +1100 Subject: [PATCH] Add Job and Resume Similarity Visualization System with New API Endpoints (#166) * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip * wip --- apps/registry/app/api/job-similarity/route.js | 82 + apps/registry/app/api/resumes/route.js | 2 +- apps/registry/app/api/similarity/route.js | 79 + apps/registry/app/components/Menu.js | 10 + apps/registry/app/job-similarity/page.js | 947 +++++++ apps/registry/app/similarity/page.js | 228 ++ apps/registry/package.json | 6 +- pnpm-lock.yaml | 2301 ++++++++++++++++- 8 files changed, 3615 insertions(+), 40 deletions(-) create mode 100644 apps/registry/app/api/job-similarity/route.js create mode 100644 apps/registry/app/api/similarity/route.js create mode 100644 apps/registry/app/job-similarity/page.js create mode 100644 apps/registry/app/similarity/page.js diff --git a/apps/registry/app/api/job-similarity/route.js b/apps/registry/app/api/job-similarity/route.js new file mode 100644 index 0000000..9afa5df --- /dev/null +++ b/apps/registry/app/api/job-similarity/route.js @@ -0,0 +1,82 @@ +import { createClient } from '@supabase/supabase-js'; +import { NextResponse } from 'next/server'; + +const supabaseUrl = 'https://itxuhvvwryeuzuyihpkp.supabase.co'; + +// This ensures the route is always dynamic +export const dynamic = 'force-dynamic'; + +export async function GET(request) { + // During build time or when SUPABASE_KEY is not available + if (!process.env.SUPABASE_KEY) { + return NextResponse.json( + { message: 'API not available during build' }, + { status: 503 } + ); + } + + try { + const supabase = createClient(supabaseUrl, process.env.SUPABASE_KEY); + const { searchParams } = new URL(request.url); + const limit = parseInt(searchParams.get('limit')) || 750; + + console.time('getJobSimilarityData'); + const { data, error } = await supabase + .from('jobs') + .select('uuid, embedding_v5, gpt_content') + .not('embedding_v5', 'is', null) + .limit(limit) + .order('created_at', { ascending: false }); + + if (error) { + console.error('Error fetching job similarity data:', error); + return NextResponse.json( + { message: 'Error fetching job similarity data' }, + { status: 500 } + ); + } + + console.timeEnd('getJobSimilarityData'); + + // Parse embeddings and job titles + const parsedData = data + .map((item) => { + let jobTitle = 'Unknown Position'; + let gptContent = null; + try { + gptContent = JSON.parse(item.gpt_content); + jobTitle = gptContent?.title || 'Unknown Position'; + } catch (e) { + console.warn('Failed to parse gpt_content for job:', item.uuid); + } + + return { + uuid: item.uuid, + title: jobTitle, + company: gptContent?.company, + countryCode: gptContent?.countryCode, + embedding: + typeof item.embedding_v5 === 'string' + ? JSON.parse(item.embedding_v5) + : Array.isArray(item.embedding_v5) + ? item.embedding_v5 + : null, + }; + }) + .filter((item) => item.embedding !== null); + + return NextResponse.json(parsedData, { + headers: { + 'Content-Type': 'application/json', + 'Cache-Control': + 'public, max-age=86400, s-maxage=86400, stale-while-revalidate=86400', + }, + }); + } catch (error) { + console.error('Error in job similarity endpoint:', error); + return NextResponse.json( + { message: 'Internal server error' }, + { status: 500 } + ); + } +} diff --git a/apps/registry/app/api/resumes/route.js b/apps/registry/app/api/resumes/route.js index 2bbc874..dab9a3d 100644 --- a/apps/registry/app/api/resumes/route.js +++ b/apps/registry/app/api/resumes/route.js @@ -19,7 +19,7 @@ export async function GET(request) { try { const supabase = createClient(supabaseUrl, process.env.SUPABASE_KEY); const { searchParams } = new URL(request.url); - const limit = parseInt(searchParams.get('limit')) || 3000; + const limit = parseInt(searchParams.get('limit')) || 2000; const page = parseInt(searchParams.get('page')) || 1; const search = searchParams.get('search') || ''; diff --git a/apps/registry/app/api/similarity/route.js b/apps/registry/app/api/similarity/route.js new file mode 100644 index 0000000..395c723 --- /dev/null +++ b/apps/registry/app/api/similarity/route.js @@ -0,0 +1,79 @@ +import { createClient } from '@supabase/supabase-js'; +import { NextResponse } from 'next/server'; + +const supabaseUrl = 'https://itxuhvvwryeuzuyihpkp.supabase.co'; + +// This ensures the route is always dynamic +export const dynamic = 'force-dynamic'; + +export async function GET(request) { + // During build time or when SUPABASE_KEY is not available + if (!process.env.SUPABASE_KEY) { + return NextResponse.json( + { message: 'API not available during build' }, + { status: 503 } + ); + } + + try { + const supabase = createClient(supabaseUrl, process.env.SUPABASE_KEY); + const { searchParams } = new URL(request.url); + const limit = parseInt(searchParams.get('limit')) || 1000; + + console.time('getResumeSimilarityData'); + const { data, error } = await supabase + .from('resumes') + .select('username, embedding, resume') + .not('embedding', 'is', null) + .limit(limit) + .order('created_at', { ascending: false }); + + if (error) { + console.error('Error fetching resume similarity data:', error); + return NextResponse.json( + { message: 'Error fetching resume similarity data' }, + { status: 500 } + ); + } + + console.timeEnd('getResumeSimilarityData'); + + // Parse embeddings from strings to numerical arrays and extract position + const parsedData = data + .map((item) => { + let resumeData; + try { + resumeData = JSON.parse(item.resume); + } catch (e) { + console.warn('Failed to parse resume for user:', item.username); + resumeData = {}; + } + + return { + username: item.username, + embedding: + typeof item.embedding === 'string' + ? JSON.parse(item.embedding) + : Array.isArray(item.embedding) + ? item.embedding + : null, + position: resumeData?.basics?.label || 'Unknown Position', + }; + }) + .filter((item) => item.embedding !== null); + + return NextResponse.json(parsedData, { + headers: { + 'Content-Type': 'application/json', + 'Cache-Control': + 'public, max-age=86400, s-maxage=86400, stale-while-revalidate=86400', + }, + }); + } catch (error) { + console.error('Error in similarity endpoint:', error); + return NextResponse.json( + { message: 'Internal server error' }, + { status: 500 } + ); + } +} diff --git a/apps/registry/app/components/Menu.js b/apps/registry/app/components/Menu.js index 8b5f956..73b7219 100644 --- a/apps/registry/app/components/Menu.js +++ b/apps/registry/app/components/Menu.js @@ -44,6 +44,16 @@ export default function Menu({ session }) { > Jobs + + Similarity + import('react-force-graph-2d'), { + ssr: false, +}); + +// Helper function to compute cosine similarity +const cosineSimilarity = (a, b) => { + if (!Array.isArray(a) || !Array.isArray(b) || a.length !== b.length) return 0; + + const dotProduct = a.reduce((sum, _, i) => sum + a[i] * b[i], 0); + const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0)); + const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0)); + + return dotProduct / (magnitudeA * magnitudeB); +}; + +// Helper function to normalize a vector +const normalizeVector = (vector) => { + if (!Array.isArray(vector) || vector.length === 0) return null; + + const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0)); + if (magnitude === 0) return null; + + return vector.map((val) => val / magnitude); +}; + +// Helper function to get average embedding +const getAverageEmbedding = (embeddings) => { + if (!Array.isArray(embeddings) || embeddings.length === 0) return null; + + const sum = embeddings.reduce((acc, curr) => { + return acc.map((val, i) => val + curr[i]); + }, new Array(embeddings[0].length).fill(0)); + + return sum.map((val) => val / embeddings.length); +}; + +// Similarity algorithms +const algorithms = { + mst: { + name: 'Minimum Spanning Tree', + compute: (nodes, minSimilarity = 0.3) => { + // Kruskal's algorithm for MST + const links = []; + const parent = new Array(nodes.length).fill(0).map((_, i) => i); + + function find(x) { + if (parent[x] !== x) parent[x] = find(parent[x]); + return parent[x]; + } + + function union(x, y) { + parent[find(x)] = find(y); + } + + // Create all possible edges with weights + const edges = []; + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + if (similarity > minSimilarity) { + edges.push({ i, j, similarity }); + } + } + } + + // Sort edges by similarity (descending) + edges.sort((a, b) => b.similarity - a.similarity); + + // Build MST + edges.forEach(({ i, j, similarity }) => { + if (find(i) !== find(j)) { + union(i, j); + links.push({ + source: nodes[i].id, + target: nodes[j].id, + value: similarity, + }); + } + }); + + return links; + }, + }, + knn: { + name: 'K-Nearest Neighbors', + compute: (nodes, K = 3, minSimilarity = 0.5) => { + const links = new Set(); + nodes.forEach((node, i) => { + const similarities = nodes.map((otherNode, j) => ({ + index: j, + similarity: + i === j + ? -1 + : cosineSimilarity(node.avgEmbedding, otherNode.avgEmbedding), + })); + + similarities + .sort((a, b) => b.similarity - a.similarity) + .slice(0, K) + .forEach(({ index, similarity }) => { + if (similarity > minSimilarity) { + links.add({ + source: nodes[i].id, + target: nodes[index].id, + value: similarity, + }); + } + }); + }); + return Array.from(links); + }, + }, + threshold: { + name: 'Similarity Threshold', + compute: (nodes, threshold = 0.7) => { + const links = new Set(); + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + if (similarity > threshold) { + links.add({ + source: nodes[i].id, + target: nodes[j].id, + value: similarity, + }); + } + } + } + return Array.from(links); + }, + }, + hierarchical: { + name: 'Hierarchical Clustering', + compute: (nodes, threshold = 0.5) => { + const links = new Set(); + let clusters = new Array(nodes.length).fill(0).map((_, i) => [i]); + const similarities = []; + + // Calculate all pairwise similarities + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + similarities.push({ i, j, similarity }); + } + } + + // Sort by similarity descending + similarities.sort((a, b) => b.similarity - a.similarity); + + // Merge clusters and add links + similarities.forEach(({ i, j, similarity }) => { + if (similarity > threshold) { + const cluster1 = clusters.find((c) => c.includes(i)); + const cluster2 = clusters.find((c) => c.includes(j)); + + if (cluster1 !== cluster2) { + // Add links between closest points in clusters + links.add({ + source: nodes[i].id, + target: nodes[j].id, + value: similarity, + }); + + // Merge clusters + const merged = [...cluster1, ...cluster2]; + clusters = clusters.filter((c) => c !== cluster1 && c !== cluster2); + clusters.push(merged); + } + } + }); + + return Array.from(links); + }, + }, + community: { + name: 'Community Detection', + compute: (nodes, threshold = 0.5, communityThreshold = 0.6) => { + const links = new Set(); + const communities = new Map(); + let communityId = 0; + + // First pass: create initial communities based on strong similarities + for (let i = 0; i < nodes.length; i++) { + if (!communities.has(i)) { + const community = new Set([i]); + communities.set(i, communityId); + + // Find strongly connected nodes + for (let j = 0; j < nodes.length; j++) { + if (i !== j && !communities.has(j)) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + if (similarity > communityThreshold) { + community.add(j); + communities.set(j, communityId); + } + } + } + communityId++; + } + } + + // Second pass: connect communities + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + const sameCommunity = communities.get(i) === communities.get(j); + + // Add links within communities and strong links between communities + if (similarity > (sameCommunity ? threshold : communityThreshold)) { + links.add({ + source: nodes[i].id, + target: nodes[j].id, + value: similarity, + }); + } + } + } + + return Array.from(links); + }, + }, + adaptive: { + name: 'Adaptive Threshold', + compute: (nodes) => { + const links = new Set(); + const similarities = []; + + // Calculate all pairwise similarities + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + similarities.push(similarity); + } + } + + // Calculate adaptive threshold using mean and standard deviation + const mean = + similarities.reduce((a, b) => a + b, 0) / similarities.length; + const std = Math.sqrt( + similarities.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / + similarities.length + ); + const adaptiveThreshold = mean + 0.5 * std; + + // Create links based on adaptive threshold + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + if (similarity > adaptiveThreshold) { + links.add({ + source: nodes[i].id, + target: nodes[j].id, + value: similarity, + }); + } + } + } + + return Array.from(links); + }, + }, + maxSpanningTree: { + name: 'Maximum Spanning Tree', + compute: (nodes, minSimilarity = 0.3) => { + const edges = []; + // Calculate all pairwise similarities + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + if (similarity >= minSimilarity) { + edges.push({ + source: nodes[i], + target: nodes[j], + similarity: similarity, + }); + } + } + } + + // Sort edges by similarity in descending order + edges.sort((a, b) => b.similarity - a.similarity); + + // Kruskal's algorithm for maximum spanning tree + const disjointSet = new Map(); + const find = (node) => { + if (!disjointSet.has(node.id)) { + disjointSet.set(node.id, node.id); + } + if (disjointSet.get(node.id) !== node.id) { + disjointSet.set(node.id, find({ id: disjointSet.get(node.id) })); + } + return disjointSet.get(node.id); + }; + + const union = (node1, node2) => { + const root1 = find(node1); + const root2 = find(node2); + if (root1 !== root2) { + disjointSet.set(root1, root2); + } + }; + + const mstEdges = edges.filter((edge) => { + const sourceRoot = find(edge.source); + const targetRoot = find(edge.target); + if (sourceRoot !== targetRoot) { + union(edge.source, edge.target); + return true; + } + return false; + }); + + return mstEdges; + }, + }, + clique: { + name: 'Maximum Cliques', + compute: (nodes, minSimilarity = 0.6) => { + // Build adjacency matrix + const n = nodes.length; + const adj = Array(n) + .fill() + .map(() => Array(n).fill(false)); + + for (let i = 0; i < n; i++) { + for (let j = i + 1; j < n; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + if (similarity >= minSimilarity) { + adj[i][j] = adj[j][i] = true; + } + } + } + + // Find maximal cliques using Bron-Kerbosch algorithm + const cliques = []; + const bronKerbosch = (r, p, x) => { + if (p.length === 0 && x.length === 0) { + if (r.length >= 3) { + // Only consider cliques of size 3 or larger + cliques.push([...r]); + } + return; + } + + const pivot = [...p, ...x][0]; + const candidates = p.filter((v) => !adj[pivot][v]); + + for (const v of candidates) { + const newR = [...r, v]; + const newP = p.filter((u) => adj[v][u]); + const newX = x.filter((u) => adj[v][u]); + bronKerbosch(newR, newP, newX); + p = p.filter((u) => u !== v); + x.push(v); + } + }; + + const vertices = Array.from({ length: n }, (_, i) => i); + bronKerbosch([], vertices, []); + + // Convert cliques to edges + const edges = []; + for (const clique of cliques) { + for (let i = 0; i < clique.length; i++) { + for (let j = i + 1; j < clique.length; j++) { + const similarity = cosineSimilarity( + nodes[clique[i]].avgEmbedding, + nodes[clique[j]].avgEmbedding + ); + edges.push({ + source: nodes[clique[i]], + target: nodes[clique[j]], + similarity, + }); + } + } + } + + return edges; + }, + }, + pathfinder: { + name: 'Pathfinder Network', + compute: (nodes, r = 2) => { + const n = nodes.length; + const distances = Array(n) + .fill() + .map(() => Array(n).fill(Infinity)); + + // Calculate initial distances + for (let i = 0; i < n; i++) { + distances[i][i] = 0; + for (let j = i + 1; j < n; j++) { + const similarity = cosineSimilarity( + nodes[i].avgEmbedding, + nodes[j].avgEmbedding + ); + // Convert similarity to distance (1 - similarity) + const distance = 1 - similarity; + distances[i][j] = distances[j][i] = distance; + } + } + + // Pathfinder network scaling + for (let k = 0; k < n; k++) { + for (let i = 0; i < n; i++) { + for (let j = 0; j < n; j++) { + const sum = Math.pow( + Math.pow(distances[i][k], r) + Math.pow(distances[k][j], r), + 1 / r + ); + if (sum < distances[i][j]) { + distances[i][j] = sum; + } + } + } + } + + // Generate edges for the minimal network + const edges = []; + for (let i = 0; i < n; i++) { + for (let j = i + 1; j < n; j++) { + let isMinimal = true; + for (let k = 0; k < n; k++) { + if (k !== i && k !== j) { + const sum = Math.pow( + Math.pow(distances[i][k], r) + Math.pow(distances[k][j], r), + 1 / r + ); + if (Math.abs(sum - distances[i][j]) < 1e-10) { + isMinimal = false; + break; + } + } + } + if (isMinimal) { + const similarity = 1 - distances[i][j]; + if (similarity > 0.3) { + // Only include edges with reasonable similarity + edges.push({ + source: nodes[i], + target: nodes[j], + similarity, + }); + } + } + } + } + + return edges; + }, + }, +}; + +const colors = [ + '#FF6B6B', // coral red + '#4ECDC4', // turquoise + '#45B7D1', // sky blue + '#96CEB4', // sage green + '#F7D794', // mellow yellow + '#9B59B6', // amethyst purple + '#FF8C94', // rose pink + '#4A90E2', // ocean blue + '#50E3C2', // mint + '#F39C12', // amber + '#D35400', // pumpkin + '#FF5E3A', // sunset orange + '#2ECC71', // emerald + '#F64747', // pomegranate + '#786FA6', // lavender + '#00B894', // mountain meadow + '#FD79A8', // pink glamour + '#6C5CE7', // blue violet + '#FDA7DF', // light pink + '#A8E6CF', // light green +]; + +const Header = memo(() => ( +
+

Job Market Simlarity

+
+

+ An interactive visualization of the tech job market, powered by data + from HN "Who's Hiring" threads and the JSON Resume Registry. Each node + represents a job category, with edges connecting similar roles. The size + of each node indicates the number of job listings in that category. +

+

+ Hover over a node to see details about the companies and locations + hiring for that role. Click a node to view the original job listing or + resume profile. +

+
    +
  • + Jobs View: Job posts from "Who's Hiring" → GPT-4 + standardization → OpenAI embeddings +
  • +
  • + Resumes View: JSON Resume profiles → OpenAI + embeddings +
  • +
+

+ Multiple graph algorithms available to explore different relationships. +

+
+
+)); +Header.displayName = 'Header'; + +const Controls = memo( + ({ dataSource, setDataSource, algorithm, setAlgorithm }) => ( +
+
+
+ + +
+
+ + +
+
+
+ ) +); +Controls.displayName = 'Controls'; + +const GraphContainer = ({ dataSource, algorithm }) => { + const [graphData, setGraphData] = useState(null); + const [hoverNode, setHoverNode] = useState(null); + const [rawNodes, setRawNodes] = useState(null); + const [highlightNodes, setHighlightNodes] = useState(new Set()); + const [highlightLinks, setHighlightLinks] = useState(new Set()); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [edges, setEdges] = useState([]); + + const handleNodeHover = useCallback( + (node) => { + setHighlightNodes(new Set(node ? [node] : [])); + setHighlightLinks( + new Set( + node + ? edges.filter( + (link) => link.source === node || link.target === node + ) + : [] + ) + ); + setHoverNode(node || null); + }, + [edges] + ); + + const handleNodeClick = useCallback( + (node) => { + if (!node) return; + if (node.uuids && node.uuids.length > 0) { + const baseUrl = dataSource === 'jobs' ? '/jobs/' : '/'; + window.open(`${baseUrl}${node.uuids[0]}`, '_blank'); + } + }, + [dataSource] + ); + + const processData = useCallback( + (data) => { + // Filter out items without valid embeddings + const validData = data.filter((item) => { + const embedding = + dataSource === 'jobs' + ? item.embedding + : typeof item.embedding === 'string' + ? JSON.parse(item.embedding) + : item.embedding; + return Array.isArray(embedding) && embedding.length > 0; + }); + + // Group similar items + const groups = {}; + + validData.forEach((item) => { + const label = + dataSource === 'jobs' + ? item.title + : item.position || 'Unknown Position'; + + if (!groups[label]) { + groups[label] = []; + } + groups[label].push(item); + }); + + // Create nodes with normalized embeddings + const nodes = Object.entries(groups) + .map(([label, items], index) => { + const embeddings = items.map((item) => { + if (dataSource === 'jobs') return item.embedding; + return typeof item.embedding === 'string' + ? JSON.parse(item.embedding) + : item.embedding; + }); + + const normalizedEmbeddings = embeddings + .map((emb) => normalizeVector(emb)) + .filter((emb) => emb !== null); + + if (normalizedEmbeddings.length === 0) return null; + + const avgEmbedding = getAverageEmbedding(normalizedEmbeddings); + if (!avgEmbedding) return null; + + return { + id: label, + group: index, + size: Math.log(items.length + 1) * 3, + count: items.length, + uuids: items.map((item) => + dataSource === 'jobs' ? item.uuid : item.username + ), + usernames: + dataSource === 'jobs' + ? null + : [...new Set(items.map((item) => item.username))], + avgEmbedding, + color: colors[index % colors.length], + companies: + dataSource === 'jobs' + ? [ + ...new Set( + items.map((item) => item.company || 'Unknown Company') + ), + ] + : null, + countryCodes: + dataSource === 'jobs' + ? [ + ...new Set( + items.map( + (item) => item.countryCode || 'Unknown Location' + ) + ), + ] + : null, + }; + }) + .filter((node) => node !== null); + + if (nodes.length === 0) { + throw new Error('No valid data found with embeddings'); + } + + return nodes; + }, + [dataSource] + ); + + const fetchData = useCallback(async () => { + setLoading(true); + setError(null); + try { + const response = await fetch( + `/api/${ + dataSource === 'jobs' ? 'job-' : '' + }similarity?limit=250&algorithm=${algorithm}` + ); + if (!response.ok) { + throw new Error('Failed to fetch data'); + } + const data = await response.json(); + const processedData = processData(data); + setRawNodes(processedData); + } catch (err) { + console.error('Error fetching data:', err); + setError(err.message); + } finally { + setLoading(false); + } + }, [dataSource, algorithm, processData]); + + const processLinks = useCallback(() => { + if (!rawNodes) return; + + const { compute } = algorithms[algorithm]; + const links = compute(rawNodes); + + setGraphData({ nodes: rawNodes, links }); + setEdges(links); + }, [rawNodes, algorithm]); + + useEffect(() => { + fetchData(); + }, [fetchData]); + + useEffect(() => { + processLinks(); + }, [processLinks]); + + if (loading) + return ( +
+
+
+
+ Loading graph data... +
+
+
+ ); + if (error) + return ( +
+
+
+ + + +
+
+ Error loading graph data +
+
{error}
+
+
+ ); + + return ( +
+ {graphData && ( + + highlightNodes.has(node) ? '#FF4757' : node.color + } + nodeCanvasObject={(node, ctx, globalScale) => { + // Draw node + const size = node.size * (4 / Math.max(1, globalScale)); + ctx.beginPath(); + ctx.arc(node.x, node.y, size, 0, 2 * Math.PI); + ctx.fillStyle = highlightNodes.has(node) ? '#FF4757' : node.color; + ctx.fill(); + + // Add a subtle glow effect + if (highlightNodes.has(node)) { + ctx.shadowColor = '#FF4757'; + ctx.shadowBlur = 15; + ctx.strokeStyle = '#FF4757'; + ctx.lineWidth = 2; + ctx.stroke(); + ctx.shadowBlur = 0; + } + // Only draw label if node is highlighted + if (highlightNodes.has(node)) { + const label = node.id; + const fontSize = Math.max(14, node.size * 1.5); + ctx.font = `${fontSize}px Sans-Serif`; + ctx.textAlign = 'center'; + ctx.textBaseline = 'middle'; + ctx.fillStyle = '#000'; + + // Add background to text + const textWidth = ctx.measureText(label).width; + const bckgDimensions = [textWidth, fontSize].map( + (n) => n + fontSize * 0.4 + ); + ctx.fillStyle = 'rgba(255, 255, 255, 0.8)'; + ctx.fillRect( + node.x - bckgDimensions[0] / 2, + node.y - bckgDimensions[1] / 2, + bckgDimensions[0], + bckgDimensions[1] + ); + + ctx.fillStyle = '#000'; + ctx.fillText(label, node.x, node.y); + + // Add count below label + const countLabel = `${node.count} ${ + dataSource === 'jobs' ? 'jobs' : 'resumes' + }`; + const smallerFont = fontSize * 0.7; + ctx.font = `${smallerFont}px Sans-Serif`; + ctx.fillText(countLabel, node.x, node.y - bckgDimensions[1]); + } + }} + nodeRelSize={4} + linkWidth={(link) => (highlightLinks.has(link) ? 2 : 1)} + linkColor={(link) => + highlightLinks.has(link) ? '#FF4757' : '#E5E9F2' + } + linkOpacity={0.5} + linkDirectionalParticles={0} + linkDirectionalParticleWidth={2} + onNodeHover={handleNodeHover} + onNodeClick={handleNodeClick} + enableNodeDrag={false} + cooldownTicks={50} + d3AlphaDecay={0.05} + d3VelocityDecay={0.4} + warmupTicks={50} + d3Force={{ + collision: 1, + charge: -30, + }} + width={window.innerWidth} + height={window.innerHeight - 32 * 16} + /> + )} + {hoverNode && ( +
+

{hoverNode.id}

+

+ {hoverNode.count}{' '} + {dataSource === 'jobs' ? 'job listings' : 'resumes'} +

+ {dataSource === 'jobs' && hoverNode.companies && ( +
+

Companies:

+

+ {hoverNode.companies.slice(0, 5).join(', ')} + {hoverNode.companies.length > 5 + ? `, +${hoverNode.companies.length - 5} more` + : ''} +

+
+ )} + {dataSource === 'jobs' && hoverNode.countryCodes && ( +
+

Locations:

+

+ {hoverNode.countryCodes.slice(0, 5).join(', ')} + {hoverNode.countryCodes.length > 5 + ? `, +${hoverNode.countryCodes.length - 5} more` + : ''} +

+
+ )} + {dataSource !== 'jobs' && hoverNode.usernames && ( +
+

Usernames:

+
+ {hoverNode.usernames.map((username, i) => ( +
window.open(`/${username}`, '_blank')} + > + {username} +
+ ))} +
+
+ )} +

+ Click to view {dataSource === 'jobs' ? 'job' : 'resume'} +

+
+ )} +
+ ); +}; + +export default function Page() { + const [dataSource, setDataSource] = useState('jobs'); + const [algorithm, setAlgorithm] = useState('mst'); + + return ( +
+
+
+ +
+
+ +
+
+ ); +} diff --git a/apps/registry/app/similarity/page.js b/apps/registry/app/similarity/page.js new file mode 100644 index 0000000..590c9c2 --- /dev/null +++ b/apps/registry/app/similarity/page.js @@ -0,0 +1,228 @@ +'use client'; + +import React, { useEffect, useState, useCallback } from 'react'; +import dynamic from 'next/dynamic'; + +// Import ForceGraph dynamically to avoid SSR issues +const ForceGraph2D = dynamic(() => import('react-force-graph-2d'), { + ssr: false, +}); + +// Helper function to compute cosine similarity +function cosineSimilarity(a, b) { + let dotProduct = 0; + let normA = 0; + let normB = 0; + for (let i = 0; i < a.length; i++) { + dotProduct += a[i] * b[i]; + normA += a[i] * a[i]; + normB += b[i] * b[i]; + } + return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB)); +} + +export default function SimilarityPage() { + const [graphData, setGraphData] = useState(null); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [highlightNodes, setHighlightNodes] = useState(new Set()); + const [highlightLinks, setHighlightLinks] = useState(new Set()); + const [hoverNode, setHoverNode] = useState(null); + + useEffect(() => { + async function fetchData() { + try { + const response = await fetch('/api/similarity'); + if (!response.ok) { + throw new Error('Failed to fetch data'); + } + const jsonData = await response.json(); + + // Group similar positions + const positionGroups = {}; + jsonData.forEach((item) => { + const position = item.position; + if (!positionGroups[position]) { + positionGroups[position] = []; + } + positionGroups[position].push(item); + }); + + // Create nodes and links + const nodes = []; + const links = []; + const similarityThreshold = 0.7; + + // Create nodes for each unique position + Object.entries(positionGroups).forEach(([position, items], index) => { + nodes.push({ + id: position, + group: index, + size: Math.log(items.length + 1) * 3, + count: items.length, + usernames: items.map((item) => item.username), + embeddings: items.map((item) => item.embedding), + color: `hsl(${Math.random() * 360}, 70%, 50%)`, + }); + }); + + // Create links between similar positions + for (let i = 0; i < nodes.length; i++) { + for (let j = i + 1; j < nodes.length; j++) { + // Calculate average similarity between groups + let totalSimilarity = 0; + let comparisons = 0; + + nodes[i].embeddings.forEach((emb1) => { + nodes[j].embeddings.forEach((emb2) => { + totalSimilarity += cosineSimilarity(emb1, emb2); + comparisons++; + }); + }); + + const avgSimilarity = totalSimilarity / comparisons; + + if (avgSimilarity > similarityThreshold) { + links.push({ + source: nodes[i].id, + target: nodes[j].id, + value: avgSimilarity, + }); + } + } + } + + setGraphData({ nodes, links }); + } catch (err) { + setError(err.message); + } finally { + setLoading(false); + } + } + + fetchData(); + }, []); + + const handleNodeHover = useCallback( + (node) => { + setHighlightNodes(new Set(node ? [node] : [])); + setHighlightLinks( + new Set( + graphData?.links.filter( + (link) => link.source.id === node?.id || link.target.id === node?.id + ) || [] + ) + ); + setHoverNode(node || null); + }, + [graphData] + ); + + if (loading) { + return ( +
+

Resume Position Network

+

Loading...

+
+ ); + } + + if (error) { + return ( +
+

Resume Position Network

+

Error: {error}

+
+ ); + } + + return ( +
+

Resume Position Network

+

+ Explore similar positions in an interactive network. Each node + represents a position, with size indicating the number of resumes. + Connected positions are similar based on resume content. Hover to + highlight connections, click to view resumes. +

+
+ {graphData && ( + + highlightNodes.has(node) ? '#ff0000' : node.color + } + nodeCanvasObject={(node, ctx, globalScale) => { + // Draw node + const size = node.size * (4 / Math.max(1, globalScale)); + ctx.beginPath(); + ctx.arc(node.x, node.y, size, 0, 2 * Math.PI); + ctx.fillStyle = highlightNodes.has(node) ? '#ff0000' : node.color; + ctx.fill(); + + // Only draw label if node is highlighted + if (highlightNodes.has(node)) { + const label = node.id; + const fontSize = Math.max(14, size * 1.5); + ctx.font = `${fontSize}px Sans-Serif`; + const textWidth = ctx.measureText(label).width; + const bckgDimensions = [textWidth, fontSize].map( + (n) => n + fontSize * 0.2 + ); + + // Draw background for label + ctx.fillStyle = 'rgba(255, 255, 255, 0.9)'; + ctx.fillRect( + node.x - bckgDimensions[0] / 2, + node.y - bckgDimensions[1] * 2, + bckgDimensions[0], + bckgDimensions[1] + ); + + // Draw label + ctx.textAlign = 'center'; + ctx.textBaseline = 'middle'; + ctx.fillStyle = '#000'; + ctx.fillText(label, node.x, node.y - bckgDimensions[1] * 1.5); + + // Draw count + const countLabel = `(${node.count})`; + const smallerFont = fontSize * 0.7; + ctx.font = `${smallerFont}px Sans-Serif`; + ctx.fillText(countLabel, node.x, node.y - bckgDimensions[1]); + } + }} + nodeRelSize={6} + linkWidth={(link) => (highlightLinks.has(link) ? 2 : 1)} + linkColor={(link) => + highlightLinks.has(link) ? '#ff0000' : '#cccccc' + } + linkOpacity={0.3} + linkDirectionalParticles={4} + linkDirectionalParticleWidth={2} + onNodeHover={handleNodeHover} + onNodeClick={(node) => { + if (node.usernames && node.usernames.length > 0) { + window.open(`/${node.usernames[0]}`, '_blank'); + } + }} + enableNodeDrag={true} + cooldownTicks={100} + d3AlphaDecay={0.02} + d3VelocityDecay={0.3} + warmupTicks={100} + /> + )} + {hoverNode && ( +
+

{hoverNode.id}

+

{hoverNode.count} resumes

+

+ Click to view a sample resume +

+
+ )} +
+
+ ); +} diff --git a/apps/registry/package.json b/apps/registry/package.json index 7371bd0..a4bb021 100644 --- a/apps/registry/package.json +++ b/apps/registry/package.json @@ -14,10 +14,10 @@ "dependencies": { "@ai-sdk/openai": "^0.0.14", "@faker-js/faker": "^8.0.2", + "@jsonresume/jsonresume-theme-professional": "workspace:*", "@jsonresume/schema": "^1.2.0", "@jsonresume/theme-papirus": "workspace:*", "@jsonresume/theme-stackoverflow": "^2", - "@jsonresume/jsonresume-theme-professional": "workspace:*", "@monaco-editor/react": "^4.6.0", "@pinecone-database/pinecone": "^0.1.6", "@prisma/client": "^4.15.0", @@ -93,6 +93,7 @@ "openai": "^4.0.0", "pg": "^8.11.0", "pinecone-client": "^1.1.0", + "plotly.js-dist": "^2.35.3", "postcss": "^8.4.39", "prisma": "^4.15.0", "prismjs": "^1.29.0", @@ -100,7 +101,10 @@ "react": "^18.3.1", "react-autocomplete": "^1.8.1", "react-dom": "^18.3.1", + "react-force-graph": "^1.45.4", + "react-force-graph-2d": "^1.26.1", "react-markdown": "^8.0.7", + "react-plotly.js": "^2.6.0", "react-speech-recognition": "^3.10.0", "recharts": "^2.12.7", "resume-schema": "^1.0.0", diff --git a/pnpm-lock.yaml b/pnpm-lock.yaml index 1d53d71..2ba465e 100644 --- a/pnpm-lock.yaml +++ b/pnpm-lock.yaml @@ -423,6 +423,9 @@ importers: pinecone-client: specifier: ^1.1.0 version: 1.1.0 + plotly.js-dist: + specifier: ^2.35.3 + version: 2.35.3 postcss: specifier: ^8.4.39 version: 8.4.39 @@ -444,9 +447,18 @@ importers: react-dom: specifier: ^18.3.1 version: 18.3.1(react@18.3.1) + react-force-graph: + specifier: ^1.45.4 + version: 1.45.4(react@18.3.1)(three@0.171.0) + react-force-graph-2d: + specifier: ^1.26.1 + version: 1.26.1(react@18.3.1) react-markdown: specifier: ^8.0.7 version: 8.0.7(@types/react@18.3.3)(react@18.3.1) + react-plotly.js: + specifier: ^2.6.0 + version: 2.6.0(plotly.js@2.35.3)(react@18.3.1) react-speech-recognition: specifier: ^3.10.0 version: 3.10.0(react@18.3.1) @@ -936,6 +948,42 @@ importers: packages: + /3d-force-graph-ar@1.9.3(three@0.171.0): + resolution: {integrity: sha512-KHcwKVF8394ioKhc4h3y5H9jPBvw+lUmD1BJd1AEV/SO+FM324CXVYTvbGg2IuW0nOPR/ChXvlWhvIZaOtyeTg==} + engines: {node: '>=12'} + dependencies: + aframe-forcegraph-component: 3.1.0(three@0.171.0) + kapsule: 1.16.0 + transitivePeerDependencies: + - three + dev: false + + /3d-force-graph-vr@2.4.3(three@0.171.0): + resolution: {integrity: sha512-os/IPpkWUqNnqWFQISaa5Snts1uUFf485SMPpPi2BIJLHbW9Jxt+1PQWc2rqT3tDFTh9J72BnxyuqegyMrqxdQ==} + engines: {node: '>=12'} + dependencies: + accessor-fn: 1.5.1 + aframe: 1.6.0 + aframe-extras: 7.5.2 + aframe-forcegraph-component: 3.1.0(three@0.171.0) + kapsule: 1.16.0 + polished: 4.2.2 + transitivePeerDependencies: + - supports-color + - three + dev: false + + /3d-force-graph@1.74.5: + resolution: {integrity: sha512-CyneQqxoFwTGOqBVe8DSED0uQrU3q4+xpgvl0kbNHctv/kdBRgOKSSJgyB7j+08mUKaHchelMmwNVmQf5XaJrA==} + engines: {node: '>=12'} + dependencies: + accessor-fn: 1.5.1 + kapsule: 1.16.0 + three: 0.171.0 + three-forcegraph: 1.42.10(three@0.171.0) + three-render-objects: 1.32.1(three@0.171.0) + dev: false + /@adobe/css-tools@4.4.1: resolution: {integrity: sha512-12WGKBQzjUAI4ayyF4IAtfw2QR/IDoqk6jTddXDhtYTJF9ASmoE1zst7cVtP0aL/F1jUJL5r+JxKXKEgHNbEUQ==} dev: true @@ -4573,6 +4621,13 @@ packages: prettier: 2.8.0 dev: false + /@choojs/findup@0.2.1: + resolution: {integrity: sha512-YstAqNb0MCN8PjdLCDfRsBcGVRN41f3vgLvaI0IrIcBp4AqILRSS0DeWNGkicC+f/zRIPJLc+9RURVSepwvfBw==} + hasBin: true + dependencies: + commander: 2.20.3 + dev: false + /@chronobserver/htmls@0.4.4: resolution: {integrity: sha512-50UaAsvJCP1Uocf5un8qrvjF23TL/mh72gfDQCPflUr4Bl6BIVzRS38hLjEvrliRkWA7435W52zvjlLi9c+FMg==} dependencies: @@ -5659,6 +5714,75 @@ packages: read-yaml-file: 1.1.0 dev: false + /@mapbox/geojson-rewind@0.5.2: + resolution: {integrity: sha512-tJaT+RbYGJYStt7wI3cq4Nl4SXxG8W7JDG5DMJu97V25RnbNg3QtQtf+KD+VLjNpWKYsRvXDNmNrBgEETr1ifA==} + hasBin: true + dependencies: + get-stream: 6.0.1 + minimist: 1.2.8 + dev: false + + /@mapbox/geojson-types@1.0.2: + resolution: {integrity: sha512-e9EBqHHv3EORHrSfbR9DqecPNn+AmuAoQxV6aL8Xu30bJMJR1o8PZLZzpk1Wq7/NfCbuhmakHTPYRhoqLsXRnw==} + dev: false + + /@mapbox/jsonlint-lines-primitives@2.0.2: + resolution: {integrity: sha512-rY0o9A5ECsTQRVhv7tL/OyDpGAoUB4tTvLiW1DSzQGq4bvTPhNw1VpSNjDJc5GFZ2XuyOtSWSVN05qOtcD71qQ==} + engines: {node: '>= 0.6'} + dev: false + + /@mapbox/mapbox-gl-supported@1.5.0(mapbox-gl@1.13.3): + resolution: {integrity: sha512-/PT1P6DNf7vjEEiPkVIRJkvibbqWtqnyGaBz3nfRdcxclNSnSdaLU5tfAgcD7I8Yt5i+L19s406YLl1koLnLbg==} + peerDependencies: + mapbox-gl: '>=0.32.1 <2.0.0' + dependencies: + mapbox-gl: 1.13.3 + dev: false + + /@mapbox/point-geometry@0.1.0: + resolution: {integrity: sha512-6j56HdLTwWGO0fJPlrZtdU/B13q8Uwmo18Ck2GnGgN9PCFyKTZ3UbXeEdRFh18i9XQ92eH2VdtpJHpBD3aripQ==} + dev: false + + /@mapbox/tiny-sdf@1.2.5: + resolution: {integrity: sha512-cD8A/zJlm6fdJOk6DqPUV8mcpyJkRz2x2R+/fYcWDYG3oWbG7/L7Yl/WqQ1VZCjnL9OTIMAn6c+BC5Eru4sQEw==} + dev: false + + /@mapbox/tiny-sdf@2.0.6: + resolution: {integrity: sha512-qMqa27TLw+ZQz5Jk+RcwZGH7BQf5G/TrutJhspsca/3SHwmgKQ1iq+d3Jxz5oysPVYTGP6aXxCo5Lk9Er6YBAA==} + dev: false + + /@mapbox/unitbezier@0.0.0: + resolution: {integrity: sha512-HPnRdYO0WjFjRTSwO3frz1wKaU649OBFPX3Zo/2WZvuRi6zMiRGui8SnPQiQABgqCf8YikDe5t3HViTVw1WUzA==} + dev: false + + /@mapbox/unitbezier@0.0.1: + resolution: {integrity: sha512-nMkuDXFv60aBr9soUG5q+GvZYL+2KZHVvsqFCzqnkGEf46U2fvmytHaEVc1/YZbiLn8X+eR3QzX1+dwDO1lxlw==} + dev: false + + /@mapbox/vector-tile@1.3.1: + resolution: {integrity: sha512-MCEddb8u44/xfQ3oD+Srl/tNcQoqTw3goGk2oLsrFxOTc3dUp+kAnby3PvAeeBYSMSjSPD1nd1AJA6W49WnoUw==} + dependencies: + '@mapbox/point-geometry': 0.1.0 + dev: false + + /@mapbox/whoots-js@3.1.0: + resolution: {integrity: sha512-Es6WcD0nO5l+2BOQS4uLfNPYQaNDfbot3X1XUoloz+x0mPDS3eeORZJl06HXjwBG1fOGwCRnzK88LMdxKRrd6Q==} + engines: {node: '>=6.0.0'} + dev: false + + /@maplibre/maplibre-gl-style-spec@20.4.0: + resolution: {integrity: sha512-AzBy3095fTFPjDjmWpR2w6HVRAZJ6hQZUCwk5Plz6EyfnfuQW1odeW5i2Ai47Y6TBA2hQnC+azscjBSALpaWgw==} + hasBin: true + dependencies: + '@mapbox/jsonlint-lines-primitives': 2.0.2 + '@mapbox/unitbezier': 0.0.1 + json-stringify-pretty-compact: 4.0.0 + minimist: 1.2.8 + quickselect: 2.0.0 + rw: 1.3.3 + tinyqueue: 3.0.0 + dev: false + /@mdx-js/react@3.1.0(@types/react@18.3.3)(react@18.3.1): resolution: {integrity: sha512-QjHtSaoameoalGnKDT3FoIl4+9RwyTmo9ZJGBdLOks/YOiWHoRDI3PUwEzOE7kEmGcV3AFcp9K6dYu9rEuKLAQ==} peerDependencies: @@ -6935,6 +7059,76 @@ packages: dependencies: playwright: 1.40.0 + /@plotly/d3-sankey-circular@0.33.1: + resolution: {integrity: sha512-FgBV1HEvCr3DV7RHhDsPXyryknucxtfnLwPtCKKxdolKyTFYoLX/ibEfX39iFYIL7DYbVeRtP43dbFcrHNE+KQ==} + dependencies: + d3-array: 1.2.4 + d3-collection: 1.0.7 + d3-shape: 1.3.7 + elementary-circuits-directed-graph: 1.3.1 + dev: false + + /@plotly/d3-sankey@0.7.2: + resolution: {integrity: sha512-2jdVos1N3mMp3QW0k2q1ph7Gd6j5PY1YihBrwpkFnKqO+cqtZq3AdEYUeSGXMeLsBDQYiqTVcihYfk8vr5tqhw==} + dependencies: + d3-array: 1.2.4 + d3-collection: 1.0.7 + d3-shape: 1.3.7 + dev: false + + /@plotly/d3@3.8.2: + resolution: {integrity: sha512-wvsNmh1GYjyJfyEBPKJLTMzgf2c2bEbSIL50lmqVUi+o1NHaLPi1Lb4v7VxXXJn043BhNyrxUrWI85Q+zmjOVA==} + dev: false + + /@plotly/mapbox-gl@1.13.4(mapbox-gl@1.13.3): + resolution: {integrity: sha512-sR3/Pe5LqT/fhYgp4rT4aSFf1rTsxMbGiH6Hojc7PH36ny5Bn17iVFUjpzycafETURuFbLZUfjODO8LvSI+5zQ==} + engines: {node: '>=6.4.0'} + dependencies: + '@mapbox/geojson-rewind': 0.5.2 + '@mapbox/geojson-types': 1.0.2 + '@mapbox/jsonlint-lines-primitives': 2.0.2 + '@mapbox/mapbox-gl-supported': 1.5.0(mapbox-gl@1.13.3) + '@mapbox/point-geometry': 0.1.0 + '@mapbox/tiny-sdf': 1.2.5 + '@mapbox/unitbezier': 0.0.0 + '@mapbox/vector-tile': 1.3.1 + '@mapbox/whoots-js': 3.1.0 + csscolorparser: 1.0.3 + earcut: 2.2.4 + geojson-vt: 3.2.1 + gl-matrix: 3.4.3 + grid-index: 1.1.0 + murmurhash-js: 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Do not use it. 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