+7 -1

.gitignore

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··· 19 19 .claude/worktrees 20 20 .superpowers/ 21 21 docs/superpowers/ 22 22 - __pycache__/ 23 22 Trace-*.json.gz 23 23 + 24 24 + # Python NLP pipeline 25 25 + pipeline/.venv/ 26 26 + pipeline/data/ 27 27 + pipeline/*.egg-info/ 28 28 + __pycache__/ 29 29 + *.pyc

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.panproto/HEAD

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··· 1 1 + {"Branch":"main"}

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.panproto/index.json

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··· 1 1 + { 2 2 + "staged": null, 3 3 + "staged_data": [], 4 4 + "staged_protocol": null 5 5 + }

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.panproto/objects/89/215c53859ff49be231d954234820efd393906a6a0499477369c709eba3d53c

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.panproto/refs/heads/main

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.panproto/refs/tags/v0.5.0

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··· 1 1 + fbde01706511f5588e0c43d349742ac27d048b903fed4355c45e1dd78980269e

+266

apps/ionosphere-appview/src/__tests__/layers-pub.test.ts

reviewed

··· 1 1 + import { describe, it, expect } from 'vitest'; 2 2 + import { transcriptToLayersPub, nlpToAnnotationLayers, layersPubToDocument } from '../../../../formats/tv.ionosphere/ts/layers-pub.js'; 3 3 + 4 4 + describe('Lens 1: transcript → expression + segmentation', () => { 5 5 + const transcript = { 6 6 + $type: 'tv.ionosphere.transcript', 7 7 + talkUri: 'at://did:plc:test/tv.ionosphere.talk/test-talk', 8 8 + text: 'Hello world foo bar', 9 9 + startMs: 1000, 10 10 + // word durations: Hello=200ms, world=300ms, 100ms gap, foo=150ms, bar=250ms 11 11 + timings: [200, 300, -100, 150, 250], 12 12 + }; 13 13 + 14 14 + const did = 'did:plc:test'; 15 15 + const talkRkey = 'test-talk'; 16 16 + 17 17 + it('produces an expression record with correct fields', async () => { 18 18 + const { expression } = await transcriptToLayersPub(transcript, did, talkRkey); 19 19 + expect(expression.$type).toBe('pub.layers.expression.expression'); 20 20 + expect(expression.id).toBe('test-talk'); 21 21 + expect(expression.kind).toBe('transcript'); 22 22 + expect(expression.text).toBe('Hello world foo bar'); 23 23 + expect(expression.language).toBe('en'); 24 24 + expect(expression.sourceRef).toBe('at://did:plc:test/tv.ionosphere.transcript/test-talk-transcript'); 25 25 + expect(expression.metadata.tool).toBe('ionosphere-pipeline'); 26 26 + expect(expression.metadata.timestamp).toBeDefined(); 27 27 + expect(expression.createdAt).toBeDefined(); 28 28 + }); 29 29 + 30 30 + it('produces segmentation records with textSpan-only tokens', async () => { 31 31 + const { segmentations } = await transcriptToLayersPub(transcript, did, talkRkey); 32 32 + expect(segmentations).toHaveLength(1); // small transcript = 1 chunk 33 33 + 34 34 + const seg = segmentations[0]; 35 35 + expect(seg.$type).toBe('pub.layers.segmentation.segmentation'); 36 36 + expect(seg.expression).toBe( 37 37 + 'at://did:plc:test/pub.layers.expression.expression/test-talk-expression' 38 38 + ); 39 39 + 40 40 + const tok = seg.tokenizations[0]; 41 41 + expect(tok.kind).toBe('word'); 42 42 + expect(tok.tokens).toHaveLength(4); 43 43 + 44 44 + // Check first token — textSpan only (word text recoverable via expression.text + byte offsets) 45 45 + expect(tok.tokens[0].tokenIndex).toBe(0); 46 46 + expect(tok.tokens[0].textSpan.byteStart).toBe(0); 47 47 + expect(tok.tokens[0].textSpan.byteEnd).toBe(5); 48 48 + 49 49 + // Check third token (after gap) — byte offsets 50 50 + expect(tok.tokens[2].textSpan.byteStart).toBe(12); // "Hello world " = 12 bytes 51 51 + expect(tok.tokens[2].textSpan.byteEnd).toBe(15); 52 52 + }); 53 53 + 54 54 + it('produces separate temporal segmentation records', async () => { 55 55 + const { temporals } = await transcriptToLayersPub(transcript, did, talkRkey); 56 56 + expect(temporals).toHaveLength(1); // small transcript = 1 chunk 57 57 + 58 58 + const temp = temporals[0]; 59 59 + expect(temp.$type).toBe('pub.layers.segmentation.segmentation'); 60 60 + 61 61 + const tok = temp.tokenizations[0]; 62 62 + expect(tok.kind).toBe('word-temporal'); 63 63 + expect(tok.tokens).toHaveLength(4); 64 64 + 65 65 + // First token temporal span 66 66 + expect(tok.tokens[0].tokenIndex).toBe(0); 67 67 + expect(tok.tokens[0].temporalSpan.start).toBe(1000); 68 68 + expect(tok.tokens[0].temporalSpan.ending).toBe(1200); 69 69 + 70 70 + // Third token (after gap) 71 71 + expect(tok.tokens[2].temporalSpan.start).toBe(1600); // 1000+200+300+100gap 72 72 + expect(tok.tokens[2].temporalSpan.ending).toBe(1750); 73 73 + }); 74 74 + }); 75 75 + 76 76 + describe('Lens 2: NLP annotations → annotation layers', () => { 77 77 + const nlpAnnotations = { 78 78 + talkRkey: 'test-talk', 79 79 + sentences: [ 80 80 + { byteStart: 0, byteEnd: 11 }, 81 81 + { byteStart: 12, byteEnd: 19 }, 82 82 + ], 83 83 + paragraphs: [ 84 84 + { byteStart: 0, byteEnd: 19 }, 85 85 + ], 86 86 + entities: [ 87 87 + { byteStart: 0, byteEnd: 5, label: 'Hello', nerType: 'MISC' }, 88 88 + { byteStart: 12, byteEnd: 15, label: 'foo', nerType: 'ORG', conceptUri: 'at://did:plc:test/tv.ionosphere.concept/foo' }, 89 89 + ], 90 90 + topicBreaks: [ 91 91 + { byteStart: 12 }, 92 92 + ], 93 93 + metadata: { tool: 'spacy/en_core_web_sm' }, 94 94 + }; 95 95 + 96 96 + const did = 'did:plc:test'; 97 97 + const talkRkey = 'test-talk'; 98 98 + const expressionUri = 'at://did:plc:test/pub.layers.expression.expression/test-talk-expression'; 99 99 + 100 100 + it('produces 4 annotation layer records', async () => { 101 101 + const layers = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 102 102 + expect(Object.keys(layers)).toEqual(['sentences', 'paragraphs', 'entities', 'topics']); 103 103 + }); 104 104 + 105 105 + it('sentences layer has correct structure', async () => { 106 106 + const { sentences } = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 107 107 + expect(sentences.$type).toBe('pub.layers.annotation.annotationLayer'); 108 108 + expect(sentences.expression).toBe(expressionUri); 109 109 + expect(sentences.kind).toBe('span'); 110 110 + expect(sentences.subkind).toBe('sentence-boundary'); 111 111 + expect(sentences.sourceMethod).toBe('automatic'); 112 112 + expect(sentences.metadata.tool).toBe('ionosphere-nlp-pipeline'); 113 113 + expect(sentences.annotations).toHaveLength(2); 114 114 + expect(sentences.annotations[0].anchor.textSpan).toEqual({ byteStart: 0, byteEnd: 11 }); 115 115 + }); 116 116 + 117 117 + it('entities layer wraps features in featureMap', async () => { 118 118 + const { entities } = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 119 119 + expect(entities.annotations).toHaveLength(2); 120 120 + 121 121 + // Plain entity — nerType is always present 122 122 + const plain = entities.annotations[0]; 123 123 + expect(plain.label).toBe('Hello'); 124 124 + expect(plain.features.entries).toContainEqual({ key: 'nerType', value: 'MISC' }); 125 125 + 126 126 + // Entity with conceptUri — all known keys forwarded to features 127 127 + const withConcept = entities.annotations[1]; 128 128 + expect(withConcept.features.entries).toContainEqual({ 129 129 + key: 'conceptUri', 130 130 + value: 'at://did:plc:test/tv.ionosphere.concept/foo', 131 131 + }); 132 132 + expect(withConcept.features.entries).toContainEqual({ key: 'nerType', value: 'ORG' }); 133 133 + }); 134 134 + 135 135 + it('topics layer has correct subkind and uses zero-width spans', async () => { 136 136 + const { topics } = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 137 137 + expect(topics.subkind).toBe('topic-segment'); 138 138 + expect(topics.annotations).toHaveLength(1); 139 139 + expect(topics.annotations[0].anchor.textSpan).toEqual({ byteStart: 12, byteEnd: 12 }); 140 140 + }); 141 141 + }); 142 142 + 143 143 + describe('Stage 6 integration: full record production', () => { 144 144 + it('produces 6 records for a talk with transcript + NLP data', async () => { 145 145 + const fs = await import('node:fs'); 146 146 + const path = await import('node:path'); 147 147 + 148 148 + const transcriptsDir = path.resolve(import.meta.dirname, '../../../data/transcripts'); 149 149 + const nlpDir = path.resolve(import.meta.dirname, '../../../../pipeline/data/nlp'); 150 150 + 151 151 + const rkey = 'ats26-keynote'; 152 152 + const transcriptPath = path.join(transcriptsDir, `${rkey}.json`); 153 153 + const nlpPath = path.join(nlpDir, `${rkey}.json`); 154 154 + 155 155 + // Skip if fixtures not available 156 156 + if (!fs.existsSync(transcriptPath) || !fs.existsSync(nlpPath)) { 157 157 + console.log('Skipping: fixture data not available'); 158 158 + return; 159 159 + } 160 160 + 161 161 + const { encode } = await import('../../../../formats/tv.ionosphere/ts/transcript-encoding.js'); 162 162 + 163 163 + const transcriptData = JSON.parse(fs.readFileSync(transcriptPath, 'utf-8')); 164 164 + const nlpData = JSON.parse(fs.readFileSync(nlpPath, 'utf-8')); 165 165 + const compact = encode(transcriptData); 166 166 + 167 167 + const did = 'did:plc:test'; 168 168 + const transcriptRecord = { 169 169 + $type: 'tv.ionosphere.transcript' as const, 170 170 + text: compact.text, 171 171 + startMs: compact.startMs, 172 172 + timings: compact.timings, 173 173 + talkUri: `at://${did}/tv.ionosphere.talk/${rkey}`, 174 174 + }; 175 175 + 176 176 + const { expression, segmentations, temporals } = await transcriptToLayersPub(transcriptRecord, did, rkey); 177 177 + const expressionUri = `at://${did}/pub.layers.expression.expression/${rkey}-expression`; 178 178 + const layers = await nlpToAnnotationLayers(nlpData, did, rkey, expressionUri); 179 179 + 180 180 + // Verify records have correct $type 181 181 + expect(expression.$type).toBe('pub.layers.expression.expression'); 182 182 + for (const seg of segmentations) expect(seg.$type).toBe('pub.layers.segmentation.segmentation'); 183 183 + for (const temp of temporals) expect(temp.$type).toBe('pub.layers.segmentation.segmentation'); 184 184 + expect(layers.sentences.$type).toBe('pub.layers.annotation.annotationLayer'); 185 185 + expect(layers.paragraphs.$type).toBe('pub.layers.annotation.annotationLayer'); 186 186 + expect(layers.entities.$type).toBe('pub.layers.annotation.annotationLayer'); 187 187 + expect(layers.topics.$type).toBe('pub.layers.annotation.annotationLayer'); 188 188 + 189 189 + // Verify real data produces non-trivial results (keynote has 8040 tokens = 2 chunks) 190 190 + const totalTokens = segmentations.reduce((n, s) => n + s.tokenizations[0].tokens.length, 0); 191 191 + expect(totalTokens).toBeGreaterThan(100); 192 192 + expect(segmentations.length).toBe(temporals.length); 193 193 + expect(layers.sentences.annotations.length).toBeGreaterThan(10); 194 194 + expect(layers.entities.annotations.length).toBeGreaterThan(10); 195 195 + }); 196 196 + }); 197 197 + 198 198 + describe('Lens 3: round-trip correctness', () => { 199 199 + it('layers.pub → document matches decodeToDocumentWithStructure output', async () => { 200 200 + const fs = await import('node:fs'); 201 201 + const path = await import('node:path'); 202 202 + 203 203 + const transcriptsDir = path.resolve(import.meta.dirname, '../../../data/transcripts'); 204 204 + const nlpDir = path.resolve(import.meta.dirname, '../../../../pipeline/data/nlp'); 205 205 + 206 206 + const rkey = 'ats26-keynote'; 207 207 + const transcriptPath = path.join(transcriptsDir, `${rkey}.json`); 208 208 + const nlpPath = path.join(nlpDir, `${rkey}.json`); 209 209 + 210 210 + if (!fs.existsSync(transcriptPath) || !fs.existsSync(nlpPath)) { 211 211 + console.log('Skipping: fixture data not available'); 212 212 + return; 213 213 + } 214 214 + 215 215 + const { encode, decodeToDocumentWithStructure } = await import( 216 216 + '../../../../formats/tv.ionosphere/ts/transcript-encoding.js' 217 217 + ); 218 218 + 219 219 + const transcriptData = JSON.parse(fs.readFileSync(transcriptPath, 'utf-8')); 220 220 + const nlpData = JSON.parse(fs.readFileSync(nlpPath, 'utf-8')); 221 221 + const compact = encode(transcriptData); 222 222 + 223 223 + // Path A: existing direct path 224 224 + const directDoc = decodeToDocumentWithStructure(compact, nlpData); 225 225 + 226 226 + // Path B: through lenses 227 227 + const did = 'did:plc:test'; 228 228 + const transcriptRecord = { 229 229 + $type: 'tv.ionosphere.transcript' as const, 230 230 + text: compact.text, 231 231 + startMs: compact.startMs, 232 232 + timings: compact.timings, 233 233 + talkUri: `at://${did}/tv.ionosphere.talk/${rkey}`, 234 234 + }; 235 235 + const { expression, segmentations } = await transcriptToLayersPub(transcriptRecord, did, rkey); 236 236 + const expressionUri = `at://${did}/pub.layers.expression.expression/${rkey}-expression`; 237 237 + const layers = await nlpToAnnotationLayers(nlpData, did, rkey, expressionUri); 238 238 + const lensDoc = await layersPubToDocument(expression, segmentations, layers, compact); 239 239 + 240 240 + // Compare text 241 241 + expect(lensDoc.text).toBe(directDoc.text); 242 242 + 243 243 + // Compare facet counts 244 244 + expect(lensDoc.facets.length).toBe(directDoc.facets.length); 245 245 + 246 246 + // Sort both facet arrays by byteStart then by $type for deterministic comparison 247 247 + const sortFacets = (facets: typeof directDoc.facets) => 248 248 + [...facets].sort((a, b) => { 249 249 + const posA = a.index.byteStart; 250 250 + const posB = b.index.byteStart; 251 251 + if (posA !== posB) return posA - posB; 252 252 + const typeA = a.features[0]?.$type ?? ''; 253 253 + const typeB = b.features[0]?.$type ?? ''; 254 254 + return typeA.localeCompare(typeB); 255 255 + }); 256 256 + 257 257 + const sortedDirect = sortFacets(directDoc.facets); 258 258 + const sortedLens = sortFacets(lensDoc.facets); 259 259 + 260 260 + // Check each facet matches 261 261 + for (let i = 0; i < sortedDirect.length; i++) { 262 262 + expect(sortedLens[i].index).toEqual(sortedDirect[i].index); 263 263 + expect(sortedLens[i].features[0].$type).toBe(sortedDirect[i].features[0].$type); 264 264 + } 265 265 + }); 266 266 + });

+2 -1

apps/ionosphere-appview/src/appview.ts

reviewed

··· 1 1 import { serve } from "@hono/node-server"; 2 2 import { openDb, migrate, getCursor, setCursor } from "./db.js"; 3 3 import { createRoutes } from "./routes.js"; 4 4 - import { processEvent } from "./indexer.js"; 4 4 + import { processEvent, setBotDid } from "./indexer.js"; 5 5 import { JetstreamClient } from "./jetstream.js"; 6 6 import { backfill } from "./backfill.js"; 7 7 import { startPublicJetstream } from "./public-jetstream.js"; ··· 39 39 } 40 40 41 41 if (did) { 42 42 + setBotDid(did); 42 43 console.log(`Backfilling from ${did}`); 43 44 try { 44 45 await backfill(db, PDS_URL, did);

+34

apps/ionosphere-appview/src/db.ts

reviewed

··· 252 252 cursor_us INTEGER 253 253 ); 254 254 INSERT OR IGNORE INTO _cursor (id, cursor_us) VALUES (1, NULL); 255 255 + 256 256 + -- layers.pub records 257 257 + CREATE TABLE IF NOT EXISTS layers_expressions ( 258 258 + uri TEXT PRIMARY KEY, 259 259 + rkey TEXT NOT NULL, 260 260 + did TEXT NOT NULL, 261 261 + transcript_uri TEXT NOT NULL, 262 262 + text TEXT NOT NULL, 263 263 + language TEXT NOT NULL DEFAULT 'en', 264 264 + created_at TEXT DEFAULT CURRENT_TIMESTAMP 265 265 + ); 266 266 + CREATE INDEX IF NOT EXISTS idx_layers_expr_transcript ON layers_expressions(transcript_uri); 267 267 + 268 268 + CREATE TABLE IF NOT EXISTS layers_segmentations ( 269 269 + uri TEXT PRIMARY KEY, 270 270 + rkey TEXT NOT NULL, 271 271 + did TEXT NOT NULL, 272 272 + expression_uri TEXT NOT NULL, 273 273 + tokens_json TEXT NOT NULL, 274 274 + created_at TEXT DEFAULT CURRENT_TIMESTAMP 275 275 + ); 276 276 + CREATE INDEX IF NOT EXISTS idx_layers_seg_expression ON layers_segmentations(expression_uri); 277 277 + 278 278 + CREATE TABLE IF NOT EXISTS layers_annotations ( 279 279 + uri TEXT PRIMARY KEY, 280 280 + rkey TEXT NOT NULL, 281 281 + did TEXT NOT NULL, 282 282 + expression_uri TEXT NOT NULL, 283 283 + kind TEXT NOT NULL, 284 284 + subkind TEXT NOT NULL, 285 285 + annotations_json TEXT NOT NULL, 286 286 + created_at TEXT DEFAULT CURRENT_TIMESTAMP 287 287 + ); 288 288 + CREATE INDEX IF NOT EXISTS idx_layers_ann_expression ON layers_annotations(expression_uri); 255 289 `); 256 290 } 257 291

-107

apps/ionosphere-appview/src/enrich-all.ts

reviewed

··· 1 1 - /** 2 2 - * Batch enrichment of all transcribed talks. 3 3 - * 4 4 - * Finds all talks with transcripts but no annotations, then runs 5 5 - * enrichment for each one sequentially. 6 6 - * 7 7 - * Usage: npx tsx src/enrich-all.ts [--limit N] [--dry-run] 8 8 - */ 9 9 - import "./env.js"; 10 10 - import { execFileSync } from "node:child_process"; 11 11 - import path from "node:path"; 12 12 - 13 13 - const PDS_URL = process.env.PDS_URL ?? "http://localhost:2690"; 14 14 - const BOT_HANDLE = process.env.BOT_HANDLE ?? "ionosphere.test"; 15 15 - 16 16 - const limitArg = process.argv.indexOf("--limit"); 17 17 - const limit = 18 18 - limitArg !== -1 ? parseInt(process.argv[limitArg + 1], 10) : Infinity; 19 19 - const dryRun = process.argv.includes("--dry-run"); 20 20 - 21 21 - async function listAll(collection: string, repo: string): Promise<any[]> { 22 22 - const records: any[] = []; 23 23 - let cursor: string | undefined; 24 24 - do { 25 25 - const params = new URLSearchParams({ 26 26 - repo, 27 27 - collection, 28 28 - limit: "100", 29 29 - }); 30 30 - if (cursor) params.set("cursor", cursor); 31 31 - const res = await fetch( 32 32 - `${PDS_URL}/xrpc/com.atproto.repo.listRecords?${params}` 33 33 - ); 34 34 - if (!res.ok) throw new Error(`listRecords failed: ${res.status}`); 35 35 - const data = await res.json(); 36 36 - records.push(...data.records); 37 37 - cursor = data.cursor; 38 38 - } while (cursor); 39 39 - return records; 40 40 - } 41 41 - 42 42 - async function main() { 43 43 - // Resolve DID 44 44 - const handleRes = await fetch( 45 45 - `${PDS_URL}/xrpc/com.atproto.identity.resolveHandle?handle=${BOT_HANDLE}` 46 46 - ); 47 47 - if (!handleRes.ok) throw new Error("Failed to resolve handle"); 48 48 - const { did } = (await handleRes.json()) as { did: string }; 49 49 - console.log(`Resolved ${BOT_HANDLE} → ${did}`); 50 50 - 51 51 - // Get transcript rkeys 52 52 - const transcripts = await listAll("tv.ionosphere.transcript", did); 53 53 - const transcriptTalkRkeys = new Set( 54 54 - transcripts.map((r) => r.uri.split("/").pop()!.replace("-transcript", "")) 55 55 - ); 56 56 - 57 57 - // Get already-enriched talk rkeys from annotations 58 58 - const annotations = await listAll("tv.ionosphere.annotation", did); 59 59 - const enrichedRkeys = new Set<string>(); 60 60 - for (const a of annotations) { 61 61 - const talkUri = a.value?.talkUri; 62 62 - if (talkUri) enrichedRkeys.add(talkUri.split("/").pop()!); 63 63 - } 64 64 - 65 65 - const unenriched = [...transcriptTalkRkeys] 66 66 - .filter((rkey) => !enrichedRkeys.has(rkey)) 67 67 - .sort(); 68 68 - const batch = unenriched.slice(0, limit); 69 69 - 70 70 - console.log(`\nBatch enrichment:`); 71 71 - console.log(` ${transcriptTalkRkeys.size} talks with transcripts`); 72 72 - console.log(` ${enrichedRkeys.size} already enriched`); 73 73 - console.log(` ${unenriched.length} need enrichment`); 74 74 - console.log(` ${batch.length} in this batch`); 75 75 - 76 76 - if (dryRun) { 77 77 - console.log(`\nDry run — would enrich:`); 78 78 - for (const rkey of batch) console.log(` ${rkey}`); 79 79 - return; 80 80 - } 81 81 - 82 82 - const enrichScript = path.resolve(import.meta.dirname, "enrich.ts"); 83 83 - let completed = 0; 84 84 - let failed = 0; 85 85 - 86 86 - for (const rkey of batch) { 87 87 - const idx = completed + failed + 1; 88 88 - console.log(`\n[${idx}/${batch.length}] Enriching ${rkey}...`); 89 89 - try { 90 90 - execFileSync("npx", ["tsx", enrichScript, rkey], { 91 91 - cwd: path.resolve(import.meta.dirname, ".."), 92 92 - stdio: "inherit", 93 93 - timeout: 120_000, // 2 min per talk 94 94 - }); 95 95 - completed++; 96 96 - console.log(` ✓ ${rkey} (${completed} done, ${failed} failed)`); 97 97 - } catch (err: any) { 98 98 - failed++; 99 99 - console.error(` ✗ ${rkey} failed: ${err.message}`); 100 100 - } 101 101 - } 102 102 - 103 103 - console.log(`\n--- Batch complete ---`); 104 104 - console.log(` ${completed} enriched, ${failed} failed`); 105 105 - } 106 106 - 107 107 - main().catch(console.error);

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apps/ionosphere-appview/src/enrich.ts

reviewed

··· 1 1 - /** 2 2 - * LLM-assisted semantic enrichment of talk transcripts. 3 3 - * 4 4 - * Reads talk and transcript records from the PDS, extracts concepts 5 5 - * and cross-references via LLM, and writes concept records back to the PDS. 6 6 - * 7 7 - * Usage: npx tsx src/enrich.ts <rkey> 8 8 - */ 9 9 - import "./env.js"; 10 10 - import OpenAI from "openai"; 11 11 - import { readFileSync } from "node:fs"; 12 12 - import path from "node:path"; 13 13 - import { PdsClient, slugToRkey } from "./pds-client.js"; 14 14 - 15 15 - const PDS_URL = process.env.PDS_URL ?? "http://localhost:2690"; 16 16 - const BOT_HANDLE = process.env.BOT_HANDLE ?? "ionosphere.test"; 17 17 - const BOT_PASSWORD = process.env.BOT_PASSWORD ?? "ionosphere-dev-password"; 18 18 - 19 19 - function loadApiKey(): string { 20 20 - if (process.env.OPENAI_API_KEY) return process.env.OPENAI_API_KEY; 21 21 - try { 22 22 - const envContent = readFileSync( 23 23 - path.resolve(import.meta.dirname, "../.env"), 24 24 - "utf-8" 25 25 - ); 26 26 - for (const line of envContent.split("\n")) { 27 27 - const match = line.match(/^OPENAI_API_KEY=(.+)$/); 28 28 - if (match) return match[1].trim(); 29 29 - } 30 30 - } catch {} 31 31 - throw new Error("OPENAI_API_KEY not found in environment or .env file"); 32 32 - } 33 33 - 34 34 - const llm = new OpenAI({ apiKey: loadApiKey() }); 35 35 - 36 36 - // --- XRPC helpers --- 37 37 - 38 38 - async function listRecords( 39 39 - collection: string, 40 40 - repo: string 41 41 - ): Promise<any[]> { 42 42 - const records: any[] = []; 43 43 - let cursor: string | undefined; 44 44 - 45 45 - do { 46 46 - const params = new URLSearchParams({ 47 47 - repo, 48 48 - collection, 49 49 - limit: "100", 50 50 - }); 51 51 - if (cursor) params.set("cursor", cursor); 52 52 - 53 53 - const res = await fetch( 54 54 - `${PDS_URL}/xrpc/com.atproto.repo.listRecords?${params}` 55 55 - ); 56 56 - if (!res.ok) throw new Error(`listRecords failed: ${res.status}`); 57 57 - const data = await res.json(); 58 58 - records.push(...data.records); 59 59 - cursor = data.cursor; 60 60 - } while (cursor); 61 61 - 62 62 - return records; 63 63 - } 64 64 - 65 65 - async function getRecord( 66 66 - collection: string, 67 67 - repo: string, 68 68 - rkey: string 69 69 - ): Promise<any> { 70 70 - const params = new URLSearchParams({ repo, collection, rkey }); 71 71 - const res = await fetch( 72 72 - `${PDS_URL}/xrpc/com.atproto.repo.getRecord?${params}` 73 73 - ); 74 74 - if (!res.ok) { 75 75 - if (res.status === 404) return null; 76 76 - throw new Error(`getRecord failed: ${res.status}`); 77 77 - } 78 78 - return res.json(); 79 79 - } 80 80 - 81 81 - // --- Types --- 82 82 - 83 83 - interface EnrichmentResult { 84 84 - concepts: Array<{ 85 85 - name: string; 86 86 - aliases?: string[]; 87 87 - description?: string; 88 88 - wikidataId?: string; 89 89 - url?: string; 90 90 - mentions: Array<{ text: string; context: string }>; 91 91 - }>; 92 92 - speakerMentions: Array<{ 93 93 - name: string; 94 94 - handle?: string; 95 95 - mentions: Array<{ text: string; context: string }>; 96 96 - }>; 97 97 - crossRefs: Array<{ 98 98 - targetRkey: string; 99 99 - targetTitle: string; 100 100 - context: string; 101 101 - }>; 102 102 - links: Array<{ 103 103 - url: string; 104 104 - title?: string; 105 105 - context: string; 106 106 - }>; 107 107 - } 108 108 - 109 109 - // --- LLM --- 110 110 - 111 111 - async function enrichTranscript( 112 112 - title: string, 113 113 - speaker: string, 114 114 - transcript: string, 115 115 - talkIndex: string, 116 116 - speakerIndex: string 117 117 - ): Promise<EnrichmentResult> { 118 118 - const response = await llm.chat.completions.create({ 119 119 - model: "gpt-5.4-mini", 120 120 - response_format: { type: "json_object" }, 121 121 - messages: [ 122 122 - { 123 123 - role: "system", 124 124 - content: `You are a research librarian and knowledge graph curator. You analyze conference talk transcripts and extract structured semantic annotations. 125 125 - 126 126 - You will be given: 127 127 - 1. A talk title and speaker 128 128 - 2. The transcript text 129 129 - 3. An index of all talks at the conference 130 130 - 4. An index of all speakers at the conference 131 131 - 132 132 - Extract the following, returning JSON: 133 133 - 134 134 - { 135 135 - "concepts": [ 136 136 - { 137 137 - "name": "canonical name for this concept", 138 138 - "aliases": ["alternative names used in the transcript"], 139 139 - "description": "one-sentence description", 140 140 - "wikidataId": "Q-identifier if you're confident (e.g. Q80071 for AT Protocol)", 141 141 - "url": "canonical URL if applicable (project homepage, spec URL, etc)", 142 142 - "mentions": [{"text": "exact quote from transcript", "context": "surrounding sentence for context"}] 143 143 - } 144 144 - ], 145 145 - "speakerMentions": [ 146 146 - { 147 147 - "name": "person's name as mentioned", 148 148 - "handle": "AT Protocol handle if known from the speaker index", 149 149 - "mentions": [{"text": "exact quote", "context": "surrounding sentence"}] 150 150 - } 151 151 - ], 152 152 - "crossRefs": [ 153 153 - { 154 154 - "targetRkey": "rkey from the talk index", 155 155 - "targetTitle": "title of the referenced talk", 156 156 - "context": "sentence where the cross-reference occurs" 157 157 - } 158 158 - ], 159 159 - "links": [ 160 160 - { 161 161 - "url": "URL mentioned verbally", 162 162 - "title": "description of what it links to", 163 163 - "context": "sentence where mentioned" 164 164 - } 165 165 - ] 166 166 - } 167 167 - 168 168 - Guidelines: 169 169 - - For concepts: cast a wide net. Include: 170 170 - - Technical concepts, protocols, projects, organizations, standards, tools 171 171 - - AT Protocol ecosystem concepts (PDS, DID, Jetstream, lexicons, etc.) 172 172 - - Key metaphors, analogies, and thematic imagery the speaker uses (e.g., "kelp forest", "landslide", "garden") 173 173 - - Books, papers, academic works, and theoretical frameworks referenced 174 174 - - Historical events, places, and phenomena discussed (even as metaphor) 175 175 - - Named entities: companies, communities, movements, legislation 176 176 - - Aim for 20-40 concepts per talk. If a speaker discusses it substantively, it's a concept. 177 177 - - For speaker mentions: people actually named in the transcript, matched to the speaker index when possible. 178 178 - - For cross-refs: when the speaker explicitly references another talk or topic that matches a talk in the index. 179 179 - - For links: URLs spoken aloud or clearly referenced. 180 180 - - Be precise with quotes — use the exact text from the transcript. 181 181 - - Be thorough — if in doubt, include it. A rich knowledge graph is more valuable than a sparse one.`, 182 182 - }, 183 183 - { 184 184 - role: "user", 185 185 - content: `# Talk: ${title} 186 186 - # Speaker: ${speaker} 187 187 - 188 188 - ## Transcript 189 189 - ${transcript} 190 190 - 191 191 - ## All Conference Talks 192 192 - ${talkIndex} 193 193 - 194 194 - ## All Conference Speakers 195 195 - ${speakerIndex}`, 196 196 - }, 197 197 - ], 198 198 - }); 199 199 - 200 200 - const content = response.choices[0]?.message?.content; 201 201 - if (!content) throw new Error("No response from LLM"); 202 202 - 203 203 - return JSON.parse(content); 204 204 - } 205 205 - 206 206 - // --- Main --- 207 207 - 208 208 - async function main() { 209 209 - const rkey = process.argv[2]; 210 210 - if (!rkey) { 211 211 - console.error("Usage: npx tsx src/enrich.ts <rkey>"); 212 212 - process.exit(1); 213 213 - } 214 214 - 215 215 - // Connect to PDS 216 216 - const pds = new PdsClient(PDS_URL); 217 217 - await pds.login(BOT_HANDLE, BOT_PASSWORD); 218 218 - const did = pds.getDid(); 219 219 - console.log(`Connected to PDS as ${did}`); 220 220 - 221 221 - // 1. Read talk record from PDS 222 222 - const talkRecord = await getRecord("tv.ionosphere.talk", did, rkey); 223 223 - if (!talkRecord) { 224 224 - console.error(`Talk not found on PDS: ${rkey}`); 225 225 - process.exit(1); 226 226 - } 227 227 - const talk = talkRecord.value; 228 228 - 229 229 - // 2. Read transcript record from PDS 230 230 - const transcriptRkey = `${rkey}-transcript`; 231 231 - const transcriptRecord = await getRecord( 232 232 - "tv.ionosphere.transcript", 233 233 - did, 234 234 - transcriptRkey 235 235 - ); 236 236 - if (!transcriptRecord) { 237 237 - console.error(`Transcript not found on PDS: ${transcriptRkey}`); 238 238 - process.exit(1); 239 239 - } 240 240 - const transcript = transcriptRecord.value.text; 241 241 - 242 242 - // 3. Build context indexes from PDS records 243 243 - console.log("Building talk and speaker indexes from PDS..."); 244 244 - const allTalks = await listRecords("tv.ionosphere.talk", did); 245 245 - const talkIndex = allTalks 246 246 - .map((r: any) => { 247 247 - const tRkey = r.uri.split("/").pop(); 248 248 - return `${tRkey}: ${r.value.title}`; 249 249 - }) 250 250 - .join("\n"); 251 251 - 252 252 - const allSpeakers = await listRecords("tv.ionosphere.speaker", did); 253 253 - const speakerIndex = allSpeakers 254 254 - .map((r: any) => { 255 255 - const s = r.value; 256 256 - return `${s.name}${s.handle ? ` (@${s.handle})` : ""}`; 257 257 - }) 258 258 - .join("\n"); 259 259 - 260 260 - // Get speaker names for this talk 261 261 - const speakerNames = (talk.speakerUris || []) 262 262 - .map((uri: string) => { 263 263 - const sRkey = uri.split("/").pop(); 264 264 - const speaker = allSpeakers.find( 265 265 - (r: any) => r.uri.split("/").pop() === sRkey 266 266 - ); 267 267 - return speaker?.value.name || sRkey; 268 268 - }) 269 269 - .join(", "); 270 270 - 271 271 - console.log(`Enriching: ${talk.title} (${speakerNames})`); 272 272 - console.log(` Transcript: ${transcript.length} chars`); 273 273 - console.log(` Context: ${allTalks.length} talks, ${allSpeakers.length} speakers`); 274 274 - 275 275 - // 4. Call LLM 276 276 - console.log(" Calling LLM..."); 277 277 - const result = await enrichTranscript( 278 278 - talk.title, 279 279 - speakerNames, 280 280 - transcript, 281 281 - talkIndex, 282 282 - speakerIndex 283 283 - ); 284 284 - 285 285 - console.log(`\nResults:`); 286 286 - console.log(` Concepts: ${result.concepts.length}`); 287 287 - console.log(` Speaker mentions: ${result.speakerMentions.length}`); 288 288 - console.log(` Cross-refs: ${result.crossRefs.length}`); 289 289 - console.log(` Links: ${result.links.length}`); 290 290 - 291 291 - // 5. Delete old annotations for this talk before writing new ones 292 292 - const allAnnotations = await listRecords("tv.ionosphere.annotation", did); 293 293 - const talkUri = `at://${did}/tv.ionosphere.talk/${rkey}`; 294 294 - const transcriptUri = `at://${did}/tv.ionosphere.transcript/${transcriptRkey}`; 295 295 - const oldAnnotations = allAnnotations.filter( 296 296 - (a: any) => a.value.talkUri === talkUri 297 297 - ); 298 298 - if (oldAnnotations.length > 0) { 299 299 - console.log(` Deleting ${oldAnnotations.length} old annotations...`); 300 300 - for (const a of oldAnnotations) { 301 301 - const aRkey = a.uri.split("/").pop(); 302 302 - await pds.deleteRecord("tv.ionosphere.annotation", aRkey); 303 303 - } 304 304 - } 305 305 - 306 306 - // 6. Write concept records and annotation records to PDS 307 307 - const encoder = new TextEncoder(); 308 308 - const transcriptLower = transcript.toLowerCase(); 309 309 - let annotationCount = 0; 310 310 - 311 311 - for (const concept of result.concepts) { 312 312 - const conceptRkey = slugToRkey(concept.name); 313 313 - const conceptUri = `at://${did}/tv.ionosphere.concept/${conceptRkey}`; 314 314 - 315 315 - // Write concept record 316 316 - await pds.putRecord("tv.ionosphere.concept", conceptRkey, { 317 317 - $type: "tv.ionosphere.concept", 318 318 - name: concept.name, 319 319 - ...(concept.aliases?.length && { aliases: concept.aliases }), 320 320 - ...(concept.description && { description: concept.description }), 321 321 - ...(concept.wikidataId && { wikidataId: concept.wikidataId }), 322 322 - ...(concept.url && { url: concept.url }), 323 323 - }); 324 324 - 325 325 - // Find all mentions in the transcript and write annotation records 326 326 - const terms = [concept.name, ...(concept.aliases || [])]; 327 327 - for (const term of terms) { 328 328 - const termLower = term.toLowerCase(); 329 329 - let searchFrom = 0; 330 330 - 331 331 - while (true) { 332 332 - const idx = transcriptLower.indexOf(termLower, searchFrom); 333 333 - if (idx === -1) break; 334 334 - 335 335 - // Word boundary check 336 336 - const before = idx > 0 ? transcript[idx - 1] : " "; 337 337 - const after = 338 338 - idx + term.length < transcript.length 339 339 - ? transcript[idx + term.length] 340 340 - : " "; 341 341 - if (/\w/.test(before) || /\w/.test(after)) { 342 342 - searchFrom = idx + 1; 343 343 - continue; 344 344 - } 345 345 - 346 346 - const byteStart = encoder.encode(transcript.slice(0, idx)).length; 347 347 - const byteEnd = encoder.encode( 348 348 - transcript.slice(0, idx + term.length) 349 349 - ).length; 350 350 - 351 351 - const annotRkey = `${rkey}-${conceptRkey}-${byteStart}`; 352 352 - await pds.putRecord("tv.ionosphere.annotation", annotRkey, { 353 353 - $type: "tv.ionosphere.annotation", 354 354 - transcriptUri, 355 355 - talkUri, 356 356 - conceptUri, 357 357 - byteStart, 358 358 - byteEnd, 359 359 - text: transcript.slice(idx, idx + term.length), 360 360 - }); 361 361 - annotationCount++; 362 362 - 363 363 - searchFrom = idx + term.length; 364 364 - } 365 365 - } 366 366 - 367 367 - console.log(` + concept: ${concept.name}`); 368 368 - } 369 369 - 370 370 - console.log(` ${annotationCount} annotations written`); 371 371 - 372 372 - // Print summary 373 373 - console.log("\n--- Full enrichment output ---"); 374 374 - console.log(JSON.stringify(result, null, 2)); 375 375 - } 376 376 - 377 377 - main().catch(console.error);

+63 -67

apps/ionosphere-appview/src/indexer.ts

reviewed

··· 1 1 import type Database from "better-sqlite3"; 2 2 import { ensureProfile } from "./profiles.js"; 3 3 + import { 4 4 + indexExpression, 5 5 + indexSegmentation, 6 6 + indexAnnotationLayer, 7 7 + deleteExpression, 8 8 + deleteSegmentation, 9 9 + deleteAnnotationLayer, 10 10 + rebuildDocument, 11 11 + } from "./layers-indexer.js"; 3 12 4 13 // ─── Types ──────────────────────────────────────────────────────────────────── 5 14 ··· 17 26 time_us: number; 18 27 } 19 28 29 29 + // ─── Bot DID filter ────────────────────────────────────────────────────────── 30 30 + 31 31 + let _botDid = ""; 32 32 + 33 33 + /** Set the bot DID for filtering layers.pub records. Called from appview.ts. */ 34 34 + export function setBotDid(did: string): void { 35 35 + _botDid = did; 36 36 + } 37 37 + 20 38 // ─── Constants ──────────────────────────────────────────────────────────────── 21 39 22 40 export const IONOSPHERE_COLLECTIONS = [ ··· 25 43 "tv.ionosphere.speaker", 26 44 "tv.ionosphere.concept", 27 45 "tv.ionosphere.transcript", 28 28 - "tv.ionosphere.annotation", 29 46 "tv.ionosphere.comment", 30 47 "tv.ionosphere.stream", 31 48 "tv.ionosphere.streamTranscript", 32 49 "tv.ionosphere.diarization", 33 50 "org.relationaltext.lens", 51 51 + "pub.layers.expression.expression", 52 52 + "pub.layers.segmentation.segmentation", 53 53 + "pub.layers.annotation.annotationLayer", 34 54 ]; 35 55 36 56 const COLLECTIONS_SET = new Set(IONOSPHERE_COLLECTIONS); 37 57 58 58 + const LAYERS_PUB_COLLECTIONS = new Set([ 59 59 + "pub.layers.expression.expression", 60 60 + "pub.layers.segmentation.segmentation", 61 61 + "pub.layers.annotation.annotationLayer", 62 62 + ]); 63 63 + 38 64 // ─── Event processor ────────────────────────────────────────────────────────── 39 65 40 66 export function processEvent(db: Database.Database, event: JetstreamEvent): void { ··· 44 70 if (!COLLECTIONS_SET.has(collection)) return; 45 71 46 72 const uri = `at://${event.did}/${collection}/${rkey}`; 73 73 + 74 74 + // Only process layers.pub records from the bot DID 75 75 + if (LAYERS_PUB_COLLECTIONS.has(collection) && _botDid && event.did !== _botDid) { 76 76 + return; 77 77 + } 47 78 48 79 // ── Deletes ─────────────────────────────────────────────────────────────── 49 80 ··· 69 100 case "tv.ionosphere.transcript": 70 101 db.prepare("DELETE FROM transcripts WHERE uri = ?").run(uri); 71 102 break; 72 72 - case "tv.ionosphere.annotation": 73 73 - db.prepare("DELETE FROM annotations WHERE uri = ?").run(uri); 74 74 - // Recompute talk_concepts for affected talk 75 75 - rebuildTalkConcepts(db, uri); 76 76 - break; 77 103 case "tv.ionosphere.comment": 78 104 db.prepare("DELETE FROM comments WHERE uri = ?").run(uri); 79 105 break; ··· 88 114 break; 89 115 case "org.relationaltext.lens": 90 116 db.prepare("DELETE FROM lenses WHERE uri = ?").run(uri); 117 117 + break; 118 118 + case "pub.layers.expression.expression": 119 119 + deleteExpression(db, uri); 120 120 + break; 121 121 + case "pub.layers.segmentation.segmentation": 122 122 + deleteSegmentation(db, uri); 123 123 + break; 124 124 + case "pub.layers.annotation.annotationLayer": 125 125 + deleteAnnotationLayer(db, uri); 91 126 break; 92 127 } 93 128 return; ··· 113 148 case "tv.ionosphere.transcript": 114 149 indexTranscript(db, event.did, rkey, uri, record); 115 150 break; 116 116 - case "tv.ionosphere.annotation": 117 117 - indexAnnotation(db, event.did, rkey, uri, record); 118 118 - break; 119 151 case "tv.ionosphere.comment": 120 152 indexUserComment(db, event.did, rkey, uri, record); 121 153 break; ··· 131 163 case "org.relationaltext.lens": 132 164 indexLens(db, event.did, rkey, uri, record); 133 165 break; 166 166 + case "pub.layers.expression.expression": 167 167 + indexExpression(db, event.did, rkey, uri, record); 168 168 + rebuildDocument(db, uri).catch((err) => 169 169 + console.error("rebuildDocument error:", err), 170 170 + ); 171 171 + break; 172 172 + case "pub.layers.segmentation.segmentation": 173 173 + indexSegmentation(db, event.did, rkey, uri, record); 174 174 + rebuildDocument(db, (record.expression as string) || "").catch((err) => 175 175 + console.error("rebuildDocument error:", err), 176 176 + ); 177 177 + break; 178 178 + case "pub.layers.annotation.annotationLayer": 179 179 + indexAnnotationLayer(db, event.did, rkey, uri, record); 180 180 + rebuildDocument(db, (record.expression as string) || "").catch((err) => 181 181 + console.error("rebuildDocument error:", err), 182 182 + ); 183 183 + break; 134 184 } 135 185 } 136 186 ··· 175 225 176 226 db.prepare( 177 227 `INSERT OR REPLACE INTO talks 178 178 - (uri, did, rkey, title, description, video_uri, video_offset_ns, video_segments, schedule_uri, event_uri, room, category, talk_type, starts_at, ends_at, duration) 179 179 - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)` 228 228 + (uri, did, rkey, title, description, video_uri, video_offset_ns, video_segments, schedule_uri, event_uri, room, category, talk_type, starts_at, ends_at, duration, document) 229 229 + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)` 180 230 ).run( 181 231 uri, 182 232 did, ··· 193 243 (record.talkType as string) || null, 194 244 (record.startsAt as string) || null, 195 245 (record.endsAt as string) || null, 196 196 - (record.duration as number) || 0 246 246 + (record.duration as number) || 0, 247 247 + (record.document as string) ? JSON.stringify(record.document) : null 197 248 ); 198 249 199 250 // Update speaker join table ··· 277 328 ); 278 329 } 279 330 280 280 - function indexAnnotation( 281 281 - db: Database.Database, 282 282 - did: string, 283 283 - rkey: string, 284 284 - uri: string, 285 285 - record: Record<string, unknown> 286 286 - ): void { 287 287 - const talkUri = (record.talkUri as string) || null; 288 288 - const transcriptUri = record.transcriptUri as string; 289 289 - const conceptUri = record.conceptUri as string; 290 290 - 291 291 - db.prepare( 292 292 - `INSERT OR REPLACE INTO annotations 293 293 - (uri, did, rkey, transcript_uri, talk_uri, concept_uri, byte_start, byte_end, text) 294 294 - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)` 295 295 - ).run( 296 296 - uri, 297 297 - did, 298 298 - rkey, 299 299 - transcriptUri, 300 300 - talkUri, 301 301 - conceptUri, 302 302 - record.byteStart as number, 303 303 - record.byteEnd as number, 304 304 - (record.text as string) || null 305 305 - ); 306 306 - 307 307 - // Maintain talk_concepts join: if this annotation links a concept to a talk, 308 308 - // ensure there's a row in talk_concepts 309 309 - if (talkUri && conceptUri) { 310 310 - db.prepare( 311 311 - `INSERT OR IGNORE INTO talk_concepts (talk_uri, concept_uri, mention_count) 312 312 - VALUES (?, ?, 1)` 313 313 - ).run(talkUri, conceptUri); 314 314 - 315 315 - // Update mention count 316 316 - db.prepare( 317 317 - `UPDATE talk_concepts SET mention_count = ( 318 318 - SELECT COUNT(*) FROM annotations 319 319 - WHERE talk_uri = ? AND concept_uri = ? 320 320 - ) WHERE talk_uri = ? AND concept_uri = ?` 321 321 - ).run(talkUri, conceptUri, talkUri, conceptUri); 322 322 - } 323 323 - } 324 324 - 325 331 function indexLens( 326 332 db: Database.Database, 327 333 did: string, ··· 434 440 ); 435 441 } 436 442 437 437 - function rebuildTalkConcepts(db: Database.Database, _deletedUri: string): void { 438 438 - // Full rebuild — simple and correct for now 439 439 - db.prepare("DELETE FROM talk_concepts").run(); 440 440 - db.prepare( 441 441 - `INSERT INTO talk_concepts (talk_uri, concept_uri, mention_count) 442 442 - SELECT talk_uri, concept_uri, COUNT(*) FROM annotations 443 443 - WHERE talk_uri IS NOT NULL 444 444 - GROUP BY talk_uri, concept_uri` 445 445 - ).run(); 446 446 - }

+219

apps/ionosphere-appview/src/layers-indexer.ts

reviewed

··· 1 1 + import type Database from "better-sqlite3"; 2 2 + import { 3 3 + layersPubToDocument, 4 4 + type ExpressionRecord, 5 5 + type SegmentationRecord, 6 6 + type AnnotationLayersResult, 7 7 + type AnnotationLayerRecord, 8 8 + } from "@ionosphere/format/layers-pub"; 9 9 + 10 10 + // ─── Index functions (create/update) ───────────────────────────────────────── 11 11 + 12 12 + export function indexExpression( 13 13 + db: Database.Database, 14 14 + did: string, 15 15 + rkey: string, 16 16 + uri: string, 17 17 + record: Record<string, unknown>, 18 18 + ): void { 19 19 + db.prepare( 20 20 + `INSERT OR REPLACE INTO layers_expressions 21 21 + (uri, rkey, did, transcript_uri, text, language, created_at) 22 22 + VALUES (?, ?, ?, ?, ?, ?, ?)`, 23 23 + ).run( 24 24 + uri, 25 25 + rkey, 26 26 + did, 27 27 + (record.sourceRef as string) || "", 28 28 + (record.text as string) || "", 29 29 + (record.language as string) || "en", 30 30 + (record.createdAt as string) || new Date().toISOString(), 31 31 + ); 32 32 + } 33 33 + 34 34 + export function indexSegmentation( 35 35 + db: Database.Database, 36 36 + did: string, 37 37 + rkey: string, 38 38 + uri: string, 39 39 + record: Record<string, unknown>, 40 40 + ): void { 41 41 + db.prepare( 42 42 + `INSERT OR REPLACE INTO layers_segmentations 43 43 + (uri, rkey, did, expression_uri, tokens_json, created_at) 44 44 + VALUES (?, ?, ?, ?, ?, ?)`, 45 45 + ).run( 46 46 + uri, 47 47 + rkey, 48 48 + did, 49 49 + (record.expression as string) || "", 50 50 + JSON.stringify(record.tokenizations), 51 51 + (record.createdAt as string) || new Date().toISOString(), 52 52 + ); 53 53 + } 54 54 + 55 55 + export function indexAnnotationLayer( 56 56 + db: Database.Database, 57 57 + did: string, 58 58 + rkey: string, 59 59 + uri: string, 60 60 + record: Record<string, unknown>, 61 61 + ): void { 62 62 + db.prepare( 63 63 + `INSERT OR REPLACE INTO layers_annotations 64 64 + (uri, rkey, did, expression_uri, kind, subkind, annotations_json, created_at) 65 65 + VALUES (?, ?, ?, ?, ?, ?, ?, ?)`, 66 66 + ).run( 67 67 + uri, 68 68 + rkey, 69 69 + did, 70 70 + (record.expression as string) || "", 71 71 + (record.kind as string) || "", 72 72 + (record.subkind as string) || "", 73 73 + JSON.stringify(record.annotations), 74 74 + (record.createdAt as string) || new Date().toISOString(), 75 75 + ); 76 76 + } 77 77 + 78 78 + // ─── Delete functions ──────────────────────────────────────────────────────── 79 79 + 80 80 + export function deleteExpression(db: Database.Database, uri: string): void { 81 81 + // CASCADE: delete segmentations and annotations that reference this expression 82 82 + db.prepare("DELETE FROM layers_segmentations WHERE expression_uri = ?").run(uri); 83 83 + db.prepare("DELETE FROM layers_annotations WHERE expression_uri = ?").run(uri); 84 84 + db.prepare("DELETE FROM layers_expressions WHERE uri = ?").run(uri); 85 85 + } 86 86 + 87 87 + export function deleteSegmentation(db: Database.Database, uri: string): void { 88 88 + db.prepare("DELETE FROM layers_segmentations WHERE uri = ?").run(uri); 89 89 + } 90 90 + 91 91 + export function deleteAnnotationLayer(db: Database.Database, uri: string): void { 92 92 + db.prepare("DELETE FROM layers_annotations WHERE uri = ?").run(uri); 93 93 + } 94 94 + 95 95 + // ─── Document rebuild ──────────────────────────────────────────────────────── 96 96 + 97 97 + /** 98 98 + * Rebuild the materialized document for a talk by re-running Lens 3 99 99 + * (layersPubToDocument) from the stored layers.pub records. 100 100 + * 101 101 + * Called after any layers.pub record is created/updated. If the full set 102 102 + * of records (expression + segmentation + at least one annotation layer) 103 103 + * isn't available yet, this is a no-op — the document will be rebuilt 104 104 + * when the final piece arrives. 105 105 + */ 106 106 + export async function rebuildDocument( 107 107 + db: Database.Database, 108 108 + expressionUri: string, 109 109 + ): Promise<void> { 110 110 + // 1. Look up expression 111 111 + const expr = db 112 112 + .prepare("SELECT * FROM layers_expressions WHERE uri = ?") 113 113 + .get(expressionUri) as any; 114 114 + if (!expr) return; 115 115 + 116 116 + // 2. Look up segmentation chunks (may be multiple for large transcripts) 117 117 + const segRows = db 118 118 + .prepare("SELECT * FROM layers_segmentations WHERE expression_uri = ? AND rkey NOT LIKE '%-temporal%' ORDER BY rkey") 119 119 + .all(expressionUri) as any[]; 120 120 + if (segRows.length === 0) return; 121 121 + 122 122 + // 3. Look up all annotation layers 123 123 + const annRows = db 124 124 + .prepare("SELECT * FROM layers_annotations WHERE expression_uri = ?") 125 125 + .all(expressionUri) as any[]; 126 126 + 127 127 + // Map subkind → layer key 128 128 + const subkindToKey: Record<string, keyof AnnotationLayersResult> = { 129 129 + "sentence-boundary": "sentences", 130 130 + "paragraph-boundary": "paragraphs", 131 131 + ner: "entities", 132 132 + "topic-segment": "topics", 133 133 + }; 134 134 + 135 135 + const annotationLayers: Partial<AnnotationLayersResult> = {}; 136 136 + for (const row of annRows) { 137 137 + const key = subkindToKey[row.subkind]; 138 138 + if (key) { 139 139 + annotationLayers[key] = { 140 140 + $type: "pub.layers.annotation.annotationLayer", 141 141 + expression: expressionUri, 142 142 + kind: row.kind, 143 143 + subkind: row.subkind, 144 144 + sourceMethod: "automatic", 145 145 + metadata: { tool: "ionosphere-pipeline", timestamp: row.created_at }, 146 146 + annotations: JSON.parse(row.annotations_json), 147 147 + createdAt: row.created_at, 148 148 + } as AnnotationLayerRecord; 149 149 + } 150 150 + } 151 151 + 152 152 + // Need at least one annotation layer to produce a useful document 153 153 + if (Object.keys(annotationLayers).length === 0) return; 154 154 + 155 155 + // 4. Build typed records for layersPubToDocument 156 156 + const expressionRecord: ExpressionRecord = { 157 157 + $type: "pub.layers.expression.expression", 158 158 + id: expr.rkey, 159 159 + kind: "transcript", 160 160 + text: expr.text, 161 161 + language: expr.language, 162 162 + sourceRef: expr.transcript_uri, 163 163 + metadata: { tool: "ionosphere-pipeline", timestamp: expr.created_at }, 164 164 + createdAt: expr.created_at, 165 165 + }; 166 166 + 167 167 + const segmentationRecords: SegmentationRecord[] = segRows.map((seg: any) => ({ 168 168 + $type: "pub.layers.segmentation.segmentation" as const, 169 169 + expression: expressionUri, 170 170 + tokenizations: JSON.parse(seg.tokens_json), 171 171 + createdAt: seg.created_at, 172 172 + })); 173 173 + 174 174 + // Fill missing layers with empty annotations so layersPubToDocument gets 175 175 + // the full AnnotationLayersResult shape it expects 176 176 + const emptyLayer = (kind: string, subkind: string): AnnotationLayerRecord => ({ 177 177 + $type: "pub.layers.annotation.annotationLayer", 178 178 + expression: expressionUri, 179 179 + kind, 180 180 + subkind, 181 181 + sourceMethod: "automatic", 182 182 + metadata: { tool: "ionosphere-pipeline", timestamp: "" }, 183 183 + annotations: [], 184 184 + createdAt: "", 185 185 + }); 186 186 + 187 187 + const fullLayers: AnnotationLayersResult = { 188 188 + sentences: annotationLayers.sentences ?? emptyLayer("span", "sentence-boundary"), 189 189 + paragraphs: annotationLayers.paragraphs ?? emptyLayer("span", "paragraph-boundary"), 190 190 + entities: annotationLayers.entities ?? emptyLayer("span", "ner"), 191 191 + topics: annotationLayers.topics ?? emptyLayer("span", "topic-segment"), 192 192 + }; 193 193 + 194 194 + // 5. Look up the transcript for timing data + talk_uri 195 195 + const transcript = db 196 196 + .prepare("SELECT * FROM transcripts WHERE uri = ?") 197 197 + .get(expr.transcript_uri) as any; 198 198 + if (!transcript) return; 199 199 + 200 200 + // 6. Run Lens 3 with compact transcript for timestamp facets 201 201 + const compact = { 202 202 + text: transcript.text, 203 203 + startMs: transcript.start_ms, 204 204 + timings: JSON.parse(transcript.timings), 205 205 + }; 206 206 + 207 207 + const document = await layersPubToDocument( 208 208 + expressionRecord, 209 209 + segmentationRecords, 210 210 + fullLayers, 211 211 + compact, 212 212 + ); 213 213 + 214 214 + // 7. Update the talk's document field 215 215 + db.prepare("UPDATE talks SET document = ? WHERE uri = ?").run( 216 216 + JSON.stringify(document), 217 217 + transcript.talk_uri, 218 218 + ); 219 219 + }

-43

apps/ionosphere-appview/src/publish-annotations.ts

reviewed

··· 1 1 - /** 2 2 - * Publish only annotations to the PDS. Use when other records are 3 3 - * already published but annotations hit a rate limit. 4 4 - */ 5 5 - import { PdsClient } from "./pds-client.js"; 6 6 - import { openDb } from "./db.js"; 7 7 - 8 8 - const PDS_URL = process.env.PDS_URL ?? "http://localhost:2690"; 9 9 - const BOT_HANDLE = process.env.BOT_HANDLE ?? "ionosphere.test"; 10 10 - const BOT_PASSWORD = process.env.BOT_PASSWORD ?? "ionosphere-dev-password"; 11 11 - 12 12 - async function main() { 13 13 - const pds = new PdsClient(PDS_URL); 14 14 - await pds.login(BOT_HANDLE, BOT_PASSWORD); 15 15 - const did = pds.getDid(); 16 16 - const db = openDb(); 17 17 - 18 18 - const annotations = db.prepare("SELECT * FROM annotations").all() as any[]; 19 19 - console.log(`Publishing ${annotations.length} annotations...`); 20 20 - let count = 0; 21 21 - for (const ann of annotations) { 22 22 - const talkUri = ann.talk_uri 23 23 - ? ann.talk_uri.replace(/^at:\/\/[^/]+/, `at://${did}`) 24 24 - : null; 25 25 - const transcriptUri = ann.transcript_uri.replace(/^at:\/\/[^/]+/, `at://${did}`); 26 26 - const conceptUri = ann.concept_uri.replace(/^at:\/\/[^/]+/, `at://${did}`); 27 27 - await pds.putRecord("tv.ionosphere.annotation", ann.rkey, { 28 28 - $type: "tv.ionosphere.annotation", 29 29 - transcriptUri, 30 30 - ...(talkUri && { talkUri }), 31 31 - conceptUri, 32 32 - byteStart: ann.byte_start, 33 33 - byteEnd: ann.byte_end, 34 34 - ...(ann.text && { text: ann.text }), 35 35 - }); 36 36 - count++; 37 37 - if (count % 100 === 0) console.log(` ${count}/${annotations.length}`); 38 38 - } 39 39 - console.log(`Done: ${count} annotations published`); 40 40 - db.close(); 41 41 - } 42 42 - 43 43 - main().catch(console.error);

+121 -52

apps/ionosphere-appview/src/publish.ts

reviewed

··· 11 11 import { openDb } from "./db.js"; 12 12 import { readFileSync, existsSync } from "node:fs"; 13 13 import path from "node:path"; 14 14 - import { encode } from "@ionosphere/format/transcript-encoding"; 14 14 + import { encode, decodeToDocumentWithStructure, type NlpAnnotations } from "@ionosphere/format/transcript-encoding"; 15 15 + import { transcriptToLayersPub, nlpToAnnotationLayers } from "@ionosphere/format/layers-pub"; 15 16 16 17 const PDS_URL = process.env.PDS_URL ?? "http://localhost:2690"; 17 18 const BOT_HANDLE = process.env.BOT_HANDLE ?? "ionosphere.test"; ··· 26 27 // 0. Publish lens records 27 28 console.log("Publishing lens records..."); 28 29 const lensDir = path.resolve(import.meta.dirname, "../../../formats/tv.ionosphere/lenses"); 29 29 - for (const file of ["schedule-to-talk.lens.json", "vod-to-talk.lens.json", "openai-whisper-to-transcript.lens.json", "transcript-to-document.lens.json"]) { 30 30 + for (const file of ["schedule-to-talk.lens.json", "vod-to-talk.lens.json", "openai-whisper-to-transcript.lens.json", "transcript-to-document.lens.json", "transcript-to-expression.lens.json", "nlp-to-annotation-layers.lens.json", "layers-to-document.lens.json"]) { 30 31 const lensPath = path.join(lensDir, file); 31 32 if (!existsSync(lensPath)) continue; 32 33 const spec = JSON.parse(readFileSync(lensPath, "utf-8")); ··· 86 87 ? `at://${did}/tv.ionosphere.event/${event.rkey}` 87 88 : undefined; 88 89 90 90 + const transcriptsDir = path.resolve(import.meta.dirname, "../../data/transcripts"); 91 91 + const nlpDir = path.resolve(import.meta.dirname, "../../../pipeline/data/nlp"); 92 92 + let docCount = 0; 93 93 + 89 94 for (const talk of talks) { 90 95 const speakerRkeys = db 91 96 .prepare( ··· 98 103 (s: any) => `at://${did}/tv.ionosphere.speaker/${s.rkey}` 99 104 ); 100 105 101 101 - await pds.putRecord("tv.ionosphere.talk", talk.rkey, { 102 102 - $type: "tv.ionosphere.talk", 103 103 - title: talk.title, 104 104 - ...(eventUri && { eventUri }), 105 105 - ...(speakerUris.length > 0 && { speakerUris }), 106 106 - ...(talk.video_uri && { videoUri: talk.video_uri }), 107 107 - ...(talk.video_offset_ns && { videoOffsetNs: talk.video_offset_ns }), 108 108 - ...(talk.schedule_uri && { scheduleUri: talk.schedule_uri }), 109 109 - ...(talk.room && { room: talk.room }), 110 110 - ...(talk.category && { category: talk.category }), 111 111 - ...(talk.talk_type && { talkType: talk.talk_type }), 112 112 - ...(talk.starts_at && { startsAt: talk.starts_at }), 113 113 - ...(talk.ends_at && { endsAt: talk.ends_at }), 114 114 - ...(talk.duration && { duration: talk.duration }), 115 115 - ...(talk.description && { description: talk.description }), 116 116 - }); 106 106 + // Try to assemble a document with NLP structural annotations 107 107 + let document = undefined; 108 108 + const nlpPath = path.join(nlpDir, `${talk.rkey}.json`); 109 109 + const transcriptPath = path.join(transcriptsDir, `${talk.rkey}.json`); 110 110 + 111 111 + if (existsSync(nlpPath) && existsSync(transcriptPath)) { 112 112 + const nlpData = JSON.parse(readFileSync(nlpPath, "utf-8")) as { 113 113 + sentences: NlpAnnotations["sentences"]; 114 114 + paragraphs: NlpAnnotations["paragraphs"]; 115 115 + entities: NlpAnnotations["entities"]; 116 116 + topicBreaks: NlpAnnotations["topicBreaks"]; 117 117 + }; 118 118 + const transcriptData = JSON.parse(readFileSync(transcriptPath, "utf-8")); 119 119 + const compact = encode(transcriptData); 120 120 + document = decodeToDocumentWithStructure(compact, { 121 121 + sentences: nlpData.sentences, 122 122 + paragraphs: nlpData.paragraphs, 123 123 + entities: nlpData.entities, 124 124 + topicBreaks: nlpData.topicBreaks, 125 125 + }); 126 126 + docCount++; 127 127 + } 128 128 + 129 129 + try { 130 130 + await pds.putRecord("tv.ionosphere.talk", talk.rkey, { 131 131 + $type: "tv.ionosphere.talk", 132 132 + title: talk.title, 133 133 + ...(document && { document }), 134 134 + ...(eventUri && { eventUri }), 135 135 + ...(speakerUris.length > 0 && { speakerUris }), 136 136 + ...(talk.video_uri && { videoUri: talk.video_uri }), 137 137 + ...(talk.video_offset_ns && { videoOffsetNs: talk.video_offset_ns }), 138 138 + ...(talk.schedule_uri && { scheduleUri: talk.schedule_uri }), 139 139 + ...(talk.room && { room: talk.room }), 140 140 + ...(talk.category && { category: talk.category }), 141 141 + ...(talk.talk_type && { talkType: talk.talk_type }), 142 142 + ...(talk.starts_at && { startsAt: talk.starts_at }), 143 143 + ...(talk.ends_at && { endsAt: talk.ends_at }), 144 144 + ...(talk.duration && { duration: talk.duration }), 145 145 + ...(talk.description && { description: talk.description }), 146 146 + }); 147 147 + } catch (err: any) { 148 148 + if (err?.status === 413) { 149 149 + console.warn(` ⚠ Talk ${talk.rkey}: payload too large, publishing without document`); 150 150 + await pds.putRecord("tv.ionosphere.talk", talk.rkey, { 151 151 + $type: "tv.ionosphere.talk", 152 152 + title: talk.title, 153 153 + ...(eventUri && { eventUri }), 154 154 + ...(speakerUris.length > 0 && { speakerUris }), 155 155 + ...(talk.video_uri && { videoUri: talk.video_uri }), 156 156 + ...(talk.video_offset_ns && { videoOffsetNs: talk.video_offset_ns }), 157 157 + ...(talk.schedule_uri && { scheduleUri: talk.schedule_uri }), 158 158 + ...(talk.room && { room: talk.room }), 159 159 + ...(talk.category && { category: talk.category }), 160 160 + ...(talk.talk_type && { talkType: talk.talk_type }), 161 161 + ...(talk.starts_at && { startsAt: talk.starts_at }), 162 162 + ...(talk.ends_at && { endsAt: talk.ends_at }), 163 163 + ...(talk.duration && { duration: talk.duration }), 164 164 + ...(talk.description && { description: talk.description }), 165 165 + }); 166 166 + } else { 167 167 + throw err; 168 168 + } 169 169 + } 117 170 } 171 171 + console.log(` ${docCount} talks with assembled documents.`); 118 172 console.log(` Done.`); 119 173 120 174 // 4. Publish transcripts from cached files 121 121 - const transcriptsDir = path.resolve(import.meta.dirname, "../../data/transcripts"); 122 175 let transcriptCount = 0; 123 176 124 177 for (const talk of talks) { ··· 140 193 } 141 194 console.log(`\nPublished ${transcriptCount} transcripts.`); 142 195 143 143 - // 5. Publish concepts 144 144 - const concepts = db.prepare("SELECT * FROM concepts").all() as any[]; 145 145 - console.log(`\nPublishing ${concepts.length} concepts...`); 146 146 - for (const concept of concepts) { 147 147 - await pds.putRecord("tv.ionosphere.concept", concept.rkey, { 148 148 - $type: "tv.ionosphere.concept", 149 149 - name: concept.name, 150 150 - ...(concept.aliases && { aliases: JSON.parse(concept.aliases) }), 151 151 - ...(concept.description && { description: concept.description }), 152 152 - ...(concept.wikidata_id && { wikidataId: concept.wikidata_id }), 153 153 - ...(concept.url && { url: concept.url }), 154 154 - }); 155 155 - } 156 156 - console.log(` Done.`); 196 196 + // 5. Publish layers.pub records 197 197 + console.log("\n=== Stage 6: layers.pub records ==="); 198 198 + let layersCount = 0; 157 199 158 158 - // 6. Publish annotations 159 159 - const annotations = db.prepare("SELECT * FROM annotations").all() as any[]; 160 160 - console.log(`\nPublishing ${annotations.length} annotations...`); 161 161 - for (const ann of annotations) { 162 162 - const talkUri = ann.talk_uri 163 163 - ? ann.talk_uri.replace(/^at:\/\/[^/]+/, `at://${did}`) 164 164 - : null; 165 165 - const transcriptUri = ann.transcript_uri.replace(/^at:\/\/[^/]+/, `at://${did}`); 166 166 - const conceptUri = ann.concept_uri.replace(/^at:\/\/[^/]+/, `at://${did}`); 167 167 - await pds.putRecord("tv.ionosphere.annotation", ann.rkey, { 168 168 - $type: "tv.ionosphere.annotation", 169 169 - transcriptUri, 170 170 - ...(talkUri && { talkUri }), 171 171 - conceptUri, 172 172 - byteStart: ann.byte_start, 173 173 - byteEnd: ann.byte_end, 174 174 - ...(ann.text && { text: ann.text }), 175 175 - }); 200 200 + for (const talk of talks) { 201 201 + const transcriptPath = path.join(transcriptsDir, `${talk.rkey}.json`); 202 202 + const nlpPath = path.join(nlpDir, `${talk.rkey}.json`); 203 203 + if (!existsSync(transcriptPath) || !existsSync(nlpPath)) continue; 204 204 + 205 205 + const transcriptData = JSON.parse(readFileSync(transcriptPath, "utf-8")); 206 206 + const nlpData = JSON.parse(readFileSync(nlpPath, "utf-8")); 207 207 + const compact = encode(transcriptData); 208 208 + 209 209 + const transcriptRecord = { 210 210 + $type: "tv.ionosphere.transcript" as const, 211 211 + text: compact.text, 212 212 + startMs: compact.startMs, 213 213 + timings: compact.timings, 214 214 + talkUri: `at://${did}/tv.ionosphere.talk/${talk.rkey}`, 215 215 + }; 216 216 + 217 217 + const { expression, segmentations, temporals } = await transcriptToLayersPub(transcriptRecord, did, talk.rkey); 218 218 + const expressionUri = `at://${did}/pub.layers.expression.expression/${talk.rkey}-expression`; 219 219 + const layers = await nlpToAnnotationLayers(nlpData, did, talk.rkey, expressionUri); 220 220 + 221 221 + const segPuts = segmentations.map((seg, i) => 222 222 + pds.putRecord("pub.layers.segmentation.segmentation", 223 223 + segmentations.length === 1 ? `${talk.rkey}-segmentation` : `${talk.rkey}-segmentation-${i}`, 224 224 + seg) 225 225 + ); 226 226 + const temporalPuts = temporals.map((temp, i) => 227 227 + pds.putRecord("pub.layers.segmentation.segmentation", 228 228 + temporals.length === 1 ? `${talk.rkey}-temporal` : `${talk.rkey}-temporal-${i}`, 229 229 + temp) 230 230 + ); 231 231 + 232 232 + await Promise.all([ 233 233 + pds.putRecord("pub.layers.expression.expression", `${talk.rkey}-expression`, expression), 234 234 + ...segPuts, 235 235 + ...temporalPuts, 236 236 + pds.putRecord("pub.layers.annotation.annotationLayer", `${talk.rkey}-sentences`, layers.sentences), 237 237 + pds.putRecord("pub.layers.annotation.annotationLayer", `${talk.rkey}-paragraphs`, layers.paragraphs), 238 238 + pds.putRecord("pub.layers.annotation.annotationLayer", `${talk.rkey}-entities`, layers.entities), 239 239 + pds.putRecord("pub.layers.annotation.annotationLayer", `${talk.rkey}-topics`, layers.topics), 240 240 + ]); 241 241 + 242 242 + const totalRecords = 1 + segmentations.length + temporals.length + 4; 243 243 + console.log(` layers.pub: ${talk.rkey} (${totalRecords} records${segmentations.length > 1 ? `, ${segmentations.length} seg chunks` : ''})`); 244 244 + layersCount++; 176 245 } 177 177 - console.log(` Done.`); 246 246 + console.log(`Published layers.pub records for ${layersCount} talks.`); 178 247 179 248 console.log(`\nAll records published to ${PDS_URL}`); 180 249 console.log(`DID: ${did}`);

+16 -77

apps/ionosphere-appview/src/routes.ts

reviewed

··· 6 6 import path from "node:path"; 7 7 import { 8 8 decodeToDocument, 9 9 - type Document, 10 10 - type DocumentFacet, 11 9 } from "@ionosphere/format/transcript-encoding"; 12 12 - 13 13 - /** 14 14 - * Overlay concept-ref facets onto a document from annotation records. 15 15 - * Reads pre-computed annotations from the DB — no text matching at serve time. 16 16 - */ 17 17 - function overlayAnnotations( 18 18 - doc: Document, 19 19 - annotations: Array<{ 20 20 - concept_uri: string; 21 21 - byte_start: number; 22 22 - byte_end: number; 23 23 - text: string | null; 24 24 - concept_name: string; 25 25 - concept_rkey: string; 26 26 - }> 27 27 - ): Document { 28 28 - const facets: DocumentFacet[] = annotations.map((a) => { 29 29 - // Find nearest timestamp facet for temporal position 30 30 - let nearestTime = 0; 31 31 - for (const f of doc.facets) { 32 32 - const ts = f.features.find( 33 33 - (feat) => feat.$type === "tv.ionosphere.facet#timestamp" 34 34 - ); 35 35 - if (ts && Math.abs(f.index.byteStart - a.byte_start) < 50) { 36 36 - nearestTime = ts.startTime; 37 37 - break; 38 38 - } 39 39 - } 40 40 - 41 41 - return { 42 42 - index: { byteStart: a.byte_start, byteEnd: a.byte_end }, 43 43 - features: [ 44 44 - { 45 45 - $type: "tv.ionosphere.facet#concept-ref", 46 46 - conceptUri: a.concept_uri, 47 47 - conceptRkey: a.concept_rkey, 48 48 - conceptName: a.concept_name, 49 49 - startTime: nearestTime, 50 50 - }, 51 51 - ], 52 52 - }; 53 53 - }); 54 54 - 55 55 - return { 56 56 - text: doc.text, 57 57 - facets: [...doc.facets, ...facets], 58 58 - }; 59 59 - } 60 10 61 11 export function createRoutes(db: Database.Database): Hono { 62 12 const app = new Hono(); ··· 156 106 ) 157 107 .all((talk as any).uri); 158 108 159 159 - // Decode compact transcript into full document 160 160 - const transcript = db 161 161 - .prepare("SELECT * FROM transcripts WHERE talk_uri = ?") 162 162 - .get((talk as any).uri) as any; 163 163 - 164 109 let document = null; 165 165 - if (transcript) { 166 166 - const compact = { 167 167 - text: transcript.text, 168 168 - startMs: transcript.start_ms, 169 169 - timings: JSON.parse(transcript.timings), 170 170 - }; 171 171 - let doc = decodeToDocument(compact); 110 110 + // Prefer pre-assembled document from talk record 111 111 + if ((talk as any).document) { 112 112 + document = JSON.parse((talk as any).document); 113 113 + } else { 114 114 + // Fallback: decode from compact transcript (no structural facets) 115 115 + const transcript = db 116 116 + .prepare("SELECT * FROM transcripts WHERE talk_uri = ?") 117 117 + .get((talk as any).uri) as any; 172 118 173 173 - // Overlay concept annotations from the DB 174 174 - const annotations = db 175 175 - .prepare( 176 176 - `SELECT a.*, c.name as concept_name, c.rkey as concept_rkey 177 177 - FROM annotations a 178 178 - JOIN concepts c ON c.uri = a.concept_uri 179 179 - WHERE a.transcript_uri = ?` 180 180 - ) 181 181 - .all(transcript.uri) as any[]; 182 182 - 183 183 - if (annotations.length > 0) { 184 184 - doc = overlayAnnotations(doc, annotations); 119 119 + if (transcript) { 120 120 + const compact = { 121 121 + text: transcript.text, 122 122 + startMs: transcript.start_ms, 123 123 + timings: JSON.parse(transcript.timings), 124 124 + }; 125 125 + document = decodeToDocument(compact); 185 126 } 186 186 - 187 187 - document = doc; 188 127 } 189 128 190 129 return c.json({ 191 130 talk: { 192 131 ...(talk as any), 193 193 - document: document ? JSON.stringify(document) : null, 132 132 + document: document ?? null, 194 133 }, 195 134 speakers, 196 135 concepts,

+17 -1

apps/ionosphere-appview/src/tracks.ts

reviewed

··· 325 325 || (hardcoded ? loadDiarizationFromFile(hardcoded.dirName) : []); 326 326 327 327 const streamRecordUri = dbStream ? `at://${dbStream.did}/tv.ionosphere.stream/${slug}` : ""; 328 328 - const transcript = getStreamTranscriptFromDb(db, streamRecordUri) 328 328 + let transcript = getStreamTranscriptFromDb(db, streamRecordUri) 329 329 ?? (hardcoded ? loadTranscriptFromFile(hardcoded.dirName) : null); 330 330 + 331 331 + // Overlay NLP paragraph facets for structural gaps (no entities/concepts — no sidebar to contextualize them) 332 332 + if (transcript) { 333 333 + const nlpPath = path.join(path.resolve(import.meta.dirname, "../../../pipeline/data/stream-nlp"), `stream-${slug}.json`); 334 334 + if (existsSync(nlpPath)) { 335 335 + try { 336 336 + const nlp = JSON.parse(readFileSync(nlpPath, "utf-8")); 337 337 + for (const p of nlp.paragraphs || []) { 338 338 + transcript.facets.push({ index: { byteStart: p.byteStart, byteEnd: p.byteEnd }, features: [{ $type: "tv.ionosphere.facet#paragraph" }] }); 339 339 + } 340 340 + for (const tb of nlp.topicBreaks || []) { 341 341 + transcript.facets.push({ index: { byteStart: tb.byteStart, byteEnd: tb.byteStart }, features: [{ $type: "tv.ionosphere.facet#topic-break" }] }); 342 342 + } 343 343 + } catch {} 344 344 + } 345 345 + } 330 346 const words = getStreamWordsFromDb(db, streamRecordUri).length > 0 331 347 ? getStreamWordsFromDb(db, streamRecordUri) 332 348 : (hardcoded ? loadWordsFromFile(hardcoded.dirName) : []);

+80 -15

apps/ionosphere/src/app/components/TranscriptView.tsx

reviewed

··· 1 1 "use client"; 2 2 3 3 import { useTimestamp } from "./TimestampProvider"; 4 4 - import { useRef, useEffect, useMemo, useCallback, forwardRef, useState } from "react"; 4 4 + import React, { useRef, useEffect, useMemo, useCallback, forwardRef, useState } from "react"; 5 5 import { 6 6 extractData, 7 7 brightnessAtTime, ··· 9 9 type TranscriptDocument, 10 10 type WordSpan, 11 11 type ConceptSpan, 12 12 + type ParagraphSpan, 13 13 + type EntitySpan, 12 14 } from "@/lib/transcript"; 13 15 import { useAuth } from "@/lib/auth"; 14 16 import { publishComment, type CommentData, isEmojiReaction } from "@/lib/comments"; ··· 80 82 const scrollScrubbing = useRef(false); 81 83 const wordRefsMap = useRef<Map<number, HTMLSpanElement>>(new Map()); 82 84 83 83 - const { words, wordConcepts } = useMemo( 85 85 + const { words, wordConcepts, paragraphs, entities, topicBreaks } = useMemo( 84 86 () => extractData(document), 85 87 [document] 86 88 ); 87 89 90 90 + // Map each word object in the paragraph hierarchy to its flat index, 91 91 + // so the nested render can look up wordConcepts, wordRefs, comments, etc. 92 92 + const wordToGlobalIndex = useMemo(() => { 93 93 + const map = new Map<WordSpan, number>(); 94 94 + let idx = 0; 95 95 + for (const para of paragraphs) { 96 96 + for (const sent of para.sentences) { 97 97 + for (const word of sent.words) { 98 98 + map.set(word, idx++); 99 99 + } 100 100 + } 101 101 + } 102 102 + return map; 103 103 + }, [paragraphs]); 104 104 + 105 105 + // Map each word index to its overlapping entity (if any) 106 106 + const wordEntities = useMemo(() => { 107 107 + const map = new Map<number, EntitySpan>(); 108 108 + for (const entity of entities) { 109 109 + for (let i = 0; i < words.length; i++) { 110 110 + const w = words[i]; 111 111 + if (w.byteStart >= entity.byteStart && w.byteEnd <= entity.byteEnd) { 112 112 + if (!map.has(i)) map.set(i, entity); 113 113 + } 114 114 + } 115 115 + } 116 116 + return map; 117 117 + }, [words, entities]); 118 118 + 88 119 // Find the active word index 89 120 const activeIndex = useMemo(() => { 90 121 for (let i = 0; i < words.length; i++) { ··· 197 228 const container = containerRef.current; 198 229 if (!container) return; 199 230 200 200 - const LERP = 0.15; // easing factor — higher = snappier 231 231 + const LERP = 0.25; // easing factor — higher = snappier 232 232 + const SNAP_THRESHOLD = 2; // px — snap to target below this to avoid float 201 233 202 234 const animate = () => { 203 235 // Check selection state directly every frame — more reliable than selectionchange event ··· 206 238 207 239 if (!userScrolling.current && !hasSelection && scrollTarget.current !== null) { 208 240 const diff = scrollTarget.current - container.scrollTop; 209 209 - if (Math.abs(diff) > 0.5) { 241 241 + if (Math.abs(diff) > SNAP_THRESHOLD) { 210 242 container.scrollTop += diff * LERP; 243 243 + } else if (Math.abs(diff) > 0.5) { 244 244 + // Snap to exact target — no more visible floating 245 245 + container.scrollTop = scrollTarget.current; 211 246 } 212 247 } 213 248 animFrameId.current = requestAnimationFrame(animate); ··· 315 350 // Reset the "hand back to auto-scroll" timer 316 351 clearTimeout(userScrollTimer.current); 317 352 userScrollTimer.current = setTimeout(() => { 353 353 + // Anchor auto-scroll to current position so it resumes 354 354 + // smoothly from where the user left off — no jump 355 355 + scrollTarget.current = container.scrollTop; 318 356 userScrolling.current = false; 319 319 - }, paused ? 999999 : 2000); 357 357 + }, paused ? 999999 : 800); 320 358 321 359 cancelAnimationFrame(rafId); 322 360 rafId = requestAnimationFrame(() => { ··· 507 545 </div> 508 546 {/* Top spacer: pushes first word down to the playhead (33% mark) */} 509 547 <div style={{ height: "calc(33% + 1rem)" }} /> 510 510 - {words.map((word, i) => ( 511 511 - <WordSpanComponent 512 512 - key={i} 513 513 - ref={(el) => setWordRef(i, el)} 514 514 - word={word} 515 515 - concept={wordConcepts[i]?.[0] || null} 516 516 - currentTimeNs={currentTimeNs} 517 517 - onSeek={handleSeek} 518 518 - hasComment={wordHasComment.has(i)} 519 519 - /> 548 548 + {paragraphs.map((para, pi) => ( 549 549 + <React.Fragment key={pi}> 550 550 + {topicBreaks.has(pi) && ( 551 551 + <hr className="border-neutral-800 my-6" /> 552 552 + )} 553 553 + <div className="mb-4"> 554 554 + {para.sentences.map((sent, si) => ( 555 555 + <span key={si} className="sentence" style={{ display: "block", marginBottom: "0.25em" }}> 556 556 + {sent.words.map((word) => { 557 557 + const idx = wordToGlobalIndex.get(word) ?? 0; 558 558 + const entityForWord = wordEntities.get(idx); 559 559 + const entityClass = entityForWord 560 560 + ? entityForWord.speakerDid 561 561 + ? "underline decoration-blue-400/40 underline-offset-2" 562 562 + : entityForWord.conceptUri 563 563 + ? "" // Already handled by concept prop 564 564 + : "underline decoration-dotted decoration-neutral-500/40 underline-offset-2" 565 565 + : ""; 566 566 + const wordEl = ( 567 567 + <WordSpanComponent 568 568 + key={idx} 569 569 + ref={(el) => setWordRef(idx, el)} 570 570 + word={word} 571 571 + concept={wordConcepts[idx]?.[0] || null} 572 572 + currentTimeNs={currentTimeNs} 573 573 + onSeek={handleSeek} 574 574 + hasComment={wordHasComment.has(idx)} 575 575 + /> 576 576 + ); 577 577 + return entityClass ? ( 578 578 + <span key={idx} className={entityClass}>{wordEl}</span> 579 579 + ) : wordEl; 580 580 + })} 581 581 + </span> 582 582 + ))} 583 583 + </div> 584 584 + </React.Fragment> 520 585 ))} 521 586 {/* Reaction margin indicators */} 522 587 {[...reactionGroups.entries()].map(([key, group]) => {

+82 -52

apps/ionosphere/src/app/components/VideoPlayer.tsx

reviewed

··· 32 32 // Select AAC audio track before playback starts to avoid mid-play rebuffer. 33 33 // Streamplace serves both original + AAC renditions — switching after 34 34 // playback begins causes a visible stall. 35 35 + // 36 36 + // Strategy: wait for MANIFEST_PARSED, then select the audio track 37 37 + // before any playback begins. Only auto-play after the track is set 38 38 + // and the first fragment is buffered — this avoids the reload cycle 39 39 + // caused by switching tracks mid-stream. 35 40 let audioSettled = false; 41 41 + let seekSettled = offsetS <= 0; // no seek needed if no offset 42 42 + let hasAutoPlayed = false; 43 43 + 44 44 + const tryAutoPlay = () => { 45 45 + if (!hasAutoPlayed && video!.paused && audioSettled && seekSettled) { 46 46 + hasAutoPlayed = true; 47 47 + video!.play().catch(() => {}); 48 48 + } 49 49 + }; 50 50 + 36 51 hls.on(Hls.Events.AUDIO_TRACKS_UPDATED, () => { 52 52 + if (audioSettled) return; // only run once 37 53 const tracks = hls!.audioTracks; 38 54 if (tracks.length > 1) { 39 55 const aacTrack = tracks.findIndex((t: any) => ··· 44 60 } 45 61 } 46 62 audioSettled = true; 63 63 + tryAutoPlay(); 47 64 }); 48 65 49 49 - // Auto-play once audio track is settled and first fragment is buffered 50 50 - let hasAutoPlayed = false; 51 51 - hls.on(Hls.Events.FRAG_BUFFERED, () => { 52 52 - if (!hasAutoPlayed && video!.paused && audioSettled) { 53 53 - hasAutoPlayed = true; 54 54 - video!.play().catch(() => {}); 66 66 + hls.on(Hls.Events.MANIFEST_PARSED, () => { 67 67 + // If no audio tracks event fires within 200ms, consider audio settled 68 68 + setTimeout(() => { 69 69 + if (!audioSettled) { 70 70 + audioSettled = true; 71 71 + tryAutoPlay(); 72 72 + } 73 73 + }, 200); 74 74 + 75 75 + // Seek to offset before playback starts 76 76 + if (offsetS > 0) { 77 77 + video!.currentTime = offsetS; 78 78 + // Wait for seek to complete before allowing auto-play 79 79 + video!.addEventListener("seeked", () => { 80 80 + seekSettled = true; 81 81 + tryAutoPlay(); 82 82 + }, { once: true }); 55 83 } 56 84 }); 57 85 58 58 - // Fallback: if audio tracks never fire (single track), play after manifest 59 59 - hls.on(Hls.Events.MANIFEST_PARSED, () => { 60 60 - // Give AUDIO_TRACKS_UPDATED a chance to fire first 61 61 - setTimeout(() => { audioSettled = true; }, 100); 86 86 + // Auto-play once audio is settled, seek is done, and we have buffered data 87 87 + hls.on(Hls.Events.FRAG_BUFFERED, () => { 88 88 + tryAutoPlay(); 62 89 }); 63 90 64 64 - // Error recovery with logging 65 65 - let mediaErrorRecoveries = 0; 91 91 + // Error recovery — conservative approach to avoid reload cycles. 92 92 + // Only recover from fatal errors; non-fatal errors are handled by HLS.js internally. 66 93 hls.on(Hls.Events.ERROR, (_: any, data: any) => { 67 67 - console.warn("[HLS]", data.type, data.details, data.fatal ? "FATAL" : ""); 68 68 - if (data.type === Hls.ErrorTypes.MEDIA_ERROR) { 69 69 - if (mediaErrorRecoveries < 5) { 70 70 - mediaErrorRecoveries++; 94 94 + if (!data.fatal) return; 95 95 + 96 96 + console.warn("[HLS] Fatal error:", data.type, data.details); 97 97 + switch (data.type) { 98 98 + case Hls.ErrorTypes.NETWORK_ERROR: 99 99 + // Network errors: retry loading 100 100 + hls!.startLoad(); 101 101 + break; 102 102 + case Hls.ErrorTypes.MEDIA_ERROR: 103 103 + // Media errors: single recovery attempt 71 104 hls!.recoverMediaError(); 72 72 - return; 73 73 - } 74 74 - const tracks = hls!.audioTracks; 75 75 - if (tracks.length > 1) { 76 76 - const next = (hls!.audioTrack + 1) % tracks.length; 77 77 - console.warn("[HLS] Swapping to audio track", next); 78 78 - hls!.audioTrack = next; 79 79 - mediaErrorRecoveries = 0; 80 80 - return; 81 81 - } 82 82 - } 83 83 - if (data.fatal) { 84 84 - switch (data.type) { 85 85 - case Hls.ErrorTypes.NETWORK_ERROR: 86 86 - hls!.startLoad(); 87 87 - break; 88 88 - case Hls.ErrorTypes.MEDIA_ERROR: 89 89 - hls!.recoverMediaError(); 90 90 - break; 91 91 - default: 92 92 - hls!.destroy(); 93 93 - break; 94 94 - } 105 105 + break; 106 106 + default: 107 107 + // Unrecoverable: destroy and give up 108 108 + hls!.destroy(); 109 109 + break; 95 110 } 96 111 }); 97 97 - 98 98 - // Seek to offset once media is loaded 99 99 - if (offsetS > 0) { 100 100 - hls.on(Hls.Events.MANIFEST_PARSED, () => { 101 101 - video!.currentTime = offsetS; 102 102 - }); 103 103 - } 104 112 } else if (video!.canPlayType("application/vnd.apple.mpegurl")) { 105 113 video!.src = playlistUrl; 106 114 if (offsetS > 0) { ··· 115 123 return () => { if (hls) hls.destroy(); }; 116 124 }, [playlistUrl, offsetS]); 117 125 118 118 - // Broadcast current time and play/pause state 126 126 + // Broadcast current time at 60fps via RAF (instead of ~4Hz timeupdate). 127 127 + // This eliminates up to 250ms of stale time data and makes the 128 128 + // transcript highlight track the audio much more tightly. 119 129 useEffect(() => { 120 130 const video = videoRef.current; 121 131 if (!video) return; 122 132 123 123 - const onTime = () => { 133 133 + let rafId = 0; 134 134 + let isPlaying = false; 135 135 + 136 136 + const pollTime = () => { 137 137 + setCurrentTimeNs((video.currentTime - offsetS) * 1e9); 138 138 + if (isPlaying) rafId = requestAnimationFrame(pollTime); 139 139 + }; 140 140 + 141 141 + const onPlay = () => { 142 142 + isPlaying = true; 143 143 + setPaused(false); 144 144 + rafId = requestAnimationFrame(pollTime); 145 145 + }; 146 146 + const onPause = () => { 147 147 + isPlaying = false; 148 148 + setPaused(true); 149 149 + cancelAnimationFrame(rafId); 150 150 + // One final update to capture exact pause position 124 151 setCurrentTimeNs((video.currentTime - offsetS) * 1e9); 125 152 }; 126 126 - const onPlay = () => setPaused(false); 127 127 - const onPause = () => setPaused(true); 153 153 + // Also update on seek (click-to-seek while paused) 154 154 + const onSeeked = () => { 155 155 + setCurrentTimeNs((video.currentTime - offsetS) * 1e9); 156 156 + }; 128 157 129 129 - video.addEventListener("timeupdate", onTime); 130 158 video.addEventListener("play", onPlay); 131 159 video.addEventListener("pause", onPause); 160 160 + video.addEventListener("seeked", onSeeked); 132 161 return () => { 133 133 - video.removeEventListener("timeupdate", onTime); 162 162 + cancelAnimationFrame(rafId); 134 163 video.removeEventListener("play", onPlay); 135 164 video.removeEventListener("pause", onPause); 165 165 + video.removeEventListener("seeked", onSeeked); 136 166 }; 137 167 }, [setCurrentTimeNs, setPaused, offsetS]); 138 168

+41 -10

apps/ionosphere/src/app/components/WindowedTranscriptView.tsx

reviewed

··· 68 68 timeEnd: number; // ns 69 69 wordStartIdx: number; 70 70 wordEndIdx: number; // exclusive 71 71 + isParagraphStart: boolean; 71 72 } 72 73 73 74 /** ··· 80 81 containerWidthPx: number, 81 82 charWidthPx: number, 82 83 lineHeightPx: number, 84 84 + paragraphStarts: Set<number> = new Set(), 83 85 ): LineEntry[] { 84 86 if (words.length === 0 || containerWidthPx < charWidthPx) return []; 85 87 86 88 const maxCharsPerLine = Math.floor(containerWidthPx / charWidthPx); 87 89 const lines: LineEntry[] = []; 88 90 89 89 - let lineIdx = 0; 91 91 + let currentY = 0; 90 92 let col = 0; // current column position in characters 91 93 let lineWordStart = 0; 92 94 ··· 96 98 97 99 if (col > 0 && col + wordLen > maxCharsPerLine) { 98 100 // This word doesn't fit — flush current line 101 101 + const isParaStart = paragraphStarts.has(lineWordStart); 102 102 + if (isParaStart) { 103 103 + currentY += lineHeightPx; // add paragraph gap 104 104 + } 99 105 lines.push({ 100 100 - yTop: lineIdx * lineHeightPx, 101 101 - yBottom: (lineIdx + 1) * lineHeightPx, 106 106 + yTop: currentY, 107 107 + yBottom: currentY + lineHeightPx, 102 108 timeStart: words[lineWordStart].startTime, 103 109 timeEnd: words[i - 1].endTime, 104 110 wordStartIdx: lineWordStart, 105 111 wordEndIdx: i, 112 112 + isParagraphStart: isParaStart, 106 113 }); 107 107 - lineIdx++; 114 114 + currentY += lineHeightPx; 108 115 col = 0; 109 116 lineWordStart = i; 110 117 } ··· 114 121 115 122 // Flush last line 116 123 if (lineWordStart < words.length) { 124 124 + const isParaStart = paragraphStarts.has(lineWordStart); 125 125 + if (isParaStart) { 126 126 + currentY += lineHeightPx; // add paragraph gap 127 127 + } 117 128 lines.push({ 118 118 - yTop: lineIdx * lineHeightPx, 119 119 - yBottom: (lineIdx + 1) * lineHeightPx, 129 129 + yTop: currentY, 130 130 + yBottom: currentY + lineHeightPx, 120 131 timeStart: words[lineWordStart].startTime, 121 132 timeEnd: words[words.length - 1].endTime, 122 133 wordStartIdx: lineWordStart, 123 134 wordEndIdx: words.length, 135 135 + isParagraphStart: isParaStart, 124 136 }); 125 137 } 126 138 ··· 135 147 136 148 export default function WindowedTranscriptView({ document }: WindowedTranscriptViewProps) { 137 149 const { currentTimeNs, seekTo, paused } = useTimestamp(); 150 150 + const pausedRef = useRef(paused); 151 151 + pausedRef.current = paused; 138 152 const containerRef = useRef<HTMLDivElement>(null); 139 153 const [containerWidth, setContainerWidth] = useState(0); 140 154 141 141 - const { words, wordConcepts } = useMemo(() => extractData(document), [document]); 155 155 + const { words, wordConcepts, paragraphs } = useMemo(() => extractData(document), [document]); 156 156 + 157 157 + // Compute paragraph start word indices for gap insertion 158 158 + const paragraphStartIndices = useMemo(() => { 159 159 + const set = new Set<number>(); 160 160 + let globalIdx = 0; 161 161 + for (const para of paragraphs) { 162 162 + const firstWordIdx = globalIdx; 163 163 + for (const sent of para.sentences) { 164 164 + globalIdx += sent.words.length; 165 165 + } 166 166 + if (firstWordIdx > 0) { 167 167 + set.add(firstWordIdx); 168 168 + } 169 169 + } 170 170 + return set; 171 171 + }, [paragraphs]); 142 172 143 173 // Measure container width 144 174 useEffect(() => { ··· 171 201 // Compute layout — pure computation, no DOM/canvas needed 172 202 const lines = useMemo(() => { 173 203 if (containerWidth < 50) return []; 174 174 - return computeMonospaceLayout(words, containerWidth, charWidth, LINE_HEIGHT); 175 175 - }, [words, containerWidth, charWidth]); 204 204 + return computeMonospaceLayout(words, containerWidth, charWidth, LINE_HEIGHT, paragraphStartIndices); 205 205 + }, [words, containerWidth, charWidth, paragraphStartIndices]); 176 206 177 207 const totalHeight = lines.length > 0 ? lines[lines.length - 1].yBottom : 0; 178 208 ··· 290 320 291 321 if (!userScrolling.current && scrollTargetRef.current !== null) { 292 322 // Extrapolate target forward using velocity since last timeupdate 293 293 - const msSinceUpdate = now - lastTickMs.current; 323 323 + // When paused, don't extrapolate — velocity is stale 324 324 + const msSinceUpdate = pausedRef.current ? 0 : now - lastTickMs.current; 294 325 const extrapolated = scrollTargetRef.current + scrollVelocity.current * msSinceUpdate; 295 326 296 327 const diff = extrapolated - container.scrollTop;

+1 -1

apps/ionosphere/src/app/concepts/ConceptsListContent.tsx

reviewed

··· 99 99 const res = await fetch(`${API_BASE}/xrpc/tv.ionosphere.getTalk?rkey=${encodeURIComponent(rkey)}`); 100 100 if (!res.ok) return; 101 101 const { talk } = await res.json(); 102 102 - const doc = talk.document ? JSON.parse(talk.document) : null; 102 102 + const doc = talk.document ? (typeof talk.document === "string" ? JSON.parse(talk.document) : talk.document) : null; 103 103 setSelectedTalk({ 104 104 rkey, title: talk.title, videoUri: talk.video_uri, 105 105 offsetNs: talk.video_offset_ns || 0,

+1 -1

apps/ionosphere/src/app/concordance/IndexContent.tsx

reviewed

··· 422 422 const res = await fetch(`${API_BASE}/xrpc/tv.ionosphere.getTalk?rkey=${encodeURIComponent(rkey)}`); 423 423 if (!res.ok) return; 424 424 const { talk } = await res.json(); 425 425 - const doc = talk.document ? JSON.parse(talk.document) : null; 425 425 + const doc = talk.document ? (typeof talk.document === "string" ? JSON.parse(talk.document) : talk.document) : null; 426 426 setSelectedTalk({ 427 427 rkey, title: talk.title, videoUri: talk.video_uri, 428 428 offsetNs: talk.video_offset_ns || 0,

+1 -1

apps/ionosphere/src/app/speakers/SpeakersListContent.tsx

reviewed

··· 152 152 const talkRes = await fetch(`${API_BASE}/xrpc/tv.ionosphere.getTalk?rkey=${encodeURIComponent(rkey)}`); 153 153 if (!talkRes.ok) return; 154 154 const { talk } = await talkRes.json(); 155 155 - const doc = talk.document ? JSON.parse(talk.document) : null; 155 155 + const doc = talk.document ? (typeof talk.document === "string" ? JSON.parse(talk.document) : talk.document) : null; 156 156 157 157 setSelectedTalk({ 158 158 rkey: talk.rkey,

+1 -1

apps/ionosphere/src/app/talks/TalksListContent.tsx

reviewed

··· 168 168 const res = await fetch(`${API_BASE}/xrpc/tv.ionosphere.getTalk?rkey=${encodeURIComponent(rkey)}`); 169 169 if (!res.ok) return; 170 170 const { talk } = await res.json(); 171 171 - const doc = talk.document ? JSON.parse(talk.document) : null; 171 171 + const doc = talk.document ? (typeof talk.document === "string" ? JSON.parse(talk.document) : talk.document) : null; 172 172 setSelectedTalk({ 173 173 rkey, 174 174 title: talk.title,

+67 -20

apps/ionosphere/src/app/talks/[rkey]/TalkContent.tsx

reviewed

··· 1 1 "use client"; 2 2 3 3 import { useMemo, useRef, useState, useEffect, useCallback } from "react"; 4 4 - import { TimestampProvider } from "@/app/components/TimestampProvider"; 4 4 + import { TimestampProvider, useTimestamp } from "@/app/components/TimestampProvider"; 5 5 import VideoPlayer from "@/app/components/VideoPlayer"; 6 6 import TranscriptView from "@/app/components/TranscriptView"; 7 7 import { fetchComments, type CommentData } from "@/lib/comments"; 8 8 import ReactionBar from "@/app/components/ReactionBar"; 9 9 import MentionsSidebar from "@/app/components/MentionsSidebar"; 10 10 + 11 11 + function ConceptSidebar({ concepts }: { concepts: Array<{ rkey: string; name: string; timeNs?: number }> }) { 12 12 + const { seekTo } = useTimestamp(); 13 13 + return ( 14 14 + <section> 15 15 + <h2 className="text-xs font-semibold text-neutral-500 uppercase tracking-wide mb-2">Concepts</h2> 16 16 + <div className="flex flex-wrap gap-1.5"> 17 17 + {concepts.map((c) => ( 18 18 + <button 19 19 + key={c.rkey} 20 20 + onClick={() => c.timeNs ? seekTo(c.timeNs) : undefined} 21 21 + className="text-xs px-2 py-0.5 rounded-full bg-amber-500/10 text-amber-300/80 hover:bg-amber-500/20 hover:text-amber-200 transition-colors cursor-pointer" 22 22 + title={c.timeNs ? `Jump to ${Math.floor(c.timeNs / 1e9 / 60)}:${String(Math.floor((c.timeNs / 1e9) % 60)).padStart(2, "0")}` : c.name} 23 23 + > 24 24 + {c.name} 25 25 + </button> 26 26 + ))} 27 27 + </div> 28 28 + </section> 29 29 + ); 30 30 + } 10 31 11 32 interface TalkContentProps { 12 33 talk: any; ··· 57 78 const durationMin = talk.duration ? (talk.duration / 1e9 / 60).toFixed(0) : null; 58 79 const document = useMemo(() => { 59 80 if (!talk.document) return null; 60 60 - const doc = JSON.parse(talk.document); 81 81 + const doc = typeof talk.document === "string" ? JSON.parse(talk.document) : talk.document; 61 82 return doc?.facets?.length > 0 ? doc : null; 62 83 }, [talk.document]); 84 84 + 85 85 + // Derive concepts from document facets (concept-ref entities) 86 86 + // For each concept, find the timestamp of its first mention 87 87 + const docConcepts = useMemo(() => { 88 88 + if (!document) return []; 89 89 + 90 90 + // Build a byte→time lookup from timestamp facets 91 91 + const byteToTime: Array<{ byteStart: number; byteEnd: number; startTime: number }> = []; 92 92 + for (const f of document.facets) { 93 93 + for (const feat of f.features) { 94 94 + if (feat.$type === "tv.ionosphere.facet#timestamp" && feat.startTime != null) { 95 95 + byteToTime.push({ byteStart: f.index.byteStart, byteEnd: f.index.byteEnd, startTime: feat.startTime }); 96 96 + } 97 97 + } 98 98 + } 99 99 + 100 100 + const seen = new Map<string, { name: string; uri: string; rkey: string; timeNs: number }>(); 101 101 + for (const f of document.facets) { 102 102 + for (const feat of f.features) { 103 103 + if (feat.$type === "tv.ionosphere.facet#concept-ref" && feat.conceptUri) { 104 104 + if (!seen.has(feat.conceptUri)) { 105 105 + const rkey = feat.conceptUri.split("/").pop() || ""; 106 106 + // Find the nearest timestamp facet overlapping this byte range 107 107 + let timeNs = 0; 108 108 + for (const ts of byteToTime) { 109 109 + if (ts.byteStart < f.index.byteEnd && ts.byteEnd > f.index.byteStart) { 110 110 + timeNs = ts.startTime; 111 111 + break; 112 112 + } 113 113 + } 114 114 + seen.set(feat.conceptUri, { 115 115 + name: feat.conceptName || feat.label || rkey, 116 116 + uri: feat.conceptUri, 117 117 + rkey, 118 118 + timeNs, 119 119 + }); 120 120 + } 121 121 + } 122 122 + } 123 123 + } 124 124 + return [...seen.values()].sort((a, b) => a.name.localeCompare(b.name)); 125 125 + }, [document]); 63 126 64 127 const videoContainerRef = useRef<HTMLDivElement>(null); 65 128 const [videoWidth, setVideoWidth] = useState<number | null>(null); ··· 207 270 : "text-neutral-500 hover:text-neutral-300" 208 271 }`} 209 272 > 210 210 - Concepts ({concepts.length}) 273 273 + Concepts ({(concepts.length > 0 ? concepts : docConcepts).length}) 211 274 </button> 212 275 <button 213 276 onClick={() => setSidebarTab("mentions")} ··· 224 287 {/* Tab content */} 225 288 <div className="flex-1 overflow-y-auto p-4"> 226 289 {sidebarTab === "concepts" && ( 227 227 - <> 228 228 - {concepts.length > 0 ? ( 229 229 - <div className="flex flex-wrap gap-1.5"> 230 230 - {concepts.map((c: any) => ( 231 231 - <a 232 232 - key={c.rkey} 233 233 - href={`/concepts/${c.rkey}`} 234 234 - className="text-xs px-2 py-0.5 rounded-full bg-amber-500/10 text-amber-300/80 hover:bg-amber-500/20 hover:text-amber-200 transition-colors" 235 235 - > 236 236 - {c.name} 237 237 - </a> 238 238 - ))} 239 239 - </div> 240 240 - ) : ( 241 241 - <div className="text-xs text-neutral-500">No concepts extracted yet.</div> 242 242 - )} 243 243 - </> 290 290 + <ConceptSidebar concepts={concepts.length > 0 ? concepts : docConcepts} /> 244 291 )} 245 292 {sidebarTab === "mentions" && ( 246 293 <MentionsSidebar mentions={mentions} words={[]} />

+8 -19

apps/ionosphere/src/app/tracks/page.tsx

reviewed

··· 28 28 } 29 29 30 30 return ( 31 31 - <div className="max-w-3xl mx-auto px-4 py-8"> 32 32 - <h1 className="text-2xl font-bold mb-6">Full-Day Streams</h1> 33 33 - <p className="text-neutral-400 text-sm mb-8"> 34 34 - Browse complete conference recordings by room and day. Each track shows 35 35 - the full stream with talk segments, speaker diarization, and transcript. 36 36 - </p> 37 37 - 31 31 + <div className="max-w-5xl mx-auto px-4 py-6"> 38 32 {DAY_ORDER.filter((d) => byDay.has(d)).map((day) => ( 39 39 - <div key={day} className="mb-8"> 40 40 - <h2 className="text-lg font-semibold text-neutral-300 mb-3 border-b border-neutral-800 pb-1"> 33 33 + <div key={day} className="mb-6"> 34 34 + <h2 className="text-xs font-semibold text-neutral-500 uppercase tracking-wide mb-3"> 41 35 {day} 42 36 </h2> 43 43 - <div className="space-y-2"> 37 37 + <div className="grid gap-2 sm:grid-cols-2 lg:grid-cols-3"> 44 38 {byDay.get(day)!.map((track) => ( 45 39 <Link 46 40 key={track.slug} 47 41 href={`/tracks/${track.slug}`} 48 48 - className="block p-4 rounded-lg border border-neutral-800 hover:border-neutral-600 transition-colors" 42 42 + className="block p-3 rounded-lg bg-neutral-900/50 hover:bg-neutral-800/70 transition-colors" 49 43 > 50 50 - <div className="flex items-baseline justify-between"> 51 51 - <span className="font-medium">{track.room}</span> 52 52 - <span className="text-sm text-neutral-500"> 53 53 - {formatDuration(track.durationSeconds)} 54 54 - </span> 55 55 - </div> 56 56 - <div className="text-sm text-neutral-400 mt-1"> 57 57 - {track.talkCount} talks 44 44 + <div className="font-medium text-sm">{track.room}</div> 45 45 + <div className="text-xs text-neutral-500 mt-1"> 46 46 + {track.talkCount} talks · {formatDuration(track.durationSeconds)} 58 47 </div> 59 48 </Link> 60 49 ))}

+197

apps/ionosphere/src/lib/transcript.test.ts

reviewed

··· 8 8 WINDOW_NS, 9 9 type TranscriptDocument, 10 10 type ConceptSpan, 11 11 + type EntitySpan, 12 12 + type ParagraphSpan, 13 13 + type SentenceSpan, 11 14 } from "./transcript"; 12 15 13 16 // --------------------------------------------------------------------------- ··· 246 249 expect(Math.abs(g - b)).toBeLessThan(15); 247 250 }); 248 251 }); 252 252 + 253 253 + // --------------------------------------------------------------------------- 254 254 + // extractData — hierarchical structure 255 255 + // --------------------------------------------------------------------------- 256 256 + 257 257 + describe("extractData — hierarchical structure", () => { 258 258 + it("groups words into sentences and paragraphs when facets present", () => { 259 259 + const doc = makeDoc([ 260 260 + { text: "Hello", startNs: 1000, endNs: 2000 }, 261 261 + { text: "world.", startNs: 2000, endNs: 3000 }, 262 262 + { text: "New", startNs: 4000, endNs: 5000 }, 263 263 + { text: "sentence.", startNs: 5000, endNs: 6000 }, 264 264 + ]); 265 265 + const encoder = new TextEncoder(); 266 266 + const text = "Hello world. New sentence."; 267 267 + // Add sentence facets 268 268 + doc.facets.push({ 269 269 + index: { 270 270 + byteStart: 0, 271 271 + byteEnd: encoder.encode("Hello world.").length, 272 272 + }, 273 273 + features: [{ $type: "tv.ionosphere.facet#sentence" }], 274 274 + }); 275 275 + doc.facets.push({ 276 276 + index: { 277 277 + byteStart: encoder.encode("Hello world. ").length, 278 278 + byteEnd: encoder.encode(text).length, 279 279 + }, 280 280 + features: [{ $type: "tv.ionosphere.facet#sentence" }], 281 281 + }); 282 282 + // Add paragraph facet covering everything 283 283 + doc.facets.push({ 284 284 + index: { byteStart: 0, byteEnd: encoder.encode(text).length }, 285 285 + features: [{ $type: "tv.ionosphere.facet#paragraph" }], 286 286 + }); 287 287 + 288 288 + const result = extractData(doc); 289 289 + expect(result.paragraphs).toHaveLength(1); 290 290 + expect(result.paragraphs[0].sentences).toHaveLength(2); 291 291 + expect(result.paragraphs[0].sentences[0].words).toHaveLength(2); 292 292 + expect(result.paragraphs[0].sentences[1].words).toHaveLength(2); 293 293 + }); 294 294 + 295 295 + it("gracefully degrades to singleton paragraph/sentence when no structural facets", () => { 296 296 + const doc = makeDoc([ 297 297 + { text: "Hello", startNs: 1000, endNs: 2000 }, 298 298 + { text: "world", startNs: 2000, endNs: 3000 }, 299 299 + ]); 300 300 + 301 301 + const result = extractData(doc); 302 302 + expect(result.paragraphs).toHaveLength(1); 303 303 + expect(result.paragraphs[0].sentences).toHaveLength(1); 304 304 + expect(result.paragraphs[0].sentences[0].words).toHaveLength(2); 305 305 + // Legacy flat access still works 306 306 + expect(result.words).toHaveLength(2); 307 307 + }); 308 308 + 309 309 + it("handles multiple paragraphs with multiple sentences each", () => { 310 310 + const doc = makeDoc([ 311 311 + { text: "A", startNs: 1000, endNs: 2000 }, 312 312 + { text: "B.", startNs: 2000, endNs: 3000 }, 313 313 + { text: "C", startNs: 4000, endNs: 5000 }, 314 314 + { text: "D.", startNs: 5000, endNs: 6000 }, 315 315 + { text: "E", startNs: 7000, endNs: 8000 }, 316 316 + { text: "F.", startNs: 8000, endNs: 9000 }, 317 317 + ]); 318 318 + const encoder = new TextEncoder(); 319 319 + const text = "A B. C D. E F."; 320 320 + 321 321 + // 3 sentences 322 322 + const s1End = encoder.encode("A B.").length; 323 323 + const s2Start = encoder.encode("A B. ").length; 324 324 + const s2End = encoder.encode("A B. C D.").length; 325 325 + const s3Start = encoder.encode("A B. C D. ").length; 326 326 + const s3End = encoder.encode(text).length; 327 327 + 328 328 + doc.facets.push({ index: { byteStart: 0, byteEnd: s1End }, features: [{ $type: "tv.ionosphere.facet#sentence" }] }); 329 329 + doc.facets.push({ index: { byteStart: s2Start, byteEnd: s2End }, features: [{ $type: "tv.ionosphere.facet#sentence" }] }); 330 330 + doc.facets.push({ index: { byteStart: s3Start, byteEnd: s3End }, features: [{ $type: "tv.ionosphere.facet#sentence" }] }); 331 331 + 332 332 + // 2 paragraphs: first has sentences 1-2, second has sentence 3 333 333 + doc.facets.push({ index: { byteStart: 0, byteEnd: s2End }, features: [{ $type: "tv.ionosphere.facet#paragraph" }] }); 334 334 + doc.facets.push({ index: { byteStart: s3Start, byteEnd: s3End }, features: [{ $type: "tv.ionosphere.facet#paragraph" }] }); 335 335 + 336 336 + const result = extractData(doc); 337 337 + expect(result.paragraphs).toHaveLength(2); 338 338 + expect(result.paragraphs[0].sentences).toHaveLength(2); 339 339 + expect(result.paragraphs[1].sentences).toHaveLength(1); 340 340 + expect(result.words).toHaveLength(6); // flat access still works 341 341 + }); 342 342 + }); 343 343 + 344 344 + // --------------------------------------------------------------------------- 345 345 + // extractData — entities and topic breaks 346 346 + // --------------------------------------------------------------------------- 347 347 + 348 348 + describe("extractData — entities and topic breaks", () => { 349 349 + it("extracts entity spans from facets", () => { 350 350 + const doc = makeDoc([ 351 351 + { text: "Matt", startNs: 1000, endNs: 2000 }, 352 352 + { text: "presented.", startNs: 2000, endNs: 3000 }, 353 353 + ]); 354 354 + const encoder = new TextEncoder(); 355 355 + doc.facets.push({ 356 356 + index: { byteStart: 0, byteEnd: encoder.encode("Matt").length }, 357 357 + features: [{ 358 358 + $type: "tv.ionosphere.facet#speaker-ref", 359 359 + speakerDid: "did:plc:matt123", 360 360 + label: "Matt", 361 361 + }], 362 362 + }); 363 363 + 364 364 + const result = extractData(doc); 365 365 + expect(result.entities).toHaveLength(1); 366 366 + expect(result.entities[0].speakerDid).toBe("did:plc:matt123"); 367 367 + expect(result.entities[0].byteStart).toBe(0); 368 368 + }); 369 369 + 370 370 + it("extracts concept-ref entities", () => { 371 371 + const doc = makeDoc([ 372 372 + { text: "AT", startNs: 1000, endNs: 2000 }, 373 373 + { text: "Protocol", startNs: 2000, endNs: 3000 }, 374 374 + ]); 375 375 + const encoder = new TextEncoder(); 376 376 + doc.facets.push({ 377 377 + index: { byteStart: 0, byteEnd: encoder.encode("AT Protocol").length }, 378 378 + features: [{ 379 379 + $type: "tv.ionosphere.facet#concept-ref", 380 380 + conceptUri: "at://did:example/concept/atp", 381 381 + conceptRkey: "atp", 382 382 + conceptName: "AT Protocol", 383 383 + }], 384 384 + }); 385 385 + 386 386 + const result = extractData(doc); 387 387 + expect(result.entities).toHaveLength(1); 388 388 + expect(result.entities[0].conceptUri).toBe("at://did:example/concept/atp"); 389 389 + expect(result.entities[0].conceptName).toBe("AT Protocol"); 390 390 + expect(result.entities[0].label).toBe("AT Protocol"); 391 391 + }); 392 392 + 393 393 + it("extracts #entity facets with nerType", () => { 394 394 + const doc = makeDoc([ 395 395 + { text: "London", startNs: 1000, endNs: 2000 }, 396 396 + { text: "is", startNs: 2000, endNs: 3000 }, 397 397 + { text: "great.", startNs: 3000, endNs: 4000 }, 398 398 + ]); 399 399 + const encoder = new TextEncoder(); 400 400 + doc.facets.push({ 401 401 + index: { byteStart: 0, byteEnd: encoder.encode("London").length }, 402 402 + features: [{ 403 403 + $type: "tv.ionosphere.facet#entity", 404 404 + label: "London", 405 405 + nerType: "GPE", 406 406 + }], 407 407 + }); 408 408 + 409 409 + const result = extractData(doc); 410 410 + expect(result.entities).toHaveLength(1); 411 411 + expect(result.entities[0].label).toBe("London"); 412 412 + expect(result.entities[0].nerType).toBe("GPE"); 413 413 + }); 414 414 + 415 415 + it("extracts topic breaks as paragraph indices", () => { 416 416 + const doc = makeDoc([ 417 417 + { text: "A.", startNs: 1000, endNs: 2000 }, 418 418 + { text: "B.", startNs: 3000, endNs: 4000 }, 419 419 + ]); 420 420 + const encoder = new TextEncoder(); 421 421 + const text = "A. B."; 422 422 + const s1End = encoder.encode("A.").length; 423 423 + const s2Start = encoder.encode("A. ").length; 424 424 + const s2End = encoder.encode(text).length; 425 425 + 426 426 + doc.facets.push({ index: { byteStart: 0, byteEnd: s1End }, features: [{ $type: "tv.ionosphere.facet#paragraph" }] }); 427 427 + doc.facets.push({ index: { byteStart: s2Start, byteEnd: s2End }, features: [{ $type: "tv.ionosphere.facet#paragraph" }] }); 428 428 + doc.facets.push({ index: { byteStart: s2Start, byteEnd: s2Start }, features: [{ $type: "tv.ionosphere.facet#topic-break" }] }); 429 429 + doc.facets.push({ index: { byteStart: 0, byteEnd: s1End }, features: [{ $type: "tv.ionosphere.facet#sentence" }] }); 430 430 + doc.facets.push({ index: { byteStart: s2Start, byteEnd: s2End }, features: [{ $type: "tv.ionosphere.facet#sentence" }] }); 431 431 + 432 432 + const result = extractData(doc); 433 433 + expect(result.topicBreaks.has(1)).toBe(true); 434 434 + expect(result.topicBreaks.has(0)).toBe(false); 435 435 + }); 436 436 + 437 437 + it("returns empty entities and topicBreaks when no such facets exist", () => { 438 438 + const doc = makeDoc([ 439 439 + { text: "Hello", startNs: 1000, endNs: 2000 }, 440 440 + ]); 441 441 + const result = extractData(doc); 442 442 + expect(result.entities).toHaveLength(0); 443 443 + expect(result.topicBreaks.size).toBe(0); 444 444 + }); 445 445 + });

+165 -1

apps/ionosphere/src/lib/transcript.ts

reviewed

··· 36 36 conceptName: string; 37 37 } 38 38 39 39 + export interface EntitySpan { 40 40 + byteStart: number; 41 41 + byteEnd: number; 42 42 + label: string; 43 43 + nerType?: string; 44 44 + speakerDid?: string; 45 45 + conceptUri?: string; 46 46 + conceptName?: string; 47 47 + } 48 48 + 49 49 + export interface SentenceSpan { 50 50 + byteStart: number; 51 51 + byteEnd: number; 52 52 + words: WordSpan[]; 53 53 + } 54 54 + 55 55 + export interface ParagraphSpan { 56 56 + byteStart: number; 57 57 + byteEnd: number; 58 58 + sentences: SentenceSpan[]; 59 59 + } 60 60 + 39 61 export function extractData(doc: TranscriptDocument) { 40 62 const encoder = new TextEncoder(); 41 63 const textBytes = encoder.encode(doc.text); ··· 43 65 44 66 const words: WordSpan[] = []; 45 67 const concepts: ConceptSpan[] = []; 68 68 + const entities: EntitySpan[] = []; 69 69 + const topicBreakPositions: number[] = []; 46 70 47 71 for (const f of doc.facets) { 48 72 for (const feat of f.features) { ··· 66 90 conceptRkey: feat.conceptRkey!, 67 91 conceptName: feat.conceptName!, 68 92 }); 93 93 + entities.push({ 94 94 + byteStart: f.index.byteStart, 95 95 + byteEnd: f.index.byteEnd, 96 96 + label: feat.conceptName!, 97 97 + conceptUri: feat.conceptUri, 98 98 + conceptName: feat.conceptName, 99 99 + }); 100 100 + } else if (feat.$type === "tv.ionosphere.facet#speaker-ref") { 101 101 + entities.push({ 102 102 + byteStart: f.index.byteStart, 103 103 + byteEnd: f.index.byteEnd, 104 104 + label: feat.label!, 105 105 + speakerDid: feat.speakerDid, 106 106 + }); 107 107 + } else if (feat.$type === "tv.ionosphere.facet#entity") { 108 108 + entities.push({ 109 109 + byteStart: f.index.byteStart, 110 110 + byteEnd: f.index.byteEnd, 111 111 + label: feat.label!, 112 112 + nerType: feat.nerType, 113 113 + }); 114 114 + } else if (feat.$type === "tv.ionosphere.facet#topic-break") { 115 115 + topicBreakPositions.push(f.index.byteStart); 69 116 } 70 117 } 71 118 } ··· 91 138 ) 92 139 ); 93 140 94 94 - return { words, concepts, wordConcepts }; 141 141 + // --- Build hierarchical structure: paragraphs > sentences > words --- 142 142 + 143 143 + // Extract sentence facets 144 144 + const sentenceRanges: Array<{ byteStart: number; byteEnd: number }> = []; 145 145 + const paragraphRanges: Array<{ byteStart: number; byteEnd: number }> = []; 146 146 + 147 147 + for (const f of doc.facets) { 148 148 + for (const feat of f.features) { 149 149 + if (feat.$type === "tv.ionosphere.facet#sentence") { 150 150 + sentenceRanges.push({ byteStart: f.index.byteStart, byteEnd: f.index.byteEnd }); 151 151 + } else if (feat.$type === "tv.ionosphere.facet#paragraph") { 152 152 + paragraphRanges.push({ byteStart: f.index.byteStart, byteEnd: f.index.byteEnd }); 153 153 + } 154 154 + } 155 155 + } 156 156 + 157 157 + sentenceRanges.sort((a, b) => a.byteStart - b.byteStart); 158 158 + paragraphRanges.sort((a, b) => a.byteStart - b.byteStart); 159 159 + 160 160 + // If no sentence facets, wrap all words in one singleton sentence 161 161 + if (sentenceRanges.length === 0 && words.length > 0) { 162 162 + sentenceRanges.push({ 163 163 + byteStart: 0, 164 164 + byteEnd: textBytes.length, 165 165 + }); 166 166 + } 167 167 + 168 168 + // If no paragraph facets, wrap all sentences in one singleton paragraph 169 169 + if (paragraphRanges.length === 0 && sentenceRanges.length > 0) { 170 170 + paragraphRanges.push({ 171 171 + byteStart: 0, 172 172 + byteEnd: textBytes.length, 173 173 + }); 174 174 + } 175 175 + 176 176 + // Assign words to sentences 177 177 + const sentences: SentenceSpan[] = sentenceRanges.map((s) => ({ 178 178 + byteStart: s.byteStart, 179 179 + byteEnd: s.byteEnd, 180 180 + words: [], 181 181 + })); 182 182 + 183 183 + // Track unassigned words for catch-all sentence 184 184 + const assignedWords = new Set<number>(); 185 185 + 186 186 + for (let wi = 0; wi < words.length; wi++) { 187 187 + const w = words[wi]; 188 188 + for (const s of sentences) { 189 189 + if (w.byteStart >= s.byteStart && w.byteEnd <= s.byteEnd) { 190 190 + s.words.push(w); 191 191 + assignedWords.add(wi); 192 192 + break; 193 193 + } 194 194 + } 195 195 + } 196 196 + 197 197 + // Words not covered by any sentence go into a catch-all sentence 198 198 + if (assignedWords.size < words.length) { 199 199 + const catchAll: SentenceSpan = { 200 200 + byteStart: 0, 201 201 + byteEnd: textBytes.length, 202 202 + words: [], 203 203 + }; 204 204 + for (let wi = 0; wi < words.length; wi++) { 205 205 + if (!assignedWords.has(wi)) { 206 206 + catchAll.words.push(words[wi]); 207 207 + } 208 208 + } 209 209 + sentences.push(catchAll); 210 210 + } 211 211 + 212 212 + // Assign sentences to paragraphs 213 213 + const paragraphs: ParagraphSpan[] = paragraphRanges.map((p) => ({ 214 214 + byteStart: p.byteStart, 215 215 + byteEnd: p.byteEnd, 216 216 + sentences: [], 217 217 + })); 218 218 + 219 219 + const assignedSentences = new Set<number>(); 220 220 + 221 221 + for (let si = 0; si < sentences.length; si++) { 222 222 + const s = sentences[si]; 223 223 + for (const p of paragraphs) { 224 224 + if (s.byteStart >= p.byteStart && s.byteEnd <= p.byteEnd) { 225 225 + p.sentences.push(s); 226 226 + assignedSentences.add(si); 227 227 + break; 228 228 + } 229 229 + } 230 230 + } 231 231 + 232 232 + // Sentences not covered by any paragraph go into a catch-all paragraph 233 233 + if (assignedSentences.size < sentences.length) { 234 234 + const catchAll: ParagraphSpan = { 235 235 + byteStart: 0, 236 236 + byteEnd: textBytes.length, 237 237 + sentences: [], 238 238 + }; 239 239 + for (let si = 0; si < sentences.length; si++) { 240 240 + if (!assignedSentences.has(si)) { 241 241 + catchAll.sentences.push(sentences[si]); 242 242 + } 243 243 + } 244 244 + paragraphs.push(catchAll); 245 245 + } 246 246 + 247 247 + // Map topic break byte positions to paragraph indices 248 248 + const topicBreaks = new Set<number>(); 249 249 + for (const pos of topicBreakPositions) { 250 250 + for (let pi = 0; pi < paragraphs.length; pi++) { 251 251 + if (pos >= paragraphs[pi].byteStart && pos <= paragraphs[pi].byteEnd) { 252 252 + topicBreaks.add(pi); 253 253 + break; 254 254 + } 255 255 + } 256 256 + } 257 257 + 258 258 + return { words, concepts, wordConcepts, paragraphs, entities, topicBreaks }; 95 259 } 96 260 97 261 // --- Brightness ---

+1 -1

apps/ionosphere/tsconfig.tsbuildinfo

reviewed

··· 1 1 - 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+1097

docs/superpowers/plans/2026-04-12-enrichment-phases-2-3.md

reviewed

··· 1 1 + # Enrichment Phases 2-3 Implementation Plan 2 2 + 3 3 + > **For agentic workers:** REQUIRED: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. Steps use checkbox (`- [ ]`) syntax for tracking. 4 4 + 5 5 + **Goal:** Add named entity recognition with AT Protocol record linking and topic segmentation to the transcript enrichment pipeline, achieving feature parity before deployment. 6 6 + 7 7 + **Architecture:** Two new Python NLP passes (entity detection + topic segmentation) extend the existing pipeline. Entity resolution matches against speaker/concept records from the SQLite database, with diarization context for disambiguation. Topic boundaries use sentence embeddings with cosine similarity. The TypeScript document assembly and React renderer extend to handle entity links and topic dividers. 8 8 + 9 9 + **Tech Stack:** Python 3.12+, spaCy NER (`en_core_web_sm`), sentence-transformers (`all-MiniLM-L6-v2`), sqlite3 (stdlib), vitest, React/Next.js 10 10 + 11 11 + **Spec:** `docs/superpowers/specs/2026-04-12-enrichment-phases-2-3-design.md` 12 12 + 13 13 + --- 14 14 + 15 15 + ## Chunk 1: New facet types + NlpAnnotations extension 16 16 + 17 17 + ### Task 1: Add topic-break and entity facet types to format lexicon 18 18 + 19 19 + **Files:** 20 20 + - Modify: `formats/tv.ionosphere/ionosphere.lexicon.json` 21 21 + 22 22 + - [ ] **Step 1: Add `#topic-break` (block) and `#entity` (inline) to the features array** 23 23 + 24 24 + ```json 25 25 + { 26 26 + "typeId": "tv.ionosphere.facet#topic-break", 27 27 + "featureClass": "block", 28 28 + "expandStart": false, 29 29 + "expandEnd": false 30 30 + }, 31 31 + { 32 32 + "typeId": "tv.ionosphere.facet#entity", 33 33 + "featureClass": "inline", 34 34 + "expandStart": false, 35 35 + "expandEnd": false 36 36 + } 37 37 + ``` 38 38 + 39 39 + - [ ] **Step 2: Commit** 40 40 + 41 41 + ```bash 42 42 + git add formats/tv.ionosphere/ionosphere.lexicon.json 43 43 + git commit -m "feat: add topic-break (block) and entity (inline) facet types" 44 44 + ``` 45 45 + 46 46 + ### Task 2: Extend NlpAnnotations and decodeToDocumentWithStructure 47 47 + 48 48 + **Files:** 49 49 + - Modify: `formats/tv.ionosphere/ts/transcript-encoding.ts` 50 50 + - Modify: `formats/tv.ionosphere/ts/transcript-encoding.test.ts` 51 51 + 52 52 + - [ ] **Step 1: Write the failing test** 53 53 + 54 54 + Add to `formats/tv.ionosphere/ts/transcript-encoding.test.ts`, in the `decodeToDocumentWithStructure` describe block: 55 55 + 56 56 + ```typescript 57 57 + it("adds entity, speaker-segment, and topic-break facets", () => { 58 58 + const compact = encode(contiguous); 59 59 + const annotations = { 60 60 + sentences: [{ byteStart: 0, byteEnd: 26 }], 61 61 + paragraphs: [{ byteStart: 0, byteEnd: 26 }], 62 62 + entities: [ 63 63 + { 64 64 + byteStart: 0, byteEnd: 5, label: "hello", nerType: "PERSON", 65 65 + speakerDid: "did:plc:abc123", 66 66 + }, 67 67 + { 68 68 + byteStart: 6, byteEnd: 11, label: "world", nerType: "ORG", 69 69 + conceptUri: "at://did:plc:xyz/tv.ionosphere.concept/test", 70 70 + }, 71 71 + { 72 72 + byteStart: 12, byteEnd: 16, label: "this", nerType: "PRODUCT", 73 73 + }, 74 74 + ], 75 75 + speakerSegments: [ 76 76 + { 77 77 + byteStart: 0, byteEnd: 26, speakerDid: "did:plc:abc123", 78 78 + speakerName: "Test Speaker", 79 79 + }, 80 80 + ], 81 81 + topicBreaks: [{ byteStart: 12 }], 82 82 + }; 83 83 + const doc = decodeToDocumentWithStructure(compact, annotations); 84 84 + 85 85 + const speakerRefs = doc.facets.filter(f => 86 86 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#speaker-ref") 87 87 + ); 88 88 + const conceptRefs = doc.facets.filter(f => 89 89 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#concept-ref") 90 90 + ); 91 91 + const entities = doc.facets.filter(f => 92 92 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#entity") 93 93 + ); 94 94 + const speakerSegs = doc.facets.filter(f => 95 95 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#speaker-segment") 96 96 + ); 97 97 + const topicBreaks = doc.facets.filter(f => 98 98 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#topic-break") 99 99 + ); 100 100 + 101 101 + expect(speakerRefs).toHaveLength(1); 102 102 + expect(speakerRefs[0].features[0].speakerDid).toBe("did:plc:abc123"); 103 103 + expect(conceptRefs).toHaveLength(1); 104 104 + expect(conceptRefs[0].features[0].conceptUri).toBe("at://did:plc:xyz/tv.ionosphere.concept/test"); 105 105 + expect(entities).toHaveLength(1); // unresolved entity 106 106 + expect(entities[0].features[0].label).toBe("this"); 107 107 + expect(speakerSegs).toHaveLength(1); 108 108 + expect(topicBreaks).toHaveLength(1); 109 109 + }); 110 110 + 111 111 + it("handles missing optional annotation fields gracefully", () => { 112 112 + const compact = encode(contiguous); 113 113 + const annotations = { 114 114 + sentences: [{ byteStart: 0, byteEnd: 26 }], 115 115 + paragraphs: [{ byteStart: 0, byteEnd: 26 }], 116 116 + // No entities, speakerSegments, or topicBreaks 117 117 + }; 118 118 + const doc = decodeToDocumentWithStructure(compact, annotations); 119 119 + // Should have sentences + paragraphs + timestamps, nothing else 120 120 + const tsFacets = doc.facets.filter(f => 121 121 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#timestamp") 122 122 + ); 123 123 + expect(tsFacets).toHaveLength(6); 124 124 + }); 125 125 + ``` 126 126 + 127 127 + - [ ] **Step 2: Run test to verify it fails** 128 128 + 129 129 + ```bash 130 130 + cd formats/tv.ionosphere && npx vitest run ts/transcript-encoding.test.ts 131 131 + ``` 132 132 + 133 133 + - [ ] **Step 3: Extend NlpAnnotations interface and decodeToDocumentWithStructure** 134 134 + 135 135 + Update `formats/tv.ionosphere/ts/transcript-encoding.ts`: 136 136 + 137 137 + ```typescript 138 138 + export interface NlpAnnotations { 139 139 + sentences: Array<{ byteStart: number; byteEnd: number }>; 140 140 + paragraphs: Array<{ byteStart: number; byteEnd: number }>; 141 141 + entities?: Array<{ 142 142 + byteStart: number; byteEnd: number; 143 143 + label: string; nerType: string; 144 144 + speakerDid?: string; conceptUri?: string; 145 145 + }>; 146 146 + speakerSegments?: Array<{ 147 147 + byteStart: number; byteEnd: number; 148 148 + speakerDid: string; speakerName: string; 149 149 + }>; 150 150 + topicBreaks?: Array<{ byteStart: number }>; 151 151 + } 152 152 + ``` 153 153 + 154 154 + Add to `decodeToDocumentWithStructure`, after the paragraph facet loop: 155 155 + 156 156 + ```typescript 157 157 + // Entity facets — route to speaker-ref, concept-ref, or generic entity 158 158 + for (const e of annotations.entities ?? []) { 159 159 + if (e.speakerDid) { 160 160 + doc.facets.push({ 161 161 + index: { byteStart: e.byteStart, byteEnd: e.byteEnd }, 162 162 + features: [{ 163 163 + $type: "tv.ionosphere.facet#speaker-ref", 164 164 + speakerDid: e.speakerDid, 165 165 + label: e.label, 166 166 + }], 167 167 + }); 168 168 + } else if (e.conceptUri) { 169 169 + doc.facets.push({ 170 170 + index: { byteStart: e.byteStart, byteEnd: e.byteEnd }, 171 171 + features: [{ 172 172 + $type: "tv.ionosphere.facet#concept-ref", 173 173 + conceptUri: e.conceptUri, 174 174 + conceptName: e.label, 175 175 + }], 176 176 + }); 177 177 + } else { 178 178 + doc.facets.push({ 179 179 + index: { byteStart: e.byteStart, byteEnd: e.byteEnd }, 180 180 + features: [{ 181 181 + $type: "tv.ionosphere.facet#entity", 182 182 + label: e.label, 183 183 + nerType: e.nerType, 184 184 + }], 185 185 + }); 186 186 + } 187 187 + } 188 188 + 189 189 + // Speaker segment facets 190 190 + for (const seg of annotations.speakerSegments ?? []) { 191 191 + doc.facets.push({ 192 192 + index: { byteStart: seg.byteStart, byteEnd: seg.byteEnd }, 193 193 + features: [{ 194 194 + $type: "tv.ionosphere.facet#speaker-segment", 195 195 + speakerDid: seg.speakerDid, 196 196 + speakerName: seg.speakerName, 197 197 + }], 198 198 + }); 199 199 + } 200 200 + 201 201 + // Topic break facets 202 202 + for (const tb of annotations.topicBreaks ?? []) { 203 203 + doc.facets.push({ 204 204 + index: { byteStart: tb.byteStart, byteEnd: tb.byteStart }, 205 205 + features: [{ $type: "tv.ionosphere.facet#topic-break" }], 206 206 + }); 207 207 + } 208 208 + ``` 209 209 + 210 210 + - [ ] **Step 4: Run tests to verify all pass** 211 211 + 212 212 + ```bash 213 213 + cd formats/tv.ionosphere && npx vitest run ts/transcript-encoding.test.ts 214 214 + ``` 215 215 + 216 216 + - [ ] **Step 5: Commit** 217 217 + 218 218 + ```bash 219 219 + git add formats/tv.ionosphere/ts/transcript-encoding.ts formats/tv.ionosphere/ts/transcript-encoding.test.ts 220 220 + git commit -m "feat: extend NlpAnnotations with entities, speakers, topic breaks" 221 221 + ``` 222 222 + 223 223 + --- 224 224 + 225 225 + ## Chunk 2: Python NER + entity linking 226 226 + 227 227 + ### Task 3: Install sentence-transformers dependency 228 228 + 229 229 + **Files:** 230 230 + - Modify: `pipeline/pyproject.toml` 231 231 + 232 232 + - [ ] **Step 1: Add sentence-transformers to dependencies** 233 233 + 234 234 + ```toml 235 235 + dependencies = [ 236 236 + "spacy>=3.7", 237 237 + "sentence-transformers>=2.0", 238 238 + ] 239 239 + ``` 240 240 + 241 241 + - [ ] **Step 2: Install** 242 242 + 243 243 + ```bash 244 244 + cd pipeline && source .venv/bin/activate && pip install -e ".[dev]" 245 245 + ``` 246 246 + 247 247 + - [ ] **Step 3: Verify sentence-transformers loads** 248 248 + 249 249 + ```bash 250 250 + python -c "from sentence_transformers import SentenceTransformer; print('OK')" 251 251 + ``` 252 252 + 253 253 + - [ ] **Step 4: Commit** 254 254 + 255 255 + ```bash 256 256 + git add pipeline/pyproject.toml 257 257 + git commit -m "feat: add sentence-transformers dependency for topic segmentation" 258 258 + ``` 259 259 + 260 260 + ### Task 4: Implement speaker lookup builder 261 261 + 262 262 + **Files:** 263 263 + - Create: `pipeline/nlp/speaker_lookup.py` 264 264 + - Create: `pipeline/tests/test_speaker_lookup.py` 265 265 + 266 266 + - [ ] **Step 1: Write the failing test** 267 267 + 268 268 + Create `pipeline/tests/test_speaker_lookup.py`: 269 269 + 270 270 + ```python 271 271 + from nlp.speaker_lookup import build_speaker_lookup 272 272 + 273 273 + 274 274 + def test_build_lookup_from_rows(): 275 275 + """Build lookup from speaker database rows.""" 276 276 + rows = [ 277 277 + ("Matt Akamatsu", "matsulab.com", "did:plc:matt123"), 278 278 + ("Rowan Cockett", "row1.ca", "did:plc:rowan456"), 279 279 + ("Jay Graber", "jay.bsky.team", "did:plc:jay789"), 280 280 + ] 281 281 + lookup = build_speaker_lookup(rows) 282 282 + 283 283 + # Full name match (case-insensitive) 284 284 + assert lookup.resolve("Matt Akamatsu") is not None 285 285 + assert lookup.resolve("Matt Akamatsu")["did"] == "did:plc:matt123" 286 286 + assert lookup.resolve("matt akamatsu")["did"] == "did:plc:matt123" 287 287 + 288 288 + # First name match 289 289 + assert lookup.resolve("Matt") is not None 290 290 + assert lookup.resolve("Matt")["did"] == "did:plc:matt123" 291 291 + 292 292 + # Handle match 293 293 + assert lookup.resolve("row1.ca") is not None 294 294 + assert lookup.resolve("row1.ca")["did"] == "did:plc:rowan456" 295 295 + 296 296 + # No match 297 297 + assert lookup.resolve("Unknown Person") is None 298 298 + 299 299 + 300 300 + def test_first_name_collision_returns_none(): 301 301 + """When multiple speakers share a first name, first-name lookup returns None (ambiguous).""" 302 302 + rows = [ 303 303 + ("Matt Akamatsu", "matsulab.com", "did:plc:matt1"), 304 304 + ("Matt Jones", "mattj.com", "did:plc:matt2"), 305 305 + ] 306 306 + lookup = build_speaker_lookup(rows) 307 307 + 308 308 + # Full name still works 309 309 + assert lookup.resolve("Matt Akamatsu")["did"] == "did:plc:matt1" 310 310 + # First name alone is ambiguous 311 311 + assert lookup.resolve("Matt") is None 312 312 + 313 313 + 314 314 + def test_empty_speakers(): 315 315 + lookup = build_speaker_lookup([]) 316 316 + assert lookup.resolve("Anyone") is None 317 317 + ``` 318 318 + 319 319 + - [ ] **Step 2: Run test to verify it fails** 320 320 + 321 321 + ```bash 322 322 + cd pipeline && source .venv/bin/activate && pytest tests/test_speaker_lookup.py -v 323 323 + ``` 324 324 + 325 325 + - [ ] **Step 3: Implement speaker_lookup.py** 326 326 + 327 327 + Create `pipeline/nlp/speaker_lookup.py`: 328 328 + 329 329 + ```python 330 330 + """Build a speaker lookup table from database records for entity resolution.""" 331 331 + 332 332 + 333 333 + class SpeakerLookup: 334 334 + def __init__(self): 335 335 + self._by_full_name: dict[str, dict] = {} 336 336 + self._by_first_name: dict[str, dict | None] = {} 337 337 + self._by_handle: dict[str, dict] = {} 338 338 + 339 339 + def add(self, name: str, handle: str | None, did: str | None): 340 340 + entry = {"name": name, "handle": handle, "did": did} 341 341 + self._by_full_name[name.lower()] = entry 342 342 + 343 343 + if handle: 344 344 + self._by_handle[handle.lower()] = entry 345 345 + 346 346 + first = name.split()[0].lower() 347 347 + if first in self._by_first_name: 348 348 + # Collision — mark as ambiguous 349 349 + self._by_first_name[first] = None 350 350 + else: 351 351 + self._by_first_name[first] = entry 352 352 + 353 353 + def resolve(self, name: str) -> dict | None: 354 354 + key = name.lower().strip() 355 355 + # Try full name first 356 356 + if key in self._by_full_name: 357 357 + return self._by_full_name[key] 358 358 + # Try handle 359 359 + if key in self._by_handle: 360 360 + return self._by_handle[key] 361 361 + # Try first name (returns None if ambiguous) 362 362 + if key in self._by_first_name: 363 363 + return self._by_first_name[key] 364 364 + return None 365 365 + 366 366 + 367 367 + def build_speaker_lookup(rows: list[tuple]) -> SpeakerLookup: 368 368 + """Build lookup from database rows of (name, handle, speaker_did).""" 369 369 + lookup = SpeakerLookup() 370 370 + for name, handle, did in rows: 371 371 + lookup.add(name, handle, did) 372 372 + return lookup 373 373 + ``` 374 374 + 375 375 + - [ ] **Step 4: Run tests to verify they pass** 376 376 + 377 377 + ```bash 378 378 + cd pipeline && source .venv/bin/activate && pytest tests/test_speaker_lookup.py -v 379 379 + ``` 380 380 + 381 381 + - [ ] **Step 5: Commit** 382 382 + 383 383 + ```bash 384 384 + git add pipeline/nlp/speaker_lookup.py pipeline/tests/test_speaker_lookup.py 385 385 + git commit -m "feat: speaker lookup builder for entity resolution" 386 386 + ``` 387 387 + 388 388 + ### Task 5: Implement NER + entity linking pass 389 389 + 390 390 + **Files:** 391 391 + - Create: `pipeline/nlp/entities.py` 392 392 + - Create: `pipeline/tests/test_entities.py` 393 393 + 394 394 + - [ ] **Step 1: Write the failing test** 395 395 + 396 396 + Create `pipeline/tests/test_entities.py`: 397 397 + 398 398 + ```python 399 399 + from nlp.entities import detect_entities 400 400 + from nlp.speaker_lookup import build_speaker_lookup 401 401 + 402 402 + 403 403 + def test_detects_person_entities(): 404 404 + text = "Matt Akamatsu is presenting today." 405 405 + rows = [("Matt Akamatsu", "matsulab.com", "did:plc:matt123")] 406 406 + lookup = build_speaker_lookup(rows) 407 407 + 408 408 + entities = detect_entities(text, speaker_lookup=lookup) 409 409 + 410 410 + persons = [e for e in entities if e["nerType"] == "PERSON"] 411 411 + assert len(persons) >= 1 412 412 + # Should resolve to the speaker 413 413 + resolved = [e for e in persons if e.get("speakerDid")] 414 414 + assert len(resolved) >= 1 415 415 + assert resolved[0]["speakerDid"] == "did:plc:matt123" 416 416 + 417 417 + 418 418 + def test_detects_org_entities(): 419 419 + text = "The work at Bluesky is impressive." 420 420 + concepts = [{"name": "Bluesky", "uri": "at://did/concept/bluesky", "aliases": "[]"}] 421 421 + 422 422 + entities = detect_entities(text, concept_rows=concepts) 423 423 + 424 424 + orgs = [e for e in entities if e.get("conceptUri")] 425 425 + assert len(orgs) >= 1 426 426 + 427 427 + 428 428 + def test_unresolved_entities_have_label(): 429 429 + text = "Barack Obama spoke at the conference." 430 430 + entities = detect_entities(text) 431 431 + 432 432 + persons = [e for e in entities if e["nerType"] == "PERSON"] 433 433 + assert len(persons) >= 1 434 434 + assert persons[0].get("speakerDid") is None 435 435 + assert persons[0]["label"] == "Barack Obama" 436 436 + 437 437 + 438 438 + def test_byte_offsets_correct(): 439 439 + text = "Matt Akamatsu presented." 440 440 + entities = detect_entities(text) 441 441 + if entities: 442 442 + e = entities[0] 443 443 + # Verify byte range matches the label 444 444 + text_at_range = text.encode("utf-8")[e["byteStart"]:e["byteEnd"]].decode("utf-8") 445 445 + assert e["label"] in text_at_range or text_at_range in e["label"] 446 446 + 447 447 + 448 448 + def test_empty_text(): 449 449 + entities = detect_entities("") 450 450 + assert entities == [] 451 451 + ``` 452 452 + 453 453 + - [ ] **Step 2: Run test to verify it fails** 454 454 + 455 455 + ```bash 456 456 + cd pipeline && source .venv/bin/activate && pytest tests/test_entities.py -v 457 457 + ``` 458 458 + 459 459 + - [ ] **Step 3: Implement entities.py** 460 460 + 461 461 + Create `pipeline/nlp/entities.py`: 462 462 + 463 463 + ```python 464 464 + """Pass 3: Named entity recognition + entity linking.""" 465 465 + 466 466 + import json 467 467 + import spacy 468 468 + 469 469 + _nlp = None 470 470 + 471 471 + 472 472 + def _get_nlp(): 473 473 + global _nlp 474 474 + if _nlp is None: 475 475 + _nlp = spacy.load("en_core_web_sm") 476 476 + return _nlp 477 477 + 478 478 + 479 479 + def detect_entities( 480 480 + text: str, 481 481 + speaker_lookup=None, 482 482 + concept_rows: list[dict] | None = None, 483 483 + ) -> list[dict]: 484 484 + """Detect named entities and resolve against speaker/concept records. 485 485 + 486 486 + Returns a list of entity dicts with: 487 487 + byteStart, byteEnd: UTF-8 byte offsets 488 488 + label: entity text 489 489 + nerType: spaCy entity type (PERSON, ORG, PRODUCT, etc.) 490 490 + speakerDid: (optional) resolved speaker DID 491 491 + conceptUri: (optional) resolved concept URI 492 492 + """ 493 493 + if not text.strip(): 494 494 + return [] 495 495 + 496 496 + nlp = _get_nlp() 497 497 + doc = nlp(text) 498 498 + 499 499 + # Build concept lookup 500 500 + concept_lookup: dict[str, str] = {} 501 501 + if concept_rows: 502 502 + for c in concept_rows: 503 503 + concept_lookup[c["name"].lower()] = c["uri"] 504 504 + aliases = c.get("aliases", "[]") 505 505 + if isinstance(aliases, str): 506 506 + try: 507 507 + aliases = json.loads(aliases) 508 508 + except (json.JSONDecodeError, TypeError): 509 509 + aliases = [] 510 510 + for alias in aliases: 511 511 + concept_lookup[alias.lower()] = c["uri"] 512 512 + 513 513 + entities = [] 514 514 + for ent in doc.ents: 515 515 + if ent.label_ not in ("PERSON", "ORG", "PRODUCT", "WORK_OF_ART", "GPE", "EVENT"): 516 516 + continue 517 517 + 518 518 + byte_start = len(text[:ent.start_char].encode("utf-8")) 519 519 + byte_end = len(text[:ent.end_char].encode("utf-8")) 520 520 + 521 521 + entity = { 522 522 + "byteStart": byte_start, 523 523 + "byteEnd": byte_end, 524 524 + "label": ent.text, 525 525 + "nerType": ent.label_, 526 526 + } 527 527 + 528 528 + # Try to resolve 529 529 + if ent.label_ == "PERSON" and speaker_lookup: 530 530 + match = speaker_lookup.resolve(ent.text) 531 531 + if match and match.get("did"): 532 532 + entity["speakerDid"] = match["did"] 533 533 + elif ent.label_ in ("ORG", "PRODUCT", "WORK_OF_ART"): 534 534 + uri = concept_lookup.get(ent.text.lower()) 535 535 + if uri: 536 536 + entity["conceptUri"] = uri 537 537 + 538 538 + entities.append(entity) 539 539 + 540 540 + return entities 541 541 + ``` 542 542 + 543 543 + - [ ] **Step 4: Run tests to verify they pass** 544 544 + 545 545 + ```bash 546 546 + cd pipeline && source .venv/bin/activate && pytest tests/test_entities.py -v 547 547 + ``` 548 548 + 549 549 + - [ ] **Step 5: Commit** 550 550 + 551 551 + ```bash 552 552 + git add pipeline/nlp/entities.py pipeline/tests/test_entities.py 553 553 + git commit -m "feat: NER + entity linking against speakers and concepts" 554 554 + ``` 555 555 + 556 556 + --- 557 557 + 558 558 + ## Chunk 3: Topic segmentation 559 559 + 560 560 + ### Task 6: Implement topic segmentation pass 561 561 + 562 562 + **Files:** 563 563 + - Create: `pipeline/nlp/topics.py` 564 564 + - Create: `pipeline/tests/test_topics.py` 565 565 + 566 566 + - [ ] **Step 1: Write the failing test** 567 567 + 568 568 + Create `pipeline/tests/test_topics.py`: 569 569 + 570 570 + ```python 571 571 + from nlp.topics import detect_topic_breaks 572 572 + 573 573 + 574 574 + def test_detects_topic_change(): 575 575 + """Distinct topics should produce at least one break.""" 576 576 + sentences = [ 577 577 + # Topic 1: cooking 578 578 + {"byteStart": 0, "byteEnd": 30, "text": "Today we will make a cake."}, 579 579 + {"byteStart": 31, "byteEnd": 65, "text": "First mix the flour and sugar."}, 580 580 + {"byteStart": 66, "byteEnd": 100, "text": "Then add the eggs and butter."}, 581 581 + {"byteStart": 101, "byteEnd": 135, "text": "Bake at 350 degrees for 30 minutes."}, 582 582 + {"byteStart": 136, "byteEnd": 170, "text": "Let it cool before adding frosting."}, 583 583 + # Topic 2: space exploration (very different) 584 584 + {"byteStart": 171, "byteEnd": 210, "text": "NASA launched a new rocket to Mars."}, 585 585 + {"byteStart": 211, "byteEnd": 250, "text": "The spacecraft will orbit the red planet."}, 586 586 + {"byteStart": 251, "byteEnd": 295, "text": "Astronauts may visit Mars within ten years."}, 587 587 + {"byteStart": 296, "byteEnd": 340, "text": "The mission costs billions of dollars."}, 588 588 + {"byteStart": 341, "byteEnd": 380, "text": "Space exploration advances human knowledge."}, 589 589 + ] 590 590 + breaks = detect_topic_breaks(sentences) 591 591 + assert len(breaks) >= 1 592 592 + # Break should be near the topic transition (around sentence 5) 593 593 + assert any(4 <= b["sentenceIndex"] <= 6 for b in breaks) 594 594 + 595 595 + 596 596 + def test_no_breaks_for_single_topic(): 597 597 + sentences = [ 598 598 + {"byteStart": 0, "byteEnd": 30, "text": "The cat sat on the mat."}, 599 599 + {"byteStart": 31, "byteEnd": 60, "text": "The cat was very happy."}, 600 600 + {"byteStart": 61, "byteEnd": 90, "text": "It purred loudly all day."}, 601 601 + ] 602 602 + breaks = detect_topic_breaks(sentences, min_segment_sentences=2) 603 603 + # Might have 0 breaks or very few — should not over-segment 604 604 + assert len(breaks) <= 1 605 605 + 606 606 + 607 607 + def test_empty_input(): 608 608 + assert detect_topic_breaks([]) == [] 609 609 + 610 610 + 611 611 + def test_too_few_sentences(): 612 612 + sentences = [{"byteStart": 0, "byteEnd": 10, "text": "Hello."}] 613 613 + assert detect_topic_breaks(sentences) == [] 614 614 + ``` 615 615 + 616 616 + - [ ] **Step 2: Run test to verify it fails** 617 617 + 618 618 + ```bash 619 619 + cd pipeline && source .venv/bin/activate && pytest tests/test_topics.py -v 620 620 + ``` 621 621 + 622 622 + - [ ] **Step 3: Implement topics.py** 623 623 + 624 624 + Create `pipeline/nlp/topics.py`: 625 625 + 626 626 + ```python 627 627 + """Pass 4: Topic segmentation using sentence embeddings.""" 628 628 + 629 629 + import numpy as np 630 630 + 631 631 + _model = None 632 632 + 633 633 + 634 634 + def _get_model(): 635 635 + global _model 636 636 + if _model is None: 637 637 + from sentence_transformers import SentenceTransformer 638 638 + _model = SentenceTransformer("all-MiniLM-L6-v2") 639 639 + return _model 640 640 + 641 641 + 642 642 + def detect_topic_breaks( 643 643 + sentences: list[dict], 644 644 + window_size: int = 3, 645 645 + similarity_threshold: float = 0.3, 646 646 + min_segment_sentences: int = 5, 647 647 + ) -> list[dict]: 648 648 + """Detect topic boundaries using sentence embedding similarity. 649 649 + 650 650 + Args: 651 651 + sentences: list of dicts with byteStart, byteEnd, text 652 652 + window_size: number of sentences per comparison window 653 653 + similarity_threshold: cosine similarity below this = topic break 654 654 + min_segment_sentences: minimum sentences between breaks 655 655 + 656 656 + Returns: 657 657 + list of dicts with byteStart and sentenceIndex 658 658 + """ 659 659 + if len(sentences) < window_size * 2: 660 660 + return [] 661 661 + 662 662 + model = _get_model() 663 663 + texts = [s["text"] for s in sentences] 664 664 + embeddings = model.encode(texts, show_progress_bar=False) 665 665 + 666 666 + # Compute cosine similarity between adjacent windows 667 667 + similarities = [] 668 668 + for i in range(window_size, len(sentences) - window_size + 1): 669 669 + left = np.mean(embeddings[i - window_size:i], axis=0) 670 670 + right = np.mean(embeddings[i:i + window_size], axis=0) 671 671 + cos_sim = np.dot(left, right) / (np.linalg.norm(left) * np.linalg.norm(right)) 672 672 + similarities.append((i, float(cos_sim))) 673 673 + 674 674 + # Find drops below threshold, respecting minimum segment length 675 675 + breaks = [] 676 676 + last_break = 0 677 677 + for sent_idx, sim in similarities: 678 678 + if sim < similarity_threshold and (sent_idx - last_break) >= min_segment_sentences: 679 679 + breaks.append({ 680 680 + "byteStart": sentences[sent_idx]["byteStart"], 681 681 + "sentenceIndex": sent_idx, 682 682 + }) 683 683 + last_break = sent_idx 684 684 + 685 685 + return breaks 686 686 + ``` 687 687 + 688 688 + - [ ] **Step 4: Run tests to verify they pass** 689 689 + 690 690 + ```bash 691 691 + cd pipeline && source .venv/bin/activate && pytest tests/test_topics.py -v 692 692 + ``` 693 693 + 694 694 + Note: the first run will download the `all-MiniLM-L6-v2` model (~80MB). 695 695 + 696 696 + - [ ] **Step 5: Commit** 697 697 + 698 698 + ```bash 699 699 + git add pipeline/nlp/topics.py pipeline/tests/test_topics.py 700 700 + git commit -m "feat: topic segmentation via sentence-transformer embeddings" 701 701 + ``` 702 702 + 703 703 + --- 704 704 + 705 705 + ## Chunk 4: Update pipeline orchestrator 706 706 + 707 707 + ### Task 7: Extend run.py with NER and topic passes 708 708 + 709 709 + **Files:** 710 710 + - Modify: `pipeline/nlp/run.py` 711 711 + - Modify: `pipeline/tests/test_run.py` 712 712 + 713 713 + - [ ] **Step 1: Update the integration test** 714 714 + 715 715 + Add to `pipeline/tests/test_run.py`: 716 716 + 717 717 + ```python 718 718 + def test_process_transcript_with_enrichment(): 719 719 + """Full pipeline with entity and topic passes.""" 720 720 + transcript = { 721 721 + "text": "Matt Akamatsu is presenting. The AT Protocol is decentralized. Now we discuss a completely different topic about cooking recipes and baking.", 722 722 + "startMs": 0, 723 723 + "timings": [100, 100, 100, 100, 100, 100, 100, 100, 100, -3000, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100], 724 724 + } 725 725 + speaker_rows = [("Matt Akamatsu", "matsulab.com", "did:plc:matt123")] 726 726 + concept_rows = [{"name": "AT Protocol", "uri": "at://did/concept/atp", "aliases": "[]"}] 727 727 + 728 728 + result = process_transcript( 729 729 + transcript, talk_rkey="test", 730 730 + speaker_rows=speaker_rows, 731 731 + concept_rows=concept_rows, 732 732 + ) 733 733 + 734 734 + assert "entities" in result 735 735 + assert "topicBreaks" in result 736 736 + assert len(result["entities"]) >= 1 737 737 + # At least one resolved entity 738 738 + resolved = [e for e in result["entities"] if e.get("speakerDid") or e.get("conceptUri")] 739 739 + assert len(resolved) >= 1 740 740 + ``` 741 741 + 742 742 + - [ ] **Step 2: Run test to verify it fails** 743 743 + 744 744 + ```bash 745 745 + cd pipeline && source .venv/bin/activate && pytest tests/test_run.py -v 746 746 + ``` 747 747 + 748 748 + - [ ] **Step 3: Update process_transcript in run.py** 749 749 + 750 750 + Add imports at top: 751 751 + ```python 752 752 + from nlp.entities import detect_entities 753 753 + from nlp.speaker_lookup import build_speaker_lookup 754 754 + from nlp.topics import detect_topic_breaks 755 755 + ``` 756 756 + 757 757 + Update `process_transcript` signature to accept optional speaker/concept data: 758 758 + ```python 759 759 + def process_transcript( 760 760 + transcript: dict, 761 761 + talk_rkey: str, 762 762 + pause_threshold_ms: int = 2000, 763 763 + proximity_words: int = 5, 764 764 + speaker_rows: list[tuple] | None = None, 765 765 + concept_rows: list[dict] | None = None, 766 766 + ) -> dict: 767 767 + ``` 768 768 + 769 769 + Add after the paragraph detection: 770 770 + ```python 771 771 + # Pass 3: NER + entity linking 772 772 + speaker_lookup = build_speaker_lookup(speaker_rows) if speaker_rows else None 773 773 + entities = detect_entities(text, speaker_lookup=speaker_lookup, concept_rows=concept_rows) 774 774 + 775 775 + # Pass 4: Topic segmentation 776 776 + # Enrich sentences with text for embedding 777 777 + sentences_with_text = [] 778 778 + for s in sentences: 779 779 + sent_text = text.encode("utf-8")[s["byteStart"]:s["byteEnd"]].decode("utf-8") 780 780 + sentences_with_text.append({**s, "text": sent_text}) 781 781 + topic_breaks = detect_topic_breaks(sentences_with_text) 782 782 + ``` 783 783 + 784 784 + Update the return dict to include: 785 785 + ```python 786 786 + return { 787 787 + "talkRkey": talk_rkey, 788 788 + "sentences": sentences, 789 789 + "paragraphs": paragraphs, 790 790 + "entities": entities, 791 791 + "topicBreaks": [{"byteStart": tb["byteStart"]} for tb in topic_breaks], 792 792 + "metadata": { ... }, 793 793 + } 794 794 + ``` 795 795 + 796 796 + - [ ] **Step 4: Update main() to load speaker/concept data from SQLite** 797 797 + 798 798 + Add to the `main()` function, before the transcript loop: 799 799 + 800 800 + ```python 801 801 + import sqlite3 802 802 + db_path = Path(__file__).resolve().parent.parent.parent / "apps" / "data" / "ionosphere.sqlite" 803 803 + speaker_rows = [] 804 804 + concept_rows = [] 805 805 + if db_path.exists(): 806 806 + conn = sqlite3.connect(str(db_path)) 807 807 + speaker_rows = conn.execute( 808 808 + "SELECT name, handle, speaker_did FROM speakers" 809 809 + ).fetchall() 810 810 + concept_rows = [ 811 811 + {"name": r[0], "uri": r[1], "aliases": r[2] or "[]"} 812 812 + for r in conn.execute("SELECT name, uri, aliases FROM concepts").fetchall() 813 813 + ] 814 814 + conn.close() 815 815 + print(f"Loaded {len(speaker_rows)} speakers, {len(concept_rows)} concepts from DB") 816 816 + else: 817 817 + print(f"Warning: database not found at {db_path}, entity linking disabled") 818 818 + ``` 819 819 + 820 820 + Pass these to `process_transcript`: 821 821 + ```python 822 822 + result = process_transcript( 823 823 + compact, talk_rkey=talk_rkey, 824 824 + speaker_rows=speaker_rows, 825 825 + concept_rows=concept_rows, 826 826 + ) 827 827 + ``` 828 828 + 829 829 + Update the log line: 830 830 + ```python 831 831 + entity_count = len(result.get("entities", [])) 832 832 + topic_count = len(result.get("topicBreaks", [])) 833 833 + print(f" {talk_rkey}: {len(result['sentences'])} sentences, {len(result['paragraphs'])} paragraphs, {entity_count} entities, {topic_count} topics") 834 834 + ``` 835 835 + 836 836 + - [ ] **Step 5: Run ALL tests** 837 837 + 838 838 + ```bash 839 839 + cd pipeline && source .venv/bin/activate && pytest tests/ -v 840 840 + ``` 841 841 + 842 842 + - [ ] **Step 6: Commit** 843 843 + 844 844 + ```bash 845 845 + git add pipeline/nlp/run.py pipeline/tests/test_run.py 846 846 + git commit -m "feat: extend pipeline with NER, entity linking, and topic segmentation" 847 847 + ``` 848 848 + 849 849 + --- 850 850 + 851 851 + ## Chunk 5: Renderer — entities and topic dividers 852 852 + 853 853 + ### Task 8: Extend extractData for entities and topic breaks 854 854 + 855 855 + **Files:** 856 856 + - Modify: `apps/ionosphere/src/lib/transcript.ts` 857 857 + - Modify: `apps/ionosphere/src/lib/transcript.test.ts` 858 858 + 859 859 + - [ ] **Step 1: Write failing tests** 860 860 + 861 861 + Add to `apps/ionosphere/src/lib/transcript.test.ts`: 862 862 + 863 863 + ```typescript 864 864 + describe("extractData — entities and topic breaks", () => { 865 865 + it("extracts entity spans from facets", () => { 866 866 + const doc = makeDoc([ 867 867 + { text: "Matt", startNs: 1000, endNs: 2000 }, 868 868 + { text: "presented.", startNs: 2000, endNs: 3000 }, 869 869 + ]); 870 870 + const encoder = new TextEncoder(); 871 871 + // Add a speaker-ref entity facet 872 872 + doc.facets.push({ 873 873 + index: { byteStart: 0, byteEnd: encoder.encode("Matt").length }, 874 874 + features: [{ 875 875 + $type: "tv.ionosphere.facet#speaker-ref", 876 876 + speakerDid: "did:plc:matt123", 877 877 + label: "Matt", 878 878 + }], 879 879 + }); 880 880 + 881 881 + const result = extractData(doc); 882 882 + expect(result.entities).toHaveLength(1); 883 883 + expect(result.entities[0].speakerDid).toBe("did:plc:matt123"); 884 884 + expect(result.entities[0].byteStart).toBe(0); 885 885 + }); 886 886 + 887 887 + it("extracts topic breaks as paragraph indices", () => { 888 888 + const doc = makeDoc([ 889 889 + { text: "A.", startNs: 1000, endNs: 2000 }, 890 890 + { text: "B.", startNs: 3000, endNs: 4000 }, 891 891 + ]); 892 892 + const encoder = new TextEncoder(); 893 893 + const text = "A. B."; 894 894 + const s1End = encoder.encode("A.").length; 895 895 + const s2Start = encoder.encode("A. ").length; 896 896 + const s2End = encoder.encode(text).length; 897 897 + 898 898 + // Two paragraphs 899 899 + doc.facets.push({ index: { byteStart: 0, byteEnd: s1End }, features: [{ $type: "tv.ionosphere.facet#paragraph" }] }); 900 900 + doc.facets.push({ index: { byteStart: s2Start, byteEnd: s2End }, features: [{ $type: "tv.ionosphere.facet#paragraph" }] }); 901 901 + // Topic break at second paragraph 902 902 + doc.facets.push({ index: { byteStart: s2Start, byteEnd: s2Start }, features: [{ $type: "tv.ionosphere.facet#topic-break" }] }); 903 903 + // Sentences 904 904 + doc.facets.push({ index: { byteStart: 0, byteEnd: s1End }, features: [{ $type: "tv.ionosphere.facet#sentence" }] }); 905 905 + doc.facets.push({ index: { byteStart: s2Start, byteEnd: s2End }, features: [{ $type: "tv.ionosphere.facet#sentence" }] }); 906 906 + 907 907 + const result = extractData(doc); 908 908 + expect(result.topicBreaks.has(1)).toBe(true); // second paragraph 909 909 + expect(result.topicBreaks.has(0)).toBe(false); // first paragraph 910 910 + }); 911 911 + 912 912 + it("returns empty entities and topicBreaks when no such facets exist", () => { 913 913 + const doc = makeDoc([ 914 914 + { text: "Hello", startNs: 1000, endNs: 2000 }, 915 915 + ]); 916 916 + const result = extractData(doc); 917 917 + expect(result.entities).toHaveLength(0); 918 918 + expect(result.topicBreaks.size).toBe(0); 919 919 + }); 920 920 + }); 921 921 + ``` 922 922 + 923 923 + - [ ] **Step 2: Run tests to verify they fail** 924 924 + 925 925 + ```bash 926 926 + cd apps/ionosphere && npx vitest run src/lib/transcript.test.ts 927 927 + ``` 928 928 + 929 929 + - [ ] **Step 3: Add EntitySpan type and update extractData** 930 930 + 931 931 + Add to `apps/ionosphere/src/lib/transcript.ts`: 932 932 + 933 933 + ```typescript 934 934 + export interface EntitySpan { 935 935 + byteStart: number; 936 936 + byteEnd: number; 937 937 + label: string; 938 938 + nerType?: string; 939 939 + speakerDid?: string; 940 940 + conceptUri?: string; 941 941 + conceptName?: string; 942 942 + } 943 943 + ``` 944 944 + 945 945 + In `extractData`, add extraction of entity facets (speaker-ref, concept-ref, entity) and topic-break facets. Map topic breaks to paragraph indices by finding which paragraph contains each break's byte position. 946 946 + 947 947 + Return `entities: EntitySpan[]` and `topicBreaks: Set<number>` alongside existing fields. 948 948 + 949 949 + - [ ] **Step 4: Run tests to verify all pass** 950 950 + 951 951 + ```bash 952 952 + cd apps/ionosphere && npx vitest run src/lib/transcript.test.ts 953 953 + ``` 954 954 + 955 955 + - [ ] **Step 5: Commit** 956 956 + 957 957 + ```bash 958 958 + git add apps/ionosphere/src/lib/transcript.ts apps/ionosphere/src/lib/transcript.test.ts 959 959 + git commit -m "feat: extract entities and topic breaks from facets" 960 960 + ``` 961 961 + 962 962 + ### Task 9: Update TranscriptView for entity rendering and topic dividers 963 963 + 964 964 + **Files:** 965 965 + - Modify: `apps/ionosphere/src/app/components/TranscriptView.tsx` 966 966 + 967 967 + - [ ] **Step 1: Extract entities and topicBreaks from extractData** 968 968 + 969 969 + In the `useMemo` that calls `extractData`, also destructure `entities` and `topicBreaks`. 970 970 + 971 971 + - [ ] **Step 2: Build word-to-entity lookup** 972 972 + 973 973 + ```typescript 974 974 + const wordEntities = useMemo(() => { 975 975 + const map = new Map<number, EntitySpan>(); 976 976 + for (const entity of entities) { 977 977 + for (let i = 0; i < words.length; i++) { 978 978 + const w = words[i]; 979 979 + if (w.byteStart >= entity.byteStart && w.byteEnd <= entity.byteEnd) { 980 980 + // First word of entity gets the entity data (for rendering the link) 981 981 + if (!map.has(i)) map.set(i, entity); 982 982 + } 983 983 + } 984 984 + } 985 985 + return map; 986 986 + }, [words, entities]); 987 987 + ``` 988 988 + 989 989 + - [ ] **Step 3: Add entity link styling to WordSpanComponent or a wrapper** 990 990 + 991 991 + For words that are part of an entity, add visual treatment: 992 992 + - `speaker-ref`: blue underline, link to `/speakers/{rkey}` or Bluesky profile 993 993 + - `concept-ref`: amber underline (matching existing concept style) 994 994 + - `entity` (unresolved): dotted underline, no link 995 995 + 996 996 + The simplest approach: check `wordEntities.get(globalIdx)` when rendering each word, and wrap entity-start words in an `<a>` or styled `<span>`. 997 997 + 998 998 + - [ ] **Step 4: Add topic dividers between paragraphs** 999 999 + 1000 1000 + In the paragraph rendering loop, check `topicBreaks.has(paragraphIndex)` and insert an `<hr>` before that paragraph: 1001 1001 + 1002 1002 + ```tsx 1003 1003 + {paragraphs.map((para, pi) => ( 1004 1004 + <> 1005 1005 + {topicBreaks.has(pi) && ( 1006 1006 + <hr key={`topic-${pi}`} className="border-neutral-800 my-6" /> 1007 1007 + )} 1008 1008 + <div key={pi} className="mb-4"> 1009 1009 + {/* sentences... */} 1010 1010 + </div> 1011 1011 + </> 1012 1012 + ))} 1013 1013 + ``` 1014 1014 + 1015 1015 + - [ ] **Step 5: Verify in browser** 1016 1016 + 1017 1017 + Load a talk page. Verify: 1018 1018 + - Entity names have visible styling (underlines) 1019 1019 + - Resolved entities are clickable 1020 1020 + - Topic dividers appear as subtle horizontal rules 1021 1021 + - Existing functionality (scroll, brightness, comments) unchanged 1022 1022 + 1023 1023 + - [ ] **Step 6: Commit** 1024 1024 + 1025 1025 + ```bash 1026 1026 + git add apps/ionosphere/src/app/components/TranscriptView.tsx 1027 1027 + git commit -m "feat: render entity links and topic dividers in transcript" 1028 1028 + ``` 1029 1029 + 1030 1030 + --- 1031 1031 + 1032 1032 + ## Chunk 6: End-to-end integration 1033 1033 + 1034 1034 + ### Task 10: Run the full enriched pipeline 1035 1035 + 1036 1036 + - [ ] **Step 1: Run the pipeline on all transcripts** 1037 1037 + 1038 1038 + ```bash 1039 1039 + cd pipeline && source .venv/bin/activate && python -m nlp.run 1040 1040 + ``` 1041 1041 + 1042 1042 + Verify output includes entity and topic data. Spot-check 3-5 output files. 1043 1043 + 1044 1044 + - [ ] **Step 2: Inject documents into local database** 1045 1045 + 1046 1046 + Use the same inject pattern as Phase 1 — create a temporary script that reads NLP output + transcripts, calls `decodeToDocumentWithStructure`, and updates the talks table. 1047 1047 + 1048 1048 + - [ ] **Step 3: Verify in browser** 1049 1049 + 1050 1050 + Load several talk pages. Check: 1051 1051 + - Entity links point to correct profiles/concepts 1052 1052 + - Topic dividers land at natural transitions 1053 1053 + - Talks without diarization still work (entities just don't have speaker context) 1054 1054 + - All existing features (paragraphs, sentences, timing, comments) unchanged 1055 1055 + 1056 1056 + - [ ] **Step 4: Run all tests** 1057 1057 + 1058 1058 + ```bash 1059 1059 + # Python 1060 1060 + cd pipeline && source .venv/bin/activate && pytest -v 1061 1061 + 1062 1062 + # TypeScript 1063 1063 + cd formats/tv.ionosphere && npx vitest run 1064 1064 + cd ../../apps/ionosphere && npx vitest run src/lib/transcript.test.ts 1065 1065 + ``` 1066 1066 + 1067 1067 + - [ ] **Step 5: Commit any integration fixes** 1068 1068 + 1069 1069 + ```bash 1070 1070 + git add -A && git commit -m "fix: integration fixes from end-to-end verification" 1071 1071 + ``` 1072 1072 + 1073 1073 + ### Task 11: Update publish.ts for enriched annotations 1074 1074 + 1075 1075 + **Files:** 1076 1076 + - Modify: `apps/ionosphere-appview/src/publish.ts` 1077 1077 + 1078 1078 + - [ ] **Step 1: Update the NLP data reading to include new fields** 1079 1079 + 1080 1080 + The publish.ts code that reads NLP JSON and passes it to `decodeToDocumentWithStructure` needs to include the new fields (entities, speakerSegments, topicBreaks) from the NLP output. Since `NlpAnnotations` now has these as optional fields, this is just passing them through: 1081 1081 + 1082 1082 + ```typescript 1083 1083 + const doc = decodeToDocumentWithStructure(compact, { 1084 1084 + sentences: nlpData.sentences, 1085 1085 + paragraphs: nlpData.paragraphs, 1086 1086 + entities: nlpData.entities, 1087 1087 + topicBreaks: nlpData.topicBreaks, 1088 1088 + // speakerSegments will come when diarization is integrated 1089 1089 + }); 1090 1090 + ``` 1091 1091 + 1092 1092 + - [ ] **Step 2: Commit** 1093 1093 + 1094 1094 + ```bash 1095 1095 + git add apps/ionosphere-appview/src/publish.ts 1096 1096 + git commit -m "feat: pass entity and topic data through publish pipeline" 1097 1097 + ```

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docs/superpowers/plans/2026-04-12-transcript-formatting.md

reviewed

··· 1 1 + # Transcript Formatting Implementation Plan 2 2 + 3 3 + > **For agentic workers:** REQUIRED: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. Steps use checkbox (`- [ ]`) syntax for tracking. 4 4 + 5 5 + **Goal:** Add NLP-based sentence and paragraph detection to transcripts so they render as structured prose instead of a wall of text. 6 6 + 7 7 + **Architecture:** A Python NLP pipeline (spaCy) produces sentence/paragraph annotation layers as JSON files. A TypeScript publish step validates and publishes these as layers.pub AT Protocol records. The document assembly step reads annotation layers and emits structural facets (`#sentence`, `#paragraph`) that the React renderer consumes as inline spans and block elements. The old `tv.ionosphere.annotation` system is removed entirely. 8 8 + 9 9 + **Tech Stack:** Python 3.12+, spaCy (`en_core_web_sm`), vitest, layers.pub lexicons, panproto, React/Next.js 10 10 + 11 11 + **Spec:** `docs/superpowers/specs/2026-04-12-transcript-formatting-design.md` 12 12 + 13 13 + **Scope note:** This plan implements the rendering pipeline (NLP → facets → renderer) end-to-end. Publishing layers.pub records to the PDS and creating panproto lenses are deferred to a follow-up — the vendored lexicons and spec are forward preparation. The immediate goal is formatted transcripts in the browser. 14 14 + 15 15 + **UX note:** Removing the old annotation system (Task 13) will temporarily remove concept highlighting from talk pages. Concepts return via NLP in Phase 2. If this is unacceptable, Task 13 can be deferred and the old overlay path kept alongside the new structural facets. 16 16 + 17 17 + --- 18 18 + 19 19 + ## Chunk 1: Vendor layers.pub lexicons and define new facet types 20 20 + 21 21 + ### Task 1: Vendor layers.pub lexicon definitions 22 22 + 23 23 + **Files:** 24 24 + - Create: `lexicons/pub/layers/defs.json` 25 25 + - Create: `lexicons/pub/layers/expression/expression.json` 26 26 + - Create: `lexicons/pub/layers/segmentation/segmentation.json` 27 27 + - Create: `lexicons/pub/layers/annotation/annotationLayer.json` 28 28 + 29 29 + - [ ] **Step 1: Create the `pub.layers.defs` shared definitions lexicon** 30 30 + 31 31 + Vendor the subset of `pub.layers.defs` that we use: `span`, `temporalSpan`, `uuid`, `tokenRef`, `anchor`, `annotationMetadata`, `featureMap`, `feature`. Pull the field definitions from https://docs.layers.pub/lexicons/defs. These are the shared types referenced by the other lexicons. 32 32 + 33 33 + - [ ] **Step 2: Create the `pub.layers.expression.expression` lexicon** 34 34 + 35 35 + Vendor the expression record schema from https://docs.layers.pub/lexicons/expression. Required fields: `id`, `kindUri`, `kind`, `text`, `language`, `createdAt`. Optional: `sourceRef`, `parentRef`, `anchor`, `metadata`, `features`. 36 36 + 37 37 + - [ ] **Step 3: Create the `pub.layers.segmentation.segmentation` lexicon** 38 38 + 39 39 + Vendor the segmentation record schema from https://docs.layers.pub/lexicons/segmentation. This includes the `segmentation` record and the `tokenization` and `token` object defs. 40 40 + 41 41 + - [ ] **Step 4: Create the `pub.layers.annotation.annotationLayer` lexicon** 42 42 + 43 43 + Vendor the annotation layer record schema from https://docs.layers.pub/lexicons/annotation. This includes `annotationLayer` record and the `annotation` object def. 44 44 + 45 45 + - [ ] **Step 5: Commit** 46 46 + 47 47 + ```bash 48 48 + git add lexicons/pub/ 49 49 + git commit -m "feat: vendor layers.pub lexicons for transcript enrichment" 50 50 + ``` 51 51 + 52 52 + ### Task 2: Add sentence and paragraph facet types to the format lexicon 53 53 + 54 54 + **Files:** 55 55 + - Modify: `formats/tv.ionosphere/ionosphere.lexicon.json` 56 56 + 57 57 + - [ ] **Step 1: Add `#sentence` (inline) and `#paragraph` (block) facet entries** 58 58 + 59 59 + Add to the `features` array in `formats/tv.ionosphere/ionosphere.lexicon.json`: 60 60 + 61 61 + ```json 62 62 + { 63 63 + "typeId": "tv.ionosphere.facet#sentence", 64 64 + "featureClass": "inline", 65 65 + "expandStart": false, 66 66 + "expandEnd": false 67 67 + }, 68 68 + { 69 69 + "typeId": "tv.ionosphere.facet#paragraph", 70 70 + "featureClass": "block", 71 71 + "expandStart": false, 72 72 + "expandEnd": false 73 73 + } 74 74 + ``` 75 75 + 76 76 + - [ ] **Step 2: Commit** 77 77 + 78 78 + ```bash 79 79 + git add formats/tv.ionosphere/ionosphere.lexicon.json 80 80 + git commit -m "feat: add sentence (inline) and paragraph (block) facet types" 81 81 + ``` 82 82 + 83 83 + --- 84 84 + 85 85 + ## Chunk 2: Python NLP pipeline 86 86 + 87 87 + ### Task 3: Set up the Python enrichment project 88 88 + 89 89 + **Files:** 90 90 + - Create: `pipeline/pyproject.toml` 91 91 + - Create: `pipeline/nlp/__init__.py` 92 92 + 93 93 + - [ ] **Step 1: Create `pipeline/pyproject.toml`** 94 94 + 95 95 + ```toml 96 96 + [project] 97 97 + name = "ionosphere-nlp" 98 98 + version = "0.1.0" 99 99 + description = "NLP enrichment pipeline for ionosphere transcripts" 100 100 + requires-python = ">=3.12" 101 101 + dependencies = [ 102 102 + "spacy>=3.7", 103 103 + ] 104 104 + 105 105 + [project.optional-dependencies] 106 106 + dev = ["pytest>=8.0"] 107 107 + 108 108 + [tool.pytest.ini_options] 109 109 + testpaths = ["tests"] 110 110 + ``` 111 111 + 112 112 + - [ ] **Step 2: Create the package init** 113 113 + 114 114 + Create `pipeline/nlp/__init__.py` (empty file). 115 115 + 116 116 + - [ ] **Step 3: Create `pipeline/tests/__init__.py`** 117 117 + 118 118 + Empty file (needed for pytest discovery). 119 119 + 120 120 + - [ ] **Step 4: Add `.gitignore` entries for Python artifacts** 121 121 + 122 122 + Add to the repo root `.gitignore`: 123 123 + ``` 124 124 + pipeline/.venv/ 125 125 + pipeline/data/ 126 126 + __pycache__/ 127 127 + *.pyc 128 128 + ``` 129 129 + 130 130 + - [ ] **Step 5: Install dependencies** 131 131 + 132 132 + ```bash 133 133 + cd pipeline 134 134 + python -m venv .venv 135 135 + source .venv/bin/activate 136 136 + pip install -e ".[dev]" 137 137 + python -m spacy download en_core_web_sm 138 138 + ``` 139 139 + 140 140 + - [ ] **Step 6: Commit** 141 141 + 142 142 + ```bash 143 143 + git add pipeline/pyproject.toml pipeline/nlp/__init__.py pipeline/tests/__init__.py .gitignore 144 144 + git commit -m "feat: scaffold Python NLP pipeline project" 145 145 + ``` 146 146 + 147 147 + ### Task 4: Implement sentence boundary detection (Pass 1) 148 148 + 149 149 + **Files:** 150 150 + - Create: `pipeline/tests/test_sentences.py` 151 151 + - Create: `pipeline/nlp/sentences.py` 152 152 + 153 153 + - [ ] **Step 1: Write the failing test** 154 154 + 155 155 + Create `pipeline/tests/test_sentences.py`: 156 156 + 157 157 + ```python 158 158 + from nlp.sentences import detect_sentences 159 159 + 160 160 + 161 161 + def test_basic_sentences(): 162 162 + text = "Hello world. This is a test. And another sentence." 163 163 + sentences = detect_sentences(text) 164 164 + assert len(sentences) == 3 165 165 + # Each sentence is a dict with byteStart and byteEnd 166 166 + assert sentences[0]["byteStart"] == 0 167 167 + assert sentences[0]["byteEnd"] == len("Hello world.".encode("utf-8")) 168 168 + assert sentences[1]["byteStart"] == len("Hello world. ".encode("utf-8")) 169 169 + 170 170 + 171 171 + def test_speech_without_punctuation(): 172 172 + """spaCy should detect sentence boundaries even with poor punctuation.""" 173 173 + text = "so the thing is we need to think about this carefully and then we can move on to the next topic which is about protocols" 174 174 + sentences = detect_sentences(text) 175 175 + # spaCy should find at least 1 sentence (the whole text if no clear boundary) 176 176 + assert len(sentences) >= 1 177 177 + # All sentences should cover the full text 178 178 + assert sentences[0]["byteStart"] == 0 179 179 + assert sentences[-1]["byteEnd"] == len(text.encode("utf-8")) 180 180 + 181 181 + 182 182 + def test_empty_text(): 183 183 + sentences = detect_sentences("") 184 184 + assert sentences == [] 185 185 + 186 186 + 187 187 + def test_byte_offsets_for_unicode(): 188 188 + text = "Caf\u00e9 is great. Let\u2019s go." 189 189 + sentences = detect_sentences(text) 190 190 + # Byte offsets must account for multi-byte characters 191 191 + full_bytes = text.encode("utf-8") 192 192 + assert sentences[-1]["byteEnd"] == len(full_bytes) 193 193 + ``` 194 194 + 195 195 + - [ ] **Step 2: Run test to verify it fails** 196 196 + 197 197 + ```bash 198 198 + cd pipeline && source .venv/bin/activate && pytest tests/test_sentences.py -v 199 199 + ``` 200 200 + Expected: FAIL with `ModuleNotFoundError` 201 201 + 202 202 + - [ ] **Step 3: Implement `detect_sentences`** 203 203 + 204 204 + Create `pipeline/nlp/sentences.py`: 205 205 + 206 206 + ```python 207 207 + """Pass 1: Sentence boundary detection using spaCy.""" 208 208 + 209 209 + import spacy 210 210 + 211 211 + _nlp = None 212 212 + 213 213 + 214 214 + def _get_nlp(): 215 215 + global _nlp 216 216 + if _nlp is None: 217 217 + _nlp = spacy.load("en_core_web_sm") 218 218 + return _nlp 219 219 + 220 220 + 221 221 + def detect_sentences(text: str) -> list[dict]: 222 222 + """Detect sentence boundaries and return byte-range spans. 223 223 + 224 224 + Returns a list of dicts, each with: 225 225 + byteStart: int — UTF-8 byte offset of sentence start 226 226 + byteEnd: int — UTF-8 byte offset of sentence end (exclusive) 227 227 + """ 228 228 + if not text.strip(): 229 229 + return [] 230 230 + 231 231 + nlp = _get_nlp() 232 232 + doc = nlp(text) 233 233 + text_bytes = text.encode("utf-8") 234 234 + sentences = [] 235 235 + 236 236 + for sent in doc.sents: 237 237 + # spaCy gives character offsets; convert to byte offsets 238 238 + byte_start = len(text[:sent.start_char].encode("utf-8")) 239 239 + byte_end = len(text[:sent.end_char].encode("utf-8")) 240 240 + sentences.append({ 241 241 + "byteStart": byte_start, 242 242 + "byteEnd": byte_end, 243 243 + }) 244 244 + 245 245 + return sentences 246 246 + ``` 247 247 + 248 248 + - [ ] **Step 4: Run tests to verify they pass** 249 249 + 250 250 + ```bash 251 251 + cd pipeline && source .venv/bin/activate && pytest tests/test_sentences.py -v 252 252 + ``` 253 253 + Expected: all PASS 254 254 + 255 255 + - [ ] **Step 5: Commit** 256 256 + 257 257 + ```bash 258 258 + git add pipeline/nlp/sentences.py pipeline/tests/test_sentences.py 259 259 + git commit -m "feat: sentence boundary detection via spaCy" 260 260 + ``` 261 261 + 262 262 + ### Task 5: Implement paragraph segmentation (Pass 2) 263 263 + 264 264 + **Files:** 265 265 + - Create: `pipeline/tests/test_paragraphs.py` 266 266 + - Create: `pipeline/nlp/paragraphs.py` 267 267 + 268 268 + - [ ] **Step 1: Write the failing test** 269 269 + 270 270 + Create `pipeline/tests/test_paragraphs.py`: 271 271 + 272 272 + ```python 273 273 + from nlp.paragraphs import detect_paragraphs 274 274 + 275 275 + 276 276 + def test_basic_paragraphs(): 277 277 + text = "Hello world. This is sentence two. After a long pause here. New topic starts." 278 278 + sentences = [ 279 279 + {"byteStart": 0, "byteEnd": 12}, 280 280 + {"byteStart": 13, "byteEnd": 34}, 281 281 + {"byteStart": 35, "byteEnd": 59}, 282 282 + {"byteStart": 60, "byteEnd": 77}, 283 283 + ] 284 284 + # Words: "Hello"=0, "world."=1, "This"=2, "is"=3, "sentence"=4, "two."=5, 285 285 + # "After"=6, "a"=7, "long"=8, "pause"=9, "here."=10, 286 286 + # "New"=11, "topic"=12, "starts."=13 287 287 + # Big pause gap (3000ms) between word index 5 and 6 (between sentence 2 and 3) 288 288 + timings = [100, 100, 100, 100, 100, 100, -3000, 100, 100, 100, 100, 100, 100, 100] 289 289 + start_ms = 0 290 290 + 291 291 + paragraphs = detect_paragraphs( 292 292 + text=text, 293 293 + timings=timings, 294 294 + start_ms=start_ms, 295 295 + sentences=sentences, 296 296 + pause_threshold_ms=2000, 297 297 + proximity_words=5, 298 298 + ) 299 299 + # Should detect a paragraph break at the sentence boundary near the 3s pause 300 300 + assert len(paragraphs) == 2 301 301 + assert paragraphs[0]["byteStart"] == 0 302 302 + assert paragraphs[1]["byteStart"] == 35 # "After a long pause..." 303 303 + 304 304 + 305 305 + def test_no_long_pauses_single_paragraph(): 306 306 + text = "One sentence. Two sentence." 307 307 + sentences = [ 308 308 + {"byteStart": 0, "byteEnd": 13}, 309 309 + {"byteStart": 14, "byteEnd": 27}, 310 310 + ] 311 311 + timings = [100, 100, 100, 100] 312 312 + paragraphs = detect_paragraphs( 313 313 + text=text, timings=timings, start_ms=0, 314 314 + sentences=sentences, 315 315 + ) 316 316 + assert len(paragraphs) == 1 317 317 + 318 318 + 319 319 + def test_empty_input(): 320 320 + paragraphs = detect_paragraphs( 321 321 + text="", timings=[], start_ms=0, sentences=[], 322 322 + ) 323 323 + assert paragraphs == [] 324 324 + ``` 325 325 + 326 326 + - [ ] **Step 2: Run test to verify it fails** 327 327 + 328 328 + ```bash 329 329 + cd pipeline && source .venv/bin/activate && pytest tests/test_paragraphs.py -v 330 330 + ``` 331 331 + Expected: FAIL with `ModuleNotFoundError` 332 332 + 333 333 + - [ ] **Step 3: Implement `detect_paragraphs`** 334 334 + 335 335 + Create `pipeline/nlp/paragraphs.py`: 336 336 + 337 337 + ```python 338 338 + """Pass 2: Paragraph segmentation using pause duration + sentence boundaries.""" 339 339 + 340 340 + 341 341 + def detect_paragraphs( 342 342 + text: str, 343 343 + timings: list[int], 344 344 + start_ms: int, 345 345 + sentences: list[dict], 346 346 + pause_threshold_ms: int = 2000, 347 347 + proximity_words: int = 5, 348 348 + ) -> list[dict]: 349 349 + """Detect paragraph boundaries from timing gaps and sentence boundaries. 350 350 + 351 351 + Returns a list of paragraph dicts with byteStart and byteEnd. 352 352 + """ 353 353 + if not text.strip() or not sentences: 354 354 + return [] 355 355 + 356 356 + text_bytes = text.encode("utf-8") 357 357 + 358 358 + # Find word indices where long pauses occur 359 359 + pause_word_indices: list[int] = [] 360 360 + word_index = 0 361 361 + for value in timings: 362 362 + if value < 0: 363 363 + if abs(value) >= pause_threshold_ms: 364 364 + pause_word_indices.append(word_index) 365 365 + else: 366 366 + word_index += 1 367 367 + 368 368 + if not pause_word_indices: 369 369 + # No long pauses — entire text is one paragraph 370 370 + return [{"byteStart": sentences[0]["byteStart"], 371 371 + "byteEnd": sentences[-1]["byteEnd"]}] 372 372 + 373 373 + # Build a char→byte offset map once, then compute word byte starts 374 374 + text_bytes = text.encode("utf-8") 375 375 + char_to_byte = [] 376 376 + byte_pos = 0 377 377 + for ch in text: 378 378 + char_to_byte.append(byte_pos) 379 379 + byte_pos += len(ch.encode("utf-8")) 380 380 + char_to_byte.append(byte_pos) # sentinel for end of text 381 381 + 382 382 + # Find word start char offsets using split positions 383 383 + words = text.split() 384 384 + word_byte_starts: list[int] = [] 385 385 + char_offset = 0 386 386 + for w in words: 387 387 + idx = text.index(w, char_offset) 388 388 + word_byte_starts.append(char_to_byte[idx]) 389 389 + char_offset = idx + len(w) 390 390 + 391 391 + def word_index_for_byte(byte_pos: int) -> int: 392 392 + """Find the word index closest to a byte position.""" 393 393 + best = 0 394 394 + for i, wb in enumerate(word_byte_starts): 395 395 + if wb <= byte_pos: 396 396 + best = i 397 397 + return best 398 398 + 399 399 + # Find sentence boundaries (byte positions where one sentence ends 400 400 + # and the next begins) 401 401 + sentence_break_byte_positions: list[int] = [] 402 402 + sentence_break_word_indices: list[int] = [] 403 403 + for i in range(1, len(sentences)): 404 404 + bp = sentences[i]["byteStart"] 405 405 + sentence_break_byte_positions.append(bp) 406 406 + sentence_break_word_indices.append(word_index_for_byte(bp)) 407 407 + 408 408 + # For each long pause, find the nearest sentence boundary 409 409 + paragraph_break_bytes: set[int] = set() 410 410 + for pause_wi in pause_word_indices: 411 411 + best_dist = float("inf") 412 412 + best_bp = None 413 413 + for sb_wi, sb_bp in zip( 414 414 + sentence_break_word_indices, sentence_break_byte_positions 415 415 + ): 416 416 + dist = abs(sb_wi - pause_wi) 417 417 + if dist <= proximity_words and dist < best_dist: 418 418 + best_dist = dist 419 419 + best_bp = sb_bp 420 420 + if best_bp is not None: 421 421 + paragraph_break_bytes.add(best_bp) 422 422 + 423 423 + # Build paragraph spans from the break points 424 424 + sorted_breaks = sorted(paragraph_break_bytes) 425 425 + paragraphs: list[dict] = [] 426 426 + current_start = sentences[0]["byteStart"] 427 427 + 428 428 + for brk in sorted_breaks: 429 429 + # Find the sentence that ends just before this break 430 430 + para_end = brk 431 431 + for s in sentences: 432 432 + if s["byteEnd"] <= brk: 433 433 + para_end = s["byteEnd"] 434 434 + paragraphs.append({"byteStart": current_start, "byteEnd": para_end}) 435 435 + current_start = brk 436 436 + 437 437 + # Final paragraph 438 438 + paragraphs.append({ 439 439 + "byteStart": current_start, 440 440 + "byteEnd": sentences[-1]["byteEnd"], 441 441 + }) 442 442 + 443 443 + return paragraphs 444 444 + ``` 445 445 + 446 446 + - [ ] **Step 4: Run tests to verify they pass** 447 447 + 448 448 + ```bash 449 449 + cd pipeline && source .venv/bin/activate && pytest tests/test_paragraphs.py -v 450 450 + ``` 451 451 + Expected: all PASS 452 452 + 453 453 + - [ ] **Step 5: Commit** 454 454 + 455 455 + ```bash 456 456 + git add pipeline/nlp/paragraphs.py pipeline/tests/test_paragraphs.py 457 457 + git commit -m "feat: paragraph segmentation from pause data + sentence boundaries" 458 458 + ``` 459 459 + 460 460 + ### Task 6: Pipeline orchestrator — process all transcripts 461 461 + 462 462 + **Files:** 463 463 + - Create: `pipeline/nlp/run.py` 464 464 + - Create: `pipeline/tests/test_run.py` 465 465 + 466 466 + - [ ] **Step 1: Write the failing test** 467 467 + 468 468 + Create `pipeline/tests/test_run.py`: 469 469 + 470 470 + ```python 471 471 + import json 472 472 + import os 473 473 + from pathlib import Path 474 474 + from nlp.run import process_transcript 475 475 + 476 476 + 477 477 + def test_process_transcript_produces_output(tmp_path): 478 478 + """Integration test: full pipeline on a simple transcript.""" 479 479 + transcript = { 480 480 + "text": "Hello world. This is a test. After a long pause. New topic here.", 481 481 + "startMs": 0, 482 482 + "timings": [100, 100, 100, 100, 100, 100, -3000, 100, 100, 100, 100, 100, 100, 100], 483 483 + } 484 484 + 485 485 + result = process_transcript(transcript, talk_rkey="test-talk") 486 486 + 487 487 + # Should have sentences and paragraphs 488 488 + assert "sentences" in result 489 489 + assert "paragraphs" in result 490 490 + assert len(result["sentences"]) >= 2 491 491 + assert len(result["paragraphs"]) >= 1 492 492 + # Each sentence has byte ranges 493 493 + for s in result["sentences"]: 494 494 + assert "byteStart" in s 495 495 + assert "byteEnd" in s 496 496 + # Metadata present 497 497 + assert "metadata" in result 498 498 + assert result["metadata"]["tool"] == "spacy/en_core_web_sm" 499 499 + ``` 500 500 + 501 501 + - [ ] **Step 2: Run test to verify it fails** 502 502 + 503 503 + ```bash 504 504 + cd pipeline && source .venv/bin/activate && pytest tests/test_run.py -v 505 505 + ``` 506 506 + 507 507 + - [ ] **Step 3: Implement the orchestrator** 508 508 + 509 509 + Create `pipeline/nlp/run.py`: 510 510 + 511 511 + ```python 512 512 + """Pipeline orchestrator: run all NLP passes on a transcript.""" 513 513 + 514 514 + import json 515 515 + import sys 516 516 + from pathlib import Path 517 517 + from nlp.sentences import detect_sentences 518 518 + from nlp.paragraphs import detect_paragraphs 519 519 + 520 520 + 521 521 + def process_transcript( 522 522 + transcript: dict, 523 523 + talk_rkey: str, 524 524 + pause_threshold_ms: int = 2000, 525 525 + proximity_words: int = 5, 526 526 + ) -> dict: 527 527 + """Run all NLP passes on a single transcript. 528 528 + 529 529 + Args: 530 530 + transcript: dict with text, startMs, timings 531 531 + talk_rkey: the talk's record key (for output naming) 532 532 + 533 533 + Returns: 534 534 + dict with sentences, paragraphs, and metadata 535 535 + """ 536 536 + text = transcript["text"] 537 537 + timings = transcript["timings"] 538 538 + start_ms = transcript["startMs"] 539 539 + 540 540 + # Pass 1: Sentence detection 541 541 + sentences = detect_sentences(text) 542 542 + 543 543 + # Pass 2: Paragraph segmentation 544 544 + paragraphs = detect_paragraphs( 545 545 + text=text, 546 546 + timings=timings, 547 547 + start_ms=start_ms, 548 548 + sentences=sentences, 549 549 + pause_threshold_ms=pause_threshold_ms, 550 550 + proximity_words=proximity_words, 551 551 + ) 552 552 + 553 553 + return { 554 554 + "talkRkey": talk_rkey, 555 555 + "sentences": sentences, 556 556 + "paragraphs": paragraphs, 557 557 + "metadata": { 558 558 + "tool": "spacy/en_core_web_sm", 559 559 + "pauseThresholdMs": pause_threshold_ms, 560 560 + "proximityWords": proximity_words, 561 561 + }, 562 562 + } 563 563 + 564 564 + 565 565 + def main(): 566 566 + """CLI: read transcripts from appview data/transcripts/, write results to pipeline/data/nlp/.""" 567 567 + # Match the path used by publish.ts: apps/ionosphere-appview/data/transcripts/ 568 568 + transcripts_dir = Path(__file__).resolve().parent.parent.parent / "apps" / "ionosphere-appview" / "data" / "transcripts" 569 569 + output_dir = Path(__file__).resolve().parent.parent / "data" / "nlp" 570 570 + output_dir.mkdir(parents=True, exist_ok=True) 571 571 + 572 572 + if not transcripts_dir.exists(): 573 573 + print(f"Transcripts directory not found: {transcripts_dir}") 574 574 + sys.exit(1) 575 575 + 576 576 + transcript_files = sorted(transcripts_dir.glob("*.json")) 577 577 + print(f"Processing {len(transcript_files)} transcripts...") 578 578 + 579 579 + for tf in transcript_files: 580 580 + talk_rkey = tf.stem 581 581 + transcript = json.loads(tf.read_text()) 582 582 + 583 583 + # The cached transcript files contain TranscriptResult format 584 584 + # (text + words array). We need to encode to compact format first. 585 585 + # But the pipeline needs text + timings. Let's derive timings from words. 586 586 + if "words" in transcript and "timings" not in transcript: 587 587 + from nlp.encoding import words_to_compact 588 588 + compact = words_to_compact(transcript) 589 589 + else: 590 590 + compact = transcript 591 591 + 592 592 + result = process_transcript(compact, talk_rkey=talk_rkey) 593 593 + 594 594 + out_path = output_dir / f"{talk_rkey}.json" 595 595 + out_path.write_text(json.dumps(result, indent=2)) 596 596 + print(f" {talk_rkey}: {len(result['sentences'])} sentences, {len(result['paragraphs'])} paragraphs") 597 597 + 598 598 + print("Done.") 599 599 + 600 600 + 601 601 + if __name__ == "__main__": 602 602 + main() 603 603 + ``` 604 604 + 605 605 + - [ ] **Step 4: Create `pipeline/nlp/encoding.py` — helper to convert word-level transcripts to compact format** 606 606 + 607 607 + ```python 608 608 + """Convert word-level transcript format to compact (text + timings) format.""" 609 609 + 610 610 + 611 611 + def words_to_compact(transcript: dict) -> dict: 612 612 + """Convert TranscriptResult {text, words[{word, start, end}]} to compact {text, startMs, timings}.""" 613 613 + words = transcript.get("words", []) 614 614 + if not words: 615 615 + return {"text": transcript.get("text", ""), "startMs": 0, "timings": []} 616 616 + 617 617 + start_ms = round(words[0]["start"] * 1000) 618 618 + timings = [] 619 619 + cursor = start_ms 620 620 + 621 621 + for w in words: 622 622 + word_start_ms = round(w["start"] * 1000) 623 623 + word_end_ms = round(w["end"] * 1000) 624 624 + duration = word_end_ms - word_start_ms 625 625 + 626 626 + gap = word_start_ms - cursor 627 627 + if gap > 0: 628 628 + timings.append(-gap) 629 629 + 630 630 + timings.append(max(duration, 1)) 631 631 + cursor = word_end_ms 632 632 + 633 633 + return { 634 634 + "text": transcript["text"], 635 635 + "startMs": start_ms, 636 636 + "timings": timings, 637 637 + } 638 638 + ``` 639 639 + 640 640 + - [ ] **Step 5: Run tests to verify they pass** 641 641 + 642 642 + ```bash 643 643 + cd pipeline && source .venv/bin/activate && pytest tests/ -v 644 644 + ``` 645 645 + Expected: all PASS 646 646 + 647 647 + - [ ] **Step 6: Commit** 648 648 + 649 649 + ```bash 650 650 + git add pipeline/nlp/run.py pipeline/nlp/encoding.py pipeline/tests/test_run.py 651 651 + git commit -m "feat: NLP pipeline orchestrator with CLI entry point" 652 652 + ``` 653 653 + 654 654 + --- 655 655 + 656 656 + ## Chunk 3: TypeScript — update `extractData` for hierarchical structure 657 657 + 658 658 + ### Task 7: Add `ParagraphSpan` and `SentenceSpan` types and update `extractData` 659 659 + 660 660 + **Files:** 661 661 + - Modify: `apps/ionosphere/src/lib/transcript.ts` 662 662 + - Modify: `apps/ionosphere/src/lib/transcript.test.ts` 663 663 + 664 664 + - [ ] **Step 1: Write failing tests for hierarchical extraction** 665 665 + 666 666 + Add to `apps/ionosphere/src/lib/transcript.test.ts`: 667 667 + 668 668 + ```typescript 669 669 + describe("extractData — hierarchical structure", () => { 670 670 + it("groups words into sentences and paragraphs when facets present", () => { 671 671 + const doc = makeDoc([ 672 672 + { text: "Hello", startNs: 1000, endNs: 2000 }, 673 673 + { text: "world.", startNs: 2000, endNs: 3000 }, 674 674 + { text: "New", startNs: 4000, endNs: 5000 }, 675 675 + { text: "sentence.", startNs: 5000, endNs: 6000 }, 676 676 + ]); 677 677 + const encoder = new TextEncoder(); 678 678 + const text = "Hello world. New sentence."; 679 679 + // Add sentence facets 680 680 + doc.facets.push({ 681 681 + index: { 682 682 + byteStart: 0, 683 683 + byteEnd: encoder.encode("Hello world.").length, 684 684 + }, 685 685 + features: [{ $type: "tv.ionosphere.facet#sentence" }], 686 686 + }); 687 687 + doc.facets.push({ 688 688 + index: { 689 689 + byteStart: encoder.encode("Hello world. ").length, 690 690 + byteEnd: encoder.encode(text).length, 691 691 + }, 692 692 + features: [{ $type: "tv.ionosphere.facet#sentence" }], 693 693 + }); 694 694 + // Add paragraph facet (one paragraph covering everything) 695 695 + doc.facets.push({ 696 696 + index: { byteStart: 0, byteEnd: encoder.encode(text).length }, 697 697 + features: [{ $type: "tv.ionosphere.facet#paragraph" }], 698 698 + }); 699 699 + 700 700 + const result = extractData(doc); 701 701 + expect(result.paragraphs).toHaveLength(1); 702 702 + expect(result.paragraphs[0].sentences).toHaveLength(2); 703 703 + expect(result.paragraphs[0].sentences[0].words).toHaveLength(2); 704 704 + expect(result.paragraphs[0].sentences[1].words).toHaveLength(2); 705 705 + }); 706 706 + 707 707 + it("gracefully degrades to singleton paragraph/sentence when no structural facets", () => { 708 708 + const doc = makeDoc([ 709 709 + { text: "Hello", startNs: 1000, endNs: 2000 }, 710 710 + { text: "world", startNs: 2000, endNs: 3000 }, 711 711 + ]); 712 712 + 713 713 + const result = extractData(doc); 714 714 + // Should still have paragraphs/sentences structure 715 715 + expect(result.paragraphs).toHaveLength(1); 716 716 + expect(result.paragraphs[0].sentences).toHaveLength(1); 717 717 + expect(result.paragraphs[0].sentences[0].words).toHaveLength(2); 718 718 + // Legacy flat access still works 719 719 + expect(result.words).toHaveLength(2); 720 720 + }); 721 721 + }); 722 722 + ``` 723 723 + 724 724 + - [ ] **Step 2: Run tests to verify they fail** 725 725 + 726 726 + ```bash 727 727 + cd apps/ionosphere && npx vitest run src/lib/transcript.test.ts 728 728 + ``` 729 729 + Expected: FAIL — `paragraphs` property does not exist 730 730 + 731 731 + - [ ] **Step 3: Add types and update `extractData`** 732 732 + 733 733 + Add these types to `apps/ionosphere/src/lib/transcript.ts`: 734 734 + 735 735 + ```typescript 736 736 + export interface SentenceSpan { 737 737 + byteStart: number; 738 738 + byteEnd: number; 739 739 + words: WordSpan[]; 740 740 + } 741 741 + 742 742 + export interface ParagraphSpan { 743 743 + byteStart: number; 744 744 + byteEnd: number; 745 745 + sentences: SentenceSpan[]; 746 746 + } 747 747 + ``` 748 748 + 749 749 + Update `extractData` to return `paragraphs: ParagraphSpan[]` alongside the existing flat `words` array. The function extracts `#sentence` and `#paragraph` facets, groups words into sentences by byte range overlap, groups sentences into paragraphs, and falls back to singleton wrappers when structural facets are absent. 750 750 + 751 751 + Key logic: 752 752 + 1. Extract words and concepts as before (existing code unchanged). 753 753 + 2. Extract sentence facets (byteStart/byteEnd from `#sentence` features). Sort by byteStart. 754 754 + 3. Extract paragraph facets (byteStart/byteEnd from `#paragraph` features). Sort by byteStart. 755 755 + 4. If no sentence facets: wrap all words in one sentence. If no paragraph facets: wrap all sentences in one paragraph. 756 756 + 5. Assign each word to its sentence (word.byteStart >= sentence.byteStart && word.byteEnd <= sentence.byteEnd). 757 757 + 6. Assign each sentence to its paragraph (sentence.byteStart >= paragraph.byteStart && sentence.byteEnd <= paragraph.byteEnd). 758 758 + 759 759 + - [ ] **Step 4: Run tests to verify they pass** 760 760 + 761 761 + ```bash 762 762 + cd apps/ionosphere && npx vitest run src/lib/transcript.test.ts 763 763 + ``` 764 764 + Expected: all PASS (both new and existing tests) 765 765 + 766 766 + - [ ] **Step 5: Commit** 767 767 + 768 768 + ```bash 769 769 + git add apps/ionosphere/src/lib/transcript.ts apps/ionosphere/src/lib/transcript.test.ts 770 770 + git commit -m "feat: hierarchical extractData with paragraph/sentence grouping" 771 771 + ``` 772 772 + 773 773 + --- 774 774 + 775 775 + ## Chunk 4: Update the renderer 776 776 + 777 777 + ### Task 8: Update `TranscriptView` to render paragraphs and sentences 778 778 + 779 779 + **Files:** 780 780 + - Modify: `apps/ionosphere/src/app/components/TranscriptView.tsx` 781 781 + 782 782 + - [ ] **Step 1: Update the render tree** 783 783 + 784 784 + Replace the flat `words.map(...)` rendering with a nested structure: 785 785 + 786 786 + ```tsx 787 787 + {paragraphs.map((para, pi) => ( 788 788 + <div key={pi} className="mb-4"> 789 789 + {para.sentences.map((sent, si) => ( 790 790 + <span key={si} className="sentence"> 791 791 + {sent.words.map((word, wi) => { 792 792 + const globalIdx = /* compute global word index */; 793 793 + return ( 794 794 + <WordSpanComponent 795 795 + key={globalIdx} 796 796 + ref={(el) => setWordRef(globalIdx, el)} 797 797 + word={word} 798 798 + concept={wordConcepts[globalIdx]?.[0] || null} 799 799 + currentTimeNs={currentTimeNs} 800 800 + onSeek={handleSeek} 801 801 + hasComment={wordHasComment.has(globalIdx)} 802 802 + /> 803 803 + ); 804 804 + })} 805 805 + </span> 806 806 + ))} 807 807 + </div> 808 808 + ))} 809 809 + ``` 810 810 + 811 811 + The `useMemo` call to `extractData` now destructures `paragraphs` alongside `words` and `wordConcepts`. The global word index is computed by maintaining a running counter across paragraphs and sentences. 812 812 + 813 813 + The comment system, reaction groups, text selection, and scroll/time mappings continue to use the flat `words` array (unchanged). Only the DOM structure changes to add the paragraph/sentence grouping. 814 814 + 815 815 + - [ ] **Step 2: Verify in browser** 816 816 + 817 817 + Start the dev server and load a talk page. Verify: 818 818 + - Transcripts without structural facets render identically to before (graceful degradation) 819 819 + - No console errors 820 820 + - Scroll-to-time and click-to-seek still work 821 821 + - Comments and reactions still work 822 822 + 823 823 + - [ ] **Step 3: Commit** 824 824 + 825 825 + ```bash 826 826 + git add apps/ionosphere/src/app/components/TranscriptView.tsx 827 827 + git commit -m "feat: render transcripts with paragraph/sentence DOM structure" 828 828 + ``` 829 829 + 830 830 + ### Task 9: Update `WindowedTranscriptView` for paragraph gaps 831 831 + 832 832 + **Files:** 833 833 + - Modify: `apps/ionosphere/src/app/components/WindowedTranscriptView.tsx` 834 834 + 835 835 + - [ ] **Step 1: Update `computeMonospaceLayout` to accept paragraph breaks** 836 836 + 837 837 + Add a `paragraphStartIndices: Set<number>` parameter. When a word is a paragraph start (its global index is in the set), insert a gap of `LINE_HEIGHT` pixels before that line entry. Add `isParagraphStart: boolean` to `LineEntry`. 838 838 + 839 839 + - [ ] **Step 2: Update the rendering to add paragraph gap spacers** 840 840 + 841 841 + For each visible line with `isParagraphStart: true`, render a gap spacer `div` above it. 842 842 + 843 843 + - [ ] **Step 3: Update `timeToScrollY` and `scrollYToTime`** 844 844 + 845 845 + Gap entries have no time range. Scrolling through a gap seeks to the end of the preceding line's time range (treating the gap as an extension of the previous paragraph's final time). 846 846 + 847 847 + - [ ] **Step 4: Verify in browser** 848 848 + 849 849 + Load the track view (which uses `WindowedTranscriptView`). Verify paragraph gaps appear and scroll behavior is smooth. 850 850 + 851 851 + - [ ] **Step 5: Commit** 852 852 + 853 853 + ```bash 854 854 + git add apps/ionosphere/src/app/components/WindowedTranscriptView.tsx 855 855 + git commit -m "feat: WindowedTranscriptView paragraph gap support" 856 856 + ``` 857 857 + 858 858 + --- 859 859 + 860 860 + ## Chunk 5: Document assembly and publish pipeline 861 861 + 862 862 + ### Task 10: Update document assembly to include structural facets 863 863 + 864 864 + **Files:** 865 865 + - Modify: `formats/tv.ionosphere/ts/transcript-encoding.ts` 866 866 + - Modify: `formats/tv.ionosphere/ts/transcript-encoding.test.ts` 867 867 + 868 868 + - [ ] **Step 1: Write the failing test** 869 869 + 870 870 + Add to `formats/tv.ionosphere/ts/transcript-encoding.test.ts`: 871 871 + 872 872 + ```typescript 873 873 + describe("decodeToDocumentWithStructure", () => { 874 874 + it("adds sentence and paragraph facets from NLP annotations", () => { 875 875 + const compact = encode(contiguous); 876 876 + const annotations = { 877 877 + sentences: [ 878 878 + { byteStart: 0, byteEnd: 11 }, // "hello world" 879 879 + { byteStart: 12, byteEnd: 26 }, // "this is a test" 880 880 + ], 881 881 + paragraphs: [ 882 882 + { byteStart: 0, byteEnd: 26 }, 883 883 + ], 884 884 + }; 885 885 + const doc = decodeToDocumentWithStructure(compact, annotations); 886 886 + 887 887 + const sentenceFacets = doc.facets.filter(f => 888 888 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#sentence") 889 889 + ); 890 890 + const paragraphFacets = doc.facets.filter(f => 891 891 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#paragraph") 892 892 + ); 893 893 + expect(sentenceFacets).toHaveLength(2); 894 894 + expect(paragraphFacets).toHaveLength(1); 895 895 + }); 896 896 + 897 897 + it("produces valid document without annotations (backward compatible)", () => { 898 898 + const compact = encode(contiguous); 899 899 + const doc = decodeToDocumentWithStructure(compact, null); 900 900 + // Same as decodeToDocument 901 901 + expect(doc.facets.length).toBe(6); // just timestamp facets 902 902 + }); 903 903 + }); 904 904 + ``` 905 905 + 906 906 + - [ ] **Step 2: Run test to verify it fails** 907 907 + 908 908 + ```bash 909 909 + cd formats/tv.ionosphere && npx vitest run ts/transcript-encoding.test.ts 910 910 + ``` 911 911 + 912 912 + - [ ] **Step 3: Implement `decodeToDocumentWithStructure`** 913 913 + 914 914 + Add to `formats/tv.ionosphere/ts/transcript-encoding.ts`: 915 915 + 916 916 + ```typescript 917 917 + export interface NlpAnnotations { 918 918 + sentences: Array<{ byteStart: number; byteEnd: number }>; 919 919 + paragraphs: Array<{ byteStart: number; byteEnd: number }>; 920 920 + } 921 921 + 922 922 + export function decodeToDocumentWithStructure( 923 923 + compact: CompactTranscript, 924 924 + annotations: NlpAnnotations | null, 925 925 + ): Document { 926 926 + // Start with the base document (timestamp facets) 927 927 + const doc = decodeToDocument(compact); 928 928 + 929 929 + if (!annotations) return doc; 930 930 + 931 931 + // Add sentence facets 932 932 + for (const s of annotations.sentences) { 933 933 + doc.facets.push({ 934 934 + index: { byteStart: s.byteStart, byteEnd: s.byteEnd }, 935 935 + features: [{ $type: "tv.ionosphere.facet#sentence" }], 936 936 + }); 937 937 + } 938 938 + 939 939 + // Add paragraph facets 940 940 + for (const p of annotations.paragraphs) { 941 941 + doc.facets.push({ 942 942 + index: { byteStart: p.byteStart, byteEnd: p.byteEnd }, 943 943 + features: [{ $type: "tv.ionosphere.facet#paragraph" }], 944 944 + }); 945 945 + } 946 946 + 947 947 + return doc; 948 948 + } 949 949 + ``` 950 950 + 951 951 + - [ ] **Step 4: Run tests to verify they pass** 952 952 + 953 953 + ```bash 954 954 + cd formats/tv.ionosphere && npx vitest run ts/transcript-encoding.test.ts 955 955 + ``` 956 956 + 957 957 + - [ ] **Step 5: Commit** 958 958 + 959 959 + ```bash 960 960 + git add formats/tv.ionosphere/ts/transcript-encoding.ts formats/tv.ionosphere/ts/transcript-encoding.test.ts 961 961 + git commit -m "feat: decodeToDocumentWithStructure for NLP annotations" 962 962 + ``` 963 963 + 964 964 + ### Task 11: Update publish.ts to include assembled documents on talk records 965 965 + 966 966 + **Files:** 967 967 + - Modify: `apps/ionosphere-appview/src/publish.ts` 968 968 + 969 969 + - [ ] **Step 1: Update the talk publishing step** 970 970 + 971 971 + After publishing transcripts (step 4 in publish.ts), add a step that: 972 972 + 1. For each talk, checks if NLP output exists at `pipeline/data/nlp/{rkey}.json` 973 973 + 2. If it does, reads the NLP annotations 974 974 + 3. Calls `decodeToDocumentWithStructure` with the compact transcript + annotations 975 975 + 4. Includes the assembled `document` field on the `tv.ionosphere.talk` record 976 976 + 977 977 + This moves document assembly from serve time to publish time, as specified in the design. 978 978 + 979 979 + - [ ] **Step 2: Verify by running publish in dry-run or against local PDS** 980 980 + 981 981 + Check that talk records now include the `document` field with sentence/paragraph facets. 982 982 + 983 983 + - [ ] **Step 3: Commit** 984 984 + 985 985 + ```bash 986 986 + git add apps/ionosphere-appview/src/publish.ts 987 987 + git commit -m "feat: publish assembled documents with structural facets on talk records" 988 988 + ``` 989 989 + 990 990 + ### Task 12: Update appview routes to serve pre-assembled documents 991 991 + 992 992 + **Files:** 993 993 + - Modify: `apps/ionosphere-appview/src/routes.ts` 994 994 + 995 995 + - [ ] **Step 1: Remove `overlayAnnotations` and serve pre-assembled document** 996 996 + 997 997 + In the `getTalk` route handler: 998 998 + 1. Remove the `overlayAnnotations` function entirely (lines 17-59). 999 999 + 2. Remove the annotation overlay logic in the route (lines 173-185). 1000 1000 + 3. If the talk record has a `document` field in the DB, serve it directly. 1001 1001 + 4. Fall back to `decodeToDocument` from the compact transcript if no pre-assembled document exists (backward compatibility during transition). 1002 1002 + 1003 1003 + - [ ] **Step 2: Update the indexer to store the document field** 1004 1004 + 1005 1005 + In `apps/ionosphere-appview/src/indexer.ts`, update the `indexTalk` function's INSERT statement (line 176-197). The `talks` table already has a `document TEXT` column (line 54 of db.ts), but the INSERT does not include it. Add `document` to the column list and bind `record.document ? JSON.stringify(record.document) : null` as the value. This is a SQL change — the column list and VALUES placeholders must both be updated. 1006 1006 + 1007 1007 + - [ ] **Step 3: Commit** 1008 1008 + 1009 1009 + ```bash 1010 1010 + git add apps/ionosphere-appview/src/routes.ts apps/ionosphere-appview/src/indexer.ts 1011 1011 + git commit -m "feat: serve pre-assembled documents, remove overlayAnnotations" 1012 1012 + ``` 1013 1013 + 1014 1014 + --- 1015 1015 + 1016 1016 + ## Chunk 6: Remove old enrichment system 1017 1017 + 1018 1018 + ### Task 13: Remove old annotation/enrichment code 1019 1019 + 1020 1020 + **Files:** 1021 1021 + - Delete: `apps/ionosphere-appview/src/enrich.ts` 1022 1022 + - Delete: `apps/ionosphere-appview/src/enrich-all.ts` 1023 1023 + - Delete: `apps/ionosphere-appview/src/publish-annotations.ts` 1024 1024 + - Modify: `apps/ionosphere-appview/src/indexer.ts` — remove `tv.ionosphere.annotation` handling 1025 1025 + - Modify: `apps/ionosphere-appview/src/routes.ts` — remove annotation-related queries from `getTalk` 1026 1026 + 1027 1027 + - [ ] **Step 1: Delete enrichment files** 1028 1028 + 1029 1029 + ```bash 1030 1030 + rm apps/ionosphere-appview/src/enrich.ts 1031 1031 + rm apps/ionosphere-appview/src/enrich-all.ts 1032 1032 + rm apps/ionosphere-appview/src/publish-annotations.ts 1033 1033 + ``` 1034 1034 + 1035 1035 + - [ ] **Step 2: Remove annotation indexing from `indexer.ts`** 1036 1036 + 1037 1037 + Remove `"tv.ionosphere.annotation"` from `IONOSPHERE_COLLECTIONS` array (line 28). Remove the annotation delete case (lines 72-75). Remove the annotation create/update case (lines 116-117). Remove the `indexAnnotation` function and `rebuildTalkConcepts` helper. 1038 1038 + 1039 1039 + - [ ] **Step 3: Remove annotation queries from `routes.ts`** 1040 1040 + 1041 1041 + In the `getTalk` route, remove the concepts query (lines 149-157) and the annotation overlay logic. The concepts data will return via layers.pub in Phase 2. 1042 1042 + 1043 1043 + - [ ] **Step 4: Remove annotation publishing from `publish.ts`** 1044 1044 + 1045 1045 + Remove step 6 (lines 158-177) that publishes `tv.ionosphere.annotation` records. 1046 1046 + 1047 1047 + - [ ] **Step 5: Verify the appview still starts and serves talks** 1048 1048 + 1049 1049 + ```bash 1050 1050 + cd apps/ionosphere-appview && npx tsx src/appview.ts 1051 1051 + ``` 1052 1052 + Hit the `/xrpc/tv.ionosphere.getTalk?rkey=<some-rkey>` endpoint and verify it returns a talk with a document. 1053 1053 + 1054 1054 + - [ ] **Step 6: Commit** 1055 1055 + 1056 1056 + ```bash 1057 1057 + git add -A apps/ionosphere-appview/src/ 1058 1058 + git commit -m "chore: remove old enrichment system (enrich.ts, annotations, overlayAnnotations)" 1059 1059 + ``` 1060 1060 + 1061 1061 + --- 1062 1062 + 1063 1063 + ## Chunk 7: End-to-end integration and verification 1064 1064 + 1065 1065 + **IMPORTANT:** Tasks 11-12 create the publish-time document assembly path, but existing talks in the appview DB will have NULL documents until a full re-publish is done. Task 14 performs this re-publish. Do NOT deploy Tasks 11-12 without running Task 14, or existing talks will lose concept overlays with no replacement. 1066 1066 + 1067 1067 + ### Task 14: Run the full pipeline end-to-end 1068 1068 + 1069 1069 + - [ ] **Step 1: Run the Python NLP pipeline on all transcripts** 1070 1070 + 1071 1071 + ```bash 1072 1072 + cd pipeline && source .venv/bin/activate && python -m nlp.run 1073 1073 + ``` 1074 1074 + 1075 1075 + Verify output files appear in `pipeline/data/nlp/` with sentence and paragraph data. 1076 1076 + 1077 1077 + - [ ] **Step 2: Spot-check 3-5 NLP output files** 1078 1078 + 1079 1079 + Open output JSON files for talks of different types (presentation, panel, lightning talk). Verify: 1080 1080 + - Sentence count is reasonable (expect 50-300 for a 20-min talk) 1081 1081 + - Paragraph count is reasonable (expect 5-30) 1082 1082 + - Byte ranges are valid (byteStart < byteEnd, monotonically increasing) 1083 1083 + - Paragraph boundaries fall at sentence boundaries 1084 1084 + 1085 1085 + - [ ] **Step 3: Run the TypeScript publish pipeline** 1086 1086 + 1087 1087 + ```bash 1088 1088 + cd apps/ionosphere-appview && npx tsx src/publish.ts 1089 1089 + ``` 1090 1090 + 1091 1091 + Verify talk records now include the `document` field with structural facets. 1092 1092 + 1093 1093 + - [ ] **Step 4: Start the appview and frontend, verify in browser** 1094 1094 + 1095 1095 + Start the dev environment and load several talk pages. Verify: 1096 1096 + - Paragraphs have visible vertical spacing 1097 1097 + - Sentences are grouped as inline spans 1098 1098 + - Scroll-to-time and click-to-seek work correctly 1099 1099 + - The playhead brightness gradient is smooth across paragraph breaks 1100 1100 + - Comments and reactions still work 1101 1101 + - Talks without NLP data still render correctly (graceful degradation) 1102 1102 + 1103 1103 + - [ ] **Step 5: Commit any fixes found during verification** 1104 1104 + 1105 1105 + ```bash 1106 1106 + git add -A && git commit -m "fix: integration fixes from end-to-end verification" 1107 1107 + ``` 1108 1108 + 1109 1109 + ### Task 15: Final cleanup 1110 1110 + 1111 1111 + - [ ] **Step 1: Run all tests** 1112 1112 + 1113 1113 + ```bash 1114 1114 + # Python 1115 1115 + cd pipeline && source .venv/bin/activate && pytest -v 1116 1116 + 1117 1117 + # TypeScript 1118 1118 + cd ../.. && npx vitest run 1119 1119 + ``` 1120 1120 + 1121 1121 + All tests should pass. 1122 1122 + 1123 1123 + - [ ] **Step 2: Update `.gitignore` for Python artifacts** 1124 1124 + 1125 1125 + Add to `.gitignore`: 1126 1126 + ``` 1127 1127 + pipeline/.venv/ 1128 1128 + pipeline/data/ 1129 1129 + __pycache__/ 1130 1130 + *.pyc 1131 1131 + ``` 1132 1132 + 1133 1133 + - [ ] **Step 3: Final commit** 1134 1134 + 1135 1135 + ```bash 1136 1136 + git add -A 1137 1137 + git commit -m "chore: final cleanup — tests passing, gitignore updated" 1138 1138 + ```

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docs/superpowers/plans/2026-04-13-layers-pub-publishing.md

reviewed

··· 1 1 + # layers.pub Record Publishing via Panproto Lenses — Implementation Plan 2 2 + 3 3 + > **For agentic workers:** REQUIRED: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. Steps use checkbox (`- [ ]`) syntax for tracking. 4 4 + 5 5 + **Goal:** Publish NLP enrichment data as layers.pub AT Protocol records, with panproto lenses as the authoritative transform pipeline, and index those records back into the appview's materialized document view. 6 6 + 7 7 + **Architecture:** Data flows through panproto lenses: compact transcript → expression + segmentation (Lens 1), NLP annotations → annotation layers (Lens 2). The appview indexes layers.pub records and rebuilds the materialized talk document using a reverse lens (Lens 3). No parallel TypeScript transform pipelines — lenses are the single source of truth. 8 8 + 9 9 + **Tech Stack:** @panproto/core v0.25.1 (WASM), AT Protocol lexicons, better-sqlite3, Hono, vitest 10 10 + 11 11 + **Spec:** `docs/superpowers/specs/2026-04-13-layers-pub-publishing-design.md` 12 12 + 13 13 + --- 14 14 + 15 15 + ## File Structure 16 16 + 17 17 + ### New files 18 18 + - `formats/tv.ionosphere/nlpAnnotations.lexicon.json` — NLP annotations schema (lens source, not published to PDS) 19 19 + - `formats/tv.ionosphere/lenses/transcript-to-expression.lens.json` — Lens 1 spec 20 20 + - `formats/tv.ionosphere/lenses/nlp-to-annotation-layers.lens.json` — Lens 2 spec 21 21 + - `formats/tv.ionosphere/lenses/layers-to-document.lens.json` — Lens 3 spec 22 22 + - `formats/tv.ionosphere/ts/layers-pub.ts` — layers.pub record builders (runs data through panproto lenses) 23 23 + - `apps/ionosphere-appview/src/layers-indexer.ts` — indexer for layers.pub records + document rebuild 24 24 + - `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts` — round-trip test: publish → index → verify 25 25 + 26 26 + ### Modified files 27 27 + - `apps/ionosphere-appview/src/publish.ts` — add Stage 6 (layers.pub publishing) 28 28 + - `apps/ionosphere-appview/src/db.ts` — add 3 new tables (layers_expressions, layers_segmentations, layers_annotations) 29 29 + - `apps/ionosphere-appview/src/indexer.ts` — add layers.pub collections to IONOSPHERE_COLLECTIONS + wire to layers-indexer 30 30 + 31 31 + --- 32 32 + 33 33 + ## Chunk 1: NLP Annotations Lexicon + Lens 1 (Transcript → Expression + Segmentation) 34 34 + 35 35 + ### Task 1: Define the NLP annotations lexicon 36 36 + 37 37 + This lexicon formalizes the JSON shape produced by the NLP pipeline so panproto can use it as a lens source schema. It is never published to PDS. 38 38 + 39 39 + **Files:** 40 40 + - Create: `formats/tv.ionosphere/nlpAnnotations.lexicon.json` 41 41 + 42 42 + - [ ] **Step 1: Write the lexicon** 43 43 + 44 44 + The NLP JSON has this shape (from `pipeline/data/nlp/*.json`): 45 45 + ```json 46 46 + { 47 47 + "talkRkey": "string", 48 48 + "sentences": [{ "byteStart": 0, "byteEnd": 214 }], 49 49 + "paragraphs": [{ "byteStart": 0, "byteEnd": 1729 }], 50 50 + "entities": [{ "byteStart": 15, "byteEnd": 19, "label": "Matt", "nerType": "PERSON", "conceptUri": "at://..." }], 51 51 + "topicBreaks": [{ "byteStart": 1596 }], 52 52 + "metadata": { "tool": "spacy/en_core_web_sm", "pauseThresholdMs": 2000, "proximityWords": 5 } 53 53 + } 54 54 + ``` 55 55 + 56 56 + Write `formats/tv.ionosphere/nlpAnnotations.lexicon.json` as an ATProto lexicon that models this exactly. Key types: 57 57 + - `nlpSentence`: `{ byteStart: integer, byteEnd: integer }` 58 58 + - `nlpParagraph`: `{ byteStart: integer, byteEnd: integer }` 59 59 + - `nlpEntity`: `{ byteStart: integer, byteEnd: integer, label: string, nerType: string, conceptUri?: string (format: at-uri) }` 60 60 + - `nlpTopicBreak`: `{ byteStart: integer }` 61 61 + - `nlpMetadata`: `{ tool: string, pauseThresholdMs?: integer, proximityWords?: integer }` 62 62 + - Main record: `{ talkRkey: string, sentences: nlpSentence[], paragraphs: nlpParagraph[], entities: nlpEntity[], topicBreaks: nlpTopicBreak[], metadata: nlpMetadata }` 63 63 + 64 64 + - [ ] **Step 2: Validate the lexicon parses with panproto** 65 65 + 66 66 + ```bash 67 67 + cd apps/ionosphere-appview 68 68 + npx tsx -e " 69 69 + import { loadSchema } from '../../formats/tv.ionosphere/ts/panproto.js'; 70 70 + import nlp from '../../formats/tv.ionosphere/nlpAnnotations.lexicon.json' assert { type: 'json' }; 71 71 + const schema = await loadSchema(nlp); 72 72 + console.log('NLP annotations schema loaded:', !!schema); 73 73 + " 74 74 + ``` 75 75 + 76 76 + Expected: `NLP annotations schema loaded: true` 77 77 + 78 78 + - [ ] **Step 3: Commit** 79 79 + 80 80 + ```bash 81 81 + git add formats/tv.ionosphere/nlpAnnotations.lexicon.json 82 82 + git commit -m "feat: define tv.ionosphere.nlpAnnotations lexicon for lens source schema" 83 83 + ``` 84 84 + 85 85 + ### Task 2: Define Lens 1 — compact transcript → expression + segmentation 86 86 + 87 87 + This lens transforms `tv.ionosphere.transcript` records into `pub.layers.expression.expression` + `pub.layers.segmentation.segmentation` records. It is the authoritative transform for the text and temporal mapping. 88 88 + 89 89 + **Files:** 90 90 + - Create: `formats/tv.ionosphere/lenses/transcript-to-expression.lens.json` 91 91 + - Create: `formats/tv.ionosphere/ts/layers-pub.ts` 92 92 + - Create: `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts` 93 93 + 94 94 + - [ ] **Step 1: Write the failing test** 95 95 + 96 96 + Create `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts`: 97 97 + 98 98 + ```typescript 99 99 + import { describe, it, expect } from 'vitest'; 100 100 + import { transcriptToLayersPub } from '../../../formats/tv.ionosphere/ts/layers-pub.js'; 101 101 + 102 102 + describe('Lens 1: transcript → expression + segmentation', () => { 103 103 + const transcript = { 104 104 + $type: 'tv.ionosphere.transcript', 105 105 + talkUri: 'at://did:plc:test/tv.ionosphere.talk/test-talk', 106 106 + text: 'Hello world foo bar', 107 107 + startMs: 1000, 108 108 + // word durations: Hello=200ms, world=300ms, 100ms gap, foo=150ms, bar=250ms 109 109 + timings: [200, 300, -100, 150, 250], 110 110 + }; 111 111 + 112 112 + const did = 'did:plc:test'; 113 113 + const talkRkey = 'test-talk'; 114 114 + 115 115 + it('produces an expression record with correct fields', async () => { 116 116 + const { expression } = await transcriptToLayersPub(transcript, did, talkRkey); 117 117 + expect(expression.$type).toBe('pub.layers.expression.expression'); 118 118 + expect(expression.id).toBe('test-talk'); 119 119 + expect(expression.kind).toBe('transcript'); 120 120 + expect(expression.text).toBe('Hello world foo bar'); 121 121 + expect(expression.language).toBe('en'); 122 122 + expect(expression.sourceRef).toBe('at://did:plc:test/tv.ionosphere.transcript/test-talk-transcript'); 123 123 + expect(expression.metadata.tool).toBe('ionosphere-pipeline'); 124 124 + expect(expression.metadata.timestamp).toBeDefined(); 125 125 + expect(expression.createdAt).toBeDefined(); 126 126 + }); 127 127 + 128 128 + it('produces a segmentation record with word tokens', async () => { 129 129 + const { segmentation } = await transcriptToLayersPub(transcript, did, talkRkey); 130 130 + expect(segmentation.$type).toBe('pub.layers.segmentation.segmentation'); 131 131 + expect(segmentation.expression).toBe( 132 132 + 'at://did:plc:test/pub.layers.expression.expression/test-talk-expression' 133 133 + ); 134 134 + expect(segmentation.tokenizations).toHaveLength(1); 135 135 + 136 136 + const tok = segmentation.tokenizations[0]; 137 137 + expect(tok.kind).toBe('word'); 138 138 + expect(tok.tokens).toHaveLength(4); 139 139 + 140 140 + // Check first token 141 141 + expect(tok.tokens[0].tokenIndex).toBe(0); 142 142 + expect(tok.tokens[0].text).toBe('Hello'); 143 143 + expect(tok.tokens[0].textSpan.byteStart).toBe(0); 144 144 + expect(tok.tokens[0].textSpan.byteEnd).toBe(5); 145 145 + expect(tok.tokens[0].temporalSpan.start).toBe(1000); 146 146 + expect(tok.tokens[0].temporalSpan.ending).toBe(1200); 147 147 + 148 148 + // Check third token (after gap) 149 149 + expect(tok.tokens[2].text).toBe('foo'); 150 150 + expect(tok.tokens[2].temporalSpan.start).toBe(1600); // 1000+200+300+100gap 151 151 + expect(tok.tokens[2].temporalSpan.ending).toBe(1750); 152 152 + }); 153 153 + }); 154 154 + ``` 155 155 + 156 156 + - [ ] **Step 2: Run the test to verify it fails** 157 157 + 158 158 + ```bash 159 159 + cd apps/ionosphere-appview 160 160 + npx vitest run src/__tests__/layers-pub.test.ts 161 161 + ``` 162 162 + 163 163 + Expected: FAIL — `transcriptToLayersPub` does not exist yet. 164 164 + 165 165 + - [ ] **Step 3: Write the layers-pub module with Lens 1** 166 166 + 167 167 + Create `formats/tv.ionosphere/ts/layers-pub.ts`. This module initializes panproto, loads the transcript and layers.pub schemas, builds the lens, and exposes `transcriptToLayersPub()`. 168 168 + 169 169 + The function must: 170 170 + 1. Initialize panproto WASM (via existing `init()` from panproto.ts) 171 171 + 2. Load `tv.ionosphere.transcript` schema and `pub.layers.expression.expression` + `pub.layers.segmentation.segmentation` schemas 172 172 + 3. Use `autoGenerateWithHints()` to create a protolens chain with hints mapping transcript fields to layers.pub fields 173 173 + 4. Run the transcript record through the lens 174 174 + 5. Post-process: inject `$type`, `sourceRef`, `expression` URI (pre-computed from DID + rkey), `createdAt` 175 175 + 6. Return `{ expression, segmentation }` 176 176 + 177 177 + The timings replay algorithm is the same as `decodeToDocument()` in `transcript-encoding.ts`: 178 178 + - Split text by whitespace to get words 179 179 + - Use TextEncoder for UTF-8 byte offsets 180 180 + - Iterate timings: negative = silence gap (advance cursor), positive = word duration 181 181 + - Each word becomes a token with `textSpan: { byteStart, byteEnd }` and `temporalSpan: { start, ending }` in ms 182 182 + 183 183 + If panproto's auto-generated lens cannot handle the timings array → token list transform natively (likely — this is algorithmic, not structural), implement the timings replay in TypeScript and feed the pre-built token array into the lens as a morphism hint's computed field. The lens still owns the structural mapping; the timings replay is a computed input. 184 184 + 185 185 + - [ ] **Step 4: Run the test to verify it passes** 186 186 + 187 187 + ```bash 188 188 + cd apps/ionosphere-appview 189 189 + npx vitest run src/__tests__/layers-pub.test.ts 190 190 + ``` 191 191 + 192 192 + Expected: PASS 193 193 + 194 194 + - [ ] **Step 5: Commit** 195 195 + 196 196 + ```bash 197 197 + git add formats/tv.ionosphere/ts/layers-pub.ts formats/tv.ionosphere/lenses/transcript-to-expression.lens.json apps/ionosphere-appview/src/__tests__/layers-pub.test.ts 198 198 + git commit -m "feat: Lens 1 — compact transcript to layers.pub expression + segmentation" 199 199 + ``` 200 200 + 201 201 + --- 202 202 + 203 203 + ## Chunk 2: Lens 2 (NLP Annotations → Annotation Layers) 204 204 + 205 205 + ### Task 3: Define Lens 2 — NLP annotations → 4 annotation layers 206 206 + 207 207 + This lens transforms the NLP pipeline JSON into 4 `pub.layers.annotation.annotationLayer` records (sentences, paragraphs, entities, topics). 208 208 + 209 209 + **Files:** 210 210 + - Create: `formats/tv.ionosphere/lenses/nlp-to-annotation-layers.lens.json` 211 211 + - Modify: `formats/tv.ionosphere/ts/layers-pub.ts` — add `nlpToAnnotationLayers()` 212 212 + - Modify: `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts` — add Lens 2 tests 213 213 + 214 214 + - [ ] **Step 1: Write the failing tests** 215 215 + 216 216 + Add to `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts`: 217 217 + 218 218 + ```typescript 219 219 + import { nlpToAnnotationLayers } from '../../../formats/tv.ionosphere/ts/layers-pub.js'; 220 220 + 221 221 + describe('Lens 2: NLP annotations → annotation layers', () => { 222 222 + const nlpAnnotations = { 223 223 + talkRkey: 'test-talk', 224 224 + sentences: [ 225 225 + { byteStart: 0, byteEnd: 11 }, 226 226 + { byteStart: 12, byteEnd: 19 }, 227 227 + ], 228 228 + paragraphs: [ 229 229 + { byteStart: 0, byteEnd: 19 }, 230 230 + ], 231 231 + entities: [ 232 232 + { byteStart: 0, byteEnd: 5, label: 'Hello', nerType: 'MISC' }, 233 233 + { byteStart: 12, byteEnd: 15, label: 'foo', nerType: 'ORG', conceptUri: 'at://did:plc:test/tv.ionosphere.concept/foo' }, 234 234 + ], 235 235 + topicBreaks: [ 236 236 + { byteStart: 12 }, 237 237 + ], 238 238 + metadata: { tool: 'spacy/en_core_web_sm' }, 239 239 + }; 240 240 + 241 241 + const did = 'did:plc:test'; 242 242 + const talkRkey = 'test-talk'; 243 243 + const expressionUri = 'at://did:plc:test/pub.layers.expression.expression/test-talk-expression'; 244 244 + 245 245 + it('produces 4 annotation layer records', async () => { 246 246 + const layers = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 247 247 + expect(Object.keys(layers)).toEqual(['sentences', 'paragraphs', 'entities', 'topics']); 248 248 + }); 249 249 + 250 250 + it('sentences layer has correct structure', async () => { 251 251 + const { sentences } = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 252 252 + expect(sentences.$type).toBe('pub.layers.annotation.annotationLayer'); 253 253 + expect(sentences.expression).toBe(expressionUri); 254 254 + expect(sentences.kind).toBe('span'); 255 255 + expect(sentences.subkind).toBe('sentence-boundary'); 256 256 + expect(sentences.sourceMethod).toBe('automatic'); 257 257 + expect(sentences.metadata.tool).toBe('ionosphere-nlp-pipeline'); 258 258 + expect(sentences.annotations).toHaveLength(2); 259 259 + expect(sentences.annotations[0].anchor.textSpan).toEqual({ byteStart: 0, byteEnd: 11 }); 260 260 + }); 261 261 + 262 262 + it('entities layer wraps features in featureMap', async () => { 263 263 + const { entities } = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 264 264 + expect(entities.annotations).toHaveLength(2); 265 265 + 266 266 + // Plain entity — nerType is always present 267 267 + const plain = entities.annotations[0]; 268 268 + expect(plain.label).toBe('Hello'); 269 269 + expect(plain.features.entries).toContainEqual({ key: 'nerType', value: 'MISC' }); 270 270 + 271 271 + // Entity with conceptUri — all known keys forwarded to features 272 272 + const withConcept = entities.annotations[1]; 273 273 + expect(withConcept.features.entries).toContainEqual({ 274 274 + key: 'conceptUri', 275 275 + value: 'at://did:plc:test/tv.ionosphere.concept/foo', 276 276 + }); 277 277 + expect(withConcept.features.entries).toContainEqual({ key: 'nerType', value: 'ORG' }); 278 278 + }); 279 279 + 280 280 + it('topics layer has correct subkind and uses zero-width spans', async () => { 281 281 + const { topics } = await nlpToAnnotationLayers(nlpAnnotations, did, talkRkey, expressionUri); 282 282 + expect(topics.subkind).toBe('topic-segment'); 283 283 + expect(topics.annotations).toHaveLength(1); 284 284 + expect(topics.annotations[0].anchor.textSpan).toEqual({ byteStart: 12, byteEnd: 12 }); 285 285 + }); 286 286 + }); 287 287 + ``` 288 288 + 289 289 + - [ ] **Step 2: Run tests to verify they fail** 290 290 + 291 291 + ```bash 292 292 + cd apps/ionosphere-appview 293 293 + npx vitest run src/__tests__/layers-pub.test.ts 294 294 + ``` 295 295 + 296 296 + Expected: FAIL — `nlpToAnnotationLayers` does not exist. 297 297 + 298 298 + - [ ] **Step 3: Implement nlpToAnnotationLayers in layers-pub.ts** 299 299 + 300 300 + Add `nlpToAnnotationLayers()` to `formats/tv.ionosphere/ts/layers-pub.ts`. 301 301 + 302 302 + The function: 303 303 + 1. Loads the NLP annotations schema and annotation layer schema via panproto 304 304 + 2. Builds protolens with hints for each annotation type 305 305 + 3. For each of the 4 annotation types, maps the NLP data to a `pub.layers.annotation.annotationLayer` record: 306 306 + 307 307 + **Sentences layer** (`{talkRkey}-sentences`): 308 308 + - Each sentence `{ byteStart, byteEnd }` → annotation with `anchor: { textSpan: { byteStart, byteEnd } }`, `label`: truncated text (or "sentence") 309 309 + 310 310 + **Paragraphs layer** (`{talkRkey}-paragraphs`): 311 311 + - Each paragraph → annotation with `anchor: { textSpan }`, `label: "paragraph"` 312 312 + 313 313 + **Entities layer** (`{talkRkey}-entities`): 314 314 + - Each entity → annotation with `anchor: { textSpan }`, `label: entity.label` 315 315 + - `features: { entries: [] }` — forward all entity keys beyond byteStart/byteEnd/label into entries: `nerType` always, `conceptUri` if present, and any future keys (e.g., `speakerDid`) passthrough automatically 316 316 + 317 317 + **Topics layer** (`{talkRkey}-topics`): 318 318 + - Each topicBreak → annotation with `anchor: { textSpan: { byteStart, byteEnd: byteStart } }` (zero-width), `label: "topic-break"` 319 319 + 320 320 + All layers get: `$type`, `expression` URI, `kind: "span"`, `sourceMethod: "automatic"`, `metadata: { tool, timestamp }`, `createdAt`. 321 321 + 322 322 + Similar to Lens 1: if the structural fan-out (one source → four targets) is beyond what panproto's protolens can express natively, implement the fan-out in TypeScript and use the lens for each individual layer's structural mapping. The lens remains authoritative for the shape of each annotation layer record. 323 323 + 324 324 + - [ ] **Step 4: Run tests to verify they pass** 325 325 + 326 326 + ```bash 327 327 + cd apps/ionosphere-appview 328 328 + npx vitest run src/__tests__/layers-pub.test.ts 329 329 + ``` 330 330 + 331 331 + Expected: All PASS 332 332 + 333 333 + - [ ] **Step 5: Commit** 334 334 + 335 335 + ```bash 336 336 + git add formats/tv.ionosphere/ts/layers-pub.ts formats/tv.ionosphere/lenses/nlp-to-annotation-layers.lens.json apps/ionosphere-appview/src/__tests__/layers-pub.test.ts 337 337 + git commit -m "feat: Lens 2 — NLP annotations to 4 annotation layer records" 338 338 + ``` 339 339 + 340 340 + --- 341 341 + 342 342 + ## Chunk 3: Publish Pipeline Stage 6 343 343 + 344 344 + ### Task 4: Add layers.pub publishing to publish.ts 345 345 + 346 346 + Wire the lens functions into the existing publish pipeline as a new Stage 6. 347 347 + 348 348 + **Files:** 349 349 + - Modify: `apps/ionosphere-appview/src/publish.ts` — add Stage 6 350 350 + - Modify: `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts` — add integration test 351 351 + 352 352 + - [ ] **Step 1: Write the failing integration test** 353 353 + 354 354 + Add to `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts`: 355 355 + 356 356 + ```typescript 357 357 + describe('Stage 6: layers.pub publish pipeline', () => { 358 358 + it('produces 6 records for a talk with transcript + NLP data', async () => { 359 359 + // Use real fixture data from pipeline/data/ 360 360 + // Read a compact transcript and NLP annotations for the same talk 361 361 + // Run both lenses, verify 6 records produced with correct rkeys and $type values 362 362 + }); 363 363 + }); 364 364 + ``` 365 365 + 366 366 + The test should use fixture data from `pipeline/data/nlp/ats26-keynote.json` and a corresponding transcript to verify end-to-end record production. It does NOT publish to a PDS — it verifies the lens output shape. 367 367 + 368 368 + - [ ] **Step 2: Run test to verify it fails** 369 369 + 370 370 + ```bash 371 371 + cd apps/ionosphere-appview 372 372 + npx vitest run src/__tests__/layers-pub.test.ts 373 373 + ``` 374 374 + 375 375 + - [ ] **Step 3: Add Stage 6 to publish.ts** 376 376 + 377 377 + Add after the existing transcript publishing stage in `apps/ionosphere-appview/src/publish.ts`: 378 378 + 379 379 + ```typescript 380 380 + // ── Stage 6: layers.pub records ──────────────────────────────────────────── 381 381 + console.log("\n=== Stage 6: layers.pub records ==="); 382 382 + ``` 383 383 + 384 384 + For each talk that has both a transcript and NLP annotations: 385 385 + - Transcripts: `apps/data/transcripts/{rkey}.json` (same path as existing Stage 5, resolved via `../../data/transcripts` from `src/`) 386 386 + - NLP: `pipeline/data/nlp/{rkey}.json` (same path as existing Stage 4) 387 387 + 388 388 + 1. Load transcript JSON → `encode()` → CompactTranscript 389 389 + 2. Load NLP annotations JSON 390 390 + 3. Call `transcriptToLayersPub(transcriptRecord, did, rkey)` → expression + segmentation 391 391 + 4. Call `nlpToAnnotationLayers(nlpData, did, rkey, expressionUri)` → 4 annotation layers 392 392 + 5. Publish all 6 records via `pds.putRecord()` — parallel within each talk using `Promise.all()` 393 393 + 6. Log progress: `Published 6 layers.pub records for {rkey}` 394 394 + 395 395 + Record collections and rkeys: 396 396 + - `pub.layers.expression.expression` / `{rkey}-expression` 397 397 + - `pub.layers.segmentation.segmentation` / `{rkey}-segmentation` 398 398 + - `pub.layers.annotation.annotationLayer` / `{rkey}-sentences` 399 399 + - `pub.layers.annotation.annotationLayer` / `{rkey}-paragraphs` 400 400 + - `pub.layers.annotation.annotationLayer` / `{rkey}-entities` 401 401 + - `pub.layers.annotation.annotationLayer` / `{rkey}-topics` 402 402 + 403 403 + Also publish the 3 new lens files (transcript-to-expression, nlp-to-annotation-layers, layers-to-document) in Stage 1 alongside the existing 4 lenses. 404 404 + 405 405 + - [ ] **Step 4: Run test to verify it passes** 406 406 + 407 407 + ```bash 408 408 + cd apps/ionosphere-appview 409 409 + npx vitest run src/__tests__/layers-pub.test.ts 410 410 + ``` 411 411 + 412 412 + - [ ] **Step 5: Commit** 413 413 + 414 414 + ```bash 415 415 + git add apps/ionosphere-appview/src/publish.ts apps/ionosphere-appview/src/__tests__/layers-pub.test.ts 416 416 + git commit -m "feat: publish layers.pub records in Stage 6 of publish pipeline" 417 417 + ``` 418 418 + 419 419 + --- 420 420 + 421 421 + ## Chunk 4: Lens 3 (Reverse) + Appview Indexer 422 422 + 423 423 + ### Task 5: Define Lens 3 — layers.pub → ionosphere document facets 424 424 + 425 425 + This is the reverse lens: given layers.pub records, produce the materialized RelationalText document with ionosphere facets. Used by the appview indexer. 426 426 + 427 427 + **Files:** 428 428 + - Create: `formats/tv.ionosphere/lenses/layers-to-document.lens.json` 429 429 + - Modify: `formats/tv.ionosphere/ts/layers-pub.ts` — add `layersPubToDocument()` 430 430 + - Modify: `apps/ionosphere-appview/src/__tests__/layers-pub.test.ts` — add round-trip test 431 431 + 432 432 + - [ ] **Step 1: Write the failing round-trip test** 433 433 + 434 434 + This is the critical correctness test. Feed a real transcript + NLP annotations through Lens 1+2, then feed the output through Lens 3, and compare the result with what `decodeToDocumentWithStructure()` produces for the same input. 435 435 + 436 436 + ```typescript 437 437 + import { decodeToDocumentWithStructure, encode } from '../../../formats/tv.ionosphere/ts/transcript-encoding.js'; 438 438 + import { transcriptToLayersPub, nlpToAnnotationLayers, layersPubToDocument } from '../../../formats/tv.ionosphere/ts/layers-pub.js'; 439 439 + 440 440 + describe('Lens 3: round-trip correctness', () => { 441 441 + it('layers.pub → document matches decodeToDocumentWithStructure output', async () => { 442 442 + // Load real fixture data 443 443 + // Transcripts: apps/data/transcripts/{rkey}.json (resolved from publish.ts: ../../data/transcripts) 444 444 + // NLP: pipeline/data/nlp/{rkey}.json 445 445 + const transcriptData = /* read from apps/data/transcripts/ats26-keynote.json */; 446 446 + const nlpData = /* read from pipeline/data/nlp/ats26-keynote.json */; 447 447 + 448 448 + // Path A: existing direct path 449 449 + const compact = encode(transcriptData); 450 450 + const directDoc = decodeToDocumentWithStructure(compact, nlpData); 451 451 + 452 452 + // Path B: through lenses 453 453 + const transcriptRecord = { text: compact.text, startMs: compact.startMs, timings: compact.timings, talkUri: 'at://test/tv.ionosphere.talk/test' }; 454 454 + const { expression, segmentation } = await transcriptToLayersPub(transcriptRecord, 'did:plc:test', 'test'); 455 455 + const annotationLayers = await nlpToAnnotationLayers(nlpData, 'did:plc:test', 'test', 'at://...'); 456 456 + const lensDoc = await layersPubToDocument(expression, segmentation, annotationLayers); 457 457 + 458 458 + // Compare 459 459 + expect(lensDoc.text).toBe(directDoc.text); 460 460 + expect(lensDoc.facets.length).toBe(directDoc.facets.length); 461 461 + // Facets may be in different order — sort by byteStart then compare 462 462 + }); 463 463 + }); 464 464 + ``` 465 465 + 466 466 + - [ ] **Step 2: Run test to verify it fails** 467 467 + 468 468 + ```bash 469 469 + cd apps/ionosphere-appview 470 470 + npx vitest run src/__tests__/layers-pub.test.ts 471 471 + ``` 472 472 + 473 473 + - [ ] **Step 3: Implement layersPubToDocument** 474 474 + 475 475 + Add `layersPubToDocument()` to `formats/tv.ionosphere/ts/layers-pub.ts`. 476 476 + 477 477 + Inputs: expression record, segmentation record, annotation layers (object with sentences/paragraphs/entities/topics). 478 478 + 479 479 + Output: `{ text: string, facets: DocumentFacet[] }` — same shape as `decodeToDocumentWithStructure()`. 480 480 + 481 481 + Transform: 482 482 + 1. `text` comes from the expression record 483 483 + 2. Timestamp facets: iterate segmentation tokens, for each token create a facet with `$type: "tv.ionosphere.facet#timestamp"`, `startTime` and `endTime` in nanoseconds (ms × 1_000_000), `byteStart`/`byteEnd` from token's textSpan 484 484 + 3. Sentence facets: from sentences annotation layer, each annotation → facet with `$type: "tv.ionosphere.facet#sentence"` 485 485 + 4. Paragraph facets: similar, `$type: "tv.ionosphere.facet#paragraph"` 486 486 + 5. Entity facets: route by features — if has `conceptUri` → `#concept-ref`, else → `#entity` 487 487 + 6. Topic facets: `$type: "tv.ionosphere.facet#topic-break"`, zero-width span 488 488 + 489 489 + - [ ] **Step 4: Run test to verify it passes** 490 490 + 491 491 + ```bash 492 492 + cd apps/ionosphere-appview 493 493 + npx vitest run src/__tests__/layers-pub.test.ts 494 494 + ``` 495 495 + 496 496 + - [ ] **Step 5: Commit** 497 497 + 498 498 + ```bash 499 499 + git add formats/tv.ionosphere/ts/layers-pub.ts formats/tv.ionosphere/lenses/layers-to-document.lens.json apps/ionosphere-appview/src/__tests__/layers-pub.test.ts 500 500 + git commit -m "feat: Lens 3 — layers.pub records to ionosphere document facets (round-trip verified)" 501 501 + ``` 502 502 + 503 503 + ### Task 6: Add layers.pub DB tables 504 504 + 505 505 + **Files:** 506 506 + - Modify: `apps/ionosphere-appview/src/db.ts` 507 507 + 508 508 + - [ ] **Step 1: Add 3 new tables to migrate()** 509 509 + 510 510 + Add after the existing `_cursor` table creation in `apps/ionosphere-appview/src/db.ts`: 511 511 + 512 512 + ```sql 513 513 + CREATE TABLE IF NOT EXISTS layers_expressions ( 514 514 + uri TEXT PRIMARY KEY, 515 515 + rkey TEXT NOT NULL, 516 516 + did TEXT NOT NULL, 517 517 + transcript_uri TEXT NOT NULL, 518 518 + text TEXT NOT NULL, 519 519 + language TEXT NOT NULL DEFAULT 'en', 520 520 + created_at TEXT DEFAULT CURRENT_TIMESTAMP 521 521 + ); 522 522 + 523 523 + -- uri IS the expression URI for this table; other tables reference it via expression_uri 524 524 + CREATE INDEX IF NOT EXISTS idx_layers_expr_transcript ON layers_expressions(transcript_uri); 525 525 + 526 526 + CREATE TABLE IF NOT EXISTS layers_segmentations ( 527 527 + uri TEXT PRIMARY KEY, 528 528 + rkey TEXT NOT NULL, 529 529 + did TEXT NOT NULL, 530 530 + expression_uri TEXT NOT NULL, 531 531 + tokens_json TEXT NOT NULL, 532 532 + created_at TEXT DEFAULT CURRENT_TIMESTAMP 533 533 + ); 534 534 + 535 535 + CREATE INDEX IF NOT EXISTS idx_layers_seg_expression ON layers_segmentations(expression_uri); 536 536 + 537 537 + CREATE TABLE IF NOT EXISTS layers_annotations ( 538 538 + uri TEXT PRIMARY KEY, 539 539 + rkey TEXT NOT NULL, 540 540 + did TEXT NOT NULL, 541 541 + expression_uri TEXT NOT NULL, 542 542 + kind TEXT NOT NULL, 543 543 + subkind TEXT NOT NULL, 544 544 + annotations_json TEXT NOT NULL, 545 545 + created_at TEXT DEFAULT CURRENT_TIMESTAMP 546 546 + ); 547 547 + 548 548 + CREATE INDEX IF NOT EXISTS idx_layers_ann_expression ON layers_annotations(expression_uri); 549 549 + ``` 550 550 + 551 551 + - [ ] **Step 2: Verify DB migration runs clean** 552 552 + 553 553 + ```bash 554 554 + cd apps/ionosphere-appview 555 555 + npx tsx -e " 556 556 + import { openDb, migrate } from './src/db.js'; 557 557 + const db = openDb(); 558 558 + migrate(db); 559 559 + const tables = db.prepare(\"SELECT name FROM sqlite_master WHERE type='table' AND name LIKE 'layers_%'\").all(); 560 560 + console.log('New tables:', tables.map(t => t.name)); 561 561 + " 562 562 + ``` 563 563 + 564 564 + Expected: `New tables: ['layers_expressions', 'layers_segmentations', 'layers_annotations']` 565 565 + 566 566 + - [ ] **Step 3: Commit** 567 567 + 568 568 + ```bash 569 569 + git add apps/ionosphere-appview/src/db.ts 570 570 + git commit -m "feat: add layers.pub DB tables (expressions, segmentations, annotations)" 571 571 + ``` 572 572 + 573 573 + ### Task 7: Wire layers.pub indexer into appview 574 574 + 575 575 + **Files:** 576 576 + - Create: `apps/ionosphere-appview/src/layers-indexer.ts` 577 577 + - Modify: `apps/ionosphere-appview/src/indexer.ts` 578 578 + 579 579 + - [ ] **Step 1: Create the layers-indexer module** 580 580 + 581 581 + Create `apps/ionosphere-appview/src/layers-indexer.ts` with these functions: 582 582 + 583 583 + ```typescript 584 584 + export function indexExpression(db, did, rkey, uri, record): void 585 585 + export function indexSegmentation(db, did, rkey, uri, record): void 586 586 + export function indexAnnotationLayer(db, did, rkey, uri, record): void 587 587 + export function deleteExpression(db, uri): void 588 588 + export function deleteSegmentation(db, uri): void 589 589 + export function deleteAnnotationLayer(db, uri): void 590 590 + export async function rebuildDocument(db, expressionUri): Promise<void> 591 591 + ``` 592 592 + 593 593 + **indexExpression:** INSERT OR REPLACE into `layers_expressions`. The record's `uri` IS the expression URI (used by other tables' `expression_uri` FK). Extract `sourceRef` as `transcript_uri`. 594 594 + 595 595 + **indexSegmentation:** INSERT OR REPLACE into `layers_segmentations`. Store tokenizations as JSON. 596 596 + 597 597 + **indexAnnotationLayer:** INSERT OR REPLACE into `layers_annotations`. Store annotations array as JSON. 598 598 + 599 599 + **deleteExpression:** DELETE from `layers_expressions` WHERE uri. CASCADE: also delete from `layers_segmentations` and `layers_annotations` WHERE expression_uri matches. Clear the talk's document field in the talks table. 600 600 + 601 601 + **deleteSegmentation/deleteAnnotationLayer:** DELETE the specific row, then call `rebuildDocument`. 602 602 + 603 603 + **rebuildDocument:** 604 604 + 1. Look up expression by `uri` (= expression_uri) → get transcript_uri 605 605 + 2. Look up segmentation by expression_uri 606 606 + 3. Look up all annotation layers by expression_uri 607 607 + 4. If expression + segmentation exist, call `layersPubToDocument()` (Lens 3) 608 608 + 5. Find the talk_uri from the transcript table using transcript_uri 609 609 + 6. UPDATE the talk's `document` field with `JSON.stringify(document)` 610 610 + 611 611 + - [ ] **Step 2: Wire into indexer.ts** 612 612 + 613 613 + Add 3 new collections to `IONOSPHERE_COLLECTIONS`: 614 614 + 615 615 + ```typescript 616 616 + "pub.layers.expression.expression", 617 617 + "pub.layers.segmentation.segmentation", 618 618 + "pub.layers.annotation.annotationLayer", 619 619 + ``` 620 620 + 621 621 + Add DID filter in `processEvent()` — only process layers.pub records from the bot DID: 622 622 + 623 623 + ```typescript 624 624 + if (collection.startsWith("pub.layers.") && event.did !== BOT_DID) return; 625 625 + ``` 626 626 + 627 627 + The `BOT_DID` is already resolved in `appview.ts` — pass it to the indexer or make it available as a module-level constant. 628 628 + 629 629 + Add delete and create/update cases for the 3 new collections in the switch statements, calling the functions from `layers-indexer.ts`. 630 630 + 631 631 + After each create/update of a layers.pub record, call `rebuildDocument()` with the expression URI. 632 632 + 633 633 + - [ ] **Step 3: Test indexer locally** 634 634 + 635 635 + ```bash 636 636 + cd apps/ionosphere-appview 637 637 + # Start local environment 638 638 + docker compose up -d 639 639 + PORT=9401 npx tsx src/appview.ts & 640 640 + # Publish records 641 641 + PDS_URL=http://localhost:2690 BOT_HANDLE=ionosphere.test BOT_PASSWORD=ionosphere-dev-password npx tsx src/publish.ts 642 642 + # Verify layers.pub records were indexed 643 643 + curl -s http://localhost:9401/xrpc/tv.ionosphere.getTalk?rkey=ats26-keynote | python3 -c "import sys,json; d=json.load(sys.stdin); print('Has document:', bool(d.get('document'))); print('Facet count:', len(d['document']['facets']) if d.get('document') else 0)" 644 644 + ``` 645 645 + 646 646 + - [ ] **Step 4: Commit** 647 647 + 648 648 + ```bash 649 649 + git add apps/ionosphere-appview/src/layers-indexer.ts apps/ionosphere-appview/src/indexer.ts 650 650 + git commit -m "feat: index layers.pub records and rebuild materialized documents via Lens 3" 651 651 + ``` 652 652 + 653 653 + --- 654 654 + 655 655 + ## Chunk 5: Schema Versioning + Final Verification 656 656 + 657 657 + ### Task 8: Initialize panproto VCS 658 658 + 659 659 + **Files:** 660 660 + - Project root — panproto VCS state 661 661 + 662 662 + - [ ] **Step 1: Initialize and commit schemas** 663 663 + 664 664 + ```bash 665 665 + # From project root 666 666 + schema init 667 667 + schema add lexicons/pub/layers/ 668 668 + schema add formats/tv.ionosphere/ionosphere.lexicon.json 669 669 + schema add formats/tv.ionosphere/nlpAnnotations.lexicon.json 670 670 + schema add formats/tv.ionosphere/lenses/ 671 671 + schema commit -m "Initial schema commit: layers.pub v0.5.0, ionosphere facets, NLP annotations, 7 lenses" 672 672 + schema tag v0.5.0 673 673 + ``` 674 674 + 675 675 + - [ ] **Step 2: Verify VCS state** 676 676 + 677 677 + ```bash 678 678 + schema log 679 679 + schema status 680 680 + ``` 681 681 + 682 682 + Expected: clean state, one commit, tagged v0.5.0. 683 683 + 684 684 + - [ ] **Step 3: Commit VCS state to git** 685 685 + 686 686 + ```bash 687 687 + git add .panproto/ # or wherever schema VCS stores its state 688 688 + git commit -m "feat: initialize panproto VCS, tag layers.pub v0.5.0" 689 689 + ``` 690 690 + 691 691 + ### Task 9: End-to-end verification 692 692 + 693 693 + - [ ] **Step 1: Run full test suite** 694 694 + 695 695 + ```bash 696 696 + cd apps/ionosphere-appview 697 697 + npx vitest run 698 698 + ``` 699 699 + 700 700 + Expected: All tests pass. 701 701 + 702 702 + - [ ] **Step 2: Run publish against local PDS and verify round-trip** 703 703 + 704 704 + ```bash 705 705 + cd apps/ionosphere-appview 706 706 + docker compose up -d 707 707 + PORT=9401 npx tsx src/appview.ts & 708 708 + PDS_URL=http://localhost:2690 BOT_HANDLE=ionosphere.test BOT_PASSWORD=ionosphere-dev-password npx tsx src/publish.ts 709 709 + ``` 710 710 + 711 711 + Verify: 712 712 + 1. layers.pub records appear in PDS (check via `com.atproto.repo.listRecords`) 713 713 + 2. Appview indexes them and rebuilds documents 714 714 + 3. Documents served via API match previous output 715 715 + 4. Frontend renders correctly at http://127.0.0.1:9402/talks 716 716 + 717 717 + - [ ] **Step 3: Commit any fixes** 718 718 + 719 719 + ### Task 10: Deploy 720 720 + 721 721 + - [ ] **Step 1: Deploy appview** 722 722 + 723 723 + ```bash 724 724 + flyctl deploy --config fly.appview.toml --remote-only 725 725 + ``` 726 726 + 727 727 + - [ ] **Step 2: Publish to production PDS** 728 728 + 729 729 + ```bash 730 730 + cd apps/ionosphere-appview 731 731 + PDS_URL=https://jellybaby.us-east.host.bsky.network \ 732 732 + BOT_HANDLE=ionosphere.tv \ 733 733 + BOT_PASSWORD=<app-password> \ 734 734 + npx tsx src/publish.ts 735 735 + ``` 736 736 + 737 737 + - [ ] **Step 3: Invalidate caches** 738 738 + 739 739 + ```bash 740 740 + curl -X POST https://api.ionosphere.tv/xrpc/tv.ionosphere.invalidate 741 741 + ``` 742 742 + 743 743 + - [ ] **Step 4: Deploy frontend** 744 744 + 745 745 + ```bash 746 746 + flyctl deploy --config fly.web.toml --remote-only 747 747 + ``` 748 748 + 749 749 + - [ ] **Step 5: Verify production** 750 750 + 751 751 + Check https://ionosphere.tv/talks — documents should render with all enrichment annotations intact.

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docs/superpowers/specs/2026-04-12-enrichment-phases-2-3-design.md

reviewed

··· 1 1 + # Enrichment Phases 2-3: NER + Entity Linking, Topic Segmentation 2 2 + 3 3 + **Date:** 2026-04-12 4 4 + **Status:** Approved 5 5 + **Depends on:** Phase 1 transcript formatting (complete) 6 6 + 7 7 + ## Goal 8 8 + 9 9 + Add named entity recognition with AT Protocol record linking (Phase 2) and topic segmentation with visual dividers (Phase 3) to the existing NLP enrichment pipeline. Achieve feature parity with the old concept system while adding speaker attribution and topic navigation, before deploying. 10 10 + 11 11 + ## Constraints 12 12 + 13 13 + - **Text is immutable.** Same as Phase 1 — annotations only, no word changes. 14 14 + - **Build-time processing.** New passes extend the existing Python pipeline. 15 15 + - **Leverage existing data.** Speaker records, diarization records, and concept records are already in the database. Use them for entity resolution. 16 16 + - **layers.pub annotation model.** Each pass produces a separate annotation layer, consistent with Phase 1's approach. 17 17 + 18 18 + ## Pipeline Passes 19 19 + 20 20 + ### Pass 3: Named Entity Recognition + Entity Linking 21 21 + 22 22 + **Input:** transcript text + speaker records (from SQLite) + diarization records (from SQLite) + concept records (from SQLite) 23 23 + 24 24 + **Steps:** 25 25 + 26 26 + 1. **Build speaker lookup.** Query `speakers` table, build a map of `{name, aliases, handle, did}` for all speakers. Include normalized variants (lowercase, first-name-only for disambiguation with diarization context). 27 27 + 28 28 + 2. **Load diarization.** Query `stream_diarizations` table for the talk's stream. Map diarization time ranges to speaker identities. Each segment tells us who is speaking when — this provides context for resolving ambiguous names. 29 29 + 30 30 + 3. **Run spaCy NER.** The existing `en_core_web_sm` model (already loaded for sentence detection) provides NER via `doc.ents`. Extract entities with types: PERSON, ORG, PRODUCT, WORK_OF_ART, GPE, EVENT. Compute byte ranges using the same char→byte conversion as sentence detection. 31 31 + 32 32 + 4. **Resolve entities:** 33 33 + - **PERSON entities:** Match against speaker lookup by name similarity. Use diarization context for disambiguation — if a first name is mentioned while a known speaker with that first name is presenting or was just speaking, prefer that match. Resolved entities get a `speakerDid` linking to the Bluesky profile. 34 34 + - **ORG/PRODUCT entities:** Match against concept records by name and aliases. Resolved entities get a `conceptUri`. 35 35 + - **Unresolved entities:** Keep as labeled spans with NER type but no link target. Available for manual curation in Phase 4. 36 36 + 37 37 + 5. **Emit speaker attribution.** For each diarization segment, emit a speaker-segment annotation spanning the corresponding byte range in the transcript. Cross-reference diarization time ranges with word timestamps to find byte boundaries. 38 38 + 39 39 + **Output:** NLP JSON with `entities` array and `speakerSegments` array. 40 40 + 41 41 + ### Pass 4: Topic Segmentation 42 42 + 43 43 + **Input:** transcript text + sentence boundaries (from Pass 1) 44 44 + 45 45 + **Steps:** 46 46 + 47 47 + 1. **Embed sentences.** Run each sentence through `all-MiniLM-L6-v2` (384-dim sentence embeddings). The model is ~80MB, downloaded on first run. Embedding 300 sentences takes ~2 seconds on CPU. 48 48 + 49 49 + 2. **Compute similarity.** For each pair of adjacent sentence windows (window size N, default 3 sentences), compute cosine similarity between the mean embedding of the left window and the right window. 50 50 + 51 51 + 3. **Detect boundaries.** Similarity drops below a threshold (tunable, default 0.3) indicate topic shifts. Apply a minimum segment length (default 5 sentences) to avoid over-segmentation. 52 52 + 53 53 + 4. **Snap to structure.** Topic breaks are snapped to the nearest paragraph boundary where possible (since paragraphs already represent pause-based thought transitions). If no paragraph boundary is within 2 sentences of the detected break, snap to the nearest sentence boundary. 54 54 + 55 55 + **Output:** NLP JSON with `topicBreaks` array (byte positions of topic boundaries). 56 56 + 57 57 + **Parameters stored in metadata:** `embeddingModel`, `windowSize`, `similarityThreshold`, `minSegmentSentences`. 58 58 + 59 59 + ## Facet Schema 60 60 + 61 61 + **Existing facets now populated:** 62 62 + 63 63 + | Facet type | Class | Use | 64 64 + |---|---|---| 65 65 + | `tv.ionosphere.facet#speaker-segment` | `block` | Wraps diarization segment — attributes text to speaker | 66 66 + | `tv.ionosphere.facet#speaker-ref` | `inline` | Links person mention to speaker DID/profile | 67 67 + | `tv.ionosphere.facet#concept-ref` | `inline` | Links ORG/PRODUCT mention to concept record | 68 68 + 69 69 + **New facets to add to format lexicon:** 70 70 + 71 71 + | Facet type | Class | Use | 72 72 + |---|---|---| 73 73 + | `tv.ionosphere.facet#topic-break` | `block` | Topic boundary — renderer inserts divider | 74 74 + | `tv.ionosphere.facet#entity` | `inline` | Unresolved entity — has label + NER type, no linked record | 75 75 + 76 76 + ## Document Assembly 77 77 + 78 78 + The `NlpAnnotations` interface in `transcript-encoding.ts` extends to: 79 79 + 80 80 + ```typescript 81 81 + interface NlpAnnotations { 82 82 + sentences: Array<{ byteStart: number; byteEnd: number }>; 83 83 + paragraphs: Array<{ byteStart: number; byteEnd: number }>; 84 84 + entities: Array<{ 85 85 + byteStart: number; byteEnd: number; 86 86 + label: string; nerType: string; 87 87 + speakerDid?: string; conceptUri?: string; 88 88 + }>; 89 89 + speakerSegments: Array<{ 90 90 + byteStart: number; byteEnd: number; 91 91 + speakerDid: string; speakerName: string; 92 92 + }>; 93 93 + topicBreaks: Array<{ byteStart: number }>; 94 94 + } 95 95 + ``` 96 96 + 97 97 + `decodeToDocumentWithStructure` maps these to facets: 98 98 + - `entities` with `speakerDid` → `#speaker-ref` facets 99 99 + - `entities` with `conceptUri` → `#concept-ref` facets 100 100 + - `entities` with neither → `#entity` facets (unresolved) 101 101 + - `speakerSegments` → `#speaker-segment` facets 102 102 + - `topicBreaks` → `#topic-break` facets 103 103 + 104 104 + ## Renderer Changes 105 105 + 106 106 + ### Entity spans 107 107 + 108 108 + `extractData` returns `entities: EntitySpan[]` with byte range, label, NER type, and optional link target. The renderer overlays these on word spans: 109 109 + 110 110 + - **`#speaker-ref`** — renders as a link styled with a subtle blue underline. Clicking navigates to the speaker page or Bluesky profile. 111 111 + - **`#concept-ref`** — renders as a link with amber underline (matching existing concept highlighting). Clicking navigates to the concept page. 112 112 + - **`#entity`** (unresolved) — renders as subtly styled text (dotted underline, slightly different color) to indicate a recognized entity without a link. 113 113 + 114 114 + These are inline facets that overlay on word spans. A word can have multiple facets (timestamp + entity). The existing `wordConcepts` pattern in `extractData` extends to handle all entity types. 115 115 + 116 116 + ### Speaker segments 117 117 + 118 118 + Not visually rendered in this phase. The data is stored in facets for future use (speaker-colored text, margin labels, etc.). Getting the attribution data right is the priority. 119 119 + 120 120 + ### Topic dividers 121 121 + 122 122 + A subtle `<hr>` between paragraphs where a topic break falls: 123 123 + 124 124 + ```html 125 125 + <div class="mb-4"><!-- paragraph --></div> 126 126 + <hr class="border-neutral-800 my-6" /> 127 127 + <div class="mb-4"><!-- paragraph --></div> 128 128 + ``` 129 129 + 130 130 + `extractData` returns `topicBreaks: Set<number>` — a set of paragraph indices where topic breaks occur. The renderer checks this set when iterating paragraphs and inserts dividers. 131 131 + 132 132 + ## Speaker Lookup Generation 133 133 + 134 134 + The Python pipeline reads speaker data directly from the SQLite database (Python's `sqlite3` is in the standard library). The lookup table is built at pipeline startup: 135 135 + 136 136 + ```python 137 137 + speakers = db.execute("SELECT name, handle, speaker_did FROM speakers").fetchall() 138 138 + lookup = {} 139 139 + for name, handle, did in speakers: 140 140 + lookup[name.lower()] = {"name": name, "handle": handle, "did": did} 141 141 + # Also index by first name for diarization-context matching 142 142 + first = name.split()[0].lower() 143 143 + if first not in lookup: 144 144 + lookup[first] = {"name": name, "handle": handle, "did": did} 145 145 + ``` 146 146 + 147 147 + This is ephemeral — rebuilt each pipeline run from the current speaker records. No separate file to maintain. 148 148 + 149 149 + ## Dependencies 150 150 + 151 151 + **New Python dependency:** `sentence-transformers>=2.0` (adds torch, transformers, tokenizers — ~2GB install). Build-time only, no runtime impact. 152 152 + 153 153 + **spaCy NER:** Zero-cost addition — `en_core_web_sm` already loaded for sentence detection. NER entities are read from `doc.ents` in the same pass. 154 154 + 155 155 + **SQLite access:** Python `sqlite3` standard library. Pipeline reads speaker, diarization, and concept records from the same database the appview uses. 156 156 + 157 157 + ## Testing Strategy 158 158 + 159 159 + **Python pipeline (pytest):** 160 160 + - Unit tests for speaker lookup construction (name variants, first-name matching) 161 161 + - Unit tests for entity resolution (exact match, first-name match with diarization context, unresolved fallback) 162 162 + - Unit tests for topic segmentation (boundary detection, minimum segment length, snap-to-paragraph) 163 163 + - Integration test: full pipeline on a known transcript, verify entity and topic output 164 164 + 165 165 + **TypeScript (vitest):** 166 166 + - `decodeToDocumentWithStructure` with entity/speaker/topic annotations 167 167 + - `extractData` with entity facets and topic breaks 168 168 + - Renderer: entity links, topic dividers between paragraphs 169 169 + 170 170 + **Manual validation:** 171 171 + - Spot-check 5-10 talks: verify entity links point to correct profiles/concepts 172 172 + - Verify topic breaks land at natural transitions, not mid-thought 173 173 + - Check that speaker attribution aligns with diarization (correct speaker for each segment)

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docs/superpowers/specs/2026-04-12-transcript-formatting-design.md

reviewed

··· 1 1 + # Transcript Formatting: NLP Enrichment Pipeline 2 2 + 3 3 + **Date:** 2026-04-12 4 4 + **Status:** Approved 5 5 + 6 6 + ## Problem 7 7 + 8 8 + Transcripts are currently rendered as an infinitely long, unbroken run of text. Word-level timing and concept facets exist, but there is no structural formatting — no sentences, no paragraphs, no visual hierarchy. The goal is for transcripts to read as though they were essays. 9 9 + 10 10 + ## Constraints 11 11 + 12 12 + - **Text is immutable.** The pipeline adds structural annotations only — no words are modified, added, or removed. Transcript editing is a separate future concern. 13 13 + - **Reliability over ambition.** Each enrichment pass must be dependable enough to run unsupervised across all transcripts. Noisy output is worse than no output. 14 14 + - **Build-time processing.** NLP runs once in the batch pipeline; results are published as AT Protocol records. Zero runtime cost. 15 15 + - **Python NLP stack.** spaCy for sentence detection and NER; sentence-transformers for topic segmentation. 16 16 + 17 17 + ## Schema Design: layers.pub Integration 18 18 + 19 19 + The enrichment pipeline uses [layers.pub](https://layers.pub) (`pub.layers.*`) lexicons — composable AT Protocol schemas for linguistic annotation. This gives us a standard, interoperable representation with built-in support for multiple annotation passes, provenance tracking, and manual overrides. 20 20 + 21 21 + **Vendoring strategy:** layers.pub is at v0.5.0 draft. We vendor the specific lexicon definitions we use into `lexicons/pub/layers/` in this repo. Panproto lenses provide forward-compatibility — when layers.pub evolves, we define migrations rather than rewriting our pipeline. This follows the project's principle of prioritizing the lens layer for forward-compat. 22 22 + 23 23 + ### Record Architecture 24 24 + 25 25 + #### 1. Source transcript (existing) 26 26 + 27 27 + `tv.ionosphere.transcript` — compact storage format with `text`, `startMs`, and `timings` array. Stays as-is. Source of truth for raw transcription output. 28 28 + 29 29 + #### 2. Expression record 30 30 + 31 31 + `pub.layers.expression.expression` (kind: `"transcript"`) — the transcript text published as a layers.pub expression. Links back to the ionosphere transcript via `sourceRef`. This is the anchoring point for all annotations. 32 32 + 33 33 + #### 3. Segmentation record 34 34 + 35 35 + `pub.layers.segmentation.segmentation` — word-level tokenization derived from the transcript's compact timing data. Each token carries: 36 36 + - `textSpan`: UTF-8 byte offsets (`byteStart`, `byteEnd`) 37 37 + - `temporalSpan`: timing in milliseconds (`start`, `ending`) 38 38 + 39 39 + This replaces the per-word timestamp facets with a standard representation. 40 40 + 41 41 + #### 4. Annotation layers 42 42 + 43 43 + `pub.layers.annotation.annotationLayer` — one record per enrichment pass: 44 44 + 45 45 + | Pass | `kind` | `subkind` | `sourceMethod` | 46 46 + |------|--------|-----------|----------------| 47 47 + | Sentence detection | `span` | `sentence-boundary` | `automatic` | 48 48 + | Paragraph segmentation | `span` | `paragraph-boundary` | `automatic` | 49 49 + | Topic segmentation (future) | `span` | `topic-segment` | `automatic` | 50 50 + | Named entity recognition (future) | `span` | `ner` | `automatic` | 51 51 + | Concept linking (future) | `span` | `concept` | `automatic` | 52 52 + | Speaker attribution (future) | `span` | `speaker` | `automatic` | 53 53 + | Manual corrections (future) | varies | varies | `manual-native` | 54 54 + 55 55 + Each layer includes `metadata` (agent, tool, confidence, timestamp) for provenance. Pipeline parameters (e.g., paragraph pause threshold) are stored in `metadata.features` so provenance is complete and results are reproducible. 56 56 + 57 57 + #### 5. Manual override layer (future) 58 58 + 59 59 + A separate annotation layer with `sourceMethod: "manual-native"` and higher `rank`. The merge step prefers higher-ranked layers. Example: correcting "Blue Sky" to link to the Bluesky concept record is an annotation in this layer that supersedes the auto-detected concept. Published as first-class AT Protocol records — auditable, attributable, and preservable across pipeline re-runs. 60 60 + 61 61 + ### Replacing `tv.ionosphere.annotation` 62 62 + 63 63 + The existing `tv.ionosphere.annotation` record type (concept mentions anchored to byte ranges) is replaced wholesale by layers.pub annotation layers. Since the entire pipeline rebuilds from raw transcripts, there is no migration burden — the next pipeline run produces layers.pub records instead of `tv.ionosphere.annotation` records, and the old records are deleted. 64 64 + 65 65 + The existing concept data is re-derived by the NLP pipeline as a concept annotation layer (Phase 2), which will produce better results than the current approach. The `tv.ionosphere.annotation` lexicon and related code (`enrich.ts`, `enrich-all.ts`, `overlayAnnotations` in the appview) are removed in Phase 1. 66 66 + 67 67 + ### Panproto Integration 68 68 + 69 69 + - **Lenses:** Transform between compact transcript format (`tv.ionosphere.transcript`) and layers.pub expression + segmentation format. Lens definitions live in `formats/tv.ionosphere/lenses/`. 70 70 + - **Schema validation:** Validate all layers.pub records before publishing to PDS. Runs in the TypeScript publish step (after the Python NLP pipeline outputs JSON). 71 71 + - **Migration support:** As layers.pub evolves from v0.5.0, panproto migrations keep ionosphere records compatible. Vendored lexicons in `lexicons/pub/layers/` are the pinned source of truth. 72 72 + - **Pipeline boundary:** The Python NLP pipeline outputs annotation layer JSON files. The TypeScript publish step validates them against panproto-parsed lexicons and publishes to PDS. This reuses the existing panproto TypeScript integration. 73 73 + 74 74 + ## Pipeline Architecture 75 75 + 76 76 + ``` 77 77 + transcript record (text + timings) 78 78 + | 79 79 + v 80 80 + +-------------------+ 81 81 + | Pass 1: Sentences | <-- spaCy sentence boundary detection 82 82 + +---------+---------+ 83 83 + | 84 84 + v 85 85 + +--------------------+ 86 86 + | Pass 2: Paragraphs | <-- pause data + sentence boundaries 87 87 + +---------+----------+ 88 88 + | 89 89 + v 90 90 + +--------------------+ 91 91 + | Pass N: (future) | <-- topics, entities, speaker linking 92 92 + +---------+----------+ 93 93 + | 94 94 + v 95 95 + +--------------------+ 96 96 + | Override layer | <-- manual corrections (higher rank) 97 97 + +---------+----------+ 98 98 + | 99 99 + v 100 100 + +--------------------+ 101 101 + | Merge & publish | <-- assemble RelationalText document 102 102 + +--------------------+ 103 103 + ``` 104 104 + 105 105 + Properties: 106 106 + - **Each pass is a standalone Python module** with a consistent interface: takes transcript text + timings + prior layer output, returns a new annotation layer. 107 107 + - **Passes are additive** — they never modify text, only emit new annotations. 108 108 + - **Override layer applies last** — manual corrections supersede auto-generated annotations at matching byte ranges via `rank`. 109 109 + - **Idempotent** — re-running the pipeline produces the same output; manual overrides are preserved because they are separate records. 110 110 + 111 111 + ### Pass 1: Sentence Boundary Detection 112 112 + 113 113 + **Tool:** spaCy with `en_core_web_sm`. The small model is nearly as accurate as the transformer model for sentence boundary detection (its most battle-tested feature), and runs without GPU on a standard dev machine. If accuracy proves insufficient on speech transcripts, upgrade to `en_core_web_trf` in a later pass. 114 114 + 115 115 + spaCy's sentence segmenter uses dependency parsing, which is significantly more robust than punctuation-splitting for speech transcripts where Whisper's punctuation can be unreliable. 116 116 + 117 117 + **Output:** An annotation layer with one annotation per sentence, anchored by byte span. 118 118 + 119 119 + **Reliability:** Very high (95%+ accuracy on messy speech text). 120 120 + 121 121 + ### Pass 2: Paragraph Segmentation 122 122 + 123 123 + **Tool:** Custom algorithm combining two signals. 124 124 + 125 125 + **Signal 1 — Pause duration:** The transcript's timing data encodes silence gaps as negative values. Pauses above a tunable threshold are paragraph boundary candidates. Default threshold: **2.0 seconds** (a conservative starting point — most speech pauses are under 1s; pauses over 2s reliably indicate topic transitions). 126 126 + 127 127 + **Signal 2 — Sentence alignment:** Paragraph breaks only occur at sentence boundaries (from Pass 1). A long pause mid-sentence is a speaker thinking, not a paragraph break. 128 128 + 129 129 + **Algorithm:** 130 130 + ``` 131 131 + for each silence gap > pause_threshold_ms (default: 2000): 132 132 + find the nearest sentence boundary (from Pass 1) 133 133 + if the sentence boundary is within proximity_words (default: 5) of the pause: 134 134 + emit paragraph break at that sentence boundary 135 135 + ``` 136 136 + 137 137 + The proximity constraint of 5 words allows for the common case where a speaker finishes a thought (pause), says a brief connective phrase ("so", "and then"), and starts the next topic — the paragraph break lands at the sentence boundary closest to the actual pause. 138 138 + 139 139 + Both `pause_threshold_ms` and `proximity_words` are stored in the annotation layer's `metadata.features` for reproducibility. 140 140 + 141 141 + **Reliability:** High. Pause duration is a genuine speech signal, and constraining to sentence boundaries eliminates false positives. 142 142 + 143 143 + ## Rendering 144 144 + 145 145 + ### Format Lexicon Updates 146 146 + 147 147 + Two new facet types added to `tv.ionosphere.facet`: 148 148 + 149 149 + | Facet type | `featureClass` | Description | 150 150 + |---|---|---| 151 151 + | `tv.ionosphere.facet#sentence` | `inline` | Wraps all words in a sentence as a contiguous inline span | 152 152 + | `tv.ionosphere.facet#paragraph` | `block` | Groups sentences into a block-level paragraph container | 153 153 + 154 154 + Note: the annotation _storage_ format is layers.pub annotation layers (on the PDS). The _rendering_ format is ionosphere facets in the RelationalText document. The document assembly step bridges these — it reads layers.pub annotations and emits ionosphere facets. This separation means the renderer does not need to know about layers.pub. 155 155 + 156 156 + ### DOM Structure 157 157 + 158 158 + The renderer groups words into sentence spans and sentences into paragraph blocks: 159 159 + 160 160 + ```html 161 161 + <div> <!-- paragraph (block) --> 162 162 + <span> <!-- sentence (inline) --> 163 163 + <span>word</span> <span>word</span> <span>word</span> 164 164 + </span> 165 165 + <span> <!-- sentence (inline) --> 166 166 + <span>word</span> <span>word</span> 167 167 + </span> 168 168 + </div> 169 169 + <div> <!-- paragraph (block) --> 170 170 + <span> <!-- sentence (inline) --> 171 171 + <span>word</span> <span>word</span> 172 172 + </span> 173 173 + </div> 174 174 + ``` 175 175 + 176 176 + This mirrors the layers.pub expression hierarchy (transcript > paragraph > sentence) and maps directly to the format lexicon's `featureClass` system (`block` for paragraphs, `inline` for sentences). 177 177 + 178 178 + Sentence spans provide styling hooks for hover, selection, and transitions at sentence granularity. Paragraph blocks provide natural vertical whitespace. 179 179 + 180 180 + ### Data Model Changes: `extractData` → Hierarchical Structure 181 181 + 182 182 + The current `extractData` function in `src/lib/transcript.ts` returns a flat `{ words: WordSpan[], concepts, wordConcepts }`. This must change to return a hierarchical structure: 183 183 + 184 184 + ```typescript 185 185 + interface ParagraphSpan { 186 186 + byteStart: number; 187 187 + byteEnd: number; 188 188 + sentences: SentenceSpan[]; 189 189 + } 190 190 + 191 191 + interface SentenceSpan { 192 192 + byteStart: number; 193 193 + byteEnd: number; 194 194 + words: WordSpan[]; // existing WordSpan type, unchanged 195 195 + } 196 196 + 197 197 + interface TranscriptStructure { 198 198 + paragraphs: ParagraphSpan[]; 199 199 + concepts: ConceptSpan[]; 200 200 + // wordConcepts lookup remains flat (indexed by global word index) 201 201 + wordConcepts: ConceptSpan[][]; 202 202 + } 203 203 + ``` 204 204 + 205 205 + `extractData` builds this hierarchy by: 206 206 + 1. Extracting all word spans from `#timestamp` facets (existing logic, unchanged). 207 207 + 2. Reading `#paragraph` facets to get paragraph byte ranges. Sorting by `byteStart`. 208 208 + 3. Reading `#sentence` facets to get sentence byte ranges. Sorting by `byteStart`. 209 209 + 4. Assigning each word to its containing sentence (by byte range overlap). 210 210 + 5. Assigning each sentence to its containing paragraph (by byte range overlap). 211 211 + 6. Words not covered by any sentence facet form singleton sentences. Sentences not covered by any paragraph facet form singleton paragraphs. This graceful degradation means the renderer works identically on transcripts that have not yet been enriched. 212 212 + 213 213 + The brightness gradient system (`boundaryStartTime`/`boundaryEndTime`) continues to use the global word ordering — paragraph visual gaps do not affect the temporal continuity of the gradient. The existing `WordSpanComponent` is reused unchanged inside the sentence/paragraph wrappers. 214 214 + 215 215 + ### Document Assembly 216 216 + 217 217 + Document assembly is a **build-time step** that runs after the NLP pipeline and before publishing. It: 218 218 + 1. Reads the compact transcript record (`tv.ionosphere.transcript`). 219 219 + 2. Reads all layers.pub annotation layer records for this transcript. 220 220 + 3. Converts layers.pub sentence/paragraph annotations into `#sentence` and `#paragraph` ionosphere facets. 221 221 + 4. Merges with existing `#timestamp` and `#concept-ref` facets from `decodeToDocument`. 222 222 + 5. Writes the assembled RelationalText document onto the `tv.ionosphere.talk` record's `document` field. 223 223 + 224 224 + This replaces the current runtime assembly in the appview serve path with a pre-computed document. The appview serves the pre-assembled document directly — zero runtime cost. 225 225 + 226 226 + Annotation layers of different `subkind` values naturally have overlapping byte ranges (a paragraph span contains sentence spans, which contain word spans). This is expected and correct — they represent different levels of the hierarchy, not conflicting annotations. 227 227 + 228 228 + ### Scroll/Time Mapping 229 229 + 230 230 + Both `TranscriptView` and `WindowedTranscriptView` must account for paragraph whitespace in their scroll-to-time and time-to-scroll mappings. 231 231 + 232 232 + **TranscriptView:** The line-map computation already handles variable-height content. Paragraph `<div>` elements with margin/padding become part of the natural layout — no special handling needed beyond the existing line grouping logic. 233 233 + 234 234 + **WindowedTranscriptView:** The `computeMonospaceLayout` function currently returns `LineEntry[]` with uniform `LINE_HEIGHT`. Changes: 235 235 + - Accept an additional `paragraphBreaks: Set<number>` parameter (set of word indices where a paragraph starts). 236 236 + - When a word is a paragraph start, insert a gap of `PARAGRAPH_GAP` pixels (default: `LINE_HEIGHT`, i.e., one blank line) before its line entry. 237 237 + - `LineEntry` gains `isParagraphStart: boolean` for rendering the gap spacer. 238 238 + - Gap entries have no time range — `timeToScrollY` and `scrollYToTime` skip gaps by treating them as extensions of the preceding line's time range (scrolling through a gap seeks to the end of the previous paragraph). 239 239 + 240 240 + ## Testing Strategy 241 241 + 242 242 + **Python pipeline (pytest):** 243 243 + - Golden-file tests: run the sentence/paragraph pipeline on 2-3 known transcripts, compare output annotation layers to curated expected output. These transcripts should cover: a clean well-punctuated talk, a messy conversational panel, and a lightning talk with rapid transitions. 244 244 + - Unit tests for the paragraph algorithm: verify that paragraph breaks only land at sentence boundaries, that pauses below threshold produce no breaks, and that the proximity constraint works correctly. 245 245 + 246 246 + **TypeScript rendering (vitest):** 247 247 + - Unit tests for the updated `extractData`: verify hierarchical output from facets, and verify graceful degradation when sentence/paragraph facets are absent (flat word array wrapped in singleton sentence/paragraph). 248 248 + - Snapshot tests for `computeMonospaceLayout` with paragraph gaps. 249 249 + 250 250 + **Manual validation:** 251 251 + - After running the pipeline on all transcripts, spot-check 5-10 talks across different rooms/talk types. Verify paragraph breaks land at natural topic transitions, not mid-thought. Measure average sentences-per-paragraph (expect 3-8 for well-structured talks). 252 252 + 253 253 + ## Phase Roadmap 254 254 + 255 255 + ### Phase 1 — Structural formatting + layers.pub migration (this work) 256 256 + 257 257 + - Vendor layers.pub lexicon definitions into `lexicons/pub/layers/` 258 258 + - Python NLP pipeline: sentence detection (spaCy) + paragraph segmentation (pause + sentence alignment) 259 259 + - layers.pub expression + segmentation records for each transcript 260 260 + - Sentence and paragraph annotation layers 261 261 + - Panproto lenses: compact transcript <-> layers.pub expression + segmentation 262 262 + - Document assembly reads annotation layers, emits structural facets 263 263 + - Renderer: sentences as inline spans, paragraphs as block elements 264 264 + - Remove `tv.ionosphere.annotation` records, `enrich.ts`/`enrich-all.ts`, `overlayAnnotations` — fully replaced by layers.pub 265 265 + - **Goal:** Transcripts read as paragraphed prose; all enrichment flows through layers.pub 266 266 + 267 267 + ### Phase 2 — Entity recognition + record linking 268 268 + 269 269 + - spaCy NER pass in the pipeline 270 270 + - AT Protocol record resolver: people -> Bluesky profiles (DID resolution via handle/display name lookup), projects -> `tv.ionosphere.concept` records 271 271 + - Concept annotation layer replaces the old `tv.ionosphere.annotation`-based concept system with richer, NLP-derived results 272 272 + - Entity annotation layer with `knowledgeRefs` to resolved records 273 273 + - Renderer: entity spans as links/tooltips to profiles and concept pages 274 274 + - **Goal:** People and projects mentioned in talks are clickable, linked to real AT Protocol identities 275 275 + 276 276 + ### Phase 3 — Topic segmentation 277 277 + 278 278 + - Sentence-transformer embedding pass (e.g., `all-MiniLM-L6-v2`) 279 279 + - Sliding-window cosine similarity topic boundary detection 280 280 + - Topic segment annotation layer 281 281 + - Renderer: section dividers or topic labels at major transitions 282 282 + - UI: topic-based navigation within a talk (jump to "Q&A", "Demo", etc.) 283 283 + - **Goal:** Long talks become navigable by topic 284 284 + 285 285 + ### Phase 4 — Manual curation layer 286 286 + 287 287 + - UI for creating manual override annotations (correct a concept link, fix an entity, adjust a paragraph break) 288 288 + - Published as AT Protocol records with `sourceMethod: "manual-native"`, higher `rank` 289 289 + - Pipeline respects overrides on re-run 290 290 + - Multi-user: anyone with write access can contribute corrections 291 291 + - **Goal:** Community-curated enrichment that improves over time 292 292 + 293 293 + ### Phase 5 — Concept enrichment + cross-talk linking 294 294 + 295 295 + - Supersede auto-detected concepts with curated concept records 296 296 + - Cross-reference talks that mention the same entities/concepts 297 297 + - `tv.ionosphere.facet#talk-xref` links between related talks 298 298 + - Knowledge graph across the entire conference 299 299 + - **Goal:** The archive becomes a connected knowledge base, not just isolated transcripts

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··· 1 1 + # layers.pub Record Publishing + Panproto Lenses — Pre-Plan 2 2 + 3 3 + **Status:** Pre-plan for next session 4 4 + **Depends on:** Phases 1-3 enrichment (complete), concept deduplication (complete) 5 5 + 6 6 + ## Context 7 7 + 8 8 + The NLP enrichment pipeline produces sentence/paragraph/entity/topic annotations that are currently stored as ionosphere facets in pre-assembled RelationalText documents. The layers.pub lexicons are vendored (`lexicons/pub/layers/`) but no actual AT Protocol records are published. 9 9 + 10 10 + This work makes the enrichment data first-class AT Protocol records — publishable, indexable, and interoperable with the broader layers.pub ecosystem. 11 11 + 12 12 + ## What Exists 13 13 + 14 14 + ### Vendored lexicons (in `lexicons/pub/layers/`) 15 15 + - `defs.json` — shared types: span, temporalSpan, uuid, tokenRef, anchor, annotationMetadata, feature/featureMap 16 16 + - `expression/expression.json` — record: id, kind, text, language, sourceRef, parentRef, anchor, metadata 17 17 + - `segmentation/segmentation.json` — record: expression ref, tokenizations (with textSpan + temporalSpan per token) 18 18 + - `annotation/annotationLayer.json` — record: expression ref, kind/subkind, sourceMethod, annotations array 19 19 + 20 20 + ### Existing panproto integration (`formats/tv.ionosphere/ts/panproto.ts`) 21 21 + - WASM-based runtime (lazy singleton init) 22 22 + - `loadSchema()` — parse lexicon JSON into BuiltSchema 23 23 + - `buildMigration()` — explicit migration between schemas 24 24 + - `createLens()` — auto-generated lens between schemas 25 25 + - `autoGenerateWithHints()` — protolens chain with morphism hints 26 26 + - `createPipeline()` — PipelineBuilder for combinator transforms 27 27 + - `serializeChain()` / `serializeMigrationSpec()` — serialization for storage 28 28 + 29 29 + ### Existing lenses (`formats/tv.ionosphere/lenses/`) 30 30 + - `openai-whisper-to-transcript.lens.json` 31 31 + - `transcript-to-document.lens.json` 32 32 + - `schedule-to-talk.lens.json` 33 33 + - `vod-to-talk.lens.json` 34 34 + 35 35 + ## What Needs to Be Built 36 36 + 37 37 + ### 1. Publish layers.pub records to PDS 38 38 + 39 39 + For each transcript, publish: 40 40 + 41 41 + **Expression record** (`pub.layers.expression.expression`): 42 42 + - `kind: "transcript"` 43 43 + - `text`: the transcript text 44 44 + - `language: "en"` 45 45 + - `sourceRef`: AT URI of the `tv.ionosphere.transcript` record 46 46 + - `createdAt`: timestamp 47 47 + 48 48 + **Segmentation record** (`pub.layers.segmentation.segmentation`): 49 49 + - `expression`: AT URI of the expression record above 50 50 + - One tokenization with `kind: "whitespace"` 51 51 + - Each token has `textSpan` (byteStart/byteEnd) and `temporalSpan` (start/ending in ms) 52 52 + - Derived from the compact transcript's timing data 53 53 + 54 54 + **Annotation layers** (`pub.layers.annotation.annotationLayer`): 55 55 + - **Sentence layer**: `kind: "span"`, `subkind: "sentence-boundary"`, `sourceMethod: "automatic"` 56 56 + - **Paragraph layer**: `kind: "span"`, `subkind: "paragraph-boundary"`, `sourceMethod: "automatic"` 57 57 + - **Entity layer**: `kind: "span"`, `subkind: "ner"`, `sourceMethod: "automatic"`, with `knowledgeRefs` for resolved entities 58 58 + - **Topic layer**: `kind: "span"`, `subkind: "topic-segment"`, `sourceMethod: "automatic"` 59 59 + - Each layer references the expression record and includes `metadata` (tool, confidence, timestamp) 60 60 + 61 61 + ### 2. Panproto lenses 62 62 + 63 63 + **Lens: compact transcript → layers.pub expression + segmentation** 64 64 + - Source: `tv.ionosphere.transcript` (text, startMs, timings) 65 65 + - Target: `pub.layers.expression.expression` + `pub.layers.segmentation.segmentation` 66 66 + - This is the transform that `decodeToDocument` / `encode` already implement in code — the lens formalizes it 67 67 + 68 68 + **Lens: NLP annotations → layers.pub annotation layers** 69 69 + - Source: NLP pipeline JSON output (sentences, paragraphs, entities, topicBreaks) 70 70 + - Target: `pub.layers.annotation.annotationLayer` records 71 71 + - Mostly structural mapping — the NLP output already has byte ranges and labels 72 72 + 73 73 + **Lens: layers.pub → ionosphere document facets** 74 74 + - Source: layers.pub records (expression + segmentation + annotation layers) 75 75 + - Target: RelationalText document with ionosphere facets (#timestamp, #sentence, #paragraph, etc.) 76 76 + - This is the reverse of what `decodeToDocumentWithStructure` does — reading layers.pub records and emitting facets 77 77 + 78 78 + ### 3. Appview indexer updates 79 79 + 80 80 + The appview needs to index layers.pub records from Jetstream: 81 81 + - Add `pub.layers.expression.expression`, `pub.layers.segmentation.segmentation`, `pub.layers.annotation.annotationLayer` to `IONOSPHERE_COLLECTIONS` 82 82 + - Create DB tables for these records 83 83 + - On indexing, rebuild the pre-assembled document from the layers.pub records (using the lens) 84 84 + 85 85 + ### 4. Schema versioning 86 86 + 87 87 + - Initialize panproto VCS for the layers.pub schemas 88 88 + - Pin to layers.pub v0.5.0 (current vendored version) 89 89 + - Define migration strategy for when layers.pub evolves 90 90 + 91 91 + ## Architecture Decision: Build-Time vs Runtime 92 92 + 93 93 + Currently, document assembly happens at **build time** (publish.ts) and the assembled document is stored on the talk record. With layers.pub records, the assembly could move to **runtime** (appview reads layers.pub records and assembles on the fly). 94 94 + 95 95 + **Recommendation:** Keep build-time assembly for the ionosphere document (fast serving), AND publish layers.pub records alongside (for interoperability). The layers.pub records are the canonical source; the ionosphere document is a materialized view. 96 96 + 97 97 + ## Suggested Task Order 98 98 + 99 99 + 1. Write the publish step for layers.pub expression + segmentation records 100 100 + 2. Write the publish step for annotation layer records 101 101 + 3. Define panproto lens: compact transcript → expression + segmentation 102 102 + 4. Define panproto lens: NLP annotations → annotation layers 103 103 + 5. Update appview indexer to handle layers.pub records 104 104 + 6. Initialize panproto VCS, tag v0.5.0 105 105 + 7. Test round-trip: publish → index → serve → verify in browser 106 106 + 8. Define panproto lens: layers.pub → ionosphere document facets (reverse lens) 107 107 + 108 108 + ## Questions for the Session 109 109 + 110 110 + - Should we publish layers.pub records under the ionosphere.tv DID, or a separate account? 111 111 + - How should the appview discover which annotation layers belong to a given transcript? (By expression URI reference? By convention?) 112 112 + - Do we want to support third-party annotation layers from other DIDs? (e.g., someone else annotating our transcripts) 113 113 + - Should the panproto lenses be published as `org.relationaltext.lens` records (like the existing ones)?

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··· 1 1 + # layers.pub Record Publishing via Panproto Lenses — Design 2 2 + 3 3 + **Status:** Approved 4 4 + **Date:** 2026-04-13 5 5 + **Depends on:** NLP enrichment pipeline (complete), concept deduplication (complete) 6 6 + 7 7 + ## Overview 8 8 + 9 9 + Publish enrichment data as first-class AT Protocol records using the layers.pub schema. Panproto lenses are the authoritative transforms — all data flows through lenses, no parallel TypeScript pipelines. The existing ionosphere document (text + facets embedded on the talk record) becomes a materialized view rebuilt from layers.pub records. 10 10 + 11 11 + ## Decisions 12 12 + 13 13 + - **DID:** Publish under ionosphere.tv 14 14 + - **Annotation layer references:** Point to the transcript's AT URI (`tv.ionosphere.transcript`) 15 15 + - **Third-party layers:** Not supported yet (no moderation). Comments and reactions are unaffected. 16 16 + - **Lens namespace:** `org.relationaltext.lens` (consistent with existing 4 lenses) 17 17 + - **Record keys:** Deterministic, derived from talk rkey (e.g., `{talk.rkey}-expression`) 18 18 + - **Expression kind:** `kind: "transcript"` (no `kindUri` — it requires AT URI format, and there's no meaningful record to reference) 19 19 + - **Segmentation tokenization:** `kind: "word"` — carries the temporal mapping; annotations use `anchor.textSpan` (byte offsets) directly 20 20 + - **Publish ordering:** Expression URI pre-computed from DID + rkey; all records publish in parallel 21 21 + - **Architecture:** Lenses-first (Approach B) — lenses are authoritative, publish step runs data through lenses 22 22 + 23 23 + ## Section 1: Record Model & Relationships 24 24 + 25 25 + For each transcript, 6 new records published under ionosphere.tv: 26 26 + 27 27 + ``` 28 28 + tv.ionosphere.transcript/{talk.rkey}-transcript (already exists) 29 29 + │ 30 30 + ▼ sourceRef 31 31 + pub.layers.expression.expression/{talk.rkey}-expression 32 32 + │ 33 33 + ├── pub.layers.segmentation.segmentation/{talk.rkey}-segmentation 34 34 + │ └── tokenization: kind "word", tokens with textSpan + temporalSpan 35 35 + │ 36 36 + ├── pub.layers.annotation.annotationLayer/{talk.rkey}-sentences 37 37 + ├── pub.layers.annotation.annotationLayer/{talk.rkey}-paragraphs 38 38 + ├── pub.layers.annotation.annotationLayer/{talk.rkey}-entities 39 39 + └── pub.layers.annotation.annotationLayer/{talk.rkey}-topics 40 40 + ``` 41 41 + 42 42 + Example AT URI: `at://did:plc:xxxxxx/pub.layers.expression.expression/atproto-for-everyone-expression` 43 43 + 44 44 + > **Future work:** A `-speakers` annotation layer (`subkind: "speaker-segment"`) for diarization spans. 45 45 + > The NLP pipeline does not yet produce `speakerSegments` data, so this layer is deferred until the 46 46 + > diarization pipeline is integrated. 47 47 + 48 48 + ### Expression record 49 49 + 50 50 + | Field | Value | 51 51 + |---|---| 52 52 + | `id` | talk rkey | 53 53 + | `$type` | `"pub.layers.expression.expression"` | 54 54 + | `kind` | `"transcript"` | 55 55 + | `text` | full transcript text | 56 56 + | `language` | `"en"` | 57 57 + | `sourceRef` | AT URI of `tv.ionosphere.transcript` record | 58 58 + | `metadata` | `{ tool: "ionosphere-pipeline", timestamp: "<ISO 8601 datetime>" }` | 59 59 + | `createdAt` | ISO 8601 timestamp | 60 60 + 61 61 + ### Segmentation record 62 62 + 63 63 + | Field | Value | 64 64 + |---|---| 65 65 + | `expression` | AT URI of expression (pre-computed) | 66 66 + | `tokenizations` | Single tokenization, `kind: "word"` | 67 67 + | `createdAt` | ISO 8601 timestamp | 68 68 + 69 69 + Each token: `tokenIndex` (0-based), `text` (word), `textSpan` (UTF-8 byte offsets), `temporalSpan` (start/ending in ms, derived from compact transcript timings). 70 70 + 71 71 + ### Annotation layers 72 72 + 73 73 + All records include `$type: "pub.layers.annotation.annotationLayer"`. All reference the expression URI. All use `sourceMethod: "automatic"`. `createdAt` is optional in the lexicon but always included. 74 74 + 75 75 + | rkey suffix | kind | subkind | annotations | 76 76 + |---|---|---|---| 77 77 + | `-sentences` | `"span"` | `"sentence-boundary"` | One annotation per sentence. `anchor: { textSpan: { byteStart, byteEnd } }`, `label`: first ~50 chars of sentence text. | 78 78 + | `-paragraphs` | `"span"` | `"paragraph-boundary"` | One annotation per paragraph. `anchor: { textSpan: { byteStart, byteEnd } }`, `label`: `"paragraph"`. | 79 79 + | `-entities` | `"span"` | `"ner"` | One annotation per entity mention. `anchor: { textSpan: { byteStart, byteEnd } }`, `label`: entity name. `features: { entries: [{ key: "nerType", value: "PERSON" }, { key: "conceptUri", value: "at://..." }] }` — `nerType` always present, `conceptUri` if resolved, plus any future entity keys (e.g., `speakerDid`) forwarded as passthrough. | 80 80 + | `-topics` | `"span"` | `"topic-segment"` | One annotation per topic break. `anchor: { textSpan: { byteStart, byteEnd } }` where `byteEnd = byteStart` (zero-width span). `label`: `"topic-break"`. | 81 81 + 82 82 + `metadata` on all layers: `{ tool: "ionosphere-nlp-pipeline", timestamp: "<ISO 8601 datetime>" }`. 83 83 + 84 84 + ## Section 2: Panproto Lens Architecture 85 85 + 86 86 + Three lenses. All authoritative — data flows through them. 87 87 + 88 88 + ### Lens 1: Compact Transcript → Expression + Segmentation 89 89 + 90 90 + - **Source:** `tv.ionosphere.transcript` (text, startMs, timings) 91 91 + - **Target:** `pub.layers.expression.expression` + `pub.layers.segmentation.segmentation` 92 92 + - **Transform:** maps text → text, injects kind/language, replays timings array to produce token list with textSpan + temporalSpan 93 93 + - **Fan-out:** Single source → two target records (protolens chain) 94 94 + - **Morphism hints:** `text → text`, `startMs + timings → tokenizations[0].tokens` 95 95 + 96 96 + ### Lens 2: NLP Annotations → Annotation Layers 97 97 + 98 98 + - **Source:** `tv.ionosphere.nlpAnnotations` (new lexicon, not published to PDS — exists as lens source schema) 99 99 + - **Target:** 4× `pub.layers.annotation.annotationLayer` 100 100 + - **Transform:** Byte ranges → `anchor.textSpan`, labels → `annotation.label`, entity metadata → `features` 101 101 + - **Fan-out:** Single source → four target records 102 102 + 103 103 + The `tv.ionosphere.nlpAnnotations` lexicon formalizes the NlpAnnotations TypeScript interface as a proper schema that panproto can parse and validate. 104 104 + 105 105 + ### Lens 3: Layers.pub → Ionosphere Document Facets (reverse) 106 106 + 107 107 + - **Source:** expression + segmentation + annotation layers 108 108 + - **Target:** RelationalText document with ionosphere facets (`{ text, facets }`) 109 109 + - **Purpose:** Materialized view builder, used by appview indexer 110 110 + - **Round-trip property:** Lens 1+2 followed by Lens 3 should reproduce the same document that `decodeToDocumentWithStructure` currently produces. This is the correctness test. 111 111 + 112 112 + ### Lens storage 113 113 + 114 114 + Published as `org.relationaltext.lens` records (consistent with existing 4 lenses). Rkeys: `transcript-to-expression`, `nlp-to-annotation-layers`, `layers-to-document`. 115 115 + 116 116 + ## Section 3: Publish Pipeline 117 117 + 118 118 + New Stage 6 in `publish.ts`, runs after transcript publishing. 119 119 + 120 120 + For each talk with both transcript and NLP annotation files: 121 121 + 122 122 + 1. Load compact transcript (`transcripts/{rkey}.json`) 123 123 + 2. Load NLP annotations (`nlp/{rkey}.json`) 124 124 + 3. Run **Lens 1** via panproto WASM → expression + segmentation records 125 125 + 4. Run **Lens 2** via panproto WASM → 4 annotation layer records 126 126 + 5. Inject AT URIs (pre-computed from DID + rkey): `sourceRef` on expression, `expression` ref on segmentation and all annotation layers 127 127 + 6. Publish all 6 records via `PdsClient.putRecord()` (parallel within each talk, sequential across talks) 128 128 + 129 129 + **Idempotency:** Deterministic rkeys mean re-publish overwrites, no duplicates. 130 130 + 131 131 + **Rate limiting:** ~98 talks × 6 records = ~588 putRecord calls. Records within a single talk publish in parallel (6 concurrent). Talks are processed sequentially. The existing PdsClient writeDelay (100ms) and 429/backoff handling apply. 132 132 + 133 133 + **Skip condition:** If a talk has transcript but no NLP annotations, skip layers.pub entirely for that talk (keep as a unit). 134 134 + 135 135 + **WASM lifecycle:** Panproto runtime initializes once (lazy singleton), schemas load once at stage start, each talk runs data through compiled lenses. 136 136 + 137 137 + ## Section 4: Appview Indexer Updates 138 138 + 139 139 + ### New Jetstream subscriptions 140 140 + 141 141 + Add to `IONOSPHERE_COLLECTIONS`: 142 142 + - `pub.layers.expression.expression` 143 143 + - `pub.layers.segmentation.segmentation` 144 144 + - `pub.layers.annotation.annotationLayer` 145 145 + 146 146 + **DID filter:** Only index records from ionosphere.tv DID (no third-party layers). 147 147 + 148 148 + ### New DB tables 149 149 + 150 150 + ```sql 151 151 + layers_expressions: 152 152 + uri TEXT PRIMARY KEY, rkey TEXT, did TEXT, transcript_uri TEXT, 153 153 + text TEXT, language TEXT, created_at TEXT 154 154 + -- uri IS the expression URI; other tables reference it via expression_uri 155 155 + 156 156 + layers_segmentations: 157 157 + rkey TEXT, did TEXT, expression_uri TEXT, 158 158 + tokens_json TEXT, created_at TEXT 159 159 + 160 160 + layers_annotations: 161 161 + rkey TEXT, did TEXT, expression_uri TEXT, 162 162 + kind TEXT, subkind TEXT, annotations_json TEXT, created_at TEXT 163 163 + ``` 164 164 + 165 165 + Key indexes: `expression_uri` (find all layers for expression), `transcript_uri` (find expression for transcript). 166 166 + 167 167 + ### Document rebuild on ingest 168 168 + 169 169 + When any layers.pub record arrives or updates: 170 170 + 171 171 + 1. Look up the expression URI → transcript URI 172 172 + 2. Check for complete set: expression + segmentation + at least one annotation layer 173 173 + 3. If complete, run **Lens 3** (layers.pub → ionosphere document facets) to produce materialized `{ text, facets }` 174 174 + 4. Update the talk record's `document` field in DB 175 175 + 176 176 + **Deletion:** If an annotation layer is deleted, rebuild with remaining layers (graceful degradation — fewer annotations). If the expression record is deleted, cascade-delete its segmentation and annotation rows from the DB and clear the talk's materialized document. Annotation/segmentation JSON is stored as TEXT blobs — all queries are by expression URI, not individual annotations, so this is sufficient. 177 177 + 178 178 + **Backfill:** Add three new collections to existing startup backfill loop; records go through same ingest → rebuild path. 179 179 + 180 180 + ## Section 5: Schema Versioning 181 181 + 182 182 + ### Panproto VCS initialization 183 183 + 184 184 + - `schema init` in project 185 185 + - Add layers.pub lexicons + ionosphere lexicons + NLP annotations lexicon + all lens definitions 186 186 + - `schema commit` initial state 187 187 + - `schema tag v0.5.0` — pin to current vendored layers.pub version 188 188 + 189 189 + ### What's tracked 190 190 + 191 191 + - `lexicons/pub/layers/*.json` 192 192 + - `formats/tv.ionosphere/ionosphere.lexicon.json` 193 193 + - `formats/tv.ionosphere/nlpAnnotations.lexicon.json` (new) 194 194 + - `formats/tv.ionosphere/lenses/*.lens.json` (existing 4 + new 3) 195 195 + 196 196 + ### Migration strategy 197 197 + 198 198 + When layers.pub evolves: vendor updated lexicons, `schema diff`, update lenses if needed. The materialized view insulates the frontend — layers.pub can change without affecting ionosphere document format until we choose to update. 199 199 + 200 200 + ## Task Order 201 201 + 202 202 + 1. Define `tv.ionosphere.nlpAnnotations` lexicon (lens source schema) 203 203 + 2. Define Lens 1: compact transcript → expression + segmentation 204 204 + 3. Define Lens 2: NLP annotations → 4 annotation layers 205 205 + 4. Add publish Stage 6: run lenses, publish 6 records per talk 206 206 + 5. Define Lens 3: layers.pub → ionosphere document facets (reverse) 207 207 + 6. Add appview DB tables + indexer for layers.pub collections (depends on 5) 208 208 + 7. Wire indexer rebuild: on layers.pub ingest, run Lens 3, update talk document 209 209 + 8. Initialize panproto VCS, tag v0.5.0 210 210 + 9. Test round-trip: publish → index → rebuild → verify document matches current output 211 211 + 10. Deploy

+24

formats/tv.ionosphere/ionosphere.lexicon.json

reviewed

··· 39 39 "featureClass": "meta", 40 40 "expandStart": false, 41 41 "expandEnd": false 42 42 + }, 43 43 + { 44 44 + "typeId": "tv.ionosphere.facet#sentence", 45 45 + "featureClass": "inline", 46 46 + "expandStart": false, 47 47 + "expandEnd": false 48 48 + }, 49 49 + { 50 50 + "typeId": "tv.ionosphere.facet#paragraph", 51 51 + "featureClass": "block", 52 52 + "expandStart": false, 53 53 + "expandEnd": false 54 54 + }, 55 55 + { 56 56 + "typeId": "tv.ionosphere.facet#topic-break", 57 57 + "featureClass": "block", 58 58 + "expandStart": false, 59 59 + "expandEnd": false 60 60 + }, 61 61 + { 62 62 + "typeId": "tv.ionosphere.facet#entity", 63 63 + "featureClass": "inline", 64 64 + "expandStart": false, 65 65 + "expandEnd": false 42 66 } 43 67 ] 44 68 }

+6

formats/tv.ionosphere/lenses/layers-to-document.lens.json

reviewed

··· 1 1 + { 2 2 + "source": ["pub.layers.expression.expression", "pub.layers.segmentation.segmentation", "pub.layers.annotation.annotationLayer"], 3 3 + "target": "tv.ionosphere.document", 4 4 + "version": 1, 5 5 + "description": "Layers.pub records to ionosphere RelationalText document with facets (materialized view builder)" 6 6 + }

+12

formats/tv.ionosphere/lenses/nlp-to-annotation-layers.lens.json

reviewed

··· 1 1 + { 2 2 + "source": "tv.ionosphere.nlpAnnotations", 3 3 + "target": ["pub.layers.annotation.annotationLayer"], 4 4 + "version": 1, 5 5 + "description": "NLP annotations to 4 annotation layer records (sentences, paragraphs, entities, topics)", 6 6 + "fanOut": { 7 7 + "sentences": { "subkind": "sentence-boundary", "from": "sentences" }, 8 8 + "paragraphs": { "subkind": "paragraph-boundary", "from": "paragraphs" }, 9 9 + "entities": { "subkind": "ner", "from": "entities" }, 10 10 + "topics": { "subkind": "topic-segment", "from": "topicBreaks" } 11 11 + } 12 12 + }

+10

formats/tv.ionosphere/lenses/transcript-to-expression.lens.json

reviewed

··· 1 1 + { 2 2 + "source": "tv.ionosphere.transcript", 3 3 + "target": ["pub.layers.expression.expression", "pub.layers.segmentation.segmentation"], 4 4 + "version": 1, 5 5 + "description": "Compact transcript to layers.pub expression + segmentation", 6 6 + "mappings": { 7 7 + "text": "expression.text", 8 8 + "startMs + timings": "segmentation.tokenizations[0].tokens" 9 9 + } 10 10 + }

+148

formats/tv.ionosphere/nlpAnnotations.lexicon.json

reviewed

··· 1 1 + { 2 2 + "lexicon": 1, 3 3 + "$type": "com.atproto.lexicon.schema", 4 4 + "id": "tv.ionosphere.nlpAnnotations", 5 5 + "revision": 1, 6 6 + "description": "NLP pipeline output for a conference talk transcript. Formalizes the JSON shape produced by the ionosphere NLP pipeline so panproto can use it as a lens source schema. Not published to PDS.", 7 7 + "defs": { 8 8 + "main": { 9 9 + "type": "record", 10 10 + "key": "any", 11 11 + "record": { 12 12 + "type": "object", 13 13 + "required": ["talkRkey", "sentences", "paragraphs", "entities", "topicBreaks", "metadata"], 14 14 + "properties": { 15 15 + "talkRkey": { 16 16 + "type": "string", 17 17 + "description": "The rkey of the tv.ionosphere.talk record this annotation set covers." 18 18 + }, 19 19 + "sentences": { 20 20 + "type": "array", 21 21 + "items": { 22 22 + "type": "ref", 23 23 + "ref": "tv.ionosphere.nlpAnnotations#nlpSentence" 24 24 + }, 25 25 + "description": "Sentence-boundary spans detected in the transcript text." 26 26 + }, 27 27 + "paragraphs": { 28 28 + "type": "array", 29 29 + "items": { 30 30 + "type": "ref", 31 31 + "ref": "tv.ionosphere.nlpAnnotations#nlpParagraph" 32 32 + }, 33 33 + "description": "Paragraph-boundary spans detected in the transcript text." 34 34 + }, 35 35 + "entities": { 36 36 + "type": "array", 37 37 + "items": { 38 38 + "type": "ref", 39 39 + "ref": "tv.ionosphere.nlpAnnotations#nlpEntity" 40 40 + }, 41 41 + "description": "Named entities detected in the transcript text." 42 42 + }, 43 43 + "topicBreaks": { 44 44 + "type": "array", 45 45 + "items": { 46 46 + "type": "ref", 47 47 + "ref": "tv.ionosphere.nlpAnnotations#nlpTopicBreak" 48 48 + }, 49 49 + "description": "Byte positions where a topic shift was detected." 50 50 + }, 51 51 + "metadata": { 52 52 + "type": "ref", 53 53 + "ref": "tv.ionosphere.nlpAnnotations#nlpMetadata", 54 54 + "description": "Provenance and configuration metadata for this annotation run." 55 55 + } 56 56 + } 57 57 + } 58 58 + }, 59 59 + "nlpSentence": { 60 60 + "type": "object", 61 61 + "description": "A sentence span identified by the NLP pipeline.", 62 62 + "required": ["byteStart", "byteEnd"], 63 63 + "properties": { 64 64 + "byteStart": { 65 65 + "type": "integer", 66 66 + "description": "Inclusive start byte offset into the transcript text." 67 67 + }, 68 68 + "byteEnd": { 69 69 + "type": "integer", 70 70 + "description": "Exclusive end byte offset into the transcript text." 71 71 + } 72 72 + } 73 73 + }, 74 74 + "nlpParagraph": { 75 75 + "type": "object", 76 76 + "description": "A paragraph span identified by the NLP pipeline.", 77 77 + "required": ["byteStart", "byteEnd"], 78 78 + "properties": { 79 79 + "byteStart": { 80 80 + "type": "integer", 81 81 + "description": "Inclusive start byte offset into the transcript text." 82 82 + }, 83 83 + "byteEnd": { 84 84 + "type": "integer", 85 85 + "description": "Exclusive end byte offset into the transcript text." 86 86 + } 87 87 + } 88 88 + }, 89 89 + "nlpEntity": { 90 90 + "type": "object", 91 91 + "description": "A named entity span identified by the NLP pipeline.", 92 92 + "required": ["byteStart", "byteEnd", "label", "nerType"], 93 93 + "properties": { 94 94 + "byteStart": { 95 95 + "type": "integer", 96 96 + "description": "Inclusive start byte offset into the transcript text." 97 97 + }, 98 98 + "byteEnd": { 99 99 + "type": "integer", 100 100 + "description": "Exclusive end byte offset into the transcript text." 101 101 + }, 102 102 + "label": { 103 103 + "type": "string", 104 104 + "description": "Surface text of the named entity (e.g. 'Matt')." 105 105 + }, 106 106 + "nerType": { 107 107 + "type": "string", 108 108 + "description": "NER category label (e.g. PERSON, ORG, GPE)." 109 109 + }, 110 110 + "conceptUri": { 111 111 + "type": "string", 112 112 + "format": "at-uri", 113 113 + "description": "AT URI of a canonical concept record for this entity, if resolved." 114 114 + } 115 115 + } 116 116 + }, 117 117 + "nlpTopicBreak": { 118 118 + "type": "object", 119 119 + "description": "A position in the transcript text where the NLP pipeline detected a topic shift.", 120 120 + "required": ["byteStart"], 121 121 + "properties": { 122 122 + "byteStart": { 123 123 + "type": "integer", 124 124 + "description": "Byte offset at which the topic shift begins." 125 125 + } 126 126 + } 127 127 + }, 128 128 + "nlpMetadata": { 129 129 + "type": "object", 130 130 + "description": "Provenance and configuration metadata for an NLP annotation run.", 131 131 + "required": ["tool"], 132 132 + "properties": { 133 133 + "tool": { 134 134 + "type": "string", 135 135 + "description": "Name and version of the NLP tool used (e.g. 'spacy/en_core_web_sm')." 136 136 + }, 137 137 + "pauseThresholdMs": { 138 138 + "type": "integer", 139 139 + "description": "Silence duration in milliseconds used to delimit paragraphs." 140 140 + }, 141 141 + "proximityWords": { 142 142 + "type": "integer", 143 143 + "description": "Word-window radius used when linking entity mentions to concepts." 144 144 + } 145 145 + } 146 146 + } 147 147 + } 148 148 + }

+2 -1

formats/tv.ionosphere/package.json

reviewed

··· 8 8 "./assemble": "./ts/assemble.ts", 9 9 "./lenses": "./ts/lenses.ts", 10 10 "./panproto": "./ts/panproto.ts", 11 11 - "./transcript-encoding": "./ts/transcript-encoding.ts" 11 11 + "./transcript-encoding": "./ts/transcript-encoding.ts", 12 12 + "./layers-pub": "./ts/layers-pub.ts" 12 13 }, 13 14 "dependencies": { 14 15 "@msgpack/msgpack": "^3.1.3",

+472

formats/tv.ionosphere/ts/layers-pub.ts

reviewed

··· 1 1 + /** 2 2 + * Lens transforms between tv.ionosphere records and pub.layers records. 3 3 + * 4 4 + * Lens 1: transcriptToLayersPub 5 5 + * tv.ionosphere.transcript → pub.layers.expression.expression 6 6 + * + pub.layers.segmentation.segmentation 7 7 + * 8 8 + * Lens 2: nlpToAnnotationLayers 9 9 + * NLP annotations → 4 pub.layers.annotation.annotationLayer records 10 10 + * 11 11 + * Lens 3: layersPubToDocument (reverse) 12 12 + * pub.layers records → tv.ionosphere document with facets 13 13 + * 14 14 + * The timings replay algorithm matches decodeToDocument() in 15 15 + * transcript-encoding.ts — uses TextEncoder for correct UTF-8 byte offsets, 16 16 + * and indexOf with searchFrom for word position tracking. 17 17 + */ 18 18 + 19 19 + import type { Document, DocumentFacet, CompactTranscript } from './transcript-encoding.js'; 20 20 + 21 21 + export interface TranscriptRecord { 22 22 + $type: string; 23 23 + talkUri: string; 24 24 + text: string; 25 25 + startMs: number; 26 26 + timings: number[]; 27 27 + } 28 28 + 29 29 + export interface ExpressionRecord { 30 30 + $type: 'pub.layers.expression.expression'; 31 31 + id: string; 32 32 + kind: string; 33 33 + text: string; 34 34 + language: string; 35 35 + sourceRef: string; 36 36 + metadata: { 37 37 + tool: string; 38 38 + timestamp: string; 39 39 + }; 40 40 + createdAt: string; 41 41 + } 42 42 + 43 43 + export interface TokenSpan { 44 44 + byteStart: number; 45 45 + byteEnd: number; 46 46 + } 47 47 + 48 48 + export interface TemporalSpan { 49 49 + start: number; 50 50 + ending: number; 51 51 + } 52 52 + 53 53 + export interface TextToken { 54 54 + tokenIndex: number; 55 55 + textSpan: TokenSpan; 56 56 + } 57 57 + 58 58 + export interface TemporalToken { 59 59 + tokenIndex: number; 60 60 + temporalSpan: TemporalSpan; 61 61 + } 62 62 + 63 63 + export interface TextTokenization { 64 64 + kind: string; 65 65 + tokens: TextToken[]; 66 66 + } 67 67 + 68 68 + export interface TemporalTokenization { 69 69 + kind: string; 70 70 + tokens: TemporalToken[]; 71 71 + } 72 72 + 73 73 + export interface SegmentationRecord { 74 74 + $type: 'pub.layers.segmentation.segmentation'; 75 75 + expression: string; 76 76 + tokenizations: TextTokenization[]; 77 77 + createdAt: string; 78 78 + } 79 79 + 80 80 + export interface TemporalSegmentationRecord { 81 81 + $type: 'pub.layers.segmentation.segmentation'; 82 82 + expression: string; 83 83 + tokenizations: TemporalTokenization[]; 84 84 + createdAt: string; 85 85 + } 86 86 + 87 87 + /** Maximum tokens per segmentation record to stay under PDS body limits (~200KB CBOR) */ 88 88 + const MAX_TOKENS_PER_RECORD = 2000; 89 89 + 90 90 + export interface LayersPubResult { 91 91 + expression: ExpressionRecord; 92 92 + segmentations: SegmentationRecord[]; 93 93 + temporals: TemporalSegmentationRecord[]; 94 94 + } 95 95 + 96 96 + /** 97 97 + * Lens 1: Transform a compact transcript record into layers.pub 98 98 + * expression + segmentation records. 99 99 + * 100 100 + * The timings replay algorithm (matching decodeToDocument): 101 101 + * - Split text by whitespace to get words 102 102 + * - Use TextEncoder for UTF-8 byte offsets 103 103 + * - Initialize cursor at startMs 104 104 + * - Iterate timings: negative = silence gap, positive = word duration 105 105 + * - Each word becomes a token with text span and temporal span 106 106 + */ 107 107 + export async function transcriptToLayersPub( 108 108 + transcript: TranscriptRecord, 109 109 + did: string, 110 110 + talkRkey: string, 111 111 + ): Promise<LayersPubResult> { 112 112 + const now = new Date().toISOString(); 113 113 + 114 114 + // Build expression record 115 115 + const expression: ExpressionRecord = { 116 116 + $type: 'pub.layers.expression.expression', 117 117 + id: talkRkey, 118 118 + kind: 'transcript', 119 119 + text: transcript.text, 120 120 + language: 'en', 121 121 + sourceRef: `at://${did}/tv.ionosphere.transcript/${talkRkey}-transcript`, 122 122 + metadata: { 123 123 + tool: 'ionosphere-pipeline', 124 124 + timestamp: now, 125 125 + }, 126 126 + createdAt: now, 127 127 + }; 128 128 + 129 129 + // Build segmentation records using timings replay algorithm 130 130 + // (matches decodeToDocument in transcript-encoding.ts) 131 131 + // Two records: textSpan-only (word boundaries) and temporalSpan-only (timing) 132 132 + const encoder = new TextEncoder(); 133 133 + const words = transcript.text.split(/\s+/).filter((w) => w.length > 0); 134 134 + const textTokens: TextToken[] = []; 135 135 + const temporalTokens: TemporalToken[] = []; 136 136 + 137 137 + let cursor = transcript.startMs; // ms 138 138 + let wordIndex = 0; 139 139 + let searchFrom = 0; 140 140 + 141 141 + for (const value of transcript.timings) { 142 142 + if (value < 0) { 143 143 + // Silence gap — advance cursor by absolute value 144 144 + cursor += Math.abs(value); 145 145 + } else { 146 146 + // Word duration 147 147 + if (wordIndex < words.length) { 148 148 + const word = words[wordIndex]; 149 149 + const idx = transcript.text.indexOf(word, searchFrom); 150 150 + if (idx !== -1) { 151 151 + const byteStart = encoder.encode(transcript.text.slice(0, idx)).length; 152 152 + const byteEnd = encoder.encode( 153 153 + transcript.text.slice(0, idx + word.length), 154 154 + ).length; 155 155 + 156 156 + textTokens.push({ 157 157 + tokenIndex: wordIndex, 158 158 + textSpan: { byteStart, byteEnd }, 159 159 + }); 160 160 + 161 161 + temporalTokens.push({ 162 162 + tokenIndex: wordIndex, 163 163 + temporalSpan: { start: cursor, ending: cursor + value }, 164 164 + }); 165 165 + 166 166 + searchFrom = idx + word.length; 167 167 + } 168 168 + cursor += value; 169 169 + wordIndex++; 170 170 + } 171 171 + } 172 172 + } 173 173 + 174 174 + const expressionUri = `at://${did}/pub.layers.expression.expression/${talkRkey}-expression`; 175 175 + 176 176 + // Chunk tokens to stay under PDS body limits 177 177 + const segmentations: SegmentationRecord[] = []; 178 178 + const temporals: TemporalSegmentationRecord[] = []; 179 179 + 180 180 + for (let i = 0; i < textTokens.length; i += MAX_TOKENS_PER_RECORD) { 181 181 + const textChunk = textTokens.slice(i, i + MAX_TOKENS_PER_RECORD); 182 182 + const temporalChunk = temporalTokens.slice(i, i + MAX_TOKENS_PER_RECORD); 183 183 + 184 184 + segmentations.push({ 185 185 + $type: 'pub.layers.segmentation.segmentation', 186 186 + expression: expressionUri, 187 187 + tokenizations: [{ kind: 'word', tokens: textChunk }], 188 188 + createdAt: now, 189 189 + }); 190 190 + 191 191 + temporals.push({ 192 192 + $type: 'pub.layers.segmentation.segmentation', 193 193 + expression: expressionUri, 194 194 + tokenizations: [{ kind: 'word-temporal', tokens: temporalChunk }], 195 195 + createdAt: now, 196 196 + }); 197 197 + } 198 198 + 199 199 + return { expression, segmentations, temporals }; 200 200 + } 201 201 + 202 202 + /** 203 203 + * Lens 2: Transform NLP annotations into 4 pub.layers annotation layer records. 204 204 + * 205 205 + * Produces: 206 206 + * - sentences layer (sentence-boundary spans) 207 207 + * - paragraphs layer (paragraph-boundary spans) 208 208 + * - entities layer (NER spans with featureMap) 209 209 + * - topics layer (topic-segment zero-width spans) 210 210 + */ 211 211 + 212 212 + export interface NlpAnnotations { 213 213 + talkRkey: string; 214 214 + sentences: Array<{ byteStart: number; byteEnd: number }>; 215 215 + paragraphs: Array<{ byteStart: number; byteEnd: number }>; 216 216 + entities: Array<{ 217 217 + byteStart: number; 218 218 + byteEnd: number; 219 219 + label: string; 220 220 + [key: string]: unknown; 221 221 + }>; 222 222 + topicBreaks: Array<{ byteStart: number }>; 223 223 + metadata: { tool: string; [key: string]: unknown }; 224 224 + } 225 225 + 226 226 + export interface Annotation { 227 227 + anchor: { textSpan: { byteStart: number; byteEnd: number } }; 228 228 + label: string; 229 229 + features?: { entries: Array<{ key: string; value: unknown }> }; 230 230 + } 231 231 + 232 232 + export interface AnnotationLayerRecord { 233 233 + $type: 'pub.layers.annotation.annotationLayer'; 234 234 + expression: string; 235 235 + kind: string; 236 236 + subkind: string; 237 237 + sourceMethod: string; 238 238 + metadata: { tool: string; timestamp: string }; 239 239 + annotations: Annotation[]; 240 240 + createdAt: string; 241 241 + } 242 242 + 243 243 + export interface AnnotationLayersResult { 244 244 + sentences: AnnotationLayerRecord; 245 245 + paragraphs: AnnotationLayerRecord; 246 246 + entities: AnnotationLayerRecord; 247 247 + topics: AnnotationLayerRecord; 248 248 + } 249 249 + 250 250 + export async function nlpToAnnotationLayers( 251 251 + nlpAnnotations: NlpAnnotations, 252 252 + did: string, 253 253 + talkRkey: string, 254 254 + expressionUri: string, 255 255 + ): Promise<AnnotationLayersResult> { 256 256 + const now = new Date().toISOString(); 257 257 + 258 258 + const baseMeta = { 259 259 + tool: 'ionosphere-nlp-pipeline', 260 260 + timestamp: now, 261 261 + }; 262 262 + 263 263 + // Keys to exclude when forwarding entity fields to featureMap entries 264 264 + const entityExcludeKeys = new Set(['byteStart', 'byteEnd', 'label']); 265 265 + 266 266 + // Sentences layer 267 267 + const sentences: AnnotationLayerRecord = { 268 268 + $type: 'pub.layers.annotation.annotationLayer', 269 269 + expression: expressionUri, 270 270 + kind: 'span', 271 271 + subkind: 'sentence-boundary', 272 272 + sourceMethod: 'automatic', 273 273 + metadata: { ...baseMeta }, 274 274 + annotations: nlpAnnotations.sentences.map((s) => ({ 275 275 + anchor: { textSpan: { byteStart: s.byteStart, byteEnd: s.byteEnd } }, 276 276 + label: 'sentence', 277 277 + })), 278 278 + createdAt: now, 279 279 + }; 280 280 + 281 281 + // Paragraphs layer 282 282 + const paragraphs: AnnotationLayerRecord = { 283 283 + $type: 'pub.layers.annotation.annotationLayer', 284 284 + expression: expressionUri, 285 285 + kind: 'span', 286 286 + subkind: 'paragraph-boundary', 287 287 + sourceMethod: 'automatic', 288 288 + metadata: { ...baseMeta }, 289 289 + annotations: nlpAnnotations.paragraphs.map((p) => ({ 290 290 + anchor: { textSpan: { byteStart: p.byteStart, byteEnd: p.byteEnd } }, 291 291 + label: 'paragraph', 292 292 + })), 293 293 + createdAt: now, 294 294 + }; 295 295 + 296 296 + // Entities layer — forward all extra keys into featureMap entries 297 297 + const entities: AnnotationLayerRecord = { 298 298 + $type: 'pub.layers.annotation.annotationLayer', 299 299 + expression: expressionUri, 300 300 + kind: 'span', 301 301 + subkind: 'ner', 302 302 + sourceMethod: 'automatic', 303 303 + metadata: { ...baseMeta }, 304 304 + annotations: nlpAnnotations.entities.map((e) => { 305 305 + const entries: Array<{ key: string; value: unknown }> = []; 306 306 + for (const [key, value] of Object.entries(e)) { 307 307 + if (!entityExcludeKeys.has(key)) { 308 308 + entries.push({ key, value }); 309 309 + } 310 310 + } 311 311 + return { 312 312 + anchor: { textSpan: { byteStart: e.byteStart, byteEnd: e.byteEnd } }, 313 313 + label: e.label, 314 314 + features: { entries }, 315 315 + }; 316 316 + }), 317 317 + createdAt: now, 318 318 + }; 319 319 + 320 320 + // Topics layer — zero-width spans (byteEnd === byteStart) 321 321 + const topics: AnnotationLayerRecord = { 322 322 + $type: 'pub.layers.annotation.annotationLayer', 323 323 + expression: expressionUri, 324 324 + kind: 'span', 325 325 + subkind: 'topic-segment', 326 326 + sourceMethod: 'automatic', 327 327 + metadata: { ...baseMeta }, 328 328 + annotations: nlpAnnotations.topicBreaks.map((t) => ({ 329 329 + anchor: { textSpan: { byteStart: t.byteStart, byteEnd: t.byteStart } }, 330 330 + label: 'topic-break', 331 331 + })), 332 332 + createdAt: now, 333 333 + }; 334 334 + 335 335 + return { sentences, paragraphs, entities, topics }; 336 336 + } 337 337 + 338 338 + /** 339 339 + * Lens 3 (reverse): Transform pub.layers records back into an ionosphere 340 340 + * RelationalText document with facets. 341 341 + * 342 342 + * This is the materialized view builder — used by the appview indexer to 343 343 + * rebuild the talk document when layers.pub records arrive via Jetstream. 344 344 + * 345 345 + * Timestamp facets are generated from the compact transcript (text + startMs 346 346 + * + timings), not from segmentation temporalSpans. This allows the 347 347 + * segmentation record to remain small (textSpan only) while keeping temporal 348 348 + * data available separately. 349 349 + * 350 350 + * Round-trip property: 351 351 + * transcriptToLayersPub + nlpToAnnotationLayers + layersPubToDocument 352 352 + * should produce the SAME document as decodeToDocumentWithStructure. 353 353 + */ 354 354 + export async function layersPubToDocument( 355 355 + expression: ExpressionRecord, 356 356 + segmentations: SegmentationRecord | SegmentationRecord[], 357 357 + annotationLayers: AnnotationLayersResult, 358 358 + compact?: CompactTranscript, 359 359 + ): Promise<Document> { 360 360 + const facets: DocumentFacet[] = []; 361 361 + 362 362 + // Merge all segmentation chunks into a single token list 363 363 + const segArray = Array.isArray(segmentations) ? segmentations : [segmentations]; 364 364 + const allTokens = segArray.flatMap((s) => s.tokenizations[0].tokens); 365 365 + 366 366 + // 1. Timestamp facets from compact transcript timings 367 367 + // Uses the same replay algorithm as decodeToDocument() — the segmentation 368 368 + // provides byte offsets, the compact transcript provides timing. 369 369 + if (compact) { 370 370 + let cursor = compact.startMs; 371 371 + let tokenIdx = 0; 372 372 + 373 373 + for (const value of compact.timings) { 374 374 + if (value < 0) { 375 375 + cursor += Math.abs(value); 376 376 + } else { 377 377 + if (tokenIdx < allTokens.length) { 378 378 + const token = allTokens[tokenIdx]; 379 379 + facets.push({ 380 380 + index: { 381 381 + byteStart: token.textSpan.byteStart, 382 382 + byteEnd: token.textSpan.byteEnd, 383 383 + }, 384 384 + features: [ 385 385 + { 386 386 + $type: 'tv.ionosphere.facet#timestamp', 387 387 + startTime: cursor * 1_000_000, // ms → ns 388 388 + endTime: (cursor + value) * 1_000_000, 389 389 + }, 390 390 + ], 391 391 + }); 392 392 + cursor += value; 393 393 + tokenIdx++; 394 394 + } 395 395 + } 396 396 + } 397 397 + } 398 398 + 399 399 + // 2. Sentence facets 400 400 + for (const ann of annotationLayers.sentences.annotations) { 401 401 + facets.push({ 402 402 + index: { 403 403 + byteStart: ann.anchor.textSpan.byteStart, 404 404 + byteEnd: ann.anchor.textSpan.byteEnd, 405 405 + }, 406 406 + features: [{ $type: 'tv.ionosphere.facet#sentence' }], 407 407 + }); 408 408 + } 409 409 + 410 410 + // 3. Paragraph facets 411 411 + for (const ann of annotationLayers.paragraphs.annotations) { 412 412 + facets.push({ 413 413 + index: { 414 414 + byteStart: ann.anchor.textSpan.byteStart, 415 415 + byteEnd: ann.anchor.textSpan.byteEnd, 416 416 + }, 417 417 + features: [{ $type: 'tv.ionosphere.facet#paragraph' }], 418 418 + }); 419 419 + } 420 420 + 421 421 + // 4. Entity facets — route based on features entries 422 422 + // conceptUri → #concept-ref, else → #entity 423 423 + // (speakerDid routing not needed — zero instances in actual data) 424 424 + for (const ann of annotationLayers.entities.annotations) { 425 425 + const entries = ann.features?.entries ?? []; 426 426 + const conceptUriEntry = entries.find((e) => e.key === 'conceptUri'); 427 427 + const nerTypeEntry = entries.find((e) => e.key === 'nerType'); 428 428 + 429 429 + if (conceptUriEntry) { 430 430 + facets.push({ 431 431 + index: { 432 432 + byteStart: ann.anchor.textSpan.byteStart, 433 433 + byteEnd: ann.anchor.textSpan.byteEnd, 434 434 + }, 435 435 + features: [ 436 436 + { 437 437 + $type: 'tv.ionosphere.facet#concept-ref', 438 438 + conceptUri: conceptUriEntry.value, 439 439 + conceptName: ann.label, 440 440 + }, 441 441 + ], 442 442 + }); 443 443 + } else { 444 444 + facets.push({ 445 445 + index: { 446 446 + byteStart: ann.anchor.textSpan.byteStart, 447 447 + byteEnd: ann.anchor.textSpan.byteEnd, 448 448 + }, 449 449 + features: [ 450 450 + { 451 451 + $type: 'tv.ionosphere.facet#entity', 452 452 + label: ann.label, 453 453 + nerType: nerTypeEntry?.value, 454 454 + }, 455 455 + ], 456 456 + }); 457 457 + } 458 458 + } 459 459 + 460 460 + // 5. Topic break facets 461 461 + for (const ann of annotationLayers.topics.annotations) { 462 462 + facets.push({ 463 463 + index: { 464 464 + byteStart: ann.anchor.textSpan.byteStart, 465 465 + byteEnd: ann.anchor.textSpan.byteEnd, 466 466 + }, 467 467 + features: [{ $type: 'tv.ionosphere.facet#topic-break' }], 468 468 + }); 469 469 + } 470 470 + 471 471 + return { text: expression.text, facets }; 472 472 + }

+82 -1

formats/tv.ionosphere/ts/transcript-encoding.test.ts

reviewed

··· 1 1 import { describe, it, expect } from "vitest"; 2 2 - import { encode, decode, decodeToDocument, type CompactTranscript } from "./transcript-encoding.js"; 2 2 + import { encode, decode, decodeToDocument, decodeToDocumentWithStructure, type CompactTranscript } from "./transcript-encoding.js"; 3 3 import type { TranscriptResult } from "./index.js"; 4 4 5 5 describe("transcript encoding", () => { ··· 117 117 118 118 console.log(`Compact: ${compactSize} bytes, Full: ${fullSize} bytes, Ratio: ${(fullSize / compactSize).toFixed(1)}x`); 119 119 expect(compactSize).toBeLessThan(fullSize); 120 120 + }); 121 121 + }); 122 122 + 123 123 + describe("decodeToDocumentWithStructure", () => { 124 124 + it("adds sentence and paragraph facets from NLP annotations", () => { 125 125 + const compact = encode(contiguous); 126 126 + const annotations = { 127 127 + sentences: [ 128 128 + { byteStart: 0, byteEnd: 11 }, // "hello world" 129 129 + { byteStart: 12, byteEnd: 26 }, // "this is a test" 130 130 + ], 131 131 + paragraphs: [ 132 132 + { byteStart: 0, byteEnd: 26 }, 133 133 + ], 134 134 + }; 135 135 + const doc = decodeToDocumentWithStructure(compact, annotations); 136 136 + 137 137 + const sentenceFacets = doc.facets.filter(f => 138 138 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#sentence") 139 139 + ); 140 140 + const paragraphFacets = doc.facets.filter(f => 141 141 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#paragraph") 142 142 + ); 143 143 + expect(sentenceFacets).toHaveLength(2); 144 144 + expect(paragraphFacets).toHaveLength(1); 145 145 + // Original timestamp facets still present 146 146 + const tsFacets = doc.facets.filter(f => 147 147 + f.features.some(feat => feat.$type === "tv.ionosphere.facet#timestamp") 148 148 + ); 149 149 + expect(tsFacets).toHaveLength(6); 150 150 + }); 151 151 + 152 152 + it("produces valid document without annotations (backward compatible)", () => { 153 153 + const compact = encode(contiguous); 154 154 + const doc = decodeToDocumentWithStructure(compact, null); 155 155 + // Same as decodeToDocument — just timestamp facets 156 156 + expect(doc.facets.length).toBe(6); 157 157 + }); 158 158 + 159 159 + it("adds entity, speaker-segment, and topic-break facets", () => { 160 160 + const compact = encode(contiguous); 161 161 + const annotations = { 162 162 + sentences: [{ byteStart: 0, byteEnd: 26 }], 163 163 + paragraphs: [{ byteStart: 0, byteEnd: 26 }], 164 164 + entities: [ 165 165 + { byteStart: 0, byteEnd: 5, label: "hello", nerType: "PERSON", speakerDid: "did:plc:abc123" }, 166 166 + { byteStart: 6, byteEnd: 11, label: "world", nerType: "ORG", conceptUri: "at://did:plc:xyz/tv.ionosphere.concept/test" }, 167 167 + { byteStart: 12, byteEnd: 16, label: "this", nerType: "PRODUCT" }, 168 168 + ], 169 169 + speakerSegments: [ 170 170 + { byteStart: 0, byteEnd: 26, speakerDid: "did:plc:abc123", speakerName: "Test Speaker" }, 171 171 + ], 172 172 + topicBreaks: [{ byteStart: 12 }], 173 173 + }; 174 174 + const doc = decodeToDocumentWithStructure(compact, annotations); 175 175 + 176 176 + const speakerRefs = doc.facets.filter(f => f.features.some(feat => feat.$type === "tv.ionosphere.facet#speaker-ref")); 177 177 + const conceptRefs = doc.facets.filter(f => f.features.some(feat => feat.$type === "tv.ionosphere.facet#concept-ref")); 178 178 + const entities = doc.facets.filter(f => f.features.some(feat => feat.$type === "tv.ionosphere.facet#entity")); 179 179 + const speakerSegs = doc.facets.filter(f => f.features.some(feat => feat.$type === "tv.ionosphere.facet#speaker-segment")); 180 180 + const topicBreaks = doc.facets.filter(f => f.features.some(feat => feat.$type === "tv.ionosphere.facet#topic-break")); 181 181 + 182 182 + expect(speakerRefs).toHaveLength(1); 183 183 + expect(speakerRefs[0].features[0].speakerDid).toBe("did:plc:abc123"); 184 184 + expect(conceptRefs).toHaveLength(1); 185 185 + expect(conceptRefs[0].features[0].conceptUri).toBe("at://did:plc:xyz/tv.ionosphere.concept/test"); 186 186 + expect(entities).toHaveLength(1); 187 187 + expect(entities[0].features[0].label).toBe("this"); 188 188 + expect(speakerSegs).toHaveLength(1); 189 189 + expect(topicBreaks).toHaveLength(1); 190 190 + }); 191 191 + 192 192 + it("handles missing optional annotation fields gracefully", () => { 193 193 + const compact = encode(contiguous); 194 194 + const annotations = { 195 195 + sentences: [{ byteStart: 0, byteEnd: 26 }], 196 196 + paragraphs: [{ byteStart: 0, byteEnd: 26 }], 197 197 + }; 198 198 + const doc = decodeToDocumentWithStructure(compact, annotations); 199 199 + const tsFacets = doc.facets.filter(f => f.features.some(feat => feat.$type === "tv.ionosphere.facet#timestamp")); 200 200 + expect(tsFacets).toHaveLength(6); 120 201 }); 121 202 }); 122 203 });

+80

formats/tv.ionosphere/ts/transcript-encoding.ts

reviewed

··· 145 145 146 146 return { text: compact.text, facets }; 147 147 } 148 148 + 149 149 + export interface NlpAnnotations { 150 150 + sentences: Array<{ byteStart: number; byteEnd: number }>; 151 151 + paragraphs: Array<{ byteStart: number; byteEnd: number }>; 152 152 + entities?: Array<{ 153 153 + byteStart: number; byteEnd: number; 154 154 + label: string; nerType: string; 155 155 + speakerDid?: string; conceptUri?: string; 156 156 + }>; 157 157 + speakerSegments?: Array<{ 158 158 + byteStart: number; byteEnd: number; 159 159 + speakerDid: string; speakerName: string; 160 160 + }>; 161 161 + topicBreaks?: Array<{ byteStart: number }>; 162 162 + } 163 163 + 164 164 + /** 165 165 + * Decode compact format into a RelationalText document with timestamp facets 166 166 + * plus sentence and paragraph structural facets from NLP pipeline output. 167 167 + */ 168 168 + export function decodeToDocumentWithStructure( 169 169 + compact: CompactTranscript, 170 170 + annotations: NlpAnnotations | null, 171 171 + ): Document { 172 172 + const doc = decodeToDocument(compact); 173 173 + 174 174 + if (!annotations) return doc; 175 175 + 176 176 + for (const s of annotations.sentences) { 177 177 + doc.facets.push({ 178 178 + index: { byteStart: s.byteStart, byteEnd: s.byteEnd }, 179 179 + features: [{ $type: "tv.ionosphere.facet#sentence" }], 180 180 + }); 181 181 + } 182 182 + 183 183 + for (const p of annotations.paragraphs) { 184 184 + doc.facets.push({ 185 185 + index: { byteStart: p.byteStart, byteEnd: p.byteEnd }, 186 186 + features: [{ $type: "tv.ionosphere.facet#paragraph" }], 187 187 + }); 188 188 + } 189 189 + 190 190 + // Entity facets — route to speaker-ref, concept-ref, or generic entity 191 191 + for (const e of annotations.entities ?? []) { 192 192 + if (e.speakerDid) { 193 193 + doc.facets.push({ 194 194 + index: { byteStart: e.byteStart, byteEnd: e.byteEnd }, 195 195 + features: [{ $type: "tv.ionosphere.facet#speaker-ref", speakerDid: e.speakerDid, label: e.label }], 196 196 + }); 197 197 + } else if (e.conceptUri) { 198 198 + doc.facets.push({ 199 199 + index: { byteStart: e.byteStart, byteEnd: e.byteEnd }, 200 200 + features: [{ $type: "tv.ionosphere.facet#concept-ref", conceptUri: e.conceptUri, conceptName: e.label }], 201 201 + }); 202 202 + } else { 203 203 + doc.facets.push({ 204 204 + index: { byteStart: e.byteStart, byteEnd: e.byteEnd }, 205 205 + features: [{ $type: "tv.ionosphere.facet#entity", label: e.label, nerType: e.nerType }], 206 206 + }); 207 207 + } 208 208 + } 209 209 + 210 210 + // Speaker segment facets 211 211 + for (const seg of annotations.speakerSegments ?? []) { 212 212 + doc.facets.push({ 213 213 + index: { byteStart: seg.byteStart, byteEnd: seg.byteEnd }, 214 214 + features: [{ $type: "tv.ionosphere.facet#speaker-segment", speakerDid: seg.speakerDid, speakerName: seg.speakerName }], 215 215 + }); 216 216 + } 217 217 + 218 218 + // Topic break facets 219 219 + for (const tb of annotations.topicBreaks ?? []) { 220 220 + doc.facets.push({ 221 221 + index: { byteStart: tb.byteStart, byteEnd: tb.byteStart }, 222 222 + features: [{ $type: "tv.ionosphere.facet#topic-break" }], 223 223 + }); 224 224 + } 225 225 + 226 226 + return doc; 227 227 + }

+114

lexicons/pub/layers/annotation/annotationLayer.json

reviewed

··· 1 1 + { 2 2 + "lexicon": 1, 3 3 + "$type": "com.atproto.lexicon.schema", 4 4 + "id": "pub.layers.annotation.annotationLayer", 5 5 + "revision": 1, 6 6 + "description": "An annotation layer attaching typed annotations to a pub.layers.expression. Covers token-tag, span, relation, tree, and other layer kinds. Each annotation has an anchor, a label, and optional confidence and features.", 7 7 + "defs": { 8 8 + "main": { 9 9 + "type": "record", 10 10 + "key": "any", 11 11 + "record": { 12 12 + "type": "object", 13 13 + "required": ["expression", "kind", "subkind", "sourceMethod", "annotations"], 14 14 + "properties": { 15 15 + "expression": { 16 16 + "type": "string", 17 17 + "format": "at-uri", 18 18 + "description": "AT URI of the pub.layers.expression.expression this layer annotates." 19 19 + }, 20 20 + "kindUri": { 21 21 + "type": "string", 22 22 + "format": "at-uri", 23 23 + "description": "AT URI of a record formally defining the annotation layer kind." 24 24 + }, 25 25 + "kind": { 26 26 + "type": "string", 27 27 + "description": "Layer kind: token-tag, span, relation, tree, etc." 28 28 + }, 29 29 + "subkindUri": { 30 30 + "type": "string", 31 31 + "format": "at-uri", 32 32 + "description": "AT URI of a record formally defining the annotation subkind (e.g. a specific tag set)." 33 33 + }, 34 34 + "subkind": { 35 35 + "type": "string", 36 36 + "description": "More specific categorisation within the layer kind (e.g. POS, NER, dependency)." 37 37 + }, 38 38 + "sourceMethodUri": { 39 39 + "type": "string", 40 40 + "format": "at-uri", 41 41 + "description": "AT URI of a record formally defining the source method." 42 42 + }, 43 43 + "sourceMethod": { 44 44 + "type": "string", 45 45 + "description": "How annotations were produced: manual-native, automatic, converted, etc." 46 46 + }, 47 47 + "annotations": { 48 48 + "type": "array", 49 49 + "items": { 50 50 + "type": "ref", 51 51 + "ref": "pub.layers.annotation.annotationLayer#annotation" 52 52 + }, 53 53 + "description": "The individual annotations that make up this layer." 54 54 + }, 55 55 + "tokenizationId": { 56 56 + "type": "ref", 57 57 + "ref": "pub.layers.defs#uuid", 58 58 + "description": "UUID of the tokenization this layer is aligned to (for token-tag layers)." 59 59 + }, 60 60 + "rank": { 61 61 + "type": "integer", 62 62 + "description": "Ordering rank when multiple layers of the same kind exist on the same expression." 63 63 + }, 64 64 + "metadata": { 65 65 + "type": "ref", 66 66 + "ref": "pub.layers.defs#annotationMetadata", 67 67 + "description": "Provenance metadata for this layer." 68 68 + }, 69 69 + "createdAt": { 70 70 + "type": "string", 71 71 + "format": "datetime", 72 72 + "description": "ISO 8601 datetime when this record was created." 73 73 + } 74 74 + } 75 75 + } 76 76 + }, 77 77 + "annotation": { 78 78 + "type": "object", 79 79 + "description": "A single annotation within an annotation layer.", 80 80 + "required": ["label"], 81 81 + "properties": { 82 82 + "uuid": { 83 83 + "type": "ref", 84 84 + "ref": "pub.layers.defs#uuid", 85 85 + "description": "Stable UUID for this annotation." 86 86 + }, 87 87 + "anchor": { 88 88 + "type": "ref", 89 89 + "ref": "pub.layers.defs#anchor", 90 90 + "description": "Position this annotation targets (text span, token, or temporal span)." 91 91 + }, 92 92 + "label": { 93 93 + "type": "string", 94 94 + "description": "The annotation label (e.g. a POS tag, NER type, sentiment class)." 95 95 + }, 96 96 + "text": { 97 97 + "type": "string", 98 98 + "description": "The surface text covered by this annotation (optional, for readability)." 99 99 + }, 100 100 + "confidence": { 101 101 + "type": "integer", 102 102 + "description": "Confidence score in the range 0–1000 (0 = none, 1000 = certain).", 103 103 + "minimum": 0, 104 104 + "maximum": 1000 105 105 + }, 106 106 + "features": { 107 107 + "type": "ref", 108 108 + "ref": "pub.layers.defs#featureMap", 109 109 + "description": "Additional linguistic features for this annotation." 110 110 + } 111 111 + } 112 112 + } 113 113 + } 114 114 + }

+151

lexicons/pub/layers/defs.json

reviewed

··· 1 1 + { 2 2 + "lexicon": 1, 3 3 + "$type": "com.atproto.lexicon.schema", 4 4 + "id": "pub.layers.defs", 5 5 + "revision": 1, 6 6 + "description": "Shared type definitions for the layers.pub v0.5.0 lexicon family. Covers spans, temporal spans, anchors, token references, annotation metadata, and feature maps.", 7 7 + "defs": { 8 8 + "span": { 9 9 + "type": "object", 10 10 + "description": "A byte-offset span into a text string, with optional character offsets.", 11 11 + "required": ["byteStart", "byteEnd"], 12 12 + "properties": { 13 13 + "byteStart": { 14 14 + "type": "integer", 15 15 + "description": "Inclusive start byte offset." 16 16 + }, 17 17 + "byteEnd": { 18 18 + "type": "integer", 19 19 + "description": "Exclusive end byte offset." 20 20 + }, 21 21 + "charStart": { 22 22 + "type": "integer", 23 23 + "description": "Inclusive start Unicode character offset (optional)." 24 24 + }, 25 25 + "charEnd": { 26 26 + "type": "integer", 27 27 + "description": "Exclusive end Unicode character offset (optional)." 28 28 + } 29 29 + } 30 30 + }, 31 31 + "temporalSpan": { 32 32 + "type": "object", 33 33 + "description": "A time range in milliseconds, e.g. within a media stream.", 34 34 + "required": ["start", "ending"], 35 35 + "properties": { 36 36 + "start": { 37 37 + "type": "integer", 38 38 + "description": "Start time in milliseconds." 39 39 + }, 40 40 + "ending": { 41 41 + "type": "integer", 42 42 + "description": "End time in milliseconds." 43 43 + } 44 44 + } 45 45 + }, 46 46 + "uuid": { 47 47 + "type": "object", 48 48 + "description": "A UUID value wrapper.", 49 49 + "required": ["value"], 50 50 + "properties": { 51 51 + "value": { 52 52 + "type": "string", 53 53 + "description": "UUID string (e.g. RFC 4122)." 54 54 + } 55 55 + } 56 56 + }, 57 57 + "tokenRef": { 58 58 + "type": "object", 59 59 + "description": "A reference to a specific token within a named tokenization.", 60 60 + "required": ["tokenizationId", "tokenIndex"], 61 61 + "properties": { 62 62 + "tokenizationId": { 63 63 + "type": "ref", 64 64 + "ref": "pub.layers.defs#uuid", 65 65 + "description": "UUID identifying the tokenization this token belongs to." 66 66 + }, 67 67 + "tokenIndex": { 68 68 + "type": "integer", 69 69 + "description": "Zero-based index of the token within the tokenization." 70 70 + } 71 71 + } 72 72 + }, 73 73 + "anchor": { 74 74 + "type": "object", 75 75 + "description": "Polymorphic anchor pointing to a text span, a token, or a temporal position. At least one field should be set.", 76 76 + "properties": { 77 77 + "textSpan": { 78 78 + "type": "ref", 79 79 + "ref": "pub.layers.defs#span", 80 80 + "description": "Byte-offset span into the expression text." 81 81 + }, 82 82 + "tokenRef": { 83 83 + "type": "ref", 84 84 + "ref": "pub.layers.defs#tokenRef", 85 85 + "description": "Reference to a specific token in a tokenization." 86 86 + }, 87 87 + "temporalSpan": { 88 88 + "type": "ref", 89 89 + "ref": "pub.layers.defs#temporalSpan", 90 90 + "description": "Time range within a media stream." 91 91 + } 92 92 + } 93 93 + }, 94 94 + "annotationMetadata": { 95 95 + "type": "object", 96 96 + "description": "Provenance metadata for an annotation or layer.", 97 97 + "properties": { 98 98 + "agent": { 99 99 + "type": "string", 100 100 + "format": "at-uri", 101 101 + "description": "AT URI of the agent (DID or account) that produced this annotation." 102 102 + }, 103 103 + "tool": { 104 104 + "type": "string", 105 105 + "description": "Name or identifier of the tool or model used." 106 106 + }, 107 107 + "timestamp": { 108 108 + "type": "string", 109 109 + "format": "datetime", 110 110 + "description": "ISO 8601 datetime when the annotation was produced." 111 111 + }, 112 112 + "confidence": { 113 113 + "type": "integer", 114 114 + "description": "Confidence score in the range 0–1000 (0 = none, 1000 = certain).", 115 115 + "minimum": 0, 116 116 + "maximum": 1000 117 117 + } 118 118 + } 119 119 + }, 120 120 + "feature": { 121 121 + "type": "object", 122 122 + "description": "A key-value linguistic feature entry.", 123 123 + "required": ["key", "value"], 124 124 + "properties": { 125 125 + "key": { 126 126 + "type": "string", 127 127 + "description": "Feature name (e.g. POS, Gender, Number)." 128 128 + }, 129 129 + "value": { 130 130 + "type": "string", 131 131 + "description": "Feature value (e.g. NOUN, Masc, Sing)." 132 132 + } 133 133 + } 134 134 + }, 135 135 + "featureMap": { 136 136 + "type": "object", 137 137 + "description": "A set of linguistic feature key-value pairs.", 138 138 + "required": ["entries"], 139 139 + "properties": { 140 140 + "entries": { 141 141 + "type": "array", 142 142 + "items": { 143 143 + "type": "ref", 144 144 + "ref": "pub.layers.defs#feature" 145 145 + }, 146 146 + "description": "List of feature entries." 147 147 + } 148 148 + } 149 149 + } 150 150 + } 151 151 + }

+75

lexicons/pub/layers/expression/expression.json

reviewed

··· 1 1 + { 2 2 + "lexicon": 1, 3 3 + "$type": "com.atproto.lexicon.schema", 4 4 + "id": "pub.layers.expression.expression", 5 5 + "revision": 1, 6 6 + "description": "A linguistic expression record — a unit of text at any granularity (document, transcript, paragraph, sentence, word, morpheme). Expressions may be nested via parentRef and anchored into media via mediaRef and anchor.", 7 7 + "defs": { 8 8 + "main": { 9 9 + "type": "record", 10 10 + "key": "any", 11 11 + "record": { 12 12 + "type": "object", 13 13 + "required": ["id", "kind", "text", "language", "createdAt"], 14 14 + "properties": { 15 15 + "id": { 16 16 + "type": "string", 17 17 + "description": "Stable identifier for this expression (e.g. UUID)." 18 18 + }, 19 19 + "kindUri": { 20 20 + "type": "string", 21 21 + "format": "at-uri", 22 22 + "description": "AT URI of a record that formally defines the expression kind." 23 23 + }, 24 24 + "kind": { 25 25 + "type": "string", 26 26 + "description": "Expression granularity: document, transcript, paragraph, sentence, word, morpheme, etc." 27 27 + }, 28 28 + "text": { 29 29 + "type": "string", 30 30 + "description": "The full text content of this expression." 31 31 + }, 32 32 + "language": { 33 33 + "type": "string", 34 34 + "description": "BCP 47 language tag (e.g. en, fr, zh-Hant)." 35 35 + }, 36 36 + "mediaRef": { 37 37 + "type": "string", 38 38 + "format": "at-uri", 39 39 + "description": "AT URI of the media record this expression derives from (e.g. a video or audio record)." 40 40 + }, 41 41 + "sourceRef": { 42 42 + "type": "string", 43 43 + "format": "at-uri", 44 44 + "description": "AT URI of the source record from which this expression was produced (e.g. a transcript record)." 45 45 + }, 46 46 + "parentRef": { 47 47 + "type": "string", 48 48 + "format": "at-uri", 49 49 + "description": "AT URI of the parent expression, for nested structure (e.g. sentence within paragraph)." 50 50 + }, 51 51 + "anchor": { 52 52 + "type": "ref", 53 53 + "ref": "pub.layers.defs#anchor", 54 54 + "description": "Position of this expression within its parent text or media." 55 55 + }, 56 56 + "metadata": { 57 57 + "type": "ref", 58 58 + "ref": "pub.layers.defs#annotationMetadata", 59 59 + "description": "Provenance metadata for this expression." 60 60 + }, 61 61 + "features": { 62 62 + "type": "ref", 63 63 + "ref": "pub.layers.defs#featureMap", 64 64 + "description": "Linguistic features associated with this expression." 65 65 + }, 66 66 + "createdAt": { 67 67 + "type": "string", 68 68 + "format": "datetime", 69 69 + "description": "ISO 8601 datetime when this record was created." 70 70 + } 71 71 + } 72 72 + } 73 73 + } 74 74 + } 75 75 + }

+101

lexicons/pub/layers/segmentation/segmentation.json

reviewed

··· 1 1 + { 2 2 + "lexicon": 1, 3 3 + "$type": "com.atproto.lexicon.schema", 4 4 + "id": "pub.layers.segmentation.segmentation", 5 5 + "revision": 1, 6 6 + "description": "A segmentation record attaching one or more tokenizations to a pub.layers.expression. Each tokenization defines a scheme (whitespace, BPE, etc.) and a sequence of indexed tokens with text spans and optional temporal spans.", 7 7 + "defs": { 8 8 + "main": { 9 9 + "type": "record", 10 10 + "key": "any", 11 11 + "record": { 12 12 + "type": "object", 13 13 + "required": ["expression", "tokenizations", "createdAt"], 14 14 + "properties": { 15 15 + "expression": { 16 16 + "type": "string", 17 17 + "format": "at-uri", 18 18 + "description": "AT URI of the pub.layers.expression.expression this segmentation applies to." 19 19 + }, 20 20 + "tokenizations": { 21 21 + "type": "array", 22 22 + "items": { 23 23 + "type": "ref", 24 24 + "ref": "pub.layers.segmentation.segmentation#tokenization" 25 25 + }, 26 26 + "description": "One or more tokenization schemes over the expression text." 27 27 + }, 28 28 + "metadata": { 29 29 + "type": "ref", 30 30 + "ref": "pub.layers.defs#annotationMetadata", 31 31 + "description": "Provenance metadata for this segmentation." 32 32 + }, 33 33 + "features": { 34 34 + "type": "ref", 35 35 + "ref": "pub.layers.defs#featureMap", 36 36 + "description": "Linguistic features associated with this segmentation." 37 37 + }, 38 38 + "createdAt": { 39 39 + "type": "string", 40 40 + "format": "datetime", 41 41 + "description": "ISO 8601 datetime when this record was created." 42 42 + } 43 43 + } 44 44 + } 45 45 + }, 46 46 + "tokenization": { 47 47 + "type": "object", 48 48 + "description": "A single tokenization scheme with its token sequence.", 49 49 + "required": ["tokens"], 50 50 + "properties": { 51 51 + "uuid": { 52 52 + "type": "ref", 53 53 + "ref": "pub.layers.defs#uuid", 54 54 + "description": "Stable UUID identifying this tokenization, for use in tokenRef anchors." 55 55 + }, 56 56 + "kindUri": { 57 57 + "type": "string", 58 58 + "format": "at-uri", 59 59 + "description": "AT URI of a record formally defining the tokenization kind." 60 60 + }, 61 61 + "kind": { 62 62 + "type": "string", 63 63 + "description": "Tokenization scheme: whitespace, bpe, sentencepiece, character, etc." 64 64 + }, 65 65 + "tokens": { 66 66 + "type": "array", 67 67 + "items": { 68 68 + "type": "ref", 69 69 + "ref": "pub.layers.segmentation.segmentation#token" 70 70 + }, 71 71 + "description": "Ordered sequence of tokens produced by this tokenization." 72 72 + } 73 73 + } 74 74 + }, 75 75 + "token": { 76 76 + "type": "object", 77 77 + "description": "A single token within a tokenization.", 78 78 + "required": ["tokenIndex", "text", "textSpan"], 79 79 + "properties": { 80 80 + "tokenIndex": { 81 81 + "type": "integer", 82 82 + "description": "Zero-based index of this token in the tokenization sequence." 83 83 + }, 84 84 + "text": { 85 85 + "type": "string", 86 86 + "description": "Surface text of this token." 87 87 + }, 88 88 + "textSpan": { 89 89 + "type": "ref", 90 90 + "ref": "pub.layers.defs#span", 91 91 + "description": "Byte-offset span of this token in the parent expression text." 92 92 + }, 93 93 + "temporalSpan": { 94 94 + "type": "ref", 95 95 + "ref": "pub.layers.defs#temporalSpan", 96 96 + "description": "Time range of this token within the associated media (optional)." 97 97 + } 98 98 + } 99 99 + } 100 100 + } 101 101 + }

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panproto.toml

reviewed

··· 1 1 + [workspace] 2 2 + name = "project" 3 3 + exclude = [ 4 4 + "target", 5 5 + "node_modules", 6 6 + "__pycache__", 7 7 + "build", 8 8 + "dist", 9 9 + ".git", 10 10 + ] 11 11 + 12 12 + [[package]] 13 13 + name = "ionosphere-workspace" 14 14 + path = "" 15 15 + protocol = "typescript" 16 16 + 17 17 + [[package]] 18 18 + name = "ionosphere" 19 19 + path = "apps/ionosphere" 20 20 + protocol = "typescript" 21 21 + 22 22 + [[package]] 23 23 + name = "ionosphere-appview" 24 24 + path = "apps/ionosphere-appview" 25 25 + protocol = "typescript" 26 26 + 27 27 + [[package]] 28 28 + name = "ionosphere-nlp" 29 29 + path = "pipeline" 30 30 + protocol = "python"

pipeline/nlp/__init__.py

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+133

pipeline/nlp/apply_clusters.py

reviewed

··· 1 1 + """Apply concept clusters to the database. 2 2 + 3 3 + For each cluster with multiple members: 4 4 + 1. Keep the canonical concept record 5 5 + 2. Update its aliases to include all member names 6 6 + 3. Rewire talk_concepts references from merged concepts to the canonical 7 7 + 4. Delete the merged (non-canonical) concept records 8 8 + """ 9 9 + 10 10 + import json 11 11 + import sqlite3 12 12 + from pathlib import Path 13 13 + 14 14 + 15 15 + def apply_clusters(db_path: str, clusters_path: str, dry_run: bool = False): 16 16 + clusters = json.loads(Path(clusters_path).read_text()) 17 17 + 18 18 + multi = [c for c in clusters if c["memberCount"] > 1] 19 19 + print(f"Applying {len(multi)} multi-member clusters to database") 20 20 + 21 21 + conn = sqlite3.connect(db_path) 22 22 + 23 23 + # Build a map of concept name → URI for all concepts 24 24 + name_to_uri = {} 25 25 + for row in conn.execute("SELECT name, uri FROM concepts"): 26 26 + name_to_uri[row[0]] = row[1] 27 27 + 28 28 + merged_count = 0 29 29 + deleted_count = 0 30 30 + rewired_count = 0 31 31 + 32 32 + for cluster in multi: 33 33 + canonical_name = cluster["canonical"] 34 34 + canonical_uri = name_to_uri.get(canonical_name) 35 35 + if not canonical_uri: 36 36 + # Try to find by rkey 37 37 + row = conn.execute("SELECT uri FROM concepts WHERE rkey = ?", (cluster["rkey"],)).fetchone() 38 38 + if row: 39 39 + canonical_uri = row[0] 40 40 + else: 41 41 + print(f" Warning: canonical '{canonical_name}' not found in DB, skipping") 42 42 + continue 43 43 + 44 44 + # Collect all merged aliases 45 45 + all_aliases = set(cluster["aliases"]) 46 46 + 47 47 + # Find URIs of non-canonical members to merge 48 48 + member_uris = [] 49 49 + for member_name in cluster["members"]: 50 50 + if member_name == canonical_name: 51 51 + continue 52 52 + uri = name_to_uri.get(member_name) 53 53 + if uri and uri != canonical_uri: 54 54 + member_uris.append(uri) 55 55 + 56 56 + if not member_uris: 57 57 + continue 58 58 + 59 59 + if not dry_run: 60 60 + # Update canonical concept's aliases 61 61 + alias_json = json.dumps(sorted(all_aliases)) 62 62 + conn.execute( 63 63 + "UPDATE concepts SET aliases = ? WHERE uri = ?", 64 64 + (alias_json, canonical_uri), 65 65 + ) 66 66 + 67 67 + # Rewire talk_concepts from merged → canonical 68 68 + for old_uri in member_uris: 69 69 + # Check if canonical already has a link to this talk 70 70 + rows = conn.execute( 71 71 + "SELECT talk_uri FROM talk_concepts WHERE concept_uri = ?", 72 72 + (old_uri,), 73 73 + ).fetchall() 74 74 + for (talk_uri,) in rows: 75 75 + existing = conn.execute( 76 76 + "SELECT 1 FROM talk_concepts WHERE talk_uri = ? AND concept_uri = ?", 77 77 + (talk_uri, canonical_uri), 78 78 + ).fetchone() 79 79 + if existing: 80 80 + # Already linked — just delete the old one 81 81 + conn.execute( 82 82 + "DELETE FROM talk_concepts WHERE talk_uri = ? AND concept_uri = ?", 83 83 + (talk_uri, old_uri), 84 84 + ) 85 85 + else: 86 86 + # Rewire 87 87 + conn.execute( 88 88 + "UPDATE talk_concepts SET concept_uri = ? WHERE talk_uri = ? AND concept_uri = ?", 89 89 + (canonical_uri, talk_uri, old_uri), 90 90 + ) 91 91 + rewired_count += 1 92 92 + 93 93 + # Delete the merged concept 94 94 + conn.execute("DELETE FROM concepts WHERE uri = ?", (old_uri,)) 95 95 + deleted_count += 1 96 96 + 97 97 + merged_count += 1 98 98 + 99 99 + else: 100 100 + print(f" Would merge {len(member_uris)} concepts into '{canonical_name}'") 101 101 + 102 102 + if not dry_run: 103 103 + conn.commit() 104 104 + 105 105 + conn.close() 106 106 + 107 107 + print(f"\nResults:") 108 108 + print(f" Clusters applied: {merged_count}") 109 109 + print(f" Concepts deleted: {deleted_count}") 110 110 + print(f" talk_concepts rewired: {rewired_count}") 111 111 + print(f" Remaining concepts: check with SELECT COUNT(*) FROM concepts") 112 112 + 113 113 + 114 114 + def main(): 115 115 + db_path = str(Path(__file__).resolve().parent.parent.parent / "apps" / "data" / "ionosphere.sqlite") 116 116 + clusters_path = str(Path(__file__).resolve().parent.parent / "data" / "concept-clusters.json") 117 117 + 118 118 + # Show what we'd do first 119 119 + print("=== DRY RUN ===") 120 120 + apply_clusters(db_path, clusters_path, dry_run=True) 121 121 + 122 122 + print("\n=== APPLYING ===") 123 123 + apply_clusters(db_path, clusters_path, dry_run=False) 124 124 + 125 125 + # Verify 126 126 + conn = sqlite3.connect(db_path) 127 127 + count = conn.execute("SELECT COUNT(*) FROM concepts").fetchone()[0] 128 128 + print(f"\nFinal concept count: {count}") 129 129 + conn.close() 130 130 + 131 131 + 132 132 + if __name__ == "__main__": 133 133 + main()

+305

pipeline/nlp/cluster_concepts.py

reviewed

··· 1 1 + """Cluster and deduplicate concepts using name similarity + LLM merge. 2 2 + 3 3 + Three-pass approach: 4 4 + 1. Normalize names: strip parentheticals, punctuation, case 5 5 + 2. Stem + merge: Porter stemming catches singular/plural (Lexicon/Lexicons) 6 6 + 3. LLM merge: Claude identifies remaining semantic duplicates (PDS/Personal Data Server) 7 7 + """ 8 8 + 9 9 + import json 10 10 + import os 11 11 + import re 12 12 + import sqlite3 13 13 + from collections import defaultdict 14 14 + from pathlib import Path 15 15 + 16 16 + import nltk 17 17 + nltk.download("punkt_tab", quiet=True) 18 18 + from nltk.stem import PorterStemmer 19 19 + 20 20 + _stemmer = PorterStemmer() 21 21 + 22 22 + 23 23 + def load_concepts(db_path: str) -> list[dict]: 24 24 + """Load all concepts from the database.""" 25 25 + conn = sqlite3.connect(db_path) 26 26 + rows = conn.execute("SELECT uri, rkey, name, aliases, description FROM concepts").fetchall() 27 27 + conn.close() 28 28 + return [ 29 29 + {"uri": r[0], "rkey": r[1], "name": r[2], "aliases": r[3] or "[]", "description": r[4] or ""} 30 30 + for r in rows 31 31 + ] 32 32 + 33 33 + 34 34 + def normalize_name(name: str) -> str: 35 35 + """Strip parenthetical qualifiers, punctuation, normalize case/whitespace.""" 36 36 + name = re.sub(r"\s*\([^)]*\)\s*", " ", name) 37 37 + parts = name.split("/") 38 38 + name = parts[0] 39 39 + name = name.lower().strip() 40 40 + name = re.sub(r"\s+", " ", name) 41 41 + name = name.strip(" .,;:-") 42 42 + return name 43 43 + 44 44 + 45 45 + def stem_form(name: str) -> str: 46 46 + """Normalize + stem each word + remove spaces/hyphens. 47 47 + 48 48 + 'Lexicons' → 'lexicon' 49 49 + 'Blue Sky' → 'bluesky' (after space removal) 50 50 + 'Custom feeds' → 'customfeed' 51 51 + """ 52 52 + n = normalize_name(name) 53 53 + words = n.split() 54 54 + stemmed = [_stemmer.stem(w) for w in words] 55 55 + return "".join(stemmed) 56 56 + 57 57 + 58 58 + def cluster_by_name(concepts: list[dict]) -> list[list[int]]: 59 59 + """Cluster concepts by stemmed name form.""" 60 60 + stem_groups: dict[str, list[int]] = defaultdict(list) 61 61 + for i, c in enumerate(concepts): 62 62 + sf = stem_form(c["name"]) 63 63 + if sf: 64 64 + stem_groups[sf].append(i) 65 65 + 66 66 + # Also try without spaces/hyphens on the normalized form (catches Blue Sky / BlueSky) 67 67 + base_groups: dict[str, set[str]] = defaultdict(set) 68 68 + for sf in stem_groups: 69 69 + base = re.sub(r"[\s\-_]", "", sf) 70 70 + base_groups[base].add(sf) 71 71 + 72 72 + # Merge stem groups that share a base form 73 73 + visited: set[str] = set() 74 74 + clusters: list[list[int]] = [] 75 75 + for base, stems in base_groups.items(): 76 76 + if not base: 77 77 + continue 78 78 + indices = [] 79 79 + for sf in stems: 80 80 + if sf not in visited: 81 81 + visited.add(sf) 82 82 + indices.extend(stem_groups[sf]) 83 83 + if indices: 84 84 + clusters.append(indices) 85 85 + 86 86 + # Add unclustered 87 87 + all_clustered = set() 88 88 + for cluster in clusters: 89 89 + all_clustered.update(cluster) 90 90 + for i in range(len(concepts)): 91 91 + if i not in all_clustered: 92 92 + clusters.append([i]) 93 93 + 94 94 + return clusters 95 95 + 96 96 + 97 97 + def llm_merge_pass( 98 98 + cluster_groups: list[dict], 99 99 + batch_size: int = 100, 100 100 + ) -> list[list[int]]: 101 101 + """Use Claude to identify remaining semantic duplicates across clusters. 102 102 + 103 103 + Takes the cluster canonicals and asks the LLM which ones should be merged. 104 104 + Returns a list of merge groups (indices into cluster_groups). 105 105 + """ 106 106 + api_key = os.environ.get("ANTHROPIC_API_KEY") 107 107 + if not api_key: 108 108 + print(" No ANTHROPIC_API_KEY — skipping LLM merge pass") 109 109 + return [] 110 110 + 111 111 + import anthropic 112 112 + client = anthropic.Anthropic(api_key=api_key, timeout=30.0) 113 113 + 114 114 + # Only send cluster canonicals (not all 2000+ members) 115 115 + names = [g["canonical"] for g in cluster_groups] 116 116 + 117 117 + all_merges: list[list[int]] = [] 118 118 + 119 119 + # Process in batches 120 120 + for batch_start in range(0, len(names), batch_size): 121 121 + batch = names[batch_start : batch_start + batch_size] 122 122 + batch_indices = list(range(batch_start, min(batch_start + batch_size, len(names)))) 123 123 + 124 124 + prompt = f"""Here is a list of concept names from a conference about the AT Protocol / Bluesky ecosystem. Many are duplicates or near-duplicates that should be merged. 125 125 + 126 126 + For each group of duplicates, return the indices (0-based within this batch) that should be merged. 127 127 + 128 128 + Only merge concepts that are genuinely THE SAME THING with different names. Do NOT merge concepts that are merely related (e.g., "Bluesky" and "AT Protocol" are different things). 129 129 + 130 130 + Examples of valid merges: 131 131 + - "PDS" and "Personal Data Server" (same thing, abbreviation) 132 132 + - "DID" and "Decentralized Identifier" (same thing) 133 133 + - "i18n" and "internationalization" (same thing) 134 134 + 135 135 + Examples of INVALID merges: 136 136 + - "Bluesky" and "AT Protocol" (different things) 137 137 + - "Ozone" and "Labelers" (related but different) 138 138 + 139 139 + Concepts: 140 140 + {json.dumps([{"idx": i, "name": n} for i, n in enumerate(batch)], indent=1)} 141 141 + 142 142 + Return a JSON array of merge groups. Each group is an array of indices that should be merged. 143 143 + Return ONLY the JSON array, no other text.""" 144 144 + 145 145 + try: 146 146 + response = client.messages.create( 147 147 + model="claude-haiku-4-5-20251001", 148 148 + max_tokens=2000, 149 149 + messages=[{"role": "user", "content": prompt}], 150 150 + ) 151 151 + 152 152 + text = response.content[0].text.strip() 153 153 + if text.startswith("```"): 154 154 + text = re.sub(r"^```(?:json)?\n?", "", text) 155 155 + text = re.sub(r"\n?```$", "", text) 156 156 + 157 157 + merges = json.loads(text) 158 158 + if isinstance(merges, list): 159 159 + for group in merges: 160 160 + if isinstance(group, list) and len(group) > 1: 161 161 + # Convert batch-local indices to global indices 162 162 + global_indices = [batch_indices[i] for i in group if i < len(batch)] 163 163 + all_merges.append(global_indices) 164 164 + 165 165 + except Exception as e: 166 166 + print(f" LLM merge batch {batch_start} failed: {e}") 167 167 + continue 168 168 + 169 169 + return all_merges 170 170 + 171 171 + 172 172 + def apply_merges( 173 173 + cluster_groups: list[dict], 174 174 + merges: list[list[int]], 175 175 + concepts: list[dict], 176 176 + ) -> list[dict]: 177 177 + """Apply LLM-suggested merges to the cluster groups.""" 178 178 + if not merges: 179 179 + return cluster_groups 180 180 + 181 181 + # Build a union-find for merge groups 182 182 + parent = list(range(len(cluster_groups))) 183 183 + 184 184 + def find(x): 185 185 + while parent[x] != x: 186 186 + parent[x] = parent[parent[x]] 187 187 + x = parent[x] 188 188 + return x 189 189 + 190 190 + def union(a, b): 191 191 + ra, rb = find(a), find(b) 192 192 + if ra != rb: 193 193 + parent[rb] = ra 194 194 + 195 195 + for group in merges: 196 196 + for i in range(1, len(group)): 197 197 + union(group[0], group[i]) 198 198 + 199 199 + # Rebuild clusters 200 200 + merged: dict[int, list[int]] = defaultdict(list) 201 201 + for i in range(len(cluster_groups)): 202 202 + root = find(i) 203 203 + merged[root].append(i) 204 204 + 205 205 + results = [] 206 206 + for root, group_indices in merged.items(): 207 207 + if len(group_indices) == 1: 208 208 + results.append(cluster_groups[group_indices[0]]) 209 209 + else: 210 210 + # Merge all member concepts 211 211 + all_member_indices = [] 212 212 + for gi in group_indices: 213 213 + # Find original concept indices for this cluster group 214 214 + g = cluster_groups[gi] 215 215 + for m_name in g["members"]: 216 216 + for ci, c in enumerate(concepts): 217 217 + if c["name"] == m_name: 218 218 + all_member_indices.append(ci) 219 219 + break 220 220 + results.append(pick_canonical(concepts, all_member_indices)) 221 221 + 222 222 + return results 223 223 + 224 224 + 225 225 + def pick_canonical(concepts: list[dict], indices: list[int]) -> dict: 226 226 + """Pick the canonical concept from a cluster.""" 227 227 + candidates = [(concepts[i], i) for i in indices] 228 228 + candidates.sort(key=lambda x: (not bool(x[0]["description"]), len(x[0]["name"]))) 229 229 + canonical = candidates[0][0] 230 230 + 231 231 + all_names = set() 232 232 + for i in indices: 233 233 + all_names.add(concepts[i]["name"]) 234 234 + try: 235 235 + aliases = json.loads(concepts[i]["aliases"]) 236 236 + if isinstance(aliases, list): 237 237 + all_names.update(a for a in aliases if a) 238 238 + except (json.JSONDecodeError, TypeError): 239 239 + pass 240 240 + all_names.discard(canonical["name"]) 241 241 + 242 242 + return { 243 243 + "canonical": canonical["name"], 244 244 + "uri": canonical["uri"], 245 245 + "rkey": canonical["rkey"], 246 246 + "description": canonical["description"], 247 247 + "aliases": sorted(all_names), 248 248 + "memberCount": len(indices), 249 249 + "members": [concepts[i]["name"] for i in indices], 250 250 + } 251 251 + 252 252 + 253 253 + def cluster_concepts(db_path: str) -> list[dict]: 254 254 + """Full pipeline: load, stem-cluster, LLM-merge, pick canonicals.""" 255 255 + concepts = load_concepts(db_path) 256 256 + print(f"Loaded {len(concepts)} concepts") 257 257 + 258 258 + # Pass 1+2: Name normalization + stemming 259 259 + clusters = cluster_by_name(concepts) 260 260 + print(f"After stemming: {len(clusters)} clusters") 261 261 + 262 262 + # Build initial groups 263 263 + groups = [] 264 264 + multi = 0 265 265 + for indices in clusters: 266 266 + g = pick_canonical(concepts, indices) 267 267 + groups.append(g) 268 268 + if len(indices) > 1: 269 269 + multi += 1 270 270 + print(f" {multi} clusters with multiple members") 271 271 + 272 272 + # Pass 3: LLM merge 273 273 + print("Running LLM merge pass...") 274 274 + merges = llm_merge_pass(groups) 275 275 + if merges: 276 276 + print(f" LLM suggested {len(merges)} additional merges") 277 277 + groups = apply_merges(groups, merges, concepts) 278 278 + print(f"After LLM merge: {len(groups)} clusters") 279 279 + 280 280 + groups.sort(key=lambda g: g["memberCount"], reverse=True) 281 281 + return groups 282 282 + 283 283 + 284 284 + def main(): 285 285 + db_path = str(Path(__file__).resolve().parent.parent.parent / "apps" / "data" / "ionosphere.sqlite") 286 286 + output_path = Path(__file__).resolve().parent.parent / "data" / "concept-clusters.json" 287 287 + output_path.parent.mkdir(parents=True, exist_ok=True) 288 288 + 289 289 + results = cluster_concepts(db_path) 290 290 + 291 291 + output_path.write_text(json.dumps(results, indent=2)) 292 292 + print(f"\nWrote {len(results)} clusters to {output_path}") 293 293 + 294 294 + print("\nTop clusters by member count:") 295 295 + for g in results[:25]: 296 296 + if g["memberCount"] > 1: 297 297 + print(f" {g['canonical']} ({g['memberCount']} members)") 298 298 + for m in g["members"][:8]: 299 299 + print(f" - {m}") 300 300 + if len(g["members"]) > 8: 301 301 + print(f" ... and {len(g['members']) - 8} more") 302 302 + 303 303 + 304 304 + if __name__ == "__main__": 305 305 + main()

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pipeline/nlp/encoding.py

reviewed

··· 1 1 + """Convert word-level transcript format to compact (text + timings) format.""" 2 2 + 3 3 + 4 4 + def words_to_compact(transcript: dict) -> dict: 5 5 + """Convert TranscriptResult {text, words[{word, start, end}]} to compact {text, startMs, timings}.""" 6 6 + words = transcript.get("words", []) 7 7 + if not words: 8 8 + return {"text": transcript.get("text", ""), "startMs": 0, "timings": []} 9 9 + 10 10 + start_ms = round(words[0]["start"] * 1000) 11 11 + timings = [] 12 12 + cursor = start_ms 13 13 + 14 14 + for w in words: 15 15 + word_start_ms = round(w["start"] * 1000) 16 16 + word_end_ms = round(w["end"] * 1000) 17 17 + duration = word_end_ms - word_start_ms 18 18 + 19 19 + gap = word_start_ms - cursor 20 20 + if gap > 0: 21 21 + timings.append(-gap) 22 22 + 23 23 + timings.append(max(duration, 1)) 24 24 + cursor = word_end_ms 25 25 + 26 26 + return { 27 27 + "text": transcript["text"], 28 28 + "startMs": start_ms, 29 29 + "timings": timings, 30 30 + }

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pipeline/nlp/entities.py

reviewed

··· 1 1 + """Pass 3: Named entity recognition + entity linking + concept text matching.""" 2 2 + 3 3 + import json 4 4 + import re 5 5 + import spacy 6 6 + 7 7 + _nlp = None 8 8 + 9 9 + # Minimum concept name length to match — avoids noise from short terms 10 10 + MIN_CONCEPT_LENGTH = 3 11 11 + 12 12 + 13 13 + def _get_nlp(): 14 14 + global _nlp 15 15 + if _nlp is None: 16 16 + _nlp = spacy.load("en_core_web_sm") 17 17 + return _nlp 18 18 + 19 19 + 20 20 + def _build_concept_lookup(concept_rows: list[dict] | None) -> dict[str, str]: 21 21 + """Build a lowercase name/alias → URI lookup from concept records.""" 22 22 + lookup: dict[str, str] = {} 23 23 + if not concept_rows: 24 24 + return lookup 25 25 + for c in concept_rows: 26 26 + lookup[c["name"].lower()] = c["uri"] 27 27 + aliases = c.get("aliases", "[]") 28 28 + if isinstance(aliases, str): 29 29 + try: 30 30 + aliases = json.loads(aliases) 31 31 + except (json.JSONDecodeError, TypeError): 32 32 + aliases = [] 33 33 + for alias in aliases: 34 34 + if alias: 35 35 + lookup[alias.lower()] = c["uri"] 36 36 + return lookup 37 37 + 38 38 + 39 39 + def _build_concept_names(concept_rows: list[dict] | None) -> list[tuple[str, str, str]]: 40 40 + """Build a list of (search_term, display_name, uri) for text matching. 41 41 + 42 42 + Returns terms sorted longest-first so longer matches take priority. 43 43 + Skips terms shorter than MIN_CONCEPT_LENGTH. 44 44 + """ 45 45 + terms: list[tuple[str, str, str]] = [] 46 46 + if not concept_rows: 47 47 + return terms 48 48 + for c in concept_rows: 49 49 + name = c["name"] 50 50 + uri = c["uri"] 51 51 + if len(name) >= MIN_CONCEPT_LENGTH: 52 52 + terms.append((name, name, uri)) 53 53 + aliases = c.get("aliases", "[]") 54 54 + if isinstance(aliases, str): 55 55 + try: 56 56 + aliases = json.loads(aliases) 57 57 + except (json.JSONDecodeError, TypeError): 58 58 + aliases = [] 59 59 + for alias in aliases: 60 60 + if alias and len(alias) >= MIN_CONCEPT_LENGTH: 61 61 + terms.append((alias, name, uri)) 62 62 + # Sort longest first so "AT Protocol" matches before "AT" 63 63 + terms.sort(key=lambda t: len(t[0]), reverse=True) 64 64 + return terms 65 65 + 66 66 + 67 67 + def _scan_concepts( 68 68 + text: str, 69 69 + concept_terms: list[tuple[str, str, str]], 70 70 + occupied_ranges: set[tuple[int, int]], 71 71 + ) -> list[dict]: 72 72 + """Scan transcript text for known concept names/aliases. 73 73 + 74 74 + Returns concept entities with byte ranges. Skips ranges already 75 75 + occupied by NER-detected entities to avoid duplicates. 76 76 + """ 77 77 + entities = [] 78 78 + text_lower = text.lower() 79 79 + # Track which character ranges we've already matched (avoid overlapping matches) 80 80 + matched_chars: set[int] = set() 81 81 + # Also add NER-occupied char ranges 82 82 + for byte_start, byte_end in occupied_ranges: 83 83 + # Convert byte ranges to approximate char ranges 84 84 + prefix_len = len(text.encode("utf-8")[:byte_start].decode("utf-8", errors="ignore")) 85 85 + suffix_len = len(text.encode("utf-8")[:byte_end].decode("utf-8", errors="ignore")) 86 86 + for i in range(prefix_len, suffix_len): 87 87 + matched_chars.add(i) 88 88 + 89 89 + for search_term, display_name, uri in concept_terms: 90 90 + pattern = re.compile(re.escape(search_term.lower()), re.IGNORECASE) 91 91 + for match in pattern.finditer(text_lower): 92 92 + start_char = match.start() 93 93 + end_char = match.end() 94 94 + 95 95 + # Skip if any character in this range is already matched 96 96 + if any(i in matched_chars for i in range(start_char, end_char)): 97 97 + continue 98 98 + 99 99 + # Word boundary check — don't match "PDServer" when looking for "PDS" 100 100 + if start_char > 0 and text[start_char - 1].isalnum(): 101 101 + continue 102 102 + if end_char < len(text) and text[end_char].isalnum(): 103 103 + continue 104 104 + 105 105 + byte_start = len(text[:start_char].encode("utf-8")) 106 106 + byte_end = len(text[:end_char].encode("utf-8")) 107 107 + 108 108 + entities.append({ 109 109 + "byteStart": byte_start, 110 110 + "byteEnd": byte_end, 111 111 + "label": text[start_char:end_char], 112 112 + "nerType": "CONCEPT", 113 113 + "conceptUri": uri, 114 114 + }) 115 115 + 116 116 + # Mark these characters as matched 117 117 + for i in range(start_char, end_char): 118 118 + matched_chars.add(i) 119 119 + 120 120 + return entities 121 121 + 122 122 + 123 123 + def detect_entities( 124 124 + text: str, 125 125 + speaker_lookup=None, 126 126 + concept_rows: list[dict] | None = None, 127 127 + ) -> list[dict]: 128 128 + """Detect named entities and concepts in transcript text. 129 129 + 130 130 + Two-pass approach: 131 131 + 1. spaCy NER for people, organizations, etc. 132 132 + 2. Direct text matching for known concept names/aliases 133 133 + 134 134 + Returns a list of entity dicts with: 135 135 + byteStart, byteEnd: UTF-8 byte offsets 136 136 + label: entity text 137 137 + nerType: spaCy entity type or "CONCEPT" 138 138 + speakerDid: (optional) resolved speaker DID 139 139 + conceptUri: (optional) resolved concept URI 140 140 + """ 141 141 + if not text.strip(): 142 142 + return [] 143 143 + 144 144 + nlp = _get_nlp() 145 145 + doc = nlp(text) 146 146 + 147 147 + concept_lookup = _build_concept_lookup(concept_rows) 148 148 + concept_terms = _build_concept_names(concept_rows) 149 149 + 150 150 + entities = [] 151 151 + occupied_ranges: set[tuple[int, int]] = set() 152 152 + 153 153 + # Pass 1: NER entities 154 154 + for ent in doc.ents: 155 155 + if ent.label_ not in ("PERSON", "ORG", "PRODUCT", "WORK_OF_ART", "GPE", "EVENT"): 156 156 + continue 157 157 + 158 158 + byte_start = len(text[:ent.start_char].encode("utf-8")) 159 159 + byte_end = len(text[:ent.end_char].encode("utf-8")) 160 160 + 161 161 + entity = { 162 162 + "byteStart": byte_start, 163 163 + "byteEnd": byte_end, 164 164 + "label": ent.text, 165 165 + "nerType": ent.label_, 166 166 + } 167 167 + 168 168 + if ent.label_ == "PERSON" and speaker_lookup: 169 169 + match = speaker_lookup.resolve(ent.text) 170 170 + if match and match.get("did"): 171 171 + entity["speakerDid"] = match["did"] 172 172 + 173 173 + # Concept linking applies across all entity types 174 174 + # Try exact match, then strip common prefixes ("The", "A", "An") 175 175 + ent_lower = ent.text.lower() 176 176 + uri = concept_lookup.get(ent_lower) 177 177 + if not uri: 178 178 + for prefix in ("the ", "a ", "an "): 179 179 + if ent_lower.startswith(prefix): 180 180 + uri = concept_lookup.get(ent_lower[len(prefix):]) 181 181 + if uri: 182 182 + break 183 183 + if uri: 184 184 + entity["conceptUri"] = uri 185 185 + 186 186 + entities.append(entity) 187 187 + occupied_ranges.add((byte_start, byte_end)) 188 188 + 189 189 + # Pass 2: Direct text matching for known concepts 190 190 + concept_entities = _scan_concepts(text, concept_terms, occupied_ranges) 191 191 + entities.extend(concept_entities) 192 192 + 193 193 + return entities

+60

pipeline/nlp/export_streams.py

reviewed

··· 1 1 + """Export stream transcripts from DB as compact JSON for the NLP pipeline.""" 2 2 + 3 3 + import json 4 4 + import sqlite3 5 5 + from pathlib import Path 6 6 + 7 7 + 8 8 + def export_stream_transcripts(db_path: str, output_dir: str): 9 9 + conn = sqlite3.connect(db_path) 10 10 + 11 11 + streams = conn.execute("SELECT uri, slug FROM streams").fetchall() 12 12 + print(f"Found {len(streams)} streams") 13 13 + 14 14 + out = Path(output_dir) 15 15 + out.mkdir(parents=True, exist_ok=True) 16 16 + 17 17 + exported = 0 18 18 + for stream_uri, slug in streams: 19 19 + chunks = conn.execute( 20 20 + "SELECT text, start_ms, timings FROM stream_transcripts WHERE stream_uri = ? ORDER BY chunk_index ASC", 21 21 + (stream_uri,) 22 22 + ).fetchall() 23 23 + 24 24 + if not chunks: 25 25 + continue 26 26 + 27 27 + # Reassemble into a single compact transcript 28 28 + full_text = "" 29 29 + all_timings = [] 30 30 + start_ms = chunks[0][1] 31 31 + 32 32 + for text, chunk_start_ms, timings_json in chunks: 33 33 + timings = json.loads(timings_json) 34 34 + 35 35 + if full_text: 36 36 + # Add a gap between chunks if needed 37 37 + full_text += " " 38 38 + 39 39 + full_text += text 40 40 + all_timings.extend(timings) 41 41 + 42 42 + result = { 43 43 + "text": full_text, 44 44 + "startMs": start_ms, 45 45 + "timings": all_timings, 46 46 + } 47 47 + 48 48 + out_path = out / f"stream-{slug}.json" 49 49 + out_path.write_text(json.dumps(result)) 50 50 + print(f" {slug}: {len(full_text)} chars, {len(all_timings)} timing values") 51 51 + exported += 1 52 52 + 53 53 + conn.close() 54 54 + print(f"Exported {exported} stream transcripts") 55 55 + 56 56 + 57 57 + if __name__ == "__main__": 58 58 + db_path = str(Path(__file__).resolve().parent.parent.parent / "apps" / "data" / "ionosphere.sqlite") 59 59 + output_dir = str(Path(__file__).resolve().parent.parent / "data" / "stream-transcripts") 60 60 + export_stream_transcripts(db_path, output_dir)

+205

pipeline/nlp/llm_concepts.py

reviewed

··· 1 1 + """Pass 5: LLM-based concept detection using Claude. 2 2 + 3 3 + Sends transcript chunks to Claude to identify domain concepts that 4 4 + text matching and NER miss — paraphrases, abbreviations, implicit 5 5 + references, and potential new concepts. 6 6 + """ 7 7 + 8 8 + import json 9 9 + import os 10 10 + import re 11 11 + import anthropic 12 12 + 13 13 + # Use Haiku for speed and cost efficiency 14 14 + MODEL = "claude-haiku-4-5-20251001" 15 15 + 16 16 + SYSTEM_PROMPT = """You are analyzing a transcript from ATmosphereConf, a conference about the AT Protocol ecosystem (Bluesky, decentralized social media, open protocols). 17 17 + 18 18 + Your job: identify technical concepts, projects, protocols, organizations, and domain-specific terms mentioned in this transcript chunk. 19 19 + 20 20 + For each concept you find, return: 21 21 + - "text": the exact text as it appears in the transcript (verbatim, for byte-range matching) 22 22 + - "label": the canonical name of the concept (e.g., "AT Protocol" even if the text says "the protocol") 23 23 + - "isNew": true if this seems like a concept not in the provided known concepts list 24 24 + 25 25 + Focus on domain-specific terms. Skip common English words, generic phrases, and obvious things. Be precise about the exact text spans. 26 26 + 27 27 + Return valid JSON array only, no other text.""" 28 28 + 29 29 + 30 30 + def detect_llm_concepts( 31 31 + text: str, 32 32 + known_concept_names: list[str], 33 33 + chunk_size: int = 8000, 34 34 + ) -> list[dict]: 35 35 + """Use Claude to identify concepts in transcript text. 36 36 + 37 37 + Args: 38 38 + text: full transcript text 39 39 + known_concept_names: list of known concept names for context 40 40 + chunk_size: characters per chunk (to stay within context limits) 41 41 + 42 42 + Returns: 43 43 + list of dicts with text, label, isNew 44 44 + """ 45 45 + api_key = os.environ.get("ANTHROPIC_API_KEY") 46 46 + if not api_key: 47 47 + print(" Warning: ANTHROPIC_API_KEY not set, skipping LLM concept pass") 48 48 + return [] 49 49 + 50 50 + client = anthropic.Anthropic(api_key=api_key, timeout=30.0) 51 51 + 52 52 + # Build a concise concept list (top 200 by name length — shorter names are more common) 53 53 + sorted_concepts = sorted(known_concept_names, key=len)[:200] 54 54 + concept_list = ", ".join(sorted_concepts) 55 55 + 56 56 + # Split text into chunks at sentence boundaries 57 57 + chunks = _split_into_chunks(text, chunk_size) 58 58 + all_concepts = [] 59 59 + 60 60 + for i, chunk in enumerate(chunks): 61 61 + user_prompt = f"""Known concepts (subset): {concept_list} 62 62 + 63 63 + Transcript chunk ({i + 1}/{len(chunks)}): 64 64 + \"\"\" 65 65 + {chunk} 66 66 + \"\"\" 67 67 + 68 68 + Identify all technical concepts, projects, protocols, and domain terms in this chunk. Return a JSON array.""" 69 69 + 70 70 + try: 71 71 + response = client.messages.create( 72 72 + model=MODEL, 73 73 + max_tokens=2000, 74 74 + system=SYSTEM_PROMPT, 75 75 + messages=[{"role": "user", "content": user_prompt}], 76 76 + ) 77 77 + 78 78 + # Parse JSON from response 79 79 + response_text = response.content[0].text.strip() 80 80 + # Handle markdown code blocks 81 81 + if response_text.startswith("```"): 82 82 + response_text = re.sub(r"^```(?:json)?\n?", "", response_text) 83 83 + response_text = re.sub(r"\n?```$", "", response_text) 84 84 + 85 85 + concepts = json.loads(response_text) 86 86 + if isinstance(concepts, list): 87 87 + all_concepts.extend(concepts) 88 88 + except (json.JSONDecodeError, anthropic.APIError, IndexError) as e: 89 89 + print(f" Warning: LLM chunk {i + 1} failed: {e}") 90 90 + continue 91 91 + 92 92 + return all_concepts 93 93 + 94 94 + 95 95 + def resolve_llm_concepts( 96 96 + text: str, 97 97 + llm_concepts: list[dict], 98 98 + concept_lookup: dict[str, str], 99 99 + occupied_ranges: set[tuple[int, int]], 100 100 + ) -> list[dict]: 101 101 + """Resolve LLM-detected concepts to known concept URIs and compute byte ranges. 102 102 + 103 103 + Args: 104 104 + text: full transcript text 105 105 + llm_concepts: output from detect_llm_concepts 106 106 + concept_lookup: lowercase name → URI mapping 107 107 + occupied_ranges: byte ranges already occupied by other entities 108 108 + 109 109 + Returns: 110 110 + list of entity dicts ready to merge into the entities list 111 111 + """ 112 112 + entities = [] 113 113 + text_lower = text.lower() 114 114 + 115 115 + # Build set of occupied character ranges for dedup 116 116 + matched_chars: set[int] = set() 117 117 + for byte_start, byte_end in occupied_ranges: 118 118 + prefix_len = len(text.encode("utf-8")[:byte_start].decode("utf-8", errors="ignore")) 119 119 + suffix_len = len(text.encode("utf-8")[:byte_end].decode("utf-8", errors="ignore")) 120 120 + for i in range(prefix_len, suffix_len): 121 121 + matched_chars.add(i) 122 122 + 123 123 + for concept in llm_concepts: 124 124 + search_text = concept.get("text", "").strip() 125 125 + label = concept.get("label", search_text) 126 126 + is_new = concept.get("isNew", False) 127 127 + 128 128 + if not search_text or len(search_text) < 3: 129 129 + continue 130 130 + 131 131 + # Find the exact text in the transcript 132 132 + search_lower = search_text.lower() 133 133 + start = 0 134 134 + while True: 135 135 + idx = text_lower.find(search_lower, start) 136 136 + if idx == -1: 137 137 + break 138 138 + 139 139 + end_idx = idx + len(search_text) 140 140 + 141 141 + # Skip if overlaps with existing entities 142 142 + if any(i in matched_chars for i in range(idx, end_idx)): 143 143 + start = end_idx 144 144 + continue 145 145 + 146 146 + byte_start = len(text[:idx].encode("utf-8")) 147 147 + byte_end = len(text[:end_idx].encode("utf-8")) 148 148 + 149 149 + # Try to resolve to a known concept 150 150 + uri = concept_lookup.get(label.lower()) or concept_lookup.get(search_lower) 151 151 + 152 152 + entity = { 153 153 + "byteStart": byte_start, 154 154 + "byteEnd": byte_end, 155 155 + "label": text[idx:end_idx], 156 156 + "nerType": "CONCEPT", 157 157 + } 158 158 + if uri: 159 159 + entity["conceptUri"] = uri 160 160 + # Skip if it's a "new" concept with no URI — we only emit linked concepts 161 161 + # (new concept discovery is for Phase 4 manual curation) 162 162 + elif is_new: 163 163 + start = end_idx 164 164 + continue 165 165 + 166 166 + entities.append(entity) 167 167 + 168 168 + # Mark as occupied 169 169 + for i in range(idx, end_idx): 170 170 + matched_chars.add(i) 171 171 + 172 172 + # Only match first occurrence per chunk to avoid over-annotation 173 173 + break 174 174 + 175 175 + return entities 176 176 + 177 177 + 178 178 + def _split_into_chunks(text: str, chunk_size: int) -> list[str]: 179 179 + """Split text into chunks at sentence boundaries.""" 180 180 + if len(text) <= chunk_size: 181 181 + return [text] 182 182 + 183 183 + chunks = [] 184 184 + start = 0 185 185 + while start < len(text): 186 186 + end = start + chunk_size 187 187 + if end >= len(text): 188 188 + chunks.append(text[start:]) 189 189 + break 190 190 + 191 191 + # Find the last sentence boundary before the chunk limit 192 192 + last_period = text.rfind(". ", start, end) 193 193 + last_question = text.rfind("? ", start, end) 194 194 + last_exclaim = text.rfind("! ", start, end) 195 195 + boundary = max(last_period, last_question, last_exclaim) 196 196 + 197 197 + if boundary > start: 198 198 + chunks.append(text[start : boundary + 1]) 199 199 + start = boundary + 2 200 200 + else: 201 201 + # No sentence boundary found — split at chunk_size 202 202 + chunks.append(text[start:end]) 203 203 + start = end 204 204 + 205 205 + return chunks

+129

pipeline/nlp/paragraphs.py

reviewed

··· 1 1 + """Pass 2: Paragraph segmentation from pause data and sentence boundaries.""" 2 2 + 3 3 + 4 4 + def detect_paragraphs( 5 5 + text: str, 6 6 + timings: list[int], 7 7 + start_ms: int, 8 8 + sentences: list[dict], 9 9 + pause_threshold_ms: int = 2000, 10 10 + proximity_words: int = 5, 11 11 + ) -> list[dict]: 12 12 + """Detect paragraph boundaries using pause duration and sentence structure. 13 13 + 14 14 + Paragraph breaks are only inserted at sentence boundaries — never mid-sentence. 15 15 + A long pause (>= pause_threshold_ms) triggers a break only when a sentence 16 16 + boundary falls within proximity_words of the pause location. 17 17 + 18 18 + Args: 19 19 + text: The full transcript text. 20 20 + timings: Mixed-sign list where positive values are word durations (ms) 21 21 + and negative values are silence gaps (absolute value in ms) 22 22 + before the next word. 23 23 + start_ms: Absolute time offset of the first word (ms). 24 24 + sentences: List of sentence dicts with byteStart and byteEnd keys. 25 25 + pause_threshold_ms: Minimum silence duration to consider a paragraph break. 26 26 + proximity_words: Max word-index distance from pause to nearest sentence boundary. 27 27 + 28 28 + Returns: 29 29 + List of paragraph dicts with byteStart and byteEnd (UTF-8 byte offsets). 30 30 + """ 31 31 + if not text or not sentences: 32 32 + return [] 33 33 + 34 34 + # Build a char→byte offset map once (O(n)) 35 35 + char_to_byte = [] 36 36 + byte_pos = 0 37 37 + for ch in text: 38 38 + char_to_byte.append(byte_pos) 39 39 + byte_pos += len(ch.encode("utf-8")) 40 40 + char_to_byte.append(byte_pos) # sentinel for end-of-string 41 41 + 42 42 + # Find word start char offsets (preserving order of words in text) 43 43 + words = text.split() 44 44 + word_byte_starts = [] 45 45 + char_offset = 0 46 46 + for w in words: 47 47 + idx = text.index(w, char_offset) 48 48 + word_byte_starts.append(char_to_byte[idx]) 49 49 + char_offset = idx + len(w) 50 50 + 51 51 + # Map each sentence boundary (byteStart) to its approximate word index. 52 52 + # We find the word whose byte start is closest to / at the sentence boundary. 53 53 + def sentence_start_word_index(byte_start: int) -> int: 54 54 + """Return the word index whose byte start is nearest to byte_start.""" 55 55 + best_idx = 0 56 56 + best_dist = abs(word_byte_starts[0] - byte_start) if word_byte_starts else 0 57 57 + for i, wb in enumerate(word_byte_starts): 58 58 + dist = abs(wb - byte_start) 59 59 + if dist < best_dist: 60 60 + best_dist = dist 61 61 + best_idx = i 62 62 + return best_idx 63 63 + 64 64 + # Pre-compute word indices for all sentence boundaries (skip index 0 — that's 65 65 + # always the start of the first paragraph, not a potential break). 66 66 + sentence_word_indices = [] 67 67 + for sent in sentences: 68 68 + sentence_word_indices.append(sentence_start_word_index(sent["byteStart"])) 69 69 + 70 70 + # Walk timings to find word indices where long pauses occur. 71 71 + # The word_index before a pause is the last word of the current span. 72 72 + # The word after the pause starts the next potential paragraph. 73 73 + pause_after_word = [] # word indices AFTER which a long pause occurs 74 74 + word_index = -1 # incremented when we encounter a word duration 75 75 + for t in timings: 76 76 + if t >= 0: 77 77 + # Positive value: duration of the next word 78 78 + word_index += 1 79 79 + else: 80 80 + # Negative value: silence gap before the next word 81 81 + gap_ms = abs(t) 82 82 + if gap_ms >= pause_threshold_ms and word_index >= 0: 83 83 + pause_after_word.append(word_index) 84 84 + 85 85 + if not pause_after_word: 86 86 + # No qualifying pauses — single paragraph spanning everything 87 87 + return [{"byteStart": sentences[0]["byteStart"], "byteEnd": sentences[-1]["byteEnd"]}] 88 88 + 89 89 + # For each long pause, find the nearest sentence boundary within proximity_words. 90 90 + # A sentence boundary is a candidate break point only if it's a sentence START 91 91 + # (index > 0, since we never break before the first sentence). 92 92 + break_byte_starts: set[int] = set() 93 93 + for pause_word in pause_after_word: 94 94 + best_sent_idx = None 95 95 + best_dist = proximity_words + 1 # exclusive upper bound 96 96 + for si, sw in enumerate(sentence_word_indices): 97 97 + if si == 0: 98 98 + continue # can't break before the first sentence 99 99 + dist = abs(sw - (pause_word + 1)) # distance from word-after-pause to sentence start 100 100 + if dist <= proximity_words and dist < best_dist: 101 101 + best_dist = dist 102 102 + best_sent_idx = si 103 103 + if best_sent_idx is not None: 104 104 + break_byte_starts.add(sentences[best_sent_idx]["byteStart"]) 105 105 + 106 106 + if not break_byte_starts: 107 107 + # Pauses exist but no sentence boundaries are nearby — single paragraph 108 108 + return [{"byteStart": sentences[0]["byteStart"], "byteEnd": sentences[-1]["byteEnd"]}] 109 109 + 110 110 + # Build paragraph spans from the collected sentence-boundary break points. 111 111 + # Sort sentences and group them into paragraphs. 112 112 + sorted_breaks = sorted(break_byte_starts) 113 113 + paragraphs = [] 114 114 + para_start_byte = sentences[0]["byteStart"] 115 115 + 116 116 + for sent in sentences[1:]: 117 117 + if sent["byteStart"] in sorted_breaks: 118 118 + # Close current paragraph just before this sentence 119 119 + prev_sent = next( 120 120 + s for s in reversed(sentences) 121 121 + if s["byteStart"] < sent["byteStart"] 122 122 + ) 123 123 + paragraphs.append({"byteStart": para_start_byte, "byteEnd": prev_sent["byteEnd"]}) 124 124 + para_start_byte = sent["byteStart"] 125 125 + 126 126 + # Close the final paragraph 127 127 + paragraphs.append({"byteStart": para_start_byte, "byteEnd": sentences[-1]["byteEnd"]}) 128 128 + 129 129 + return paragraphs

+147

pipeline/nlp/run.py

reviewed

··· 1 1 + """Pipeline orchestrator: run all NLP passes on a transcript.""" 2 2 + 3 3 + import json 4 4 + import sys 5 5 + from pathlib import Path 6 6 + from nlp.sentences import detect_sentences 7 7 + from nlp.paragraphs import detect_paragraphs 8 8 + from nlp.entities import detect_entities 9 9 + from nlp.speaker_lookup import build_speaker_lookup 10 10 + from nlp.topics import detect_topic_breaks 11 11 + from nlp.llm_concepts import detect_llm_concepts, resolve_llm_concepts 12 12 + from nlp.entities import _build_concept_lookup 13 13 + 14 14 + 15 15 + def process_transcript( 16 16 + transcript: dict, 17 17 + talk_rkey: str, 18 18 + pause_threshold_ms: int = 2000, 19 19 + proximity_words: int = 5, 20 20 + speaker_rows: list[tuple] | None = None, 21 21 + concept_rows: list[dict] | None = None, 22 22 + ) -> dict: 23 23 + """Run all NLP passes on a single transcript. 24 24 + 25 25 + Args: 26 26 + transcript: dict with text, startMs, timings 27 27 + talk_rkey: the talk's record key (for output naming) 28 28 + speaker_rows: optional list of (name, handle, did) tuples for entity linking 29 29 + concept_rows: optional list of concept dicts for entity linking 30 30 + 31 31 + Returns: 32 32 + dict with sentences, paragraphs, entities, topicBreaks, and metadata 33 33 + """ 34 34 + text = transcript["text"] 35 35 + timings = transcript["timings"] 36 36 + start_ms = transcript["startMs"] 37 37 + 38 38 + # Pass 1: Sentence detection 39 39 + sentences = detect_sentences(text) 40 40 + 41 41 + # Pass 2: Paragraph segmentation 42 42 + paragraphs = detect_paragraphs( 43 43 + text=text, 44 44 + timings=timings, 45 45 + start_ms=start_ms, 46 46 + sentences=sentences, 47 47 + pause_threshold_ms=pause_threshold_ms, 48 48 + proximity_words=proximity_words, 49 49 + ) 50 50 + 51 51 + # Pass 3: NER + entity linking 52 52 + speaker_lookup = build_speaker_lookup(speaker_rows) if speaker_rows else None 53 53 + entities = detect_entities(text, speaker_lookup=speaker_lookup, concept_rows=concept_rows) 54 54 + 55 55 + # Pass 4: Topic segmentation 56 56 + sentences_with_text = [] 57 57 + for s in sentences: 58 58 + sent_text = text.encode("utf-8")[s["byteStart"]:s["byteEnd"]].decode("utf-8") 59 59 + sentences_with_text.append({**s, "text": sent_text}) 60 60 + topic_breaks = detect_topic_breaks(sentences_with_text) 61 61 + 62 62 + # Pass 5: LLM concept detection (optional — requires ANTHROPIC_API_KEY) 63 63 + if concept_rows: 64 64 + concept_lookup = _build_concept_lookup(concept_rows) 65 65 + known_names = [c["name"] for c in concept_rows] 66 66 + llm_concepts = detect_llm_concepts(text, known_names) 67 67 + if llm_concepts: 68 68 + occupied = {(e["byteStart"], e["byteEnd"]) for e in entities} 69 69 + llm_entities = resolve_llm_concepts(text, llm_concepts, concept_lookup, occupied) 70 70 + entities.extend(llm_entities) 71 71 + 72 72 + return { 73 73 + "talkRkey": talk_rkey, 74 74 + "sentences": sentences, 75 75 + "paragraphs": paragraphs, 76 76 + "entities": entities, 77 77 + "topicBreaks": [{"byteStart": tb["byteStart"]} for tb in topic_breaks], 78 78 + "metadata": { 79 79 + "tool": "spacy/en_core_web_sm", 80 80 + "pauseThresholdMs": pause_threshold_ms, 81 81 + "proximityWords": proximity_words, 82 82 + }, 83 83 + } 84 84 + 85 85 + 86 86 + def main(): 87 87 + """CLI: read transcripts from appview data/transcripts/, write results to pipeline/data/nlp/.""" 88 88 + # Match the path used by publish.ts: apps/ionosphere-appview/data/transcripts/ 89 89 + transcripts_dir = Path(__file__).resolve().parent.parent.parent / "apps" / "ionosphere-appview" / "data" / "transcripts" 90 90 + output_dir = Path(__file__).resolve().parent.parent / "data" / "nlp" 91 91 + output_dir.mkdir(parents=True, exist_ok=True) 92 92 + 93 93 + if not transcripts_dir.exists(): 94 94 + print(f"Transcripts directory not found: {transcripts_dir}") 95 95 + print("Run from repo root, or ensure appview data/transcripts/ exists.") 96 96 + sys.exit(1) 97 97 + 98 98 + import sqlite3 99 99 + db_path = Path(__file__).resolve().parent.parent.parent / "apps" / "data" / "ionosphere.sqlite" 100 100 + speaker_rows = [] 101 101 + concept_rows = [] 102 102 + if db_path.exists(): 103 103 + conn = sqlite3.connect(str(db_path)) 104 104 + speaker_rows = conn.execute( 105 105 + "SELECT name, handle, speaker_did FROM speakers" 106 106 + ).fetchall() 107 107 + concept_rows = [ 108 108 + {"name": r[0], "uri": r[1], "aliases": r[2] or "[]"} 109 109 + for r in conn.execute("SELECT name, uri, aliases FROM concepts").fetchall() 110 110 + ] 111 111 + conn.close() 112 112 + print(f"Loaded {len(speaker_rows)} speakers, {len(concept_rows)} concepts from DB") 113 113 + else: 114 114 + print(f"Warning: database not found at {db_path}, entity linking disabled") 115 115 + 116 116 + transcript_files = sorted(transcripts_dir.glob("*.json")) 117 117 + print(f"Processing {len(transcript_files)} transcripts...") 118 118 + 119 119 + for tf in transcript_files: 120 120 + talk_rkey = tf.stem 121 121 + transcript = json.loads(tf.read_text()) 122 122 + 123 123 + # The cached transcript files may be in TranscriptResult format 124 124 + # (text + words array). Convert to compact format if needed. 125 125 + if "words" in transcript and "timings" not in transcript: 126 126 + from nlp.encoding import words_to_compact 127 127 + compact = words_to_compact(transcript) 128 128 + else: 129 129 + compact = transcript 130 130 + 131 131 + result = process_transcript( 132 132 + compact, talk_rkey=talk_rkey, 133 133 + speaker_rows=speaker_rows, 134 134 + concept_rows=concept_rows, 135 135 + ) 136 136 + 137 137 + out_path = output_dir / f"{talk_rkey}.json" 138 138 + out_path.write_text(json.dumps(result, indent=2)) 139 139 + entity_count = len(result.get("entities", [])) 140 140 + topic_count = len(result.get("topicBreaks", [])) 141 141 + print(f" {talk_rkey}: {len(result['sentences'])} sentences, {len(result['paragraphs'])} paragraphs, {entity_count} entities, {topic_count} topics", flush=True) 142 142 + 143 143 + print("Done.") 144 144 + 145 145 + 146 146 + if __name__ == "__main__": 147 147 + main()

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pipeline/nlp/run_streams.py

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··· 1 1 + """Run NLP pipeline on stream transcripts (without LLM pass — too large).""" 2 2 + 3 3 + import json 4 4 + import sqlite3 5 5 + import sys 6 6 + from pathlib import Path 7 7 + from nlp.sentences import detect_sentences 8 8 + from nlp.paragraphs import detect_paragraphs 9 9 + from nlp.entities import detect_entities 10 10 + from nlp.speaker_lookup import build_speaker_lookup 11 11 + from nlp.topics import detect_topic_breaks 12 12 + 13 13 + 14 14 + def main(): 15 15 + streams_dir = Path(__file__).resolve().parent.parent / "data" / "stream-transcripts" 16 16 + output_dir = Path(__file__).resolve().parent.parent / "data" / "stream-nlp" 17 17 + output_dir.mkdir(parents=True, exist_ok=True) 18 18 + 19 19 + if not streams_dir.exists(): 20 20 + print("Run export_streams.py first") 21 21 + sys.exit(1) 22 22 + 23 23 + # Load speaker/concept data 24 24 + db_path = Path(__file__).resolve().parent.parent.parent / "apps" / "data" / "ionosphere.sqlite" 25 25 + speaker_rows = [] 26 26 + concept_rows = [] 27 27 + if db_path.exists(): 28 28 + conn = sqlite3.connect(str(db_path)) 29 29 + speaker_rows = conn.execute("SELECT name, handle, speaker_did FROM speakers").fetchall() 30 30 + concept_rows = [ 31 31 + {"name": r[0], "uri": r[1], "aliases": r[2] or "[]"} 32 32 + for r in conn.execute("SELECT name, uri, aliases FROM concepts").fetchall() 33 33 + ] 34 34 + conn.close() 35 35 + print(f"Loaded {len(speaker_rows)} speakers, {len(concept_rows)} concepts", flush=True) 36 36 + 37 37 + speaker_lookup = build_speaker_lookup(speaker_rows) if speaker_rows else None 38 38 + 39 39 + for tf in sorted(streams_dir.glob("*.json")): 40 40 + slug = tf.stem.replace("stream-", "") 41 41 + transcript = json.loads(tf.read_text()) 42 42 + text = transcript["text"] 43 43 + timings = transcript["timings"] 44 44 + start_ms = transcript["startMs"] 45 45 + 46 46 + print(f"Processing {slug} ({len(text)} chars)...", flush=True) 47 47 + 48 48 + # Pass 1: Sentences 49 49 + sentences = detect_sentences(text) 50 50 + print(f" {len(sentences)} sentences", flush=True) 51 51 + 52 52 + # Pass 2: Paragraphs 53 53 + paragraphs = detect_paragraphs( 54 54 + text=text, timings=timings, start_ms=start_ms, 55 55 + sentences=sentences, 56 56 + ) 57 57 + print(f" {len(paragraphs)} paragraphs", flush=True) 58 58 + 59 59 + # Pass 3: NER + entity linking (no LLM — too large) 60 60 + entities = detect_entities(text, speaker_lookup=speaker_lookup, concept_rows=concept_rows) 61 61 + print(f" {len(entities)} entities", flush=True) 62 62 + 63 63 + # Pass 4: Topic segmentation 64 64 + sentences_with_text = [] 65 65 + for s in sentences: 66 66 + sent_text = text.encode("utf-8")[s["byteStart"]:s["byteEnd"]].decode("utf-8") 67 67 + sentences_with_text.append({**s, "text": sent_text}) 68 68 + topic_breaks = detect_topic_breaks(sentences_with_text) 69 69 + print(f" {len(topic_breaks)} topics", flush=True) 70 70 + 71 71 + result = { 72 72 + "slug": slug, 73 73 + "sentences": sentences, 74 74 + "paragraphs": paragraphs, 75 75 + "entities": entities, 76 76 + "topicBreaks": [{"byteStart": tb["byteStart"]} for tb in topic_breaks], 77 77 + } 78 78 + 79 79 + out_path = output_dir / f"stream-{slug}.json" 80 80 + out_path.write_text(json.dumps(result, indent=2)) 81 81 + print(f" Done: {slug}", flush=True) 82 82 + 83 83 + print("All streams processed.") 84 84 + 85 85 + 86 86 + if __name__ == "__main__": 87 87 + main()

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pipeline/nlp/sentences.py

reviewed

··· 1 1 + """Pass 1: Sentence boundary detection using spaCy.""" 2 2 + 3 3 + import spacy 4 4 + 5 5 + _nlp = None 6 6 + 7 7 + 8 8 + def _get_nlp(): 9 9 + global _nlp 10 10 + if _nlp is None: 11 11 + _nlp = spacy.load("en_core_web_sm") 12 12 + return _nlp 13 13 + 14 14 + 15 15 + def detect_sentences(text: str) -> list[dict]: 16 16 + """Detect sentence boundaries and return byte-range spans. 17 17 + 18 18 + Returns a list of dicts, each with: 19 19 + byteStart: int — UTF-8 byte offset of sentence start 20 20 + byteEnd: int — UTF-8 byte offset of sentence end (exclusive) 21 21 + """ 22 22 + if not text.strip(): 23 23 + return [] 24 24 + 25 25 + nlp = _get_nlp() 26 26 + doc = nlp(text) 27 27 + sentences = [] 28 28 + 29 29 + for sent in doc.sents: 30 30 + # spaCy gives character offsets; convert to byte offsets 31 31 + byte_start = len(text[:sent.start_char].encode("utf-8")) 32 32 + byte_end = len(text[:sent.end_char].encode("utf-8")) 33 33 + sentences.append({ 34 34 + "byteStart": byte_start, 35 35 + "byteEnd": byte_end, 36 36 + }) 37 37 + 38 38 + return sentences

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pipeline/nlp/speaker_lookup.py

reviewed

··· 1 1 + """Build a speaker lookup table from database records for entity resolution.""" 2 2 + 3 3 + 4 4 + class SpeakerLookup: 5 5 + def __init__(self): 6 6 + self._by_full_name: dict[str, dict] = {} 7 7 + self._by_first_name: dict[str, dict | None] = {} 8 8 + self._by_handle: dict[str, dict] = {} 9 9 + 10 10 + def add(self, name: str, handle: str | None, did: str | None): 11 11 + entry = {"name": name, "handle": handle, "did": did} 12 12 + self._by_full_name[name.lower()] = entry 13 13 + if handle: 14 14 + self._by_handle[handle.lower()] = entry 15 15 + first = name.split()[0].lower() 16 16 + if first in self._by_first_name: 17 17 + self._by_first_name[first] = None # ambiguous 18 18 + else: 19 19 + self._by_first_name[first] = entry 20 20 + 21 21 + def resolve(self, name: str) -> dict | None: 22 22 + key = name.lower().strip() 23 23 + if key in self._by_full_name: 24 24 + return self._by_full_name[key] 25 25 + if key in self._by_handle: 26 26 + return self._by_handle[key] 27 27 + if key in self._by_first_name: 28 28 + return self._by_first_name[key] 29 29 + return None 30 30 + 31 31 + 32 32 + def build_speaker_lookup(rows: list[tuple]) -> SpeakerLookup: 33 33 + lookup = SpeakerLookup() 34 34 + for name, handle, did in rows: 35 35 + lookup.add(name, handle, did) 36 36 + return lookup

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pipeline/nlp/topics.py

reviewed

··· 1 1 + """Pass 4: Topic segmentation using sentence embeddings.""" 2 2 + 3 3 + import numpy as np 4 4 + 5 5 + _model = None 6 6 + 7 7 + 8 8 + def _get_model(): 9 9 + global _model 10 10 + if _model is None: 11 11 + from sentence_transformers import SentenceTransformer 12 12 + _model = SentenceTransformer("all-MiniLM-L6-v2") 13 13 + return _model 14 14 + 15 15 + 16 16 + def detect_topic_breaks( 17 17 + sentences: list[dict], 18 18 + window_size: int = 3, 19 19 + similarity_threshold: float = 0.3, 20 20 + min_segment_sentences: int = 5, 21 21 + ) -> list[dict]: 22 22 + """Detect topic boundaries using sentence embedding similarity. 23 23 + 24 24 + Args: 25 25 + sentences: list of dicts with byteStart, byteEnd, text 26 26 + window_size: number of sentences per comparison window 27 27 + similarity_threshold: cosine similarity below this = topic break 28 28 + min_segment_sentences: minimum sentences between breaks 29 29 + 30 30 + Returns: 31 31 + list of dicts with byteStart and sentenceIndex 32 32 + """ 33 33 + if len(sentences) < window_size * 2: 34 34 + return [] 35 35 + 36 36 + model = _get_model() 37 37 + texts = [s["text"] for s in sentences] 38 38 + embeddings = model.encode(texts, show_progress_bar=False) 39 39 + 40 40 + similarities = [] 41 41 + for i in range(window_size, len(sentences) - window_size + 1): 42 42 + left = np.mean(embeddings[i - window_size:i], axis=0) 43 43 + right = np.mean(embeddings[i:i + window_size], axis=0) 44 44 + cos_sim = np.dot(left, right) / (np.linalg.norm(left) * np.linalg.norm(right)) 45 45 + similarities.append((i, float(cos_sim))) 46 46 + 47 47 + breaks = [] 48 48 + last_break = 0 49 49 + for sent_idx, sim in similarities: 50 50 + if sim < similarity_threshold and (sent_idx - last_break) >= min_segment_sentences: 51 51 + breaks.append({ 52 52 + "byteStart": sentences[sent_idx]["byteStart"], 53 53 + "sentenceIndex": sent_idx, 54 54 + }) 55 55 + last_break = sent_idx 56 56 + 57 57 + return breaks

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pipeline/pyproject.toml

reviewed

··· 1 1 + [project] 2 2 + name = "ionosphere-nlp" 3 3 + version = "0.1.0" 4 4 + description = "NLP enrichment pipeline for ionosphere transcripts" 5 5 + requires-python = ">=3.12" 6 6 + dependencies = [ 7 7 + "spacy>=3.7", 8 8 + "sentence-transformers>=2.0", 9 9 + "anthropic>=0.90", 10 10 + ] 11 11 + 12 12 + [project.optional-dependencies] 13 13 + dev = ["pytest>=8.0"] 14 14 + 15 15 + [tool.setuptools.packages.find] 16 16 + include = ["nlp*"] 17 17 + 18 18 + [tool.pytest.ini_options] 19 19 + testpaths = ["tests"]

pipeline/tests/__init__.py

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pipeline/tests/test_entities.py

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··· 1 1 + from nlp.entities import detect_entities 2 2 + from nlp.speaker_lookup import build_speaker_lookup 3 3 + 4 4 + 5 5 + def test_detects_person_entities(): 6 6 + text = "Matt Akamatsu is presenting today." 7 7 + rows = [("Matt Akamatsu", "matsulab.com", "did:plc:matt123")] 8 8 + lookup = build_speaker_lookup(rows) 9 9 + 10 10 + entities = detect_entities(text, speaker_lookup=lookup) 11 11 + 12 12 + persons = [e for e in entities if e["nerType"] == "PERSON"] 13 13 + assert len(persons) >= 1 14 14 + resolved = [e for e in persons if e.get("speakerDid")] 15 15 + assert len(resolved) >= 1 16 16 + assert resolved[0]["speakerDid"] == "did:plc:matt123" 17 17 + 18 18 + 19 19 + def test_detects_org_entities(): 20 20 + text = "The work at Bluesky is impressive." 21 21 + concepts = [{"name": "Bluesky", "uri": "at://did/concept/bluesky", "aliases": "[]"}] 22 22 + 23 23 + entities = detect_entities(text, concept_rows=concepts) 24 24 + 25 25 + orgs = [e for e in entities if e.get("conceptUri")] 26 26 + assert len(orgs) >= 1 27 27 + 28 28 + 29 29 + def test_unresolved_entities_have_label(): 30 30 + text = "Barack Obama spoke at the conference." 31 31 + entities = detect_entities(text) 32 32 + 33 33 + persons = [e for e in entities if e["nerType"] == "PERSON"] 34 34 + assert len(persons) >= 1 35 35 + assert persons[0].get("speakerDid") is None 36 36 + assert persons[0]["label"] == "Barack Obama" 37 37 + 38 38 + 39 39 + def test_byte_offsets_correct(): 40 40 + text = "Matt Akamatsu presented." 41 41 + entities = detect_entities(text) 42 42 + if entities: 43 43 + e = entities[0] 44 44 + text_at_range = text.encode("utf-8")[e["byteStart"]:e["byteEnd"]].decode("utf-8") 45 45 + assert e["label"] in text_at_range or text_at_range in e["label"] 46 46 + 47 47 + 48 48 + def test_empty_text(): 49 49 + entities = detect_entities("") 50 50 + assert entities == [] 51 51 + 52 52 + 53 53 + # --- Text matching for known concepts --- 54 54 + 55 55 + 56 56 + def test_text_matching_finds_domain_concepts(): 57 57 + """Concepts like 'AT Protocol' and 'PDS' aren't NER entities but should be found by text matching.""" 58 58 + text = "The AT Protocol uses a PDS for data storage. Each lexicon defines a schema." 59 59 + concepts = [ 60 60 + {"name": "AT Protocol", "uri": "at://did/concept/atp", "aliases": '["atproto"]'}, 61 61 + {"name": "PDS", "uri": "at://did/concept/pds", "aliases": '["Personal Data Server"]'}, 62 62 + {"name": "Lexicon", "uri": "at://did/concept/lexicon", "aliases": "[]"}, 63 63 + ] 64 64 + 65 65 + entities = detect_entities(text, concept_rows=concepts) 66 66 + 67 67 + concept_entities = [e for e in entities if e.get("conceptUri")] 68 68 + concept_names = {e["label"] for e in concept_entities} 69 69 + assert "AT Protocol" in concept_names 70 70 + assert "PDS" in concept_names 71 71 + assert "lexicon" in concept_names or "Lexicon" in concept_names 72 72 + 73 73 + 74 74 + def test_text_matching_case_insensitive(): 75 75 + text = "We use the at protocol for federation." 76 76 + concepts = [{"name": "AT Protocol", "uri": "at://did/concept/atp", "aliases": "[]"}] 77 77 + 78 78 + entities = detect_entities(text, concept_rows=concepts) 79 79 + 80 80 + concept_entities = [e for e in entities if e.get("conceptUri")] 81 81 + # Should match case-insensitively 82 82 + assert len(concept_entities) >= 1 83 83 + 84 84 + 85 85 + def test_text_matching_finds_aliases(): 86 86 + text = "The Personal Data Server stores your data." 87 87 + concepts = [{"name": "PDS", "uri": "at://did/concept/pds", "aliases": '["Personal Data Server"]'}] 88 88 + 89 89 + entities = detect_entities(text, concept_rows=concepts) 90 90 + 91 91 + concept_entities = [e for e in entities if e.get("conceptUri")] 92 92 + assert len(concept_entities) >= 1 93 93 + assert concept_entities[0]["conceptUri"] == "at://did/concept/pds" 94 94 + 95 95 + 96 96 + def test_text_matching_no_duplicates_with_ner(): 97 97 + """If NER already detected an entity and text matching also finds it, don't duplicate.""" 98 98 + text = "Bluesky is a social network." 99 99 + concepts = [{"name": "Bluesky", "uri": "at://did/concept/bluesky", "aliases": "[]"}] 100 100 + 101 101 + entities = detect_entities(text, concept_rows=concepts) 102 102 + 103 103 + bluesky_entities = [e for e in entities if e["label"] == "Bluesky" or e["label"] == "bluesky"] 104 104 + # Should have exactly one, not two 105 105 + assert len(bluesky_entities) <= 2 # NER might split differently, but no exact byte-range duplicates 106 106 + byte_ranges = {(e["byteStart"], e["byteEnd"]) for e in bluesky_entities} 107 107 + assert len(byte_ranges) == len(bluesky_entities) # no duplicate ranges 108 108 + 109 109 + 110 110 + def test_text_matching_skips_short_concepts(): 111 111 + """Very short concept names (1-2 chars) should be skipped to avoid noise.""" 112 112 + text = "I think we should use AT for this." 113 113 + concepts = [{"name": "AT", "uri": "at://did/concept/at", "aliases": "[]"}] 114 114 + 115 115 + entities = detect_entities(text, concept_rows=concepts) 116 116 + 117 117 + # "AT" is too short and common — should not match 118 118 + concept_entities = [e for e in entities if e.get("conceptUri")] 119 119 + assert len(concept_entities) == 0

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pipeline/tests/test_paragraphs.py

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··· 1 1 + from nlp.paragraphs import detect_paragraphs 2 2 + 3 3 + 4 4 + def test_basic_paragraphs(): 5 5 + text = "Hello world. This is sentence two. After a long pause here. New topic starts." 6 6 + sentences = [ 7 7 + {"byteStart": 0, "byteEnd": 12}, 8 8 + {"byteStart": 13, "byteEnd": 34}, 9 9 + {"byteStart": 35, "byteEnd": 59}, 10 10 + {"byteStart": 60, "byteEnd": 77}, 11 11 + ] 12 12 + # Big pause gap (3000ms) between word 5 ("two.") and word 6 ("After") 13 13 + # Words: Hello(0) world.(1) This(2) is(3) sentence(4) two.(5) 14 14 + # After(6) a(7) long(8) pause(9) here.(10) New(11) topic(12) starts.(13) 15 15 + timings = [100, 100, 100, 100, 100, 100, -3000, 100, 100, 100, 100, 100, 100, 100] 16 16 + start_ms = 0 17 17 + 18 18 + paragraphs = detect_paragraphs( 19 19 + text=text, 20 20 + timings=timings, 21 21 + start_ms=start_ms, 22 22 + sentences=sentences, 23 23 + pause_threshold_ms=2000, 24 24 + proximity_words=5, 25 25 + ) 26 26 + # Should detect a paragraph break at the sentence boundary near the 3s pause 27 27 + assert len(paragraphs) == 2 28 28 + assert paragraphs[0]["byteStart"] == 0 29 29 + assert paragraphs[1]["byteStart"] == 35 # "After a long pause..." 30 30 + 31 31 + 32 32 + def test_no_long_pauses_single_paragraph(): 33 33 + text = "One sentence. Two sentence." 34 34 + sentences = [ 35 35 + {"byteStart": 0, "byteEnd": 13}, 36 36 + {"byteStart": 14, "byteEnd": 27}, 37 37 + ] 38 38 + timings = [100, 100, 100, 100] 39 39 + paragraphs = detect_paragraphs( 40 40 + text=text, timings=timings, start_ms=0, 41 41 + sentences=sentences, 42 42 + ) 43 43 + assert len(paragraphs) == 1 44 44 + 45 45 + 46 46 + def test_empty_input(): 47 47 + paragraphs = detect_paragraphs( 48 48 + text="", timings=[], start_ms=0, sentences=[], 49 49 + ) 50 50 + assert paragraphs == []

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pipeline/tests/test_run.py

reviewed

··· 1 1 + import json 2 2 + from pathlib import Path 3 3 + from nlp.run import process_transcript 4 4 + 5 5 + 6 6 + def test_process_transcript_produces_output(): 7 7 + """Integration test: full pipeline on a simple transcript.""" 8 8 + transcript = { 9 9 + "text": "Hello world. This is a test. After a long pause. New topic here.", 10 10 + "startMs": 0, 11 11 + "timings": [100, 100, 100, 100, 100, 100, -3000, 100, 100, 100, 100, 100, 100, 100], 12 12 + } 13 13 + 14 14 + result = process_transcript(transcript, talk_rkey="test-talk") 15 15 + 16 16 + # Should have sentences and paragraphs 17 17 + assert "sentences" in result 18 18 + assert "paragraphs" in result 19 19 + assert len(result["sentences"]) >= 2 20 20 + assert len(result["paragraphs"]) >= 1 21 21 + # Each sentence has byte ranges 22 22 + for s in result["sentences"]: 23 23 + assert "byteStart" in s 24 24 + assert "byteEnd" in s 25 25 + # Metadata present 26 26 + assert "metadata" in result 27 27 + assert result["metadata"]["tool"] == "spacy/en_core_web_sm" 28 28 + 29 29 + 30 30 + def test_process_transcript_with_enrichment(): 31 31 + """Full pipeline with entity and topic passes.""" 32 32 + transcript = { 33 33 + "text": "Matt Akamatsu is presenting. The AT Protocol is decentralized. Now we discuss a completely different topic about cooking recipes and baking cakes with flour and sugar and eggs.", 34 34 + "startMs": 0, 35 35 + "timings": [100, 100, 100, 100, 100, 100, 100, 100, 100, -3000, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100], 36 36 + } 37 37 + speaker_rows = [("Matt Akamatsu", "matsulab.com", "did:plc:matt123")] 38 38 + concept_rows = [{"name": "AT Protocol", "uri": "at://did/concept/atp", "aliases": "[]"}] 39 39 + 40 40 + result = process_transcript( 41 41 + transcript, talk_rkey="test", 42 42 + speaker_rows=speaker_rows, 43 43 + concept_rows=concept_rows, 44 44 + ) 45 45 + 46 46 + assert "entities" in result 47 47 + assert "topicBreaks" in result 48 48 + assert len(result["entities"]) >= 1 49 49 + # At least one resolved entity 50 50 + resolved = [e for e in result["entities"] if e.get("speakerDid") or e.get("conceptUri")] 51 51 + assert len(resolved) >= 1

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pipeline/tests/test_sentences.py

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··· 1 1 + from nlp.sentences import detect_sentences 2 2 + 3 3 + 4 4 + def test_basic_sentences(): 5 5 + text = "Hello world. This is a test. And another sentence." 6 6 + sentences = detect_sentences(text) 7 7 + assert len(sentences) == 3 8 8 + # Each sentence is a dict with byteStart and byteEnd 9 9 + assert sentences[0]["byteStart"] == 0 10 10 + assert sentences[0]["byteEnd"] == len("Hello world.".encode("utf-8")) 11 11 + assert sentences[1]["byteStart"] == len("Hello world. ".encode("utf-8")) 12 12 + 13 13 + 14 14 + def test_speech_without_punctuation(): 15 15 + """spaCy should detect sentence boundaries even with poor punctuation.""" 16 16 + text = "so the thing is we need to think about this carefully and then we can move on to the next topic which is about protocols" 17 17 + sentences = detect_sentences(text) 18 18 + # spaCy should find at least 1 sentence (the whole text if no clear boundary) 19 19 + assert len(sentences) >= 1 20 20 + # All sentences should cover the full text 21 21 + assert sentences[0]["byteStart"] == 0 22 22 + assert sentences[-1]["byteEnd"] == len(text.encode("utf-8")) 23 23 + 24 24 + 25 25 + def test_empty_text(): 26 26 + sentences = detect_sentences("") 27 27 + assert sentences == [] 28 28 + 29 29 + 30 30 + def test_byte_offsets_for_unicode(): 31 31 + text = "Caf\u00e9 is great. Let\u2019s go." 32 32 + sentences = detect_sentences(text) 33 33 + # Byte offsets must account for multi-byte characters 34 34 + full_bytes = text.encode("utf-8") 35 35 + assert sentences[-1]["byteEnd"] == len(full_bytes)

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pipeline/tests/test_speaker_lookup.py

reviewed

··· 1 1 + from nlp.speaker_lookup import build_speaker_lookup 2 2 + 3 3 + 4 4 + def test_build_lookup_from_rows(): 5 5 + rows = [ 6 6 + ("Matt Akamatsu", "matsulab.com", "did:plc:matt123"), 7 7 + ("Rowan Cockett", "row1.ca", "did:plc:rowan456"), 8 8 + ("Jay Graber", "jay.bsky.team", "did:plc:jay789"), 9 9 + ] 10 10 + lookup = build_speaker_lookup(rows) 11 11 + 12 12 + assert lookup.resolve("Matt Akamatsu") is not None 13 13 + assert lookup.resolve("Matt Akamatsu")["did"] == "did:plc:matt123" 14 14 + assert lookup.resolve("matt akamatsu")["did"] == "did:plc:matt123" 15 15 + assert lookup.resolve("Matt") is not None 16 16 + assert lookup.resolve("Matt")["did"] == "did:plc:matt123" 17 17 + assert lookup.resolve("row1.ca") is not None 18 18 + assert lookup.resolve("row1.ca")["did"] == "did:plc:rowan456" 19 19 + assert lookup.resolve("Unknown Person") is None 20 20 + 21 21 + 22 22 + def test_first_name_collision_returns_none(): 23 23 + rows = [ 24 24 + ("Matt Akamatsu", "matsulab.com", "did:plc:matt1"), 25 25 + ("Matt Jones", "mattj.com", "did:plc:matt2"), 26 26 + ] 27 27 + lookup = build_speaker_lookup(rows) 28 28 + 29 29 + assert lookup.resolve("Matt Akamatsu")["did"] == "did:plc:matt1" 30 30 + assert lookup.resolve("Matt") is None 31 31 + 32 32 + 33 33 + def test_empty_speakers(): 34 34 + lookup = build_speaker_lookup([]) 35 35 + assert lookup.resolve("Anyone") is None

+39

pipeline/tests/test_topics.py

reviewed

··· 1 1 + from nlp.topics import detect_topic_breaks 2 2 + 3 3 + 4 4 + def test_detects_topic_change(): 5 5 + """Distinct topics should produce at least one break.""" 6 6 + sentences = [ 7 7 + {"byteStart": 0, "byteEnd": 30, "text": "Today we will make a cake."}, 8 8 + {"byteStart": 31, "byteEnd": 65, "text": "First mix the flour and sugar."}, 9 9 + {"byteStart": 66, "byteEnd": 100, "text": "Then add the eggs and butter."}, 10 10 + {"byteStart": 101, "byteEnd": 135, "text": "Bake at 350 degrees for 30 minutes."}, 11 11 + {"byteStart": 136, "byteEnd": 170, "text": "Let it cool before adding frosting."}, 12 12 + {"byteStart": 171, "byteEnd": 210, "text": "NASA launched a new rocket to Mars."}, 13 13 + {"byteStart": 211, "byteEnd": 250, "text": "The spacecraft will orbit the red planet."}, 14 14 + {"byteStart": 251, "byteEnd": 295, "text": "Astronauts may visit Mars within ten years."}, 15 15 + {"byteStart": 296, "byteEnd": 340, "text": "The mission costs billions of dollars."}, 16 16 + {"byteStart": 341, "byteEnd": 380, "text": "Space exploration advances human knowledge."}, 17 17 + ] 18 18 + breaks = detect_topic_breaks(sentences) 19 19 + assert len(breaks) >= 1 20 20 + assert any(4 <= b["sentenceIndex"] <= 6 for b in breaks) 21 21 + 22 22 + 23 23 + def test_no_breaks_for_single_topic(): 24 24 + sentences = [ 25 25 + {"byteStart": 0, "byteEnd": 30, "text": "The cat sat on the mat."}, 26 26 + {"byteStart": 31, "byteEnd": 60, "text": "The cat was very happy."}, 27 27 + {"byteStart": 61, "byteEnd": 90, "text": "It purred loudly all day."}, 28 28 + ] 29 29 + breaks = detect_topic_breaks(sentences, min_segment_sentences=2) 30 30 + assert len(breaks) <= 1 31 31 + 32 32 + 33 33 + def test_empty_input(): 34 34 + assert detect_topic_breaks([]) == [] 35 35 + 36 36 + 37 37 + def test_too_few_sentences(): 38 38 + sentences = [{"byteStart": 0, "byteEnd": 10, "text": "Hello."}] 39 39 + assert detect_topic_breaks(sentences) == []