{"type":"thinking_level_change","id":"bdc10029","parentId":null,"timestamp":"2026-03-30T19:10:28.894Z","thinkingLevel":"medium"}
{"type":"model_change","id":"95cbaf65","parentId":"bdc10029","timestamp":"2026-03-30T19:10:28.897Z","provider":"anthropic","modelId":"claude-opus-4-6"}
{"type":"custom_message","customType":"gsd-auto","content":"You are executing GSD auto-mode.\n\n## Working Directory\n\nYour working directory is `/home/aux/projects/content-to-kb-automator`. All file reads, writes, and shell commands MUST operate relative to this directory. Do NOT `cd` to any other directory.\n\nAll relevant context has been preloaded below — start working immediately without re-reading these files.\n\n## UNIT: Research Slice S03 (\"Transcript Folder Watcher — Auto-Ingest Service\") — Milestone M007\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M007/M007-ROADMAP.md`\n\n# M007: \n\n## Vision\nMake the pipeline fully transparent — every LLM call's full prompt and response viewable, copyable, exportable, with per-stage token accounting. Automate transcript ingestion via folder monitoring instead of manual POST. Tighten the admin UX: prune dead weight, improve workflow clarity. Fix frontend overflow and mobile responsiveness bugs. Set the foundation for iterating on content quality through better visibility into what the LLM is actually doing.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting | medium | — | ✅ | Toggle debug mode on for a video, trigger pipeline, see full prompt + response stored for every LLM call with per-stage token breakdown |\n| S02 | Debug Payload Viewer — Inline View, Copy, and Export in Admin UI | low | S01 | ✅ | Admin clicks an LLM call event, sees full prompt and response in a readable viewer, copies to clipboard, or downloads as JSON |\n| S03 | Transcript Folder Watcher — Auto-Ingest Service | medium | — | ⬜ | Drop a transcript JSON into the watch folder on ub01 → file is detected, validated, POSTed to /ingest, pipeline triggers automatically |\n| S04 | Admin UX Audit — Prune, Streamline, and Polish | low | S02 | ⬜ | Admin pipeline page is cleaner and more efficient for daily content management. Dead UI elements removed. Workflow improvements visible. |\n| S05 | Key Moment Card Text Overflow Fix | low | — | ⬜ | Key moment cards with long filenames and timestamp ranges display cleanly — text truncated with ellipsis or wrapped within card bounds, no horizontal bleed |\n| S06 | Mobile Viewport Overflow Fix — Technique Pages and Global Content | low | S05 | ⬜ | Technique page on Galaxy S25 Ultra renders cleanly — body text wraps, tags don't overflow, metadata stays within viewport, no horizontal scroll |\n\n---\n\n### Decisions\nSource: `.gsd/DECISIONS.md`\n\n# Decisions Register\n\n<!-- Append-only. Never edit or remove existing rows.\n     To reverse a decision, add a new row that supersedes it.\n     Read this file at the start of any planning or research phase. -->\n\n| # | When | Scope | Decision | Choice | Rationale | Revisable? | Made By |\n|---|------|-------|----------|--------|-----------|------------|---------|\n| D001 |  | architecture | Storage layer selection | PostgreSQL for structured data, Qdrant (existing instance at 10.0.0.10) for vector embeddings, local filesystem for transcript JSON files | Spec specifies PostgreSQL for JSONB support and Qdrant already running on hypervisor. File store on local filesystem organized by creator slug. Qdrant gets a dedicated collection, not a new instance. | Yes | collaborative |\n| D002 |  | architecture | Timestamp handling for asyncpg with TIMESTAMP WITHOUT TIME ZONE columns | Use datetime.now(timezone.utc).replace(tzinfo=None) in _now() helper | asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns. Stripping tzinfo at the application layer preserves UTC semantics while staying compatible with the existing schema. | Yes | agent |\n| D003 |  | requirement | R002 Transcript Ingestion API status | validated | 6 passing integration tests prove the full POST /api/v1/ingest flow: creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, idempotent re-upload, and invalid input rejection. | Yes | agent |\n| D004 |  | architecture | Sync vs async approach for Celery worker pipeline tasks | Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine. | Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine. | Yes | agent |\n| D005 |  | architecture | Embedding/Qdrant failure handling strategy in pipeline | Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger. | Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time. | Yes | agent |\n| D006 |  | requirement | R013 Prompt Template System status | validated | 4 prompt template files in prompts/ directory loaded from configurable settings.prompts_path. Templates use XML-style content fencing. Pipeline stages read templates from disk at runtime, enabling edits without code changes. Manual re-trigger endpoint (POST /api/v1/pipeline/trigger/{video_id}) allows re-processing after prompt edits. | Yes | agent |\n| D007 | M001/S04 | architecture | Runtime review mode toggle persistence mechanism | Store review mode toggle in Redis key `chrysopedia:review_mode` with async redis client. Fall back to `settings.review_mode` config default when key is absent. | The config.py `review_mode` setting is loaded via lru_cache from environment variables and cannot be mutated at runtime. Redis is already used by the project (Celery broker, stage 4 classification data) so it adds no new infrastructure. A system_settings DB table would work but Redis is simpler for a single boolean toggle on a single-admin tool. The pipeline's stages.py reads settings.review_mode from config — the admin toggle only affects new pipeline runs if stages.py is updated to check Redis too, but that's deferred since the toggle is primarily a UI-level concept for the review queue. | Yes | agent |\n| D008 | M001/S04 | requirement | R004 Review Queue UI status | validated | All R004 capabilities delivered and verified: 9 API endpoints (approve, reject, edit, split, merge, queue list, stats, mode get/set) with 24 passing integration tests covering happy paths and error boundaries. React+TypeScript frontend with queue page (filter tabs, stats, pagination), moment detail page (all review actions with modals), and review-vs-auto mode toggle. Frontend builds with zero TypeScript errors. | Yes | agent |\n| D009 | M001/S05 | architecture | Async search service pattern for FastAPI request path | Create a separate `SearchService` class using `openai.AsyncOpenAI` and `qdrant_client.AsyncQdrantClient` for the search endpoint. Keep existing sync `EmbeddingClient` and `QdrantManager` for Celery pipeline. Search endpoint has 300ms timeout on embedding API and falls back to SQL ILIKE keyword search on Qdrant/embedding failure. | The existing EmbeddingClient and QdrantManager are sync (using `openai.OpenAI` and `QdrantClient`) because Celery tasks run synchronously. FastAPI request handlers are async — reusing sync clients would block the event loop. Creating a thin async wrapper avoids modifying the battle-tested pipeline code while providing non-blocking search. The 300ms timeout and keyword fallback ensure the search endpoint always returns results, even when Qdrant or the embedding service is degraded. | Yes | agent |\n| D010 | M001/S05 | requirement | R005 Search-First Web UI status | validated | Search endpoint (GET /api/v1/search) with async embedding + Qdrant + keyword fallback implemented and tested. Frontend Home.tsx has prominent search bar with 300ms debounced typeahead, scope toggle via URL params, nav cards for Topics/Creators, Recently Added section. SearchResults.tsx displays grouped results. 5 integration tests verify search happy path, empty query, keyword fallback, scope filter, and no-results. Frontend production build succeeds with zero TypeScript errors. | Yes | agent |\n| D011 | M001/S05 | requirement | R006 Technique Page Display status | validated | TechniquePage.tsx renders all specified sections: header with topic_category badge and topic_tags pills, creator name linked to creator detail, source_quality indicator, amber banner for unstructured content, body_sections JSONB prose (handles both string and object values), key moments index ordered by start_time, signal chains, plugins pill list, and related techniques links. Backend GET /api/v1/techniques/{slug} returns full detail with eager-loaded key_moments and related links. 404 for unknown slug tested. | Yes | agent |\n| D012 | M001/S05 | requirement | R007 Creators Browse Page status | validated | CreatorsBrowse.tsx implements genre filter pills, type-to-narrow name filter, sort toggle (Random default/A-Z/Views). Each creator row shows name, genre tags, technique_count, video_count. Links to CreatorDetail page. Backend GET /api/v1/creators supports sort=random\\|alpha\\|views and genre filter. Integration tests verify random sort, alpha sort, genre filter, detail endpoint, 404, and counts. | Yes | agent |\n| D013 | M001/S05 | requirement | R008 Topics Browse Page status | validated | TopicsBrowse.tsx renders two-level topic hierarchy (6 categories from canonical_tags.yaml with expandable sub-topics showing technique_count and creator_count). Filter input narrows categories/sub-topics. Clicking sub-topic navigates to search with scope=topics. Backend GET /api/v1/topics aggregates counts from DB per sub-topic. Integration test verifies topic hierarchy response shape. | Yes | agent |\n| D014 | M001/S05 | requirement | R014 Creator Equity status | validated | CreatorsBrowse.tsx defaults to sort=random. Backend uses func.random() ORDER BY for randomized sort. Integration test verifies random sort returns all creators (order may vary). All creators get equal visual weight in the UI — no featured/highlighted treatment. Equal-weight row layout confirmed in CSS. | Yes | agent |\n| D015 | M002/S01 | architecture | Docker network subnet for Chrysopedia on ub01 | 172.32.0.0/24 — free on ub01, replaces 172.24.0.0/24 which is used by xpltd_docs_default | 172.24.0.0/24 was already allocated to xpltd_docs_default network on ub01. Scanned all existing subnets and 172.32.0.0/24 was the first unoccupied /24 in the 172.x range. | Yes | agent |\n| D016 | M002/S01 | architecture | Embedding service for the pipeline and search — OpenWebUI doesn't serve /v1/embeddings | Add Ollama container (ollama/ollama:latest) to the compose stack running nomic-embed-text on CPU | The FYN OpenWebUI instance at chat.forgetyour.name returns 404/500 on /v1/embeddings. No Ollama instance exists on the network. Ollama provides the standard OpenAI-compatible /v1/embeddings endpoint that both EmbeddingClient and SearchService expect. | Yes | agent |\n| D017 |  | frontend | CSS theming approach for dark mode | 77 semantic CSS custom properties in :root block, all hardcoded colors replaced with var(--*) references | A complete variable-based palette enables theme switching, ensures consistency, and makes future color changes single-point edits. 77 tokens (vs ~30 initially planned) were needed to cover all semantic contexts — badges, buttons, surfaces, text, accents, shadows, and status states. Cyan (#22d3ee) replaces indigo as the accent color throughout. | Yes | agent |\n| D018 | M004/S04 | architecture | Version snapshot failure handling strategy in stage 5 | Best-effort versioning — snapshot INSERT failure logs ERROR but does not block page update (existing content overwrites proceed regardless) | Versioning is a diagnostic/benchmarking feature. Losing a version snapshot is far less damaging than failing the entire page synthesis. Follows the same non-blocking side-effect pattern established in D005 for embedding/Qdrant failures. | Yes | agent |\n| D019 | M005/S02 | frontend-layout | Technique page layout structure | CSS grid 2-column layout: 1fr main + 22rem sticky sidebar, collapsing to single column at 768px. Page max-width widened from 48rem to 64rem. | 22rem sidebar provides enough room for moment cards and plugin lists without cramming. 64rem total width accommodates both columns comfortably on standard desktop displays. Sticky sidebar keeps navigation aids visible while scrolling prose. 768px breakpoint aligns with existing mobile styles in the codebase. | Yes | agent |\n| D020 | M006/S05 | frontend | Topics page card layout visual differentiation approach | 3px colored left border + small colored dot next to category name, using existing badge CSS custom properties per category | Subtler than a full colored header — maintains dark theme cohesion while providing clear visual differentiation between 7 category cards. Reuses existing --color-badge-cat-*-bg/text custom properties, avoiding new color definitions. | Yes | agent |\n\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** validated\n**Description:** Desktop Python script that accepts video files (MP4/MKV), extracts audio via ffmpeg, runs Whisper large-v3 on RTX 4090, and outputs timestamped transcript JSON with segment-level timestamps and word-level timing. Must be resumable.\n**Validation:** Script processes a sample video and produces valid JSON with timestamped segments.\n**Primary Owner:** M001/S01\n\n## R002 — Transcript Ingestion API\n**Status:** validated\n**Description:** FastAPI endpoint that accepts transcript JSON uploads, creates/updates Creator and Source Video records, and stores transcript data in PostgreSQL. Handles new creator detection from folder names.\n**Validation:** POST transcript JSON → 200 OK, records created in DB, file stored on filesystem.\n**Primary Owner:** M001/S02\n\n## R003 — LLM-Powered Extraction Pipeline (Stages 2-5)\n**Status:** validated\n**Description:** Background worker pipeline: transcript segmentation → key moment extraction → classification/tagging → technique page synthesis. Uses OpenAI-compatible API with primary (DGX Sparks Qwen) and fallback (local Ollama) endpoints. Pipeline must be resumable per-video per-stage.\n**Validation:** End-to-end: transcript JSON in → technique pages with key moments, tags, and cross-references out.\n**Primary Owner:** M001/S03\n\n## R004 — Review Queue UI\n**Status:** validated\n**Description:** Admin interface for reviewing extracted key moments: approve, edit+approve, split, merge, reject. Organized by source video for contextual review. Includes mode toggle (review vs auto-publish).\n**Validation:** Admin can review, edit, and approve/reject moments; mode toggle controls whether new moments require review.\n**Primary Owner:** M001/S04\n\n## R005 — Search-First Web UI\n**Status:** validated\n**Description:** Landing page with prominent search bar, live typeahead (results after 2-3 chars), scope toggle (All/Topics/Creators), and two navigation cards (Topics, Creators). Recently added section. Search powered by Qdrant semantic search with keyword fallback.\n**Validation:** User types query → results appear within 500ms, grouped by type, with clickable navigation.\n**Primary Owner:** M001/S05\n\n## R006 — Technique Page Display\n**Status:** validated\n**Description:** Core content unit: header (tags, title, creator, meta), study guide prose (organized by sub-aspects with signal chain blocks and quotes), key moments index (timestamped list), related techniques, plugins referenced. Amber banner for livestream-sourced content.\n**Validation:** Technique page renders with all sections populated from synthesized data.\n**Primary Owner:** M001/S05\n\n## R007 — Creators Browse Page\n**Status:** validated\n**Description:** Filterable creator list with genre filter pills, type-to-narrow, sort options (randomized default, alphabetical, view count). Each row: name, genre tags, technique count, video count, view count. Links to creator detail page.\n**Validation:** Page loads with randomized order, genre filtering works, clicking row navigates to creator detail.\n**Primary Owner:** M001/S05\n\n## R008 — Topics Browse Page\n**Status:** validated\n**Description:** Two-level topic hierarchy (6 top-level categories → sub-topics). Filter input, genre filter pills. Each sub-topic shows technique count and creator count. Clicking sub-topic shows technique pages.\n**Validation:** Hierarchy renders, filtering works, sub-topic links show correct technique pages.\n**Primary Owner:** M001/S05\n\n## R009 — Qdrant Vector Search Integration\n**Status:** validated\n**Description:** Embed key moment summaries, technique page content, and transcript segments in Qdrant using configurable embedding model (nomic-embed-text default). Power semantic search with metadata filtering.\n**Validation:** Semantic search returns relevant results for natural language queries; embeddings update when content changes.\n**Primary Owner:** M001/S03\n\n## R010 — Docker Compose Deployment\n**Status:** validated\n**Description:** Single docker-compose.yml packaging API, web UI, PostgreSQL, and worker services. Follows XPLTD conventions: bind mounts at /vmPool/r/services/, compose at /vmPool/r/compose/chrysopedia/, xpltd_chrysopedia project name, dedicated Docker network.\n**Validation:** `docker compose up -d` brings up all services; data persists across restarts.\n**Primary Owner:** M001/S01\n\n## R011 — Canonical Tag System\n**Status:** validated\n**Description:** Editable canonical tag list (config file) with aliases. Pipeline references tags during classification. New tags can be proposed by LLM and queued for admin approval or auto-added within existing categories.\n**Validation:** Tag list is editable; pipeline uses canonical tags consistently; alias normalization works.\n**Primary Owner:** M001/S03\n\n## R012 — Incremental Content Addition\n**Status:** validated\n**Description:** System handles ongoing content: new videos processed through pipeline, new creators auto-detected, existing technique pages updated when new moments are added for same creator+topic.\n**Validation:** Adding a new video for an existing creator updates their technique pages; new creator folder creates new Creator record.\n**Primary Owner:** M001/S03\n\n## R013 — Prompt Template System\n**Status:** validated\n**Description:** Extraction prompts (stages 2-5) stored as editable configuration files, not hardcoded. Admin can edit prompts and re-run extraction on specific or all videos for calibration.\n**Validation:** Prompt files are editable; re-processing a video with updated prompts produces different output.\n**Primary Owner:** M001/S03\n\n## R014 — Creator Equity\n**Status:** validated\n**Description:** No creator is privileged in the UI. Default sort on Creators page is randomized on every page load. All creators get equal visual weight.\n**Validation:** Refreshing Creators page shows different order each time; no creator gets larger/bolder display.\n**Primary Owner:** M001/S05\n\n## R015 — 30-Second Retrieval Target\n**Status:** active\n**Description:** A producer mid-session can find a specific technique in under 30 seconds from Alt+Tab to reading the key insight.\n**Validation:** Timed test: Alt+Tab → search → read technique → under 30 seconds.\n**Primary Owner:** M001/S05\n\n---\n\n[...truncated 6 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what to watch out for.\n\n- (no dependencies)\n\n## Your Role in the Pipeline\n\nYou are the scout. After you finish, a **planner agent** reads your output in a fresh context with no memory of your exploration. It uses your findings to decompose this slice into executable tasks — deciding what files change, what order to build things, how to verify the work. Then **executor agents** build each task in isolated context windows.\n\nWrite for the planner, not for a human. The planner needs:\n- **What files exist and what they do** — so it can scope tasks to specific files\n- **Where the natural seams are** — where work divides into independent units\n- **What to build or prove first** — what's riskiest, what unblocks everything else\n- **How to verify the result** — what commands, tests, or checks confirm the slice works\n\nIf the research doc is vague, the planner will waste its context re-exploring code you already read. If it's precise, the planner can decompose immediately.\n\n## Calibrate Depth\n\nRead the slice title, the roadmap excerpt, and any milestone research above. Ask: does this slice involve unfamiliar technology, risky integration, novel architecture, or ambiguous requirements? Or is it straightforward application of known patterns to known code?\n\n- **Deep research** — new technology, unfamiliar APIs, risky integration, multiple viable approaches, or ambiguous scope. Explore broadly, look up docs, check libraries, investigate constraints. Write all template sections. This is the default when you're genuinely uncertain.\n- **Targeted research** — known technology but new to this codebase, or moderately complex integration. Explore the relevant code, check one or two libraries, identify constraints. Omit Don't Hand-Roll and Sources if nothing applies.\n- **Light research** — well-understood work using established patterns already in the codebase (wiring up existing APIs, adding standard UI components, CRUD operations, configuration changes). Read the relevant files to confirm the pattern, note any constraints, write Summary + Recommendation + Implementation Landscape. Skip the rest. A light research doc can be 15-20 lines. Don't manufacture pitfalls or risks for work that doesn't have them.\n\nAn honest \"this is straightforward, here's the pattern to follow\" is more valuable than invented complexity.\n\n## Steps\n\nResearch what this slice needs. Narrate key findings and surprises as you go — what exists, what's missing, what constrains the approach.\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements this slice owns or supports. Research should target these requirements — surfacing risks, unknowns, and implementation constraints that could affect whether the slice actually delivers them.\n1.  Reference specific rules from loaded skills in your findings where they inform the implementation approach.\n2. **Skill Discovery (suggest):**\n   Identify the key technologies, frameworks, and services this work depends on (e.g. Stripe, Clerk, Supabase, JUCE, SwiftUI).\n   For each, check if a professional agent skill already exists:\n   - First check `<available_skills>` in your system prompt — a skill may already be installed.\n   - For technologies without an installed skill, run: `npx skills find \"<technology>\"`\n   - Only consider skills that are **directly relevant** to core technologies — not tangentially related.\n   - Evaluate results by install count and relevance to the actual work.\n   - Note promising skills in your research output with their install commands, but do NOT install them.\n   - The user will decide which to install.\n3. Explore relevant code for this slice's scope. For targeted exploration, use `rg`, `find`, and reads. For broad or unfamiliar subsystems, use `scout` to map the relevant area first.\n4. Use `resolve_library` / `get_library_docs` for unfamiliar libraries — skip this for libraries already used in the codebase\n5. **Web search budget:** You have a limited budget of web searches (max ~15 per session). Use them strategically — prefer `resolve_library` / `get_library_docs` for library documentation. Do NOT repeat the same or similar queries. If a search didn't find what you need, rephrase once or move on. Target 3-5 total web searches for a typical research unit.\n6. Use the **Research** output template from the inlined context above — include only sections that have real content. The template is already inlined above; do NOT attempt to read any template file from disk (there is no `templates/SLICE-RESEARCH.md` — the correct template is already present in this prompt).\n7. Call `gsd_summary_save` with `milestone_id: M007`, `slice_id: S03`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M007/slices/S03/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S03 researched.\"","display":false,"id":"7d33f3e6","parentId":"95cbaf65","timestamp":"2026-03-30T19:10:28.900Z"}
{"type":"message","id":"55ddd6c5","parentId":"7d33f3e6","timestamp":"2026-03-30T19:10:40.149Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me understand the slice: \"Transcript Folder Watcher — Auto-Ingest Service\". The goal is to drop a transcript JSON into a watch folder on ub01 → file is detected, validated, POSTed to /ingest, pipeline triggers automatically.\n\nLet me explore the codebase to understand the current ingest flow, deployment setup, and what's needed.","thinkingSignature":"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"},{"type":"text","text":"I need to understand the current ingest flow, deployment topology, and what a folder watcher service would look like. Let me explore the relevant code."},{"type":"toolCall","id":"toolu_015A74e3AF6gs62wZqrWNK7c","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend -type f -name \"*.py\" | head -40"}},{"type":"toolCall","id":"toolu_01KyKhCdjWDDQffreHB11cn8","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat docker-compose.yml | head -80"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":274,"cacheRead":58112,"cacheWrite":6519,"totalTokens":64908,"cost":{"input":0.000015000000000000002,"output":0.00685,"cacheRead":0.029056,"cacheWrite":0.04074375,"total":0.07666475}},"stopReason":"toolUse","timestamp":1774897828898}}
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{"type":"message","id":"5ac3e680","parentId":"da83c383","timestamp":"2026-03-30T19:10:44.303Z","message":{"role":"toolResult","toolCallId":"toolu_01KiPwZ7nEyqNhbqqVzLbmTr","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774897844303}}
{"type":"message","id":"e826db0c","parentId":"5ac3e680","timestamp":"2026-03-30T19:10:44.305Z","message":{"role":"toolResult","toolCallId":"toolu_01VQdAr2aU2guhMqGS2Evx7k","toolName":"bash","content":[{"type":"text","text":"./backend/pipeline/stages.py\n"}],"isError":false,"timestamp":1774897844303}}
{"type":"message","id":"21ea4962","parentId":"e826db0c","timestamp":"2026-03-30T19:10:49.270Z","message":{"role":"assistant","content":[{"type":"text","text":"Interesting — the repo structure is minimal locally. The CLAUDE.md says the canonical codebase is on ub01. Let me check what's actually here and look at the remote repo structure."},{"type":"toolCall","id":"toolu_01DL4ZWvEAbsKrRKQiTt7moG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls -la"}},{"type":"toolCall","id":"toolu_017WX4y9eGc7PVxvQToyQHPz","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && git log --oneline -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":172,"cacheRead":64892,"cacheWrite":235,"totalTokens":65300,"cost":{"input":0.000005,"output":0.0043,"cacheRead":0.032445999999999996,"cacheWrite":0.00146875,"total":0.03821974999999999}},"stopReason":"toolUse","timestamp":1774897844304}}
{"type":"message","id":"be6326fa","parentId":"21ea4962","timestamp":"2026-03-30T19:10:49.283Z","message":{"role":"toolResult","toolCallId":"toolu_01DL4ZWvEAbsKrRKQiTt7moG","toolName":"bash","content":[{"type":"text","text":"total 48\ndrwxrwxr-x 10 aux aux 4096 Mar 30 19:01 .\ndrwxrwxr-x  4 aux aux 4096 Mar 29 17:47 ..\ndrwxrwxr-x  3 aux aux 4096 Mar 30 08:33 .artifacts\ndrwxrwxr-x  3 aux aux 4096 Mar 30 19:01 backend\ndrwxrwxr-x  2 aux aux 4096 Mar 30 07:38 .bg-shell\ndrwxrwxr-x  2 aux aux 4096 Mar 30 12:44 .claude\n-rw-rw-r--  1 aux aux 2119 Mar 30 19:01 CLAUDE.md\ndrwxrwxr-x  5 aux aux 4096 Mar 30 19:01 frontend\ndrwxrwxr-x  8 aux aux 4096 Mar 30 19:07 .git\n-rw-rw-r--  1 aux aux  353 Mar 29 21:39 .gitignore\ndrwxrwxr-x  6 aux aux 4096 Mar 30 19:10 .gsd\ndrwxrwxr-x  2 aux aux 4096 Mar 30 19:10 .gsd.lock\n"}],"isError":false,"timestamp":1774897849283}}
{"type":"message","id":"42868a3e","parentId":"be6326fa","timestamp":"2026-03-30T19:10:49.291Z","message":{"role":"toolResult","toolCallId":"toolu_017WX4y9eGc7PVxvQToyQHPz","toolName":"bash","content":[{"type":"text","text":"07eaa7f feat: Added DebugPayloadViewer component with collapsible System Prompt…\n653c927 feat: DebugPayloadViewer component with copy, export, collapsible sections\n8c89b5d feat: Wired debug-mode-aware LLM I/O capture into pipeline callback — _…\nbe6b2d5 feat: Added 3 TEXT columns to pipeline_events (system_prompt_text, user…\n0484c15 chore: auto-commit after complete-milestone\n2e9ef20 feat: Updated Dockerfile.web and docker-compose.yml on ub01 to pass VIT…\ne6ce650 feat: Added AppFooter component displaying app version, build date, com…\n7533234 feat: Rewrote TopicsBrowse.tsx from vertical accordion to responsive 2-…\n3f3fe06 feat: Added Music Theory as 7th category in canonical_tags.yaml with 8…\n61d52d7 feat: Reordered technique page sidebar (plugins first), added prominent…\nb4d4cae feat: Added \"Commit\" row to version metadata panel on TechniquePage — r…\n12f9fb7 chore: Added GIT_COMMIT_SHA build arg to Dockerfile.api, compose build…\nee24731 feat: Added Head/Tail segmented toggle to EventLog with order param wir…\nbf126f4 feat: Added `order` query parameter (asc/desc, default desc) to pipelin…\n05c7ba3 feat: Created AdminDropdown component with click-outside/Escape close,…\n94460fa chore: auto-commit after complete-milestone\nc6efec8 feat: Split key moment card header into standalone h3 title and flex-ro…\naa71387 feat: Added CSS grid layout splitting technique page into prose (left)…\n26556ba feat: Built AdminPipeline.tsx page at /admin/pipeline with video table,…\nb3d405b fix: All five admin pipeline endpoints respond correctly — fix was ngin…\n"}],"isError":false,"timestamp":1774897849289}}
{"type":"message","id":"af91bae9","parentId":"42868a3e","timestamp":"2026-03-30T19:10:53.017Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01HQCd21Z1wiwf3mYzKfaJHE","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && git ls-tree -r HEAD --name-only | grep -E \"\\.py$|\\.ts$|\\.tsx$|docker|Dockerfile|compose\" | sort"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":105,"cacheRead":65127,"cacheWrite":1078,"totalTokens":66311,"cost":{"input":0.000005,"output":0.002625,"cacheRead":0.032563499999999995,"cacheWrite":0.0067375,"total":0.041930999999999996}},"stopReason":"toolUse","timestamp":1774897849290}}
{"type":"message","id":"d304aefb","parentId":"af91bae9","timestamp":"2026-03-30T19:10:53.034Z","message":{"role":"toolResult","toolCallId":"toolu_01HQCd21Z1wiwf3mYzKfaJHE","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/stages.py\nfrontend/src/api/client.ts\nfrontend/src/api/public-client.ts\nfrontend/src/App.tsx\nfrontend/src/components/AdminDropdown.tsx\nfrontend/src/components/AppFooter.tsx\nfrontend/src/components/ModeToggle.tsx\nfrontend/src/components/ReportIssueModal.tsx\nfrontend/src/components/StatusBadge.tsx\nfrontend/src/main.tsx\nfrontend/src/pages/AdminPipeline.tsx\nfrontend/src/pages/AdminReports.tsx\nfrontend/src/pages/CreatorDetail.tsx\nfrontend/src/pages/CreatorsBrowse.tsx\nfrontend/src/pages/Home.tsx\nfrontend/src/pages/MomentDetail.tsx\nfrontend/src/pages/ReviewQueue.tsx\nfrontend/src/pages/SearchResults.tsx\nfrontend/src/pages/TechniquePage.tsx\nfrontend/src/pages/TopicsBrowse.tsx\nfrontend/src/vite-env.d.ts\nfrontend/vite.config.ts\n"}],"isError":false,"timestamp":1774897853033}}
{"type":"message","id":"a20c39d1","parentId":"d304aefb","timestamp":"2026-03-30T19:10:58.469Z","message":{"role":"assistant","content":[{"type":"text","text":"The local repo has very few backend files — most of the code lives on ub01. Let me check what's on the remote."},{"type":"toolCall","id":"toolu_01UJ43GLev7sywgjT4fHtc8E","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && find backend -type f -name '*.py' | sort\""}},{"type":"toolCall","id":"toolu_01Pfs8eFFmtaM4jT6irUiexu","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat docker-compose.yml\" 2>/dev/null | head -120"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":195,"cacheRead":66205,"cacheWrite":373,"totalTokens":66774,"cost":{"input":0.000005,"output":0.004875,"cacheRead":0.0331025,"cacheWrite":0.00233125,"total":0.04031375}},"stopReason":"toolUse","timestamp":1774897853033}}
{"type":"message","id":"b8e3bfcb","parentId":"a20c39d1","timestamp":"2026-03-30T19:10:59.253Z","message":{"role":"toolResult","toolCallId":"toolu_01UJ43GLev7sywgjT4fHtc8E","toolName":"bash","content":[{"type":"text","text":"backend/config.py\nbackend/database.py\nbackend/main.py\nbackend/models.py\nbackend/pipeline/embedding_client.py\nbackend/pipeline/__init__.py\nbackend/pipeline/llm_client.py\nbackend/pipeline/qdrant_client.py\nbackend/pipeline/schemas.py\nbackend/pipeline/stages.py\nbackend/redis_client.py\nbackend/routers/creators.py\nbackend/routers/health.py\nbackend/routers/ingest.py\nbackend/routers/__init__.py\nbackend/routers/pipeline.py\nbackend/routers/reports.py\nbackend/routers/review.py\nbackend/routers/search.py\nbackend/routers/techniques.py\nbackend/routers/topics.py\nbackend/routers/videos.py\nbackend/schemas.py\nbackend/search_service.py\nbackend/tests/conftest.py\nbackend/tests/fixtures/mock_llm_responses.py\nbackend/tests/__init__.py\nbackend/tests/test_ingest.py\nbackend/tests/test_pipeline.py\nbackend/tests/test_public_api.py\nbackend/tests/test_review.py\nbackend/tests/test_search.py\nbackend/worker.py\n"}],"isError":false,"timestamp":1774897859253}}
{"type":"message","id":"c9e4eeda","parentId":"b8e3bfcb","timestamp":"2026-03-30T19:10:59.278Z","message":{"role":"toolResult","toolCallId":"toolu_01Pfs8eFFmtaM4jT6irUiexu","toolName":"bash","content":[{"type":"text","text":"# Chrysopedia — Docker Compose\n# XPLTD convention: xpltd_chrysopedia project, bind mounts, dedicated bridge\n# Deployed to: /vmPool/r/compose/xpltd_chrysopedia/ (symlinked)\nname: xpltd_chrysopedia\n\nservices:\n  # ── PostgreSQL 16 ──\n  chrysopedia-db:\n    image: postgres:16-alpine\n    container_name: chrysopedia-db\n    restart: unless-stopped\n    environment:\n      POSTGRES_USER: ${POSTGRES_USER:-chrysopedia}\n      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-changeme}\n      POSTGRES_DB: ${POSTGRES_DB:-chrysopedia}\n    volumes:\n      - /vmPool/r/services/chrysopedia_db:/var/lib/postgresql/data\n    ports:\n      - \"127.0.0.1:5433:5432\"\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD-SHELL\", \"pg_isready -U ${POSTGRES_USER:-chrysopedia}\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n    stop_grace_period: 30s\n\n  # ── Redis (Celery broker + runtime config) ──\n  chrysopedia-redis:\n    image: redis:7-alpine\n    container_name: chrysopedia-redis\n    restart: unless-stopped\n    command: redis-server --save 60 1 --loglevel warning\n    volumes:\n      - /vmPool/r/services/chrysopedia_redis:/data\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD\", \"redis-cli\", \"ping\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n    stop_grace_period: 15s\n\n  # ── Qdrant vector database ──\n  chrysopedia-qdrant:\n    image: qdrant/qdrant:v1.13.2\n    container_name: chrysopedia-qdrant\n    restart: unless-stopped\n    volumes:\n      - /vmPool/r/services/chrysopedia_qdrant:/qdrant/storage\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD-SHELL\", \"bash -c 'echo > /dev/tcp/localhost/6333'\"]\n      interval: 15s\n      timeout: 5s\n      retries: 5\n      start_period: 10s\n    stop_grace_period: 30s\n\n  # ── Ollama (embedding model server) ──\n  chrysopedia-ollama:\n    image: ollama/ollama:latest\n    container_name: chrysopedia-ollama\n    restart: unless-stopped\n    volumes:\n      - /vmPool/r/services/chrysopedia_ollama:/root/.ollama\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD\", \"ollama\", \"list\"]\n      interval: 15s\n      timeout: 5s\n      retries: 5\n      start_period: 30s\n    stop_grace_period: 15s\n\n  # ── FastAPI application ──\n  chrysopedia-api:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-api\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD:-changeme}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n      QDRANT_URL: http://chrysopedia-qdrant:6333\n      EMBEDDING_API_URL: http://chrysopedia-ollama:11434/v1\n      PROMPTS_PATH: /prompts\n    volumes:\n      - /vmPool/r/services/chrysopedia_data:/data\n      - ./config:/config:ro\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n      chrysopedia-qdrant:\n        condition: service_healthy\n      chrysopedia-ollama:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n    stop_grace_period: 15s\n\n  # ── Celery worker (pipeline stages 2-6) ──\n  chrysopedia-worker:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-worker\n    restart: unless-stopped\n    env_file:\n      - path: .env\n"}],"isError":false,"timestamp":1774897859277}}
{"type":"message","id":"bcd19ee7","parentId":"c9e4eeda","timestamp":"2026-03-30T19:11:03.353Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Esg8gRgycyxJBBKTAWW2PZ","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat docker-compose.yml\" 2>/dev/null | tail -80"}},{"type":"toolCall","id":"toolu_01KeqkdDZkj13DgzaKqfmGTg","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat backend/routers/ingest.py\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":163,"cacheRead":66578,"cacheWrite":1731,"totalTokens":68473,"cost":{"input":0.000005,"output":0.0040750000000000005,"cacheRead":0.033289,"cacheWrite":0.01081875,"total":0.04818775}},"stopReason":"toolUse","timestamp":1774897859278}}
{"type":"message","id":"ab44c4a9","parentId":"bcd19ee7","timestamp":"2026-03-30T19:11:03.686Z","message":{"role":"toolResult","toolCallId":"toolu_01Esg8gRgycyxJBBKTAWW2PZ","toolName":"bash","content":[{"type":"text","text":"        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n      chrysopedia-qdrant:\n        condition: service_healthy\n      chrysopedia-ollama:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n    stop_grace_period: 15s\n\n  # ── Celery worker (pipeline stages 2-6) ──\n  chrysopedia-worker:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-worker\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD:-changeme}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n      QDRANT_URL: http://chrysopedia-qdrant:6333\n      EMBEDDING_API_URL: http://chrysopedia-ollama:11434/v1\n      PROMPTS_PATH: /prompts\n    command: [\"celery\", \"-A\", \"worker\", \"worker\", \"--loglevel=info\", \"--concurrency=1\"]\n    healthcheck:\n      test: [\"CMD-SHELL\", \"celery -A worker inspect ping --timeout=5 2>/dev/null | grep -q pong || exit 1\"]\n      interval: 30s\n      timeout: 10s\n      retries: 3\n      start_period: 30s\n    volumes:\n      - /vmPool/r/services/chrysopedia_data:/data\n      - ./prompts:/prompts:ro\n      - ./config:/config:ro\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n      chrysopedia-qdrant:\n        condition: service_healthy\n      chrysopedia-ollama:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n    stop_grace_period: 30s\n\n  # ── React web UI (nginx) ──\n  chrysopedia-web:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.web\n      args:\n        VITE_GIT_COMMIT: ${GIT_COMMIT_SHA:-dev}\n    container_name: chrysopedia-web-8096\n    restart: unless-stopped\n    ports:\n      - \"0.0.0.0:8096:80\"\n    depends_on:\n      - chrysopedia-api\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD-SHELL\", \"curl -sf http://127.0.0.1:80/ || exit 1\"]\n      interval: 30s\n      timeout: 5s\n      retries: 3\n      start_period: 10s\n    stop_grace_period: 15s\n\nnetworks:\n  chrysopedia:\n    driver: bridge\n    ipam:\n      config:\n        - subnet: \"172.32.0.0/24\"\n"}],"isError":false,"timestamp":1774897863686}}
{"type":"message","id":"546bc9c6","parentId":"ab44c4a9","timestamp":"2026-03-30T19:11:03.714Z","message":{"role":"toolResult","toolCallId":"toolu_01KeqkdDZkj13DgzaKqfmGTg","toolName":"bash","content":[{"type":"text","text":"\"\"\"Transcript ingestion endpoint for the Chrysopedia API.\n\nAccepts a Whisper-format transcript JSON via multipart file upload, finds or\ncreates a Creator, upserts a SourceVideo, bulk-inserts TranscriptSegments,\npersists the raw JSON to disk, and returns a structured response.\n\"\"\"\n\nimport hashlib\nimport json\nimport logging\nimport os\nimport re\nimport uuid\n\nfrom fastapi import APIRouter, Depends, HTTPException, UploadFile\nfrom sqlalchemy import delete, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import ContentType, Creator, ProcessingStatus, SourceVideo, TranscriptSegment\nfrom schemas import TranscriptIngestResponse\n\nlogger = logging.getLogger(\"chrysopedia.ingest\")\n\nrouter = APIRouter(prefix=\"/ingest\", tags=[\"ingest\"])\n\nREQUIRED_KEYS = {\"source_file\", \"creator_folder\", \"duration_seconds\", \"segments\"}\n\n\ndef slugify(value: str) -> str:\n    \"\"\"Lowercase, replace non-alphanumeric chars with hyphens, collapse/strip.\"\"\"\n    value = value.lower()\n    value = re.sub(r\"[^a-z0-9]+\", \"-\", value)\n    value = value.strip(\"-\")\n    value = re.sub(r\"-{2,}\", \"-\", value)\n    return value\n\n\n\ndef compute_content_hash(segments: list[dict]) -> str:\n    \"\"\"Compute a stable SHA-256 hash from transcript segment text.\n\n    Hashes only the segment text content in order, ignoring metadata like\n    filenames, timestamps, or dates. Two transcripts of the same audio will\n    produce identical hashes even if ingested with different filenames.\n    \"\"\"\n    h = hashlib.sha256()\n    for seg in segments:\n        h.update(str(seg.get(\"text\", \"\")).encode(\"utf-8\"))\n    return h.hexdigest()\n\n\n@router.post(\"\", response_model=TranscriptIngestResponse)\nasync def ingest_transcript(\n    file: UploadFile,\n    db: AsyncSession = Depends(get_session),\n) -> TranscriptIngestResponse:\n    \"\"\"Ingest a Whisper transcript JSON file.\n\n    Workflow:\n      1. Parse and validate the uploaded JSON.\n      2. Find-or-create a Creator by folder_name.\n      3. Upsert a SourceVideo by (creator_id, filename).\n      4. Bulk-insert TranscriptSegment rows.\n      5. Save raw JSON to transcript_storage_path.\n      6. Return structured response.\n    \"\"\"\n    settings = get_settings()\n\n    # ── 1. Read & parse JSON ─────────────────────────────────────────────\n    try:\n        raw_bytes = await file.read()\n        raw_text = raw_bytes.decode(\"utf-8\")\n    except Exception as exc:\n        raise HTTPException(status_code=400, detail=f\"Invalid file: {exc}\") from exc\n\n    try:\n        data = json.loads(raw_text)\n    except json.JSONDecodeError as exc:\n        raise HTTPException(\n            status_code=422, detail=f\"JSON parse error: {exc}\"\n        ) from exc\n\n    if not isinstance(data, dict):\n        raise HTTPException(status_code=422, detail=\"Expected a JSON object at the top level\")\n\n    missing = REQUIRED_KEYS - data.keys()\n    if missing:\n        raise HTTPException(\n            status_code=422,\n            detail=f\"Missing required keys: {', '.join(sorted(missing))}\",\n        )\n\n    source_file: str = data[\"source_file\"]\n    creator_folder: str = data[\"creator_folder\"]\n    duration_seconds: int | None = data.get(\"duration_seconds\")\n    segments_data: list = data[\"segments\"]\n\n    if not isinstance(segments_data, list):\n        raise HTTPException(status_code=422, detail=\"'segments' must be an array\")\n\n    content_hash = compute_content_hash(segments_data)\n    logger.info(\"Content hash for %s: %s\", source_file, content_hash)\n\n    # ── 2. Find-or-create Creator ────────────────────────────────────────\n    stmt = select(Creator).where(Creator.folder_name == creator_folder)\n    result = await db.execute(stmt)\n    creator = result.scalar_one_or_none()\n\n    if creator is None:\n        creator = Creator(\n            name=creator_folder,\n            slug=slugify(creator_folder),\n            folder_name=creator_folder,\n        )\n        db.add(creator)\n        await db.flush()  # assign id\n\n    # ── 3. Upsert SourceVideo ────────────────────────────────────────────\n    # First check for exact filename match (original behavior)\n    stmt = select(SourceVideo).where(\n        SourceVideo.creator_id == creator.id,\n        SourceVideo.filename == source_file,\n    )\n    result = await db.execute(stmt)\n    existing_video = result.scalar_one_or_none()\n\n    # Tier 2: content hash match (same audio, different filename/metadata)\n    matched_video = None\n    match_reason = None\n    if existing_video is None:\n        stmt = select(SourceVideo).where(\n            SourceVideo.content_hash == content_hash,\n        )\n        result = await db.execute(stmt)\n        matched_video = result.scalar_one_or_none()\n        if matched_video:\n            match_reason = \"content_hash\"\n\n    # Tier 3: filename + duration match (same yt-dlp download, re-encoded)\n    if existing_video is None and matched_video is None and duration_seconds is not None:\n        # Strip common prefixes like dates (e.g. \"2023-07-19 \") and extensions\n        # to get a normalized base name for fuzzy matching\n        base_name = re.sub(r\"^\\d{4}-\\d{2}-\\d{2}\\s+\", \"\", source_file)\n        base_name = re.sub(r\"\\s*\\(\\d+p\\).*$\", \"\", base_name)  # strip resolution suffix\n        base_name = os.path.splitext(base_name)[0].strip()\n\n        stmt = select(SourceVideo).where(\n            SourceVideo.creator_id == creator.id,\n            SourceVideo.duration_seconds == duration_seconds,\n        )\n        result = await db.execute(stmt)\n        candidates = result.scalars().all()\n        for candidate in candidates:\n            cand_name = re.sub(r\"^\\d{4}-\\d{2}-\\d{2}\\s+\", \"\", candidate.filename)\n            cand_name = re.sub(r\"\\s*\\(\\d+p\\).*$\", \"\", cand_name)\n            cand_name = os.path.splitext(cand_name)[0].strip()\n            if cand_name == base_name:\n                matched_video = candidate\n                match_reason = \"filename+duration\"\n                break\n\n    is_reupload = existing_video is not None\n    is_duplicate_content = matched_video is not None\n\n    if is_duplicate_content:\n        logger.info(\n            \"Duplicate detected via %s: '%s' matches existing video '%s' (%s)\",\n            match_reason, source_file, matched_video.filename, matched_video.id,\n        )\n\n    if is_reupload:\n        video = existing_video\n        # Delete old segments for idempotent re-upload\n        await db.execute(\n            delete(TranscriptSegment).where(\n                TranscriptSegment.source_video_id == video.id\n            )\n        )\n        video.duration_seconds = duration_seconds\n        video.content_hash = content_hash\n        video.processing_status = ProcessingStatus.transcribed\n    elif is_duplicate_content:\n        # Same content, different filename — update the existing record\n        video = matched_video\n        await db.execute(\n            delete(TranscriptSegment).where(\n                TranscriptSegment.source_video_id == video.id\n            )\n        )\n        video.filename = source_file\n        video.file_path = f\"{creator_folder}/{source_file}\"\n        video.duration_seconds = duration_seconds\n        video.content_hash = content_hash\n        video.processing_status = ProcessingStatus.transcribed\n        is_reupload = True  # Treat as reupload for response\n    else:\n        video = SourceVideo(\n            creator_id=creator.id,\n            filename=source_file,\n            file_path=f\"{creator_folder}/{source_file}\",\n            duration_seconds=duration_seconds,\n            content_type=ContentType.tutorial,\n            content_hash=content_hash,\n            processing_status=ProcessingStatus.transcribed,\n        )\n        db.add(video)\n        await db.flush()  # assign id\n\n    # ── 4. Bulk-insert TranscriptSegments ────────────────────────────────\n    segment_objs = [\n        TranscriptSegment(\n            source_video_id=video.id,\n            start_time=float(seg[\"start\"]),\n            end_time=float(seg[\"end\"]),\n            text=str(seg[\"text\"]),\n            segment_index=idx,\n        )\n        for idx, seg in enumerate(segments_data)\n    ]\n    db.add_all(segment_objs)\n\n    # ── 5. Save raw JSON to disk ─────────────────────────────────────────\n    transcript_dir = os.path.join(\n        settings.transcript_storage_path, creator_folder\n    )\n    transcript_path = os.path.join(transcript_dir, f\"{source_file}.json\")\n\n    try:\n        os.makedirs(transcript_dir, exist_ok=True)\n        with open(transcript_path, \"w\", encoding=\"utf-8\") as f:\n            f.write(raw_text)\n    except OSError as exc:\n        raise HTTPException(\n            status_code=500, detail=f\"Failed to save transcript: {exc}\"\n        ) from exc\n\n    video.transcript_path = transcript_path\n\n    # ── 6. Commit & respond ──────────────────────────────────────────────\n    try:\n        await db.commit()\n    except Exception as exc:\n        await db.rollback()\n        logger.error(\"Database commit failed during ingest: %s\", exc)\n        raise HTTPException(\n            status_code=500, detail=\"Database error during ingest\"\n        ) from exc\n\n    await db.refresh(video)\n    await db.refresh(creator)\n\n    # ── 7. Dispatch LLM pipeline (best-effort) ──────────────────────────\n    try:\n        from pipeline.stages import run_pipeline\n\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline dispatched for video_id=%s\", video.id)\n    except Exception as exc:\n        logger.warning(\n            \"Pipeline dispatch failed for video_id=%s (ingest still succeeds): %s\",\n            video.id,\n            exc,\n        )\n\n    logger.info(\n        \"Ingested transcript: creator=%s, file=%s, segments=%d, reupload=%s\",\n        creator.name,\n        source_file,\n        len(segment_objs),\n        is_reupload,\n    )\n\n    return TranscriptIngestResponse(\n        video_id=video.id,\n        creator_id=creator.id,\n        creator_name=creator.name,\n        filename=source_file,\n        segments_stored=len(segment_objs),\n        processing_status=video.processing_status.value,\n        is_reupload=is_reupload,\n        content_hash=content_hash,\n    )\n"}],"isError":false,"timestamp":1774897863713}}
{"type":"message","id":"e99628ba","parentId":"546bc9c6","timestamp":"2026-03-30T19:11:09.245Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — the ingest endpoint accepts multipart file upload, validates, creates DB records, and dispatches the pipeline. Now let me check the config, the watch folder expectations, and how transcripts are currently produced."},{"type":"toolCall","id":"toolu_01Cr5UikXTYNxWsdYwi82b2W","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat backend/config.py\""}},{"type":"toolCall","id":"toolu_01FSgtULMVfyM8st7dMkXzK2","name":"bash","arguments":{"command":"ssh ub01 \"ls /vmPool/r/services/chrysopedia_data/ 2>/dev/null\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":190,"cacheRead":68309,"cacheWrite":3911,"totalTokens":72411,"cost":{"input":0.000005,"output":0.00475,"cacheRead":0.0341545,"cacheWrite":0.02444375,"total":0.06335325}},"stopReason":"toolUse","timestamp":1774897863713}}
{"type":"message","id":"f840c74c","parentId":"e99628ba","timestamp":"2026-03-30T19:11:09.590Z","message":{"role":"toolResult","toolCallId":"toolu_01Cr5UikXTYNxWsdYwi82b2W","toolName":"bash","content":[{"type":"text","text":"\"\"\"Application configuration loaded from environment variables.\"\"\"\n\nfrom functools import lru_cache\n\nfrom pydantic_settings import BaseSettings\n\n\nclass Settings(BaseSettings):\n    \"\"\"Chrysopedia API settings.\n\n    Values are loaded from environment variables (or .env file via\n    pydantic-settings' dotenv support).\n    \"\"\"\n\n    # Database\n    database_url: str = \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\"\n\n    # Redis\n    redis_url: str = \"redis://localhost:6379/0\"\n\n    # Application\n    app_env: str = \"development\"\n    app_log_level: str = \"info\"\n    app_secret_key: str = \"changeme-generate-a-real-secret\"\n\n    # CORS\n    cors_origins: list[str] = [\"*\"]\n\n    # LLM endpoint (OpenAI-compatible)\n    llm_api_url: str = \"http://localhost:11434/v1\"\n    llm_api_key: str = \"sk-placeholder\"\n    llm_model: str = \"fyn-llm-agent-chat\"\n    llm_fallback_url: str = \"http://localhost:11434/v1\"\n    llm_fallback_model: str = \"fyn-llm-agent-chat\"\n\n    # Per-stage model overrides (optional — falls back to llm_model / \"chat\")\n    llm_stage2_model: str | None = \"fyn-llm-agent-chat\"   # segmentation — mechanical, fast chat\n    llm_stage2_modality: str = \"chat\"\n    llm_stage3_model: str | None = \"fyn-llm-agent-think\"  # extraction — reasoning\n    llm_stage3_modality: str = \"thinking\"\n    llm_stage4_model: str | None = \"fyn-llm-agent-chat\"   # classification — mechanical, fast chat\n    llm_stage4_modality: str = \"chat\"\n    llm_stage5_model: str | None = \"fyn-llm-agent-think\"  # synthesis — reasoning\n    llm_stage5_modality: str = \"thinking\"\n\n    # Dynamic token estimation — each stage calculates max_tokens from input size\n    llm_max_tokens_hard_limit: int = 32768   # Hard ceiling for dynamic estimator\n    llm_max_tokens: int = 65536              # Fallback when no estimate is provided\n\n    # Embedding endpoint\n    embedding_api_url: str = \"http://localhost:11434/v1\"\n    embedding_model: str = \"nomic-embed-text\"\n    embedding_dimensions: int = 768\n\n    # Qdrant\n    qdrant_url: str = \"http://localhost:6333\"\n    qdrant_collection: str = \"chrysopedia\"\n\n    # Prompt templates\n    prompts_path: str = \"./prompts\"\n\n    # Review mode — when True, extracted moments go to review queue before publishing\n    review_mode: bool = True\n\n    # Debug mode — when True, pipeline captures full LLM prompts and responses\n    debug_mode: bool = False\n\n    # File storage\n    transcript_storage_path: str = \"/data/transcripts\"\n    video_metadata_path: str = \"/data/video_meta\"\n\n    # Git commit SHA (set at Docker build time or via env var)\n    git_commit_sha: str = \"unknown\"\n\n    model_config = {\n        \"env_file\": \".env\",\n        \"env_file_encoding\": \"utf-8\",\n        \"case_sensitive\": False,\n    }\n\n\n@lru_cache\ndef get_settings() -> Settings:\n    \"\"\"Return cached application settings (singleton).\"\"\"\n    return Settings()\n"}],"isError":false,"timestamp":1774897869590}}
{"type":"message","id":"ea0011c8","parentId":"f840c74c","timestamp":"2026-03-30T19:11:09.594Z","message":{"role":"toolResult","toolCallId":"toolu_01FSgtULMVfyM8st7dMkXzK2","toolName":"bash","content":[{"type":"text","text":"transcripts\nvideo_meta\n"}],"isError":false,"timestamp":1774897869592}}
{"type":"message","id":"538de28b","parentId":"ea0011c8","timestamp":"2026-03-30T19:11:13.713Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0197TCKQU7vo3MSMNvFgC4xn","name":"bash","arguments":{"command":"ssh ub01 \"ls /vmPool/r/services/chrysopedia_data/transcripts/ 2>/dev/null\""}},{"type":"toolCall","id":"toolu_01VucchhnTTfqtWg3h9akGFj","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat backend/worker.py\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":148,"cacheRead":72220,"cacheWrite":1165,"totalTokens":73534,"cost":{"input":0.000005,"output":0.0037,"cacheRead":0.036109999999999996,"cacheWrite":0.00728125,"total":0.04709625}},"stopReason":"toolUse","timestamp":1774897869592}}
{"type":"message","id":"cd77b1d0","parentId":"538de28b","timestamp":"2026-03-30T19:11:14.063Z","message":{"role":"toolResult","toolCallId":"toolu_0197TCKQU7vo3MSMNvFgC4xn","toolName":"bash","content":[{"type":"text","text":"Break\nCaracal Project, The\nChee\nCOPYCATT\nGlorkglunk\nSkope\n"}],"isError":false,"timestamp":1774897874063}}
{"type":"message","id":"55071f2a","parentId":"cd77b1d0","timestamp":"2026-03-30T19:11:14.068Z","message":{"role":"toolResult","toolCallId":"toolu_01VucchhnTTfqtWg3h9akGFj","toolName":"bash","content":[{"type":"text","text":"\"\"\"Celery application instance for the Chrysopedia pipeline.\n\nUsage:\n    celery -A worker worker --loglevel=info\n\"\"\"\n\nfrom celery import Celery\n\nfrom config import get_settings\n\nsettings = get_settings()\n\ncelery_app = Celery(\n    \"chrysopedia\",\n    broker=settings.redis_url,\n    backend=settings.redis_url,\n)\n\ncelery_app.conf.update(\n    task_serializer=\"json\",\n    result_serializer=\"json\",\n    accept_content=[\"json\"],\n    timezone=\"UTC\",\n    enable_utc=True,\n    task_track_started=True,\n    task_acks_late=True,\n    worker_prefetch_multiplier=1,\n)\n\n# Import pipeline.stages so that @celery_app.task decorators register tasks.\n# This import must come after celery_app is defined.\nimport pipeline.stages  # noqa: E402, F401\n"}],"isError":false,"timestamp":1774897874066}}
{"type":"message","id":"9ee10c06","parentId":"55071f2a","timestamp":"2026-03-30T19:11:20.433Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the Whisper transcription script on hal0022 to understand the transcript JSON format being produced."},{"type":"toolCall","id":"toolu_01K2HWzeN16VGCsBZA5fq9Fm","name":"bash","arguments":{"command":"ssh ub01 \"ls /vmPool/r/services/chrysopedia_data/transcripts/Break/ 2>/dev/null | head -5\""}},{"type":"toolCall","id":"toolu_01EvsCXoR7NRBpL3a4jWQCiF","name":"bash","arguments":{"command":"ssh ub01 \"head -30 /vmPool/r/services/chrysopedia_data/transcripts/Break/*.json 2>/dev/null | head -40\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":189,"cacheRead":73385,"cacheWrite":468,"totalTokens":74043,"cost":{"input":0.000005,"output":0.004725,"cacheRead":0.036692499999999996,"cacheWrite":0.002925,"total":0.04434749999999999}},"stopReason":"toolUse","timestamp":1774897874067}}
{"type":"message","id":"ac014929","parentId":"9ee10c06","timestamp":"2026-03-30T19:11:20.754Z","message":{"role":"toolResult","toolCallId":"toolu_01K2HWzeN16VGCsBZA5fq9Fm","toolName":"bash","content":[{"type":"text","text":"BREAK_Part1_DnB_Masterclass[1920x1080][800MB].mp4.json\nBREAK_Part2_DnB_Masterclass[1920x1080][760MB].mp4.json\nComputer Music - Break Masterclass.json\nComputer Music - Break Masterclass.mp4.json\nDigital Labz BREAK DnB Masterclass\n"}],"isError":false,"timestamp":1774897880754}}
{"type":"message","id":"c9b83a84","parentId":"ac014929","timestamp":"2026-03-30T19:11:20.758Z","message":{"role":"toolResult","toolCallId":"toolu_01EvsCXoR7NRBpL3a4jWQCiF","toolName":"bash","content":[{"type":"text","text":"==> /vmPool/r/services/chrysopedia_data/transcripts/Break/BREAK_Part1_DnB_Masterclass[1920x1080][800MB].mp4.json <==\n{\r\n  \"source_file\": \"BREAK_Part1_DnB_Masterclass[1920x1080][800MB].mp4\",\r\n  \"creator_folder\": \"Break\",\r\n  \"duration_seconds\": 2547,\r\n  \"segments\": [\r\n    {\r\n      \"start\": 9.64,\r\n      \"end\": 16.12,\r\n      \"text\": \"My name is Brake, I do drum and bass and various other music production for about the last\",\r\n      \"words\": [\r\n        {\r\n          \"word\": \"My\",\r\n          \"start\": 9.64,\r\n          \"end\": 10.12\r\n        },\r\n        {\r\n          \"word\": \"name\",\r\n          \"start\": 10.12,\r\n          \"end\": 10.34\r\n        },\r\n        {\r\n          \"word\": \"is\",\r\n          \"start\": 10.34,\r\n          \"end\": 10.46\r\n        },\r\n        {\r\n          \"word\": \"Brake,\",\r\n          \"start\": 10.46,\r\n          \"end\": 10.74\r\n        },\r\n\n==> /vmPool/r/services/chrysopedia_data/transcripts/Break/BREAK_Part2_DnB_Masterclass[1920x1080][760MB].mp4.json <==\n{\r\n  \"source_file\": \"BREAK_Part2_DnB_Masterclass[1920x1080][760MB].mp4\",\r\n  \"creator_folder\": \"Break\",\r\n  \"duration_seconds\": 2418,\r\n  \"segments\": [\r\n    {\r\n      \"start\": 0.0,\r\n"}],"isError":false,"timestamp":1774897880756}}
{"type":"message","id":"7797f358","parentId":"c9b83a84","timestamp":"2026-03-30T19:11:26.451Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I can see the transcript JSON format. Now let me understand the deployment topology better for where the watch folder should live."},{"type":"toolCall","id":"toolu_01MQuuM86BkUn8THWVSQqB2q","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat docker/Dockerfile.api\""}},{"type":"toolCall","id":"toolu_012W8NYG4qfDUsZNi6Ed9Bsk","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && find . -name '*.py' -path '*/pipeline/*' | sort && echo '---' && cat backend/main.py\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":200,"cacheRead":73853,"cacheWrite":772,"totalTokens":74826,"cost":{"input":0.000005,"output":0.005,"cacheRead":0.0369265,"cacheWrite":0.004825,"total":0.046756500000000006}},"stopReason":"toolUse","timestamp":1774897880756}}
{"type":"message","id":"6420e48d","parentId":"7797f358","timestamp":"2026-03-30T19:11:26.766Z","message":{"role":"toolResult","toolCallId":"toolu_01MQuuM86BkUn8THWVSQqB2q","toolName":"bash","content":[{"type":"text","text":"FROM python:3.12-slim\n\nWORKDIR /app\n\n# System deps\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n    gcc libpq-dev curl \\\n    && rm -rf /var/lib/apt/lists/*\n\n# Python deps (cached layer)\nCOPY backend/requirements.txt /app/requirements.txt\nRUN pip install --no-cache-dir -r requirements.txt\n\n# Git commit SHA for version tracking\nARG GIT_COMMIT_SHA=unknown\n\n# Application code\nCOPY backend/ /app/\nRUN echo \"${GIT_COMMIT_SHA}\" > /app/.git-commit\nCOPY prompts/ /prompts/\nCOPY config/ /config/\nCOPY alembic.ini /app/alembic.ini\nCOPY alembic/ /app/alembic/\n\nEXPOSE 8000\n\nHEALTHCHECK --interval=15s --timeout=5s --retries=3 --start-period=10s \\\n    CMD curl -f http://localhost:8000/health || exit 1\n\nCMD [\"uvicorn\", \"main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"]\n"}],"isError":false,"timestamp":1774897886765}}
{"type":"message","id":"d9ba3c05","parentId":"6420e48d","timestamp":"2026-03-30T19:11:26.786Z","message":{"role":"toolResult","toolCallId":"toolu_012W8NYG4qfDUsZNi6Ed9Bsk","toolName":"bash","content":[{"type":"text","text":"./backend/pipeline/embedding_client.py\n./backend/pipeline/__init__.py\n./backend/pipeline/llm_client.py\n./backend/pipeline/qdrant_client.py\n./backend/pipeline/schemas.py\n./backend/pipeline/stages.py\n---\n\"\"\"Chrysopedia API — Knowledge extraction and retrieval system.\n\nEntry point for the FastAPI application. Configures middleware,\nstructured logging, and mounts versioned API routers.\n\"\"\"\n\nimport logging\nimport sys\nfrom contextlib import asynccontextmanager\n\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom config import get_settings\nfrom routers import creators, health, ingest, pipeline, reports, review, search, techniques, topics, videos\n\n\ndef _setup_logging() -> None:\n    \"\"\"Configure structured logging to stdout.\"\"\"\n    settings = get_settings()\n    level = getattr(logging, settings.app_log_level.upper(), logging.INFO)\n\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(\n        logging.Formatter(\n            fmt=\"%(asctime)s | %(levelname)-8s | %(name)s | %(message)s\",\n            datefmt=\"%Y-%m-%dT%H:%M:%S\",\n        )\n    )\n\n    root = logging.getLogger()\n    root.setLevel(level)\n    # Avoid duplicate handlers on reload\n    root.handlers.clear()\n    root.addHandler(handler)\n\n    # Quiet noisy libraries\n    logging.getLogger(\"uvicorn.access\").setLevel(logging.WARNING)\n    logging.getLogger(\"sqlalchemy.engine\").setLevel(logging.WARNING)\n\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):  # noqa: ARG001\n    \"\"\"Application lifespan: setup on startup, teardown on shutdown.\"\"\"\n    _setup_logging()\n    logger = logging.getLogger(\"chrysopedia\")\n    settings = get_settings()\n    logger.info(\n        \"Chrysopedia API starting (env=%s, log_level=%s)\",\n        settings.app_env,\n        settings.app_log_level,\n    )\n    yield\n    logger.info(\"Chrysopedia API shutting down\")\n\n\napp = FastAPI(\n    title=\"Chrysopedia API\",\n    description=\"Knowledge extraction and retrieval for music production content\",\n    version=\"0.1.0\",\n    lifespan=lifespan,\n)\n\n# ── Middleware ────────────────────────────────────────────────────────────────\n\nsettings = get_settings()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=settings.cors_origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# ── Routers ──────────────────────────────────────────────────────────────────\n\n# Root-level health (no prefix)\napp.include_router(health.router)\n\n# Versioned API\napp.include_router(creators.router, prefix=\"/api/v1\")\napp.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(pipeline.router, prefix=\"/api/v1\")\napp.include_router(review.router, prefix=\"/api/v1\")\napp.include_router(reports.router, prefix=\"/api/v1\")\napp.include_router(search.router, prefix=\"/api/v1\")\napp.include_router(techniques.router, prefix=\"/api/v1\")\napp.include_router(topics.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")\n\n\n@app.get(\"/api/v1/health\")\nasync def api_health():\n    \"\"\"Lightweight version-prefixed health endpoint (no DB check).\"\"\"\n    return {\"status\": \"ok\", \"version\": \"0.1.0\"}\n"}],"isError":false,"timestamp":1774897886784}}
{"type":"message","id":"5583caf6","parentId":"d9ba3c05","timestamp":"2026-03-30T19:11:31.392Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the requirements.txt for what's already available and look at the existing Whisper script."},{"type":"toolCall","id":"toolu_01RM172pdtqy9kAy3bM9hiw2","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat backend/requirements.txt\""}},{"type":"toolCall","id":"toolu_01FpHoEDvNZ7qUAYAp1e7qga","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && find . -name 'whisper*' -o -name 'transcribe*' -o -name 'watcher*' 2>/dev/null\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":203,"cacheRead":74625,"cacheWrite":1492,"totalTokens":76321,"cost":{"input":0.000005,"output":0.0050750000000000005,"cacheRead":0.0373125,"cacheWrite":0.009325,"total":0.0517175}},"stopReason":"toolUse","timestamp":1774897886785}}
{"type":"message","id":"bb69ccfd","parentId":"5583caf6","timestamp":"2026-03-30T19:11:31.697Z","message":{"role":"toolResult","toolCallId":"toolu_01RM172pdtqy9kAy3bM9hiw2","toolName":"bash","content":[{"type":"text","text":"fastapi>=0.115.0,<1.0\nuvicorn[standard]>=0.32.0,<1.0\nsqlalchemy[asyncio]>=2.0,<3.0\nasyncpg>=0.30.0,<1.0\nalembic>=1.14.0,<2.0\npydantic>=2.0,<3.0\npydantic-settings>=2.0,<3.0\ncelery[redis]>=5.4.0,<6.0\nredis>=5.0,<6.0\npython-dotenv>=1.0,<2.0\npython-multipart>=0.0.9,<1.0\nhttpx>=0.27.0,<1.0\nopenai>=1.0,<2.0\nqdrant-client>=1.9,<2.0\npyyaml>=6.0,<7.0\npsycopg2-binary>=2.9,<3.0\n# Test dependencies\npytest>=8.0,<10.0\npytest-asyncio>=0.24,<1.0\n"}],"isError":false,"timestamp":1774897891697}}
{"type":"message","id":"dfe799df","parentId":"bb69ccfd","timestamp":"2026-03-30T19:11:31.719Z","message":{"role":"toolResult","toolCallId":"toolu_01FpHoEDvNZ7qUAYAp1e7qga","toolName":"bash","content":[{"type":"text","text":"./whisper\n./whisper/transcribe.py\n"}],"isError":false,"timestamp":1774897891717}}
{"type":"message","id":"a2fb669a","parentId":"dfe799df","timestamp":"2026-03-30T19:11:35.150Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01AKmnnhjwtAw2nv5jHS6gMK","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat whisper/transcribe.py\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":85,"cacheRead":76117,"cacheWrite":542,"totalTokens":76745,"cost":{"input":0.000005,"output":0.002125,"cacheRead":0.038058499999999995,"cacheWrite":0.0033875000000000003,"total":0.043576}},"stopReason":"toolUse","timestamp":1774897891718}}
{"type":"message","id":"9671d1a4","parentId":"a2fb669a","timestamp":"2026-03-30T19:11:35.461Z","message":{"role":"toolResult","toolCallId":"toolu_01AKmnnhjwtAw2nv5jHS6gMK","toolName":"bash","content":[{"type":"text","text":"#!/usr/bin/env python3\n\"\"\"\nChrysopedia — Whisper Transcription Script\n\nDesktop transcription tool for extracting timestamped text from video files\nusing OpenAI's Whisper model (large-v3). Designed to run on a machine with\nan NVIDIA GPU (e.g., RTX 4090).\n\nOutputs JSON matching the Chrysopedia spec format:\n{\n  \"source_file\": \"filename.mp4\",\n  \"creator_folder\": \"CreatorName\",\n  \"duration_seconds\": 7243,\n  \"segments\": [\n    {\n      \"start\": 0.0,\n      \"end\": 4.52,\n      \"text\": \"...\",\n      \"words\": [{\"word\": \"Hey\", \"start\": 0.0, \"end\": 0.28}, ...]\n    }\n  ]\n}\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport logging\nimport os\nimport shutil\nimport subprocess\nimport sys\nimport tempfile\nimport time\nfrom pathlib import Path\n\n# ---------------------------------------------------------------------------\n# Logging\n# ---------------------------------------------------------------------------\n\nLOG_FORMAT = \"%(asctime)s [%(levelname)s] %(message)s\"\nlogging.basicConfig(format=LOG_FORMAT, level=logging.INFO)\nlogger = logging.getLogger(\"chrysopedia.transcribe\")\n\n# ---------------------------------------------------------------------------\n# Constants\n# ---------------------------------------------------------------------------\n\nSUPPORTED_EXTENSIONS = {\".mp4\", \".mkv\", \".avi\", \".mov\", \".webm\", \".flv\", \".wmv\"}\nDEFAULT_MODEL = \"large-v3\"\nDEFAULT_DEVICE = \"cuda\"\n\n# ---------------------------------------------------------------------------\n# Helpers\n# ---------------------------------------------------------------------------\n\n\ndef check_ffmpeg() -> bool:\n    \"\"\"Return True if ffmpeg is available on PATH.\"\"\"\n    return shutil.which(\"ffmpeg\") is not None\n\n\ndef get_audio_duration(video_path: Path) -> float | None:\n    \"\"\"Use ffprobe to get duration in seconds. Returns None on failure.\"\"\"\n    ffprobe = shutil.which(\"ffprobe\")\n    if ffprobe is None:\n        return None\n    try:\n        result = subprocess.run(\n            [\n                ffprobe,\n                \"-v\", \"error\",\n                \"-show_entries\", \"format=duration\",\n                \"-of\", \"default=noprint_wrappers=1:nokey=1\",\n                str(video_path),\n            ],\n            capture_output=True,\n            text=True,\n            timeout=30,\n        )\n        return float(result.stdout.strip())\n    except (subprocess.TimeoutExpired, ValueError, OSError) as exc:\n        logger.warning(\"Could not determine duration for %s: %s\", video_path.name, exc)\n        return None\n\n\ndef extract_audio(video_path: Path, audio_path: Path) -> None:\n    \"\"\"Extract audio from video to 16kHz mono WAV using ffmpeg.\"\"\"\n    logger.info(\"Extracting audio: %s -> %s\", video_path.name, audio_path.name)\n    cmd = [\n        \"ffmpeg\",\n        \"-i\", str(video_path),\n        \"-vn\",                    # no video\n        \"-acodec\", \"pcm_s16le\",   # 16-bit PCM\n        \"-ar\", \"16000\",           # 16kHz (Whisper expects this)\n        \"-ac\", \"1\",               # mono\n        \"-y\",                     # overwrite\n        str(audio_path),\n    ]\n    result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)\n    if result.returncode != 0:\n        raise RuntimeError(\n            f\"ffmpeg audio extraction failed (exit {result.returncode}): {result.stderr[:500]}\"\n        )\n\n\ndef transcribe_audio(\n    audio_path: Path,\n    model_name: str = DEFAULT_MODEL,\n    device: str = DEFAULT_DEVICE,\n) -> dict:\n    \"\"\"Run Whisper on the audio file and return the raw result dict.\"\"\"\n    # Import whisper here so --help works without the dependency installed\n    try:\n        import whisper  # type: ignore[import-untyped]\n    except ImportError:\n        logger.error(\n            \"openai-whisper is not installed. \"\n            \"Install it with: pip install openai-whisper\"\n        )\n        sys.exit(1)\n\n    logger.info(\"Loading Whisper model '%s' on device '%s'...\", model_name, device)\n    t0 = time.time()\n    model = whisper.load_model(model_name, device=device)\n    logger.info(\"Model loaded in %.1f s\", time.time() - t0)\n\n    logger.info(\"Transcribing %s ...\", audio_path.name)\n    t0 = time.time()\n    result = model.transcribe(\n        str(audio_path),\n        word_timestamps=True,\n        verbose=False,\n    )\n    elapsed = time.time() - t0\n    logger.info(\n        \"Transcription complete in %.1f s (%.1fx real-time)\",\n        elapsed,\n        (result.get(\"duration\", elapsed) / elapsed) if elapsed > 0 else 0,\n    )\n    return result\n\n\ndef format_output(\n    whisper_result: dict,\n    source_file: str,\n    creator_folder: str,\n    duration_seconds: float | None,\n) -> dict:\n    \"\"\"Convert Whisper result to the Chrysopedia spec JSON format.\"\"\"\n    segments = []\n    for seg in whisper_result.get(\"segments\", []):\n        words = []\n        for w in seg.get(\"words\", []):\n            words.append(\n                {\n                    \"word\": w.get(\"word\", \"\").strip(),\n                    \"start\": round(w.get(\"start\", 0.0), 2),\n                    \"end\": round(w.get(\"end\", 0.0), 2),\n                }\n            )\n        segments.append(\n            {\n                \"start\": round(seg.get(\"start\", 0.0), 2),\n                \"end\": round(seg.get(\"end\", 0.0), 2),\n                \"text\": seg.get(\"text\", \"\").strip(),\n                \"words\": words,\n            }\n        )\n\n    # Use duration from ffprobe if available, otherwise from whisper\n    if duration_seconds is None:\n        duration_seconds = whisper_result.get(\"duration\", 0.0)\n\n    return {\n        \"source_file\": source_file,\n        \"creator_folder\": creator_folder,\n        \"duration_seconds\": round(duration_seconds),\n        \"segments\": segments,\n    }\n\n\ndef infer_creator_folder(video_path: Path) -> str:\n    \"\"\"\n    Infer creator folder name from directory structure.\n\n    Expected layout: /path/to/<CreatorName>/video.mp4\n    Falls back to parent directory name.\n    \"\"\"\n    return video_path.parent.name\n\n\ndef output_path_for(video_path: Path, output_dir: Path) -> Path:\n    \"\"\"Compute the output JSON path for a given video file.\"\"\"\n    return output_dir / f\"{video_path.stem}.json\"\n\n\ndef process_single(\n    video_path: Path,\n    output_dir: Path,\n    model_name: str,\n    device: str,\n    creator_folder: str | None = None,\n) -> Path | None:\n    \"\"\"\n    Process a single video file. Returns the output path on success, None if skipped.\n    \"\"\"\n    out_path = output_path_for(video_path, output_dir)\n\n    # Resumability: skip if output already exists\n    if out_path.exists():\n        logger.info(\"SKIP (output exists): %s\", out_path)\n        return None\n\n    logger.info(\"Processing: %s\", video_path)\n\n    # Determine creator folder\n    folder = creator_folder or infer_creator_folder(video_path)\n\n    # Get duration via ffprobe\n    duration = get_audio_duration(video_path)\n    if duration is not None:\n        logger.info(\"Video duration: %.0f s (%.1f min)\", duration, duration / 60)\n\n    # Extract audio to temp file\n    with tempfile.TemporaryDirectory(prefix=\"chrysopedia_\") as tmpdir:\n        audio_path = Path(tmpdir) / \"audio.wav\"\n        extract_audio(video_path, audio_path)\n\n        # Transcribe\n        whisper_result = transcribe_audio(audio_path, model_name, device)\n\n    # Format and write output\n    output = format_output(whisper_result, video_path.name, folder, duration)\n\n    output_dir.mkdir(parents=True, exist_ok=True)\n    with open(out_path, \"w\", encoding=\"utf-8\") as f:\n        json.dump(output, f, indent=2, ensure_ascii=False)\n\n    segment_count = len(output[\"segments\"])\n    logger.info(\"Wrote %s (%d segments)\", out_path, segment_count)\n    return out_path\n\n\ndef find_videos(input_path: Path) -> list[Path]:\n    \"\"\"Find all supported video files in a directory (non-recursive).\"\"\"\n    videos = sorted(\n        p for p in input_path.iterdir()\n        if p.is_file() and p.suffix.lower() in SUPPORTED_EXTENSIONS\n    )\n    return videos\n\n\n# ---------------------------------------------------------------------------\n# CLI\n# ---------------------------------------------------------------------------\n\n\ndef build_parser() -> argparse.ArgumentParser:\n    parser = argparse.ArgumentParser(\n        prog=\"transcribe\",\n        description=(\n            \"Chrysopedia Whisper Transcription — extract timestamped transcripts \"\n            \"from video files using OpenAI's Whisper model.\"\n        ),\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        epilog=(\n            \"Examples:\\n\"\n            \"  # Single file\\n\"\n            \"  python transcribe.py --input video.mp4 --output-dir ./transcripts\\n\"\n            \"\\n\"\n            \"  # Batch mode (all videos in directory)\\n\"\n            \"  python transcribe.py --input ./videos/ --output-dir ./transcripts\\n\"\n            \"\\n\"\n            \"  # Use a smaller model on CPU\\n\"\n            \"  python transcribe.py --input video.mp4 --model base --device cpu\\n\"\n        ),\n    )\n    parser.add_argument(\n        \"--input\",\n        required=True,\n        type=str,\n        help=\"Path to a video file or directory of video files\",\n    )\n    parser.add_argument(\n        \"--output-dir\",\n        required=True,\n        type=str,\n        help=\"Directory to write transcript JSON files\",\n    )\n    parser.add_argument(\n        \"--model\",\n        default=DEFAULT_MODEL,\n        type=str,\n        help=f\"Whisper model name (default: {DEFAULT_MODEL})\",\n    )\n    parser.add_argument(\n        \"--device\",\n        default=DEFAULT_DEVICE,\n        type=str,\n        help=f\"Compute device: cuda, cpu (default: {DEFAULT_DEVICE})\",\n    )\n    parser.add_argument(\n        \"--creator\",\n        default=None,\n        type=str,\n        help=\"Override creator folder name (default: inferred from parent directory)\",\n    )\n    parser.add_argument(\n        \"-v\", \"--verbose\",\n        action=\"store_true\",\n        help=\"Enable debug logging\",\n    )\n    return parser\n\n\ndef main(argv: list[str] | None = None) -> int:\n    parser = build_parser()\n    args = parser.parse_args(argv)\n\n    if args.verbose:\n        logging.getLogger().setLevel(logging.DEBUG)\n\n    # Validate ffmpeg availability\n    if not check_ffmpeg():\n        logger.error(\n            \"ffmpeg is not installed or not on PATH. \"\n            \"Install it with: sudo apt install ffmpeg  (or equivalent)\"\n        )\n        return 1\n\n    input_path = Path(args.input).resolve()\n    output_dir = Path(args.output_dir).resolve()\n\n    if not input_path.exists():\n        logger.error(\"Input path does not exist: %s\", input_path)\n        return 1\n\n    # Single file mode\n    if input_path.is_file():\n        if input_path.suffix.lower() not in SUPPORTED_EXTENSIONS:\n            logger.error(\n                \"Unsupported file type '%s'. Supported: %s\",\n                input_path.suffix,\n                \", \".join(sorted(SUPPORTED_EXTENSIONS)),\n            )\n            return 1\n        result = process_single(\n            input_path, output_dir, args.model, args.device, args.creator\n        )\n        if result is None:\n            logger.info(\"Nothing to do (output already exists).\")\n        return 0\n\n    # Batch mode (directory)\n    if input_path.is_dir():\n        videos = find_videos(input_path)\n        if not videos:\n            logger.warning(\"No supported video files found in %s\", input_path)\n            return 0\n\n        logger.info(\"Found %d video(s) in %s\", len(videos), input_path)\n        processed = 0\n        skipped = 0\n        failed = 0\n\n        for i, video in enumerate(videos, 1):\n            logger.info(\"--- [%d/%d] %s ---\", i, len(videos), video.name)\n            try:\n                result = process_single(\n                    video, output_dir, args.model, args.device, args.creator\n                )\n                if result is not None:\n                    processed += 1\n                else:\n                    skipped += 1\n            except Exception:\n                logger.exception(\"FAILED: %s\", video.name)\n                failed += 1\n\n        logger.info(\n            \"Batch complete: %d processed, %d skipped, %d failed\",\n            processed, skipped, failed,\n        )\n        return 1 if failed > 0 else 0\n\n    logger.error(\"Input is neither a file nor a directory: %s\", input_path)\n    return 1\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n"}],"isError":false,"timestamp":1774897895456}}
{"type":"message","id":"4298fe7a","parentId":"9671d1a4","timestamp":"2026-03-30T19:11:47.833Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Now I have a good picture. Let me check if there's a watchdog or inotify library already available, and understand the deployment model for the watcher service.\n\nThe watcher needs to:\n1. Monitor a folder on ub01 for new .json transcript files\n2. When a new file appears, validate it matches the transcript spec\n3. POST it to the /api/v1/ingest endpoint\n4. The pipeline triggers automatically (already wired in the ingest endpoint)\n\nKey decisions:\n- Where does the watcher run? As a new Docker service or as a host-level service?\n- What library? watchdog (Python), inotifywait (shell), or polling?\n\n\n- Watch folder location on ub01\n\nNow I'm checking what's available in the compose file and whether watchdog is a practical choice for this setup.","thinkingSignature":"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"},{"type":"text","text":"Now I have enough context. Let me check one more thing — the nginx config that fronts the API, to know the right URL for the watcher to POST to."},{"type":"toolCall","id":"toolu_01956UsiAUbgFZSKbpVswkmp","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat docker/nginx.conf 2>/dev/null || find docker -type f | sort\""}},{"type":"toolCall","id":"toolu_01RuXzsPjCxdGVPNxN4JLrdM","name":"bash","arguments":{"command":"ssh ub01 \"docker ps --filter name=chrysopedia --format '{{.Names}} {{.Ports}}'\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":394,"cacheRead":76659,"cacheWrite":3571,"totalTokens":80625,"cost":{"input":0.000005,"output":0.009850000000000001,"cacheRead":0.038329499999999996,"cacheWrite":0.022318750000000002,"total":0.07050325}},"stopReason":"toolUse","timestamp":1774897895459}}
{"type":"message","id":"935b993e","parentId":"4298fe7a","timestamp":"2026-03-30T19:11:48.176Z","message":{"role":"toolResult","toolCallId":"toolu_01956UsiAUbgFZSKbpVswkmp","toolName":"bash","content":[{"type":"text","text":"server {\n    listen 80;\n    server_name _;\n    root /usr/share/nginx/html;\n    index index.html;\n\n    # Use Docker's embedded DNS with 30s TTL so upstream IPs refresh\n    # after container recreates\n    resolver 127.0.0.11 valid=30s ipv6=off;\n\n    # Allow large transcript uploads (up to 50MB)\n    client_max_body_size 50m;\n\n    # SPA fallback\n    location / {\n        try_files $uri $uri/ /index.html;\n    }\n\n    # API proxy — variable forces nginx to re-resolve on each request\n    location /api/ {\n        set $backend http://chrysopedia-api:8000;\n        proxy_pass $backend;\n        proxy_set_header Host $host;\n        proxy_set_header X-Real-IP $remote_addr;\n        proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;\n        proxy_set_header X-Forwarded-Proto $scheme;\n        proxy_read_timeout 300s;\n        proxy_send_timeout 300s;\n    }\n\n    location /health {\n        set $backend http://chrysopedia-api:8000;\n        proxy_pass $backend;\n    }\n}\n"}],"isError":false,"timestamp":1774897908176}}
{"type":"message","id":"76d83d1d","parentId":"935b993e","timestamp":"2026-03-30T19:11:48.191Z","message":{"role":"toolResult","toolCallId":"toolu_01RuXzsPjCxdGVPNxN4JLrdM","toolName":"bash","content":[{"type":"text","text":"chrysopedia-web-8096 0.0.0.0:8096->80/tcp\nchrysopedia-api 8000/tcp\nchrysopedia-worker 8000/tcp\nchrysopedia-qdrant 6333-6334/tcp\nchrysopedia-ollama 11434/tcp\nchrysopedia-db 127.0.0.1:5433->5432/tcp\nchrysopedia-redis 6379/tcp\n"}],"isError":false,"timestamp":1774897908190}}
{"type":"message","id":"330d5e67","parentId":"76d83d1d","timestamp":"2026-03-30T19:11:53.385Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have the full picture. Let me check if Python's `watchdog` library is commonly used for this and what the `httpx` library (already a dependency) looks like for file upload."},{"type":"toolCall","id":"toolu_01Evm7YwqMYkJ2bJhyhLuEDH","name":"bash","arguments":{"command":"ssh ub01 \"cd /vmPool/r/repos/xpltdco/chrysopedia && cat backend/schemas.py | grep -A 15 'TranscriptIngest'\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":139,"cacheRead":80230,"cacheWrite":681,"totalTokens":81051,"cost":{"input":0.000005,"output":0.0034750000000000002,"cacheRead":0.040115,"cacheWrite":0.0042562500000000005,"total":0.04785125}},"stopReason":"toolUse","timestamp":1774897908191}}
{"type":"message","id":"979249e5","parentId":"330d5e67","timestamp":"2026-03-30T19:11:53.702Z","message":{"role":"toolResult","toolCallId":"toolu_01Evm7YwqMYkJ2bJhyhLuEDH","toolName":"bash","content":[{"type":"text","text":"class TranscriptIngestResponse(BaseModel):\n    \"\"\"Response returned after successfully ingesting a transcript.\"\"\"\n    video_id: uuid.UUID\n    creator_id: uuid.UUID\n    creator_name: str\n    filename: str\n    segments_stored: int\n    processing_status: str\n    is_reupload: bool\n    content_hash: str\n\n\n# ── Pagination wrapper ───────────────────────────────────────────────────────\n\nclass PaginatedResponse(BaseModel):\n    \"\"\"Generic paginated list response.\"\"\"\n"}],"isError":false,"timestamp":1774897913700}}
{"type":"message","id":"94b5a66b","parentId":"979249e5","timestamp":"2026-03-30T19:11:58.730Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I have everything I need. Let me now look at `watchdog` briefly — it's the standard Python library for filesystem monitoring."},{"type":"toolCall","id":"toolu_01E9EhbKicZZiYEs29R4wmUb","name":"resolve_library","arguments":{"libraryName":"watchdog","query":"file system monitoring watch directory for new files"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":110,"cacheRead":80911,"cacheWrite":276,"totalTokens":81298,"cost":{"input":0.000005,"output":0.0027500000000000003,"cacheRead":0.0404555,"cacheWrite":0.0017250000000000002,"total":0.044935499999999996}},"stopReason":"toolUse","timestamp":1774897913701}}
{"type":"message","id":"dbc4007d","parentId":"94b5a66b","timestamp":"2026-03-30T19:11:59.666Z","message":{"role":"toolResult","toolCallId":"toolu_01E9EhbKicZZiYEs29R4wmUb","toolName":"resolve_library","content":[{"type":"text","text":"Found 1 library matching \"watchdog\":\n\n• Watchdog  (ID: /gorakhargosh/watchdog)\n  Watchdog is a Python API and shell utilities for monitoring file system events across Windows, macOS, and Linux platforms.\n  trust: 8.8/10 · benchmark: 69.3 · 132 snippets · 17k tokens · updated: 2026-03-27\n\nUse the ID (e.g. /websites/react_dev) with get_library_docs to fetch documentation."}],"details":{"query":"watchdog","resultCount":1,"cached":false},"isError":false,"timestamp":1774897919664}}
{"type":"message","id":"5a8d54d7","parentId":"dbc4007d","timestamp":"2026-03-30T19:12:03.003Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_015a16mcNzXJwyj5MaEK2nbC","name":"get_library_docs","arguments":{"libraryId":"/gorakhargosh/watchdog","query":"watch directory for new files observer handler","tokens":3000}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":105,"cacheRead":81187,"cacheWrite":242,"totalTokens":81535,"cost":{"input":0.000005,"output":0.002625,"cacheRead":0.0405935,"cacheWrite":0.0015125000000000002,"total":0.044736}},"stopReason":"toolUse","timestamp":1774897919665}}
{"type":"message","id":"01b898b8","parentId":"5a8d54d7","timestamp":"2026-03-30T19:12:04.261Z","message":{"role":"toolResult","toolCallId":"toolu_015a16mcNzXJwyj5MaEK2nbC","toolName":"get_library_docs","content":[{"type":"text","text":"### Monitor Directory Changes with Observer\n\nSource: https://github.com/gorakhargosh/watchdog/blob/master/docs/source/quickstart.md\n\nDemonstrates how to monitor a directory recursively for file system events. It uses a custom event handler and an observer thread to detect and print changes to the console.\n\n```python\nimport time\n\nfrom watchdog.events import FileSystemEvent, FileSystemEventHandler\nfrom watchdog.observers import Observer\n\n\nclass MyEventHandler(FileSystemEventHandler):\n    def on_any_event(self, event: FileSystemEvent) -> None:\n        print(event)\n\n\nevent_handler = MyEventHandler()\nobserver = Observer()\nobserver.schedule(event_handler, \".\", recursive=True)\nobserver.start()\ntry:\n    while True:\n        time.sleep(1)\nfinally:\n    observer.stop()\n    observer.join()\n```\n\n--------------------------------\n\n### Python API: Monitor Directory Events\n\nSource: https://github.com/gorakhargosh/watchdog/blob/master/README.rst\n\nThis Python code snippet demonstrates how to use the watchdog library to monitor a directory for file system events. It defines a custom event handler to print any detected event and uses an Observer to schedule the handler. The observer runs until manually stopped.\n\n```python\nimport time\n\nfrom watchdog.events import FileSystemEvent, FileSystemEventHandler\nfrom watchdog.observers import Observer\n\n\nclass MyEventHandler(FileSystemEventHandler):\n    def on_any_event(self, event: FileSystemEvent) -> None:\n        print(event)\n\n\nevent_handler = MyEventHandler()\nobserver = Observer()\nobserver.schedule(event_handler, \".\", recursive=True)\nobserver.start()\ntry:\n    while True:\n        time.sleep(1)\nfinally:\n    observer.stop()\n    observer.join()\n```\n\n```python\nimport time\n\nfrom watchdog.events import FileSystemEvent, FileSystemEventHandler\nfrom watchdog.observers import Observer\n\n\nclass MyEventHandler(FileSystemEventHandler):\n    def on_any_event(self, event: FileSystemEvent) -> None:\n        print(event)\n\n\nevent_handler = MyEventHandler()\nobserver = Observer()\nobserver.schedule(event_handler, \".\", recursive=True)\n\nwith observer:\n    while True:\n        time.sleep(1)\n```\n\n--------------------------------\n\n### Python Observer API\n\nSource: https://github.com/gorakhargosh/watchdog/blob/master/README.rst\n\nThe Observer API allows developers to programmatically monitor directories for file system changes by scheduling event handlers.\n\n```APIDOC\n## Observer.schedule\n\n### Description\nSchedules an event handler to monitor a specific path for file system events.\n\n### Method\nPython Method Call\n\n### Parameters\n#### Arguments\n- **event_handler** (FileSystemEventHandler) - Required - The handler instance to process events.\n- **path** (str) - Required - The directory path to monitor.\n- **recursive** (bool) - Optional - Whether to monitor subdirectories recursively.\n\n### Request Example\n```python\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\n\nobserver = Observer()\nobserver.schedule(MyEventHandler(), \".\", recursive=True)\nobserver.start()\n```\n```\n\n--------------------------------\n\n### Monitor Directory Changes with Context Manager\n\nSource: https://github.com/gorakhargosh/watchdog/blob/master/docs/source/quickstart.md\n\nAn alternative implementation using the observer as a context manager to ensure automatic starting and stopping of the observer thread.\n\n```python\nimport time\n\nfrom watchdog.events import FileSystemEvent, FileSystemEventHandler\nfrom watchdog.observers import Observer\n\n\nclass MyEventHandler(FileSystemEventHandler):\n    def on_any_event(self, event: FileSystemEvent) -> None:\n        print(event)\n\n\nevent_handler = MyEventHandler()\nobserver = Observer()\nobserver.schedule(event_handler, \".\", recursive=True)\n\nwith observer:\n    while True:\n        time.sleep(1)\n```\n\n### Quickstart > A Simple Example\n\nSource: https://github.com/gorakhargosh/watchdog/blob/master/docs/source/quickstart.md\n\nThe example program monitors the current directory recursively for file system changes and prints them to the console. It defines a custom event handler `MyEventHandler` that overrides the `on_any_event` method to print any detected event. An `Observer` instance is created, scheduled to monitor the current directory (`.`) recursively, and then started. The program enters an infinite loop with a sleep interval to keep the main thread alive while the observer runs in the background. A `try...finally` block ensures that the observer is properly stopped and joined when the program is terminated, for instance, by pressing Control-C."}],"details":{"libraryId":"/gorakhargosh/watchdog","query":"watch directory for new files observer handler","tokens":3000,"cached":false,"truncated":false,"charCount":4544},"isError":false,"timestamp":1774897924260}}
{"type":"message","id":"d5c825ad","parentId":"01b898b8","timestamp":"2026-03-30T19:12:55.056Z","message":{"role":"assistant","content":[{"type":"text","text":"That's all I need. Now let me write the research document. The approach is clear: a Python service using `watchdog` that monitors a folder, validates JSON files, and POSTs them to the ingest endpoint via `httpx`. Runs as a new Docker Compose service."},{"type":"toolCall","id":"toolu_01RVtSMrfpm6qSaiYP5Q7ixM","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S03 Research — Transcript Folder Watcher — Auto-Ingest Service\n\n## Summary\n\nAdd a folder-watching service that monitors a directory for new transcript JSON files, validates them, and POSTs them to the existing `/api/v1/ingest` endpoint. This replaces manual curl/upload for getting transcripts into the pipeline.\n\nThe work is straightforward — filesystem monitoring + HTTP POST using well-understood Python libraries already in the project. The ingest endpoint is mature (6 integration tests, handles idempotent re-upload, auto-dispatches pipeline). The watcher just needs to be a reliable bridge from \"file appears on disk\" to \"POST /api/v1/ingest\".\n\n## Recommendation\n\n**watchdog** library + **httpx** (already a dependency) in a lightweight Python script, deployed as a new Docker Compose service with the watch folder bind-mounted. Polling fallback via `PollingObserver` for reliability on network-mounted filesystems.\n\n## Implementation Landscape\n\n### Current Ingest Flow\n- `POST /api/v1/ingest` accepts multipart file upload (`UploadFile`)\n- Validates JSON structure (requires `source_file`, `creator_folder`, `duration_seconds`, `segments`)\n- Creates/updates Creator, upserts SourceVideo, bulk-inserts TranscriptSegments\n- Saves raw JSON to `/data/transcripts/{creator_folder}/`\n- Dispatches Celery pipeline via `run_pipeline.delay(str(video.id))`\n- Returns `TranscriptIngestResponse` with `video_id`, `processing_status`, etc.\n- Already handles idempotent re-upload (content hash matching, duplicate detection)\n\n### Transcript JSON Format\nFiles produced by `whisper/transcribe.py` on hal0022:\n```json\n{\n  \"source_file\": \"filename.mp4\",\n  \"creator_folder\": \"CreatorName\",\n  \"duration_seconds\": 2547,\n  \"segments\": [{\"start\": 0.0, \"end\": 4.52, \"text\": \"...\", \"words\": [...]}]\n}\n```\n\n### Deployment Topology\n- All services run on **ub01** via Docker Compose (`xpltd_chrysopedia` project)\n- Data volume: `/vmPool/r/services/chrysopedia_data` → mounted at `/data` in API/worker containers\n- Existing transcripts live at `/vmPool/r/services/chrysopedia_data/transcripts/{CreatorName}/`\n- The watcher needs a **separate watch folder** (not the transcripts store) — the ingest endpoint copies files there itself\n- Proposed watch folder: `/vmPool/r/services/chrysopedia_watch/` → mounted at `/watch` in the watcher container\n- API is reachable from other containers at `http://chrysopedia-api:8000` on the `chrysopedia` Docker network\n\n### Key Design Decisions\n\n**Watch folder ≠ transcript storage.** The ingest endpoint already saves transcripts to `/data/transcripts/`. The watch folder is an inbox — files land there, get POSTed, then get moved to a `processed/` or `failed/` subfolder. This prevents double-processing and gives visibility into what's been handled.\n\n**File stability check.** When watchdog fires `on_created`, the file may still be being written (especially over network mounts or SCP). The watcher should wait until the file size stops changing (poll twice with a 1-2s delay) before attempting to read and POST it.\n\n**Recursive vs flat watching.** Watch recursively — the whisper script outputs to `{output_dir}/{video_stem}.json` but users might organize into creator subfolders. The `creator_folder` field inside the JSON is authoritative, not the filesystem path.\n\n**Error handling.** Failed files move to `failed/` with a `.error` sidecar file containing the HTTP response or exception. This avoids infinite retry loops and gives the admin a clear queue of problems to fix.\n\n### Files to Create/Modify\n\n| File | Action | Purpose |\n|------|--------|---------|\n| `backend/watcher.py` | **Create** | Main watcher script — watchdog Observer + httpx POST logic |\n| `docker/Dockerfile.watcher` | **Create** | Lightweight image (could reuse Dockerfile.api or be minimal) |\n| `docker-compose.yml` | **Modify** | Add `chrysopedia-watcher` service with watch folder bind mount |\n| `backend/config.py` | **Modify** | Add `watch_folder_path` and `watcher_api_url` settings |\n\n### Dependencies\n\n- **watchdog** — needs adding to `requirements.txt` (not currently a dependency)\n- **httpx** — already in `requirements.txt`, used for HTTP client\n- **json** / **os** / **shutil** / **time** / **logging** — stdlib only\n\n### Watcher Service Architecture\n\n```\nchrysopedia-watcher container:\n  ┌──────────────────────────────────┐\n  │  watchdog Observer               │\n  │  monitors /watch/ recursively    │\n  │                                  │\n  │  on_created *.json:              │\n  │    1. Wait for file stability    │\n  │    2. Read & validate JSON keys  │\n  │    3. POST to chrysopedia-api    │\n  │    4. Move to processed/ or      │\n  │       failed/ with sidecar       │\n  └──────────────────────────────────┘\n```\n\n### Docker Compose Service\n\n```yaml\nchrysopedia-watcher:\n  build:\n    context: .\n    dockerfile: docker/Dockerfile.api  # reuse same image\n  container_name: chrysopedia-watcher\n  restart: unless-stopped\n  command: [\"python\", \"watcher.py\"]\n  environment:\n    WATCHER_API_URL: http://chrysopedia-api:8000/api/v1/ingest\n    WATCH_FOLDER: /watch\n  volumes:\n    - /vmPool/r/services/chrysopedia_watch:/watch\n  depends_on:\n    chrysopedia-api:\n      condition: service_healthy\n  networks:\n    - chrysopedia\n  healthcheck:\n    test: [\"CMD-SHELL\", \"pgrep -f watcher.py || exit 1\"]\n    interval: 30s\n    timeout: 5s\n    retries: 3\n```\n\n### Natural Task Seams\n\n1. **T01: Create watcher.py** — the core script with watchdog Observer, file stability check, JSON validation, httpx multipart POST, processed/failed file management, structured logging. This is ~80% of the work.\n2. **T02: Docker integration** — Dockerfile (or reuse), compose service definition, config settings, host folder creation. Verify end-to-end: drop file → watcher detects → POSTs to API → pipeline triggers.\n\n### Verification Strategy\n\n- **Unit-level:** Drop a valid transcript JSON into the watch folder on ub01 → confirm watcher logs pickup, API returns 200, video appears in DB with `transcribed` status, pipeline dispatches.\n- **Error case:** Drop an invalid JSON → confirm it moves to `failed/` with error sidecar, watcher continues running.\n- **Stability:** Watcher survives API container restart (retry logic or health-check-gated startup).\n- **Idempotency:** Drop same file twice → second copy moves to processed (API handles idempotent re-upload).\n\n### Risks\n\n**Low risk overall.** The ingest endpoint is battle-tested. The watcher is a thin bridge. Main concern is file stability detection on the bind mount — watchdog's inotify works well on local Linux filesystems, which is what `/vmPool/r/services/` is (ZFS on ub01). If files arrive via SCP or rsync, partial writes could trigger premature pickup — the stability check (size-stable for 2s) handles this.\n\n### Skills\n\nNo specialized skills needed. Standard Python, Docker Compose, watchdog library. No frontend work.\n","milestone_id":"M007","slice_id":"S03"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2088,"cacheRead":81429,"cacheWrite":1201,"totalTokens":84719,"cost":{"input":0.000005,"output":0.0522,"cacheRead":0.0407145,"cacheWrite":0.007506250000000001,"total":0.10042575000000001}},"stopReason":"toolUse","timestamp":1774897924260}}
{"type":"message","id":"909f75fb","parentId":"d5c825ad","timestamp":"2026-03-30T19:12:55.061Z","message":{"role":"toolResult","toolCallId":"toolu_01RVtSMrfpm6qSaiYP5Q7ixM","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M007/slices/S03/S03-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M007/slices/S03/S03-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774897975059}}
{"type":"message","id":"671a1b4a","parentId":"909f75fb","timestamp":"2026-03-30T19:12:58.046Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S03 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":82630,"cacheWrite":2126,"totalTokens":84768,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.041315,"cacheWrite":0.0132875,"total":0.0548825}},"stopReason":"stop","timestamp":1774897975060}}
