{"type":"thinking_level_change","id":"9ca4a42d","parentId":null,"timestamp":"2026-03-30T18:11:50.847Z","thinkingLevel":"medium"}
{"type":"model_change","id":"38e287a1","parentId":"9ca4a42d","timestamp":"2026-03-30T18:11:50.850Z","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 S01 (\"Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\") — 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: S01`, `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/S01/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S01 researched.\"","display":false,"id":"87a0a342","parentId":"38e287a1","timestamp":"2026-03-30T18:11:50.855Z"}
{"type":"message","id":"e5da11d1","parentId":"87a0a342","timestamp":"2026-03-30T18:11:59.990Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me research what's needed for S01: \"Pipeline Debug Mode — Full LLM I/O Capture and Token Accounting\". I need to understand the current pipeline architecture, how LLM calls are made, and where to hook in debug capture.\n\nLet me start by exploring the relevant code.","thinkingSignature":"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"},{"type":"text","text":"I'll explore the pipeline's LLM call patterns and the existing event/instrumentation infrastructure to understand where debug capture hooks belong."},{"type":"toolCall","id":"toolu_01WbEtTqjhqbbwGJzKqWo59F","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend/pipeline -type f -name '*.py' | head -20 && echo \"---\" && find backend -name 'models.py' -o -name 'events*' -o -name 'instrument*' | head -10"}},{"type":"toolCall","id":"toolu_01UprfVUSWu4REduJmqaB51i","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"openai|OpenAI|chat\\.completions\" backend/ --type py | head -15"}},{"type":"toolCall","id":"toolu_01Nq5eWpm6UujbrdwqVgKkNN","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -l \"PipelineEvent|pipeline_event\" backend/ --type py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":383,"cacheRead":58112,"cacheWrite":6524,"totalTokens":65022,"cost":{"input":0.000015000000000000002,"output":0.009575,"cacheRead":0.029056,"cacheWrite":0.040775,"total":0.07942099999999999}},"stopReason":"toolUse","timestamp":1774894310851}}
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{"type":"message","id":"ea93e776","parentId":"8ffff6f2","timestamp":"2026-03-30T18:12:00.022Z","message":{"role":"toolResult","toolCallId":"toolu_01Nq5eWpm6UujbrdwqVgKkNN","toolName":"bash","content":[{"type":"text","text":"backend/pipeline/stages.py\n"}],"isError":false,"timestamp":1774894320020}}
{"type":"message","id":"0186d39b","parentId":"ea93e776","timestamp":"2026-03-30T18:12:06.203Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Kz4jxrFhxMt5rTaxV4qKj8","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && wc -l backend/pipeline/stages.py && echo \"---\" && rg -n \"def |class |openai|chat\\.completions|PipelineEvent|_llm_call|prompt|completion|usage|token\" backend/pipeline/stages.py | head -50"}},{"type":"toolCall","id":"toolu_01Ukks1TsTSzrnGFJnwjyN4R","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && rg -n \"class.*Model|class.*Event|class.*Debug\" backend/models/ --type py 2>/dev/null || rg -n \"class.*Model|class.*Event\" backend/models.py 2>/dev/null | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":242,"cacheRead":64636,"cacheWrite":410,"totalTokens":65289,"cost":{"input":0.000005,"output":0.006050000000000001,"cacheRead":0.032318,"cacheWrite":0.0025625,"total":0.0409355}},"stopReason":"toolUse","timestamp":1774894320021}}
{"type":"message","id":"532b278d","parentId":"0186d39b","timestamp":"2026-03-30T18:12:06.222Z","message":{"role":"toolResult","toolCallId":"toolu_01Kz4jxrFhxMt5rTaxV4qKj8","toolName":"bash","content":[{"type":"text","text":"979 backend/pipeline/stages.py\n---\n3:Each stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\n29:    PipelineEvent,\n52:def _emit_event(\n57:    prompt_tokens: int | None = None,\n58:    completion_tokens: int | None = None,\n59:    total_tokens: int | None = None,\n68:            event = PipelineEvent(\n72:                prompt_tokens=prompt_tokens,\n73:                completion_tokens=completion_tokens,\n74:                total_tokens=total_tokens,\n87:def _make_llm_callback(video_id: str, stage: str):\n89:    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n90:                 total_tokens=None, content=None, finish_reason=None,\n99:            prompt_tokens=prompt_tokens,\n100:            completion_tokens=completion_tokens,\n101:            total_tokens=total_tokens,\n117:def _get_sync_engine():\n129:def _get_sync_session() -> Session:\n137:def _load_prompt(template_name: str) -> str:\n138:    \"\"\"Read a prompt template from the prompts directory.\n143:    path = Path(settings.prompts_path) / template_name\n150:def _get_llm_client() -> LLMClient:\n155:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n168:def _load_canonical_tags() -> dict:\n184:def _format_taxonomy_for_prompt(tags_data: dict) -> str:\n185:    \"\"\"Format the canonical tags taxonomy as readable text for the LLM prompt.\"\"\"\n195:def _safe_parse_llm_response(\n199:    system_prompt: str,\n200:    user_prompt: str,\n220:        nudge_prompt = user_prompt + \"\\n\\nIMPORTANT: Output ONLY valid JSON. No markdown, no explanation.\"\n222:            system_prompt, nudge_prompt, response_model=model_cls,\n231:def stage2_segmentation(self, video_id: str) -> str:\n268:        # Load prompt and call LLM\n269:        system_prompt = _load_prompt(\"stage2_segmentation.txt\")\n270:        user_prompt = f\"<transcript>\\n{transcript_text}\\n</transcript>\"\n275:        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\"),\n277:        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt,\n297:        raise  # Don't retry missing prompt files\n310:def stage3_extraction(self, video_id: str) -> str:\n345:        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n360:            user_prompt = (\n365:            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\"),\n367:            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt,\n420:def stage4_classification(self, video_id: str) -> str:\n456:        taxonomy_text = _format_taxonomy_for_prompt(tags_data)\n469:        system_prompt = _load_prompt(\"stage4_classification.txt\")\n470:        user_prompt = (\n478:        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult, on_complete=_make_llm_callback(video_id, \"stage4_classification\"),\n480:        result = _safe_parse_llm_response(raw, ClassificationResult, llm, system_prompt, user_prompt,\n528:def _store_classification_data(video_id: str, data: list[dict]) -> None:\n"}],"isError":false,"timestamp":1774894326222}}
{"type":"message","id":"1a81f149","parentId":"532b278d","timestamp":"2026-03-30T18:12:06.229Z","message":{"role":"toolResult","toolCallId":"toolu_01Ukks1TsTSzrnGFJnwjyN4R","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774894326227}}
{"type":"message","id":"220f39f0","parentId":"1a81f149","timestamp":"2026-03-30T18:12:11.010Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01PxiSUPonXSG7AJMk31CLjS","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend -name 'models*' -type f | head -10 && echo \"---\" && ls backend/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":94,"cacheRead":65046,"cacheWrite":1303,"totalTokens":66444,"cost":{"input":0.000005,"output":0.00235,"cacheRead":0.032522999999999996,"cacheWrite":0.00814375,"total":0.04302175}},"stopReason":"toolUse","timestamp":1774894326227}}
{"type":"message","id":"4540ea59","parentId":"220f39f0","timestamp":"2026-03-30T18:12:11.026Z","message":{"role":"toolResult","toolCallId":"toolu_01PxiSUPonXSG7AJMk31CLjS","toolName":"bash","content":[{"type":"text","text":"---\npipeline\n"}],"isError":false,"timestamp":1774894331024}}
{"type":"message","id":"0bf624cf","parentId":"4540ea59","timestamp":"2026-03-30T18:12:14.681Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_0171beEdJVNUsy257SffrdtP","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls -la backend/ && echo \"---\" && find . -name 'models.py' -not -path './.gsd/*' -not -path './node_modules/*' | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":113,"cacheRead":66349,"cacheWrite":110,"totalTokens":66573,"cost":{"input":0.000005,"output":0.0028250000000000003,"cacheRead":0.033174499999999996,"cacheWrite":0.0006875000000000001,"total":0.036691999999999995}},"stopReason":"toolUse","timestamp":1774894331025}}
{"type":"message","id":"b12ea1de","parentId":"0bf624cf","timestamp":"2026-03-30T18:12:14.729Z","message":{"role":"toolResult","toolCallId":"toolu_0171beEdJVNUsy257SffrdtP","toolName":"bash","content":[{"type":"text","text":"total 12\ndrwxrwxr-x  3 aux aux 4096 Mar 30 11:57 .\ndrwxrwxr-x 10 aux aux 4096 Mar 30 18:11 ..\ndrwxrwxr-x  2 aux aux 4096 Mar 30 11:57 pipeline\n---\n"}],"isError":false,"timestamp":1774894334727}}
{"type":"message","id":"b5f8cbea","parentId":"b12ea1de","timestamp":"2026-03-30T18:12:25.252Z","message":{"role":"assistant","content":[{"type":"text","text":"This is the local workspace, but per CLAUDE.md the canonical code is on ub01. Let me check the actual repo structure."},{"type":"toolCall","id":"toolu_01PhDxR8XYAFfwL7v1vxsZo8","name":"bash","arguments":{"command":"ssh ub01 \"ls /vmPool/r/repos/xpltdco/chrysopedia/backend/\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_014doVGbMqap82jPdZCo1Gk1","name":"bash","arguments":{"command":"ssh ub01 \"find /vmPool/r/repos/xpltdco/chrysopedia/backend -name 'models.py' -o -name 'models' -type d\" 2>/dev/null | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":204,"cacheRead":66459,"cacheWrite":215,"totalTokens":66879,"cost":{"input":0.000005,"output":0.0051,"cacheRead":0.033229499999999995,"cacheWrite":0.0013437500000000001,"total":0.03967824999999999}},"stopReason":"toolUse","timestamp":1774894334727}}
{"type":"message","id":"15a83743","parentId":"b5f8cbea","timestamp":"2026-03-30T18:12:25.996Z","message":{"role":"toolResult","toolCallId":"toolu_01PhDxR8XYAFfwL7v1vxsZo8","toolName":"bash","content":[{"type":"text","text":"config.py\ndatabase.py\nmain.py\nmodels.py\npipeline\n__pycache__\npytest.ini\nredis_client.py\nrequirements.txt\nrouters\nschemas.py\nsearch_service.py\ntests\nworker.py\n"}],"isError":false,"timestamp":1774894345996}}
{"type":"message","id":"aeaee4de","parentId":"15a83743","timestamp":"2026-03-30T18:12:25.998Z","message":{"role":"toolResult","toolCallId":"toolu_014doVGbMqap82jPdZCo1Gk1","toolName":"bash","content":[{"type":"text","text":"/vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\n"}],"isError":false,"timestamp":1774894345997}}
{"type":"message","id":"1d7f2524","parentId":"aeaee4de","timestamp":"2026-03-30T18:12:30.030Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01NWRh6MipqKv8VprrRQwGZW","name":"bash","arguments":{"command":"ssh ub01 \"cat /vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\" 2>/dev/null | head -100"}},{"type":"toolCall","id":"toolu_01YN4UY2dekiJ1379GSNUReX","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'class.*Base\\|class Pipeline' /vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":175,"cacheRead":66674,"cacheWrite":345,"totalTokens":67195,"cost":{"input":0.000005,"output":0.004375,"cacheRead":0.033337,"cacheWrite":0.00215625,"total":0.03987325}},"stopReason":"toolUse","timestamp":1774894345997}}
{"type":"message","id":"37d35674","parentId":"1d7f2524","timestamp":"2026-03-30T18:12:30.372Z","message":{"role":"toolResult","toolCallId":"toolu_01NWRh6MipqKv8VprrRQwGZW","toolName":"bash","content":[{"type":"text","text":"\"\"\"SQLAlchemy ORM models for the Chrysopedia knowledge base.\n\nSeven entities matching chrysopedia-spec.md §6.1:\n  Creator, SourceVideo, TranscriptSegment, KeyMoment,\n  TechniquePage, RelatedTechniqueLink, Tag\n\"\"\"\n\nfrom __future__ import annotations\n\nimport enum\nimport uuid\nfrom datetime import datetime, timezone\n\nfrom sqlalchemy import (\n    Enum,\n    Float,\n    ForeignKey,\n    Integer,\n    String,\n    Text,\n    UniqueConstraint,\n    func,\n)\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\nfrom sqlalchemy.orm import Mapped, mapped_column\nfrom sqlalchemy.orm import relationship as sa_relationship\n\nfrom database import Base\n\n\n# ── Enums ────────────────────────────────────────────────────────────────────\n\nclass ContentType(str, enum.Enum):\n    \"\"\"Source video content type.\"\"\"\n    tutorial = \"tutorial\"\n    livestream = \"livestream\"\n    breakdown = \"breakdown\"\n    short_form = \"short_form\"\n\n\nclass ProcessingStatus(str, enum.Enum):\n    \"\"\"Pipeline processing status for a source video.\"\"\"\n    pending = \"pending\"\n    transcribed = \"transcribed\"\n    extracted = \"extracted\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass KeyMomentContentType(str, enum.Enum):\n    \"\"\"Content classification for a key moment.\"\"\"\n    technique = \"technique\"\n    settings = \"settings\"\n    reasoning = \"reasoning\"\n    workflow = \"workflow\"\n\n\nclass ReviewStatus(str, enum.Enum):\n    \"\"\"Human review status for key moments.\"\"\"\n    pending = \"pending\"\n    approved = \"approved\"\n    edited = \"edited\"\n    rejected = \"rejected\"\n\n\nclass SourceQuality(str, enum.Enum):\n    \"\"\"Derived source quality for technique pages.\"\"\"\n    structured = \"structured\"\n    mixed = \"mixed\"\n    unstructured = \"unstructured\"\n\n\nclass PageReviewStatus(str, enum.Enum):\n    \"\"\"Review lifecycle for technique pages.\"\"\"\n    draft = \"draft\"\n    reviewed = \"reviewed\"\n    published = \"published\"\n\n\nclass RelationshipType(str, enum.Enum):\n    \"\"\"Types of links between technique pages.\"\"\"\n    same_technique_other_creator = \"same_technique_other_creator\"\n    same_creator_adjacent = \"same_creator_adjacent\"\n    general_cross_reference = \"general_cross_reference\"\n\n\n# ── Helpers ──────────────────────────────────────────────────────────────────\n\ndef _uuid_pk() -> Mapped[uuid.UUID]:\n    return mapped_column(\n        UUID(as_uuid=True),\n        primary_key=True,\n        default=uuid.uuid4,\n        server_default=func.gen_random_uuid(),\n    )\n\n\ndef _now() -> datetime:\n    \"\"\"Return current UTC time as a naive datetime (no tzinfo).\n\n"}],"isError":false,"timestamp":1774894350372}}
{"type":"message","id":"a392f398","parentId":"37d35674","timestamp":"2026-03-30T18:12:30.392Z","message":{"role":"toolResult","toolCallId":"toolu_01YN4UY2dekiJ1379GSNUReX","toolName":"bash","content":[{"type":"text","text":"109:class Creator(Base):\n130:class SourceVideo(Base):\n164:class TranscriptSegment(Base):\n181:class KeyMoment(Base):\n220:class TechniquePage(Base):\n268:class RelatedTechniqueLink(Base):\n295:class TechniquePageVersion(Base):\n316:class Tag(Base):\n346:class ContentReport(Base):\n386:class PipelineEvent(Base):\n"}],"isError":false,"timestamp":1774894350391}}
{"type":"message","id":"e3fb0d90","parentId":"a392f398","timestamp":"2026-03-30T18:12:35.522Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_013LY13wugXm6FvBbZaSSpgZ","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '386,430p' /vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_019feebCHb726Z94kyoW5BQ7","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '130,165p' /vmPool/r/repos/xpltdco/chrysopedia/backend/models.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":175,"cacheRead":67019,"cacheWrite":1053,"totalTokens":68248,"cost":{"input":0.000005,"output":0.004375,"cacheRead":0.0335095,"cacheWrite":0.00658125,"total":0.04447075}},"stopReason":"toolUse","timestamp":1774894350392}}
{"type":"message","id":"45800c76","parentId":"e3fb0d90","timestamp":"2026-03-30T18:12:35.850Z","message":{"role":"toolResult","toolCallId":"toolu_013LY13wugXm6FvBbZaSSpgZ","toolName":"bash","content":[{"type":"text","text":"class PipelineEvent(Base):\n    \"\"\"Structured log entry for pipeline execution.\n\n    Captures per-stage start/complete/error/llm_call events with\n    token usage and optional response payloads for debugging.\n    \"\"\"\n    __tablename__ = \"pipeline_events\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    video_id: Mapped[uuid.UUID] = mapped_column(\n        UUID(as_uuid=True), nullable=False, index=True,\n    )\n    stage: Mapped[str] = mapped_column(\n        String(50), nullable=False, doc=\"stage2_segmentation, stage3_extraction, etc.\"\n    )\n    event_type: Mapped[str] = mapped_column(\n        String(30), nullable=False, doc=\"start, complete, error, llm_call\"\n    )\n    prompt_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    completion_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    total_tokens: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    model: Mapped[str | None] = mapped_column(String(100), nullable=True)\n    duration_ms: Mapped[int | None] = mapped_column(Integer, nullable=True)\n    payload: Mapped[dict | None] = mapped_column(\n        JSONB, nullable=True, doc=\"LLM response content, error details, stage metadata\"\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n"}],"isError":false,"timestamp":1774894355850}}
{"type":"message","id":"a71a7ee5","parentId":"45800c76","timestamp":"2026-03-30T18:12:35.853Z","message":{"role":"toolResult","toolCallId":"toolu_019feebCHb726Z94kyoW5BQ7","toolName":"bash","content":[{"type":"text","text":"class SourceVideo(Base):\n    __tablename__ = \"source_videos\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    creator_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    filename: Mapped[str] = mapped_column(String(500), nullable=False)\n    file_path: Mapped[str] = mapped_column(String(1000), nullable=False)\n    duration_seconds: Mapped[int] = mapped_column(Integer, nullable=True)\n    content_type: Mapped[ContentType] = mapped_column(\n        Enum(ContentType, name=\"content_type\", create_constraint=True),\n        nullable=False,\n    )\n    transcript_path: Mapped[str | None] = mapped_column(String(1000), nullable=True)\n    content_hash: Mapped[str | None] = mapped_column(String(64), nullable=True, index=True)\n    processing_status: Mapped[ProcessingStatus] = mapped_column(\n        Enum(ProcessingStatus, name=\"processing_status\", create_constraint=True),\n        default=ProcessingStatus.pending,\n        server_default=\"pending\",\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    updated_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now(), onupdate=_now\n    )\n\n    # relationships\n    creator: Mapped[Creator] = sa_relationship(back_populates=\"videos\")\n    segments: Mapped[list[TranscriptSegment]] = sa_relationship(back_populates=\"source_video\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(back_populates=\"source_video\")\n\n\nclass TranscriptSegment(Base):\n    __tablename__ = \"transcript_segments\"\n"}],"isError":false,"timestamp":1774894355850}}
{"type":"message","id":"de83fae8","parentId":"a71a7ee5","timestamp":"2026-03-30T18:12:40.512Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — PipelineEvent already has token columns and a JSONB `payload`. Now let me see the current `_emit_event` and `_make_llm_callback` implementations to understand what's already captured vs what's missing (the full prompt and response)."},{"type":"toolCall","id":"toolu_018FBLHDU6zD56MQaTnNda5o","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '1,110p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":157,"cacheRead":68072,"cacheWrite":1129,"totalTokens":69359,"cost":{"input":0.000005,"output":0.0039250000000000005,"cacheRead":0.034036,"cacheWrite":0.00705625,"total":0.04502225}},"stopReason":"toolUse","timestamp":1774894355851}}
{"type":"message","id":"e74c3793","parentId":"de83fae8","timestamp":"2026-03-30T18:12:40.818Z","message":{"role":"toolResult","toolCallId":"toolu_018FBLHDU6zD56MQaTnNda5o","toolName":"bash","content":[{"type":"text","text":"\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n\nEach stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\ntemplate from disk, calls the LLM client, parses the response, writes\nresults back, and updates processing_status on SourceVideo.\n\nCelery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport hashlib\nimport json\nimport logging\nimport subprocess\nimport time\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\nfrom celery import chain as celery_chain\nfrom pydantic import ValidationError\nfrom sqlalchemy import create_engine, func, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\nfrom config import get_settings\nfrom models import (\n    KeyMoment,\n    KeyMomentContentType,\n    PipelineEvent,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TechniquePageVersion,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\nfrom worker import celery_app\n\nlogger = logging.getLogger(__name__)\n\n\n# ── Pipeline event persistence ───────────────────────────────────────────────\n\ndef _emit_event(\n    video_id: str,\n    stage: str,\n    event_type: str,\n    *,\n    prompt_tokens: int | None = None,\n    completion_tokens: int | None = None,\n    total_tokens: int | None = None,\n    model: str | None = None,\n    duration_ms: int | None = None,\n    payload: dict | None = None,\n) -> None:\n    \"\"\"Persist a pipeline event to the DB. Best-effort -- failures logged, not raised.\"\"\"\n    try:\n        session = _get_sync_session()\n        try:\n            event = PipelineEvent(\n                video_id=video_id,\n                stage=stage,\n                event_type=event_type,\n                prompt_tokens=prompt_tokens,\n                completion_tokens=completion_tokens,\n                total_tokens=total_tokens,\n                model=model,\n                duration_ms=duration_ms,\n                payload=payload,\n            )\n            session.add(event)\n            session.commit()\n        finally:\n            session.close()\n    except Exception as exc:\n        logger.warning(\"Failed to emit pipeline event: %s\", exc)\n\n\ndef _make_llm_callback(video_id: str, stage: str):\n    \"\"\"Create an on_complete callback for LLMClient that emits llm_call events.\"\"\"\n    def callback(*, model=None, prompt_tokens=None, completion_tokens=None,\n                 total_tokens=None, content=None, finish_reason=None,\n                 is_fallback=False, **_kwargs):\n        # Truncate content for storage — keep first 2000 chars for debugging\n        truncated = content[:2000] if content and len(content) > 2000 else content\n        _emit_event(\n            video_id=video_id,\n            stage=stage,\n            event_type=\"llm_call\",\n            model=model,\n            prompt_tokens=prompt_tokens,\n            completion_tokens=completion_tokens,\n            total_tokens=total_tokens,\n            payload={\n                \"content_preview\": truncated,\n                \"content_length\": len(content) if content else 0,\n                \"finish_reason\": finish_reason,\n                \"is_fallback\": is_fallback,\n            },\n        )\n    return callback\n"}],"isError":false,"timestamp":1774894360816}}
{"type":"message","id":"2fd16c48","parentId":"e74c3793","timestamp":"2026-03-30T18:12:52.333Z","message":{"role":"assistant","content":[{"type":"text","text":"Key finding: the callback truncates response content to 2000 chars and stores it as `content_preview`. **No prompt text is captured at all.** This is the core gap."},{"type":"toolCall","id":"toolu_011LYPmX7HPwCcbcCDMcwMbC","name":"bash","arguments":{"command":"ssh ub01 \"cat /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/llm_client.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":134,"cacheRead":69201,"cacheWrite":1097,"totalTokens":70433,"cost":{"input":0.000005,"output":0.00335,"cacheRead":0.0346005,"cacheWrite":0.00685625,"total":0.04481175}},"stopReason":"toolUse","timestamp":1774894360817}}
{"type":"message","id":"848200d0","parentId":"2fd16c48","timestamp":"2026-03-30T18:12:52.677Z","message":{"role":"toolResult","toolCallId":"toolu_011LYPmX7HPwCcbcCDMcwMbC","toolName":"bash","content":[{"type":"text","text":"\"\"\"Synchronous LLM client with primary/fallback endpoint logic.\n\nUses the OpenAI-compatible API (works with Ollama, vLLM, OpenWebUI, etc.).\nCelery tasks run synchronously, so this uses ``openai.OpenAI`` (not Async).\n\nSupports two modalities:\n- **chat**: Standard JSON mode with ``response_format: {\"type\": \"json_object\"}``\n- **thinking**: For reasoning models that emit ``<think>...</think>`` blocks\n  before their answer. Skips ``response_format``, appends JSON instructions to\n  the system prompt, and strips think tags from the response.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport logging\nimport re\nfrom typing import TYPE_CHECKING, TypeVar\n\nif TYPE_CHECKING:\n    from collections.abc import Callable\n\nimport openai\nfrom pydantic import BaseModel\n\nfrom config import Settings\n\nlogger = logging.getLogger(__name__)\n\nT = TypeVar(\"T\", bound=BaseModel)\n\n# ── Think-tag stripping ──────────────────────────────────────────────────────\n\n_THINK_PATTERN = re.compile(r\"<think>.*?</think>\", re.DOTALL)\n\n\ndef strip_think_tags(text: str) -> str:\n    \"\"\"Remove ``<think>...</think>`` blocks from LLM output.\n\n    Thinking/reasoning models often prefix their JSON with a reasoning trace\n    wrapped in ``<think>`` tags. This strips all such blocks (including\n    multiline and multiple occurrences) and returns the cleaned text.\n\n    Handles:\n    - Single ``<think>...</think>`` block\n    - Multiple blocks in one response\n    - Multiline content inside think tags\n    - Responses with no think tags (passthrough)\n    - Empty input (passthrough)\n    \"\"\"\n    if not text:\n        return text\n    cleaned = _THINK_PATTERN.sub(\"\", text)\n    return cleaned.strip()\n\n\n\n# ── Token estimation ─────────────────────────────────────────────────────────\n\n# Stage-specific output multipliers: estimated output tokens as a ratio of input tokens.\n# These are empirically tuned based on observed pipeline behavior.\n_STAGE_OUTPUT_RATIOS: dict[str, float] = {\n    \"stage2_segmentation\": 0.3,    # Compact topic groups — much smaller than input\n    \"stage3_extraction\": 1.2,      # Detailed moments with summaries — can exceed input\n    \"stage4_classification\": 0.15,  # Index + category + tags per moment — very compact\n    \"stage5_synthesis\": 1.5,       # Full prose technique pages — heaviest output\n}\n\n# Minimum floor so we never send a trivially small max_tokens\n_MIN_MAX_TOKENS = 2048\n\n\ndef estimate_tokens(text: str) -> int:\n    \"\"\"Estimate token count from text using a chars-per-token heuristic.\n\n    Uses 3.5 chars/token which is conservative for English + JSON markup.\n    \"\"\"\n    if not text:\n        return 0\n    return max(1, int(len(text) / 3.5))\n\n\ndef estimate_max_tokens(\n    system_prompt: str,\n    user_prompt: str,\n    stage: str | None = None,\n    hard_limit: int = 32768,\n) -> int:\n    \"\"\"Estimate the max_tokens parameter for an LLM call.\n\n    Calculates expected output size based on input size and stage-specific\n    multipliers. The result is clamped between _MIN_MAX_TOKENS and hard_limit.\n\n    Parameters\n    ----------\n    system_prompt:\n        The system prompt text.\n    user_prompt:\n        The user prompt text (transcript, moments, etc.).\n    stage:\n        Pipeline stage name (e.g. \"stage3_extraction\"). If None or unknown,\n        uses a default 1.0x multiplier.\n    hard_limit:\n        Absolute ceiling — never exceed this value.\n\n    Returns\n    -------\n    int\n        Estimated max_tokens value to pass to the LLM API.\n    \"\"\"\n    input_tokens = estimate_tokens(system_prompt) + estimate_tokens(user_prompt)\n    ratio = _STAGE_OUTPUT_RATIOS.get(stage or \"\", 1.0)\n    estimated_output = int(input_tokens * ratio)\n\n    # Add a 20% buffer for JSON overhead and variability\n    estimated_output = int(estimated_output * 1.2)\n\n    # Clamp to [_MIN_MAX_TOKENS, hard_limit]\n    result = max(_MIN_MAX_TOKENS, min(estimated_output, hard_limit))\n\n    logger.info(\n        \"Token estimate: input≈%d, stage=%s, ratio=%.2f, estimated_output=%d, max_tokens=%d (hard_limit=%d)\",\n        input_tokens, stage or \"default\", ratio, estimated_output, result, hard_limit,\n    )\n    return result\n\n\nclass LLMClient:\n    \"\"\"Sync LLM client that tries a primary endpoint and falls back on failure.\"\"\"\n\n    def __init__(self, settings: Settings) -> None:\n        self.settings = settings\n        self._primary = openai.OpenAI(\n            base_url=settings.llm_api_url,\n            api_key=settings.llm_api_key,\n        )\n        self._fallback = openai.OpenAI(\n            base_url=settings.llm_fallback_url,\n            api_key=settings.llm_api_key,\n        )\n\n    # ── Core completion ──────────────────────────────────────────────────\n\n    def complete(\n        self,\n        system_prompt: str,\n        user_prompt: str,\n        response_model: type[BaseModel] | None = None,\n        modality: str = \"chat\",\n        model_override: str | None = None,\n        on_complete: \"Callable | None\" = None,\n        max_tokens: int | None = None,\n    ) -> str:\n        \"\"\"Send a chat completion request, falling back on connection/timeout errors.\n\n        Parameters\n        ----------\n        system_prompt:\n            System message content.\n        user_prompt:\n            User message content.\n        response_model:\n            If provided and modality is \"chat\", ``response_format`` is set to\n            ``{\"type\": \"json_object\"}``. For \"thinking\" modality, JSON\n            instructions are appended to the system prompt instead.\n        modality:\n            Either \"chat\" (default) or \"thinking\". Thinking modality skips\n            response_format and strips ``<think>`` tags from output.\n        model_override:\n            Model name to use instead of the default. If None, uses the\n            configured default for the endpoint.\n        max_tokens:\n            Override for max_tokens on this call. If None, falls back to\n            the configured ``llm_max_tokens`` from settings.\n\n        Returns\n        -------\n        str\n            Raw completion text from the model (think tags stripped if thinking).\n        \"\"\"\n        kwargs: dict = {}\n        effective_system = system_prompt\n\n        if modality == \"thinking\":\n            # Thinking models often don't support response_format: json_object.\n            # Instead, append explicit JSON instructions to the system prompt.\n            if response_model is not None:\n                json_schema_hint = (\n                    \"\\n\\nYou MUST respond with ONLY valid JSON. \"\n                    \"No markdown code fences, no explanation, no preamble — \"\n                    \"just the raw JSON object.\"\n                )\n                effective_system = system_prompt + json_schema_hint\n        else:\n            # Chat modality — use standard JSON mode\n            if response_model is not None:\n                kwargs[\"response_format\"] = {\"type\": \"json_object\"}\n\n        messages = [\n            {\"role\": \"system\", \"content\": effective_system},\n            {\"role\": \"user\", \"content\": user_prompt},\n        ]\n\n        primary_model = model_override or self.settings.llm_model\n        fallback_model = self.settings.llm_fallback_model\n        effective_max_tokens = max_tokens if max_tokens is not None else self.settings.llm_max_tokens\n\n        logger.info(\n            \"LLM request: model=%s, modality=%s, response_model=%s, max_tokens=%d\",\n            primary_model,\n            modality,\n            response_model.__name__ if response_model else None,\n            effective_max_tokens,\n        )\n\n        # --- Try primary endpoint ---\n        try:\n            response = self._primary.chat.completions.create(\n                model=primary_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response: prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=primary_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=response.choices[0].finish_reason if response.choices else None,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return raw\n\n        except (openai.APIConnectionError, openai.APITimeoutError) as exc:\n            logger.warning(\n                \"Primary LLM endpoint failed (%s: %s), trying fallback at %s\",\n                type(exc).__name__,\n                exc,\n                self.settings.llm_fallback_url,\n            )\n\n        # --- Try fallback endpoint ---\n        try:\n            response = self._fallback.chat.completions.create(\n                model=fallback_model,\n                messages=messages,\n                max_tokens=effective_max_tokens,\n                **kwargs,\n            )\n            raw = response.choices[0].message.content or \"\"\n            usage = getattr(response, \"usage\", None)\n            if usage:\n                logger.info(\n                    \"LLM response (fallback): prompt_tokens=%s, completion_tokens=%s, total=%s, content_len=%d, finish=%s\",\n                    usage.prompt_tokens, usage.completion_tokens, usage.total_tokens,\n                    len(raw), response.choices[0].finish_reason,\n                )\n            if modality == \"thinking\":\n                raw = strip_think_tags(raw)\n            if on_complete is not None:\n                try:\n                    on_complete(\n                        model=fallback_model,\n                        prompt_tokens=usage.prompt_tokens if usage else None,\n                        completion_tokens=usage.completion_tokens if usage else None,\n                        total_tokens=usage.total_tokens if usage else None,\n                        content=raw,\n                        finish_reason=response.choices[0].finish_reason if response.choices else None,\n                        is_fallback=True,\n                    )\n                except Exception as cb_exc:\n                    logger.warning(\"on_complete callback failed: %s\", cb_exc)\n            return raw\n\n        except (openai.APIConnectionError, openai.APITimeoutError, openai.APIError) as exc:\n            logger.error(\n                \"Fallback LLM endpoint also failed (%s: %s). Giving up.\",\n                type(exc).__name__,\n                exc,\n            )\n            raise\n\n    # ── Response parsing ─────────────────────────────────────────────────\n\n    def parse_response(self, text: str, model: type[T]) -> T:\n        \"\"\"Parse raw LLM output as JSON and validate against a Pydantic model.\n\n        Parameters\n        ----------\n        text:\n            Raw JSON string from the LLM.\n        model:\n            Pydantic model class to validate against.\n\n        Returns\n        -------\n        T\n            Validated Pydantic model instance.\n\n        Raises\n        ------\n        pydantic.ValidationError\n            If the JSON doesn't match the schema.\n        ValueError\n            If the text is not valid JSON.\n        \"\"\"\n        try:\n            return model.model_validate_json(text)\n        except Exception:\n            logger.error(\n                \"Failed to parse LLM response as %s. Response text: %.500s\",\n                model.__name__,\n                text,\n            )\n            raise\n"}],"isError":false,"timestamp":1774894372675}}
{"type":"message","id":"738133bd","parentId":"848200d0","timestamp":"2026-03-30T18:13:07.471Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I see the full picture. The `on_complete` callback receives the response content but **not** the prompts. The prompts are only known at the call site in stages.py. Let me check the existing API endpoints for pipeline events and see how the debug flag might be toggled."},{"type":"toolCall","id":"toolu_01TjH5iUrNYtnhEfGvTz5ZDd","name":"bash","arguments":{"command":"ssh ub01 \"ls /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_019ArzfFgELe7nosrLGSUpUF","name":"bash","arguments":{"command":"ssh ub01 \"grep -rn 'pipeline_event\\|PipelineEvent\\|debug\\|debug_mode' /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/ --include='*.py'\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":249,"cacheRead":70298,"cacheWrite":3298,"totalTokens":73846,"cost":{"input":0.000005,"output":0.0062250000000000005,"cacheRead":0.035149,"cacheWrite":0.020612500000000002,"total":0.061991500000000005}},"stopReason":"toolUse","timestamp":1774894372676}}
{"type":"message","id":"2a37ae8f","parentId":"738133bd","timestamp":"2026-03-30T18:13:07.787Z","message":{"role":"toolResult","toolCallId":"toolu_01TjH5iUrNYtnhEfGvTz5ZDd","toolName":"bash","content":[{"type":"text","text":"creators.py\nhealth.py\ningest.py\n__init__.py\npipeline.py\nreports.py\nreview.py\nsearch.py\ntechniques.py\ntopics.py\nvideos.py\n"}],"isError":false,"timestamp":1774894387787}}
{"type":"message","id":"1afc90f1","parentId":"2a37ae8f","timestamp":"2026-03-30T18:13:07.790Z","message":{"role":"toolResult","toolCallId":"toolu_019ArzfFgELe7nosrLGSUpUF","toolName":"bash","content":[{"type":"text","text":"/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:23:from models import PipelineEvent, SourceVideo, Creator\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:74:            PipelineEvent.video_id,\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:77:                (PipelineEvent.event_type == \"llm_call\", PipelineEvent.total_tokens),\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:80:            func.max(PipelineEvent.created_at).label(\"last_event_at\"),\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:82:        .group_by(PipelineEvent.video_id)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:179:async def list_pipeline_events(\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:189:    stmt = select(PipelineEvent).where(PipelineEvent.video_id == video_id)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:192:        stmt = stmt.where(PipelineEvent.stage == stage)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:194:        stmt = stmt.where(PipelineEvent.event_type == event_type)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py:205:    order_clause = PipelineEvent.created_at.asc() if order == \"asc\" else PipelineEvent.created_at.desc()\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/videos.py:35:    logger.debug(\"Listed %d videos (offset=%d, limit=%d)\", len(videos), offset, limit)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/creators.py:89:    logger.debug(\n"}],"isError":false,"timestamp":1774894387789}}
{"type":"message","id":"989269b2","parentId":"1afc90f1","timestamp":"2026-03-30T18:13:17.838Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01LHQsfqHV6XGzD7tQMCCqBe","name":"bash","arguments":{"command":"ssh ub01 \"cat /vmPool/r/repos/xpltdco/chrysopedia/backend/routers/pipeline.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":73596,"cacheWrite":937,"totalTokens":74625,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.036798,"cacheWrite":0.00585625,"total":0.044934249999999995}},"stopReason":"toolUse","timestamp":1774894387790}}
{"type":"message","id":"7d31a0b8","parentId":"989269b2","timestamp":"2026-03-30T18:13:18.158Z","message":{"role":"toolResult","toolCallId":"toolu_01LHQsfqHV6XGzD7tQMCCqBe","toolName":"bash","content":[{"type":"text","text":"\"\"\"Pipeline management endpoints — public trigger + admin dashboard.\n\nPublic:\n  POST /pipeline/trigger/{video_id}    Trigger pipeline for a video\n\nAdmin:\n  GET  /admin/pipeline/videos          Video list with status + event counts\n  POST /admin/pipeline/trigger/{video_id}  Retrigger (same as public but under admin prefix)\n  POST /admin/pipeline/revoke/{video_id}   Revoke/cancel active tasks for a video\n  GET  /admin/pipeline/events/{video_id}   Event log for a video (paginated)\n  GET  /admin/pipeline/worker-status       Active/reserved tasks from Celery inspect\n\"\"\"\n\nimport logging\nimport uuid\nfrom typing import Annotated\n\nfrom fastapi import APIRouter, Depends, HTTPException, Query\nfrom sqlalchemy import func, select, case\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom database import get_session\nfrom models import PipelineEvent, SourceVideo, Creator\n\nlogger = logging.getLogger(\"chrysopedia.pipeline\")\n\nrouter = APIRouter(tags=[\"pipeline\"])\n\n\n# ── Public trigger ───────────────────────────────────────────────────────────\n\n@router.post(\"/pipeline/trigger/{video_id}\")\nasync def trigger_pipeline(\n    video_id: str,\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Manually trigger (or re-trigger) the LLM extraction pipeline for a video.\"\"\"\n    stmt = select(SourceVideo).where(SourceVideo.id == video_id)\n    result = await db.execute(stmt)\n    video = result.scalar_one_or_none()\n\n    if video is None:\n        raise HTTPException(status_code=404, detail=f\"Video not found: {video_id}\")\n\n    from pipeline.stages import run_pipeline\n\n    try:\n        run_pipeline.delay(str(video.id))\n        logger.info(\"Pipeline manually triggered for video_id=%s\", video_id)\n    except Exception as exc:\n        logger.warning(\"Failed to dispatch pipeline for video_id=%s: %s\", video_id, exc)\n        raise HTTPException(\n            status_code=503,\n            detail=\"Pipeline dispatch failed — Celery/Redis may be unavailable\",\n        ) from exc\n\n    return {\n        \"status\": \"triggered\",\n        \"video_id\": str(video.id),\n        \"current_processing_status\": video.processing_status.value,\n    }\n\n\n# ── Admin: Video list ────────────────────────────────────────────────────────\n\n@router.get(\"/admin/pipeline/videos\")\nasync def list_pipeline_videos(\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"List all videos with processing status and pipeline event counts.\"\"\"\n    # Subquery for event counts per video\n    event_counts = (\n        select(\n            PipelineEvent.video_id,\n            func.count().label(\"event_count\"),\n            func.sum(case(\n                (PipelineEvent.event_type == \"llm_call\", PipelineEvent.total_tokens),\n                else_=0\n            )).label(\"total_tokens_used\"),\n            func.max(PipelineEvent.created_at).label(\"last_event_at\"),\n        )\n        .group_by(PipelineEvent.video_id)\n        .subquery()\n    )\n\n    stmt = (\n        select(\n            SourceVideo.id,\n            SourceVideo.filename,\n            SourceVideo.processing_status,\n            SourceVideo.content_hash,\n            SourceVideo.created_at,\n            SourceVideo.updated_at,\n            Creator.name.label(\"creator_name\"),\n            event_counts.c.event_count,\n            event_counts.c.total_tokens_used,\n            event_counts.c.last_event_at,\n        )\n        .join(Creator, SourceVideo.creator_id == Creator.id)\n        .outerjoin(event_counts, SourceVideo.id == event_counts.c.video_id)\n        .order_by(SourceVideo.updated_at.desc())\n    )\n\n    result = await db.execute(stmt)\n    rows = result.all()\n\n    return {\n        \"items\": [\n            {\n                \"id\": str(r.id),\n                \"filename\": r.filename,\n                \"processing_status\": r.processing_status.value if hasattr(r.processing_status, 'value') else str(r.processing_status),\n                \"content_hash\": r.content_hash,\n                \"creator_name\": r.creator_name,\n                \"created_at\": r.created_at.isoformat() if r.created_at else None,\n                \"updated_at\": r.updated_at.isoformat() if r.updated_at else None,\n                \"event_count\": r.event_count or 0,\n                \"total_tokens_used\": r.total_tokens_used or 0,\n                \"last_event_at\": r.last_event_at.isoformat() if r.last_event_at else None,\n            }\n            for r in rows\n        ],\n        \"total\": len(rows),\n    }\n\n\n# ── Admin: Retrigger ─────────────────────────────────────────────────────────\n\n@router.post(\"/admin/pipeline/trigger/{video_id}\")\nasync def admin_trigger_pipeline(\n    video_id: str,\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Admin retrigger — same as public trigger.\"\"\"\n    return await trigger_pipeline(video_id, db)\n\n\n# ── Admin: Revoke ────────────────────────────────────────────────────────────\n\n@router.post(\"/admin/pipeline/revoke/{video_id}\")\nasync def revoke_pipeline(video_id: str):\n    \"\"\"Revoke/cancel active Celery tasks for a video.\n    \n    Uses Celery's revoke with terminate=True to kill running tasks.\n    This is best-effort — the task may have already completed.\n    \"\"\"\n    from worker import celery_app\n    \n    try:\n        # Get active tasks and revoke any matching this video_id\n        inspector = celery_app.control.inspect()\n        active = inspector.active() or {}\n        revoked_count = 0\n        \n        for _worker, tasks in active.items():\n            for task in tasks:\n                task_args = task.get(\"args\", [])\n                if task_args and str(task_args[0]) == video_id:\n                    celery_app.control.revoke(task[\"id\"], terminate=True)\n                    revoked_count += 1\n                    logger.info(\"Revoked task %s for video_id=%s\", task[\"id\"], video_id)\n        \n        return {\n            \"status\": \"revoked\" if revoked_count > 0 else \"no_active_tasks\",\n            \"video_id\": video_id,\n            \"tasks_revoked\": revoked_count,\n        }\n    except Exception as exc:\n        logger.warning(\"Failed to revoke tasks for video_id=%s: %s\", video_id, exc)\n        raise HTTPException(\n            status_code=503,\n            detail=\"Failed to communicate with Celery worker\",\n        ) from exc\n\n\n# ── Admin: Event log ─────────────────────────────────────────────────────────\n\n@router.get(\"/admin/pipeline/events/{video_id}\")\nasync def list_pipeline_events(\n    video_id: str,\n    offset: Annotated[int, Query(ge=0)] = 0,\n    limit: Annotated[int, Query(ge=1, le=200)] = 100,\n    stage: Annotated[str | None, Query(description=\"Filter by stage name\")] = None,\n    event_type: Annotated[str | None, Query(description=\"Filter by event type\")] = None,\n    order: Annotated[str, Query(description=\"Sort order: asc or desc\")] = \"desc\",\n    db: AsyncSession = Depends(get_session),\n):\n    \"\"\"Get pipeline events for a video. Default: newest first (desc).\"\"\"\n    stmt = select(PipelineEvent).where(PipelineEvent.video_id == video_id)\n    \n    if stage:\n        stmt = stmt.where(PipelineEvent.stage == stage)\n    if event_type:\n        stmt = stmt.where(PipelineEvent.event_type == event_type)\n    \n    # Validate order param\n    if order not in (\"asc\", \"desc\"):\n        raise HTTPException(status_code=400, detail=\"order must be 'asc' or 'desc'\")\n\n    # Count\n    count_stmt = select(func.count()).select_from(stmt.subquery())\n    total = (await db.execute(count_stmt)).scalar() or 0\n    \n    # Fetch\n    order_clause = PipelineEvent.created_at.asc() if order == \"asc\" else PipelineEvent.created_at.desc()\n    stmt = stmt.order_by(order_clause)\n    stmt = stmt.offset(offset).limit(limit)\n    result = await db.execute(stmt)\n    events = result.scalars().all()\n    \n    return {\n        \"items\": [\n            {\n                \"id\": str(e.id),\n                \"video_id\": str(e.video_id),\n                \"stage\": e.stage,\n                \"event_type\": e.event_type,\n                \"prompt_tokens\": e.prompt_tokens,\n                \"completion_tokens\": e.completion_tokens,\n                \"total_tokens\": e.total_tokens,\n                \"model\": e.model,\n                \"duration_ms\": e.duration_ms,\n                \"payload\": e.payload,\n                \"created_at\": e.created_at.isoformat() if e.created_at else None,\n            }\n            for e in events\n        ],\n        \"total\": total,\n        \"offset\": offset,\n        \"limit\": limit,\n    }\n\n\n# ── Admin: Worker status ─────────────────────────────────────────────────────\n\n@router.get(\"/admin/pipeline/worker-status\")\nasync def worker_status():\n    \"\"\"Get current Celery worker status — active, reserved, and stats.\"\"\"\n    from worker import celery_app\n    \n    try:\n        inspector = celery_app.control.inspect()\n        active = inspector.active() or {}\n        reserved = inspector.reserved() or {}\n        stats = inspector.stats() or {}\n        \n        workers = []\n        for worker_name in set(list(active.keys()) + list(reserved.keys()) + list(stats.keys())):\n            worker_active = active.get(worker_name, [])\n            worker_reserved = reserved.get(worker_name, [])\n            worker_stats = stats.get(worker_name, {})\n            \n            workers.append({\n                \"name\": worker_name,\n                \"active_tasks\": [\n                    {\n                        \"id\": t.get(\"id\"),\n                        \"name\": t.get(\"name\"),\n                        \"args\": t.get(\"args\", []),\n                        \"time_start\": t.get(\"time_start\"),\n                    }\n                    for t in worker_active\n                ],\n                \"reserved_tasks\": len(worker_reserved),\n                \"total_completed\": worker_stats.get(\"total\", {}).get(\"tasks.pipeline.stages.stage2_segmentation\", 0)\n                    + worker_stats.get(\"total\", {}).get(\"tasks.pipeline.stages.stage3_extraction\", 0)\n                    + worker_stats.get(\"total\", {}).get(\"tasks.pipeline.stages.stage4_classification\", 0)\n                    + worker_stats.get(\"total\", {}).get(\"tasks.pipeline.stages.stage5_synthesis\", 0),\n                \"uptime\": worker_stats.get(\"clock\", None),\n                \"pool_size\": worker_stats.get(\"pool\", {}).get(\"max-concurrency\") if isinstance(worker_stats.get(\"pool\"), dict) else None,\n            })\n        \n        return {\n            \"online\": len(workers) > 0,\n            \"workers\": workers,\n        }\n    except Exception as exc:\n        logger.warning(\"Failed to inspect Celery workers: %s\", exc)\n        return {\n            \"online\": False,\n            \"workers\": [],\n            \"error\": str(exc),\n        }\n"}],"isError":false,"timestamp":1774894398156}}
{"type":"message","id":"0668a6ba","parentId":"7d31a0b8","timestamp":"2026-03-30T18:13:23.624Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how SourceVideo stores debug mode and if there's any existing toggle mechanism."},{"type":"toolCall","id":"toolu_01KXU8nJTHPdYXj9Mhra539X","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'debug\\|DEBUG' /vmPool/r/repos/xpltdco/chrysopedia/backend/config.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01LXRkckjjZc7XaqAKsVMLjD","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'debug\\|redis\\|Redis' /vmPool/r/repos/xpltdco/chrysopedia/backend/redis_client.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01SBt3LRhyT7HG7RMT6tGHoU","name":"bash","arguments":{"command":"ssh ub01 \"cat /vmPool/r/repos/xpltdco/chrysopedia/backend/schemas.py\" 2>/dev/null | head -60"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":278,"cacheRead":74533,"cacheWrite":3129,"totalTokens":77941,"cost":{"input":0.000005,"output":0.0069500000000000004,"cacheRead":0.0372665,"cacheWrite":0.01955625,"total":0.06377775000000001}},"stopReason":"toolUse","timestamp":1774894398157}}
{"type":"message","id":"daa5b24a","parentId":"0668a6ba","timestamp":"2026-03-30T18:13:23.943Z","message":{"role":"toolResult","toolCallId":"toolu_01KXU8nJTHPdYXj9Mhra539X","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774894403942}}
{"type":"message","id":"22c65766","parentId":"daa5b24a","timestamp":"2026-03-30T18:13:23.954Z","message":{"role":"toolResult","toolCallId":"toolu_01LXRkckjjZc7XaqAKsVMLjD","toolName":"bash","content":[{"type":"text","text":"1:\"\"\"Async Redis client helper for Chrysopedia.\"\"\"\n3:import redis.asyncio as aioredis\n8:async def get_redis() -> aioredis.Redis:\n9:    \"\"\"Return an async Redis client from the configured URL.\n15:    return aioredis.from_url(settings.redis_url, decode_responses=True)\n"}],"isError":false,"timestamp":1774894403953}}
{"type":"message","id":"eb302c04","parentId":"22c65766","timestamp":"2026-03-30T18:13:23.956Z","message":{"role":"toolResult","toolCallId":"toolu_01SBt3LRhyT7HG7RMT6tGHoU","toolName":"bash","content":[{"type":"text","text":"\"\"\"Pydantic schemas for the Chrysopedia API.\n\nRead-only schemas for list/detail endpoints and input schemas for creation.\nEach schema mirrors the corresponding SQLAlchemy model in models.py.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport uuid\nfrom datetime import datetime\n\nfrom pydantic import BaseModel, ConfigDict, Field\n\n\n# ── Health ───────────────────────────────────────────────────────────────────\n\nclass HealthResponse(BaseModel):\n    status: str = \"ok\"\n    service: str = \"chrysopedia-api\"\n    version: str = \"0.1.0\"\n    database: str = \"unknown\"\n\n\n# ── Creator ──────────────────────────────────────────────────────────────────\n\nclass CreatorBase(BaseModel):\n    name: str\n    slug: str\n    genres: list[str] | None = None\n    folder_name: str\n\nclass CreatorCreate(CreatorBase):\n    pass\n\nclass CreatorRead(CreatorBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    view_count: int = 0\n    created_at: datetime\n    updated_at: datetime\n\n\nclass CreatorDetail(CreatorRead):\n    \"\"\"Creator with nested video count.\"\"\"\n    video_count: int = 0\n\n\n# ── SourceVideo ──────────────────────────────────────────────────────────────\n\nclass SourceVideoBase(BaseModel):\n    filename: str\n    file_path: str\n    duration_seconds: int | None = None\n    content_type: str\n    transcript_path: str | None = None\n\nclass SourceVideoCreate(SourceVideoBase):\n    creator_id: uuid.UUID\n\n"}],"isError":false,"timestamp":1774894403953}}
{"type":"message","id":"cf7c89f5","parentId":"eb302c04","timestamp":"2026-03-30T18:13:31.067Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019vYZFXzXRfKPrVWFRu3GKQ","name":"bash","arguments":{"command":"ssh ub01 \"grep -rn 'debug_mode\\|debug_enabled\\|chrysopedia:debug\\|review_mode' /vmPool/r/repos/xpltdco/chrysopedia/backend/ --include='*.py'\" 2>/dev/null | head -20"}},{"type":"toolCall","id":"toolu_0119ZYJTwmtqaRm7jiZt8Pae","name":"bash","arguments":{"command":"ssh ub01 \"cat /vmPool/r/repos/xpltdco/chrysopedia/backend/config.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":191,"cacheRead":77662,"cacheWrite":868,"totalTokens":78722,"cost":{"input":0.000005,"output":0.004775000000000001,"cacheRead":0.038831,"cacheWrite":0.005425,"total":0.049035999999999996}},"stopReason":"toolUse","timestamp":1774894403954}}
{"type":"message","id":"47751442","parentId":"cf7c89f5","timestamp":"2026-03-30T18:13:31.862Z","message":{"role":"toolResult","toolCallId":"toolu_019vYZFXzXRfKPrVWFRu3GKQ","toolName":"bash","content":[{"type":"text","text":"/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py:654:    Sets processing_status to 'reviewed' (or 'published' if review_mode is False).\n/vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py:828:        if settings.review_mode:\n/vmPool/r/repos/xpltdco/chrysopedia/backend/schemas.py:247:    review_mode: bool\n/vmPool/r/repos/xpltdco/chrysopedia/backend/schemas.py:252:    review_mode: bool\n/vmPool/r/repos/xpltdco/chrysopedia/backend/config.py:63:    review_mode: bool = True\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py:35:REDIS_MODE_KEY = \"chrysopedia:review_mode\"\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py:346:                return ReviewModeResponse(review_mode=value.lower() == \"true\")\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py:353:    return ReviewModeResponse(review_mode=settings.review_mode)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py:364:            await redis.set(REDIS_MODE_KEY, str(body.review_mode))\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py:374:    logger.info(\"Review mode set to %s\", body.review_mode)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/routers/review.py:375:    return ReviewModeResponse(review_mode=body.review_mode)\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_pipeline.py:315:        s.review_mode = True\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_pipeline.py:394:        s.review_mode = True\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_pipeline.py:524:        s.review_mode = True\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_review.py:450:    assert resp.json()[\"review_mode\"] is True\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_review.py:461:        resp = await client.put(MODE_URL, json={\"review_mode\": False})\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_review.py:463:    assert resp.json()[\"review_mode\"] is False\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_review.py:464:    mock_redis.set.assert_called_once_with(\"chrysopedia:review_mode\", \"False\")\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_review.py:477:    assert resp.json()[\"review_mode\"] is False\n/vmPool/r/repos/xpltdco/chrysopedia/backend/tests/test_review.py:487:    assert resp.json()[\"review_mode\"] is True\n"}],"isError":false,"timestamp":1774894411862}}
{"type":"message","id":"0bda44d7","parentId":"47751442","timestamp":"2026-03-30T18:13:31.895Z","message":{"role":"toolResult","toolCallId":"toolu_0119ZYJTwmtqaRm7jiZt8Pae","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    # 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":1774894411893}}
{"type":"message","id":"776adf69","parentId":"0bda44d7","timestamp":"2026-03-30T18:13:40.977Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the stage 5 synthesis and the `_safe_parse_llm_response` to see all the LLM call sites, plus what the existing Alembic migrations look like."},{"type":"toolCall","id":"toolu_01Ncxq2YKxUwkd1iqfLdyfXg","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '195,230p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01WLiXkovEvoHGV6pZ3mR9dF","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'llm.complete\\|_safe_parse_llm_response' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01RMzzJpfsM2f9nwibzfXUjs","name":"bash","arguments":{"command":"ssh ub01 \"ls /vmPool/r/repos/xpltdco/chrysopedia/alembic/versions/ | tail -5\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":313,"cacheRead":78530,"cacheWrite":2010,"totalTokens":80854,"cost":{"input":0.000005,"output":0.007825,"cacheRead":0.039265,"cacheWrite":0.0125625,"total":0.0596575}},"stopReason":"toolUse","timestamp":1774894411894}}
{"type":"message","id":"52feebec","parentId":"776adf69","timestamp":"2026-03-30T18:13:41.325Z","message":{"role":"toolResult","toolCallId":"toolu_01Ncxq2YKxUwkd1iqfLdyfXg","toolName":"bash","content":[{"type":"text","text":"\ndef _safe_parse_llm_response(\n    raw: str,\n    model_cls,\n    llm: LLMClient,\n    system_prompt: str,\n    user_prompt: str,\n    modality: str = \"chat\",\n    model_override: str | None = None,\n):\n    \"\"\"Parse LLM response with one retry on failure.\n\n    On malformed response: log the raw text, retry once with a JSON nudge,\n    then raise on second failure.\n    \"\"\"\n    try:\n        return llm.parse_response(raw, model_cls)\n    except (ValidationError, ValueError, json.JSONDecodeError) as exc:\n        logger.warning(\n            \"First parse attempt failed for %s (%s). Retrying with JSON nudge. \"\n            \"Raw response (first 500 chars): %.500s\",\n            model_cls.__name__,\n            type(exc).__name__,\n            raw,\n        )\n        # Retry with explicit JSON instruction\n        nudge_prompt = user_prompt + \"\\n\\nIMPORTANT: Output ONLY valid JSON. No markdown, no explanation.\"\n        retry_raw = llm.complete(\n            system_prompt, nudge_prompt, response_model=model_cls,\n            modality=modality, model_override=model_override,\n        )\n        return llm.parse_response(retry_raw, model_cls)\n\n\n# ── Stage 2: Segmentation ───────────────────────────────────────────────────\n\n"}],"isError":false,"timestamp":1774894421325}}
{"type":"message","id":"90b37673","parentId":"52feebec","timestamp":"2026-03-30T18:13:41.328Z","message":{"role":"toolResult","toolCallId":"toolu_01WLiXkovEvoHGV6pZ3mR9dF","toolName":"bash","content":[{"type":"text","text":"196:def _safe_parse_llm_response(\n222:        retry_raw = llm.complete(\n278:        raw = llm.complete(system_prompt, user_prompt, response_model=SegmentationResult, on_complete=_make_llm_callback(video_id, \"stage2_segmentation\"),\n280:        result = _safe_parse_llm_response(raw, SegmentationResult, llm, system_prompt, user_prompt,\n370:            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\"),\n372:            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt,\n485:        raw = llm.complete(system_prompt, user_prompt, response_model=ClassificationResult, on_complete=_make_llm_callback(video_id, \"stage4_classification\"),\n487:        result = _safe_parse_llm_response(raw, ClassificationResult, llm, system_prompt, user_prompt,\n724:            raw = llm.complete(system_prompt, user_prompt, response_model=SynthesisResult, on_complete=_make_llm_callback(video_id, \"stage5_synthesis\"),\n726:            result = _safe_parse_llm_response(raw, SynthesisResult, llm, system_prompt, user_prompt,\n"}],"isError":false,"timestamp":1774894421328}}
{"type":"message","id":"9f0845ce","parentId":"90b37673","timestamp":"2026-03-30T18:13:41.332Z","message":{"role":"toolResult","toolCallId":"toolu_01RMzzJpfsM2f9nwibzfXUjs","toolName":"bash","content":[{"type":"text","text":"001_initial.py\n002_technique_page_versions.py\n003_content_reports.py\n004_pipeline_events.py\n005_content_hash.py\n"}],"isError":false,"timestamp":1774894421330}}
{"type":"message","id":"cf474fc7","parentId":"9f0845ce","timestamp":"2026-03-30T18:13:45.899Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01BMwtnyJU8ZeS2iA83AhSGv","name":"bash","arguments":{"command":"ssh ub01 \"cat /vmPool/r/repos/xpltdco/chrysopedia/alembic/versions/004_pipeline_events.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":96,"cacheRead":80540,"cacheWrite":1169,"totalTokens":81806,"cost":{"input":0.000005,"output":0.0024000000000000002,"cacheRead":0.04027,"cacheWrite":0.00730625,"total":0.04998125}},"stopReason":"toolUse","timestamp":1774894421331}}
{"type":"message","id":"38a5533e","parentId":"cf474fc7","timestamp":"2026-03-30T18:13:46.182Z","message":{"role":"toolResult","toolCallId":"toolu_01BMwtnyJU8ZeS2iA83AhSGv","toolName":"bash","content":[{"type":"text","text":"\"\"\"Create pipeline_events table.\n\nRevision ID: 004_pipeline_events\nRevises: 003_content_reports\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects.postgresql import UUID, JSONB\n\nrevision = \"004_pipeline_events\"\ndown_revision = \"003_content_reports\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    op.create_table(\n        \"pipeline_events\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.func.gen_random_uuid()),\n        sa.Column(\"video_id\", UUID(as_uuid=True), nullable=False, index=True),\n        sa.Column(\"stage\", sa.String(50), nullable=False),\n        sa.Column(\"event_type\", sa.String(30), nullable=False),\n        sa.Column(\"prompt_tokens\", sa.Integer(), nullable=True),\n        sa.Column(\"completion_tokens\", sa.Integer(), nullable=True),\n        sa.Column(\"total_tokens\", sa.Integer(), nullable=True),\n        sa.Column(\"model\", sa.String(100), nullable=True),\n        sa.Column(\"duration_ms\", sa.Integer(), nullable=True),\n        sa.Column(\"payload\", JSONB(), nullable=True),\n        sa.Column(\"created_at\", sa.DateTime(), server_default=sa.func.now(), nullable=False),\n    )\n    # Composite index for event log queries (video + newest first)\n    op.create_index(\"ix_pipeline_events_video_created\", \"pipeline_events\", [\"video_id\", \"created_at\"])\n\n\ndef downgrade() -> None:\n    op.drop_index(\"ix_pipeline_events_video_created\")\n    op.drop_table(\"pipeline_events\")\n"}],"isError":false,"timestamp":1774894426180}}
{"type":"message","id":"deb0e224","parentId":"38a5533e","timestamp":"2026-03-30T18:13:52.127Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how the frontend currently displays pipeline events — that's S02's territory but understanding it helps scope S01."},{"type":"toolCall","id":"toolu_01KFFbNrHDupoTCnx2z5b6RH","name":"bash","arguments":{"command":"ssh ub01 \"grep -rn 'debug\\|pipeline/events\\|llm_call\\|prompt_text\\|debug_mode' /vmPool/r/repos/xpltdco/chrysopedia/frontend/src/ --include='*.tsx' --include='*.ts'\" 2>/dev/null | head -15"}},{"type":"toolCall","id":"toolu_01Gzx6fgjgji3ECYZkYJhKU4","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '600,700p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":238,"cacheRead":80540,"cacheWrite":1717,"totalTokens":82496,"cost":{"input":0.000005,"output":0.00595,"cacheRead":0.04027,"cacheWrite":0.010731250000000001,"total":0.05695625}},"stopReason":"toolUse","timestamp":1774894426181}}
{"type":"message","id":"5fc05a0b","parentId":"deb0e224","timestamp":"2026-03-30T18:13:52.491Z","message":{"role":"toolResult","toolCallId":"toolu_01KFFbNrHDupoTCnx2z5b6RH","toolName":"bash","content":[{"type":"text","text":"/vmPool/r/repos/xpltdco/chrysopedia/frontend/src/api/public-client.ts:452:    `${BASE}/admin/pipeline/events/${videoId}${query ? `?${query}` : \"\"}`,\n/vmPool/r/repos/xpltdco/chrysopedia/frontend/src/pages/AdminPipeline.tsx:67:    case \"llm_call\":\n"}],"isError":false,"timestamp":1774894432491}}
{"type":"message","id":"9d73ad7f","parentId":"5fc05a0b","timestamp":"2026-03-30T18:13:52.494Z","message":{"role":"toolResult","toolCallId":"toolu_01Gzx6fgjgji3ECYZkYJhKU4","toolName":"bash","content":[{"type":"text","text":"    modality settings. Handles missing prompt files gracefully.\n    \"\"\"\n    settings = get_settings()\n    prompts_path = Path(settings.prompts_path)\n\n    # Hash each prompt template file\n    prompt_hashes: dict[str, str] = {}\n    prompt_files = [\n        \"stage2_segmentation.txt\",\n        \"stage3_extraction.txt\",\n        \"stage4_classification.txt\",\n        \"stage5_synthesis.txt\",\n    ]\n    for filename in prompt_files:\n        filepath = prompts_path / filename\n        try:\n            content = filepath.read_bytes()\n            prompt_hashes[filename] = hashlib.sha256(content).hexdigest()\n        except FileNotFoundError:\n            logger.warning(\"Prompt file not found for metadata capture: %s\", filepath)\n            prompt_hashes[filename] = \"\"\n        except OSError as exc:\n            logger.warning(\"Could not read prompt file %s: %s\", filepath, exc)\n            prompt_hashes[filename] = \"\"\n\n    return {\n        \"git_commit_sha\": _get_git_commit_sha(),\n        \"models\": {\n            \"stage2\": settings.llm_stage2_model,\n            \"stage3\": settings.llm_stage3_model,\n            \"stage4\": settings.llm_stage4_model,\n            \"stage5\": settings.llm_stage5_model,\n            \"embedding\": settings.embedding_model,\n        },\n        \"modalities\": {\n            \"stage2\": settings.llm_stage2_modality,\n            \"stage3\": settings.llm_stage3_modality,\n            \"stage4\": settings.llm_stage4_modality,\n            \"stage5\": settings.llm_stage5_modality,\n        },\n        \"prompt_hashes\": prompt_hashes,\n    }\n\n\n# ── Stage 5: Synthesis ───────────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage5_synthesis(self, video_id: str) -> str:\n    \"\"\"Synthesize technique pages from classified key moments.\n\n    Groups moments by (creator, topic_category), calls the LLM to synthesize\n    each group into a TechniquePage, creates/updates page rows, and links\n    KeyMoments to their TechniquePage.\n\n    Sets processing_status to 'reviewed' (or 'published' if review_mode is False).\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 5 (synthesis) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage5_synthesis\", \"start\")\n\n    settings = get_settings()\n    session = _get_sync_session()\n    try:\n        # Load video and moments\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not moments:\n            logger.info(\"Stage 5: No moments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Load classification data from stage 4\n        classification_data = _load_classification_data(video_id)\n        cls_by_moment_id = {c[\"moment_id\"]: c for c in classification_data}\n\n        # Group moments by topic_category (from classification)\n        groups: dict[str, list[tuple[KeyMoment, dict]]] = defaultdict(list)\n        for moment in moments:\n            cls_info = cls_by_moment_id.get(str(moment.id), {})\n            category = cls_info.get(\"topic_category\", \"Uncategorized\")\n            groups[category].append((moment, cls_info))\n\n        system_prompt = _load_prompt(\"stage5_synthesis.txt\")\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(5)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n        logger.info(\"Stage 5 using model=%s, modality=%s\", model_override or \"default\", modality)\n        pages_created = 0\n"}],"isError":false,"timestamp":1774894432491}}
{"type":"message","id":"ebe4b1ac","parentId":"9d73ad7f","timestamp":"2026-03-30T18:14:05.771Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check stage 3 (which has loop-based calls — important for debug capture):"},{"type":"toolCall","id":"toolu_01X2jANx5KANxaWDUSbq6Ujn","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '310,400p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":120,"cacheRead":82257,"cacheWrite":1518,"totalTokens":83896,"cost":{"input":0.000005,"output":0.003,"cacheRead":0.0411285,"cacheWrite":0.009487500000000001,"total":0.053621}},"stopReason":"toolUse","timestamp":1774894432492}}
{"type":"message","id":"46837932","parentId":"ebe4b1ac","timestamp":"2026-03-30T18:14:06.100Z","message":{"role":"toolResult","toolCallId":"toolu_01X2jANx5KANxaWDUSbq6Ujn","toolName":"bash","content":[{"type":"text","text":"# ── Stage 3: Extraction ─────────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=3, default_retry_delay=30)\ndef stage3_extraction(self, video_id: str) -> str:\n    \"\"\"Extract key moments from each topic segment group.\n\n    Groups segments by topic_label, calls the LLM for each group to extract\n    moments, creates KeyMoment rows, and sets processing_status=extracted.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 3 (extraction) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage3_extraction\", \"start\")\n\n    session = _get_sync_session()\n    try:\n        # Load segments with topic labels\n        segments = (\n            session.execute(\n                select(TranscriptSegment)\n                .where(TranscriptSegment.source_video_id == video_id)\n                .order_by(TranscriptSegment.segment_index)\n            )\n            .scalars()\n            .all()\n        )\n\n        if not segments:\n            logger.info(\"Stage 3: No segments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Group segments by topic_label\n        groups: dict[str, list[TranscriptSegment]] = defaultdict(list)\n        for seg in segments:\n            label = seg.topic_label or \"unlabeled\"\n            groups[label].append(seg)\n\n        system_prompt = _load_prompt(\"stage3_extraction.txt\")\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(3)\n        hard_limit = get_settings().llm_max_tokens_hard_limit\n        logger.info(\"Stage 3 using model=%s, modality=%s\", model_override or \"default\", modality)\n        total_moments = 0\n\n        for topic_label, group_segs in groups.items():\n            # Build segment text for this group\n            seg_lines = []\n            for seg in group_segs:\n                seg_lines.append(\n                    f\"({seg.start_time:.1f}s - {seg.end_time:.1f}s) {seg.text}\"\n                )\n            segment_text = \"\\n\".join(seg_lines)\n\n            user_prompt = (\n                f\"Topic: {topic_label}\\n\\n\"\n                f\"<segment>\\n{segment_text}\\n</segment>\"\n            )\n\n            max_tokens = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage3_extraction\", hard_limit=hard_limit)\n            raw = llm.complete(system_prompt, user_prompt, response_model=ExtractionResult, on_complete=_make_llm_callback(video_id, \"stage3_extraction\"),\n                               modality=modality, model_override=model_override, max_tokens=max_tokens)\n            result = _safe_parse_llm_response(raw, ExtractionResult, llm, system_prompt, user_prompt,\n                                              modality=modality, model_override=model_override)\n\n            # Create KeyMoment rows\n            for moment in result.moments:\n                # Validate content_type against enum\n                try:\n                    ct = KeyMomentContentType(moment.content_type)\n                except ValueError:\n                    ct = KeyMomentContentType.technique\n\n                km = KeyMoment(\n                    source_video_id=video_id,\n                    title=moment.title,\n                    summary=moment.summary,\n                    start_time=moment.start_time,\n                    end_time=moment.end_time,\n                    content_type=ct,\n                    plugins=moment.plugins if moment.plugins else None,\n                    raw_transcript=moment.raw_transcript or None,\n                )\n                session.add(km)\n                total_moments += 1\n\n        # Update processing_status to extracted\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n        video.processing_status = ProcessingStatus.extracted\n"}],"isError":false,"timestamp":1774894446098}}
{"type":"message","id":"3c535684","parentId":"46837932","timestamp":"2026-03-30T18:14:11.463Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check how the trigger endpoint passes any flags to `run_pipeline`:"},{"type":"toolCall","id":"toolu_01XAAHQsdejH1cQEHd9BtyNF","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'def run_pipeline\\|run_pipeline' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_016xTrxY5GnAEUCYM5ZEa22V","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '790,850p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":203,"cacheRead":83775,"cacheWrite":1179,"totalTokens":85158,"cost":{"input":0.000005,"output":0.0050750000000000005,"cacheRead":0.0418875,"cacheWrite":0.00736875,"total":0.05433625}},"stopReason":"toolUse","timestamp":1774894446099}}
{"type":"message","id":"e0165401","parentId":"3c535684","timestamp":"2026-03-30T18:14:11.793Z","message":{"role":"toolResult","toolCallId":"toolu_01XAAHQsdejH1cQEHd9BtyNF","toolName":"bash","content":[{"type":"text","text":"1:\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n1039:def run_pipeline(video_id: str) -> str:\n1050:    logger.info(\"run_pipeline starting for video_id=%s\", video_id)\n1059:            logger.error(\"run_pipeline: video_id=%s not found\", video_id)\n1064:            \"run_pipeline: video_id=%s current status=%s\", video_id, status.value\n1090:            \"run_pipeline: video_id=%s already at status=%s, nothing to do.\",\n1099:            \"run_pipeline: dispatched %d stages for video_id=%s\",\n"}],"isError":false,"timestamp":1774894451793}}
{"type":"message","id":"ef2d025d","parentId":"e0165401","timestamp":"2026-03-30T18:14:11.800Z","message":{"role":"toolResult","toolCallId":"toolu_016xTrxY5GnAEUCYM5ZEa22V","toolName":"bash","content":[{"type":"text","text":"                        logger.error(\n                            \"Failed to create version snapshot for page slug=%s: %s\",\n                            existing.slug, snap_exc,\n                        )\n                        # Best-effort versioning — continue with page update\n\n                    # Update existing page\n                    existing.title = page_data.title\n                    existing.summary = page_data.summary\n                    existing.body_sections = page_data.body_sections\n                    existing.signal_chains = page_data.signal_chains\n                    existing.plugins = page_data.plugins if page_data.plugins else None\n                    existing.topic_tags = list(all_tags) if all_tags else None\n                    existing.source_quality = page_data.source_quality\n                    page = existing\n                else:\n                    page = TechniquePage(\n                        creator_id=video.creator_id,\n                        title=page_data.title,\n                        slug=page_data.slug,\n                        topic_category=page_data.topic_category or category,\n                        topic_tags=list(all_tags) if all_tags else None,\n                        summary=page_data.summary,\n                        body_sections=page_data.body_sections,\n                        signal_chains=page_data.signal_chains,\n                        plugins=page_data.plugins if page_data.plugins else None,\n                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page\n                for m, _ in moment_group:\n                    m.technique_page_id = page.id\n\n        # Update processing_status\n        if settings.review_mode:\n            video.processing_status = ProcessingStatus.reviewed\n        else:\n            video.processing_status = ProcessingStatus.published\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        _emit_event(video_id, \"stage5_synthesis\", \"complete\")\n        logger.info(\n            \"Stage 5 (synthesis) completed for video_id=%s in %.1fs — %d pages created/updated\",\n            video_id, elapsed, pages_created,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        _emit_event(video_id, \"stage5_synthesis\", \"error\", payload={\"error\": str(exc)})\n        logger.error(\"Stage 5 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n"}],"isError":false,"timestamp":1774894451798}}
{"type":"message","id":"84cccdb3","parentId":"ef2d025d","timestamp":"2026-03-30T18:14:18.693Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EV8oapLtN1Xe5Rv9J1uGhE","name":"bash","arguments":{"command":"ssh ub01 \"sed -n '1039,1100p' /vmPool/r/repos/xpltdco/chrysopedia/backend/pipeline/stages.py\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":100,"cacheRead":84954,"cacheWrite":1079,"totalTokens":86134,"cost":{"input":0.000005,"output":0.0025,"cacheRead":0.042477,"cacheWrite":0.006743750000000001,"total":0.05172575}},"stopReason":"toolUse","timestamp":1774894451799}}
{"type":"message","id":"9e387d20","parentId":"84cccdb3","timestamp":"2026-03-30T18:14:19.029Z","message":{"role":"toolResult","toolCallId":"toolu_01EV8oapLtN1Xe5Rv9J1uGhE","toolName":"bash","content":[{"type":"text","text":"def run_pipeline(video_id: str) -> str:\n    \"\"\"Orchestrate the full pipeline (stages 2-5) with resumability.\n\n    Checks the current processing_status of the video and chains only the\n    stages that still need to run. For example:\n      - pending/transcribed → stages 2, 3, 4, 5\n      - extracted → stages 4, 5\n      - reviewed/published → no-op\n\n    Returns the video_id.\n    \"\"\"\n    logger.info(\"run_pipeline starting for video_id=%s\", video_id)\n\n    session = _get_sync_session()\n    try:\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one_or_none()\n\n        if video is None:\n            logger.error(\"run_pipeline: video_id=%s not found\", video_id)\n            raise ValueError(f\"Video not found: {video_id}\")\n\n        status = video.processing_status\n        logger.info(\n            \"run_pipeline: video_id=%s current status=%s\", video_id, status.value\n        )\n    finally:\n        session.close()\n\n    # Snapshot prior technique pages before pipeline resets key_moments\n    _snapshot_prior_pages(video_id)\n\n    # Build the chain based on current status\n    stages = []\n    if status in (ProcessingStatus.pending, ProcessingStatus.transcribed):\n        stages = [\n            stage2_segmentation.s(video_id),\n            stage3_extraction.s(),  # receives video_id from previous\n            stage4_classification.s(),\n            stage5_synthesis.s(),\n            stage6_embed_and_index.s(),\n        ]\n    elif status == ProcessingStatus.extracted:\n        stages = [\n            stage4_classification.s(video_id),\n            stage5_synthesis.s(),\n            stage6_embed_and_index.s(),\n        ]\n    elif status in (ProcessingStatus.reviewed, ProcessingStatus.published):\n        logger.info(\n            \"run_pipeline: video_id=%s already at status=%s, nothing to do.\",\n            video_id, status.value,\n        )\n        return video_id\n\n    if stages:\n        pipeline = celery_chain(*stages)\n        pipeline.apply_async()\n        logger.info(\n            \"run_pipeline: dispatched %d stages for video_id=%s\",\n            len(stages), video_id,\n"}],"isError":false,"timestamp":1774894459028}}
{"type":"message","id":"6c177d9c","parentId":"9e387d20","timestamp":"2026-03-30T18:14:23.310Z","message":{"role":"assistant","content":[{"type":"text","text":"I have a thorough understanding now. Let me check what prompt files look like to understand size:"},{"type":"toolCall","id":"toolu_01WQEjhHDmbLag9Y6FfUBHTT","name":"bash","arguments":{"command":"ssh ub01 \"ls -la /vmPool/r/repos/xpltdco/chrysopedia/prompts/\" 2>/dev/null && echo \"---\" && ssh ub01 \"wc -c /vmPool/r/repos/xpltdco/chrysopedia/prompts/*.txt\" 2>/dev/null"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":153,"cacheRead":86033,"cacheWrite":709,"totalTokens":86896,"cost":{"input":0.000005,"output":0.0038250000000000003,"cacheRead":0.0430165,"cacheWrite":0.00443125,"total":0.05127775}},"stopReason":"toolUse","timestamp":1774894459029}}
{"type":"message","id":"7d7a8c47","parentId":"6c177d9c","timestamp":"2026-03-30T18:14:23.932Z","message":{"role":"toolResult","toolCallId":"toolu_01WQEjhHDmbLag9Y6FfUBHTT","toolName":"bash","content":[{"type":"text","text":"total 68\ndrwxrwsr-x  2 jlightner jlightner     7 Mar 29 20:12 .\ndrwxr-s--- 12 jlightner jlightner    20 Mar 30 07:02 ..\n-rw-rw-r--  1 jlightner jlightner   111 Mar 29 20:12 README.md\n-rw-rw-r--  1 jlightner jlightner  4604 Mar 30 01:57 stage2_segmentation.txt\n-rw-rw-r--  1 jlightner jlightner  7770 Mar 30 02:00 stage3_extraction.txt\n-rw-rw-r--  1 jlightner jlightner  4130 Mar 30 02:01 stage4_classification.txt\n-rw-rw-r--  1 jlightner jlightner 12475 Mar 30 02:05 stage5_synthesis.txt\n---\n 4604 /vmPool/r/repos/xpltdco/chrysopedia/prompts/stage2_segmentation.txt\n 7770 /vmPool/r/repos/xpltdco/chrysopedia/prompts/stage3_extraction.txt\n 4130 /vmPool/r/repos/xpltdco/chrysopedia/prompts/stage4_classification.txt\n12475 /vmPool/r/repos/xpltdco/chrysopedia/prompts/stage5_synthesis.txt\n28979 total\n"}],"isError":false,"timestamp":1774894463930}}
{"type":"message","id":"731be638","parentId":"7d7a8c47","timestamp":"2026-03-30T18:14:31.002Z","message":{"role":"assistant","content":[{"type":"text","text":"System prompts are 4-12KB. User prompts include full transcript text — could be very large. This is important for storage decisions.\n\nLet me also check the frontend pipeline admin page to understand the current event display:"},{"type":"toolCall","id":"toolu_01HrNACAYgHmXa34px33TQ4p","name":"bash","arguments":{"command":"ssh ub01 \"wc -l /vmPool/r/repos/xpltdco/chrysopedia/frontend/src/pages/AdminPipeline.tsx\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_012uC3sbTWBobFKtqFW2f96X","name":"bash","arguments":{"command":"ssh ub01 \"grep -n 'debug\\|payload\\|prompt\\|llm_call\\|token' /vmPool/r/repos/xpltdco/chrysopedia/frontend/src/pages/AdminPipeline.tsx\" 2>/dev/null | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":249,"cacheRead":86742,"cacheWrite":559,"totalTokens":87551,"cost":{"input":0.000005,"output":0.0062250000000000005,"cacheRead":0.043371,"cacheWrite":0.0034937500000000003,"total":0.053094749999999996}},"stopReason":"toolUse","timestamp":1774894463931}}
{"type":"message","id":"454b879b","parentId":"731be638","timestamp":"2026-03-30T18:14:31.331Z","message":{"role":"toolResult","toolCallId":"toolu_01HrNACAYgHmXa34px33TQ4p","toolName":"bash","content":[{"type":"text","text":"436 /vmPool/r/repos/xpltdco/chrysopedia/frontend/src/pages/AdminPipeline.tsx\n"}],"isError":false,"timestamp":1774894471331}}
{"type":"message","id":"9b34fad3","parentId":"454b879b","timestamp":"2026-03-30T18:14:31.339Z","message":{"role":"toolResult","toolCallId":"toolu_012uC3sbTWBobFKtqFW2f96X","toolName":"bash","content":[{"type":"text","text":"3: * expandable event log with token usage and collapsible JSON viewer.\n67:    case \"llm_call\":\n87:        {open ? \"▾ Hide payload\" : \"▸ Show payload\"}\n169:              {evt.total_tokens != null && evt.total_tokens > 0 && (\n170:                <span className=\"pipeline-event__tokens\" title={`prompt: ${evt.prompt_tokens ?? 0} / completion: ${evt.completion_tokens ?? 0}`}>\n171:                  {formatTokens(evt.total_tokens)} tok\n179:            <JsonViewer data={evt.payload} />\n386:                  <span className=\"pipeline-video__stat\" title=\"Total tokens used\">\n387:                    {formatTokens(video.total_tokens_used)} tokens\n"}],"isError":false,"timestamp":1774894471336}}
{"type":"message","id":"1617a6da","parentId":"9b34fad3","timestamp":"2026-03-30T18:15:26.527Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I have all the context I need. Let me build the research document.\n\nThe architecture is clear:\n\n1. **PipelineEvent model** already has `payload` JSONB — currently stores truncated `content_preview` (2000 chars). Need to add dedicated `prompt_text` and `response_text` TEXT columns (not JSONB — these are large strings).\n2. **Debug mode toggle** — follow the `review_mode` pattern: Redis key + config default. Per-video or global. \n3. **`_make_llm_callback`** doesn't receive prompt text. Need to thread prompts through, either by expanding the callback signature or by capturing at the `_emit_event` call site.\n4. **Per-stage token accounting** — events already have per-call tokens. Need an aggregation endpoint that groups by stage.\n5. **Stage 3 and 5 loop** — multiple LLM calls per stage execution. Each call is already a separate event."},{"type":"toolCall","id":"toolu_01Xg6rf4Go4dYVqGerCfrftP","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S01 Research — Pipeline Debug Mode: Full LLM I/O Capture and Token Accounting\n\n## Summary\n\nThe existing `PipelineEvent` model and `_make_llm_callback` infrastructure captures token counts and a truncated 2000-char `content_preview` for each LLM call. **No prompt text is stored.** Full response text is discarded. This slice needs to:\n\n1. Add a debug mode toggle (global, Redis-backed like `review_mode`)\n2. When debug is on, capture full system prompt, user prompt, and full response text for every LLM call\n3. Expose per-stage token aggregation via API\n\nThe work is straightforward — extending an established event-capture pattern with two new columns, a toggle, and an aggregation query.\n\n## Recommendation\n\n**Targeted approach — extend existing infrastructure, don't rebuild it.**\n\n- Add `system_prompt_text` and `user_prompt_text` TEXT columns to `pipeline_events` (alongside existing `payload` JSONB which keeps the response content). Alternatively, store full response in a dedicated `response_text` TEXT column for symmetry and queryability, keeping `payload` for metadata only.\n- Debug mode: global Redis key `chrysopedia:debug_mode` (follows `review_mode` pattern from D007). Config default `debug_mode: bool = False`. When debug is **off**, behavior is unchanged (truncated preview only). When **on**, full prompts + full response stored.\n- Thread prompt text into the callback by expanding `_make_llm_callback` to accept `system_prompt` and `user_prompt` parameters. The callback closure already captures `video_id` and `stage`.\n- Token aggregation: add `GET /admin/pipeline/token-summary/{video_id}` that groups `pipeline_events` by `stage` and sums token columns.\n\n## Implementation Landscape\n\n### Existing Code (what to modify)\n\n| File | Role | What changes |\n|------|------|-------------|\n| `backend/models.py` (line ~386) | `PipelineEvent` model | Add `system_prompt_text`, `user_prompt_text`, `response_text` — all `Text, nullable=True` |\n| `backend/pipeline/stages.py` (line ~52) | `_emit_event()` | Accept new `system_prompt_text`, `user_prompt_text`, `response_text` kwargs, pass to model |\n| `backend/pipeline/stages.py` (line ~87) | `_make_llm_callback()` | Accept `system_prompt`, `user_prompt` params; conditionally capture full text when debug mode is on |\n| `backend/pipeline/stages.py` (lines 275-280, 365-372, 478-487, 720-726) | Stage `llm.complete()` call sites | Pass `system_prompt` and `user_prompt` to `_make_llm_callback()` |\n| `backend/config.py` | `Settings` | Add `debug_mode: bool = False` |\n| `backend/routers/pipeline.py` | Pipeline admin endpoints | Add debug mode GET/PUT, token summary endpoint, include new fields in event response |\n| `backend/schemas.py` | Pydantic schemas | Add `DebugModeResponse`, `DebugModeUpdate`, `TokenSummary` schemas |\n| `alembic/versions/006_debug_columns.py` | New migration | Add 3 TEXT columns to `pipeline_events` |\n\n### Key Design Details\n\n**Storage sizing:** System prompts are 4-12KB. User prompts include transcript text — can be 50-200KB for long videos. Full LLM responses are similar magnitude to user prompts. At ~4 LLM calls per video (stages 2-5, though stage 3 and 5 may loop), worst case is ~1-2MB per video pipeline run. PostgreSQL TEXT columns handle this fine. No need for filesystem or blob storage at this scale.\n\n**Stage 3 and 5 looping:** Stage 3 calls LLM once per topic group; stage 5 calls once per (creator, category) group. Each call already produces a separate `PipelineEvent` with `event_type=\"llm_call\"`. The existing infrastructure handles multi-call stages correctly — no special grouping needed.\n\n**Debug mode check location:** The `_make_llm_callback` factory runs in the Celery worker (sync context). It needs to read debug mode. Two options:\n- Read from config `settings.debug_mode` (simpler, requires worker restart to change)\n- Read from Redis via sync `redis.Redis` (matches `review_mode` runtime toggle pattern, no restart needed)\n\n**Recommendation:** Use sync Redis read in the callback. The worker already imports `config.get_settings()` which has `redis_url`. Create a thin sync Redis helper (or just `redis.from_url()` inline) — the async `redis_client.py` helper is for FastAPI only.\n\n**`_safe_parse_llm_response` retry calls:** When the first LLM parse fails, `_safe_parse_llm_response` makes a retry call with a nudge prompt. This retry call does NOT have an `on_complete` callback — it won't emit a debug event. This is acceptable for now (retry is a diagnostic edge case), but note it for S02 if full call tracing is needed.\n\n**Per-stage token aggregation query:**\n```sql\nSELECT stage,\n       COUNT(*) AS call_count,\n       SUM(prompt_tokens) AS total_prompt_tokens,\n       SUM(completion_tokens) AS total_completion_tokens,\n       SUM(total_tokens) AS total_tokens\nFROM pipeline_events\nWHERE video_id = :vid AND event_type = 'llm_call'\nGROUP BY stage\nORDER BY MIN(created_at)\n```\n\n### Natural Task Seams\n\n1. **DB migration + model** — Add 3 TEXT columns to `pipeline_events`, update `PipelineEvent` model. Pure schema work, verifiable with `alembic upgrade head`.\n2. **Debug mode toggle** — Config setting + Redis key + GET/PUT endpoints + schemas. Follows `review_mode` pattern exactly. Verifiable via API calls.\n3. **Capture instrumentation** — Modify `_make_llm_callback` to accept and conditionally store prompts/response. Modify all 4 stage call sites to pass prompt text. This is the core work. Verifiable by triggering pipeline with debug on and querying events.\n4. **Token summary endpoint** — New `GET /admin/pipeline/token-summary/{video_id}` with aggregation query. Verifiable via API call after pipeline run.\n\nTasks 1-2 are independent. Task 3 depends on 1 (columns must exist). Task 4 depends on nothing new (uses existing columns).\n\n### Constraints & Risks\n\n- **Celery worker sync context:** The debug mode Redis read must use sync `redis.Redis`, not the async client from `redis_client.py`. This is the same constraint that led to sync SQLAlchemy in the worker (D004).\n- **Large TEXT columns:** No index needed on prompt/response text. These are write-once, read-rarely columns for debugging. The existing `ix_pipeline_events_video_created` index handles all query patterns.\n- **Migration on production data:** The `pipeline_events` table has existing rows. Adding nullable TEXT columns is a non-blocking ALTER TABLE in PostgreSQL — no data rewrite needed.\n\n### Verification Strategy\n\n1. Migration: `docker exec chrysopedia-api alembic upgrade head` succeeds, columns visible in `\\d pipeline_events`\n2. Debug toggle: `curl` GET/PUT on debug mode endpoint returns expected values\n3. Full capture: Enable debug mode, trigger pipeline for a video, query events — `system_prompt_text`, `user_prompt_text`, `response_text` populated for `llm_call` events\n4. Token summary: `GET /admin/pipeline/token-summary/{video_id}` returns per-stage breakdown\n5. Debug off: Disable debug mode, trigger pipeline — new events have NULL prompt/response text columns (existing truncated `content_preview` in payload still works)\n","milestone_id":"M007","slice_id":"S01"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":2267,"cacheRead":87301,"cacheWrite":533,"totalTokens":90102,"cost":{"input":0.000005,"output":0.056675,"cacheRead":0.043650499999999995,"cacheWrite":0.00333125,"total":0.10366174999999998}},"stopReason":"toolUse","timestamp":1774894471337}}
{"type":"message","id":"aa032370","parentId":"1617a6da","timestamp":"2026-03-30T18:15:26.533Z","message":{"role":"toolResult","toolCallId":"toolu_01Xg6rf4Go4dYVqGerCfrftP","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M007/slices/S01/S01-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M007/slices/S01/S01-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774894526531}}
{"type":"message","id":"c2327ec5","parentId":"aa032370","timestamp":"2026-03-30T18:15:29.420Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S01 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":87834,"cacheWrite":2305,"totalTokens":90151,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.043917,"cacheWrite":0.01440625,"total":0.05860325}},"stopReason":"stop","timestamp":1774894526532}}
