{"type":"thinking_level_change","id":"53fe9474","parentId":null,"timestamp":"2026-03-29T22:19:57.781Z","thinkingLevel":"off"}
{"type":"model_change","id":"d3a822bc","parentId":"53fe9474","timestamp":"2026-03-29T22:19:57.782Z","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## Requirements Advanced\n\n- R002 — POST /api/v1/ingest endpoint accepts transcript JSON uploads, creates/updates Creator and SourceVideo records, stores transcript data in PostgreSQL, and persists raw JSON to filesystem. 6 integration tests prove the full flow.\n\n## Requirements Validated\n\n- R002 — 6 passing integration tests: happy-path ingestion creates Creator+SourceVideo+5 TranscriptSegments, re-upload is idempotent (same video_id, segments replaced not doubled), existing creators are reused, raw JSON saved to disk, invalid/malformed JSON rejected with proper HTTP status codes.\n\n## Requirements Invalidated or Re-scoped\n\nNone.\n\n## UNIT: Research Slice S03 (\"LLM Extraction Pipeline + Qdrant Integration\") — Milestone M001\n\n## Inlined Context (preloaded — do not re-read these files)\n\n### Milestone Roadmap\nSource: `.gsd/milestones/M001/M001-ROADMAP.md`\n\n# M001: Chrysopedia Foundation — Infrastructure, Pipeline Core, and Skeleton UI\n\n## Vision\nStand up the complete Chrysopedia stack: Docker Compose deployment on ub01, PostgreSQL data model, FastAPI backend with transcript ingestion, Whisper transcription script for the desktop, LLM extraction pipeline (stages 2-5), review queue, Qdrant integration, and the search-first web UI with technique pages, creators, and topics browsing. By the end, a video file can be transcribed → ingested → extracted → reviewed → searched and read in the web UI.\n\n## Slice Overview\n| ID | Slice | Risk | Depends | Done | After this |\n|----|-------|------|---------|------|------------|\n| S01 | Docker Compose + Database + Whisper Script | low | — | ✅ | docker compose up -d starts all services on ub01; Whisper script transcribes a sample video to JSON |\n| S02 | Transcript Ingestion API | low | S01 | ✅ | POST a transcript JSON file to the API; Creator and Source Video records appear in PostgreSQL |\n| S03 | LLM Extraction Pipeline + Qdrant Integration | high | S02 | ⬜ | A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings. |\n| S04 | Review Queue Admin UI | medium | S03 | ⬜ | Admin views pending key moments, approves/edits/rejects them, toggles between review and auto mode |\n| S05 | Search-First Web UI | medium | S03 | ⬜ | User searches for a technique, gets semantic results in <500ms, clicks through to a full technique page with study guide prose, key moments, and related links |\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\n---\n\n### Requirements\nSource: `.gsd/REQUIREMENTS.md`\n\n# Requirements\n\n## R001 — Whisper Transcription Pipeline\n**Status:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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:** active\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### Project Knowledge\nSource: `.gsd/KNOWLEDGE.md`\n\n# KNOWLEDGE\n\n## SQLAlchemy column names that shadow ORM functions\n\n**Context:** If a model has a column named `relationship` (or any other ORM function name like `query`, `metadata`), Python resolves the name to the column's `MappedColumn` descriptor within the class body. This causes `relationship(...)` calls below it to fail with \"MappedColumn object is not callable\".\n\n**Fix:** Import with an alias: `from sqlalchemy.orm import relationship as sa_relationship` and use `sa_relationship(...)` throughout. Alternatively, rename the database column, but the spec defines `relationship` as the column name so we preserved it.\n\n## Docker Compose variable interpolation and `:?` syntax\n\n**Context:** `${VAR:?error message}` in docker-compose.yml makes `docker compose config` fail when the variable is unset, even if `env_file: required: false` is used. The `required: false` only controls whether the `.env` file must exist — it does NOT provide defaults for variables referenced with `:?`.\n\n**Fix:** Use `${VAR:-default}` for variables that need a fallback, or ensure the variable is always set in the environment.\n\n## Host port 8000 conflict with kerf-engine\n\n**Context:** The kerf-engine Docker container (`kerf-engine-kerf-engine-1`) binds to `0.0.0.0:8000`. When testing the Chrysopedia API locally (outside Docker), curl requests to `localhost:8000` hit the kerf-engine container instead of the local uvicorn process, even when uvicorn binds to `127.0.0.1:8000`.\n\n**Fix:** Use an alternate port (e.g., 8001) for local testing. In Docker Compose, chrysopedia-api maps `127.0.0.1:8000:8000` which is fine because it goes through Docker's port forwarding, but will conflict with kerf-engine if both stacks run simultaneously. Consider changing one project's external port mapping.\n\n## asyncpg rejects timezone-aware datetimes for TIMESTAMP WITHOUT TIME ZONE columns\n\n**Context:** SQLAlchemy models using `default=datetime.now(timezone.utc)` produce timezone-aware Python datetimes. When the PostgreSQL column type is `TIMESTAMP WITHOUT TIME ZONE` (the default), asyncpg raises `DataError: can't subtract offset-naive and offset-aware datetimes`. This only surfaces when running against a real PostgreSQL database — SQLite-based tests won't catch it.\n\n**Fix:** Use `datetime.now(timezone.utc).replace(tzinfo=None)` in the `_now()` helper, or change the column type to `TIMESTAMP WITH TIME ZONE`. We chose the former since the existing schema uses naive timestamps.\n\n## asyncpg NullPool required for pytest-asyncio integration tests\n\n**Context:** When using a session-scoped SQLAlchemy async engine with asyncpg in pytest-asyncio tests, the connection pool reuses connections across fixtures and test functions. This causes `InterfaceError: cannot perform operation: another operation is in progress` because the ASGI test client's session holds a connection while cleanup/verification fixtures try to use the same pool.\n\n**Fix:** Use `poolclass=NullPool` when creating the test engine. Each connection is created fresh and immediately closed, eliminating contention. Performance cost is negligible for test suites.\n\n---\n\n# {{scope}} — Research\n\n**Date:** {{date}}\n\n<!-- Required sections: Summary, Recommendation, Implementation Landscape.\n     All other sections: include only when they have real content.\n     For light research (straightforward work with known patterns),\n     the required sections alone are sufficient. -->\n\n## Summary\n\n{{summary — 2-3 paragraphs with primary recommendation}}\n\n## Recommendation\n\n{{whatApproachToTake_AND_why}}\n\n## Implementation Landscape\n\n<!-- This section is the primary input for the planner agent.\n     Be specific — file paths, function names, patterns to follow.\n     The planner uses this to scope tasks to files and decide build order. -->\n\n### Key Files\n\n- `{{filePath}}` — {{whatItDoesAndHowItRelates}}\n- `{{filePath}}` — {{whatNeedsToChange}}\n\n### Build Order\n\n{{whatToProveOrBuildFirst_AND_why — whatUnblocksDownstreamWork}}\n\n### Verification Approach\n\n{{howToConfirmTheSliceWorks — commands, tests, observable behaviors}}\n\n<!-- Sections below: include when applicable, omit entirely when not. -->\n\n## Don't Hand-Roll\n\n<!-- Include when existing libraries/tools solve problems the slice would otherwise reimplement. -->\n\n| Problem | Existing Solution | Why Use It |\n|---------|------------------|------------|\n| {{problem}} | {{solution}} | {{why}} |\n\n## Constraints\n\n<!-- Include when the codebase, runtime, or dependencies impose hard limits on approach. -->\n\n- {{hardConstraintFromCodebaseOrRuntime}}\n- {{constraintFromDependencies}}\n\n## Common Pitfalls\n\n<!-- Include when there are non-obvious failure modes worth flagging. -->\n\n- **{{pitfall}}** — {{howToAvoid}}\n- **{{pitfall}}** — {{howToAvoid}}\n\n## Open Risks\n\n<!-- Include when execution could hit unknowns that affect the plan. -->\n\n- {{riskThatCouldSurfaceDuringExecution}}\n\n## Skills Discovered\n\n<!-- Include when skill discovery found relevant skills. -->\n\n| Technology | Skill | Status |\n|------------|-------|--------|\n| {{technology}} | {{owner/repo@skill}} | {{installed / available / none found}} |\n\n## Sources\n\n<!-- Include when external docs, articles, or references informed the research. -->\n\n- {{whatWasLearned}} (source: [{{title}}]({{url}}))\n\n### Output Template: Research\nSource: `templates/research.md`\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#### S02 Summary\nSource: `.gsd/milestones/M001/slices/S02/S02-SUMMARY.md`\n\n---\nid: S02\nparent: M001\nmilestone: M001\nprovides:\n  - POST /api/v1/ingest endpoint accepting Whisper transcript JSON\n  - Creator and SourceVideo records in PostgreSQL with TranscriptSegments\n  - Raw transcript JSON persisted to transcript_storage_path\n  - pytest-asyncio test infrastructure with async fixtures and ASGI client\n  - TranscriptIngestResponse Pydantic schema\nrequires:\n  []\naffects:\n  - S03\nkey_files:\n  - backend/routers/ingest.py\n  - backend/schemas.py\n  - backend/main.py\n  - backend/requirements.txt\n  - backend/tests/conftest.py\n  - backend/tests/test_ingest.py\n  - backend/tests/fixtures/sample_transcript.json\n  - backend/pytest.ini\n  - backend/models.py\nkey_decisions:\n  - Used NullPool for test engine to avoid asyncpg connection contention in pytest-asyncio\n  - Fixed _now() helper to return naive UTC datetimes for asyncpg TIMESTAMP WITHOUT TIME ZONE compatibility\n  - Used slugify helper inline in ingest.py rather than a shared utils module\n  - Set file_path to {creator_folder}/{source_file} for new SourceVideo records\npatterns_established:\n  - pytest-asyncio integration test pattern: function-scoped NullPool engine + ASGI transport client with dependency overrides\n  - Multipart JSON file upload pattern for FastAPI endpoints\n  - Creator auto-detection from folder_name with find-or-create and slugify\nobservability_surfaces:\n  - INFO-level structured logging on successful ingest (creator name, filename, segment count)\n  - pytest output with per-test pass/fail and timing via -v flag\ndrill_down_paths:\n  - .gsd/milestones/M001/slices/S02/tasks/T01-SUMMARY.md\n  - .gsd/milestones/M001/slices/S02/tasks/T02-SUMMARY.md\nduration: \"\"\nverification_result: passed\ncompleted_at: 2026-03-29T22:19:19.537Z\nblocker_discovered: false\n---\n\n# S02: Transcript Ingestion API\n\n**Delivered POST /api/v1/ingest endpoint with creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence, and 6 passing integration tests against real PostgreSQL.**\n\n## What Happened\n\nThis slice built the transcript ingestion API — the critical bridge between Whisper transcription output (S01) and the LLM extraction pipeline (S03).\n\n**T01 — Ingest Endpoint** created `POST /api/v1/ingest` accepting multipart JSON uploads. The endpoint parses Whisper transcript JSON, finds-or-creates a Creator by `folder_name` (with slugify), upserts a SourceVideo by `(creator_id, filename)` — deleting old segments on re-upload for idempotency — bulk-inserts TranscriptSegments with `segment_index`, saves the raw JSON to the configured `transcript_storage_path`, and returns a structured `TranscriptIngestResponse`. Error handling covers invalid files (400), malformed JSON (422), and DB/filesystem failures (500). The router is mounted in `main.py` under `/api/v1`.\n\n**T02 — Integration Tests** established the pytest + pytest-asyncio test infrastructure with async fixtures: function-scoped engine with NullPool for connection isolation, ASGI transport test client with dependency overrides, and a sample transcript fixture. Six tests prove: (1) happy-path creator+video+segments creation, (2) existing creator reuse, (3) idempotent re-upload (same video_id, no duplicate segments), (4) raw JSON persistence to disk, (5) invalid JSON rejection, (6) missing fields rejection. During T02, a bug was found and fixed in `models.py` — the `_now()` helper produced timezone-aware datetimes that asyncpg rejects for TIMESTAMP WITHOUT TIME ZONE columns.\n\nAll slice verification checks pass: router routes, schema fields, dependency presence, main.py wiring, 6/6 tests green, docker compose config valid.\n\n## Verification\n\nAll verification checks passed:\n1. `python -m pytest tests/test_ingest.py -v` — 6/6 tests passed in 2.93s\n2. `from routers.ingest import router; print(router.routes)` — outputs `['/ingest']`\n3. `from schemas import TranscriptIngestResponse; print(model_fields.keys())` — shows all 7 fields\n4. `grep -q 'python-multipart' requirements.txt` — exits 0\n5. `grep -q 'ingest' main.py` — exits 0\n6. `docker compose config > /dev/null` — exits 0\n\n## New Requirements Surfaced\n\nNone.\n\n## Deviations\n\nFixed bug in backend/models.py _now() function during T02 — changed from datetime.now(timezone.utc) to datetime.now(timezone.utc).replace(tzinfo=None) to match TIMESTAMP WITHOUT TIME ZONE column types. This was discovered during integration testing and was not in the original plan.\n\n## Known Limitations\n\nTests require a running PostgreSQL instance on localhost:5433 — they are integration tests, not unit tests. No SQLite fallback. The test database (chrysopedia_test) must be created manually before running tests.\n\n## Follow-ups\n\nNone.\n\n## Files Created/Modified\n\n- `backend/routers/ingest.py` — New file — POST /api/v1/ingest endpoint with creator auto-detection, SourceVideo upsert, TranscriptSegment bulk insert, raw JSON persistence\n- `backend/schemas.py` — Added TranscriptIngestResponse Pydantic model with 7 fields\n- `backend/main.py` — Mounted ingest router under /api/v1 prefix\n- `backend/requirements.txt` — Added python-multipart, pytest, pytest-asyncio, httpx dependencies\n- `backend/models.py` — Fixed _now() to return naive UTC datetimes for asyncpg compatibility\n- `backend/tests/conftest.py` — New file — async test fixtures: NullPool engine, ASGI client, sample transcript path\n- `backend/tests/test_ingest.py` — New file — 6 integration tests for ingest endpoint\n- `backend/tests/fixtures/sample_transcript.json` — New file — 5-segment sample transcript JSON fixture\n- `backend/pytest.ini` — New file — asyncio_mode = auto configuration\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: M001`, `slice_id: S03`, `artifact_type: \"RESEARCH\"`, and the full research markdown as `content` — the tool computes the file path and persists to both DB and disk.\n\nThe slice directory already exists at `.gsd/milestones/M001/slices/S03/`. Do NOT mkdir.\n\n**You MUST call `gsd_summary_save` with the research content before finishing.**\n\nWhen done, say: \"Slice S03 researched.\"","display":false,"id":"88b4c3b8","parentId":"d3a822bc","timestamp":"2026-03-29T22:19:57.783Z"}
{"type":"message","id":"ef70dfcb","parentId":"88b4c3b8","timestamp":"2026-03-29T22:20:05.501Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll research S03 — the LLM Extraction Pipeline + Qdrant Integration. This is the highest-risk slice in M001, involving LLM API integration, a multi-stage pipeline, vector embeddings, and Qdrant. Let me explore the codebase and understand what exists."},{"type":"toolCall","id":"toolu_01LGVsQXEU6go7su3z7hEPai","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f -name \"*.py\" -o -name \"*.yml\" -o -name \"*.yaml\" -o -name \"*.toml\" -o -name \"*.json\" -o -name \"*.cfg\" -o -name \"*.ini\" -o -name \"*.env\" -o -name \"*.env.*\" | grep -v __pycache__ | grep -v node_modules | grep -v .git/ | sort"}},{"type":"toolCall","id":"toolu_01H4gfVF5rTG7zyAnAqTUi85","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/models.py"}},{"type":"toolCall","id":"toolu_01G3d8MZJqXHAS449JoCnemd","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/requirements.txt"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":3,"output":348,"cacheRead":49033,"cacheWrite":7192,"totalTokens":56576,"cost":{"input":0.000015000000000000002,"output":0.008700000000000001,"cacheRead":0.0245165,"cacheWrite":0.044950000000000004,"total":0.0781815}},"stopReason":"toolUse","timestamp":1774822797782}}
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{"type":"message","id":"8a60848e","parentId":"4b8112be","timestamp":"2026-03-29T22:20:05.639Z","message":{"role":"toolResult","toolCallId":"toolu_01H4gfVF5rTG7zyAnAqTUi85","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    PostgreSQL TIMESTAMP WITHOUT TIME ZONE columns require naive datetimes.\n    asyncpg rejects timezone-aware datetimes for such columns.\n    \"\"\"\n    return datetime.now(timezone.utc).replace(tzinfo=None)\n\n\n# ── Models ───────────────────────────────────────────────────────────────────\n\nclass Creator(Base):\n    __tablename__ = \"creators\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), nullable=False)\n    slug: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    genres: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    folder_name: Mapped[str] = mapped_column(String(255), nullable=False)\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\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    videos: Mapped[list[SourceVideo]] = sa_relationship(back_populates=\"creator\")\n    technique_pages: Mapped[list[TechniquePage]] = sa_relationship(back_populates=\"creator\")\n\n\nclass 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    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\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    text: Mapped[str] = mapped_column(Text, nullable=False)\n    segment_index: Mapped[int] = mapped_column(Integer, nullable=False)\n    topic_label: Mapped[str | None] = mapped_column(String(255), nullable=True)\n\n    # relationships\n    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"segments\")\n\n\nclass KeyMoment(Base):\n    __tablename__ = \"key_moments\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    technique_page_id: Mapped[uuid.UUID | None] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True\n    )\n    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    summary: Mapped[str] = mapped_column(Text, nullable=False)\n    start_time: Mapped[float] = mapped_column(Float, nullable=False)\n    end_time: Mapped[float] = mapped_column(Float, nullable=False)\n    content_type: Mapped[KeyMomentContentType] = mapped_column(\n        Enum(KeyMomentContentType, name=\"key_moment_content_type\", create_constraint=True),\n        nullable=False,\n    )\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    review_status: Mapped[ReviewStatus] = mapped_column(\n        Enum(ReviewStatus, name=\"review_status\", create_constraint=True),\n        default=ReviewStatus.pending,\n        server_default=\"pending\",\n    )\n    raw_transcript: Mapped[str | None] = mapped_column(Text, nullable=True)\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    source_video: Mapped[SourceVideo] = sa_relationship(back_populates=\"key_moments\")\n    technique_page: Mapped[TechniquePage | None] = sa_relationship(\n        back_populates=\"key_moments\", foreign_keys=[technique_page_id]\n    )\n\n\nclass TechniquePage(Base):\n    __tablename__ = \"technique_pages\"\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    title: Mapped[str] = mapped_column(String(500), nullable=False)\n    slug: Mapped[str] = mapped_column(String(500), unique=True, nullable=False)\n    topic_category: Mapped[str] = mapped_column(String(255), nullable=False)\n    topic_tags: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    summary: Mapped[str | None] = mapped_column(Text, nullable=True)\n    body_sections: Mapped[dict | None] = mapped_column(JSONB, nullable=True)\n    signal_chains: Mapped[list | None] = mapped_column(JSONB, nullable=True)\n    plugins: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n    source_quality: Mapped[SourceQuality | None] = mapped_column(\n        Enum(SourceQuality, name=\"source_quality\", create_constraint=True),\n        nullable=True,\n    )\n    view_count: Mapped[int] = mapped_column(Integer, default=0, server_default=\"0\")\n    review_status: Mapped[PageReviewStatus] = mapped_column(\n        Enum(PageReviewStatus, name=\"page_review_status\", create_constraint=True),\n        default=PageReviewStatus.draft,\n        server_default=\"draft\",\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=\"technique_pages\")\n    key_moments: Mapped[list[KeyMoment]] = sa_relationship(\n        back_populates=\"technique_page\", foreign_keys=[KeyMoment.technique_page_id]\n    )\n    outgoing_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.source_page_id\", back_populates=\"source_page\"\n    )\n    incoming_links: Mapped[list[RelatedTechniqueLink]] = sa_relationship(\n        foreign_keys=\"RelatedTechniqueLink.target_page_id\", back_populates=\"target_page\"\n    )\n\n\nclass RelatedTechniqueLink(Base):\n    __tablename__ = \"related_technique_links\"\n    __table_args__ = (\n        UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    source_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    target_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    relationship: Mapped[RelationshipType] = mapped_column(\n        Enum(RelationshipType, name=\"relationship_type\", create_constraint=True),\n        nullable=False,\n    )\n\n    # relationships\n    source_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[source_page_id], back_populates=\"outgoing_links\"\n    )\n    target_page: Mapped[TechniquePage] = sa_relationship(\n        foreign_keys=[target_page_id], back_populates=\"incoming_links\"\n    )\n\n\nclass Tag(Base):\n    __tablename__ = \"tags\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    name: Mapped[str] = mapped_column(String(255), unique=True, nullable=False)\n    category: Mapped[str] = mapped_column(String(255), nullable=False)\n    aliases: Mapped[list[str] | None] = mapped_column(ARRAY(String), nullable=True)\n"}],"isError":false,"timestamp":1774822805634}}
{"type":"message","id":"e2e9cc70","parentId":"8a60848e","timestamp":"2026-03-29T22:20:05.641Z","message":{"role":"toolResult","toolCallId":"toolu_01G3d8MZJqXHAS449JoCnemd","toolName":"bash","content":[{"type":"text","text":"fastapi>=0.115.0,<1.0\nuvicorn[standard]>=0.32.0,<1.0\nsqlalchemy[asyncio]>=2.0,<3.0\nasyncpg>=0.30.0,<1.0\nalembic>=1.14.0,<2.0\npydantic>=2.0,<3.0\npydantic-settings>=2.0,<3.0\ncelery[redis]>=5.4.0,<6.0\nredis>=5.0,<6.0\npython-dotenv>=1.0,<2.0\npython-multipart>=0.0.9,<1.0\nhttpx>=0.27.0,<1.0\n# Test dependencies\npytest>=8.0,<10.0\npytest-asyncio>=0.24,<1.0\n"}],"isError":false,"timestamp":1774822805638}}
{"type":"message","id":"ea2a4a4d","parentId":"e2e9cc70","timestamp":"2026-03-29T22:20:11.987Z","message":{"role":"assistant","content":[{"type":"text","text":"The file listing is dominated by venv files. Let me filter properly and explore the actual project source code:"},{"type":"toolCall","id":"toolu_0155QjewUKvrFBs5nb3HbQAY","name":"bash","arguments":{"command":"find /home/aux/projects/content-to-kb-automator -type f \\( -name \"*.py\" -o -name \"*.yml\" -o -name \"*.yaml\" -o -name \"*.toml\" -o -name \"*.json\" -o -name \"*.cfg\" -o -name \"*.ini\" -o -name \"*.env\" -o -name \"*.env.*\" -o -name \"*.md\" -o -name \"*.txt\" \\) -not -path \"*/.venv/*\" -not -path \"*/__pycache__/*\" -not -path \"*/.git/*\" -not -path \"*/.gsd/*\" -not -path \"*/node_modules/*\" | sort"}},{"type":"toolCall","id":"toolu_012HMrZn52VfDK4Ax29twiEw","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/database.py"}},{"type":"toolCall","id":"toolu_01MNAhSUKsjP9KZFtmwKH8qN","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/main.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":351,"cacheRead":56225,"cacheWrite":23245,"totalTokens":79822,"cost":{"input":0.000005,"output":0.008775,"cacheRead":0.0281125,"cacheWrite":0.14528125,"total":0.18217375}},"stopReason":"toolUse","timestamp":1774822805639}}
{"type":"message","id":"50e4cde4","parentId":"ea2a4a4d","timestamp":"2026-03-29T22:20:12.031Z","message":{"role":"toolResult","toolCallId":"toolu_0155QjewUKvrFBs5nb3HbQAY","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/alembic/env.py\n/home/aux/projects/content-to-kb-automator/alembic.ini\n/home/aux/projects/content-to-kb-automator/alembic/versions/001_initial.py\n/home/aux/projects/content-to-kb-automator/backend/config.py\n/home/aux/projects/content-to-kb-automator/backend/database.py\n/home/aux/projects/content-to-kb-automator/backend/main.py\n/home/aux/projects/content-to-kb-automator/backend/models.py\n/home/aux/projects/content-to-kb-automator/backend/.pytest_cache/README.md\n/home/aux/projects/content-to-kb-automator/backend/pytest.ini\n/home/aux/projects/content-to-kb-automator/backend/requirements.txt\n/home/aux/projects/content-to-kb-automator/backend/routers/creators.py\n/home/aux/projects/content-to-kb-automator/backend/routers/health.py\n/home/aux/projects/content-to-kb-automator/backend/routers/ingest.py\n/home/aux/projects/content-to-kb-automator/backend/routers/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/routers/videos.py\n/home/aux/projects/content-to-kb-automator/backend/schemas.py\n/home/aux/projects/content-to-kb-automator/backend/tests/conftest.py\n/home/aux/projects/content-to-kb-automator/backend/tests/fixtures/sample_transcript.json\n/home/aux/projects/content-to-kb-automator/backend/tests/__init__.py\n/home/aux/projects/content-to-kb-automator/backend/tests/test_ingest.py\n/home/aux/projects/content-to-kb-automator/.bg-shell/manifest.json\n/home/aux/projects/content-to-kb-automator/chrysopedia-spec.md\n/home/aux/projects/content-to-kb-automator/config/canonical_tags.yaml\n/home/aux/projects/content-to-kb-automator/docker-compose.yml\n/home/aux/projects/content-to-kb-automator/.env.example\n/home/aux/projects/content-to-kb-automator/frontend/package.json\n/home/aux/projects/content-to-kb-automator/prompts/README.md\n/home/aux/projects/content-to-kb-automator/README.md\n/home/aux/projects/content-to-kb-automator/tests/fixtures/sample_transcript.json\n/home/aux/projects/content-to-kb-automator/whisper/README.md\n/home/aux/projects/content-to-kb-automator/whisper/requirements.txt\n/home/aux/projects/content-to-kb-automator/whisper/transcribe.py\n"}],"isError":false,"timestamp":1774822812031}}
{"type":"message","id":"428bdf65","parentId":"50e4cde4","timestamp":"2026-03-29T22:20:12.032Z","message":{"role":"toolResult","toolCallId":"toolu_012HMrZn52VfDK4Ax29twiEw","toolName":"bash","content":[{"type":"text","text":"\"\"\"Database engine, session factory, and declarative base for Chrysopedia.\"\"\"\n\nimport os\n\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine\nfrom sqlalchemy.orm import DeclarativeBase\n\nDATABASE_URL = os.getenv(\n    \"DATABASE_URL\",\n    \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia\",\n)\n\nengine = create_async_engine(DATABASE_URL, echo=False, pool_pre_ping=True)\n\nasync_session = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)\n\n\nclass Base(DeclarativeBase):\n    \"\"\"Declarative base for all ORM models.\"\"\"\n    pass\n\n\nasync def get_session() -> AsyncSession:  # type: ignore[misc]\n    \"\"\"FastAPI dependency that yields an async DB session.\"\"\"\n    async with async_session() as session:\n        yield session\n"}],"isError":false,"timestamp":1774822812031}}
{"type":"message","id":"9acebfe0","parentId":"428bdf65","timestamp":"2026-03-29T22:20:12.034Z","message":{"role":"toolResult","toolCallId":"toolu_01MNAhSUKsjP9KZFtmwKH8qN","toolName":"bash","content":[{"type":"text","text":"\"\"\"Chrysopedia API — Knowledge extraction and retrieval system.\n\nEntry point for the FastAPI application. Configures middleware,\nstructured logging, and mounts versioned API routers.\n\"\"\"\n\nimport logging\nimport sys\nfrom contextlib import asynccontextmanager\n\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom config import get_settings\nfrom routers import creators, health, ingest, videos\n\n\ndef _setup_logging() -> None:\n    \"\"\"Configure structured logging to stdout.\"\"\"\n    settings = get_settings()\n    level = getattr(logging, settings.app_log_level.upper(), logging.INFO)\n\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(\n        logging.Formatter(\n            fmt=\"%(asctime)s | %(levelname)-8s | %(name)s | %(message)s\",\n            datefmt=\"%Y-%m-%dT%H:%M:%S\",\n        )\n    )\n\n    root = logging.getLogger()\n    root.setLevel(level)\n    # Avoid duplicate handlers on reload\n    root.handlers.clear()\n    root.addHandler(handler)\n\n    # Quiet noisy libraries\n    logging.getLogger(\"uvicorn.access\").setLevel(logging.WARNING)\n    logging.getLogger(\"sqlalchemy.engine\").setLevel(logging.WARNING)\n\n\n@asynccontextmanager\nasync def lifespan(app: FastAPI):  # noqa: ARG001\n    \"\"\"Application lifespan: setup on startup, teardown on shutdown.\"\"\"\n    _setup_logging()\n    logger = logging.getLogger(\"chrysopedia\")\n    settings = get_settings()\n    logger.info(\n        \"Chrysopedia API starting (env=%s, log_level=%s)\",\n        settings.app_env,\n        settings.app_log_level,\n    )\n    yield\n    logger.info(\"Chrysopedia API shutting down\")\n\n\napp = FastAPI(\n    title=\"Chrysopedia API\",\n    description=\"Knowledge extraction and retrieval for music production content\",\n    version=\"0.1.0\",\n    lifespan=lifespan,\n)\n\n# ── Middleware ────────────────────────────────────────────────────────────────\n\nsettings = get_settings()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=settings.cors_origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n# ── Routers ──────────────────────────────────────────────────────────────────\n\n# Root-level health (no prefix)\napp.include_router(health.router)\n\n# Versioned API\napp.include_router(creators.router, prefix=\"/api/v1\")\napp.include_router(ingest.router, prefix=\"/api/v1\")\napp.include_router(videos.router, prefix=\"/api/v1\")\n\n\n@app.get(\"/api/v1/health\")\nasync def api_health():\n    \"\"\"Lightweight version-prefixed health endpoint (no DB check).\"\"\"\n    return {\"status\": \"ok\", \"version\": \"0.1.0\"}\n"}],"isError":false,"timestamp":1774822812032}}
{"type":"message","id":"13bf75c1","parentId":"9acebfe0","timestamp":"2026-03-29T22:20:18.289Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. Now let me explore the spec, existing config, prompt templates, canonical tags, and Docker setup:"},{"type":"toolCall","id":"toolu_013aMPWJdqpxDJi4XXVfQ85d","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/config/canonical_tags.yaml"}},{"type":"toolCall","id":"toolu_01BA7uD4GTYmXAuoKERRu8h4","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/prompts/README.md"}},{"type":"toolCall","id":"toolu_01DdxJTRCLwe67HNrRiuHap3","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/config.py"}},{"type":"toolCall","id":"toolu_01XE7jzasCnVpX9eBEsKwd9X","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/docker-compose.yml"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":267,"cacheRead":79470,"cacheWrite":2175,"totalTokens":81913,"cost":{"input":0.000005,"output":0.006675,"cacheRead":0.039735,"cacheWrite":0.01359375,"total":0.06000875}},"stopReason":"toolUse","timestamp":1774822812032}}
{"type":"message","id":"f5e8ea91","parentId":"13bf75c1","timestamp":"2026-03-29T22:20:18.308Z","message":{"role":"toolResult","toolCallId":"toolu_013aMPWJdqpxDJi4XXVfQ85d","toolName":"bash","content":[{"type":"text","text":"# Canonical tags — 6 top-level production categories\n# Sub-topics grow organically during pipeline extraction\ncategories:\n  - name: Sound design\n    description: Creating and shaping sounds from scratch or samples\n    sub_topics: [bass, drums, kick, snare, hi-hat, percussion, pads, leads, fx, foley, vocals, textures]\n\n  - name: Mixing\n    description: Balancing, processing, and spatializing elements\n    sub_topics: [eq, compression, bus processing, reverb, delay, stereo imaging, gain staging, automation]\n\n  - name: Synthesis\n    description: Methods of generating sound\n    sub_topics: [fm, wavetable, granular, additive, subtractive, modular, physical modeling]\n\n  - name: Arrangement\n    description: Structuring a track from intro to outro\n    sub_topics: [song structure, transitions, tension, energy flow, breakdowns, drops]\n\n  - name: Workflow\n    description: Creative process, session management, productivity\n    sub_topics: [daw setup, templates, creative process, collaboration, file management, resampling]\n\n  - name: Mastering\n    description: Final stage processing for release\n    sub_topics: [limiting, stereo width, loudness, format delivery, referencing]\n\n# Genre taxonomy (assigned to Creators, not techniques)\ngenres:\n  - Bass music\n  - Drum & bass\n  - Dubstep\n  - Halftime\n  - House\n  - Techno\n  - IDM\n  - Glitch\n  - Downtempo\n  - Neuro\n  - Ambient\n  - Experimental\n  - Cinematic\n"}],"isError":false,"timestamp":1774822818308}}
{"type":"message","id":"89dbd803","parentId":"f5e8ea91","timestamp":"2026-03-29T22:20:18.309Z","message":{"role":"toolResult","toolCallId":"toolu_01BA7uD4GTYmXAuoKERRu8h4","toolName":"bash","content":[{"type":"text","text":"# Prompt templates for LLM pipeline stages\n# These files are bind-mounted read-only into the worker container.\n"}],"isError":false,"timestamp":1774822818308}}
{"type":"message","id":"844e794f","parentId":"89dbd803","timestamp":"2026-03-29T22:20:18.309Z","message":{"role":"toolResult","toolCallId":"toolu_01DdxJTRCLwe67HNrRiuHap3","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    # File storage\n    transcript_storage_path: str = \"/data/transcripts\"\n    video_metadata_path: str = \"/data/video_meta\"\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":1774822818309}}
{"type":"message","id":"1471474e","parentId":"844e794f","timestamp":"2026-03-29T22:20:18.312Z","message":{"role":"toolResult","toolCallId":"toolu_01XE7jzasCnVpX9eBEsKwd9X","toolName":"bash","content":[{"type":"text","text":"# Chrysopedia — Docker Compose\n# XPLTD convention: xpltd_chrysopedia project, bind mounts, dedicated bridge\nname: xpltd_chrysopedia\n\nservices:\n  # ── PostgreSQL 16 ──\n  chrysopedia-db:\n    image: postgres:16-alpine\n    container_name: chrysopedia-db\n    restart: unless-stopped\n    environment:\n      POSTGRES_USER: ${POSTGRES_USER:-chrysopedia}\n      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD:-changeme}\n      POSTGRES_DB: ${POSTGRES_DB:-chrysopedia}\n    volumes:\n      - /vmPool/r/services/chrysopedia_db:/var/lib/postgresql/data\n    ports:\n      - \"127.0.0.1:5433:5432\"\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD-SHELL\", \"pg_isready -U ${POSTGRES_USER:-chrysopedia}\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n\n  # ── Redis (Celery broker) ──\n  chrysopedia-redis:\n    image: redis:7-alpine\n    container_name: chrysopedia-redis\n    restart: unless-stopped\n    volumes:\n      - /vmPool/r/services/chrysopedia_redis:/data\n    networks:\n      - chrysopedia\n    healthcheck:\n      test: [\"CMD\", \"redis-cli\", \"ping\"]\n      interval: 10s\n      timeout: 5s\n      retries: 5\n\n  # ── FastAPI application ──\n  chrysopedia-api:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-api\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n    volumes:\n      - ./backend:/app\n      - /vmPool/r/services/chrysopedia_data:/data\n    ports:\n      - \"127.0.0.1:8000:8000\"\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n\n  # ── Celery worker (pipeline stages 2-5) ──\n  chrysopedia-worker:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-worker\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n    command: [\"celery\", \"-A\", \"worker\", \"worker\", \"--loglevel=info\"]\n    volumes:\n      - ./backend:/app\n      - /vmPool/r/services/chrysopedia_data:/data\n      - ./prompts:/prompts:ro\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n\n  # ── React web UI (nginx) ──\n  chrysopedia-web:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.web\n    container_name: chrysopedia-web\n    restart: unless-stopped\n    ports:\n      - \"127.0.0.1:3000:80\"\n    depends_on:\n      - chrysopedia-api\n    networks:\n      - chrysopedia\n\nnetworks:\n  chrysopedia:\n    driver: bridge\n    ipam:\n      config:\n        - subnet: 172.24.0.0/24\n"}],"isError":false,"timestamp":1774822818311}}
{"type":"message","id":"ffa0bcd3","parentId":"1471474e","timestamp":"2026-03-29T22:20:23.067Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the spec for the LLM pipeline details, and existing schemas:"},{"type":"toolCall","id":"toolu_013486ND1xVd1RrnhfMxrxEu","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/chrysopedia-spec.md"}},{"type":"toolCall","id":"toolu_013sFpSxeUyXcHM3YH7JvzhV","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/schemas.py"}},{"type":"toolCall","id":"toolu_01ByDR6sAiwj4M53C33sr6Aa","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/routers/ingest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":211,"cacheRead":81645,"cacheWrite":2137,"totalTokens":83994,"cost":{"input":0.000005,"output":0.005275,"cacheRead":0.0408225,"cacheWrite":0.01335625,"total":0.05945875}},"stopReason":"toolUse","timestamp":1774822818312}}
{"type":"message","id":"809e9291","parentId":"ffa0bcd3","timestamp":"2026-03-29T22:20:23.091Z","message":{"role":"toolResult","toolCallId":"toolu_013486ND1xVd1RrnhfMxrxEu","toolName":"bash","content":[{"type":"text","text":"# Chrysopedia — Project Specification\n\n> **Etymology:** From *chrysopoeia* (the alchemical transmutation of base material into gold) + *encyclopedia* (an organized body of knowledge). Chrysopedia transmutes raw video content into refined, searchable production knowledge.\n\n---\n\n## 1. Project overview\n\n### 1.1 Problem statement\n\nHundreds of hours of educational video content from electronic music producers sit on local storage — tutorials, livestreams, track breakdowns, and deep dives covering techniques in sound design, mixing, arrangement, synthesis, and more. This content is extremely valuable but nearly impossible to retrieve: videos are unsearchable, unchaptered, and undocumented. A 4-hour livestream may contain 6 minutes of actionable gold buried among tangents and chat interaction. The current retrieval method is \"scrub through from memory and hope\" — or more commonly, the knowledge is simply lost.\n\n### 1.2 Solution\n\nChrysopedia is a self-hosted knowledge extraction and retrieval system that:\n\n1. **Transcribes** video content using local Whisper inference\n2. **Extracts** key moments, techniques, and insights using LLM analysis\n3. **Classifies** content by topic, creator, plugins, and production stage\n4. **Synthesizes** knowledge across multiple sources into coherent technique pages\n5. **Serves** a fast, search-first web UI for mid-session retrieval\n\nThe system transforms raw video files into a browsable, searchable knowledge base with direct timestamp links back to source material.\n\n### 1.3 Design principles\n\n- **Search-first.** The primary interaction is typing a query and getting results in seconds. Browse is secondary, for exploration.\n- **Surgical retrieval.** A producer mid-session should be able to Alt+Tab, find the technique they need, absorb the key insight, and get back to their DAW in under 2 minutes.\n- **Creator equity.** No artist is privileged in the UI. All creators get equal visual weight. Default sort is randomized.\n- **Dual-axis navigation.** Content is accessible by Topic (technique/production stage) and by Creator (artist), with both paths being first-class citizens.\n- **Incremental, not one-time.** The system must handle ongoing content additions, not just an initial batch.\n- **Self-hosted and portable.** Packaged as a Docker Compose project, deployable on existing infrastructure.\n\n### 1.4 Name and identity\n\n- **Project name:** Chrysopedia\n- **Suggested subdomain:** `chrysopedia.xpltd.co`\n- **Docker project name:** `chrysopedia`\n\n---\n\n## 2. Content inventory and source material\n\n### 2.1 Current state\n\n- **Volume:** 100–500 video files\n- **Creators:** 50+ distinct artists/producers\n- **Formats:** Primarily MP4/MKV, mixed quality and naming conventions\n- **Organization:** Folders per artist, filenames loosely descriptive\n- **Location:** Local desktop storage (not yet on the hypervisor/NAS)\n- **Content types:**\n  - Full-length tutorials (30min–4hrs, structured walkthroughs)\n  - Livestream recordings (long, unstructured, conversational)\n  - Track breakdowns / start-to-finish productions\n\n### 2.2 Content characteristics\n\nThe audio track carries the vast majority of the value. Visual demonstrations (screen recordings of DAW work) are useful context but are not the primary extraction target. The transcript is the primary ore.\n\n**Structured content** (tutorials, breakdowns) tends to have natural topic boundaries — the producer announces what they're about to cover, then demonstrates. These are easier to segment.\n\n**Unstructured content** (livestreams) is chaotic: tangents, chat interaction, rambling, with gems appearing without warning. The extraction pipeline must handle both structured and unstructured content using semantic understanding, not just topic detection from speaker announcements.\n\n---\n\n## 3. Terminology\n\n| Term | Definition |\n|------|-----------|\n| **Creator** | An artist, producer, or educator whose video content is in the system. Formerly \"artist\" — renamed for flexibility. |\n| **Technique page** | The primary knowledge unit: a structured page covering one technique or concept from one creator, compiled from one or more source videos. |\n| **Key moment** | A discrete, timestamped insight extracted from a video — a specific technique, setting, or piece of reasoning worth capturing. |\n| **Topic** | A production domain or concept category (e.g., \"sound design,\" \"mixing,\" \"snare design\"). Organized hierarchically. |\n| **Genre** | A broad musical style tag (e.g., \"dubstep,\" \"drum & bass,\" \"halftime\"). Stored as metadata on Creators, not on techniques. Used as a filter across all views. |\n| **Source video** | An original video file that has been processed by the pipeline. |\n| **Transcript** | The timestamped text output of Whisper processing a source video's audio. |\n\n---\n\n## 4. User experience\n\n### 4.1 UX philosophy\n\nThe system is accessed via Alt+Tab from a DAW on the same desktop machine. Every design decision optimizes for speed of retrieval and minimal cognitive load. The interface should feel like a tool, not a destination.\n\n**Primary access method:** Same machine, Alt+Tab to browser.\n\n### 4.2 Landing page (Launchpad)\n\nThe landing page is a decision point, not a dashboard. Minimal, focused, fast.\n\n**Layout (top to bottom):**\n\n1. **Search bar** — prominent, full-width, with live typeahead (results appear after 2–3 characters). This is the primary interaction for most visits. Scope toggle tabs below the search input: `All | Topics | Creators`\n2. **Two navigation cards** — side-by-side:\n   - **Topics** — \"Browse by technique, production stage, or concept\" with count of total techniques and categories\n   - **Creators** — \"Browse by artist, filterable by genre\" with count of total creators and genres\n3. **Recently added** — a short list of the most recently processed/published technique pages with creator name, topic tag, and relative timestamp\n\n**Future feature (not v1):** Trending / popular section alongside recently added, driven by view counts and cross-reference frequency.\n\n### 4.3 Live search (typeahead)\n\nThe search bar is the primary interface. Behavior:\n\n- Results begin appearing after 2–3 characters typed\n- Scope toggle: `All | Topics | Creators` — filters what types of results appear\n- **\"All\" scope** groups results by type:\n  - **Topics** — technique pages matching the query, showing title, creator name(s), parent topic tag\n  - **Key moments** — individual timestamped insights matching the query, showing moment title, creator, source file, and timestamp. Clicking jumps to the technique page (or eventually direct to the video moment)\n  - **Creators** — creator names matching the query\n- **\"Topics\" scope** — shows only technique pages\n- **\"Creators\" scope** — shows only creator matches\n- Genre filter is accessible on Creators scope and cross-filters Topics scope (using creator-level genre metadata)\n- Search is semantic where possible (powered by Qdrant vector search), with keyword fallback\n\n### 4.4 Technique page (A+C hybrid format)\n\nThe core content unit. Each technique page covers one technique or concept from one creator. The format adapts by content type but follows a consistent structure.\n\n**Layout (top to bottom):**\n\n1. **Header:**\n   - Topic tags (e.g., \"sound design,\" \"drums,\" \"snare\")\n   - Technique title (e.g., \"Snare design\")\n   - Creator name\n   - Meta line: \"Compiled from N sources · M key moments · Last updated [date]\"\n   - Source quality warning (amber banner) if content came from an unstructured livestream\n\n2. **Study guide prose (Section A):**\n   - Organized by sub-aspects of the technique (e.g., \"Layer construction,\" \"Saturation & character,\" \"Mix context\")\n   - Rich prose capturing:\n     - The specific technique/method described (highest priority)\n     - Exact settings, plugins, and parameters when the creator was *teaching* the setting (not incidental use)\n     - The reasoning/philosophy behind choices when the creator explains *why*\n   - Signal chain blocks rendered in monospace when a creator walks through a routing chain\n   - Direct quotes of creator opinions/warnings when they add value (e.g., \"He says it 'smears the transient into mush'\")\n\n3. **Key moments index (Section C):**\n   - Compact list of individual timestamped insights\n   - Each row: moment title, source video filename, clickable timestamp\n   - Sorted chronologically within each source video\n\n4. **Related techniques:**\n   - Links to related technique pages — same technique by other creators, adjacent techniques by the same creator, general/cross-creator technique pages\n   - Renders as clickable pill-shaped tags\n\n5. **Plugins referenced:**\n   - List of all plugins/tools mentioned in the technique page\n   - Each is a clickable tag that could lead to \"all techniques referencing this plugin\" (future: dedicated plugin pages)\n\n**Content type adaptation:**\n- **Technique-heavy content** (sound design, specific methods): Full A+C treatment with signal chains, plugin details, parameter specifics\n- **Philosophy/workflow content** (mixdown approach, creative process): More prose-heavy, fewer signal chain blocks, but same overall structure. These pages are still browsable but also serve as rich context for future RAG/chat retrieval\n- **Livestream-sourced content:** Amber warning banner noting source quality. Timestamps may land in messy context with tangents nearby\n\n### 4.5 Creators browse page\n\nAccessed from the landing page \"Creators\" card.\n\n**Layout:**\n- Page title: \"Creators\" with total count\n- Filter input: type-to-narrow the list\n- Genre filter pills: `All genres | Bass music | Drum & bass | Dubstep | Halftime | House | IDM | Neuro | Techno | ...` — clicking a genre filters the list to creators tagged with that genre\n- Sort options: Randomized (default, re-shuffled on every page load), Alphabetical, View count\n- Creator list: flat, equal-weight rows. Each row shows:\n  - Creator name\n  - Genre tags (multiple allowed)\n  - Technique count\n  - Video count\n  - View count (sum of activity across all content derived from this creator)\n- Clicking a row navigates to that creator's detail page (list of all their technique pages)\n\n**Default sort is randomized on every page load** to prevent discovery bias. Users can toggle to alphabetical or sort by view count.\n\n### 4.6 Topics browse page\n\nAccessed from the landing page \"Topics\" card.\n\n**Layout:**\n- Page title: \"Topics\" with total technique count\n- Filter input: type-to-narrow\n- Genre filter pills (uses creator-level genre metadata to filter): show only techniques from creators tagged with the selected genre\n- **Two-level hierarchy displayed:**\n  - **Top-level categories:** Sound design, Mixing, Synthesis, Arrangement, Workflow, Mastering\n  - **Sub-topics within each:** clicking a top-level category expands or navigates to show sub-topics (e.g., Sound Design → Bass, Drums, Pads, Leads, FX, Foley; Drums → Kick, Snare, Hi-hat, Percussion)\n- Each sub-topic shows: technique count, number of creators covering it\n- Clicking a sub-topic shows all technique pages in that category, filterable by creator and genre\n\n### 4.7 Search results page\n\nFor complex queries that go beyond typeahead (e.g., hitting Enter after typing a full query).\n\n**Layout:**\n- Search bar at top (retains query)\n- Scope tabs: `All results (N) | Techniques (N) | Key moments (N) | Creators (N)`\n- Results split into two tiers:\n  - **Technique pages** — first-class results with title, creator, summary snippet, tags, moment count, plugin list\n  - **Also mentioned in** — cross-references where the search term appears inside other technique pages (e.g., searching \"snare\" surfaces \"drum bus processing\" because it mentions snare bus techniques)\n\n---\n\n## 5. Taxonomy and topic hierarchy\n\n### 5.1 Top-level categories\n\nThese are broad production stages/domains. They should cover the full scope of music production education:\n\n| Category | Description | Example sub-topics |\n|----------|-------------|-------------------|\n| Sound design | Creating and shaping sounds from scratch or samples | Bass, drums (kick, snare, hi-hat, percussion), pads, leads, FX, foley, vocals, textures |\n| Mixing | Balancing, processing, and spatializing elements in a session | EQ, compression, bus processing, reverb/delay, stereo imaging, gain staging, automation |\n| Synthesis | Methods of generating sound | FM, wavetable, granular, additive, subtractive, modular, physical modeling |\n| Arrangement | Structuring a track from intro to outro | Song structure, transitions, tension/release, energy flow, breakdowns, drops |\n| Workflow | Creative process, session management, productivity | DAW setup, templates, creative process, collaboration, file management, resampling |\n| Mastering | Final stage processing for release | Limiting, stereo width, loudness, format delivery, referencing |\n\n### 5.2 Sub-topic management\n\nSub-topics are not rigidly pre-defined. The extraction pipeline proposes sub-topic tags during classification, and the taxonomy grows organically as content is processed. However, the system maintains a **canonical tag list** that the LLM references during classification to ensure consistency (e.g., always \"snare\" not sometimes \"snare drum\" and sometimes \"snare design\").\n\nThe canonical tag list is editable by the administrator and should be stored as a configuration file that the pipeline references. New tags can be proposed by the pipeline and queued for admin approval, or auto-added if they fit within an existing top-level category.\n\n### 5.3 Genre taxonomy\n\nGenres are broad, general-level tags. Sub-genre classification is explicitly out of scope to avoid complexity.\n\n**Initial genre set (expandable):**\nBass music, Drum & bass, Dubstep, Halftime, House, Techno, IDM, Glitch, Downtempo, Neuro, Ambient, Experimental, Cinematic\n\n**Rules:**\n- Genres are metadata on Creators, not on techniques\n- A Creator can have multiple genre tags\n- Genre is available as a filter on both the Creators browse page and the Topics browse page (filtering Topics by genre shows techniques from creators tagged with that genre)\n- Genre tags are assigned during initial creator setup (manually or LLM-suggested based on content analysis) and can be edited by the administrator\n\n---\n\n## 6. Data model\n\n### 6.1 Core entities\n\n**Creator**\n```\nid              UUID\nname            string (display name, e.g., \"KOAN Sound\")\nslug            string (URL-safe, e.g., \"koan-sound\")\ngenres          string[] (e.g., [\"glitch hop\", \"neuro\", \"bass music\"])\nfolder_name     string (matches the folder name on disk for source mapping)\nview_count      integer (aggregated from child technique page views)\ncreated_at      timestamp\nupdated_at      timestamp\n```\n\n**Source Video**\n```\nid              UUID\ncreator_id      FK → Creator\nfilename        string (original filename)\nfile_path       string (path on disk)\nduration_seconds integer\ncontent_type    enum: tutorial | livestream | breakdown | short_form\ntranscript_path string (path to transcript JSON)\nprocessing_status enum: pending | transcribed | extracted | reviewed | published\ncreated_at      timestamp\nupdated_at      timestamp\n```\n\n**Transcript Segment**\n```\nid              UUID\nsource_video_id FK → Source Video\nstart_time      float (seconds)\nend_time        float (seconds)\ntext            text\nsegment_index   integer (order within video)\ntopic_label     string (LLM-assigned topic label for this segment)\n```\n\n**Key Moment**\n```\nid              UUID\nsource_video_id FK → Source Video\ntechnique_page_id FK → Technique Page (nullable until assigned)\ntitle           string (e.g., \"Three-layer snare construction\")\nsummary         text (1-3 sentence description)\nstart_time      float (seconds)\nend_time        float (seconds)\ncontent_type    enum: technique | settings | reasoning | workflow\nplugins         string[] (plugin names detected)\nreview_status   enum: pending | approved | edited | rejected\nraw_transcript  text (the original transcript text for this segment)\ncreated_at      timestamp\nupdated_at      timestamp\n```\n\n**Technique Page**\n```\nid              UUID\ncreator_id      FK → Creator\ntitle           string (e.g., \"Snare design\")\nslug            string (URL-safe)\ntopic_category  string (top-level: \"sound design\")\ntopic_tags      string[] (sub-topics: [\"drums\", \"snare\", \"layering\", \"saturation\"])\nsummary         text (synthesized overview paragraph)\nbody_sections   JSONB (structured prose sections with headings)\nsignal_chains   JSONB[] (structured signal chain representations)\nplugins         string[] (all plugins referenced across all moments)\nsource_quality  enum: structured | mixed | unstructured (derived from source video types)\nview_count      integer\nreview_status   enum: draft | reviewed | published\ncreated_at      timestamp\nupdated_at      timestamp\n```\n\n**Related Technique Link**\n```\nid              UUID\nsource_page_id  FK → Technique Page\ntarget_page_id  FK → Technique Page\nrelationship    enum: same_technique_other_creator | same_creator_adjacent | general_cross_reference\n```\n\n**Tag (canonical)**\n```\nid              UUID\nname            string (e.g., \"snare\")\ncategory        string (parent top-level category: \"sound design\")\naliases         string[] (alternative phrasings the LLM should normalize: [\"snare drum\", \"snare design\"])\n```\n\n### 6.2 Storage layer\n\n| Store | Purpose | Technology |\n|-------|---------|------------|\n| Relational DB | All structured data (creators, videos, moments, technique pages, tags) | PostgreSQL (preferred) or SQLite for initial simplicity |\n| Vector DB | Semantic search embeddings for transcripts, key moments, and technique page content | Qdrant (already running on hypervisor) |\n| File store | Raw transcript JSON files, source video reference metadata | Local filesystem on hypervisor, organized by creator slug |\n\n### 6.3 Vector embeddings\n\nThe following content gets embedded in Qdrant for semantic search:\n\n- Key moment summaries (with metadata: creator, topic, timestamp, source video)\n- Technique page summaries and body sections\n- Transcript segments (for future RAG/chat retrieval)\n\nEmbedding model: configurable. Can use a local model via Ollama (e.g., `nomic-embed-text`) or an API-based model. The embedding endpoint should be a configurable URL, same pattern as the LLM endpoint.\n\n---\n\n## 7. Pipeline architecture\n\n### 7.1 Infrastructure topology\n\n```\nDesktop (RTX 4090)                    Hypervisor (Docker host)\n┌─────────────────────┐               ┌─────────────────────────────────┐\n│ Video files (local)  │               │ Chrysopedia Docker Compose      │\n│ Whisper (local GPU)  │──2.5GbE──────▶│ ├─ API / pipeline service       │\n│ Output: transcript   │  (text only)  │ ├─ Web UI                       │\n│         JSON files   │               │ ├─ PostgreSQL                   │\n└─────────────────────┘               │ ├─ Qdrant (existing)            │\n                                       │ └─ File store                   │\n                                       └────────────┬────────────────────┘\n                                                     │ API calls (text)\n                                       ┌─────────────▼────────────────────┐\n                                       │ Friend's DGX Sparks              │\n                                       │ Qwen via Open WebUI API          │\n                                       │ (2Gb fiber, high uptime)         │\n                                       └──────────────────────────────────┘\n```\n\n**Bandwidth analysis:** Transcript JSON files are 200–500KB each. At 50Mbit upload, the entire library's transcripts could transfer in under a minute. The bandwidth constraint is irrelevant for this workload. The only large files (videos) stay on the desktop.\n\n**Future centralization:** The Docker Compose project should be structured so that when all hardware is co-located, the only change is config (moving Whisper into the compose stack and pointing file paths to local storage). No architectural rewrite.\n\n### 7.2 Processing stages\n\n#### Stage 1: Audio extraction and transcription (Desktop)\n\n**Tool:** Whisper large-v3 running locally on RTX 4090\n**Input:** Video file (MP4/MKV)\n**Process:**\n1. Extract audio track from video (ffmpeg → WAV or direct pipe)\n2. Run Whisper with word-level or segment-level timestamps\n3. Output: JSON file with timestamped transcript\n\n**Output format:**\n```json\n{\n  \"source_file\": \"Skope — Sound Design Masterclass pt2.mp4\",\n  \"creator_folder\": \"Skope\",\n  \"duration_seconds\": 7243,\n  \"segments\": [\n    {\n      \"start\": 0.0,\n      \"end\": 4.52,\n      \"text\": \"Hey everyone welcome back to part two...\",\n      \"words\": [\n        {\"word\": \"Hey\", \"start\": 0.0, \"end\": 0.28},\n        {\"word\": \"everyone\", \"start\": 0.32, \"end\": 0.74}\n      ]\n    }\n  ]\n}\n```\n\n**Performance estimate:** Whisper large-v3 on a 4090 processes audio at roughly 10-20x real-time. A 2-hour video takes ~6-12 minutes to transcribe. For 300 videos averaging 1.5 hours each, the initial transcription pass is roughly 15-40 hours of GPU time.\n\n#### Stage 2: Transcript segmentation (Hypervisor → LLM)\n\n**Tool:** LLM (Qwen on DGX Sparks, or local Ollama as fallback)\n**Input:** Full timestamped transcript JSON\n**Process:** The LLM analyzes the transcript to identify topic boundaries — points where the creator shifts from one subject to another. Output is a segmented transcript with topic labels per segment.\n\n**This stage can use a lighter model** if needed (segmentation is more mechanical than extraction). However, for simplicity in v1, use the same model endpoint as stages 3-5.\n\n#### Stage 3: Key moment extraction (Hypervisor → LLM)\n\n**Tool:** LLM (Qwen on DGX Sparks)\n**Input:** Individual transcript segments from Stage 2\n**Process:** The LLM reads each segment and identifies actionable insights. The extraction prompt should distinguish between:\n\n- **Instructional content** (the creator is *teaching* something) → extract as a key moment\n- **Incidental content** (the creator is *using* a tool without explaining it) → skip\n- **Philosophical/reasoning content** (the creator explains *why* they make a choice) → extract with `content_type: reasoning`\n- **Settings/parameters** (specific plugin settings, values, configurations being demonstrated) → extract with `content_type: settings`\n\n**Extraction rule for plugin detail:** Capture plugin names and settings when the creator is *teaching* the setting — spending time explaining why they chose it, what it does, how to configure it. Skip incidental plugin usage (a plugin is visible but not discussed).\n\n#### Stage 4: Classification and tagging (Hypervisor → LLM)\n\n**Tool:** LLM (Qwen on DGX Sparks)\n**Input:** Extracted key moments from Stage 3\n**Process:** Each moment is classified with:\n- Top-level topic category\n- Sub-topic tags (referencing the canonical tag list)\n- Plugin names (normalized to canonical names)\n- Content type classification\n\nThe LLM is provided the canonical tag list as context and instructed to use existing tags where possible, proposing new tags only when no existing tag fits.\n\n#### Stage 5: Synthesis (Hypervisor → LLM)\n\n**Tool:** LLM (Qwen on DGX Sparks)\n**Input:** All approved/published key moments for a given creator + topic combination\n**Process:** When multiple key moments from the same creator cover overlapping or related topics, the synthesis stage merges them into a coherent technique page. This includes:\n- Writing the overview summary paragraph\n- Organizing body sections by sub-aspect\n- Generating signal chain blocks where applicable\n- Identifying related technique pages for cross-linking\n- Compiling the plugin reference list\n\nThis stage runs whenever new key moments are approved for a creator+topic combination that already has a technique page (updating it), or when enough moments accumulate to warrant a new page.\n\n### 7.3 LLM endpoint configuration\n\nThe pipeline talks to an **OpenAI-compatible API endpoint** (which both Ollama and Open WebUI expose). The LLM is not hardcoded — it's configured via environment variables:\n\n```\nLLM_API_URL=https://friend-openwebui.example.com/api\nLLM_API_KEY=sk-...\nLLM_MODEL=qwen2.5-72b\nLLM_FALLBACK_URL=http://localhost:11434/v1  # local Ollama\nLLM_FALLBACK_MODEL=qwen2.5:14b-q8_0\n```\n\nThe pipeline should attempt the primary endpoint first and fall back to the local model if the primary is unavailable.\n\n### 7.4 Embedding endpoint configuration\n\nSame configurable pattern:\n\n```\nEMBEDDING_API_URL=http://localhost:11434/v1\nEMBEDDING_MODEL=nomic-embed-text\n```\n\n### 7.5 Processing estimates for initial seeding\n\n| Stage | Per video | 300 videos total |\n|-------|----------|-----------------|\n| Transcription (Whisper, 4090) | 6–12 min | 30–60 hours |\n| Segmentation (LLM) | ~1 min | ~5 hours |\n| Extraction (LLM) | ~2 min | ~10 hours |\n| Classification (LLM) | ~30 sec | ~2.5 hours |\n| Synthesis (LLM) | ~2 min per technique page | Varies by page count |\n\n**Recommendation:** Tell the DGX Sparks friend to expect a weekend of sustained processing for the initial seed. The pipeline must be **resumable** — if it drops, it picks up from the last successfully processed video/stage, not from the beginning.\n\n---\n\n## 8. Review and approval workflow\n\n### 8.1 Modes\n\nThe system supports two modes:\n\n- **Review mode (initial calibration):** All extracted key moments enter a review queue. The administrator reviews, edits, approves, or rejects each moment before it's published.\n- **Auto mode (post-calibration):** Extracted moments are published automatically. The review queue still exists but functions as an audit log rather than a gate.\n\nThe mode is a system-level toggle. The transition from review to auto mode happens when the administrator is satisfied with extraction quality — typically after reviewing the first several videos and tuning prompts.\n\n### 8.2 Review queue interface\n\nThe review UI is part of the Chrysopedia web application (an admin section, not a separate tool).\n\n**Queue view:**\n- Counts: pending, approved, edited, rejected\n- Filter tabs: Pending | Approved | Edited | Rejected\n- Items organized by source video (review all moments from one video in sequence for context)\n\n**Individual moment review:**\n- Extracted moment: title, timestamp range, summary, tags, plugins detected\n- Raw transcript segment displayed alongside for comparison\n- Five actions:\n  - **Approve** — publish as-is\n  - **Edit & approve** — modify summary, tags, timestamp, or plugins, then publish\n  - **Split** — the moment actually contains two distinct insights; split into two separate moments\n  - **Merge with adjacent** — the system over-segmented; combine with the next or previous moment\n  - **Reject** — not a key moment; discard\n\n### 8.3 Prompt tuning\n\nThe extraction prompts (stages 2-5) should be stored as editable configuration, not hardcoded. If review reveals systematic issues (e.g., the LLM consistently misclassifies mixing techniques as sound design), the administrator should be able to:\n\n1. Edit the prompt templates\n2. Re-run extraction on specific videos or all videos\n3. Review the new output\n\nThis is the \"calibration loop\" — run pipeline, review output, tune prompts, re-run, repeat until quality is sufficient for auto mode.\n\n---\n\n## 9. New content ingestion workflow\n\n### 9.1 Adding new videos\n\nThe ongoing workflow for adding new content after initial seeding:\n\n1. **Drop file:** Place new video file(s) in the appropriate creator folder on the desktop (or create a new folder for a new creator)\n2. **Trigger transcription:** Run the Whisper transcription stage on the new file(s). This could be a manual CLI command, a watched-folder daemon, or an n8n workflow trigger.\n3. **Ship transcript:** Transfer the transcript JSON to the hypervisor (automated via the pipeline)\n4. **Process:** Stages 2-5 run automatically on the new transcript\n5. **Review or auto-publish:** Depending on mode, moments enter the review queue or publish directly\n6. **Synthesis update:** If the new content covers a topic that already has a technique page for this creator, the synthesis stage updates the existing page. If it's a new topic, a new technique page is created.\n\n### 9.2 Adding new creators\n\nWhen a new creator's content is added:\n\n1. Create a new folder on the desktop with the creator's name\n2. Add video files\n3. The pipeline detects the new folder name and creates a Creator record\n4. Genre tags can be auto-suggested by the LLM based on content analysis, or manually assigned by the administrator\n5. Process videos as normal\n\n### 9.3 Watched folder (optional, future)\n\nFor maximum automation, a filesystem watcher on the desktop could detect new video files and automatically trigger the transcription pipeline. This is a nice-to-have for v2, not a v1 requirement. In v1, transcription is triggered manually.\n\n---\n\n## 10. Deployment and infrastructure\n\n### 10.1 Docker Compose project\n\nThe entire Chrysopedia stack (excluding Whisper, which runs on the desktop GPU) is packaged as a single `docker-compose.yml`:\n\n```yaml\n# Indicative structure — not final\nservices:\n  chrysopedia-api:\n    # FastAPI or similar — handles pipeline orchestration, API endpoints\n  chrysopedia-web:\n    # Web UI — React, Svelte, or similar SPA\n  chrysopedia-db:\n    # PostgreSQL\n  chrysopedia-qdrant:\n    # Only if not using the existing Qdrant instance\n  chrysopedia-worker:\n    # Background job processor for pipeline stages 2-5\n```\n\n### 10.2 Existing infrastructure integration\n\n**IMPORTANT:** The implementing agent should reference **XPLTD Lore** when making deployment decisions. This includes:\n\n- Existing Docker conventions, naming patterns, and network configuration\n- The hypervisor's current resource allocation and available capacity (~60 containers already running)\n- Existing Qdrant instance (may be shared or a new collection created)\n- Existing n8n instance (potential for workflow triggers)\n- Storage paths and volume mount conventions\n- Any reverse proxy or DNS configuration patterns\n\nDo not assume infrastructure details — consult XPLTD Lore for how applications are typically deployed in this environment.\n\n### 10.3 Whisper on desktop\n\nWhisper runs separately on the desktop with the RTX 4090. It is NOT part of the Docker Compose stack (for now). It should be packaged as a simple Python script or lightweight container that:\n\n1. Accepts a video file path (or watches a directory)\n2. Extracts audio via ffmpeg\n3. Runs Whisper large-v3\n4. Outputs transcript JSON\n5. Ships the JSON to the hypervisor (SCP, rsync, or API upload to the Chrysopedia API)\n\n**Future centralization:** When all hardware is co-located, Whisper can be added to the Docker Compose stack with GPU passthrough, and the video files can be mounted directly. The pipeline should be designed so this migration is a config change, not a rewrite.\n\n### 10.4 Network considerations\n\n- Desktop ↔ Hypervisor: 2.5GbE (ample for transcript JSON transfer)\n- Hypervisor ↔ DGX Sparks: Internet (50Mbit up from Chrysopedia side, 2Gb fiber on the DGX side). Transcript text payloads are tiny; this is not a bottleneck.\n- Web UI: Served from hypervisor, accessed via local network (same machine Alt+Tab) or from other devices on the network. Eventually shareable with external users.\n\n---\n\n## 11. Technology recommendations\n\nThese are recommendations, not mandates. The implementing agent should evaluate alternatives based on current best practices and XPLTD Lore.\n\n| Component | Recommendation | Rationale |\n|-----------|---------------|-----------|\n| Transcription | Whisper large-v3 (local, 4090) | Best accuracy, local processing keeps media files on-network |\n| LLM inference | Qwen via Open WebUI API (DGX Sparks) | Free, powerful, high uptime. Ollama on 4090 as fallback |\n| Embedding | nomic-embed-text via Ollama (local) | Good quality, runs easily alongside other local models |\n| Vector DB | Qdrant | Already running on hypervisor |\n| Relational DB | PostgreSQL | Robust, good JSONB support for flexible schema fields |\n| API framework | FastAPI (Python) | Strong async support, good for pipeline orchestration |\n| Web UI | React or Svelte SPA | Fast, component-based, good for search-heavy UIs |\n| Background jobs | Celery with Redis, or a simpler task queue | Pipeline stages 2-5 run as background jobs |\n| Audio extraction | ffmpeg | Universal, reliable |\n\n---\n\n## 12. Open questions and future considerations\n\nThese items are explicitly out of scope for v1 but should be considered in architectural decisions:\n\n### 12.1 Chat / RAG retrieval\n\nNot required for v1, but the system should be **architected to support it easily.** The Qdrant embeddings and structured knowledge base provide the foundation. A future chat interface could use the Qwen instance (or any compatible LLM) with RAG over the Chrysopedia knowledge base to answer natural language questions like \"How does Skope approach snare design differently from Au5?\"\n\n### 12.2 Direct video playback\n\nv1 provides file paths and timestamps (\"Skope — Sound Design Masterclass pt2.mp4 @ 1:42:30\"). Future versions could embed video playback directly in the web UI, jumping to the exact timestamp. This requires the video files to be network-accessible from the web UI, which depends on centralizing storage.\n\n### 12.3 Access control\n\nNot needed for v1. The system is initially for personal/local use. Future versions may add authentication for sharing with friends or external users. The architecture should not preclude this (e.g., don't hardcode single-user assumptions into the data model).\n\n### 12.4 Multi-user features\n\nEventually: user-specific bookmarks, personal notes on technique pages, view history, and personalized \"trending\" based on individual usage patterns.\n\n### 12.5 Content types beyond video\n\nThe extraction pipeline is fundamentally transcript-based. It could be extended to process podcast episodes, audio-only recordings, or even written tutorials/blog posts with minimal architectural changes.\n\n### 12.6 Plugin knowledge base\n\nPlugins referenced across all technique pages could be promoted to a first-class entity with their own browse page: \"All techniques that reference Serum\" or \"Signal chains using Pro-Q 3.\" The data model already captures plugin references — this is primarily a UI feature.\n\n---\n\n## 13. Success criteria\n\nThe system is successful when:\n\n1. **A producer mid-session can find a specific technique in under 30 seconds** — from Alt+Tab to reading the key insight\n2. **The extraction pipeline correctly identifies 80%+ of key moments** without human intervention (post-calibration)\n3. **New content can be added and processed within hours**, not days\n4. **The knowledge base grows more useful over time** — cross-references and related techniques create a web of connected knowledge that surfaces unexpected insights\n5. **The system runs reliably on existing infrastructure** without requiring significant new hardware or ongoing cloud costs\n\n---\n\n## 14. Implementation phases\n\n### Phase 1: Foundation\n- Set up Docker Compose project with PostgreSQL, API service, and web UI skeleton\n- Implement Whisper transcription script for desktop\n- Build transcript ingestion endpoint on the API\n- Implement basic Creator and Source Video management\n\n### Phase 2: Extraction pipeline\n- Implement stages 2-5 (segmentation, extraction, classification, synthesis)\n- Build the review queue UI\n- Process a small batch of videos (5-10) for calibration\n- Tune extraction prompts based on review feedback\n\n### Phase 3: Knowledge UI\n- Build the search-first web UI: landing page, live search, technique pages\n- Implement Qdrant integration for semantic search\n- Build Creators and Topics browse pages\n- Implement related technique cross-linking\n\n### Phase 4: Initial seeding\n- Process the full video library through the pipeline\n- Review and approve extractions (transitioning toward auto mode)\n- Populate the canonical tag list and genre taxonomy\n- Build out cross-references and related technique links\n\n### Phase 5: Polish and ongoing\n- Transition to auto mode for new content\n- Implement view count tracking\n- Optimize search ranking and relevance\n- Begin sharing with trusted external users\n\n---\n\n*This specification was developed through collaborative ideation between the project owner and Claude. The implementing agent should treat this as a comprehensive guide while exercising judgment on technical implementation details, consulting XPLTD Lore for infrastructure conventions, and adapting to discoveries made during development.*\n"}],"isError":false,"timestamp":1774822823088}}
{"type":"message","id":"44d986dd","parentId":"809e9291","timestamp":"2026-03-29T22:20:23.092Z","message":{"role":"toolResult","toolCallId":"toolu_013sFpSxeUyXcHM3YH7JvzhV","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\nclass SourceVideoRead(SourceVideoBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    processing_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TranscriptSegment ────────────────────────────────────────────────────────\n\nclass TranscriptSegmentBase(BaseModel):\n    start_time: float\n    end_time: float\n    text: str\n    segment_index: int\n    topic_label: str | None = None\n\nclass TranscriptSegmentCreate(TranscriptSegmentBase):\n    source_video_id: uuid.UUID\n\nclass TranscriptSegmentRead(TranscriptSegmentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n\n\n# ── KeyMoment ────────────────────────────────────────────────────────────────\n\nclass KeyMomentBase(BaseModel):\n    title: str\n    summary: str\n    start_time: float\n    end_time: float\n    content_type: str\n    plugins: list[str] | None = None\n    raw_transcript: str | None = None\n\nclass KeyMomentCreate(KeyMomentBase):\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n\nclass KeyMomentRead(KeyMomentBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    source_video_id: uuid.UUID\n    technique_page_id: uuid.UUID | None = None\n    review_status: str = \"pending\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── TechniquePage ────────────────────────────────────────────────────────────\n\nclass TechniquePageBase(BaseModel):\n    title: str\n    slug: str\n    topic_category: str\n    topic_tags: list[str] | None = None\n    summary: str | None = None\n    body_sections: dict | None = None\n    signal_chains: list | None = None\n    plugins: list[str] | None = None\n\nclass TechniquePageCreate(TechniquePageBase):\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n\nclass TechniquePageRead(TechniquePageBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n    creator_id: uuid.UUID\n    source_quality: str | None = None\n    view_count: int = 0\n    review_status: str = \"draft\"\n    created_at: datetime\n    updated_at: datetime\n\n\n# ── RelatedTechniqueLink ─────────────────────────────────────────────────────\n\nclass RelatedTechniqueLinkBase(BaseModel):\n    source_page_id: uuid.UUID\n    target_page_id: uuid.UUID\n    relationship: str\n\nclass RelatedTechniqueLinkCreate(RelatedTechniqueLinkBase):\n    pass\n\nclass RelatedTechniqueLinkRead(RelatedTechniqueLinkBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Tag ──────────────────────────────────────────────────────────────────────\n\nclass TagBase(BaseModel):\n    name: str\n    category: str\n    aliases: list[str] | None = None\n\nclass TagCreate(TagBase):\n    pass\n\nclass TagRead(TagBase):\n    model_config = ConfigDict(from_attributes=True)\n\n    id: uuid.UUID\n\n\n# ── Transcript Ingestion ─────────────────────────────────────────────────────\n\nclass TranscriptIngestResponse(BaseModel):\n    \"\"\"Response returned after successfully ingesting a transcript.\"\"\"\n    video_id: uuid.UUID\n    creator_id: uuid.UUID\n    creator_name: str\n    filename: str\n    segments_stored: int\n    processing_status: str\n    is_reupload: bool\n\n\n# ── Pagination wrapper ───────────────────────────────────────────────────────\n\nclass PaginatedResponse(BaseModel):\n    \"\"\"Generic paginated list response.\"\"\"\n    items: list = Field(default_factory=list)\n    total: int = 0\n    offset: int = 0\n    limit: int = 50\n"}],"isError":false,"timestamp":1774822823088}}
{"type":"message","id":"d66db9c0","parentId":"44d986dd","timestamp":"2026-03-29T22:20:23.099Z","message":{"role":"toolResult","toolCallId":"toolu_01ByDR6sAiwj4M53C33sr6Aa","toolName":"bash","content":[{"type":"text","text":"\"\"\"Transcript ingestion endpoint for the Chrysopedia API.\n\nAccepts a Whisper-format transcript JSON via multipart file upload, finds or\ncreates a Creator, upserts a SourceVideo, bulk-inserts TranscriptSegments,\npersists the raw JSON to disk, and returns a structured response.\n\"\"\"\n\nimport json\nimport logging\nimport os\nimport re\nimport uuid\n\nfrom fastapi import APIRouter, Depends, HTTPException, UploadFile\nfrom sqlalchemy import delete, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom config import get_settings\nfrom database import get_session\nfrom models import ContentType, Creator, ProcessingStatus, SourceVideo, TranscriptSegment\nfrom schemas import TranscriptIngestResponse\n\nlogger = logging.getLogger(\"chrysopedia.ingest\")\n\nrouter = APIRouter(prefix=\"/ingest\", tags=[\"ingest\"])\n\nREQUIRED_KEYS = {\"source_file\", \"creator_folder\", \"duration_seconds\", \"segments\"}\n\n\ndef slugify(value: str) -> str:\n    \"\"\"Lowercase, replace non-alphanumeric chars with hyphens, collapse/strip.\"\"\"\n    value = value.lower()\n    value = re.sub(r\"[^a-z0-9]+\", \"-\", value)\n    value = value.strip(\"-\")\n    value = re.sub(r\"-{2,}\", \"-\", value)\n    return value\n\n\n@router.post(\"\", response_model=TranscriptIngestResponse)\nasync def ingest_transcript(\n    file: UploadFile,\n    db: AsyncSession = Depends(get_session),\n) -> TranscriptIngestResponse:\n    \"\"\"Ingest a Whisper transcript JSON file.\n\n    Workflow:\n      1. Parse and validate the uploaded JSON.\n      2. Find-or-create a Creator by folder_name.\n      3. Upsert a SourceVideo by (creator_id, filename).\n      4. Bulk-insert TranscriptSegment rows.\n      5. Save raw JSON to transcript_storage_path.\n      6. Return structured response.\n    \"\"\"\n    settings = get_settings()\n\n    # ── 1. Read & parse JSON ─────────────────────────────────────────────\n    try:\n        raw_bytes = await file.read()\n        raw_text = raw_bytes.decode(\"utf-8\")\n    except Exception as exc:\n        raise HTTPException(status_code=400, detail=f\"Invalid file: {exc}\") from exc\n\n    try:\n        data = json.loads(raw_text)\n    except json.JSONDecodeError as exc:\n        raise HTTPException(\n            status_code=422, detail=f\"JSON parse error: {exc}\"\n        ) from exc\n\n    if not isinstance(data, dict):\n        raise HTTPException(status_code=422, detail=\"Expected a JSON object at the top level\")\n\n    missing = REQUIRED_KEYS - data.keys()\n    if missing:\n        raise HTTPException(\n            status_code=422,\n            detail=f\"Missing required keys: {', '.join(sorted(missing))}\",\n        )\n\n    source_file: str = data[\"source_file\"]\n    creator_folder: str = data[\"creator_folder\"]\n    duration_seconds: int | None = data.get(\"duration_seconds\")\n    segments_data: list = data[\"segments\"]\n\n    if not isinstance(segments_data, list):\n        raise HTTPException(status_code=422, detail=\"'segments' must be an array\")\n\n    # ── 2. Find-or-create Creator ────────────────────────────────────────\n    stmt = select(Creator).where(Creator.folder_name == creator_folder)\n    result = await db.execute(stmt)\n    creator = result.scalar_one_or_none()\n\n    if creator is None:\n        creator = Creator(\n            name=creator_folder,\n            slug=slugify(creator_folder),\n            folder_name=creator_folder,\n        )\n        db.add(creator)\n        await db.flush()  # assign id\n\n    # ── 3. Upsert SourceVideo ────────────────────────────────────────────\n    stmt = select(SourceVideo).where(\n        SourceVideo.creator_id == creator.id,\n        SourceVideo.filename == source_file,\n    )\n    result = await db.execute(stmt)\n    existing_video = result.scalar_one_or_none()\n\n    is_reupload = existing_video is not None\n\n    if is_reupload:\n        video = existing_video\n        # Delete old segments for idempotent re-upload\n        await db.execute(\n            delete(TranscriptSegment).where(\n                TranscriptSegment.source_video_id == video.id\n            )\n        )\n        video.duration_seconds = duration_seconds\n        video.processing_status = ProcessingStatus.transcribed\n    else:\n        video = SourceVideo(\n            creator_id=creator.id,\n            filename=source_file,\n            file_path=f\"{creator_folder}/{source_file}\",\n            duration_seconds=duration_seconds,\n            content_type=ContentType.tutorial,\n            processing_status=ProcessingStatus.transcribed,\n        )\n        db.add(video)\n        await db.flush()  # assign id\n\n    # ── 4. Bulk-insert TranscriptSegments ────────────────────────────────\n    segment_objs = [\n        TranscriptSegment(\n            source_video_id=video.id,\n            start_time=float(seg[\"start\"]),\n            end_time=float(seg[\"end\"]),\n            text=str(seg[\"text\"]),\n            segment_index=idx,\n        )\n        for idx, seg in enumerate(segments_data)\n    ]\n    db.add_all(segment_objs)\n\n    # ── 5. Save raw JSON to disk ─────────────────────────────────────────\n    transcript_dir = os.path.join(\n        settings.transcript_storage_path, creator_folder\n    )\n    transcript_path = os.path.join(transcript_dir, f\"{source_file}.json\")\n\n    try:\n        os.makedirs(transcript_dir, exist_ok=True)\n        with open(transcript_path, \"w\", encoding=\"utf-8\") as f:\n            f.write(raw_text)\n    except OSError as exc:\n        raise HTTPException(\n            status_code=500, detail=f\"Failed to save transcript: {exc}\"\n        ) from exc\n\n    video.transcript_path = transcript_path\n\n    # ── 6. Commit & respond ──────────────────────────────────────────────\n    try:\n        await db.commit()\n    except Exception as exc:\n        await db.rollback()\n        logger.error(\"Database commit failed during ingest: %s\", exc)\n        raise HTTPException(\n            status_code=500, detail=\"Database error during ingest\"\n        ) from exc\n\n    await db.refresh(video)\n    await db.refresh(creator)\n\n    logger.info(\n        \"Ingested transcript: creator=%s, file=%s, segments=%d, reupload=%s\",\n        creator.name,\n        source_file,\n        len(segment_objs),\n        is_reupload,\n    )\n\n    return TranscriptIngestResponse(\n        video_id=video.id,\n        creator_id=creator.id,\n        creator_name=creator.name,\n        filename=source_file,\n        segments_stored=len(segment_objs),\n        processing_status=video.processing_status.value,\n        is_reupload=is_reupload,\n    )\n"}],"isError":false,"timestamp":1774822823097}}
{"type":"message","id":"1b0ba7c3","parentId":"d66db9c0","timestamp":"2026-03-29T22:20:28.227Z","message":{"role":"assistant","content":[{"type":"text","text":"Excellent. Now let me check the existing .env.example, Alembic migration, sample transcript, and explore what else exists:"},{"type":"toolCall","id":"toolu_013cXxqUSBmYzP8dZNb2xtpf","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/.env.example"}},{"type":"toolCall","id":"toolu_01MaXWdzfKE81LKEzEVDdKQb","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/alembic/versions/001_initial.py"}},{"type":"toolCall","id":"toolu_019TpuQhkeMqxnbbCcSh3tqH","name":"bash","arguments":{"command":"cat /home/aux/projects/content-to-kb-automator/backend/tests/fixtures/sample_transcript.json"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":225,"cacheRead":83782,"cacheWrite":12803,"totalTokens":96811,"cost":{"input":0.000005,"output":0.005625000000000001,"cacheRead":0.041891,"cacheWrite":0.08001875,"total":0.12753975}},"stopReason":"toolUse","timestamp":1774822823098}}
{"type":"message","id":"bc96386b","parentId":"1b0ba7c3","timestamp":"2026-03-29T22:20:28.242Z","message":{"role":"toolResult","toolCallId":"toolu_013cXxqUSBmYzP8dZNb2xtpf","toolName":"bash","content":[{"type":"text","text":"# ─── Chrysopedia Environment Variables ───\n\n# PostgreSQL\nPOSTGRES_USER=chrysopedia\nPOSTGRES_PASSWORD=changeme\nPOSTGRES_DB=chrysopedia\nDATABASE_URL=postgresql+asyncpg://${POSTGRES_USER}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB}\n\n# Redis (Celery broker)\nREDIS_URL=redis://chrysopedia-redis:6379/0\n\n# LLM endpoint (OpenAI-compatible)\nLLM_API_URL=https://friend-openwebui.example.com/api\nLLM_API_KEY=sk-changeme\nLLM_MODEL=qwen2.5-72b\nLLM_FALLBACK_URL=http://localhost:11434/v1\nLLM_FALLBACK_MODEL=qwen2.5:14b-q8_0\n\n# Embedding endpoint\nEMBEDDING_API_URL=http://localhost:11434/v1\nEMBEDDING_MODEL=nomic-embed-text\n\n# Qdrant\nQDRANT_URL=http://qdrant:6333\nQDRANT_COLLECTION=chrysopedia\n\n# Application\nAPP_ENV=production\nAPP_LOG_LEVEL=info\nAPP_SECRET_KEY=changeme-generate-a-real-secret\n\n# File storage paths (inside container)\nTRANSCRIPT_STORAGE_PATH=/data/transcripts\nVIDEO_METADATA_PATH=/data/video_meta\n\n# Review mode toggle\nREVIEW_MODE=true\n"}],"isError":false,"timestamp":1774822828242}}
{"type":"message","id":"e507c47c","parentId":"bc96386b","timestamp":"2026-03-29T22:20:28.242Z","message":{"role":"toolResult","toolCallId":"toolu_01MaXWdzfKE81LKEzEVDdKQb","toolName":"bash","content":[{"type":"text","text":"\"\"\"initial schema — 7 core entities\n\nRevision ID: 001_initial\nRevises:\nCreate Date: 2026-03-29\n\"\"\"\nfrom typing import Sequence, Union\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects.postgresql import ARRAY, JSONB, UUID\n\n# revision identifiers, used by Alembic.\nrevision: str = \"001_initial\"\ndown_revision: Union[str, None] = None\nbranch_labels: Union[str, Sequence[str], None] = None\ndepends_on: Union[str, Sequence[str], None] = None\n\n\ndef upgrade() -> None:\n    # ── Enum types ───────────────────────────────────────────────────────\n    content_type = sa.Enum(\n        \"tutorial\", \"livestream\", \"breakdown\", \"short_form\",\n        name=\"content_type\",\n    )\n    processing_status = sa.Enum(\n        \"pending\", \"transcribed\", \"extracted\", \"reviewed\", \"published\",\n        name=\"processing_status\",\n    )\n    key_moment_content_type = sa.Enum(\n        \"technique\", \"settings\", \"reasoning\", \"workflow\",\n        name=\"key_moment_content_type\",\n    )\n    review_status = sa.Enum(\n        \"pending\", \"approved\", \"edited\", \"rejected\",\n        name=\"review_status\",\n    )\n    source_quality = sa.Enum(\n        \"structured\", \"mixed\", \"unstructured\",\n        name=\"source_quality\",\n    )\n    page_review_status = sa.Enum(\n        \"draft\", \"reviewed\", \"published\",\n        name=\"page_review_status\",\n    )\n    relationship_type = sa.Enum(\n        \"same_technique_other_creator\", \"same_creator_adjacent\", \"general_cross_reference\",\n        name=\"relationship_type\",\n    )\n\n    # ── creators ─────────────────────────────────────────────────────────\n    op.create_table(\n        \"creators\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"name\", sa.String(255), nullable=False),\n        sa.Column(\"slug\", sa.String(255), nullable=False, unique=True),\n        sa.Column(\"genres\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"folder_name\", sa.String(255), nullable=False),\n        sa.Column(\"view_count\", sa.Integer, nullable=False, server_default=\"0\"),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n\n    # ── source_videos ────────────────────────────────────────────────────\n    op.create_table(\n        \"source_videos\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"creator_id\", UUID(as_uuid=True), sa.ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"filename\", sa.String(500), nullable=False),\n        sa.Column(\"file_path\", sa.String(1000), nullable=False),\n        sa.Column(\"duration_seconds\", sa.Integer, nullable=True),\n        sa.Column(\"content_type\", content_type, nullable=False),\n        sa.Column(\"transcript_path\", sa.String(1000), nullable=True),\n        sa.Column(\"processing_status\", processing_status, nullable=False, server_default=\"pending\"),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n    op.create_index(\"ix_source_videos_creator_id\", \"source_videos\", [\"creator_id\"])\n\n    # ── transcript_segments ──────────────────────────────────────────────\n    op.create_table(\n        \"transcript_segments\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"source_video_id\", UUID(as_uuid=True), sa.ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"start_time\", sa.Float, nullable=False),\n        sa.Column(\"end_time\", sa.Float, nullable=False),\n        sa.Column(\"text\", sa.Text, nullable=False),\n        sa.Column(\"segment_index\", sa.Integer, nullable=False),\n        sa.Column(\"topic_label\", sa.String(255), nullable=True),\n    )\n    op.create_index(\"ix_transcript_segments_video_id\", \"transcript_segments\", [\"source_video_id\"])\n\n    # ── technique_pages (must come before key_moments due to FK) ─────────\n    op.create_table(\n        \"technique_pages\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"creator_id\", UUID(as_uuid=True), sa.ForeignKey(\"creators.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"title\", sa.String(500), nullable=False),\n        sa.Column(\"slug\", sa.String(500), nullable=False, unique=True),\n        sa.Column(\"topic_category\", sa.String(255), nullable=False),\n        sa.Column(\"topic_tags\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"summary\", sa.Text, nullable=True),\n        sa.Column(\"body_sections\", JSONB, nullable=True),\n        sa.Column(\"signal_chains\", JSONB, nullable=True),\n        sa.Column(\"plugins\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"source_quality\", source_quality, nullable=True),\n        sa.Column(\"view_count\", sa.Integer, nullable=False, server_default=\"0\"),\n        sa.Column(\"review_status\", page_review_status, nullable=False, server_default=\"draft\"),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n    op.create_index(\"ix_technique_pages_creator_id\", \"technique_pages\", [\"creator_id\"])\n    op.create_index(\"ix_technique_pages_topic_category\", \"technique_pages\", [\"topic_category\"])\n\n    # ── key_moments ──────────────────────────────────────────────────────\n    op.create_table(\n        \"key_moments\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"source_video_id\", UUID(as_uuid=True), sa.ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"technique_page_id\", UUID(as_uuid=True), sa.ForeignKey(\"technique_pages.id\", ondelete=\"SET NULL\"), nullable=True),\n        sa.Column(\"title\", sa.String(500), nullable=False),\n        sa.Column(\"summary\", sa.Text, nullable=False),\n        sa.Column(\"start_time\", sa.Float, nullable=False),\n        sa.Column(\"end_time\", sa.Float, nullable=False),\n        sa.Column(\"content_type\", key_moment_content_type, nullable=False),\n        sa.Column(\"plugins\", ARRAY(sa.String), nullable=True),\n        sa.Column(\"review_status\", review_status, nullable=False, server_default=\"pending\"),\n        sa.Column(\"raw_transcript\", sa.Text, nullable=True),\n        sa.Column(\"created_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n        sa.Column(\"updated_at\", sa.DateTime(), nullable=False, server_default=sa.func.now()),\n    )\n    op.create_index(\"ix_key_moments_source_video_id\", \"key_moments\", [\"source_video_id\"])\n    op.create_index(\"ix_key_moments_technique_page_id\", \"key_moments\", [\"technique_page_id\"])\n\n    # ── related_technique_links ──────────────────────────────────────────\n    op.create_table(\n        \"related_technique_links\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"source_page_id\", UUID(as_uuid=True), sa.ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"target_page_id\", UUID(as_uuid=True), sa.ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False),\n        sa.Column(\"relationship\", relationship_type, nullable=False),\n        sa.UniqueConstraint(\"source_page_id\", \"target_page_id\", \"relationship\", name=\"uq_technique_link\"),\n    )\n\n    # ── tags ─────────────────────────────────────────────────────────────\n    op.create_table(\n        \"tags\",\n        sa.Column(\"id\", UUID(as_uuid=True), primary_key=True, server_default=sa.text(\"gen_random_uuid()\")),\n        sa.Column(\"name\", sa.String(255), nullable=False, unique=True),\n        sa.Column(\"category\", sa.String(255), nullable=False),\n        sa.Column(\"aliases\", ARRAY(sa.String), nullable=True),\n    )\n    op.create_index(\"ix_tags_category\", \"tags\", [\"category\"])\n\n\ndef downgrade() -> None:\n    op.drop_table(\"tags\")\n    op.drop_table(\"related_technique_links\")\n    op.drop_table(\"key_moments\")\n    op.drop_table(\"technique_pages\")\n    op.drop_table(\"transcript_segments\")\n    op.drop_table(\"source_videos\")\n    op.drop_table(\"creators\")\n\n    # Drop enum types\n    for name in [\n        \"relationship_type\", \"page_review_status\", \"source_quality\",\n        \"review_status\", \"key_moment_content_type\", \"processing_status\",\n        \"content_type\",\n    ]:\n        sa.Enum(name=name).drop(op.get_bind(), checkfirst=True)\n"}],"isError":false,"timestamp":1774822828242}}
{"type":"message","id":"0b5dd5cd","parentId":"e507c47c","timestamp":"2026-03-29T22:20:28.246Z","message":{"role":"toolResult","toolCallId":"toolu_019TpuQhkeMqxnbbCcSh3tqH","toolName":"bash","content":[{"type":"text","text":"{\n  \"source_file\": \"mixing-basics-ep1.mp4\",\n  \"creator_folder\": \"Skope\",\n  \"duration_seconds\": 1234,\n  \"segments\": [\n    {\"start\": 0.0, \"end\": 5.2, \"text\": \"Welcome to mixing basics episode one.\"},\n    {\"start\": 5.2, \"end\": 12.8, \"text\": \"Today we are going to talk about gain staging.\"},\n    {\"start\": 12.8, \"end\": 20.1, \"text\": \"First thing you want to do is set your levels.\"},\n    {\"start\": 20.1, \"end\": 28.5, \"text\": \"Make sure nothing is clipping on the master bus.\"},\n    {\"start\": 28.5, \"end\": 35.0, \"text\": \"That wraps up this quick overview of gain staging.\"}\n  ]\n}\n"}],"isError":false,"timestamp":1774822828244}}
{"type":"message","id":"71d5a647","parentId":"0b5dd5cd","timestamp":"2026-03-29T22:20:36.580Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check Qdrant connectivity and understand the existing infrastructure:"},{"type":"toolCall","id":"toolu_01KUShJzob7pfNFZuC8Kmsm6","name":"bash","arguments":{"command":"# Check what's installed in the venv that's relevant to S03\ncd /home/aux/projects/content-to-kb-automator && .venv/bin/pip list 2>/dev/null | grep -iE 'qdrant|openai|celery|redis|ollama|httpx|pydantic|jinja|yaml'"}},{"type":"toolCall","id":"toolu_01KfNdSdcCfetps93XDgtaSz","name":"bash","arguments":{"command":"# Check for any existing worker/celery/pipeline files\nfind /home/aux/projects/content-to-kb-automator/backend -name \"worker*\" -o -name \"celery*\" -o -name \"pipeline*\" -o -name \"task*\" -o -name \"extract*\" 2>/dev/null | grep -v __pycache__ | grep -v .gsd"}},{"type":"toolCall","id":"toolu_015LPh16cgxwxtHMgNCLTxmR","name":"bash","arguments":{"command":"# Check Qdrant is reachable at 10.0.0.10\ncurl -s --connect-timeout 3 http://10.0.0.10:6333/healthz 2>/dev/null || echo \"Qdrant not reachable at 10.0.0.10:6333\"\n# Also check localhost\ncurl -s --connect-timeout 3 http://localhost:6333/healthz 2>/dev/null || echo \"Qdrant not reachable at localhost:6333\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":431,"cacheRead":96585,"cacheWrite":3577,"totalTokens":100594,"cost":{"input":0.000005,"output":0.010775,"cacheRead":0.048292499999999995,"cacheWrite":0.02235625,"total":0.08142875}},"stopReason":"toolUse","timestamp":1774822828245}}
{"type":"message","id":"ac2a8c96","parentId":"71d5a647","timestamp":"2026-03-29T22:20:36.888Z","message":{"role":"toolResult","toolCallId":"toolu_01KUShJzob7pfNFZuC8Kmsm6","toolName":"bash","content":[{"type":"text","text":"celery            5.6.3\nhttpx             0.28.1\npydantic          2.12.5\npydantic_core     2.41.5\npydantic-settings 2.13.1\nPyYAML            6.0.3\nredis             5.3.1\n"}],"isError":false,"timestamp":1774822836887}}
{"type":"message","id":"cc5c4564","parentId":"ac2a8c96","timestamp":"2026-03-29T22:20:36.889Z","message":{"role":"toolResult","toolCallId":"toolu_01KfNdSdcCfetps93XDgtaSz","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774822836887}}
{"type":"message","id":"2ba3cb48","parentId":"cc5c4564","timestamp":"2026-03-29T22:20:36.891Z","message":{"role":"toolResult","toolCallId":"toolu_015LPh16cgxwxtHMgNCLTxmR","toolName":"bash","content":[{"type":"text","text":"Qdrant not reachable at 10.0.0.10:6333\nQdrant not reachable at localhost:6333\n"}],"isError":false,"timestamp":1774822836888}}
{"type":"message","id":"1dbe5df2","parentId":"2ba3cb48","timestamp":"2026-03-29T22:20:42.793Z","message":{"role":"assistant","content":[{"type":"text","text":"Qdrant isn't reachable from this environment (expected — we're not on the hypervisor). No worker/pipeline/celery files exist yet — this is all greenfield. Let me check what libraries are needed:"},{"type":"toolCall","id":"toolu_01Pjs9VVZF63ZuLpdVkfgW1K","name":"bash","arguments":{"command":"# Check if qdrant-client, openai are available\ncd /home/aux/projects/content-to-kb-automator && .venv/bin/pip list 2>/dev/null | grep -iE 'qdrant|openai'"}},{"type":"toolCall","id":"toolu_01R1bR1VAibhU3Fs9UZLfy9h","name":"bash","arguments":{"command":"# Check the docker compose for worker config details\ngrep -A 20 \"chrysopedia-worker\" /home/aux/projects/content-to-kb-automator/docker-compose.yml"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":238,"cacheRead":100162,"cacheWrite":633,"totalTokens":101034,"cost":{"input":0.000005,"output":0.00595,"cacheRead":0.050081,"cacheWrite":0.0039562500000000006,"total":0.059992250000000004}},"stopReason":"toolUse","timestamp":1774822836889}}
{"type":"message","id":"7ddcf8a2","parentId":"1dbe5df2","timestamp":"2026-03-29T22:20:43.113Z","message":{"role":"toolResult","toolCallId":"toolu_01Pjs9VVZF63ZuLpdVkfgW1K","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1774822843113}}
{"type":"message","id":"42d285cb","parentId":"7ddcf8a2","timestamp":"2026-03-29T22:20:43.116Z","message":{"role":"toolResult","toolCallId":"toolu_01R1bR1VAibhU3Fs9UZLfy9h","toolName":"bash","content":[{"type":"text","text":"  chrysopedia-worker:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.api\n    container_name: chrysopedia-worker\n    restart: unless-stopped\n    env_file:\n      - path: .env\n        required: false\n    environment:\n      DATABASE_URL: postgresql+asyncpg://${POSTGRES_USER:-chrysopedia}:${POSTGRES_PASSWORD}@chrysopedia-db:5432/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n    command: [\"celery\", \"-A\", \"worker\", \"worker\", \"--loglevel=info\"]\n    volumes:\n      - ./backend:/app\n      - /vmPool/r/services/chrysopedia_data:/data\n      - ./prompts:/prompts:ro\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n\n"}],"isError":false,"timestamp":1774822843113}}
{"type":"message","id":"faa794a3","parentId":"42d285cb","timestamp":"2026-03-29T22:20:47.224Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look up the key libraries we'll need — `qdrant-client` and `openai` Python SDK:"},{"type":"toolCall","id":"toolu_01PXfhgqQJ7DYoNpKoZYcF8s","name":"resolve_library","arguments":{"libraryName":"qdrant-client","query":"create collection upsert points search with embeddings python"}},{"type":"toolCall","id":"toolu_01Phi3oLNaDGkSVNmGoCMjAz","name":"resolve_library","arguments":{"libraryName":"openai python","query":"chat completions structured output json mode"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":177,"cacheRead":100795,"cacheWrite":554,"totalTokens":101527,"cost":{"input":0.000005,"output":0.004425,"cacheRead":0.0503975,"cacheWrite":0.0034625000000000003,"total":0.05829}},"stopReason":"toolUse","timestamp":1774822843114}}
{"type":"message","id":"a0de08d0","parentId":"faa794a3","timestamp":"2026-03-29T22:20:48.300Z","message":{"role":"toolResult","toolCallId":"toolu_01PXfhgqQJ7DYoNpKoZYcF8s","toolName":"resolve_library","content":[{"type":"text","text":"Found 5 libraries matching \"qdrant-client\":\n\n• Qdrant Client  (ID: /qdrant/qdrant-client)\n  The Qdrant Python Client is a library for interacting with the Qdrant vector search engine, supporting both synchronous and asynchronous requests, local mode, and REST/gRPC communication.\n  trust: 9.8/10 · benchmark: 71.0 · 114 snippets · 24k tokens · updated: 2026-03-25\n• Qdrant Client  (ID: /websites/rs_qdrant-client_qdrant_client)\n  Qdrant Client is a Rust library that provides an easy-to-use API interface to connect to and interact with Qdrant vector search servers over gRPC.\n  trust: 9.7/10 · benchmark: 73.8 · 10,967 snippets · 1677k tokens · updated: 2026-02-15\n• Qdrant Rust Client  (ID: /qdrant/rust-client)\n  The official Rust client for Qdrant, a high-performance vector search engine, enabling efficient interaction with Qdrant for scalable vector search applications.\n  trust: 9.8/10 · benchmark: 89.6 · 40 snippets · 13k tokens · updated: 2026-02-05\n• Qdrant Go Client  (ID: /websites/pkg_go_dev_github_com_qdrant_go-client_v1_16_2)\n  A Go client library for the Qdrant vector search engine that provides utilities for creating collections, upserting vectors, and performing similarity searches with filtering capabilities.\n  trust: 10/10 · benchmark: 67.3 · 722 snippets · 118k tokens · updated: 2025-12-21\n• Qdrant  (ID: /websites/qdrant_tech)\n  Qdrant is an AI-native vector database and semantic search engine that extracts meaningful information from unstructured data using vector search capabilities.\n  trust: 10/10 · benchmark: 88.1 · 5,682 snippets · 1068k tokens · updated: 2026-03-25\n\nUse the ID (e.g. /websites/react_dev) with get_library_docs to fetch documentation."}],"details":{"query":"qdrant-client","resultCount":5,"cached":false},"isError":false,"timestamp":1774822848300}}
{"type":"message","id":"121a822a","parentId":"a0de08d0","timestamp":"2026-03-29T22:20:48.342Z","message":{"role":"toolResult","toolCallId":"toolu_01Phi3oLNaDGkSVNmGoCMjAz","toolName":"resolve_library","content":[{"type":"text","text":"Found 5 libraries matching \"openai python\":\n\n• OpenAI Python  (ID: /openai/openai-python)\n  The OpenAI Python library provides convenient access to the OpenAI REST API from any Python 3.8+ application, offering both synchronous and asynchronous clients.\n  trust: 9.1/10 · benchmark: 79.7 · 348 snippets · 62k tokens · updated: 2026-03-26\n• OpenAI API  (ID: /websites/developers_openai_api)\n  Provides comprehensive guides, concepts, and API reference for developers to build applications using OpenAI's artificial intelligence models and services.\n  trust: 9.4/10 · benchmark: 71.2 · 8,495 snippets · 1742k tokens · updated: 2026-03-25\n• OpenAI Developers  (ID: /websites/developers_openai)\n  OpenAI Developers is a platform providing documentation, APIs, and tools for building AI applications with OpenAI's models, including Codex, ChatGPT integration, and the Apps SDK.\n  trust: 9.4/10 · benchmark: 66.7 · 41,252 snippets · 7521k tokens · updated: 2026-03-05\n• OpenAI Cookbook  (ID: /openai/openai-cookbook)\n  Example code and guides for accomplishing common tasks with the OpenAI API, written primarily in Python.\n  trust: 9.1/10 · benchmark: 75.3 · 3,591 snippets · 1199k tokens · updated: 2026-03-25\n• OpenAI Guardrails Python  (ID: /openai/openai-guardrails-python)\n  OpenAI Guardrails Python is a package for adding configurable safety and compliance guardrails to LLM applications, providing a drop-in wrapper for OpenAI's Python client for automatic input/output validation and moderation.\n  trust: 9.1/10 · benchmark: 69.2 · 83 snippets · 14k tokens · updated: 2025-10-14\n\nUse the ID (e.g. /websites/react_dev) with get_library_docs to fetch documentation."}],"details":{"query":"openai python","resultCount":5,"cached":false},"isError":false,"timestamp":1774822848341}}
{"type":"message","id":"63a97203","parentId":"121a822a","timestamp":"2026-03-29T22:20:55.055Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017pBXBArjUyTV5wRhtAUKFx","name":"get_library_docs","arguments":{"libraryId":"/qdrant/qdrant-client","query":"create collection upsert points search with embeddings async python","tokens":5000}},{"type":"toolCall","id":"toolu_01QwPWdw7Gupi7i4Bc1zEzbd","name":"get_library_docs","arguments":{"libraryId":"/openai/openai-python","query":"chat completions async client structured output json response_format","tokens":5000}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":204,"cacheRead":101349,"cacheWrite":1335,"totalTokens":102889,"cost":{"input":0.000005,"output":0.0051,"cacheRead":0.0506745,"cacheWrite":0.00834375,"total":0.06412324999999999}},"stopReason":"toolUse","timestamp":1774822848341}}
{"type":"message","id":"5c4e3e40","parentId":"63a97203","timestamp":"2026-03-29T22:20:56.601Z","message":{"role":"toolResult","toolCallId":"toolu_017pBXBArjUyTV5wRhtAUKFx","toolName":"get_library_docs","content":[{"type":"text","text":"### Async Operations: Create Collection and Upsert\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/docs/source/index.rst\n\nProvides an example of asynchronous operations for creating a collection and upserting points using the AsyncQdrantClient. It includes necessary imports and an asyncio event loop.\n\n```python\nfrom qdrant_client import AsyncQdrantClient, models\nimport numpy as np\nimport asyncio\n\nasync def main():\n   client = AsyncQdrantClient(url=\"http://localhost:6333\")\n\n   if not await client.collection_exists(\"my_collection\"):\n      await client.create_collection(\n         collection_name=\"my_collection\",\n         vectors_config=models.VectorParams(size=10, distance=models.Distance.COSINE),\n      )\n\n   await client.upsert(\n      collection_name=\"my_collection\",\n      points=[\n            models.PointStruct(\n               id=i,\n               vector=np.random.rand(10).tolist(),\n            )\n            for i in range(100)\n      ],\n   )\n\n   res = await client.search(\n      collection_name=\"my_collection\",\n      query_vector=np.random.rand(10).tolist(),  # type: ignore\n      limit=10,\n   )\n\n   print(res)\n\nasyncio.run(main())\n```\n\n--------------------------------\n\n### Async Qdrant Client Operations in Python\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/README.md\n\nDemonstrates how to initialize and use the `AsyncQdrantClient` for asynchronous interactions with a Qdrant instance. This includes creating a collection, upserting points, and querying for similar points. Requires the `qdrant-client` and `numpy` libraries.\n\n```python\nimport asyncio\n\nimport numpy as np\n\nfrom qdrant_client import AsyncQdrantClient, models\n\n\nasync def main():\n    # Your async code using QdrantClient might be put here\n    client = AsyncQdrantClient(url=\"http://localhost:6333\")\n\n    await client.create_collection(\n        collection_name=\"my_collection\",\n        vectors_config=models.VectorParams(size=10, distance=models.Distance.COSINE),\n    )\n\n    await client.upsert(\n        collection_name=\"my_collection\",\n        points=[\n            models.PointStruct(\n                id=i,\n                vector=np.random.rand(10).tolist(),\n            )\n            for i in range(100)\n        ],\n    )\n\n    res = await client.query_points(\n        collection_name=\"my_collection\",\n        query=np.random.rand(10).tolist(),  # type: ignore\n        limit=10,\n    )\n\n    print(res)\n\nasyncio.run(main())\n```\n\n--------------------------------\n\n### Upsert Points into Collection\n\nSource: https://context7.com/qdrant/qdrant-client/llms.txt\n\nInserts or updates vectors and associated payloads into a specified collection, supporting both integer and string IDs.\n\n```python\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import PointStruct, VectorParams, Distance\n\nclient = QdrantClient(\":memory:\")\nclient.create_collection(\"products\", vectors_config=VectorParams(size=4, distance=Distance.COSINE))\n\nclient.upsert(\n    collection_name=\"products\",\n    points=[\n        PointStruct(id=1, vector=[0.1, 0.2, 0.3, 0.4], payload={\"name\": \"Laptop\", \"price\": 999.99, \"category\": \"electronics\"}),\n        PointStruct(id=2, vector=[0.2, 0.3, 0.4, 0.5], payload={\"name\": \"Phone\", \"price\": 599.99, \"category\": \"electronics\"}),\n        PointStruct(id=\"uuid-3\", vector=[0.3, 0.4, 0.5, 0.6], payload={\"name\": \"Tablet\", \"price\": 399.99, \"category\": \"electronics\"})\n    ],\n    wait=True\n)\n```\n\n--------------------------------\n\n### POST /upsert\n\nSource: https://context7.com/qdrant/qdrant-client/llms.txt\n\nInserts or updates points (vectors with optional payloads) in a collection. If a point with the same ID exists, it will be overwritten.\n\n```APIDOC\n## POST /upsert\n\n### Description\nInserts or updates points (vectors with optional payloads) in a collection. If a point with the same ID exists, it will be overwritten.\n\n### Method\nPOST\n\n### Endpoint\n/collections/{collection_name}/points\n\n### Parameters\n#### Path Parameters\n- **collection_name** (string) - Required - The name of the collection to upsert points into.\n\n#### Request Body\n- **points** (array of PointStruct) - Required - A list of points to insert or update.\n- **wait** (boolean) - Optional - If true, the operation will wait until it is completed.\n\n### Request Example\n```python\nfrom qdrant_client import QdrantClient\nfrom qdrant_client.models import PointStruct, VectorParams, Distance\nimport numpy as np\n\nclient = QdrantClient(\":memory:\")\nclient.create_collection(\"products\", vectors_config=VectorParams(size=4, distance=Distance.COSINE))\n\n# Insert points with payloads\nclient.upsert(\n    collection_name=\"products\",\n    points=[\n        PointStruct(\n            id=1,\n            vector=[0.1, 0.2, 0.3, 0.4],\n            payload={\"name\": \"Laptop\", \"price\": 999.99, \"category\": \"electronics\"}\n        ),\n        PointStruct(\n            id=2,\n            vector=[0.2, 0.3, 0.4, 0.5],\n            payload={\"name\": \"Phone\", \"price\": 599.99, \"category\": \"electronics\"}\n        ),\n        PointStruct(\n            id=\"uuid-3\",  # String IDs supported\n            vector=[0.3, 0.4, 0.5, 0.6],\n            payload={\"name\": \"Tablet\", \"price\": 399.99, \"category\": \"electronics\"}\n        ),\n    ],\n    wait=True,  # Wait for operation to complete\n)\n```\n\n### Response\n#### Success Response (200)\n- **status** (boolean) - Indicates if the operation was successful.\n- **operation_id** (integer) - The ID of the operation.\n\n#### Response Example\n```json\n{\n  \"status\": true,\n  \"operation_id\": 12345\n}\n```\n```\n\n--------------------------------\n\n### Upsert Points\n\nSource: https://github.com/qdrant/qdrant-client/blob/master/docs/source/quickstart.ipynb\n\nInsert or update points in a collection, providing unique IDs, vectors, and optional payloads.\n\n```APIDOC\n## Upsert Points\n\n### Description\nInsert or update points in a collection, providing unique IDs, vectors, and optional payloads.\n\n### Method\n`client.upsert()`\n\n### Endpoint\nN/A\n\n### Parameters\n- `collection_name` (string) - Required - The name of the collection to upsert points into.\n- `wait` (bool) - Optional - Whether to wait for the operation to complete.\n- `points` (list[PointStruct]) - Required - A list of `PointStruct` objects to upsert.\n  - `id` (any) - Required - The unique identifier for the point.\n  - `vector` (list[float]) - Required - The vector of floats representing the point.\n  - `payload` (dict) - Optional - A dictionary of key-value pairs associated with the point.\n\n### Request Example\n```python\nfrom qdrant_client.http.models import PointStruct\n\noperation_info = client.upsert(\n    collection_name=\"test_collection\",\n    wait=True,\n    points=[\n        PointStruct(id=1, vector=[0.05, 0.61, 0.76, 0.74], payload={\"city\": \"Berlin\"}),\n        PointStruct(id=2, vector=[0.19, 0.81, 0.75, 0.11], payload={\"city\": \"London\"}),\n        PointStruct(id=3, vector=[0.36, 0.55, 0.47, 0.94], payload={\"city\": \"Moscow\"}),\n        PointStruct(id=4, vector=[0.18, 0.01, 0.85, 0.80], payload={\"city\": \"New York\"}),\n        PointStruct(id=5, vector=[0.24, 0.18, 0.22, 0.44], payload={\"city\": \"Beijing\"}),\n        PointStruct(id=6, vector=[0.35, 0.08, 0.11, 0.44], payload={\"city\": \"Mumbai\"}),\n    ]\n)\nprint(operation_info)\n```\n\n### Response\n- `OperationInfo` - Information about the upsert operation, including its status.\n```"}],"details":{"libraryId":"/qdrant/qdrant-client","query":"create collection upsert points search with embeddings async python","tokens":5000,"cached":false,"truncated":false,"charCount":7277},"isError":false,"timestamp":1774822856600}}
{"type":"message","id":"cbd34db9","parentId":"5c4e3e40","timestamp":"2026-03-29T22:20:56.604Z","message":{"role":"toolResult","toolCallId":"toolu_01QwPWdw7Gupi7i4Bc1zEzbd","toolName":"get_library_docs","content":[{"type":"text","text":"### POST /v1/chat/completions - Structured Output Parsing\n\nSource: https://context7.com/openai/openai-python/llms.txt\n\nThis example demonstrates parsing chat completion responses into Pydantic models, ensuring typed Python objects with JSON schema validation.\n\n```APIDOC\n## POST /v1/chat/completions\n\n### Description\nGenerate structured text output from OpenAI models, parsed into a Pydantic model based on a provided JSON schema.\n\n### Method\nPOST\n\n### Endpoint\n/v1/chat/completions\n\n### Parameters\n#### Request Body\n- **model** (string) - Required - The ID of the model to use (e.g., \"gpt-4o-2024-08-06\").\n- **messages** (array of objects) - Required - A list of messages comprising the conversation so far.\n    - **role** (string) - Required - The role of the author of this message.\n    - **content** (string) - Required - The contents of the message.\n- **response_format** (object) - Required - Specifies the format of the model's output.\n    - **type** (string) - Required - Must be \"json_object\".\n    - **schema** (object) - Required - The JSON schema for the desired output structure.\n\n### Request Example\n```json\n{\n  \"model\": \"gpt-4o-2024-08-06\",\n  \"messages\": [\n    {\n      \"role\": \"system\",\n      \"content\": \"You are a helpful math tutor.\"\n    },\n    {\n      \"role\": \"user\",\n      \"content\": \"solve 8x + 31 = 2\"\n    }\n  ],\n  \"response_format\": {\n    \"type\": \"json_object\",\n    \"schema\": {\n      \"type\": \"object\",\n      \"properties\": {\n        \"steps\": {\n          \"type\": \"array\",\n          \"items\": {\n            \"type\": \"object\",\n            \"properties\": {\n              \"explanation\": { \"type\": \"string\" },\n              \"output\": { \"type\": \"string\" }\n            },\n            \"required\": [\"explanation\", \"output\"]\n          }\n        },\n        \"final_answer\": { \"type\": \"string\" }\n      },\n      \"required\": [\"steps\", \"final_answer\"]\n    }\n  }\n}\n```\n\n### Response\n#### Success Response (200)\n- **choices** (array of objects) - A list of completion choices.\n    - **message** (object) - The message generated by the model.\n        - **parsed** (object) - The parsed Pydantic object conforming to the provided schema.\n            - **steps** (array of objects) - Steps of the math solution.\n            - **final_answer** (string) - The final answer.\n\n#### Response Example\n```json\n{\n  \"choices\": [\n    {\n      \"message\": {\n        \"parsed\": {\n          \"steps\": [\n            {\n              \"explanation\": \"Subtract 31 from both sides\",\n              \"output\": \"8x = -29\"\n            },\n            {\n              \"explanation\": \"Divide by 8\",\n              \"output\": \"x = -29/8\"\n            }\n          ],\n          \"final_answer\": \"-29/8\"\n        }\n      }\n    }\n  ]\n}\n```\n```\n\n--------------------------------\n\n### Auto-parse Chat Completion Response with Pydantic Model in Python\n\nSource: https://github.com/openai/openai-python/blob/main/helpers.md\n\nThis snippet demonstrates how to use `client.chat.completions.parse()` with a Pydantic model to automatically convert the model into a JSON schema for the API and parse the response content back into the given model. It defines `Step` and `MathResponse` Pydantic models to structure the expected output from a math tutor AI, then calls the API and prints the parsed steps and final answer or refusal.\n\n```python\nfrom typing import List\nfrom pydantic import BaseModel\nfrom openai import OpenAI\n\nclass Step(BaseModel):\n    explanation: str\n    output: str\n\nclass MathResponse(BaseModel):\n    steps: List[Step]\n    final_answer: str\n\nclient = OpenAI()\ncompletion = client.chat.completions.parse(\n    model=\"gpt-4o-2024-08-06\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful math tutor.\"},\n        {\"role\": \"user\", \"content\": \"solve 8x + 31 = 2\"},\n    ],\n    response_format=MathResponse,\n)\n\nmessage = completion.choices[0].message\nif message.parsed:\n    print(message.parsed.steps)\n    print(\"answer: \", message.parsed.final_answer)\nelse:\n    print(message.refusal)\n```\n\n--------------------------------\n\n### Parse Structured Output with Pydantic Models\n\nSource: https://context7.com/openai/openai-python/llms.txt\n\nDemonstrates structured output parsing using Pydantic models with the .parse() method. Automatically validates responses against JSON schema and parses them into typed Python objects. Handles both successful parsing and refusal cases.\n\n```Python\nfrom typing import List\nfrom pydantic import BaseModel\nfrom openai import OpenAI\n\nclass Step(BaseModel):\n    explanation: str\n    output: str\n\nclass MathResponse(BaseModel):\n    steps: List[Step]\n    final_answer: str\n\nclient = OpenAI()\n\ncompletion = client.chat.completions.parse(\n    model=\"gpt-4o-2024-08-06\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"You are a helpful math tutor.\"},\n        {\"role\": \"user\", \"content\": \"solve 8x + 31 = 2\"},\n    ],\n    response_format=MathResponse,\n)\n\nmessage = completion.choices[0].message\nif message.parsed:\n    for step in message.parsed.steps:\n        print(f\"Step: {step.explanation} -> {step.output}\")\n    print(f\"Final Answer: {message.parsed.final_answer}\")\nelse:\n    print(f\"Refusal: {message.refusal}\")\n```\n\n### Structured Outputs Parsing Helpers\n\nSource: https://github.com/openai/openai-python/blob/main/helpers.md\n\nThe OpenAI API supports extracting JSON from the model with the `response_format` request param. The SDK provides a `client.chat.completions.parse()` method which is a wrapper over the `client.chat.completions.create()` that provides richer integrations with Python specific types and returns a `ParsedChatCompletion` object, which is a subclass of the standard `ChatCompletion` class. This method allows for automatic parsing of response content with Pydantic models, where the SDK converts the model into a JSON schema, sends it to the API, and then parses the response back into the given model. It also supports auto-parsing of `function` tool calls under specific conditions, such as using the `openai.pydantic_function_tool()` helper method and marking the tool schema with `\"strict\": True`.\n\n--------------------------------\n\n### Responses API > Streaming\n\nSource: https://context7.com/openai/openai-python/llms.txt\n\nThe Responses API supports streaming responses with event-based handling, allowing you to process model outputs as they are generated. You can define structured output formats using Pydantic models to ensure responses conform to specific schemas, and retrieve the final response object after streaming completes for accessing complete output data."}],"details":{"libraryId":"/openai/openai-python","query":"chat completions async client structured output json response_format","tokens":5000,"cached":false,"truncated":false,"charCount":6507},"isError":false,"timestamp":1774822856601}}
{"type":"message","id":"566da502","parentId":"cbd34db9","timestamp":"2026-03-29T22:21:01.236Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check for skill discovery for the key technologies:"},{"type":"toolCall","id":"toolu_01E27o4Dd3jFjN4gXxcc4WRG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && npx skills find \"celery\" 2>/dev/null | head -30"}},{"type":"toolCall","id":"toolu_01S7bUfU4pGpYJBGkmMfCFqt","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && npx skills find \"qdrant\" 2>/dev/null | head -30"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":169,"cacheRead":102684,"cacheWrite":4310,"totalTokens":107164,"cost":{"input":0.000005,"output":0.0042250000000000005,"cacheRead":0.051342,"cacheWrite":0.0269375,"total":0.0825095}},"stopReason":"toolUse","timestamp":1774822856602}}
{"type":"message","id":"22ed396d","parentId":"566da502","timestamp":"2026-03-29T22:21:03.045Z","message":{"role":"toolResult","toolCallId":"toolu_01E27o4Dd3jFjN4gXxcc4WRG","toolName":"bash","content":[{"type":"text","text":"\n\u001b[38;5;250m███████╗██╗  ██╗██╗██╗     ██╗     ███████╗\u001b[0m\n\u001b[38;5;248m██╔════╝██║ ██╔╝██║██║     ██║     ██╔════╝\u001b[0m\n\u001b[38;5;245m███████╗█████╔╝ ██║██║     ██║     ███████╗\u001b[0m\n\u001b[38;5;243m╚════██║██╔═██╗ ██║██║     ██║     ╚════██║\u001b[0m\n\u001b[38;5;240m███████║██║  ██╗██║███████╗███████╗███████║\u001b[0m\n\u001b[38;5;238m╚══════╝╚═╝  ╚═╝╚═╝╚══════╝╚══════╝╚══════╝\u001b[0m\n\n\u001b[38;5;102mInstall with\u001b[0m npx skills add <owner/repo@skill>\n\n\u001b[38;5;145mvintasoftware/django-ai-plugins@django-celery-expert\u001b[0m \u001b[36m367 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/vintasoftware/django-ai-plugins/django-celery-expert\u001b[0m\n\n\u001b[38;5;145mmartinholovsky/claude-skills-generator@celery-expert\u001b[0m \u001b[36m185 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/martinholovsky/claude-skills-generator/celery-expert\u001b[0m\n\n\u001b[38;5;145mbobmatnyc/claude-mpm-skills@celery\u001b[0m \u001b[36m143 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/bobmatnyc/claude-mpm-skills/celery\u001b[0m\n\n\u001b[38;5;145myonatangross/orchestkit@celery-advanced\u001b[0m \u001b[36m18 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/yonatangross/orchestkit/celery-advanced\u001b[0m\n\n\u001b[38;5;145mkjnez/claude-code-django@celery-patterns\u001b[0m \u001b[36m10 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/kjnez/claude-code-django/celery-patterns\u001b[0m\n\n\u001b[38;5;145mdavidcastagnetoa/skills@celery_canvas\u001b[0m \u001b[36m10 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/davidcastagnetoa/skills/celery_canvas\u001b[0m\n\n"}],"isError":false,"timestamp":1774822863044}}
{"type":"message","id":"e45a2abb","parentId":"22ed396d","timestamp":"2026-03-29T22:21:03.048Z","message":{"role":"toolResult","toolCallId":"toolu_01S7bUfU4pGpYJBGkmMfCFqt","toolName":"bash","content":[{"type":"text","text":"\n\u001b[38;5;250m███████╗██╗  ██╗██╗██╗     ██╗     ███████╗\u001b[0m\n\u001b[38;5;248m██╔════╝██║ ██╔╝██║██║     ██║     ██╔════╝\u001b[0m\n\u001b[38;5;245m███████╗█████╔╝ ██║██║     ██║     ███████╗\u001b[0m\n\u001b[38;5;243m╚════██║██╔═██╗ ██║██║     ██║     ╚════██║\u001b[0m\n\u001b[38;5;240m███████║██║  ██╗██║███████╗███████╗███████║\u001b[0m\n\u001b[38;5;238m╚══════╝╚═╝  ╚═╝╚═╝╚══════╝╚══════╝╚══════╝\u001b[0m\n\n\u001b[38;5;102mInstall with\u001b[0m npx skills add <owner/repo@skill>\n\n\u001b[38;5;145mdavila7/claude-code-templates@qdrant-vector-search\u001b[0m \u001b[36m470 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/davila7/claude-code-templates/qdrant-vector-search\u001b[0m\n\n\u001b[38;5;145mgiuseppe-trisciuoglio/developer-kit@qdrant\u001b[0m \u001b[36m339 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/giuseppe-trisciuoglio/developer-kit/qdrant\u001b[0m\n\n\u001b[38;5;145myoanbernabeu/grepai-skills@grepai-storage-qdrant\u001b[0m \u001b[36m223 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/yoanbernabeu/grepai-skills/grepai-storage-qdrant\u001b[0m\n\n\u001b[38;5;145morchestra-research/ai-research-skills@qdrant-vector-search\u001b[0m \u001b[36m80 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/orchestra-research/ai-research-skills/qdrant-vector-search\u001b[0m\n\n\u001b[38;5;145mgiuseppe-trisciuoglio/developer-kit@qdrant-vector-database-integration\u001b[0m \u001b[36m74 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/giuseppe-trisciuoglio/developer-kit/qdrant-vector-database-integration\u001b[0m\n\n\u001b[38;5;145mvm0-ai/vm0-skills@qdrant\u001b[0m \u001b[36m63 installs\u001b[0m\n\u001b[38;5;102m└ https://skills.sh/vm0-ai/vm0-skills/qdrant\u001b[0m\n\n"}],"isError":false,"timestamp":1774822863044}}
{"type":"message","id":"4d8eff6e","parentId":"e45a2abb","timestamp":"2026-03-29T22:21:08.919Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me look at the existing Celery setup in the docker compose more carefully and check if there's any existing Celery app definition:"},{"type":"toolCall","id":"toolu_01EaBDDbPKJyS3WNQhxSV82V","name":"bash","arguments":{"command":"# Check if there's a worker.py or celery_app.py anywhere\nfind /home/aux/projects/content-to-kb-automator -name \"*.py\" -not -path \"*/.venv/*\" -not -path \"*/__pycache__/*\" | xargs grep -l \"celery\\|Celery\" 2>/dev/null"}},{"type":"toolCall","id":"toolu_01LtjD6PWHjqHJNBUSKnCEdm","name":"bash","arguments":{"command":"# Check the docker dir for Dockerfiles\nfind /home/aux/projects/content-to-kb-automator/docker -type f 2>/dev/null"}},{"type":"toolCall","id":"toolu_015EJgehD829SRUi621U8BEt","name":"bash","arguments":{"command":"# Check the prompts directory structure\nls -la /home/aux/projects/content-to-kb-automator/prompts/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":300,"cacheRead":106994,"cacheWrite":1639,"totalTokens":108934,"cost":{"input":0.000005,"output":0.007500000000000001,"cacheRead":0.053496999999999996,"cacheWrite":0.010243750000000001,"total":0.07124575}},"stopReason":"toolUse","timestamp":1774822863046}}
{"type":"message","id":"97dff445","parentId":"4d8eff6e","timestamp":"2026-03-29T22:21:08.951Z","message":{"role":"toolResult","toolCallId":"toolu_01EaBDDbPKJyS3WNQhxSV82V","toolName":"bash","content":[{"type":"text","text":"(no output)\n\nCommand exited with code 123"}],"details":{},"isError":true,"timestamp":1774822868951}}
{"type":"message","id":"7e29efca","parentId":"97dff445","timestamp":"2026-03-29T22:21:08.952Z","message":{"role":"toolResult","toolCallId":"toolu_01LtjD6PWHjqHJNBUSKnCEdm","toolName":"bash","content":[{"type":"text","text":"/home/aux/projects/content-to-kb-automator/docker/Dockerfile.api\n/home/aux/projects/content-to-kb-automator/docker/Dockerfile.web\n/home/aux/projects/content-to-kb-automator/docker/nginx.conf\n"}],"isError":false,"timestamp":1774822868951}}
{"type":"message","id":"e8f00b93","parentId":"7e29efca","timestamp":"2026-03-29T22:21:08.954Z","message":{"role":"toolResult","toolCallId":"toolu_015EJgehD829SRUi621U8BEt","toolName":"bash","content":[{"type":"text","text":"total 12\ndrwxrwxr-x  2 aux aux 4096 Mar 29 21:41 .\ndrwxrwxr-x 15 aux aux 4096 Mar 29 22:19 ..\n-rw-rw-r--  1 aux aux  111 Mar 29 21:41 README.md\n"}],"isError":false,"timestamp":1774822868951}}
{"type":"message","id":"17a1c565","parentId":"e8f00b93","timestamp":"2026-03-29T22:21:12.486Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Bpq3BAmWmy6QSstRVhQ5pc","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/docker/Dockerfile.api"}},{"type":"toolCall","id":"toolu_01WcgKjd1nKvkXov5Ay44hSG","name":"bash","arguments":{"command":"# Check what the existing tests look like for patterns\ncat /home/aux/projects/content-to-kb-automator/backend/tests/conftest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":142,"cacheRead":108633,"cacheWrite":552,"totalTokens":109328,"cost":{"input":0.000005,"output":0.00355,"cacheRead":0.0543165,"cacheWrite":0.0034500000000000004,"total":0.0613215}},"stopReason":"toolUse","timestamp":1774822868952}}
{"type":"message","id":"81554434","parentId":"17a1c565","timestamp":"2026-03-29T22:21:12.498Z","message":{"role":"toolResult","toolCallId":"toolu_01Bpq3BAmWmy6QSstRVhQ5pc","toolName":"read","content":[{"type":"text","text":"FROM python:3.12-slim\n\nWORKDIR /app\n\n# System deps\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n    gcc libpq-dev \\\n    && rm -rf /var/lib/apt/lists/*\n\n# Python deps (cached layer)\nCOPY backend/requirements.txt /app/requirements.txt\nRUN pip install --no-cache-dir -r requirements.txt\n\n# Application code\nCOPY backend/ /app/\n\nEXPOSE 8000\n\nCMD [\"uvicorn\", \"main:app\", \"--host\", \"0.0.0.0\", \"--port\", \"8000\"]\n"}],"isError":false,"timestamp":1774822872498}}
{"type":"message","id":"3f12b6e0","parentId":"81554434","timestamp":"2026-03-29T22:21:12.500Z","message":{"role":"toolResult","toolCallId":"toolu_01WcgKjd1nKvkXov5Ay44hSG","toolName":"bash","content":[{"type":"text","text":"\"\"\"Shared fixtures for Chrysopedia integration tests.\n\nProvides:\n- Async SQLAlchemy engine/session against a real PostgreSQL test database\n- httpx.AsyncClient wired to the FastAPI app with dependency overrides\n- Sample transcript fixture path and temporary storage directory\n\nKey design choice: function-scoped engine with NullPool avoids asyncpg\n\"another operation in progress\" errors caused by session-scoped connection\nreuse between the ASGI test client and verification queries.\n\"\"\"\n\nimport os\nimport pathlib\n\nimport pytest\nimport pytest_asyncio\nfrom httpx import ASGITransport, AsyncClient\nfrom sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine\nfrom sqlalchemy.pool import NullPool\n\n# Ensure backend/ is on sys.path so \"from models import ...\" works\nimport sys\nsys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent))\n\nfrom database import Base, get_session  # noqa: E402\nfrom main import app  # noqa: E402\n\nTEST_DATABASE_URL = os.getenv(\n    \"TEST_DATABASE_URL\",\n    \"postgresql+asyncpg://chrysopedia:changeme@localhost:5433/chrysopedia_test\",\n)\n\n\n@pytest_asyncio.fixture()\nasync def db_engine():\n    \"\"\"Create a per-test async engine (NullPool) and create/drop all tables.\"\"\"\n    engine = create_async_engine(TEST_DATABASE_URL, echo=False, poolclass=NullPool)\n\n    # Create all tables fresh for each test\n    async with engine.begin() as conn:\n        await conn.run_sync(Base.metadata.drop_all)\n        await conn.run_sync(Base.metadata.create_all)\n\n    yield engine\n\n    # Drop all tables after test\n    async with engine.begin() as conn:\n        await conn.run_sync(Base.metadata.drop_all)\n\n    await engine.dispose()\n\n\n@pytest_asyncio.fixture()\nasync def client(db_engine, tmp_path):\n    \"\"\"Async HTTP test client wired to FastAPI with dependency overrides.\"\"\"\n    session_factory = async_sessionmaker(\n        db_engine, class_=AsyncSession, expire_on_commit=False\n    )\n\n    async def _override_get_session():\n        async with session_factory() as session:\n            yield session\n\n    # Override DB session dependency\n    app.dependency_overrides[get_session] = _override_get_session\n\n    # Override transcript_storage_path via environment variable\n    os.environ[\"TRANSCRIPT_STORAGE_PATH\"] = str(tmp_path)\n    # Clear the lru_cache so Settings picks up the new env var\n    from config import get_settings\n    get_settings.cache_clear()\n\n    transport = ASGITransport(app=app)\n    async with AsyncClient(transport=transport, base_url=\"http://testserver\") as ac:\n        yield ac\n\n    # Teardown: clean overrides and restore settings cache\n    app.dependency_overrides.clear()\n    os.environ.pop(\"TRANSCRIPT_STORAGE_PATH\", None)\n    get_settings.cache_clear()\n\n\n@pytest.fixture()\ndef sample_transcript_path() -> pathlib.Path:\n    \"\"\"Path to the sample 5-segment transcript JSON fixture.\"\"\"\n    return pathlib.Path(__file__).parent / \"fixtures\" / \"sample_transcript.json\"\n\n\n@pytest.fixture()\ndef tmp_transcript_dir(tmp_path) -> pathlib.Path:\n    \"\"\"Temporary directory for transcript storage during tests.\"\"\"\n    return tmp_path\n"}],"isError":false,"timestamp":1774822872498}}
{"type":"message","id":"e1bc988b","parentId":"3f12b6e0","timestamp":"2026-03-29T22:21:17.636Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a thorough picture. Let me check the nomic-embed-text embedding dimension:"},{"type":"toolCall","id":"toolu_01RN2cXTrCAKYu1FNi5xitDH","name":"bash","arguments":{"command":"# Check what embedding dimension nomic-embed-text uses\n# nomic-embed-text outputs 768-dimensional vectors"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":99,"cacheRead":109185,"cacheWrite":1210,"totalTokens":110495,"cost":{"input":0.000005,"output":0.002475,"cacheRead":0.054592499999999995,"cacheWrite":0.007562500000000001,"total":0.064635}},"stopReason":"toolUse","timestamp":1774822872499}}
{"type":"message","id":"2e3512df","parentId":"e1bc988b","timestamp":"2026-03-29T22:21:17.645Z","message":{"role":"toolResult","toolCallId":"toolu_01RN2cXTrCAKYu1FNi5xitDH","toolName":"bash","content":[{"type":"text","text":"(no output)"}],"isError":false,"timestamp":1774822877643}}
{"type":"message","id":"0685a55a","parentId":"2e3512df","timestamp":"2026-03-29T22:22:58.358Z","message":{"role":"assistant","content":[{"type":"text","text":"I now have all the information needed. Let me compose the research document and save it."},{"type":"toolCall","id":"toolu_01MMhBbKghXapYGFkvpXK3Gb","name":"gsd_summary_save","arguments":{"artifact_type":"RESEARCH","content":"# S03 — LLM Extraction Pipeline + Qdrant Integration — Research\n\n**Date:** 2026-03-29\n\n## Summary\n\nS03 is the highest-risk, highest-complexity slice in M001. It builds the entire LLM extraction pipeline (stages 2–5: segmentation → extraction → classification → synthesis), the Qdrant vector embedding pipeline, the Celery worker infrastructure, the prompt template system, and the config extensions for LLM/embedding endpoints. This slice touches requirements R003, R009, R011, R012, and R013.\n\nThe codebase currently has **no worker/Celery/pipeline code at all** — it's entirely greenfield for this slice. Docker Compose already declares a `chrysopedia-worker` service that runs `celery -A worker worker`, but the `worker` module doesn't exist yet. The data model is complete — `KeyMoment`, `TechniquePage`, `RelatedTechniqueLink`, and `Tag` tables with their enums (`ProcessingStatus`, `ReviewStatus`, `KeyMomentContentType`, etc.) are all defined in `models.py` and migrated via `001_initial.py`. The S02 ingest endpoint already creates `TranscriptSegment` rows and sets `processing_status = transcribed`, which is the trigger state for the pipeline.\n\nThe primary risk is LLM integration: the pipeline must talk to an OpenAI-compatible API (Qwen on DGX Sparks primary, Ollama fallback), parse structured JSON responses, handle failures gracefully, and be resumable per-video per-stage. Secondary risk is Qdrant integration — creating collections with the right schema, generating embeddings via a configurable endpoint, and upserting points with metadata payloads. The Celery+Redis infrastructure is lower risk since both are already in Docker Compose and `celery[redis]` is in requirements.txt.\n\n## Recommendation\n\nBuild this slice in a layered approach: (1) config/settings extensions first, (2) Celery app + task skeleton, (3) LLM client with fallback, (4) prompt templates on disk, (5) pipeline stages 2–5 as individual Celery tasks with a chain orchestrator, (6) Qdrant client + embedding pipeline, (7) trigger mechanism (post-ingest hook or API endpoint), (8) integration tests with mocked LLM responses. \n\nUse the `openai` Python SDK for LLM calls — it works with any OpenAI-compatible API (Open WebUI, Ollama, vLLM). Use `qdrant-client` with `AsyncQdrantClient` for vector operations. Use Pydantic models to define expected LLM output schemas for each stage, enabling `response_format` structured output where the model supports it, with JSON-parse fallback for models that don't. Each pipeline stage should be an individual Celery task chained together, with `processing_status` updated after each stage completes, enabling resumability.\n\n## Implementation Landscape\n\n### Key Files\n\n- `backend/models.py` — Complete. All 7 entities exist with correct enums. `ProcessingStatus` has the progression: `pending → transcribed → extracted → reviewed → published`. `KeyMoment` has `review_status`, `content_type`, `plugins`, `raw_transcript`. `TechniquePage` has `body_sections` (JSONB), `signal_chains` (JSONB), `topic_tags` (ARRAY), `source_quality`. No schema changes needed.\n- `backend/database.py` — Provides `engine`, `async_session`, `get_session`. Worker tasks will need their own session management (not FastAPI dependency injection).\n- `backend/config.py` — Currently has DB, Redis, CORS, file storage settings. **Must be extended** with: `llm_api_url`, `llm_api_key`, `llm_model`, `llm_fallback_url`, `llm_fallback_model`, `embedding_api_url`, `embedding_model`, `qdrant_url`, `qdrant_collection`, `review_mode` (bool). All referenced in `.env.example` but not yet in `Settings`.\n- `backend/requirements.txt` — Has `celery[redis]`, `redis`, `httpx`, `pydantic`. **Must add**: `openai`, `qdrant-client`.\n- `backend/routers/ingest.py` — After successful ingest, should trigger the pipeline. Currently sets `processing_status = transcribed` and commits. Add a post-commit Celery task dispatch: `run_pipeline.delay(video_id)`.\n- `backend/worker.py` — **New file.** Celery app definition. Docker Compose expects `celery -A worker worker`.\n- `backend/pipeline/` — **New directory.** Individual stage modules:\n  - `backend/pipeline/__init__.py`\n  - `backend/pipeline/llm_client.py` — OpenAI-compatible client with primary/fallback logic\n  - `backend/pipeline/embedding_client.py` — Embedding generation via OpenAI-compatible `/v1/embeddings` endpoint\n  - `backend/pipeline/qdrant_client.py` — Qdrant collection management and upsert operations\n  - `backend/pipeline/stages.py` — Celery tasks for stages 2–5 + orchestrator chain\n  - `backend/pipeline/schemas.py` — Pydantic models for LLM input/output per stage\n- `prompts/` — **New template files:**\n  - `prompts/stage2_segmentation.txt` — Topic boundary detection prompt\n  - `prompts/stage3_extraction.txt` — Key moment extraction prompt\n  - `prompts/stage4_classification.txt` — Classification/tagging prompt\n  - `prompts/stage5_synthesis.txt` — Technique page synthesis prompt\n- `config/canonical_tags.yaml` — Already exists with 6 categories, sub-topics, and genre taxonomy. Pipeline reads this during stage 4 classification.\n\n### Build Order\n\n1. **Config + dependencies first** — Extend `Settings` with LLM/embedding/Qdrant/review_mode vars. Add `openai` and `qdrant-client` to requirements.txt. This unblocks everything.\n\n2. **Celery app + skeleton tasks** — Create `backend/worker.py` with Celery app instance pointing to Redis. Create `backend/pipeline/stages.py` with placeholder tasks for stages 2–5 and a `run_pipeline` orchestrator that chains them. Verify `celery -A worker worker` starts without errors. This proves the Celery infrastructure works.\n\n3. **LLM client with fallback** — Create `backend/pipeline/llm_client.py` using the `openai` SDK's `AsyncOpenAI` client. The key insight: the openai SDK works with any OpenAI-compatible API by setting `base_url`. Build a wrapper that tries the primary endpoint, catches connection/timeout errors, and falls back to the secondary. Define Pydantic schemas for each stage's expected LLM output in `backend/pipeline/schemas.py`.\n\n4. **Prompt templates** — Write the 4 prompt template files. These are the soul of the extraction pipeline. Stage 2 (segmentation) needs the full transcript and asks for topic boundaries. Stage 3 (extraction) takes individual segments and extracts key moments. Stage 4 (classification) takes key moments and assigns tags from the canonical list. Stage 5 (synthesis) takes all moments for a creator+topic and produces technique page content.\n\n5. **Pipeline stages implementation** — Implement the actual logic in each Celery task. Each stage:\n   - Reads its input from PostgreSQL (transcript segments, key moments, etc.)\n   - Loads the appropriate prompt template from disk\n   - Calls the LLM client\n   - Parses the structured response\n   - Writes results to PostgreSQL (topic_labels on segments, new KeyMoment rows, tags, TechniquePage records)\n   - Updates `processing_status` on the SourceVideo\n   - Pipeline is resumable: if a video is at `transcribed`, start from stage 2; if at `extracted`, skip to stage 4 (classification), etc.\n\n6. **Qdrant integration** — Create `backend/pipeline/qdrant_client.py` and `backend/pipeline/embedding_client.py`. Create the `chrysopedia` collection (if not exists) with vector size matching the embedding model (768 for nomic-embed-text). After stage 5 synthesis (or after key moment creation), embed summaries and upsert to Qdrant with metadata payloads (creator_id, topic, timestamps, source_video_id).\n\n7. **Ingest trigger** — Modify `backend/routers/ingest.py` to dispatch `run_pipeline.delay(video_id)` after successful commit. Add an API endpoint `POST /api/v1/pipeline/trigger/{video_id}` for manual re-triggering.\n\n8. **Integration tests** — Test with mocked LLM responses (don't call real LLMs in CI). Verify: pipeline stages update processing_status correctly, key moments are created with correct fields, technique pages are synthesized, Qdrant upsert is called with correct payloads.\n\n### Verification Approach\n\n1. `celery -A worker worker --loglevel=debug` starts cleanly with registered tasks visible in output\n2. `python -c \"from pipeline.stages import run_pipeline; print('import ok')\"` — module imports work\n3. `python -c \"from pipeline.llm_client import LLMClient; print('client ok')\"` — LLM client importable\n4. Pipeline integration test: ingest a sample transcript → verify processing_status progresses through `transcribed → extracted → reviewed → published` (with mocked LLM)\n5. Key moment creation test: verify stage 3 output produces KeyMoment rows with correct fields\n6. Technique page synthesis test: verify stage 5 produces TechniquePage with body_sections, signal_chains, plugins\n7. Qdrant test: verify embedding client produces vectors and qdrant_client upserts with correct payloads (mock Qdrant)\n8. Prompt template loading: verify all 4 templates load from `prompts/` directory\n9. Config test: verify all new Settings fields have correct defaults and load from env vars\n10. `docker compose config > /dev/null` still passes with worker service\n\n## Don't Hand-Roll\n\n| Problem | Existing Solution | Why Use It |\n|---------|------------------|------------|\n| OpenAI-compatible API calls | `openai` Python SDK | Works with Open WebUI, Ollama, vLLM by setting `base_url`. Has async support, structured output parsing, retry logic. No need to build raw httpx calls. |\n| Qdrant vector operations | `qdrant-client` | Official Python client with `AsyncQdrantClient`, typed models for `PointStruct`, `VectorParams`, metadata filtering. Already mature. |\n| Background job queue | `celery[redis]` | Already in requirements.txt and Docker Compose. Task chaining, retries, result tracking built in. |\n| Structured LLM output parsing | Pydantic models + openai SDK `response_format` | The openai SDK supports `response_format={\"type\": \"json_object\"}` for structured output. Combined with Pydantic `model_validate_json()` for robust parsing. |\n| YAML config loading | `PyYAML` (already installed) | For loading `canonical_tags.yaml`. Already a dependency. |\n\n## Constraints\n\n- **Celery is sync, DB is async.** The Celery worker runs in a sync event loop. Pipeline tasks using SQLAlchemy async require either: (a) running `asyncio.run()` inside each task, (b) using `asgiref.sync_to_async`, or (c) creating a sync SQLAlchemy engine for the worker. Option (a) is simplest — each Celery task wraps an async function with `asyncio.run()`. The openai SDK has both sync and async clients; use the sync client in Celery tasks to avoid complexity.\n- **Qdrant is at 10.0.0.10 from the hypervisor, not localhost.** The `QDRANT_URL` env var must be configured per-environment. In Docker Compose on the hypervisor, it should point to `http://10.0.0.10:6333`. Tests should mock Qdrant entirely.\n- **LLM structured output support varies by model.** Qwen via Open WebUI may or may not support `response_format: json_object`. The pipeline must handle both: try structured output first, fall back to prompting for JSON and parsing the raw text response. Always validate with Pydantic regardless.\n- **Prompts are bind-mounted read-only.** Docker Compose mounts `./prompts:/prompts:ro` for the worker. Prompt templates should be loaded from `/prompts/` in production and `./prompts/` in development. Config should have a `prompts_path` setting.\n- **`processing_status` enum progression is: `pending → transcribed → extracted → reviewed → published`.** The pipeline must respect this: stage 2+3 set `extracted`, stage 4 doesn't change status (classification is metadata), stage 5 sets `reviewed` (or `published` if `review_mode=false`).\n- **Naive datetimes required.** Per KNOWLEDGE.md, all datetime values must use `_now()` (naive UTC) for asyncpg compatibility with TIMESTAMP WITHOUT TIME ZONE columns.\n\n## Common Pitfalls\n\n- **Celery `asyncio.run()` nesting** — Celery tasks run in a sync context. If you try to call async functions without `asyncio.run()`, you get \"no running event loop\" errors. If a Celery worker happens to use an async-aware event loop (e.g., with gevent), `asyncio.run()` may fail with \"cannot run nested event loop\". Safest approach: use sync clients (sync `openai.OpenAI`, sync `QdrantClient`) in Celery tasks, or use a dedicated sync SQLAlchemy engine for the worker.\n- **LLM JSON parse failures** — LLMs don't always produce valid JSON even when asked. Every LLM response must be wrapped in try/except for `json.JSONDecodeError` and `pydantic.ValidationError`. Log the raw response on failure for debugging. Consider retry with a \"please output valid JSON\" follow-up.\n- **Qdrant collection recreation** — `create_collection` fails if collection exists. Always use `collection_exists()` check first, or use `recreate_collection()` only during initial setup.\n- **Token limits on long transcripts** — A 4-hour livestream transcript could be 50k+ tokens. Stage 2 (segmentation) must handle this via chunking — split the transcript into overlapping windows and segment each independently, then merge boundary segments. This is the hardest part of stage 2.\n- **Prompt template injection** — Transcript text is user-generated content inserted into prompts. While this is a self-hosted system with trusted content, prompts should clearly delineate the transcript from instructions using XML-style tags or markdown fences.\n\n## Open Risks\n\n- **LLM endpoint availability during testing.** Neither DGX Sparks nor local Ollama are available in the CI/test environment. All LLM calls in tests must use mocked responses. This means test coverage proves the pipeline plumbing but not extraction quality — quality validation requires real LLM calls in a staging environment.\n- **Token window sizing for long transcripts.** The spec mentions 4-hour livestreams. Qwen 2.5-72B has a 128K context window, but sending a full 4-hour transcript (~50K tokens) in one call may degrade quality. The chunking strategy for stage 2 needs careful design — overlapping windows with merge logic at boundaries.\n- **Embedding model dimension mismatch.** `nomic-embed-text` produces 768-dimensional vectors. If the embedding model is changed later, the Qdrant collection schema must be recreated. The collection creation should read the dimension from config, not hardcode it.\n- **Open WebUI API compatibility.** Open WebUI wraps Ollama/vLLM with its own API layer. The exact endpoint path and authentication method (API key format) may differ from standard OpenAI API conventions. The LLM client should be tested against the actual endpoint before the pipeline processes real data.\n\n## Skills Discovered\n\n| Technology | Skill | Status |\n|------------|-------|--------|\n| Celery | `bobmatnyc/claude-mpm-skills@celery` (143 installs) | available |\n| Qdrant | `davila7/claude-code-templates@qdrant-vector-search` (470 installs) | available |\n| Qdrant | `giuseppe-trisciuoglio/developer-kit@qdrant` (339 installs) | available |\n\nInstall commands (user decision):\n- `npx skills add bobmatnyc/claude-mpm-skills@celery`\n- `npx skills add davila7/claude-code-templates@qdrant-vector-search`\n","milestone_id":"M001","slice_id":"S03"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":4206,"cacheRead":110395,"cacheWrite":113,"totalTokens":114715,"cost":{"input":0.000005,"output":0.10515000000000001,"cacheRead":0.0551975,"cacheWrite":0.0007062500000000001,"total":0.16105875}},"stopReason":"toolUse","timestamp":1774822877644}}
{"type":"message","id":"37401cb9","parentId":"0685a55a","timestamp":"2026-03-29T22:22:58.362Z","message":{"role":"toolResult","toolCallId":"toolu_01MMhBbKghXapYGFkvpXK3Gb","toolName":"gsd_summary_save","content":[{"type":"text","text":"Saved RESEARCH artifact to milestones/M001/slices/S03/S03-RESEARCH.md"}],"details":{"operation":"save_summary","path":"milestones/M001/slices/S03/S03-RESEARCH.md","artifact_type":"RESEARCH"},"isError":false,"timestamp":1774822978360}}
{"type":"message","id":"3b198036","parentId":"37401cb9","timestamp":"2026-03-29T22:23:01.130Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S03 researched."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":11,"cacheRead":110508,"cacheWrite":4244,"totalTokens":114764,"cost":{"input":0.000005,"output":0.000275,"cacheRead":0.055254,"cacheWrite":0.026525,"total":0.08205899999999999}},"stopReason":"stop","timestamp":1774822978361}}
