{"type":"thinking_level_change","id":"73cf031b","parentId":null,"timestamp":"2026-03-29T22:23:01.446Z","thinkingLevel":"off"}
{"type":"model_change","id":"b42aaf81","parentId":"73cf031b","timestamp":"2026-03-29T22:23:01.447Z","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## Executor Context Constraints\n\nThe agent that executes each task has a **200K token** context window.\n- Recommended task count for this slice: **2–6 tasks**\n- Each task gets ~320K chars of inline context (plans, code, decisions)\n- Keep individual tasks completable within a single context window — if a task needs more context than fits, split it\n\nThen:\n0. If `REQUIREMENTS.md` was preloaded above, identify which Active requirements the roadmap says this slice owns or supports. These are the requirements this plan must deliver — every owned requirement needs at least one task that directly advances it, and verification must prove the requirement is met.\n1. Read the templates:\n   - `~/.gsd/agent/extensions/gsd/templates/plan.md`\n   - `~/.gsd/agent/extensions/gsd/templates/task-plan.md`\n2.  Record the installed skills you expect executors to use in each task plan's `skills_used` frontmatter.\n3. Define slice-level verification — the objective stopping condition for this slice:\n   - For non-trivial slices: plan actual test files with real assertions. Name the files.\n   - For simple slices: executable commands or script assertions are fine.\n   - If the project is non-trivial and has no test framework, the first task should set one up.\n   - If this slice establishes a boundary contract, verification must exercise that contract.\n4. **For non-trivial slices only** — plan observability, proof level, and integration closure:\n   - Include `Observability / Diagnostics` for backend, integration, async, stateful, or UI slices where failure diagnosis matters.\n   - Fill `Proof Level` and `Integration Closure` when the slice crosses runtime boundaries or has meaningful integration concerns.\n   - **Omit these sections entirely for simple slices** where they would all be \"none\" or trivially obvious.\n5. **Quality gates** — for non-trivial slices, fill the Threat Surface (Q3) and Requirement Impact (Q4) sections in the slice plan:\n   - **Threat Surface:** Identify abuse scenarios, data exposure risks, and input trust boundaries. Required when the slice handles user input, authentication, authorization, or sensitive data. Omit entirely for internal refactoring or simple changes.\n   - **Requirement Impact:** List which existing requirements this slice touches, what must be re-verified after shipping, and which prior decisions should be reconsidered. Omit entirely if no existing requirements are affected.\n   - For each task in a non-trivial slice, fill Failure Modes (Q5), Load Profile (Q6), and Negative Tests (Q7) in the task plan when the task has external dependencies, shared resources, or non-trivial input handling. Omit for simple tasks.\n6. Decompose the slice into tasks, each fitting one context window. Each task needs:\n   - a concrete, action-oriented title\n   - the inline task entry fields defined in the plan.md template (Why / Files / Do / Verify / Done when)\n   - a matching task plan file with description, steps, must-haves, verification, inputs, and expected output\n   - **Inputs and Expected Output must list concrete backtick-wrapped file paths** (e.g. `` `src/types.ts` ``). These are machine-parsed to derive task dependencies — vague prose without paths breaks parallel execution. Every task must have at least one output file path.\n   - Observability Impact section **only if the task touches runtime boundaries, async flows, or error paths** — omit it otherwise\n7. **Persist planning state through `gsd_plan_slice`.** Call it with the full slice planning payload (goal, demo, must-haves, verification, tasks, and metadata). The tool inserts all tasks in the same transaction, writes to the DB, and renders `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/S03-PLAN.md` and `.gsd/milestones/M001/slices/S03/tasks/T##-PLAN.md` files automatically. Do **not** call `gsd_plan_task` separately — `gsd_plan_slice` handles task persistence. Do **not** rely on direct `PLAN.md` writes as the source of truth; the DB-backed tool is the canonical write path for slice and task planning state.\n8. **Self-audit the plan.** Walk through each check — if any fail, fix the plan files before moving on:\n    - **Completion semantics:** If every task were completed exactly as written, the slice goal/demo should actually be true.\n    - **Requirement coverage:** Every must-have in the slice maps to at least one task. No must-have is orphaned. If `REQUIREMENTS.md` exists, every Active requirement this slice owns maps to at least one task.\n    - **Task completeness:** Every task has steps, must-haves, verification, inputs, and expected output — none are blank or vague. Inputs and Expected Output list backtick-wrapped file paths, not prose descriptions.\n    - **Dependency correctness:** Task ordering is consistent. No task references work from a later task.\n    - **Key links planned:** For every pair of artifacts that must connect, there is an explicit step that wires them.\n    - **Scope sanity:** Target 2–5 steps and 3–8 files per task. 10+ steps or 12+ files — must split. Each task must be completable in a single fresh context window.\n    - **Feature completeness:** Every task produces real, user-facing progress — not just internal scaffolding.\n    - **Quality gate coverage:** For non-trivial slices, Threat Surface and Requirement Impact sections are present and specific (not placeholder text). For non-trivial tasks, Failure Modes, Load Profile, and Negative Tests are addressed in the task plan.\n10. If planning produced structural decisions, append them to `.gsd/DECISIONS.md`\n11. Do not commit — .gsd/ planning docs are managed externally and not tracked in git.\n\nThe slice directory and tasks/ subdirectory already exist. Do NOT mkdir. All work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_plan_slice` to persist the planning state before finishing.**\n\nWhen done, say: \"Slice S03 planned.\"\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: Plan 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### Slice Research\nSource: `.gsd/milestones/M001/slices/S03/S03-RESEARCH.md`\n\n# 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\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[...truncated 2 sections]\n\n### Dependency Slice Summaries\n\nPay particular attention to **Forward Intelligence** sections — they contain hard-won knowledge about what's fragile, what assumptions changed, and what this slice should 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 have full tool access. Before decomposing, explore the relevant code to ground your plan in reality.\n\n### Verify Roadmap Assumptions\n\nCheck prior slice summaries (inlined above as dependency summaries, if present). If prior slices discovered constraints, changed approaches, or flagged fragility, adjust your plan accordingly. The roadmap description may be stale — verify it against the current codebase state.\n\n### Explore Slice Scope\n\nRead the code files relevant to this slice. Confirm the roadmap's description of what exists, what needs to change, and what boundaries apply. Use `rg`, `find`, and targeted reads.\n\n### Source Files\n\n- **Requirements**: `.gsd/REQUIREMENTS.md`\n- **Decisions**: `.gsd/DECISIONS.md`\n- **Roadmap**: `.gsd/milestones/M001/M001-ROADMAP.md`\n- **Slice Research**: `.gsd/milestones/M001/slices/S03/S03-RESEARCH.md`\n\nIf slice research exists (inlined above), trust those findings and skip redundant exploration.\n\nAfter you finish, **executor agents** implement each task in isolated fresh context windows. They see only their task plan, the slice plan excerpt (goal/demo/verification), and compressed summaries of prior tasks. They do not see the research doc, the roadmap, or REQUIREMENTS.md. Everything an executor needs must be in the task plan itself — file paths, specific steps, expected inputs and outputs.\n\nNarrate your decomposition reasoning — why you're grouping work this way, what risks are driving the order, what verification strategy you're choosing and why. Keep the narration proportional to the work — a simple slice doesn't need a long justification — but write in complete sentences, not planner shorthand.\n\n**Right-size the plan.** If the slice is simple enough to be 1 task, plan 1 task. Don't split into multiple tasks just because you can identify sub-steps. Don't fill in sections with \"None\" when the section doesn't apply — omit them entirely. The plan's job is to guide execution, not to fill a template.\n","display":false,"id":"018b066f","parentId":"b42aaf81","timestamp":"2026-03-29T22:23:01.449Z"}
{"type":"message","id":"4e1c552f","parentId":"018b066f","timestamp":"2026-03-29T22:23:07.162Z","message":{"role":"assistant","content":[{"type":"text","text":"\n\nI'll start by exploring the current codebase to ground the plan in reality, then read the templates."},{"type":"toolCall","id":"toolu_01HPH6geGXGjEu98LppNLCBG","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && find backend -type f -name '*.py' | head -40 && echo \"---\" && ls -la backend/ && echo \"---\" && ls -la prompts/ 2>/dev/null || echo \"no prompts dir\" && echo \"---\" && ls -la config/ 2>/dev/null || echo \"no config dir\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":169,"cacheRead":49033,"cacheWrite":11542,"totalTokens":60746,"cost":{"input":0.00001,"output":0.0042250000000000005,"cacheRead":0.0245165,"cacheWrite":0.07213750000000001,"total":0.100889}},"stopReason":"toolUse","timestamp":1774822981448}}
{"type":"message","id":"ec7b1a21","parentId":"4e1c552f","timestamp":"2026-03-29T22:23:07.177Z","message":{"role":"toolResult","toolCallId":"toolu_01HPH6geGXGjEu98LppNLCBG","toolName":"bash","content":[{"type":"text","text":"backend/models.py\nbackend/main.py\nbackend/routers/health.py\nbackend/routers/videos.py\nbackend/routers/__init__.py\nbackend/routers/creators.py\nbackend/routers/ingest.py\nbackend/database.py\nbackend/tests/test_ingest.py\nbackend/tests/__init__.py\nbackend/tests/conftest.py\nbackend/config.py\nbackend/schemas.py\n---\ntotal 64\ndrwxrwxr-x  6 aux aux  4096 Mar 29 22:12 .\ndrwxrwxr-x 15 aux aux  4096 Mar 29 22:19 ..\n-rw-rw-r--  1 aux aux  1066 Mar 29 21:49 config.py\n-rw-rw-r--  1 aux aux   791 Mar 29 21:44 database.py\n-rw-rw-r--  1 aux aux  2810 Mar 29 22:08 main.py\n-rw-rw-r--  1 aux aux 11382 Mar 29 22:14 models.py\ndrwxrwxr-x  2 aux aux  4096 Mar 29 22:14 __pycache__\ndrwxrwxr-x  3 aux aux  4096 Mar 29 22:12 .pytest_cache\n-rw-rw-r--  1 aux aux    47 Mar 29 22:11 pytest.ini\n-rw-rw-r--  1 aux aux   350 Mar 29 22:11 requirements.txt\ndrwxrwxr-x  3 aux aux  4096 Mar 29 22:07 routers\n-rw-rw-r--  1 aux aux  6137 Mar 29 22:07 schemas.py\ndrwxrwxr-x  4 aux aux  4096 Mar 29 22:12 tests\n---\ntotal 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---\ntotal 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 1409 Mar 29 21:41 canonical_tags.yaml\n"}],"isError":false,"timestamp":1774822987176}}
{"type":"message","id":"be343d5c","parentId":"ec7b1a21","timestamp":"2026-03-29T22:23:10.016Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01HsZNLLNSUKjz4fPuec3kcN","name":"read","arguments":{"path":"backend/config.py"}},{"type":"toolCall","id":"toolu_01CNMaYZ9X52jSrebX18h3zN","name":"read","arguments":{"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01F8XLPLpbsLzwp654eVr5rg","name":"read","arguments":{"path":"backend/database.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":131,"cacheRead":60575,"cacheWrite":861,"totalTokens":61568,"cost":{"input":0.000005,"output":0.003275,"cacheRead":0.0302875,"cacheWrite":0.005381250000000001,"total":0.038948750000000004}},"stopReason":"toolUse","timestamp":1774822987177}}
{"type":"message","id":"248f82f7","parentId":"be343d5c","timestamp":"2026-03-29T22:23:10.018Z","message":{"role":"toolResult","toolCallId":"toolu_01HsZNLLNSUKjz4fPuec3kcN","toolName":"read","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":1774822990018}}
{"type":"message","id":"8f66d412","parentId":"248f82f7","timestamp":"2026-03-29T22:23:10.019Z","message":{"role":"toolResult","toolCallId":"toolu_01CNMaYZ9X52jSrebX18h3zN","toolName":"read","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":1774822990018}}
{"type":"message","id":"66b9b759","parentId":"8f66d412","timestamp":"2026-03-29T22:23:10.021Z","message":{"role":"toolResult","toolCallId":"toolu_01F8XLPLpbsLzwp654eVr5rg","toolName":"read","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":1774822990020}}
{"type":"message","id":"cb9f960b","parentId":"66b9b759","timestamp":"2026-03-29T22:23:13.133Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JLQJHg5NzSKPdjZnYLUbTG","name":"read","arguments":{"path":"backend/routers/ingest.py"}},{"type":"toolCall","id":"toolu_016mhTdBgPrhfGaT2jP1Yswe","name":"read","arguments":{"path":"backend/requirements.txt"}},{"type":"toolCall","id":"toolu_014Uk8GFUBiYHiwa3qMEGv96","name":"read","arguments":{"path":"config/canonical_tags.yaml"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":137,"cacheRead":61436,"cacheWrite":4135,"totalTokens":65709,"cost":{"input":0.000005,"output":0.003425,"cacheRead":0.030718,"cacheWrite":0.025843750000000002,"total":0.059991749999999996}},"stopReason":"toolUse","timestamp":1774822990020}}
{"type":"message","id":"006a1e2a","parentId":"cb9f960b","timestamp":"2026-03-29T22:23:13.135Z","message":{"role":"toolResult","toolCallId":"toolu_01JLQJHg5NzSKPdjZnYLUbTG","toolName":"read","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":1774822993135}}
{"type":"message","id":"10d06c0b","parentId":"006a1e2a","timestamp":"2026-03-29T22:23:13.137Z","message":{"role":"toolResult","toolCallId":"toolu_016mhTdBgPrhfGaT2jP1Yswe","toolName":"read","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":1774822993136}}
{"type":"message","id":"34bfe7f9","parentId":"10d06c0b","timestamp":"2026-03-29T22:23:13.139Z","message":{"role":"toolResult","toolCallId":"toolu_014Uk8GFUBiYHiwa3qMEGv96","toolName":"read","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":1774822993138}}
{"type":"message","id":"22852c7b","parentId":"34bfe7f9","timestamp":"2026-03-29T22:23:16.263Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01JSj628JgRxiJApuXKkHQS2","name":"read","arguments":{"path":"backend/main.py"}},{"type":"toolCall","id":"toolu_01M9Yg7ggVKKhnu65DdbwAGi","name":"read","arguments":{"path":"backend/schemas.py"}},{"type":"toolCall","id":"toolu_01VsqqTEWFubEwWMMpkHRCn1","name":"read","arguments":{"path":"backend/tests/conftest.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":134,"cacheRead":65571,"cacheWrite":2671,"totalTokens":68377,"cost":{"input":0.000005,"output":0.00335,"cacheRead":0.032785499999999995,"cacheWrite":0.01669375,"total":0.05283424999999999}},"stopReason":"toolUse","timestamp":1774822993138}}
{"type":"message","id":"d912ebef","parentId":"22852c7b","timestamp":"2026-03-29T22:23:16.265Z","message":{"role":"toolResult","toolCallId":"toolu_01JSj628JgRxiJApuXKkHQS2","toolName":"read","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":1774822996265}}
{"type":"message","id":"86acaef8","parentId":"d912ebef","timestamp":"2026-03-29T22:23:16.266Z","message":{"role":"toolResult","toolCallId":"toolu_01M9Yg7ggVKKhnu65DdbwAGi","toolName":"read","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":1774822996265}}
{"type":"message","id":"408f4b13","parentId":"86acaef8","timestamp":"2026-03-29T22:23:16.268Z","message":{"role":"toolResult","toolCallId":"toolu_01VsqqTEWFubEwWMMpkHRCn1","toolName":"read","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":1774822996267}}
{"type":"message","id":"02febfd8","parentId":"408f4b13","timestamp":"2026-03-29T22:23:20.193Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the docker-compose to understand the worker service setup:"},{"type":"toolCall","id":"toolu_015iGux5AhumArchzycZYZvh","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && cat docker-compose.yml 2>/dev/null || echo \"not found\" && echo \"---\" && ls -la .env* 2>/dev/null || echo \"no .env files\" && echo \"---\" && cat .env.example 2>/dev/null || echo \"no .env.example\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":154,"cacheRead":65571,"cacheWrite":5990,"totalTokens":71716,"cost":{"input":0.000005,"output":0.00385,"cacheRead":0.032785499999999995,"cacheWrite":0.0374375,"total":0.07407799999999999}},"stopReason":"toolUse","timestamp":1774822996268}}
{"type":"message","id":"e73daeb3","parentId":"02febfd8","timestamp":"2026-03-29T22:23:20.207Z","message":{"role":"toolResult","toolCallId":"toolu_015iGux5AhumArchzycZYZvh","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/${POSTGRES_DB:-chrysopedia}\n      REDIS_URL: redis://chrysopedia-redis:6379/0\n    command: [\"celery\", \"-A\", \"worker\", \"worker\", \"--loglevel=info\"]\n    volumes:\n      - ./backend:/app\n      - /vmPool/r/services/chrysopedia_data:/data\n      - ./prompts:/prompts:ro\n    depends_on:\n      chrysopedia-db:\n        condition: service_healthy\n      chrysopedia-redis:\n        condition: service_healthy\n    networks:\n      - chrysopedia\n\n  # ── React web UI (nginx) ──\n  chrysopedia-web:\n    build:\n      context: .\n      dockerfile: docker/Dockerfile.web\n    container_name: chrysopedia-web\n    restart: unless-stopped\n    ports:\n      - \"127.0.0.1:3000:80\"\n    depends_on:\n      - chrysopedia-api\n    networks:\n      - chrysopedia\n\nnetworks:\n  chrysopedia:\n    driver: bridge\n    ipam:\n      config:\n        - subnet: 172.24.0.0/24\n---\n-rw-rw-r-- 1 aux aux 964 Mar 29 21:40 .env.example\n---\n# ─── Chrysopedia Environment Variables ───\n\n# PostgreSQL\nPOSTGRES_USER=chrysopedia\nPOSTGRES_PASSWORD=changeme\nPOSTGRES_DB=chrysopedia\nDATABASE_URL=postgresql+asyncpg://${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":1774823000206}}
{"type":"message","id":"796e58f8","parentId":"e73daeb3","timestamp":"2026-03-29T22:23:25.728Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the test fixture and check the existing test infrastructure:"},{"type":"toolCall","id":"toolu_01PftTSwvyiEdMkw3WHRmuxQ","name":"read","arguments":{"path":"backend/tests/fixtures/sample_transcript.json"}},{"type":"toolCall","id":"toolu_0188zuRsNnvooeykg9G9ooDP","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls -la backend/tests/ && echo \"---\" && ls -la backend/pipeline/ 2>/dev/null || echo \"no pipeline dir\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":160,"cacheRead":71561,"cacheWrite":1596,"totalTokens":73318,"cost":{"input":0.000005,"output":0.004,"cacheRead":0.0357805,"cacheWrite":0.009975000000000001,"total":0.0497605}},"stopReason":"toolUse","timestamp":1774823000206}}
{"type":"message","id":"90ff16f3","parentId":"796e58f8","timestamp":"2026-03-29T22:23:25.734Z","message":{"role":"toolResult","toolCallId":"toolu_01PftTSwvyiEdMkw3WHRmuxQ","toolName":"read","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":1774823005734}}
{"type":"message","id":"b88eff60","parentId":"90ff16f3","timestamp":"2026-03-29T22:23:25.738Z","message":{"role":"toolResult","toolCallId":"toolu_0188zuRsNnvooeykg9G9ooDP","toolName":"bash","content":[{"type":"text","text":"total 28\ndrwxrwxr-x 4 aux aux 4096 Mar 29 22:12 .\ndrwxrwxr-x 6 aux aux 4096 Mar 29 22:12 ..\n-rw-rw-r-- 1 aux aux 3094 Mar 29 22:14 conftest.py\ndrwxrwxr-x 2 aux aux 4096 Mar 29 22:11 fixtures\n-rw-rw-r-- 1 aux aux    0 Mar 29 22:11 __init__.py\ndrwxrwxr-x 2 aux aux 4096 Mar 29 22:14 __pycache__\n-rw-rw-r-- 1 aux aux 7446 Mar 29 22:13 test_ingest.py\n---\nno pipeline dir\n"}],"isError":false,"timestamp":1774823005737}}
{"type":"message","id":"29d18e97","parentId":"b88eff60","timestamp":"2026-03-29T22:23:29.215Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me read the plan templates:"},{"type":"toolCall","id":"toolu_0112YtQ3D3uvwGda1PrXWDke","name":"read","arguments":{"path":"/home/aux/.gsd/agent/extensions/gsd/templates/plan.md"}},{"type":"toolCall","id":"toolu_01MFqLsFXTwJ3vzNMsjAqZnz","name":"read","arguments":{"path":"/home/aux/.gsd/agent/extensions/gsd/templates/task-plan.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":134,"cacheRead":73157,"cacheWrite":658,"totalTokens":73950,"cost":{"input":0.000005,"output":0.00335,"cacheRead":0.0365785,"cacheWrite":0.0041125,"total":0.044045999999999995}},"stopReason":"toolUse","timestamp":1774823005737}}
{"type":"message","id":"0b03120c","parentId":"29d18e97","timestamp":"2026-03-29T22:23:29.218Z","message":{"role":"toolResult","toolCallId":"toolu_0112YtQ3D3uvwGda1PrXWDke","toolName":"read","content":[{"type":"text","text":"# {{sliceId}}: {{sliceTitle}}\n\n**Goal:** {{goal}}\n**Demo:** {{demo}}\n\n## Must-Haves\n\n- {{mustHave}}\n- {{mustHave}}\n\n## Threat Surface\n\n<!-- Q3: How can this be exploited? OMIT ENTIRELY for simple slices with no auth, user input, or data exposure. -->\n\n- **Abuse**: {{abuseScenarios — parameter tampering, replay, privilege escalation, or N/A}}\n- **Data exposure**: {{sensitiveDataAccessible — PII, tokens, secrets, or none}}\n- **Input trust**: {{untrustedInput — user input reaching DB/API/filesystem, or none}}\n\n## Requirement Impact\n\n<!-- Q4: What existing promises does this break? OMIT ENTIRELY if no existing requirements are affected. -->\n\n- **Requirements touched**: {{requirementIds — e.g. R001, R003, or none}}\n- **Re-verify**: {{whatMustBeRetested — e.g. login flow, API contract, or N/A}}\n- **Decisions revisited**: {{decisionIds — e.g. D002, or none}}\n\n## Proof Level\n\n<!-- Omit this section entirely for simple slices where the answer is trivially obvious. -->\n\n- This slice proves: {{contract | integration | operational | final-assembly}}\n- Real runtime required: {{yes/no}}\n- Human/UAT required: {{yes/no}}\n\n## Verification\n\n<!-- Define what \"done\" looks like BEFORE detailing tasks.\n     This section is the slice's objective stopping condition — execution isn't done\n     until everything here passes.\n\n     For non-trivial projects:\n     - Write actual test files into the codebase during the first task\n     - Tests should assert on the slice's demo outcome and boundary contracts\n     - Name the test files here so execution has an unambiguous target\n\n     For simple projects or scripts:\n     - Executable verification commands (bash assertions, curl checks, etc.) are sufficient\n\n     If the project has no test framework and the work is non-trivial,\n     the first task should set one up. A test runner costs 2 minutes\n     and pays for itself immediately.\n\n     For non-trivial backend, integration, async, stateful, or UI work:\n     - Include at least one verification check for an observability or failure-path signal\n     - Verify not just that the feature works, but that a future agent can inspect its state when it fails -->\n\n- {{testFileOrCommand — e.g. `npm test -- --grep \"auth flow\"` or `bash scripts/verify-s01.sh`}}\n- {{testFileOrCommand}}\n\n## Observability / Diagnostics\n\n<!-- Include this section for non-trivial backend, integration, async, stateful, or UI slices.\n     OMIT ENTIRELY for simple slices where all fields would be \"none\".\n\n     When included, describe how a future agent will inspect current state, detect failure,\n     and localize the problem with minimal ambiguity. Keep it concise and high-signal. -->\n\n- Runtime signals: {{structured log/event, state transition, metric, or none}}\n- Inspection surfaces: {{status endpoint, CLI command, script, UI state, DB table, or none}}\n- Failure visibility: {{last error, retry count, phase, timestamp, correlation id, or none}}\n- Redaction constraints: {{secret/PII boundary or none}}\n\n## Integration Closure\n\n<!-- Omit this section entirely for simple slices with no meaningful integration concerns. -->\n\n- Upstream surfaces consumed: {{specific files / modules / contracts}}\n- New wiring introduced in this slice: {{entrypoint / composition / runtime hookup, or none}}\n- What remains before the milestone is truly usable end-to-end: {{list or \"nothing\"}}\n\n## Tasks\n\n<!--\n  If every task below is completed exactly as written, the Goal and Demo above\n  should be true at the stated proof level. Tasks should close the loop on the\n  slice, not merely prepare for later work unless the Demo truthfully says the\n  slice only proves fixture/contract-level behavior.\n\n  Write each task as an executable increment, not a vague intention.\n\n  Prefer action-oriented titles:\n  - \"Wire real auth middleware into dashboard routes\"\n  - \"Persist job status and expose failure diagnostics\"\n  - \"Add browser test covering empty-state recovery\"\n\n  Avoid vague titles:\n  - \"Set up auth\"\n  - \"Handle errors\"\n  - \"Improve UI\"\n\n  Each task should usually include:\n  - Why: why this task exists / what part of the slice it closes\n  - Files: the main files likely touched\n  - Do: concrete implementation steps and important constraints\n  - Verify: the command, test, or runtime check that proves it worked\n  - Done when: a measurable acceptance condition\n\n  Keep the checkbox line format exactly:\n  - [ ] **T01: Title** `est:30m`\n-->\n\n- [ ] **T01: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n- [ ] **T02: {{taskTitle}}** `est:{{estimate}}`\n  - Why: {{whyThisTaskExists}}\n  - Files: `{{filePath}}`, `{{filePath}}`\n  - Do: {{specificImplementationStepsAndConstraints}}\n  - Verify: {{testCommandOrRuntimeCheck}}\n  - Done when: {{measurableAcceptanceCondition}}\n<!--\n  Format rules (parsers depend on this exact structure):\n  - Checkbox line: - [ ] **T01: Title** `est:30m`\n  - Description:   indented text on the next line(s)\n  - Mark done:     change [ ] to [x]\n  - Tasks execute sequentially in order (T01, T02, T03, ...)\n  - est: is informational (e.g. 30m, 1h, 2h) and optional\n\n  Verify field rules:\n  - MUST be a mechanically executable command: `npm test`, `grep -q \"pattern\" file`, `test -f path`\n  - For content/document tasks: verify file existence, section count, YAML validity, or word count\n    NOT exact phrasing, specific formulas, or \"zero TBD\" aspirational criteria\n  - If no command can verify the output, write: \"Manual review — file exists and is non-empty\"\n  - BAD: \"Sections 3.1 and 3.2 exist with exact formulas. Zero TBD/TODO.\"\n  - GOOD: `grep -c \"^## \" doc.md` returns >= 4 (4+ sections), `! grep -q \"TBD\\|TODO\" doc.md`\n\n  Integration closure rule:\n  - At least one slice in any multi-boundary milestone should perform real composition/wiring, not just contract hardening\n  - For the final assembly slice, verification must exercise the real entrypoint or runtime path\n-->\n\n## Files Likely Touched\n\n- `{{filePath}}`\n- `{{filePath}}`\n"}],"isError":false,"timestamp":1774823009217}}
{"type":"message","id":"09d8f143","parentId":"0b03120c","timestamp":"2026-03-29T22:23:29.220Z","message":{"role":"toolResult","toolCallId":"toolu_01MFqLsFXTwJ3vzNMsjAqZnz","toolName":"read","content":[{"type":"text","text":"---\n# Optional scope estimate — helps the plan quality validator detect over-scoped tasks.\n# Tasks with 10+ estimated steps or 12+ estimated files trigger a warning to consider splitting.\nestimated_steps: {{estimatedSteps}}\nestimated_files: {{estimatedFiles}}\n# Installed skills the planner expects the executor to load before coding.\nskills_used:\n  - {{skillName}}\n---\n\n# {{taskId}}: {{taskTitle}}\n\n**Slice:** {{sliceId}} — {{sliceTitle}}\n**Milestone:** {{milestoneId}}\n\n## Description\n\n{{description}}\n\n## Failure Modes\n\n<!-- Q5: What breaks when dependencies fail? OMIT ENTIRELY for tasks with no external dependencies. -->\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| {{dependency}} | {{errorStrategy}} | {{timeoutStrategy}} | {{malformedStrategy}} |\n\n## Load Profile\n\n<!-- Q6: What breaks at 10x load? OMIT ENTIRELY for tasks with no shared resources or scaling concerns. -->\n\n- **Shared resources**: {{sharedResources — DB connections, caches, rate limiters, or none}}\n- **Per-operation cost**: {{perOpCost — N API calls, M DB queries, K bytes, or trivial}}\n- **10x breakpoint**: {{whatBreaksFirst — pool exhaustion, rate limit, memory, or N/A}}\n\n## Negative Tests\n\n<!-- Q7: What negative tests prove robustness? OMIT ENTIRELY for trivial tasks. -->\n\n- **Malformed inputs**: {{malformedInputTests — empty string, null, oversized, wrong type}}\n- **Error paths**: {{errorPathTests — network timeout, auth failure, 5xx, invalid JSON}}\n- **Boundary conditions**: {{boundaryTests — empty list, max length, zero, off-by-one}}\n\n## Steps\n\n1. {{step}}\n2. {{step}}\n3. {{step}}\n\n## Must-Haves\n\n- [ ] {{mustHave}}\n- [ ] {{mustHave}}\n\n## Verification\n\n- {{howToVerifyThisTaskIsActuallyDone}}\n- {{commandToRun_OR_behaviorToCheck}}\n\n## Observability Impact\n\n<!-- OMIT THIS SECTION ENTIRELY for simple tasks that don't touch runtime boundaries,\n     async flows, APIs, background processes, or error paths.\n     Include it only when the task meaningfully changes how failures are detected or diagnosed. -->\n\n- Signals added/changed: {{structured logs, statuses, errors, metrics}}\n- How a future agent inspects this: {{command, endpoint, file, UI state}}\n- Failure state exposed: {{what becomes visible on failure}}\n\n## Inputs\n\n<!-- Every input MUST be a backtick-wrapped file path. These paths are machine-parsed to\n     derive task dependencies — vague descriptions without paths break dependency detection.\n     For the first task in a slice with no prior task outputs, list the existing source files\n     this task reads or modifies. -->\n\n- `{{filePath}}` — {{whatThisTaskNeedsFromPriorWork}}\n\n## Expected Output\n\n<!-- Every output MUST be a backtick-wrapped file path — the specific files this task creates\n     or modifies. These paths are machine-parsed to derive task dependencies.\n     This task should produce a real increment toward making the slice goal/demo true. A full\n     slice plan should not be able to mark every task complete while the claimed slice behavior\n     still does not work at the stated proof level. -->\n\n- `{{filePath}}` — {{whatThisTaskCreatesOrModifies}}\n"}],"isError":false,"timestamp":1774823009217}}
{"type":"message","id":"b6771226","parentId":"09d8f143","timestamp":"2026-03-29T22:26:36.102Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I now have a thorough understanding of the codebase. Let me reason through the decomposition.\n\n## Decomposition Reasoning\n\nThis is the highest-risk slice in M001 — it builds the entire LLM extraction pipeline, prompt system, Celery worker, and Qdrant integration. The research doc identifies 8 build steps but many can be collapsed. Here's my thinking:\n\n**Requirements owned by S03:** R003 (LLM Pipeline), R009 (Qdrant Vector Search), R011 (Canonical Tag System), R012 (Incremental Content Addition), R013 (Prompt Template System).\n\n**Key constraints:**\n1. Celery worker is sync — use sync `openai.OpenAI` and sync `QdrantClient` to avoid async nesting issues\n2. No real LLM/Qdrant in tests — everything must be mockable\n3. `processing_status` progression: `transcribed → extracted → reviewed → published`\n4. Docker Compose already expects `celery -A worker worker` and mounts `./prompts:/prompts:ro`\n5. Config needs extending with ~12 new env vars\n\n**Task decomposition (5 tasks):**\n\n1. **Config + Dependencies + Celery App + LLM Client** — Extend Settings, add pip deps, create `worker.py` with Celery app, create `pipeline/llm_client.py` with primary/fallback, create `pipeline/schemas.py` with Pydantic models for LLM I/O. This is pure infrastructure — no stages yet, but proves the worker starts and LLM client is importable.\n\n2. **Prompt Templates + Pipeline Stages 2-5** — Write the 4 prompt templates, implement all 4 stages in `pipeline/stages.py` with the orchestrator chain. Each stage reads from DB, calls LLM, writes back. This is the core logic.\n\n3. **Qdrant Integration + Embedding Client** — Create `pipeline/embedding_client.py` and `pipeline/qdrant_client.py`. Wire embedding generation into the pipeline after stage 5. Create/manage the collection.\n\n4. **Ingest Trigger + Manual Re-trigger Endpoint** — Modify `ingest.py` to dispatch `run_pipeline.delay()` post-commit. Add `POST /api/v1/pipeline/trigger/{video_id}` endpoint. This wires the pipeline to the existing ingest flow.\n\n5. **Integration Tests** — Full test suite with mocked LLM and Qdrant. Tests: pipeline stages update status, key moments created, technique pages synthesized, Qdrant upsert called, prompt loading, config fields, error handling.\n\nThis gives us 5 tasks, each scoped to ~5-8 files. Tasks 1-3 can be understood in isolation. Task 4 is small but connects the pipeline to the API. Task 5 verifies everything.\n\nLet me persist this plan:"},{"type":"toolCall","id":"toolu_014MciRVXREo6EEhxzGGEQTc","name":"gsd_plan_slice","arguments":{"goal":"A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings. Pipeline is resumable per-video per-stage, uses configurable LLM endpoints with fallback, and loads prompt templates from disk.","integrationClosure":"Upstream surfaces consumed: `backend/routers/ingest.py` (POST /api/v1/ingest sets processing_status=transcribed), `backend/models.py` (all 7 ORM models), `backend/database.py` (engine, async_session, get_session), `backend/config.py` (Settings class), `config/canonical_tags.yaml` (tag taxonomy), `backend/tests/conftest.py` (test infrastructure pattern). New wiring introduced: Celery worker process (`backend/worker.py`), pipeline package (`backend/pipeline/`), prompt templates (`prompts/`), ingest→pipeline dispatch hook, manual trigger API endpoint. What remains before milestone is truly usable end-to-end: S04 (Review Queue Admin UI) for human review workflow, S05 (Search-First Web UI) for user-facing search and technique page display.","milestoneId":"M001","observabilityImpact":"Runtime signals: structured logging at INFO level for each pipeline stage start/complete with video_id, processing_status transitions logged, LLM fallback events logged at WARNING. Inspection surfaces: `processing_status` column on `source_videos` table shows pipeline progress per video, `GET /api/v1/pipeline/trigger/{video_id}` for manual re-run, Celery task result backend in Redis. Failure visibility: LLM parse errors logged with raw response body, Qdrant connection failures logged with URL, pipeline stage failures leave video at last successful status (resumable). Redaction constraints: LLM API keys must not appear in logs.","proofLevel":"This slice proves: integration (Celery worker ↔ PostgreSQL ↔ LLM client ↔ Qdrant, all through real code paths with mocked external services). Real runtime required: no (LLM and Qdrant are mocked in tests). Human/UAT required: no (quality of LLM extraction requires real LLM calls in staging, but pipeline plumbing is proven by tests).","sliceId":"S03","successCriteria":"## Must-Haves\n\n- Celery worker starts cleanly with `celery -A worker worker` and registers all pipeline tasks\n- Config extends Settings with LLM, embedding, Qdrant, prompts_path, and review_mode env vars\n- LLM client supports primary + fallback endpoints using sync `openai.OpenAI` SDK\n- 4 prompt template files exist in `prompts/` and are loaded by pipeline stages at runtime\n- Pipeline stages 2-5 implemented as Celery tasks chained by `run_pipeline` orchestrator\n- Stage 2+3 (segmentation+extraction) sets `processing_status = extracted` on SourceVideo\n- Stage 5 (synthesis) sets status to `reviewed` (or `published` if `review_mode=false`)\n- KeyMoment rows created with correct fields (title, summary, timestamps, content_type, plugins, raw_transcript)\n- TechniquePage rows created with body_sections, signal_chains, topic_tags, source_quality\n- Qdrant embedding client generates vectors via OpenAI-compatible `/v1/embeddings` endpoint\n- Qdrant client creates collection (if not exists) and upserts points with metadata payloads\n- Ingest endpoint dispatches `run_pipeline.delay(video_id)` after successful commit\n- Manual re-trigger endpoint: `POST /api/v1/pipeline/trigger/{video_id}`\n- Pipeline reads canonical tags from `config/canonical_tags.yaml` during classification (stage 4)\n- 8+ integration tests with mocked LLM/Qdrant verify the full pipeline flow\n- R003: End-to-end pipeline from transcript to technique pages with key moments\n- R009: Qdrant embedding and upsert with metadata filtering\n- R011: Canonical tag system used during classification stage\n- R012: Pipeline handles new videos for existing creators, updates technique pages\n- R013: Prompt templates stored as editable files, loaded at runtime","tasks":[{"description":"Extend Settings with LLM/embedding/Qdrant/prompt config vars, add openai and qdrant-client to requirements.txt, create the Celery app in worker.py, build the sync LLM client with primary/fallback logic, and define Pydantic schemas for all pipeline stage inputs/outputs. This task establishes all infrastructure the pipeline stages depend on.\n\n## Steps\n\n1. Add `openai>=1.0,<2.0`, `qdrant-client>=1.9,<2.0`, and `pyyaml>=6.0,<7.0` to `backend/requirements.txt`.\n\n2. Extend `backend/config.py` `Settings` class with these fields (all with sensible defaults from .env.example):\n   - `llm_api_url: str = 'http://localhost:11434/v1'`\n   - `llm_api_key: str = 'sk-placeholder'`\n   - `llm_model: str = 'qwen2.5:14b-q8_0'`\n   - `llm_fallback_url: str = 'http://localhost:11434/v1'`\n   - `llm_fallback_model: str = 'qwen2.5:14b-q8_0'`\n   - `embedding_api_url: str = 'http://localhost:11434/v1'`\n   - `embedding_model: str = 'nomic-embed-text'`\n   - `embedding_dimensions: int = 768`\n   - `qdrant_url: str = 'http://localhost:6333'`\n   - `qdrant_collection: str = 'chrysopedia'`\n   - `prompts_path: str = './prompts'`\n   - `review_mode: bool = True`\n\n3. Create `backend/worker.py`: Celery app instance using `config.get_settings().redis_url` as broker. Import and register tasks from `pipeline.stages` (import the module so decorators register). Worker must be importable as `celery -A worker worker`.\n\n4. Create `backend/pipeline/__init__.py` (empty).\n\n5. Create `backend/pipeline/schemas.py` with Pydantic models:\n   - `TopicSegment(start_index, end_index, topic_label, summary)` — stage 2 output per segment group\n   - `SegmentationResult(segments: list[TopicSegment])` — stage 2 full output\n   - `ExtractedMoment(title, summary, start_time, end_time, content_type, plugins, raw_transcript)` — stage 3 output per moment\n   - `ExtractionResult(moments: list[ExtractedMoment])` — stage 3 full output\n   - `ClassifiedMoment(moment_index, topic_category, topic_tags, content_type_override)` — stage 4 output per moment\n   - `ClassificationResult(classifications: list[ClassifiedMoment])` — stage 4 full output\n   - `SynthesizedPage(title, slug, topic_category, topic_tags, summary, body_sections, signal_chains, plugins, source_quality)` — stage 5 output\n   - `SynthesisResult(pages: list[SynthesizedPage])` — stage 5 full output\n   All fields should use appropriate types: `content_type` as str (enum value), `body_sections` as dict, `signal_chains` as list[dict], etc.\n\n6. Create `backend/pipeline/llm_client.py`:\n   - Class `LLMClient` initialized with `settings: Settings`\n   - Uses sync `openai.OpenAI(base_url=settings.llm_api_url, api_key=settings.llm_api_key)`\n   - Method `complete(system_prompt: str, user_prompt: str, response_model: type[BaseModel] | None = None) -> str` that:\n     a. Tries primary endpoint with `response_format={'type': 'json_object'}` if response_model is provided\n     b. On connection/timeout error, tries fallback endpoint (`openai.OpenAI(base_url=settings.llm_fallback_url)` with `settings.llm_fallback_model`)\n     c. Returns the raw completion text\n     d. Logs WARNING on fallback, ERROR on total failure\n   - Method `parse_response(text: str, model: type[T]) -> T` that validates JSON via Pydantic `model_validate_json()` with error handling\n   - Catch `openai.APIConnectionError`, `openai.APITimeoutError` for fallback trigger\n   - IMPORTANT: Use sync `openai.OpenAI`, not async — Celery tasks run in sync context\n\n## Must-Haves\n\n- [ ] Settings has all 12 new fields with correct defaults\n- [ ] `openai`, `qdrant-client`, `pyyaml` in requirements.txt\n- [ ] `celery -A worker worker` is syntactically valid (worker.py creates Celery app and imports pipeline.stages)\n- [ ] LLMClient uses sync openai.OpenAI, not AsyncOpenAI\n- [ ] LLMClient has primary/fallback logic with proper exception handling\n- [ ] All 8 Pydantic schema classes defined with correct field types\n\n## Verification\n\n- `cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\"` prints defaults\n- `cd backend && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\"` exits 0\n- `cd backend && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\"` exits 0\n- `cd backend && python -c \"from worker import celery_app; print(celery_app.main)\"` exits 0\n- `grep -q 'openai' backend/requirements.txt && grep -q 'qdrant-client' backend/requirements.txt`","estimate":"1.5h","expectedOutput":["`backend/config.py` — Settings extended with 12 new LLM/embedding/Qdrant/prompt/review fields","`backend/requirements.txt` — openai, qdrant-client, pyyaml added","`backend/worker.py` — Celery app instance configured with Redis broker","`backend/pipeline/__init__.py` — empty package init","`backend/pipeline/schemas.py` — 8 Pydantic models for pipeline stage I/O","`backend/pipeline/llm_client.py` — LLMClient with sync OpenAI primary/fallback"],"files":["backend/config.py","backend/requirements.txt","backend/worker.py","backend/pipeline/__init__.py","backend/pipeline/schemas.py","backend/pipeline/llm_client.py"],"inputs":["`backend/config.py` — existing Settings class to extend","`backend/requirements.txt` — existing dependencies to append to","`backend/database.py` — provides engine/session patterns used by worker","`.env.example` — reference for env var names and defaults"],"taskId":"T01","title":"Config extensions, Celery app, LLM client, and pipeline schemas","verify":"cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\" && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\" && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\" && python -c \"from worker import celery_app; print(celery_app.main)\""},{"description":"Write the 4 prompt template files for stages 2-5 and implement all pipeline stage logic as Celery tasks in `pipeline/stages.py`. Each stage reads from PostgreSQL, loads its prompt template, calls the LLM client, parses the response, writes results back to PostgreSQL, and updates processing_status. The `run_pipeline` orchestrator chains stages and handles resumability.\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| LLM API (primary) | Fall back to secondary endpoint | Fall back to secondary endpoint | Log raw response, retry once with 'output valid JSON' nudge, then fail stage |\n| PostgreSQL | Task fails, Celery retries (max 3) | Task fails, Celery retries | N/A |\n| Prompt template file | Task fails immediately with FileNotFoundError logged | N/A | N/A |\n\n## Negative Tests\n\n- **Malformed inputs**: LLM returns non-JSON text → caught by parse_response, logged with raw text\n- **Error paths**: LLM returns valid JSON but wrong schema → Pydantic ValidationError caught, logged\n- **Boundary conditions**: Empty transcript (0 segments) → stage 2 returns empty segmentation, stages 3-5 skip gracefully\n\n## Steps\n\n1. Create `prompts/stage2_segmentation.txt`: System prompt instructing the LLM to analyze transcript segments and identify topic boundaries. Input: full transcript text with segment indices. Output: JSON matching `SegmentationResult` schema — list of topic segments with start_index, end_index, topic_label, summary. Use XML-style `<transcript>` tags to fence user content from instructions.\n\n2. Create `prompts/stage3_extraction.txt`: System prompt for extracting key moments from a topic segment. Input: segment text with timestamps. Output: JSON matching `ExtractionResult` — list of moments with title, summary, timestamps, content_type (technique/settings/reasoning/workflow), plugins mentioned, raw_transcript excerpt.\n\n3. Create `prompts/stage4_classification.txt`: System prompt for classifying key moments against the canonical tag taxonomy. Input: moment title + summary + list of canonical categories/sub-topics from `canonical_tags.yaml`. Output: JSON matching `ClassificationResult` — topic_category, topic_tags for each moment.\n\n4. Create `prompts/stage5_synthesis.txt`: System prompt for synthesizing a technique page from key moments. Input: all moments for a creator+topic group. Output: JSON matching `SynthesisResult` — title, slug, summary, body_sections (structured prose with sub-aspects), signal_chains, plugins, source_quality assessment.\n\n5. Create `backend/pipeline/stages.py` with these components:\n   a. Helper `_load_prompt(template_name: str) -> str` — reads from `settings.prompts_path / template_name`\n   b. Helper `_get_sync_session()` — creates a sync SQLAlchemy session for Celery tasks using `create_engine()` (sync, not async) with the DATABASE_URL converted from `postgresql+asyncpg://` to `postgresql+psycopg2://` (or use `sqlalchemy.create_engine` with sync URL). **CRITICAL**: Celery is sync, so use `sqlalchemy.orm.Session`, not async.\n   c. `@celery_app.task(bind=True, max_retries=3)` for `stage2_segmentation(self, video_id: str)`: Load transcript segments from DB ordered by segment_index, build prompt with full text, call LLM, parse SegmentationResult, update `topic_label` on each TranscriptSegment row.\n   d. `@celery_app.task(bind=True, max_retries=3)` for `stage3_extraction(self, video_id: str)`: Group segments by topic_label, for each group call LLM to extract moments, create KeyMoment rows in DB, set `processing_status = extracted` on SourceVideo.\n   e. `@celery_app.task(bind=True, max_retries=3)` for `stage4_classification(self, video_id: str)`: Load key moments, load canonical tags from `config/canonical_tags.yaml`, call LLM to classify, update topic_tags on moments. Stage 4 does NOT change processing_status.\n   f. `@celery_app.task(bind=True, max_retries=3)` for `stage5_synthesis(self, video_id: str)`: Group key moments by (creator, topic_category), for each group call LLM to synthesize, create/update TechniquePage rows with body_sections, signal_chains, plugins, topic_tags, summary. Link KeyMoments to their TechniquePage via technique_page_id. Set `processing_status = reviewed` (or `published` if `settings.review_mode is False`).\n   g. `@celery_app.task` for `run_pipeline(video_id: str)`: Check current processing_status, chain the appropriate stages (e.g., if `transcribed`, chain 2→3→4→5; if `extracted`, chain 4→5). Use `celery.chain()` for sequential execution.\n\n6. Import `stages` module in `worker.py` to register tasks (ensure the import line `from pipeline import stages` is present after celery_app creation).\n\n## Must-Haves\n\n- [ ] 4 prompt template files in `prompts/` with clear instruction/data separation\n- [ ] All 5 stage tasks + run_pipeline orchestrator defined in stages.py\n- [ ] Celery tasks use SYNC SQLAlchemy sessions (not async)\n- [ ] Stage 2 updates topic_label on TranscriptSegment rows\n- [ ] Stage 3 creates KeyMoment rows and sets processing_status=extracted\n- [ ] Stage 4 loads canonical_tags.yaml and classifies moments\n- [ ] Stage 5 creates TechniquePage rows and sets processing_status=reviewed/published\n- [ ] run_pipeline handles resumability based on current processing_status\n- [ ] Prompts fence user content with XML-style tags\n\n## Verification\n\n- `test -f prompts/stage2_segmentation.txt && test -f prompts/stage3_extraction.txt && test -f prompts/stage4_classification.txt && test -f prompts/stage5_synthesis.txt` — all 4 prompt files exist\n- `cd backend && python -c \"from pipeline.stages import run_pipeline, stage2_segmentation, stage3_extraction, stage4_classification, stage5_synthesis; print('all stages imported')\"` — imports succeed\n- `cd backend && python -c \"from worker import celery_app; print([t for t in celery_app.tasks if 'stage' in t or 'pipeline' in t])\"` — shows registered tasks\n\n## Observability Impact\n\n- Signals added: INFO log at start/end of each stage with video_id and duration, WARNING on LLM fallback, ERROR on parse failure with raw response excerpt\n- How a future agent inspects this: query `source_videos.processing_status` to see pipeline progress, check Celery worker logs for stage timing\n- Failure state exposed: video stays at last successful processing_status, error logged with stage name and video_id","estimate":"2.5h","expectedOutput":["`prompts/stage2_segmentation.txt` — topic boundary detection prompt template","`prompts/stage3_extraction.txt` — key moment extraction prompt template","`prompts/stage4_classification.txt` — classification/tagging prompt template","`prompts/stage5_synthesis.txt` — technique page synthesis prompt template","`backend/pipeline/stages.py` — 5 Celery tasks (stages 2-5 + run_pipeline orchestrator)","`backend/worker.py` — updated with pipeline.stages import for task registration"],"files":["prompts/stage2_segmentation.txt","prompts/stage3_extraction.txt","prompts/stage4_classification.txt","prompts/stage5_synthesis.txt","backend/pipeline/stages.py","backend/worker.py"],"inputs":["`backend/pipeline/schemas.py` — Pydantic models for stage I/O from T01","`backend/pipeline/llm_client.py` — LLMClient with primary/fallback from T01","`backend/config.py` — Settings with prompts_path, review_mode, llm_* fields from T01","`backend/worker.py` — Celery app instance from T01","`backend/models.py` — ORM models (TranscriptSegment, KeyMoment, TechniquePage, SourceVideo, ProcessingStatus)","`config/canonical_tags.yaml` — canonical tag taxonomy for stage 4 classification"],"taskId":"T02","title":"Prompt templates and pipeline stages 2-5 with orchestrator","verify":"test -f prompts/stage2_segmentation.txt && test -f prompts/stage3_extraction.txt && test -f prompts/stage4_classification.txt && test -f prompts/stage5_synthesis.txt && cd backend && python -c \"from pipeline.stages import run_pipeline, stage2_segmentation, stage3_extraction, stage4_classification, stage5_synthesis; print('all stages imported')\""},{"description":"Create the embedding client (sync OpenAI-compatible /v1/embeddings) and Qdrant client (collection management + upsert). Wire embedding generation into the pipeline after stage 5 synthesis, so technique page summaries and key moment summaries are embedded and upserted to Qdrant with metadata payloads.\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Embedding API | Log error, skip embedding for this batch, pipeline continues | Same as error | Validate vector dimensions match config, log mismatch |\n| Qdrant server | Log error, skip upsert, pipeline continues (embeddings are not blocking) | Same as error | N/A |\n\n## Steps\n\n1. Create `backend/pipeline/embedding_client.py`:\n   - Class `EmbeddingClient` initialized with `settings: Settings`\n   - Uses sync `openai.OpenAI(base_url=settings.embedding_api_url, api_key=settings.llm_api_key)`\n   - Method `embed(texts: list[str]) -> list[list[float]]` that calls `client.embeddings.create(model=settings.embedding_model, input=texts)` and returns the vector list\n   - Handle `openai.APIConnectionError` gracefully — log and return empty list\n   - Validate returned vector dimensions match `settings.embedding_dimensions`\n\n2. Create `backend/pipeline/qdrant_client.py`:\n   - Class `QdrantManager` initialized with `settings: Settings`\n   - Uses sync `qdrant_client.QdrantClient(url=settings.qdrant_url)`\n   - Method `ensure_collection()` — checks `collection_exists()`, creates with `VectorParams(size=settings.embedding_dimensions, distance=Distance.COSINE)` if not\n   - Method `upsert_points(points: list[PointStruct])` — wraps `client.upsert()`\n   - Method `upsert_technique_pages(pages: list[dict], vectors: list[list[float]])` — builds PointStruct list with payload (page_id, creator_id, title, topic_category, topic_tags, summary) and upserts\n   - Method `upsert_key_moments(moments: list[dict], vectors: list[list[float]])` — builds PointStruct list with payload (moment_id, source_video_id, title, start_time, end_time, content_type) and upserts\n   - Handle `qdrant_client.http.exceptions.UnexpectedResponse` for connection errors\n\n3. Add a new Celery task `stage6_embed_and_index(video_id: str)` in `backend/pipeline/stages.py`:\n   - Load all KeyMoments and TechniquePages created for this video\n   - Build text strings for embedding (moment: title + summary; page: title + summary + topic_category)\n   - Call EmbeddingClient.embed() to get vectors\n   - Call QdrantManager.ensure_collection() then upsert_technique_pages() and upsert_key_moments()\n   - This stage runs after stage 5 in the pipeline chain but does NOT update processing_status (it's a side-effect)\n   - If embedding/Qdrant fails, log error but don't fail the pipeline — embeddings can be regenerated later\n\n4. Update `run_pipeline` in `stages.py` to append `stage6_embed_and_index` to the chain.\n\n## Must-Haves\n\n- [ ] EmbeddingClient uses sync openai.OpenAI for /v1/embeddings calls\n- [ ] QdrantManager creates collection only if not exists\n- [ ] Qdrant points include metadata payloads (page_id/moment_id, creator_id, topic, timestamps)\n- [ ] stage6 is non-blocking — embedding/Qdrant failures don't fail the pipeline\n- [ ] Vector dimension from config, not hardcoded\n\n## Verification\n\n- `cd backend && python -c \"from pipeline.embedding_client import EmbeddingClient; print('embed ok')\"` exits 0\n- `cd backend && python -c \"from pipeline.qdrant_client import QdrantManager; print('qdrant ok')\"` exits 0\n- `cd backend && python -c \"from pipeline.stages import stage6_embed_and_index; print('stage6 ok')\"` exits 0\n\n## Observability Impact\n\n- Signals added: INFO log for embedding batch size and Qdrant upsert count, WARNING on embedding/Qdrant failures with error details\n- How a future agent inspects this: check Qdrant dashboard at QDRANT_URL, query collection point count\n- Failure state exposed: embedding failures logged but pipeline completes successfully","estimate":"1.5h","expectedOutput":["`backend/pipeline/embedding_client.py` — EmbeddingClient with sync OpenAI embeddings","`backend/pipeline/qdrant_client.py` — QdrantManager with collection management and upsert","`backend/pipeline/stages.py` — updated with stage6_embed_and_index task and run_pipeline chain"],"files":["backend/pipeline/embedding_client.py","backend/pipeline/qdrant_client.py","backend/pipeline/stages.py"],"inputs":["`backend/pipeline/stages.py` — pipeline stages from T02 to add stage6 and update run_pipeline","`backend/config.py` — Settings with embedding_api_url, embedding_model, embedding_dimensions, qdrant_url, qdrant_collection from T01","`backend/pipeline/schemas.py` — Pydantic schemas from T01","`backend/models.py` — KeyMoment and TechniquePage ORM models"],"taskId":"T03","title":"Qdrant integration and embedding client","verify":"cd backend && python -c \"from pipeline.embedding_client import EmbeddingClient; print('embed ok')\" && python -c \"from pipeline.qdrant_client import QdrantManager; print('qdrant ok')\" && python -c \"from pipeline.stages import stage6_embed_and_index; print('stage6 ok')\""},{"description":"Connect the existing ingest endpoint to the pipeline by dispatching `run_pipeline.delay()` after successful commit, and add a manual re-trigger API endpoint for re-processing videos.\n\n## Steps\n\n1. Modify `backend/routers/ingest.py`:\n   - After the `await db.commit()` and refresh calls (after the successful response is ready), dispatch the pipeline: `from pipeline.stages import run_pipeline; run_pipeline.delay(str(video.id))`\n   - Wrap the dispatch in try/except to avoid failing the ingest response if Redis/Celery is down — log WARNING on dispatch failure\n   - The dispatch must happen AFTER commit so the worker can find the data in PostgreSQL\n\n2. Create `backend/routers/pipeline.py`:\n   - Router with `prefix='/pipeline'`, tag `['pipeline']`\n   - `POST /trigger/{video_id}`: Look up SourceVideo by id, verify it exists (404 if not), dispatch `run_pipeline.delay(str(video_id))`, return `{\"status\": \"triggered\", \"video_id\": str(video_id), \"current_processing_status\": video.processing_status.value}`\n   - Uses `get_session` dependency for DB access\n\n3. Mount the pipeline router in `backend/main.py` under `/api/v1`.\n\n## Must-Haves\n\n- [ ] Ingest endpoint dispatches run_pipeline.delay() after commit\n- [ ] Pipeline dispatch failure does not fail the ingest response\n- [ ] POST /api/v1/pipeline/trigger/{video_id} exists and returns status\n- [ ] 404 returned for non-existent video_id\n- [ ] Pipeline router mounted in main.py\n\n## Verification\n\n- `cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\"` shows `['/trigger/{video_id}']`\n- `grep -q 'pipeline' backend/main.py` exits 0\n- `grep -q 'run_pipeline' backend/routers/ingest.py` exits 0","estimate":"45m","expectedOutput":["`backend/routers/ingest.py` — updated with run_pipeline.delay() dispatch after commit","`backend/routers/pipeline.py` — new router with POST /trigger/{video_id} endpoint","`backend/main.py` — updated with pipeline router mount"],"files":["backend/routers/ingest.py","backend/routers/pipeline.py","backend/main.py"],"inputs":["`backend/routers/ingest.py` — existing ingest endpoint to add pipeline dispatch to","`backend/main.py` — existing app to mount new router","`backend/pipeline/stages.py` — run_pipeline task from T02","`backend/models.py` — SourceVideo model for lookup"],"taskId":"T04","title":"Wire ingest-to-pipeline trigger and add manual re-trigger endpoint","verify":"cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\" && grep -q 'pipeline' backend/main.py && grep -q 'run_pipeline' backend/routers/ingest.py"},{"description":"Write comprehensive integration tests with mocked LLM and Qdrant that verify the entire pipeline flow. Tests use the existing pytest-asyncio infrastructure from S02, extended with mocks for the OpenAI client and Qdrant client.\n\n## Negative Tests\n\n- **Malformed inputs**: LLM returns invalid JSON → pipeline handles gracefully, logs error\n- **Error paths**: LLM primary endpoint connection refused → falls back to secondary\n- **Boundary conditions**: Video with 0 segments → pipeline completes without errors, no moments created\n\n## Steps\n\n1. Create `backend/tests/fixtures/mock_llm_responses.py`:\n   - Dictionary mapping stage names to valid JSON response strings matching each Pydantic schema\n   - Stage 2: SegmentationResult with 2 topic segments covering the 5 sample transcript segments\n   - Stage 3: ExtractionResult with 2 key moments (one technique, one settings)\n   - Stage 4: ClassificationResult mapping moments to canonical tags\n   - Stage 5: SynthesisResult with a technique page including body_sections, signal_chains, plugins\n   - Embedding response: list of 768-dimensional vectors (can be random floats for testing)\n\n2. Create `backend/tests/test_pipeline.py` with these tests using `unittest.mock.patch` on `openai.OpenAI` and `qdrant_client.QdrantClient`:\n   a. `test_stage2_segmentation_updates_topic_labels`: Ingest sample transcript, run stage2 with mocked LLM, verify TranscriptSegment rows have topic_label set\n   b. `test_stage3_extraction_creates_key_moments`: Run stages 2+3, verify KeyMoment rows created with correct fields, processing_status = extracted\n   c. `test_stage4_classification_assigns_tags`: Run stages 2+3+4, verify moments have topic_category and topic_tags from canonical list\n   d. `test_stage5_synthesis_creates_technique_pages`: Run full pipeline stages 2-5, verify TechniquePage rows created with body_sections, signal_chains, summary. Verify KeyMoments linked to TechniquePage via technique_page_id\n   e. `test_stage6_embeds_and_upserts_to_qdrant`: Run full pipeline, verify EmbeddingClient.embed called with correct texts, QdrantManager.upsert called with correct payloads\n   f. `test_run_pipeline_resumes_from_extracted`: Set video processing_status to extracted, run pipeline, verify only stages 4+5+6 execute (not 2+3)\n   g. `test_pipeline_trigger_endpoint`: POST to /api/v1/pipeline/trigger/{video_id} with mocked Celery delay, verify 200 response\n   h. `test_pipeline_trigger_404_for_missing_video`: POST to /api/v1/pipeline/trigger/{nonexistent_id}, verify 404\n   i. `test_ingest_dispatches_pipeline`: POST transcript to ingest endpoint with mocked run_pipeline.delay, verify delay was called with video_id\n   j. `test_llm_fallback_on_primary_failure`: Mock primary endpoint to raise APIConnectionError, verify fallback endpoint is called\n\n   IMPORTANT: Pipeline stages use SYNC SQLAlchemy (not async). Tests that call stage functions directly need a real PostgreSQL test database. Use the existing conftest.py pattern but create sync engine/sessions for pipeline stage calls. Mock the LLM and Qdrant clients, not the DB.\n\n3. Update `backend/tests/conftest.py` to add:\n   - A fixture that creates a sync SQLAlchemy engine pointing to the test database (for pipeline stage tests)\n   - A fixture that pre-ingests a sample transcript and returns the video_id (for pipeline tests to use)\n   - Any necessary mock fixtures for LLMClient and QdrantManager\n\n## Must-Haves\n\n- [ ] 10 integration tests covering all pipeline stages, trigger endpoints, resumability, and fallback\n- [ ] All LLM calls mocked with realistic response fixtures\n- [ ] Qdrant calls mocked — no real Qdrant needed\n- [ ] Pipeline stage tests use sync DB sessions (matching production Celery behavior)\n- [ ] At least one negative test (LLM fallback on primary failure)\n- [ ] All tests pass with `python -m pytest tests/test_pipeline.py -v`\n\n## Verification\n\n- `cd backend && python -m pytest tests/test_pipeline.py -v` — all tests pass\n- `cd backend && python -m pytest tests/ -v` — all tests (including S02 ingest tests) still pass\n\n## Observability Impact\n\n- Signals added: test output shows per-test pass/fail with timing, mock call assertions verify logging would fire in production\n- How a future agent inspects this: run `pytest tests/test_pipeline.py -v` to see all pipeline test results","estimate":"2h","expectedOutput":["`backend/tests/fixtures/mock_llm_responses.py` — mock LLM response fixtures for all stages","`backend/tests/test_pipeline.py` — 10 integration tests for full pipeline flow","`backend/tests/conftest.py` — extended with sync engine fixture and pre-ingest fixture"],"files":["backend/tests/fixtures/mock_llm_responses.py","backend/tests/test_pipeline.py","backend/tests/conftest.py"],"inputs":["`backend/pipeline/stages.py` — all pipeline stages from T02+T03","`backend/pipeline/llm_client.py` — LLMClient to mock from T01","`backend/pipeline/embedding_client.py` — EmbeddingClient to mock from T03","`backend/pipeline/qdrant_client.py` — QdrantManager to mock from T03","`backend/pipeline/schemas.py` — Pydantic models for response fixtures from T01","`backend/routers/pipeline.py` — trigger endpoint from T04","`backend/routers/ingest.py` — updated ingest with pipeline dispatch from T04","`backend/tests/conftest.py` — existing test infrastructure from S02","`backend/tests/fixtures/sample_transcript.json` — sample data from S02","`backend/models.py` — ORM models for DB verification"],"taskId":"T05","title":"Integration tests for pipeline, embedding, Qdrant, and trigger endpoints","verify":"cd backend && python -m pytest tests/test_pipeline.py -v && python -m pytest tests/ -v"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":10438,"cacheRead":73815,"cacheWrite":2742,"totalTokens":86996,"cost":{"input":0.000005,"output":0.26095,"cacheRead":0.036907499999999996,"cacheWrite":0.0171375,"total":0.31499999999999995}},"stopReason":"toolUse","timestamp":1774823009218}}
{"type":"message","id":"c4084f24","parentId":"b6771226","timestamp":"2026-03-29T22:26:36.120Z","message":{"role":"toolResult","toolCallId":"toolu_014MciRVXREo6EEhxzGGEQTc","toolName":"gsd_plan_slice","content":[{"type":"text","text":"Planned slice S03 (M001)"}],"details":{"operation":"plan_slice","milestoneId":"M001","sliceId":"S03","planPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/S03-PLAN.md","taskPlanPaths":["/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T01-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T02-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T03-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T04-PLAN.md","/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/T05-PLAN.md"]},"isError":false,"timestamp":1774823196118}}
{"type":"message","id":"420f1ce5","parentId":"c4084f24","timestamp":"2026-03-29T22:26:40.501Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me self-audit the plan by checking the rendered files:"},{"type":"toolCall","id":"toolu_014ezd4Gjny6PsUk42i8dPPe","name":"read","arguments":{"path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/S03-PLAN.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":104,"cacheRead":76557,"cacheWrite":10492,"totalTokens":87154,"cost":{"input":0.000005,"output":0.0026000000000000003,"cacheRead":0.0382785,"cacheWrite":0.06557500000000001,"total":0.10645850000000001}},"stopReason":"toolUse","timestamp":1774823196119}}
{"type":"message","id":"befa5eff","parentId":"420f1ce5","timestamp":"2026-03-29T22:26:40.505Z","message":{"role":"toolResult","toolCallId":"toolu_014ezd4Gjny6PsUk42i8dPPe","toolName":"read","content":[{"type":"text","text":"# S03: LLM Extraction Pipeline + Qdrant Integration\n\n**Goal:** A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings. Pipeline is resumable per-video per-stage, uses configurable LLM endpoints with fallback, and loads prompt templates from disk.\n**Demo:** After this: A transcript JSON triggers stages 2-5: segmentation → extraction → classification → synthesis. Technique pages with key moments appear in DB. Qdrant has searchable embeddings.\n\n## Tasks\n- [ ] **T01: Config extensions, Celery app, LLM client, and pipeline schemas** — Extend Settings with LLM/embedding/Qdrant/prompt config vars, add openai and qdrant-client to requirements.txt, create the Celery app in worker.py, build the sync LLM client with primary/fallback logic, and define Pydantic schemas for all pipeline stage inputs/outputs. This task establishes all infrastructure the pipeline stages depend on.\n\n## Steps\n\n1. Add `openai>=1.0,<2.0`, `qdrant-client>=1.9,<2.0`, and `pyyaml>=6.0,<7.0` to `backend/requirements.txt`.\n\n2. Extend `backend/config.py` `Settings` class with these fields (all with sensible defaults from .env.example):\n   - `llm_api_url: str = 'http://localhost:11434/v1'`\n   - `llm_api_key: str = 'sk-placeholder'`\n   - `llm_model: str = 'qwen2.5:14b-q8_0'`\n   - `llm_fallback_url: str = 'http://localhost:11434/v1'`\n   - `llm_fallback_model: str = 'qwen2.5:14b-q8_0'`\n   - `embedding_api_url: str = 'http://localhost:11434/v1'`\n   - `embedding_model: str = 'nomic-embed-text'`\n   - `embedding_dimensions: int = 768`\n   - `qdrant_url: str = 'http://localhost:6333'`\n   - `qdrant_collection: str = 'chrysopedia'`\n   - `prompts_path: str = './prompts'`\n   - `review_mode: bool = True`\n\n3. Create `backend/worker.py`: Celery app instance using `config.get_settings().redis_url` as broker. Import and register tasks from `pipeline.stages` (import the module so decorators register). Worker must be importable as `celery -A worker worker`.\n\n4. Create `backend/pipeline/__init__.py` (empty).\n\n5. Create `backend/pipeline/schemas.py` with Pydantic models:\n   - `TopicSegment(start_index, end_index, topic_label, summary)` — stage 2 output per segment group\n   - `SegmentationResult(segments: list[TopicSegment])` — stage 2 full output\n   - `ExtractedMoment(title, summary, start_time, end_time, content_type, plugins, raw_transcript)` — stage 3 output per moment\n   - `ExtractionResult(moments: list[ExtractedMoment])` — stage 3 full output\n   - `ClassifiedMoment(moment_index, topic_category, topic_tags, content_type_override)` — stage 4 output per moment\n   - `ClassificationResult(classifications: list[ClassifiedMoment])` — stage 4 full output\n   - `SynthesizedPage(title, slug, topic_category, topic_tags, summary, body_sections, signal_chains, plugins, source_quality)` — stage 5 output\n   - `SynthesisResult(pages: list[SynthesizedPage])` — stage 5 full output\n   All fields should use appropriate types: `content_type` as str (enum value), `body_sections` as dict, `signal_chains` as list[dict], etc.\n\n6. Create `backend/pipeline/llm_client.py`:\n   - Class `LLMClient` initialized with `settings: Settings`\n   - Uses sync `openai.OpenAI(base_url=settings.llm_api_url, api_key=settings.llm_api_key)`\n   - Method `complete(system_prompt: str, user_prompt: str, response_model: type[BaseModel] | None = None) -> str` that:\n     a. Tries primary endpoint with `response_format={'type': 'json_object'}` if response_model is provided\n     b. On connection/timeout error, tries fallback endpoint (`openai.OpenAI(base_url=settings.llm_fallback_url)` with `settings.llm_fallback_model`)\n     c. Returns the raw completion text\n     d. Logs WARNING on fallback, ERROR on total failure\n   - Method `parse_response(text: str, model: type[T]) -> T` that validates JSON via Pydantic `model_validate_json()` with error handling\n   - Catch `openai.APIConnectionError`, `openai.APITimeoutError` for fallback trigger\n   - IMPORTANT: Use sync `openai.OpenAI`, not async — Celery tasks run in sync context\n\n## Must-Haves\n\n- [ ] Settings has all 12 new fields with correct defaults\n- [ ] `openai`, `qdrant-client`, `pyyaml` in requirements.txt\n- [ ] `celery -A worker worker` is syntactically valid (worker.py creates Celery app and imports pipeline.stages)\n- [ ] LLMClient uses sync openai.OpenAI, not AsyncOpenAI\n- [ ] LLMClient has primary/fallback logic with proper exception handling\n- [ ] All 8 Pydantic schema classes defined with correct field types\n\n## Verification\n\n- `cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\"` prints defaults\n- `cd backend && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\"` exits 0\n- `cd backend && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\"` exits 0\n- `cd backend && python -c \"from worker import celery_app; print(celery_app.main)\"` exits 0\n- `grep -q 'openai' backend/requirements.txt && grep -q 'qdrant-client' backend/requirements.txt`\n  - Estimate: 1.5h\n  - Files: backend/config.py, backend/requirements.txt, backend/worker.py, backend/pipeline/__init__.py, backend/pipeline/schemas.py, backend/pipeline/llm_client.py\n  - Verify: cd backend && python -c \"from config import Settings; s = Settings(); print(s.llm_api_url, s.qdrant_url, s.review_mode)\" && python -c \"from pipeline.schemas import SegmentationResult, ExtractionResult, ClassificationResult, SynthesisResult; print('schemas ok')\" && python -c \"from pipeline.llm_client import LLMClient; print('client ok')\" && python -c \"from worker import celery_app; print(celery_app.main)\"\n- [ ] **T02: Prompt templates and pipeline stages 2-5 with orchestrator** — Write the 4 prompt template files for stages 2-5 and implement all pipeline stage logic as Celery tasks in `pipeline/stages.py`. Each stage reads from PostgreSQL, loads its prompt template, calls the LLM client, parses the response, writes results back to PostgreSQL, and updates processing_status. The `run_pipeline` orchestrator chains stages and handles resumability.\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| LLM API (primary) | Fall back to secondary endpoint | Fall back to secondary endpoint | Log raw response, retry once with 'output valid JSON' nudge, then fail stage |\n| PostgreSQL | Task fails, Celery retries (max 3) | Task fails, Celery retries | N/A |\n| Prompt template file | Task fails immediately with FileNotFoundError logged | N/A | N/A |\n\n## Negative Tests\n\n- **Malformed inputs**: LLM returns non-JSON text → caught by parse_response, logged with raw text\n- **Error paths**: LLM returns valid JSON but wrong schema → Pydantic ValidationError caught, logged\n- **Boundary conditions**: Empty transcript (0 segments) → stage 2 returns empty segmentation, stages 3-5 skip gracefully\n\n## Steps\n\n1. Create `prompts/stage2_segmentation.txt`: System prompt instructing the LLM to analyze transcript segments and identify topic boundaries. Input: full transcript text with segment indices. Output: JSON matching `SegmentationResult` schema — list of topic segments with start_index, end_index, topic_label, summary. Use XML-style `<transcript>` tags to fence user content from instructions.\n\n2. Create `prompts/stage3_extraction.txt`: System prompt for extracting key moments from a topic segment. Input: segment text with timestamps. Output: JSON matching `ExtractionResult` — list of moments with title, summary, timestamps, content_type (technique/settings/reasoning/workflow), plugins mentioned, raw_transcript excerpt.\n\n3. Create `prompts/stage4_classification.txt`: System prompt for classifying key moments against the canonical tag taxonomy. Input: moment title + summary + list of canonical categories/sub-topics from `canonical_tags.yaml`. Output: JSON matching `ClassificationResult` — topic_category, topic_tags for each moment.\n\n4. Create `prompts/stage5_synthesis.txt`: System prompt for synthesizing a technique page from key moments. Input: all moments for a creator+topic group. Output: JSON matching `SynthesisResult` — title, slug, summary, body_sections (structured prose with sub-aspects), signal_chains, plugins, source_quality assessment.\n\n5. Create `backend/pipeline/stages.py` with these components:\n   a. Helper `_load_prompt(template_name: str) -> str` — reads from `settings.prompts_path / template_name`\n   b. Helper `_get_sync_session()` — creates a sync SQLAlchemy session for Celery tasks using `create_engine()` (sync, not async) with the DATABASE_URL converted from `postgresql+asyncpg://` to `postgresql+psycopg2://` (or use `sqlalchemy.create_engine` with sync URL). **CRITICAL**: Celery is sync, so use `sqlalchemy.orm.Session`, not async.\n   c. `@celery_app.task(bind=True, max_retries=3)` for `stage2_segmentation(self, video_id: str)`: Load transcript segments from DB ordered by segment_index, build prompt with full text, call LLM, parse SegmentationResult, update `topic_label` on each TranscriptSegment row.\n   d. `@celery_app.task(bind=True, max_retries=3)` for `stage3_extraction(self, video_id: str)`: Group segments by topic_label, for each group call LLM to extract moments, create KeyMoment rows in DB, set `processing_status = extracted` on SourceVideo.\n   e. `@celery_app.task(bind=True, max_retries=3)` for `stage4_classification(self, video_id: str)`: Load key moments, load canonical tags from `config/canonical_tags.yaml`, call LLM to classify, update topic_tags on moments. Stage 4 does NOT change processing_status.\n   f. `@celery_app.task(bind=True, max_retries=3)` for `stage5_synthesis(self, video_id: str)`: Group key moments by (creator, topic_category), for each group call LLM to synthesize, create/update TechniquePage rows with body_sections, signal_chains, plugins, topic_tags, summary. Link KeyMoments to their TechniquePage via technique_page_id. Set `processing_status = reviewed` (or `published` if `settings.review_mode is False`).\n   g. `@celery_app.task` for `run_pipeline(video_id: str)`: Check current processing_status, chain the appropriate stages (e.g., if `transcribed`, chain 2→3→4→5; if `extracted`, chain 4→5). Use `celery.chain()` for sequential execution.\n\n6. Import `stages` module in `worker.py` to register tasks (ensure the import line `from pipeline import stages` is present after celery_app creation).\n\n## Must-Haves\n\n- [ ] 4 prompt template files in `prompts/` with clear instruction/data separation\n- [ ] All 5 stage tasks + run_pipeline orchestrator defined in stages.py\n- [ ] Celery tasks use SYNC SQLAlchemy sessions (not async)\n- [ ] Stage 2 updates topic_label on TranscriptSegment rows\n- [ ] Stage 3 creates KeyMoment rows and sets processing_status=extracted\n- [ ] Stage 4 loads canonical_tags.yaml and classifies moments\n- [ ] Stage 5 creates TechniquePage rows and sets processing_status=reviewed/published\n- [ ] run_pipeline handles resumability based on current processing_status\n- [ ] Prompts fence user content with XML-style tags\n\n## Verification\n\n- `test -f prompts/stage2_segmentation.txt && test -f prompts/stage3_extraction.txt && test -f prompts/stage4_classification.txt && test -f prompts/stage5_synthesis.txt` — all 4 prompt files exist\n- `cd backend && python -c \"from pipeline.stages import run_pipeline, stage2_segmentation, stage3_extraction, stage4_classification, stage5_synthesis; print('all stages imported')\"` — imports succeed\n- `cd backend && python -c \"from worker import celery_app; print([t for t in celery_app.tasks if 'stage' in t or 'pipeline' in t])\"` — shows registered tasks\n\n## Observability Impact\n\n- Signals added: INFO log at start/end of each stage with video_id and duration, WARNING on LLM fallback, ERROR on parse failure with raw response excerpt\n- How a future agent inspects this: query `source_videos.processing_status` to see pipeline progress, check Celery worker logs for stage timing\n- Failure state exposed: video stays at last successful processing_status, error logged with stage name and video_id\n  - Estimate: 2.5h\n  - Files: prompts/stage2_segmentation.txt, prompts/stage3_extraction.txt, prompts/stage4_classification.txt, prompts/stage5_synthesis.txt, backend/pipeline/stages.py, backend/worker.py\n  - Verify: test -f prompts/stage2_segmentation.txt && test -f prompts/stage3_extraction.txt && test -f prompts/stage4_classification.txt && test -f prompts/stage5_synthesis.txt && cd backend && python -c \"from pipeline.stages import run_pipeline, stage2_segmentation, stage3_extraction, stage4_classification, stage5_synthesis; print('all stages imported')\"\n- [ ] **T03: Qdrant integration and embedding client** — Create the embedding client (sync OpenAI-compatible /v1/embeddings) and Qdrant client (collection management + upsert). Wire embedding generation into the pipeline after stage 5 synthesis, so technique page summaries and key moment summaries are embedded and upserted to Qdrant with metadata payloads.\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| Embedding API | Log error, skip embedding for this batch, pipeline continues | Same as error | Validate vector dimensions match config, log mismatch |\n| Qdrant server | Log error, skip upsert, pipeline continues (embeddings are not blocking) | Same as error | N/A |\n\n## Steps\n\n1. Create `backend/pipeline/embedding_client.py`:\n   - Class `EmbeddingClient` initialized with `settings: Settings`\n   - Uses sync `openai.OpenAI(base_url=settings.embedding_api_url, api_key=settings.llm_api_key)`\n   - Method `embed(texts: list[str]) -> list[list[float]]` that calls `client.embeddings.create(model=settings.embedding_model, input=texts)` and returns the vector list\n   - Handle `openai.APIConnectionError` gracefully — log and return empty list\n   - Validate returned vector dimensions match `settings.embedding_dimensions`\n\n2. Create `backend/pipeline/qdrant_client.py`:\n   - Class `QdrantManager` initialized with `settings: Settings`\n   - Uses sync `qdrant_client.QdrantClient(url=settings.qdrant_url)`\n   - Method `ensure_collection()` — checks `collection_exists()`, creates with `VectorParams(size=settings.embedding_dimensions, distance=Distance.COSINE)` if not\n   - Method `upsert_points(points: list[PointStruct])` — wraps `client.upsert()`\n   - Method `upsert_technique_pages(pages: list[dict], vectors: list[list[float]])` — builds PointStruct list with payload (page_id, creator_id, title, topic_category, topic_tags, summary) and upserts\n   - Method `upsert_key_moments(moments: list[dict], vectors: list[list[float]])` — builds PointStruct list with payload (moment_id, source_video_id, title, start_time, end_time, content_type) and upserts\n   - Handle `qdrant_client.http.exceptions.UnexpectedResponse` for connection errors\n\n3. Add a new Celery task `stage6_embed_and_index(video_id: str)` in `backend/pipeline/stages.py`:\n   - Load all KeyMoments and TechniquePages created for this video\n   - Build text strings for embedding (moment: title + summary; page: title + summary + topic_category)\n   - Call EmbeddingClient.embed() to get vectors\n   - Call QdrantManager.ensure_collection() then upsert_technique_pages() and upsert_key_moments()\n   - This stage runs after stage 5 in the pipeline chain but does NOT update processing_status (it's a side-effect)\n   - If embedding/Qdrant fails, log error but don't fail the pipeline — embeddings can be regenerated later\n\n4. Update `run_pipeline` in `stages.py` to append `stage6_embed_and_index` to the chain.\n\n## Must-Haves\n\n- [ ] EmbeddingClient uses sync openai.OpenAI for /v1/embeddings calls\n- [ ] QdrantManager creates collection only if not exists\n- [ ] Qdrant points include metadata payloads (page_id/moment_id, creator_id, topic, timestamps)\n- [ ] stage6 is non-blocking — embedding/Qdrant failures don't fail the pipeline\n- [ ] Vector dimension from config, not hardcoded\n\n## Verification\n\n- `cd backend && python -c \"from pipeline.embedding_client import EmbeddingClient; print('embed ok')\"` exits 0\n- `cd backend && python -c \"from pipeline.qdrant_client import QdrantManager; print('qdrant ok')\"` exits 0\n- `cd backend && python -c \"from pipeline.stages import stage6_embed_and_index; print('stage6 ok')\"` exits 0\n\n## Observability Impact\n\n- Signals added: INFO log for embedding batch size and Qdrant upsert count, WARNING on embedding/Qdrant failures with error details\n- How a future agent inspects this: check Qdrant dashboard at QDRANT_URL, query collection point count\n- Failure state exposed: embedding failures logged but pipeline completes successfully\n  - Estimate: 1.5h\n  - Files: backend/pipeline/embedding_client.py, backend/pipeline/qdrant_client.py, backend/pipeline/stages.py\n  - Verify: cd backend && python -c \"from pipeline.embedding_client import EmbeddingClient; print('embed ok')\" && python -c \"from pipeline.qdrant_client import QdrantManager; print('qdrant ok')\" && python -c \"from pipeline.stages import stage6_embed_and_index; print('stage6 ok')\"\n- [ ] **T04: Wire ingest-to-pipeline trigger and add manual re-trigger endpoint** — Connect the existing ingest endpoint to the pipeline by dispatching `run_pipeline.delay()` after successful commit, and add a manual re-trigger API endpoint for re-processing videos.\n\n## Steps\n\n1. Modify `backend/routers/ingest.py`:\n   - After the `await db.commit()` and refresh calls (after the successful response is ready), dispatch the pipeline: `from pipeline.stages import run_pipeline; run_pipeline.delay(str(video.id))`\n   - Wrap the dispatch in try/except to avoid failing the ingest response if Redis/Celery is down — log WARNING on dispatch failure\n   - The dispatch must happen AFTER commit so the worker can find the data in PostgreSQL\n\n2. Create `backend/routers/pipeline.py`:\n   - Router with `prefix='/pipeline'`, tag `['pipeline']`\n   - `POST /trigger/{video_id}`: Look up SourceVideo by id, verify it exists (404 if not), dispatch `run_pipeline.delay(str(video_id))`, return `{\"status\": \"triggered\", \"video_id\": str(video_id), \"current_processing_status\": video.processing_status.value}`\n   - Uses `get_session` dependency for DB access\n\n3. Mount the pipeline router in `backend/main.py` under `/api/v1`.\n\n## Must-Haves\n\n- [ ] Ingest endpoint dispatches run_pipeline.delay() after commit\n- [ ] Pipeline dispatch failure does not fail the ingest response\n- [ ] POST /api/v1/pipeline/trigger/{video_id} exists and returns status\n- [ ] 404 returned for non-existent video_id\n- [ ] Pipeline router mounted in main.py\n\n## Verification\n\n- `cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\"` shows `['/trigger/{video_id}']`\n- `grep -q 'pipeline' backend/main.py` exits 0\n- `grep -q 'run_pipeline' backend/routers/ingest.py` exits 0\n  - Estimate: 45m\n  - Files: backend/routers/ingest.py, backend/routers/pipeline.py, backend/main.py\n  - Verify: cd backend && python -c \"from routers.pipeline import router; print([r.path for r in router.routes])\" && grep -q 'pipeline' backend/main.py && grep -q 'run_pipeline' backend/routers/ingest.py\n- [ ] **T05: Integration tests for pipeline, embedding, Qdrant, and trigger endpoints** — Write comprehensive integration tests with mocked LLM and Qdrant that verify the entire pipeline flow. Tests use the existing pytest-asyncio infrastructure from S02, extended with mocks for the OpenAI client and Qdrant client.\n\n## Negative Tests\n\n- **Malformed inputs**: LLM returns invalid JSON → pipeline handles gracefully, logs error\n- **Error paths**: LLM primary endpoint connection refused → falls back to secondary\n- **Boundary conditions**: Video with 0 segments → pipeline completes without errors, no moments created\n\n## Steps\n\n1. Create `backend/tests/fixtures/mock_llm_responses.py`:\n   - Dictionary mapping stage names to valid JSON response strings matching each Pydantic schema\n   - Stage 2: SegmentationResult with 2 topic segments covering the 5 sample transcript segments\n   - Stage 3: ExtractionResult with 2 key moments (one technique, one settings)\n   - Stage 4: ClassificationResult mapping moments to canonical tags\n   - Stage 5: SynthesisResult with a technique page including body_sections, signal_chains, plugins\n   - Embedding response: list of 768-dimensional vectors (can be random floats for testing)\n\n2. Create `backend/tests/test_pipeline.py` with these tests using `unittest.mock.patch` on `openai.OpenAI` and `qdrant_client.QdrantClient`:\n   a. `test_stage2_segmentation_updates_topic_labels`: Ingest sample transcript, run stage2 with mocked LLM, verify TranscriptSegment rows have topic_label set\n   b. `test_stage3_extraction_creates_key_moments`: Run stages 2+3, verify KeyMoment rows created with correct fields, processing_status = extracted\n   c. `test_stage4_classification_assigns_tags`: Run stages 2+3+4, verify moments have topic_category and topic_tags from canonical list\n   d. `test_stage5_synthesis_creates_technique_pages`: Run full pipeline stages 2-5, verify TechniquePage rows created with body_sections, signal_chains, summary. Verify KeyMoments linked to TechniquePage via technique_page_id\n   e. `test_stage6_embeds_and_upserts_to_qdrant`: Run full pipeline, verify EmbeddingClient.embed called with correct texts, QdrantManager.upsert called with correct payloads\n   f. `test_run_pipeline_resumes_from_extracted`: Set video processing_status to extracted, run pipeline, verify only stages 4+5+6 execute (not 2+3)\n   g. `test_pipeline_trigger_endpoint`: POST to /api/v1/pipeline/trigger/{video_id} with mocked Celery delay, verify 200 response\n   h. `test_pipeline_trigger_404_for_missing_video`: POST to /api/v1/pipeline/trigger/{nonexistent_id}, verify 404\n   i. `test_ingest_dispatches_pipeline`: POST transcript to ingest endpoint with mocked run_pipeline.delay, verify delay was called with video_id\n   j. `test_llm_fallback_on_primary_failure`: Mock primary endpoint to raise APIConnectionError, verify fallback endpoint is called\n\n   IMPORTANT: Pipeline stages use SYNC SQLAlchemy (not async). Tests that call stage functions directly need a real PostgreSQL test database. Use the existing conftest.py pattern but create sync engine/sessions for pipeline stage calls. Mock the LLM and Qdrant clients, not the DB.\n\n3. Update `backend/tests/conftest.py` to add:\n   - A fixture that creates a sync SQLAlchemy engine pointing to the test database (for pipeline stage tests)\n   - A fixture that pre-ingests a sample transcript and returns the video_id (for pipeline tests to use)\n   - Any necessary mock fixtures for LLMClient and QdrantManager\n\n## Must-Haves\n\n- [ ] 10 integration tests covering all pipeline stages, trigger endpoints, resumability, and fallback\n- [ ] All LLM calls mocked with realistic response fixtures\n- [ ] Qdrant calls mocked — no real Qdrant needed\n- [ ] Pipeline stage tests use sync DB sessions (matching production Celery behavior)\n- [ ] At least one negative test (LLM fallback on primary failure)\n- [ ] All tests pass with `python -m pytest tests/test_pipeline.py -v`\n\n## Verification\n\n- `cd backend && python -m pytest tests/test_pipeline.py -v` — all tests pass\n- `cd backend && python -m pytest tests/ -v` — all tests (including S02 ingest tests) still pass\n\n## Observability Impact\n\n- Signals added: test output shows per-test pass/fail with timing, mock call assertions verify logging would fire in production\n- How a future agent inspects this: run `pytest tests/test_pipeline.py -v` to see all pipeline test results\n  - Estimate: 2h\n  - Files: backend/tests/fixtures/mock_llm_responses.py, backend/tests/test_pipeline.py, backend/tests/conftest.py\n  - Verify: cd backend && python -m pytest tests/test_pipeline.py -v && python -m pytest tests/ -v\n"}],"isError":false,"timestamp":1774823200503}}
{"type":"message","id":"514b0703","parentId":"befa5eff","timestamp":"2026-03-29T22:26:44.538Z","message":{"role":"assistant","content":[{"type":"text","text":"Good, the plan rendered correctly. Let me verify the task plan files were created:"},{"type":"toolCall","id":"toolu_016XFmXY51WTjmjvr5hxJq6W","name":"bash","arguments":{"command":"ls -la /home/aux/projects/content-to-kb-automator/.gsd/milestones/M001/slices/S03/tasks/"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":106,"cacheRead":87049,"cacheWrite":7054,"totalTokens":94210,"cost":{"input":0.000005,"output":0.00265,"cacheRead":0.0435245,"cacheWrite":0.0440875,"total":0.090267}},"stopReason":"toolUse","timestamp":1774823200504}}
{"type":"message","id":"7c44bffe","parentId":"514b0703","timestamp":"2026-03-29T22:26:44.548Z","message":{"role":"toolResult","toolCallId":"toolu_016XFmXY51WTjmjvr5hxJq6W","toolName":"bash","content":[{"type":"text","text":"total 44\ndrwxrwxr-x 2 aux aux 4096 Mar 29 22:26 .\ndrwxrwxr-x 3 aux aux 4096 Mar 29 22:26 ..\n-rw-rw-r-- 1 aux aux 5866 Mar 29 22:26 T01-PLAN.md\n-rw-rw-r-- 1 aux aux 7944 Mar 29 22:26 T02-PLAN.md\n-rw-rw-r-- 1 aux aux 5086 Mar 29 22:26 T03-PLAN.md\n-rw-rw-r-- 1 aux aux 2590 Mar 29 22:26 T04-PLAN.md\n-rw-rw-r-- 1 aux aux 5617 Mar 29 22:26 T05-PLAN.md\n"}],"isError":false,"timestamp":1774823204546}}
{"type":"message","id":"fdeeaa85","parentId":"7c44bffe","timestamp":"2026-03-29T22:27:03.932Z","message":{"role":"assistant","content":[{"type":"text","text":"All 5 task plans created. Let me do the self-audit:\n\n**Completion semantics check:** ✅ If T01-T05 complete as written:\n- T01: Config + deps + worker + LLM client + schemas → infrastructure ready\n- T02: Prompts + stages 2-5 → pipeline logic complete\n- T03: Embedding + Qdrant + stage6 → vector search ready\n- T04: Ingest trigger + manual endpoint → pipeline wired to API\n- T05: 10 integration tests → everything verified\n→ The goal \"transcript triggers stages 2-5, technique pages in DB, Qdrant has embeddings\" is achieved.\n\n**Requirement coverage:** ✅\n- R003 (LLM Pipeline): T01 (LLM client), T02 (stages 2-5), T05 (tests)\n- R009 (Qdrant Vector Search): T03 (embedding + Qdrant), T05 (tests)\n- R011 (Canonical Tag System): T02 (stage 4 loads canonical_tags.yaml)\n- R012 (Incremental Content Addition): T02 (stage 5 creates/updates TechniquePage), T04 (trigger on ingest)\n- R013 (Prompt Template System): T02 (4 prompt files + _load_prompt helper)\n\n**Task completeness:** ✅ All tasks have steps, must-haves, verification, inputs, and expected output with backtick-wrapped file paths.\n\n**Dependency correctness:** ✅ T01 → T02 → T03 → T04 → T05 (linear chain, each builds on prior).\n\n**Scope sanity:** ✅ T01: 6 steps/6 files, T02: 6 steps/6 files, T03: 4 steps/3 files, T04: 3 steps/3 files, T05: 3 steps/3 files. All within limits.\n\nNow let me record a key structural decision:"},{"type":"toolCall","id":"toolu_01Ddxc4BrMfpppDZkdxB1sQs","name":"gsd_decision_save","arguments":{"choice":"Use sync openai.OpenAI, sync QdrantClient, and sync SQLAlchemy (create_engine with psycopg2) inside Celery tasks. Convert DATABASE_URL from postgresql+asyncpg:// to postgresql:// for the sync engine.","decision":"Sync vs async approach for Celery worker pipeline tasks","made_by":"agent","rationale":"Celery workers run in a synchronous context. Using asyncio.run() inside tasks risks nested event loop errors with gevent/eventlet workers. Using sync clients throughout eliminates this class of bug entirely. The async engine/session from database.py is only used by FastAPI (ASGI); the worker gets its own sync engine.","scope":"architecture"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":749,"cacheRead":94103,"cacheWrite":348,"totalTokens":95201,"cost":{"input":0.000005,"output":0.018725000000000002,"cacheRead":0.047051499999999996,"cacheWrite":0.0021750000000000003,"total":0.06795649999999999}},"stopReason":"toolUse","timestamp":1774823204547}}
{"type":"message","id":"96be9888","parentId":"fdeeaa85","timestamp":"2026-03-29T22:27:03.937Z","message":{"role":"toolResult","toolCallId":"toolu_01Ddxc4BrMfpppDZkdxB1sQs","toolName":"gsd_decision_save","content":[{"type":"text","text":"Saved decision D004"}],"details":{"operation":"save_decision","id":"D004"},"isError":false,"timestamp":1774823223935}}
{"type":"message","id":"366692c1","parentId":"96be9888","timestamp":"2026-03-29T22:27:11.505Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_012XKMSrjhRVEef6gQoJ2N9x","name":"gsd_decision_save","arguments":{"choice":"Embedding/Qdrant failures (stage 6) log errors but do not fail the pipeline. Processing_status is set by stages 2-5 only. Embeddings can be regenerated by manual re-trigger.","decision":"Embedding/Qdrant failure handling strategy in pipeline","made_by":"agent","rationale":"Qdrant is at 10.0.0.10 on the hypervisor network and may not be reachable during all pipeline runs. Making embedding a non-blocking side-effect ensures core pipeline output (KeyMoments, TechniquePages in PostgreSQL) is never lost due to vector store issues. The manual re-trigger endpoint allows regenerating embeddings at any time.","scope":"architecture"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":268,"cacheRead":94451,"cacheWrite":766,"totalTokens":95486,"cost":{"input":0.000005,"output":0.0067,"cacheRead":0.0472255,"cacheWrite":0.0047875,"total":0.058718}},"stopReason":"toolUse","timestamp":1774823223936}}
{"type":"message","id":"d38aef16","parentId":"366692c1","timestamp":"2026-03-29T22:27:11.509Z","message":{"role":"toolResult","toolCallId":"toolu_012XKMSrjhRVEef6gQoJ2N9x","toolName":"gsd_decision_save","content":[{"type":"text","text":"Saved decision D005"}],"details":{"operation":"save_decision","id":"D005"},"isError":false,"timestamp":1774823231508}}
{"type":"message","id":"0c2f5271","parentId":"d38aef16","timestamp":"2026-03-29T22:27:14.408Z","message":{"role":"assistant","content":[{"type":"text","text":"Slice S03 planned."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":10,"cacheRead":95217,"cacheWrite":285,"totalTokens":95513,"cost":{"input":0.000005,"output":0.00025,"cacheRead":0.0476085,"cacheWrite":0.00178125,"total":0.049644749999999994}},"stopReason":"stop","timestamp":1774823231508}}
