{"type":"thinking_level_change","id":"22eb51e7","parentId":null,"timestamp":"2026-04-03T01:26:11.072Z","thinkingLevel":"medium"}
{"type":"model_change","id":"5d3fccaf","parentId":"22eb51e7","timestamp":"2026-04-03T01:26:11.074Z","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\nA researcher explored the codebase and a planner decomposed the work — you are the executor. The task plan below is your authoritative contract for the slice goal and verification bar, but it is not a substitute for local reality. Verify the referenced files and surrounding code before changing them. Do not do broad re-research or spontaneous re-planning. Small factual corrections, file-path fixes, and local implementation adaptations are part of execution. Escalate to `blocker_discovered: true` only when the slice contract or downstream task graph no longer holds.\n\n\n\n\n\n## Backing Source Artifacts\n- Slice plan: `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/S04-PLAN.md`\n- Task plan source: `.gsd/milestones/M014/slices/S04/tasks/T01-PLAN.md`\n- Prior task summaries in this slice:\n- (no prior tasks)\n\nThen:\n0. Narrate step transitions, key implementation decisions, and verification outcomes as you work. Keep it terse — one line between tool-call clusters, not between every call — but write complete sentences in user-facing prose, not shorthand notes or scratchpad fragments.\n1.  Follow any activated skills before writing code. If no skills match this task, skip this step.\n2. Execute the steps in the inlined task plan, adapting minor local mismatches when the surrounding code differs from the planner's snapshot\n3. Build the real thing. If the task plan says \"create login endpoint\", build an endpoint that actually authenticates against a real store, not one that returns a hardcoded success response. If the task plan says \"create dashboard page\", build a page that renders real data from the API, not a component with hardcoded props. Stubs and mocks are for tests, not for the shipped feature.\n4. Write or update tests as part of execution — tests are verification, not an afterthought. If the slice plan defines test files in its Verification section and this is the first task, create them (they should initially fail).\n5. When implementing non-trivial runtime behavior (async flows, API boundaries, background processes, error paths), add or preserve agent-usable observability. Skip this for simple changes where it doesn't apply.\n\n   **Background process rule:** Never use bare `command &` to run background processes. The shell's `&` operator leaves stdout/stderr attached to the parent, which causes the Bash tool to hang indefinitely waiting for those streams to close. Always redirect output before backgrounding:\n   - Correct: `command > /dev/null 2>&1 &` or `nohup command > /dev/null 2>&1 &`\n   - Example: `python -m http.server 8080 > /dev/null 2>&1 &` (NOT `python -m http.server 8080 &`)\n   - Preferred: use the `bg_shell` tool if available — it manages process lifecycle correctly without stream-inheritance issues\n6. If the task plan includes a **Failure Modes** section (Q5), implement the error/timeout/malformed handling specified. Verify each dependency's failure path is handled. Skip if the section is absent.\n7. If the task plan includes a **Load Profile** section (Q6), implement protections for the identified 10x breakpoint (connection pooling, rate limiting, pagination, etc.). Skip if absent.\n8. If the task plan includes a **Negative Tests** section (Q7), write the specified negative test cases alongside the happy-path tests — malformed inputs, error paths, and boundary conditions. Skip if absent.\n9. Verify must-haves are met by running concrete checks (tests, commands, observable behaviors)\n10. Run the slice-level verification checks defined in the slice plan's Verification section. Track which pass. On the final task of the slice, all must pass before marking done. On intermediate tasks, partial passes are expected — note which ones pass in the summary.\n11. After the verification gate runs (you'll see gate results in stderr/notify output), populate the `## Verification Evidence` table in your task summary with the check results. Use the `formatEvidenceTable` format: one row per check with command, exit code, verdict (✅ pass / ❌ fail), and duration. If no verification commands were discovered, note that in the section.\n12. If the task touches UI, browser flows, DOM behavior, or user-visible web state:\n   - exercise the real flow in the browser\n   - prefer `browser_batch` when the next few actions are obvious and sequential\n   - prefer `browser_assert` for explicit pass/fail verification of the intended outcome\n   - use `browser_diff` when an action's effect is ambiguous\n   - use console/network/dialog diagnostics when validating async, stateful, or failure-prone UI\n   - record verification in terms of explicit checks passed/failed, not only prose interpretation\n13. If the task plan includes an Observability Impact section, verify those signals directly. Skip this step if the task plan omits the section.\n14. **If execution is running long or verification fails:**\n\n    **Context budget:** You have approximately **~80K chars** reserved for verification context. If you've used most of your context and haven't finished all steps, stop implementing and prioritize writing the task summary with clear notes on what's done and what remains. A partial summary that enables clean resumption is more valuable than one more half-finished step with no documentation. Never sacrifice summary quality for one more implementation step.\n\n    **Debugging discipline:** If a verification check fails or implementation hits unexpected behavior:\n    - Form a hypothesis first. State what you think is wrong and why, then test that specific theory. Don't shotgun-fix.\n    - Change one variable at a time. Make one change, test, observe. Multiple simultaneous changes mean you can't attribute what worked.\n    - Read completely. When investigating, read entire functions and their imports, not just the line that looks relevant.\n    - Distinguish \"I know\" from \"I assume.\" Observable facts (the error says X) are strong evidence. Assumptions (this library should work this way) need verification.\n    - Know when to stop. If you've tried 3+ fixes without progress, your mental model is probably wrong. Stop. List what you know for certain. List what you've ruled out. Form fresh hypotheses from there.\n    - Don't fix symptoms. Understand *why* something fails before changing code. A test that passes after a change you don't understand is luck, not a fix.\n15. **Blocker discovery:** If execution reveals that the remaining slice plan is fundamentally invalid — not just a bug or minor deviation, but a plan-invalidating finding like a wrong API, missing capability, or architectural mismatch — set `blocker_discovered: true` in the task summary frontmatter and describe the blocker clearly in the summary narrative. Do NOT set `blocker_discovered: true` for ordinary debugging, minor deviations, or issues that can be fixed within the current task or the remaining plan. This flag triggers an automatic replan of the slice.\n16. If you made an architectural, pattern, library, or observability decision during this task that downstream work should know about, append it to `.gsd/DECISIONS.md` (read the template at `~/.gsd/agent/extensions/gsd/templates/decisions.md` if the file doesn't exist yet). Not every task produces decisions — only append when a meaningful choice was made.\n17. If you discover a non-obvious rule, recurring gotcha, or useful pattern during execution, append it to `.gsd/KNOWLEDGE.md`. Only add entries that would save future agents from repeating your investigation. Don't add obvious things.\n18. Read the template at `~/.gsd/agent/extensions/gsd/templates/task-summary.md`\n19. Write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/tasks/T01-SUMMARY.md`\n20. Call `gsd_complete_task` with milestone_id, slice_id, task_id, and a summary of what was accomplished. This is your final required step — do NOT manually edit PLAN.md checkboxes. The tool marks the task complete, updates the DB, and renders PLAN.md automatically.\n21. Do not run git commands — the system reads your task summary after completion and creates a meaningful commit from it (type inferred from title, message from your one-liner, key files from frontmatter). Write a clear, specific one-liner in the summary — it becomes the commit message.\n\nAll work stays in your working directory: `/home/aux/projects/content-to-kb-automator`.\n\n**You MUST call `gsd_complete_task` AND write `/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/tasks/T01-SUMMARY.md` before finishing.**\n\nWhen done, say: \"Task T01 complete.\"\n## Slice Plan Excerpt\nSource: `.gsd/milestones/M014/slices/S04/S04-PLAN.md`\n**Goal:** Stage 5 detects existing technique pages by creator+category and uses the compose prompt to merge new video content into them. All pages get body_sections_format='v2' and technique_page_videos rows tracking contributing videos.\n**Demo:** After this: Process two COPYCATT videos. Second video's moments composed into existing page. technique_page_videos has both video IDs.\n\n## UNIT: Execute Task T01 (\"Add compose helpers and wire compose-or-create logic into stage5_synthesis\") — Slice S04 (\"Pipeline Compose-or-Create Logic\"), Milestone M014\n\n## Resume State\n- No continue file present. Start from the top of the task plan.\n\n## Carry-Forward Context\n- No prior task summaries in this slice.\n\n## Inlined Task Plan (authoritative local execution contract)\nSource: `.gsd/milestones/M014/slices/S04/tasks/T01-PLAN.md`\n\n---\nestimated_steps: 67\nestimated_files: 1\nskills_used: []\n---\n\n# T01: Add compose helpers and wire compose-or-create logic into stage5_synthesis\n\nAdd two helper functions to stages.py and modify the stage5_synthesis per-category loop to detect existing pages and branch to the compose path.\n\n## Steps\n\n1. Add `TechniquePageVideo` to the imports from `models` at line ~27.\n\n2. Add `_build_compose_user_prompt(existing_page, existing_moments, new_moments, creator_name)` helper function:\n   - Takes an existing `TechniquePage` ORM object, a list of `KeyMoment` ORM objects (existing), a list of `(KeyMoment, dict)` tuples (new moments with classification), and creator name string\n   - Serialize existing page to dict matching SynthesizedPage shape: title, slug, topic_category, summary, body_sections, signal_chains, plugins, source_quality\n   - Format existing moments as `[0]-[N-1]` using `_build_moments_text()` pattern but from plain KeyMoment objects (not tuples with cls_info — existing moments don't have classification data, use empty dict)\n   - Format new moments as `[N]-[N+M-1]` using `_build_moments_text()` with offset indices applied\n   - Build XML-tagged user prompt: `<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>` tags (same structure as test_harness.py's `build_compose_prompt()`)\n   - Return the user prompt string\n\n3. Add `_compose_into_existing(existing_page, existing_moments, new_moment_group, category, creator_name, system_prompt, llm, model_override, modality, hard_limit, video_id, run_id)` helper function:\n   - Load compose system prompt via `_load_prompt('stage5_compose.txt', video_id=video_id)`\n   - Call `_build_compose_user_prompt()` to build user prompt\n   - Estimate tokens via `estimate_max_tokens()`\n   - Call `llm.complete()` with compose system prompt, response_model=SynthesisResult, same callback/param pattern as `_synthesize_chunk()`\n   - Parse via `_safe_parse_llm_response()` and return SynthesisResult\n\n4. Modify the per-category loop in `stage5_synthesis()` (around line 1200):\n   - **Before** the existing chunked synthesis block, add compose detection:\n     ```\n     existing_page = session.execute(\n         select(TechniquePage).where(\n             TechniquePage.creator_id == video.creator_id,\n             func.lower(TechniquePage.topic_category) == func.lower(category),\n         )\n     ).scalars().first()\n     ```\n   - If `existing_page` is found, load its linked moments:\n     ```\n     existing_moments = session.execute(\n         select(KeyMoment)\n         .where(KeyMoment.technique_page_id == existing_page.id)\n         .order_by(KeyMoment.start_time)\n     ).scalars().all()\n     ```\n   - If existing_page AND existing_moments → compose path: call `_compose_into_existing()`, use result.pages as synthesized_pages\n   - Log INFO: 'Stage 5: Composing into existing page \\'%s\\' (%d existing moments + %d new moments)'\n   - If >1 page matches, log WARNING about multiple matches and proceed with first\n   - If no existing_page → fall through to existing synthesis block (unchanged)\n   - Wrap in `else` so existing chunked synthesis only runs when not composing\n\n5. In the persist block (around line 1380), after the `if existing:` / `else:` branch that creates/updates the page:\n   - Set `page.body_sections_format = 'v2'` on every page (both new and updated)\n   - Add TechniquePageVideo INSERT:\n     ```python\n     from sqlalchemy.dialects.postgresql import insert as pg_insert\n     stmt = pg_insert(TechniquePageVideo.__table__).values(\n         technique_page_id=page.id,\n         source_video_id=video.id,\n     ).on_conflict_do_nothing()\n     session.execute(stmt)\n     ```\n\n## Must-Haves\n\n- [ ] `_build_compose_user_prompt()` produces XML-tagged prompt with correct offset indices\n- [ ] `_compose_into_existing()` calls LLM with compose system prompt and returns SynthesisResult\n- [ ] Compose-or-create decision queries DB by creator_id + LOWER(topic_category)\n- [ ] Existing synthesis path unchanged when no existing page found\n- [ ] body_sections_format = 'v2' set on all pages\n- [ ] TechniquePageVideo row inserted for every page+video combination\n- [ ] Case-insensitive category matching (func.lower)\n\n## Failure Modes\n\n| Dependency | On error | On timeout | On malformed response |\n|------------|----------|-----------|----------------------|\n| LLM (compose) | _safe_parse_llm_response retries once, then raises (existing retry mechanism) | Celery task retry (max_retries=3) | SynthesisResult validation rejects, retry with feedback |\n| DB (existing page query) | Exception propagates to stage-level handler, triggers retry | Same | N/A |\n\n## Negative Tests\n\n- No existing page for creator+category → falls through to standard synthesis (no compose)\n- Existing page found but zero linked moments → should still compose (empty existing_moments list)\n- Multiple pages match creator+category → uses first, logs warning\n\n## Inputs\n\n- ``backend/pipeline/stages.py` — existing stage5_synthesis function to modify`\n- ``backend/pipeline/test_harness.py` — reference implementation of build_compose_prompt() for prompt structure`\n- ``backend/models.py` — TechniquePageVideo model, body_sections_format column`\n- ``prompts/stage5_compose.txt` — compose system prompt loaded by _compose_into_existing()`\n- ``backend/pipeline/schemas.py` — SynthesisResult schema for LLM response parsing`\n\n## Expected Output\n\n- ``backend/pipeline/stages.py` — modified with _build_compose_user_prompt(), _compose_into_existing(), compose-or-create branch, TechniquePageVideo INSERT, body_sections_format='v2'`\n\n## Verification\n\ncd /home/aux/projects/content-to-kb-automator && python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\" && grep -q 'body_sections_format' backend/pipeline/stages.py && grep -q 'TechniquePageVideo' backend/pipeline/stages.py && grep -q 'stage5_compose' backend/pipeline/stages.py\n","display":false,"id":"fc58e9f8","parentId":"5d3fccaf","timestamp":"2026-04-03T01:26:11.077Z"}
{"type":"message","id":"45b3001d","parentId":"fc58e9f8","timestamp":"2026-04-03T01:26:17.003Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"Let me start by reading the task plan and understanding the current state of the codebase before making changes.","thinkingSignature":"EqwCClkIDBgCKkCTjsalA9I78XjOZIg8MMyjRv6tN3xiPV8EHRDwLf0RPlrnIQQvnGpA0FK6T+zQboqVEp1RoYTtmoYj/CNZVohtMg9jbGF1ZGUtb3B1cy00LTY4ABIMvw5mk9GAQRc5YwdMGgwkauh7l6Kh3NyB+lUiMDadDGmOTJEF+n8Z7WYvx5FoCALDu4SqUpsrbWio196OPO/rGdP50JlrOfUzsOUelyqAAQ5XuFEj99Tf6srs0g5UyYHTytnyxUEl85TPTNJz2DwT9kKTLtLseU/DfYfMJDN0ulJltG5PYXAeVgHql81YQXgxRgRC2YkPfmQg9qzxldzJbX9W7Pq00xeQbOXB8j6NYfiQTYOEI2HEqR2MoNXoQOn1d0zTANsf9IhIQFcvWX7pGAE="},{"type":"text","text":"Starting T01 — compose helpers and compose-or-create logic for stage5_synthesis. Let me read the relevant source files first."},{"type":"toolCall","id":"toolu_01NMet3Qz7Kwwo3XhwUUvdbk","name":"read","arguments":{"limit":50,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_016VbPweEdeoVqzseYQvqivN","name":"read","arguments":{"limit":80,"path":"backend/pipeline/test_harness.py"}},{"type":"toolCall","id":"toolu_017wc4Ys9GFqyTMzVLp5dmQR","name":"read","arguments":{"limit":50,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":2,"output":257,"cacheRead":59865,"cacheWrite":4380,"totalTokens":64504,"cost":{"input":0.00001,"output":0.006425,"cacheRead":0.029932499999999997,"cacheWrite":0.027375,"total":0.0637425}},"stopReason":"toolUse","timestamp":1775179571075}}
{"type":"message","id":"c68895e6","parentId":"45b3001d","timestamp":"2026-04-03T01:26:17.006Z","message":{"role":"toolResult","toolCallId":"toolu_01NMet3Qz7Kwwo3XhwUUvdbk","toolName":"read","content":[{"type":"text","text":"\"\"\"Pipeline stage tasks (stages 2-5) and run_pipeline orchestrator.\n\nEach stage reads from PostgreSQL via sync SQLAlchemy, loads its prompt\ntemplate from disk, calls the LLM client, parses the response, writes\nresults back, and updates processing_status on SourceVideo.\n\nCelery tasks are synchronous — all DB access uses ``sqlalchemy.orm.Session``.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport hashlib\nimport json\nimport logging\nimport subprocess\nimport time\nfrom collections import defaultdict\nfrom pathlib import Path\n\nimport yaml\n\nfrom pydantic import ValidationError\nfrom sqlalchemy import create_engine, func, select\nfrom sqlalchemy.orm import Session, sessionmaker\n\nfrom config import get_settings\nfrom models import (\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    PipelineEvent,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TechniquePageVersion,\n    TranscriptSegment,\n)\nfrom pipeline.embedding_client import EmbeddingClient\nfrom pipeline.llm_client import LLMClient, LLMResponse, estimate_max_tokens\nfrom pipeline.qdrant_client import QdrantManager\nfrom pipeline.schemas import (\n    ClassificationResult,\n    ExtractionResult,\n    SegmentationResult,\n    SynthesisResult,\n)\nfrom worker import celery_app\n\nlogger = logging.getLogger(__name__)\n\n\n[2053 more lines in file. Use offset=51 to continue.]"}],"isError":false,"timestamp":1775179577005}}
{"type":"message","id":"caafb4cc","parentId":"c68895e6","timestamp":"2026-04-03T01:26:17.007Z","message":{"role":"toolResult","toolCallId":"toolu_016VbPweEdeoVqzseYQvqivN","toolName":"read","content":[{"type":"text","text":"\"\"\"Offline prompt test harness for Chrysopedia synthesis.\n\nLoads a fixture JSON (exported by export_fixture.py) and a prompt file,\ncalls the LLM, and outputs the synthesized result. No Docker, no database,\nno Redis, no Celery — just prompt + fixture + LLM endpoint.\n\nUsage:\n    python -m pipeline.test_harness \\\\\n      --fixture fixtures/real_video_xyz.json \\\\\n      --prompt prompts/stage5_synthesis.txt \\\\\n      --output /tmp/result.json\n\n    # Run all categories in a fixture:\n    python -m pipeline.test_harness --fixture fixtures/video.json\n\n    # Run a specific category only:\n    python -m pipeline.test_harness --fixture fixtures/video.json --category \"Sound Design\"\n\nExit codes: 0=success, 1=LLM error, 2=parse error, 3=fixture error\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport sys\nimport time\nfrom collections import Counter, defaultdict\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import NamedTuple\n\nfrom pydantic import ValidationError\n\nfrom config import get_settings\nfrom pipeline.citation_utils import validate_citations\nfrom pipeline.llm_client import LLMClient, estimate_max_tokens\nfrom pipeline.schemas import SynthesizedPage, SynthesisResult\n\n\n# ── Lightweight stand-in for KeyMoment ORM model ───────────────────────────\n\nclass _MockContentType:\n    \"\"\"Mimics KeyMomentContentType enum with a .value property.\"\"\"\n    def __init__(self, value: str) -> None:\n        self.value = value\n\n\nclass MockKeyMoment(NamedTuple):\n    \"\"\"Lightweight stand-in for the ORM KeyMoment.\n\n    Has the same attributes that _build_moments_text() accesses:\n    title, summary, content_type, start_time, end_time, plugins, raw_transcript.\n    \"\"\"\n    title: str\n    summary: str\n    content_type: object  # _MockContentType\n    start_time: float\n    end_time: float\n    plugins: list[str]\n    raw_transcript: str\n\n\ndef _log(tag: str, msg: str, level: str = \"INFO\") -> None:\n    \"\"\"Write structured log line to stderr.\"\"\"\n    print(f\"[HARNESS] [{level}] {tag}: {msg}\", file=sys.stderr)\n\n\n# ── Moment text builder (mirrors stages.py _build_moments_text) ────────────\n\ndef build_moments_text(\n    moment_group: list[tuple[MockKeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text — matches _build_moments_text in stages.py.\"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n\n[751 more lines in file. Use offset=81 to continue.]"}],"isError":false,"timestamp":1775179577006}}
{"type":"message","id":"8431dce7","parentId":"caafb4cc","timestamp":"2026-04-03T01:26:17.009Z","message":{"role":"toolResult","toolCallId":"toolu_017wc4Ys9GFqyTMzVLp5dmQR","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\n    User-facing lifecycle: not_started → queued → processing → complete\n    Error branch: processing → error (retrigger resets to queued)\n    \"\"\"\n    not_started = \"not_started\"\n    queued = \"queued\"\n    processing = \"processing\"\n    error = \"error\"\n\n[447 more lines in file. Use offset=51 to continue.]"}],"isError":false,"timestamp":1775179577008}}
{"type":"message","id":"2936e71c","parentId":"8431dce7","timestamp":"2026-04-03T01:26:20.563Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01E9oofmdPgGfJLpvVvgKxya","name":"bash","arguments":{"command":"grep -n 'TechniquePageVideo\\|body_sections_format\\|TechniquePageVersion' backend/models.py | head -30"}},{"type":"toolCall","id":"toolu_01HYhL3S9eK6GyEnoupXUqWT","name":"bash","arguments":{"command":"grep -n 'build_compose_prompt\\|compose' backend/pipeline/test_harness.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":149,"cacheRead":64245,"cacheWrite":1805,"totalTokens":66200,"cost":{"input":0.000005,"output":0.003725,"cacheRead":0.0321225,"cacheWrite":0.011281250000000001,"total":0.047133749999999995}},"stopReason":"toolUse","timestamp":1775179577008}}
{"type":"message","id":"2984a630","parentId":"2936e71c","timestamp":"2026-04-03T01:26:20.575Z","message":{"role":"toolResult","toolCallId":"toolu_01E9oofmdPgGfJLpvVvgKxya","toolName":"bash","content":[{"type":"text","text":"219:    body_sections_format: Mapped[str] = mapped_column(\n241:    versions: Mapped[list[TechniquePageVersion]] = sa_relationship(\n242:        back_populates=\"technique_page\", order_by=\"TechniquePageVersion.version_number\"\n250:    source_video_links: Mapped[list[TechniquePageVideo]] = sa_relationship(\n282:class TechniquePageVersion(Base):\n312:class TechniquePageVideo(Base):\n"}],"isError":false,"timestamp":1775179580575}}
{"type":"message","id":"67852890","parentId":"2984a630","timestamp":"2026-04-03T01:26:20.579Z","message":{"role":"toolResult","toolCallId":"toolu_01HYhL3S9eK6GyEnoupXUqWT","toolName":"bash","content":[{"type":"text","text":"332:def build_compose_prompt(\n376:def run_compose(\n452:    _log(\"PROMPT\", f\"Loading compose prompt: {prompt_path} ({len(system_prompt)} chars)\")\n479:            _log(\"WARN\", f\"  No new moments for category '{page_category}' — nothing to compose\", level=\"WARN\")\n497:        # Build compose prompt\n498:        user_prompt = build_compose_prompt(\n553:        # Log compose-specific metrics\n697:    # -- compose subcommand --\n698:    compose_parser = sub.add_parser(\"compose\", help=\"Merge new moments into an existing page\")\n699:    compose_parser.add_argument(\"--existing-page\", type=str, required=True, help=\"Existing page JSON (harness output or raw SynthesizedPage)\")\n700:    compose_parser.add_argument(\"--fixture\", \"-f\", type=str, required=True, help=\"New moments fixture JSON\")\n701:    compose_parser.add_argument(\"--existing-fixture\", type=str, required=True, help=\"Original moments fixture JSON (for citation context)\")\n702:    compose_parser.add_argument(\"--prompt\", \"-p\", type=str, default=None, help=\"Compose prompt file (default: stage5_compose.txt)\")\n703:    compose_parser.add_argument(\"--output\", \"-o\", type=str, default=None, help=\"Output file path\")\n704:    compose_parser.add_argument(\"--category\", \"-c\", type=str, default=None, help=\"Filter to a specific category\")\n705:    compose_parser.add_argument(\"--model\", type=str, default=None, help=\"Override LLM model\")\n706:    compose_parser.add_argument(\"--modality\", type=str, default=None, choices=[\"chat\", \"thinking\"])\n719:    if args.command == \"compose\":\n720:        # Resolve default compose prompt\n724:            prompt_path = str(Path(settings.prompts_path) / \"stage5_compose.txt\")\n"}],"isError":false,"timestamp":1775179580578}}
{"type":"message","id":"ba975fe4","parentId":"67852890","timestamp":"2026-04-03T01:26:24.396Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01X5Zz3ht6f2ManqJyWBVGaQ","name":"read","arguments":{"limit":100,"offset":312,"path":"backend/models.py"}},{"type":"toolCall","id":"toolu_01LjE5wBjnQzxfRw1Jn8jthV","name":"read","arguments":{"limit":80,"offset":332,"path":"backend/pipeline/test_harness.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":166,"cacheRead":66050,"cacheWrite":819,"totalTokens":67036,"cost":{"input":0.000005,"output":0.00415,"cacheRead":0.033025,"cacheWrite":0.00511875,"total":0.042298749999999996}},"stopReason":"toolUse","timestamp":1775179580578}}
{"type":"message","id":"628fd70c","parentId":"ba975fe4","timestamp":"2026-04-03T01:26:24.398Z","message":{"role":"toolResult","toolCallId":"toolu_01X5Zz3ht6f2ManqJyWBVGaQ","toolName":"read","content":[{"type":"text","text":"class TechniquePageVideo(Base):\n    \"\"\"Association linking a technique page to its contributing source videos.\"\"\"\n    __tablename__ = \"technique_page_videos\"\n    __table_args__ = (\n        UniqueConstraint(\"technique_page_id\", \"source_video_id\", name=\"uq_page_video\"),\n    )\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    technique_page_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"technique_pages.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    source_video_id: Mapped[uuid.UUID] = mapped_column(\n        ForeignKey(\"source_videos.id\", ondelete=\"CASCADE\"), nullable=False\n    )\n    added_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n\n    # relationships\n    technique_page: Mapped[TechniquePage] = sa_relationship(\n        back_populates=\"source_video_links\"\n    )\n    source_video: Mapped[SourceVideo] = sa_relationship()\n\n\n# ── Content Report Enums ─────────────────────────────────────────────────────\n\nclass ReportType(str, enum.Enum):\n    \"\"\"Classification of user-submitted content reports.\"\"\"\n    inaccurate = \"inaccurate\"\n    missing_info = \"missing_info\"\n    wrong_attribution = \"wrong_attribution\"\n    formatting = \"formatting\"\n    other = \"other\"\n\n\nclass ReportStatus(str, enum.Enum):\n    \"\"\"Triage status for content reports.\"\"\"\n    open = \"open\"\n    acknowledged = \"acknowledged\"\n    resolved = \"resolved\"\n    dismissed = \"dismissed\"\n\n\n# ── Content Report ───────────────────────────────────────────────────────────\n\nclass ContentReport(Base):\n    \"\"\"User-submitted report about a content issue.\n\n    Generic: content_type + content_id can reference any entity\n    (technique_page, key_moment, creator, or general).\n    \"\"\"\n    __tablename__ = \"content_reports\"\n\n    id: Mapped[uuid.UUID] = _uuid_pk()\n    content_type: Mapped[str] = mapped_column(\n        String(50), nullable=False, doc=\"Entity type: technique_page, key_moment, creator, general\"\n    )\n    content_id: Mapped[uuid.UUID | None] = mapped_column(\n        UUID(as_uuid=True), nullable=True, doc=\"FK to the reported entity (null for general reports)\"\n    )\n    content_title: Mapped[str | None] = mapped_column(\n        String(500), nullable=True, doc=\"Snapshot of entity title at report time\"\n    )\n    report_type: Mapped[ReportType] = mapped_column(\n        Enum(ReportType, name=\"report_type\", create_constraint=True),\n        nullable=False,\n    )\n    description: Mapped[str] = mapped_column(Text, nullable=False)\n    status: Mapped[ReportStatus] = mapped_column(\n        Enum(ReportStatus, name=\"report_status\", create_constraint=True),\n        default=ReportStatus.open,\n        server_default=\"open\",\n    )\n    admin_notes: Mapped[str | None] = mapped_column(Text, nullable=True)\n    page_url: Mapped[str | None] = mapped_column(\n        String(1000), nullable=True, doc=\"URL the user was on when reporting\"\n    )\n    created_at: Mapped[datetime] = mapped_column(\n        default=_now, server_default=func.now()\n    )\n    resolved_at: Mapped[datetime | None] = mapped_column(nullable=True)\n\n\n# ── Pipeline Event ───────────────────────────────────────────────────────────\n\nclass PipelineRunStatus(str, enum.Enum):\n    \"\"\"Status of a pipeline run.\"\"\"\n    running = \"running\"\n    complete = \"complete\"\n    error = \"error\"\n    cancelled = \"cancelled\"\n\n\nclass PipelineRunTrigger(str, enum.Enum):\n    \"\"\"What initiated a pipeline run.\"\"\"\n    manual = \"manual\"\n    clean_reprocess = \"clean_reprocess\"\n    auto_ingest = \"auto_ingest\"\n    bulk = \"bulk\"\n\n[86 more lines in file. Use offset=412 to continue.]"}],"isError":false,"timestamp":1775179584397}}
{"type":"message","id":"51dfda15","parentId":"628fd70c","timestamp":"2026-04-03T01:26:24.400Z","message":{"role":"toolResult","toolCallId":"toolu_01LjE5wBjnQzxfRw1Jn8jthV","toolName":"read","content":[{"type":"text","text":"def build_compose_prompt(\n    existing_page: dict,\n    existing_moments: list[tuple[MockKeyMoment, dict]],\n    new_moments: list[tuple[MockKeyMoment, dict]],\n    creator_name: str,\n) -> str:\n    \"\"\"Build the user prompt for composition (merging new moments into an existing page).\n\n    Existing moments keep indices [0]-[N-1].\n    New moments get indices [N]-[N+M-1].\n    Uses build_moments_text() for formatting, with index offsets applied for new moments.\n    \"\"\"\n    category = existing_page.get(\"topic_category\", \"Uncategorized\")\n\n    # Format existing moments [0]-[N-1]\n    existing_text, _ = build_moments_text(existing_moments, category)\n\n    # Format new moments with offset indices [N]-[N+M-1]\n    n = len(existing_moments)\n    new_lines = []\n    for i, (m, cls_info) in enumerate(new_moments):\n        tags = cls_info.get(\"topic_tags\", [])\n        new_lines.append(\n            f\"[{n + i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    new_text = \"\\n\\n\".join(new_lines)\n\n    page_json = json.dumps(existing_page, indent=2, ensure_ascii=False)\n\n    return (\n        f\"<existing_page>\\n{page_json}\\n</existing_page>\\n\"\n        f\"<existing_moments>\\n{existing_text}\\n</existing_moments>\\n\"\n        f\"<new_moments>\\n{new_text}\\n</new_moments>\\n\"\n        f\"<creator>{creator_name}</creator>\"\n    )\n\n\ndef run_compose(\n    existing_page_path: str,\n    existing_fixture_path: str,\n    new_fixture_path: str,\n    prompt_path: str,\n    category_filter: str | None = None,\n    model_override: str | None = None,\n    modality: str | None = None,\n) -> tuple[list[dict], int]:\n    \"\"\"Run composition: merge new fixture moments into an existing page.\n\n    Returns (pages, exit_code) — same shape as run_synthesis().\n    \"\"\"\n    # Load existing page JSON\n    existing_page_file = Path(existing_page_path)\n    if not existing_page_file.exists():\n        _log(\"ERROR\", f\"Existing page not found: {existing_page_path}\", level=\"ERROR\")\n        return [], 3\n\n    try:\n        existing_raw = json.loads(existing_page_file.read_text(encoding=\"utf-8\"))\n    except json.JSONDecodeError as exc:\n        _log(\"ERROR\", f\"Invalid JSON in existing page: {exc}\", level=\"ERROR\")\n        return [], 3\n\n    # The existing page file might be a harness output (with .pages[]) or a raw SynthesizedPage\n    if \"pages\" in existing_raw and isinstance(existing_raw[\"pages\"], list):\n        page_dicts = existing_raw[\"pages\"]\n        _log(\"COMPOSE\", f\"Loaded harness output with {len(page_dicts)} pages\")\n    elif \"title\" in existing_raw and \"body_sections\" in existing_raw:\n        page_dicts = [existing_raw]\n        _log(\"COMPOSE\", \"Loaded single SynthesizedPage\")\n    else:\n        _log(\"ERROR\", \"Existing page JSON must be a SynthesizedPage or harness output with 'pages' key\", level=\"ERROR\")\n        return [], 3\n\n\n[420 more lines in file. Use offset=412 to continue.]"}],"isError":false,"timestamp":1775179584399}}
{"type":"message","id":"17708331","parentId":"51dfda15","timestamp":"2026-04-03T01:26:29.036Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — I have the TechniquePageVideo model and the reference `build_compose_prompt()`. Now let me read the stage5_synthesis function itself."},{"type":"toolCall","id":"toolu_011GLSqQw2UmNwDFtHLgBWnf","name":"bash","arguments":{"command":"grep -n 'def stage5_synthesis\\|def _build_moments_text\\|def _synthesize_chunk\\|def _safe_parse_llm_response\\|def _load_prompt' backend/pipeline/stages.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":145,"cacheRead":66869,"cacheWrite":2338,"totalTokens":69353,"cost":{"input":0.000005,"output":0.003625,"cacheRead":0.0334345,"cacheWrite":0.0146125,"total":0.051677}},"stopReason":"toolUse","timestamp":1775179584399}}
{"type":"message","id":"10aea71c","parentId":"17708331","timestamp":"2026-04-03T01:26:29.047Z","message":{"role":"toolResult","toolCallId":"toolu_011GLSqQw2UmNwDFtHLgBWnf","toolName":"bash","content":[{"type":"text","text":"244:def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n325:def _safe_parse_llm_response(\n957:def _build_moments_text(\n983:def _synthesize_chunk(\n1127:def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n"}],"isError":false,"timestamp":1775179589045}}
{"type":"message","id":"2aa62647","parentId":"10aea71c","timestamp":"2026-04-03T01:26:32.658Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01Tu5JeCnvEoXUhqjZPcG6E4","name":"read","arguments":{"limit":70,"offset":957,"path":"backend/pipeline/stages.py"}},{"type":"toolCall","id":"toolu_01GbNbmpG8NqcmthKutyh2H8","name":"read","arguments":{"limit":200,"offset":1127,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":166,"cacheRead":69207,"cacheWrite":251,"totalTokens":69625,"cost":{"input":0.000005,"output":0.00415,"cacheRead":0.034603499999999995,"cacheWrite":0.00156875,"total":0.040327249999999995}},"stopReason":"toolUse","timestamp":1775179589046}}
{"type":"message","id":"46ff4018","parentId":"2aa62647","timestamp":"2026-04-03T01:26:32.663Z","message":{"role":"toolResult","toolCallId":"toolu_01Tu5JeCnvEoXUhqjZPcG6E4","toolName":"read","content":[{"type":"text","text":"def _build_moments_text(\n    moment_group: list[tuple[KeyMoment, dict]],\n    category: str,\n) -> tuple[str, set[str]]:\n    \"\"\"Build the moments prompt text and collect all tags for a group of moments.\n\n    Returns (moments_text, all_tags).\n    \"\"\"\n    moments_lines = []\n    all_tags: set[str] = set()\n    for i, (m, cls_info) in enumerate(moment_group):\n        tags = cls_info.get(\"topic_tags\", [])\n        all_tags.update(tags)\n        moments_lines.append(\n            f\"[{i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n    system_prompt: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n    chunk_label: str,\n) -> SynthesisResult:\n    \"\"\"Run a single synthesis LLM call for a chunk of moments.\n\n    Returns the parsed SynthesisResult.\n    \"\"\"\n    moments_text, _ = _build_moments_text(chunk, category)\n    user_prompt = f\"<creator>{creator_name}</creator>\\n<moments>\\n{moments_text}\\n</moments>\"\n\n    estimated_input = estimate_max_tokens(system_prompt, user_prompt, stage=\"stage5_synthesis\", hard_limit=hard_limit)\n    logger.info(\n        \"Stage 5: Synthesizing %s — %d moments, max_tokens=%d\",\n        chunk_label, len(chunk), estimated_input,\n    )\n\n    raw = llm.complete(\n        system_prompt, user_prompt, response_model=SynthesisResult,\n        on_complete=_make_llm_callback(\n            video_id, \"stage5_synthesis\",\n            system_prompt=system_prompt, user_prompt=user_prompt,\n            run_id=run_id, context_label=chunk_label,\n            request_params=_build_request_params(estimated_input, model_override, modality, \"SynthesisResult\", hard_limit),\n        ),\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n    return _safe_parse_llm_response(\n        raw, SynthesisResult, llm, system_prompt, user_prompt,\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n\n\ndef _slug_base(slug: str) -> str:\n    \"\"\"Extract the slug prefix before the creator name suffix for merge grouping.\n\n[1077 more lines in file. Use offset=1027 to continue.]"}],"isError":false,"timestamp":1775179592663}}
{"type":"message","id":"ca42f424","parentId":"46ff4018","timestamp":"2026-04-03T01:26:32.668Z","message":{"role":"toolResult","toolCallId":"toolu_01GbNbmpG8NqcmthKutyh2H8","toolName":"read","content":[{"type":"text","text":"def stage5_synthesis(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Synthesize technique pages from classified key moments.\n\n    Groups moments by (creator, topic_category), calls the LLM to synthesize\n    each group into a TechniquePage, creates/updates page rows, and links\n    KeyMoments to their TechniquePage.\n\n    For large category groups (exceeding synthesis_chunk_size), moments are\n    split into chronological chunks, synthesized independently, then pages\n    with matching slugs are merged via a dedicated merge LLM call.\n\n    Sets processing_status to 'complete'.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 5 (synthesis) starting for video_id=%s\", video_id)\n    _emit_event(video_id, \"stage5_synthesis\", \"start\", run_id=run_id)\n\n    settings = get_settings()\n    chunk_size = settings.synthesis_chunk_size\n    session = _get_sync_session()\n    try:\n        # Load video and moments\n        video = session.execute(\n            select(SourceVideo).where(SourceVideo.id == video_id)\n        ).scalar_one()\n\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        # Resolve creator name for the LLM prompt\n        creator = session.execute(\n            select(Creator).where(Creator.id == video.creator_id)\n        ).scalar_one_or_none()\n        creator_name = creator.name if creator else \"Unknown\"\n\n        if not moments:\n            logger.info(\"Stage 5: No moments found for video_id=%s, skipping.\", video_id)\n            return video_id\n\n        # Load classification data from stage 4\n        classification_data = _load_classification_data(video_id)\n        cls_by_moment_id = {c[\"moment_id\"]: c for c in classification_data}\n\n        # Group moments by topic_category (from classification)\n        # Normalize category casing to prevent near-duplicate groups\n        # (e.g., \"Sound design\" vs \"Sound Design\")\n        groups: dict[str, list[tuple[KeyMoment, dict]]] = defaultdict(list)\n        for moment in moments:\n            cls_info = cls_by_moment_id.get(str(moment.id), {})\n            category = cls_info.get(\"topic_category\", \"Uncategorized\").strip().title()\n            groups[category].append((moment, cls_info))\n\n        system_prompt = _load_prompt(\"stage5_synthesis.txt\", video_id=video_id)\n        llm = _get_llm_client()\n        model_override, modality = _get_stage_config(5)\n        hard_limit = settings.llm_max_tokens_hard_limit\n        logger.info(\"Stage 5 using model=%s, modality=%s\", model_override or \"default\", modality)\n        pages_created = 0\n\n        for category, moment_group in groups.items():\n            # Collect all tags across the full group (used for DB writes later)\n            all_tags: set[str] = set()\n            for _, cls_info in moment_group:\n                all_tags.update(cls_info.get(\"topic_tags\", []))\n\n            # ── Chunked synthesis with truncation recovery ─────────\n            if len(moment_group) <= chunk_size:\n                # Small group — try single LLM call first\n                try:\n                    result = _synthesize_chunk(\n                        moment_group, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, f\"category:{category}\",\n                    )\n                    synthesized_pages = list(result.pages)\n                    logger.info(\n                        \"Stage 5: category '%s' — %d moments, %d page(s) from single call\",\n                        category, len(moment_group), len(synthesized_pages),\n                    )\n                except LLMTruncationError:\n                    # Output too large for model context — split in half and retry\n                    logger.warning(\n                        \"Stage 5: category '%s' truncated with %d moments. \"\n                        \"Splitting into smaller chunks and retrying.\",\n                        category, len(moment_group),\n                    )\n                    half = max(1, len(moment_group) // 2)\n                    chunk_pages = []\n                    for sub_start in range(0, len(moment_group), half):\n                        sub_chunk = moment_group[sub_start:sub_start + half]\n                        sub_label = f\"category:{category} recovery-chunk:{sub_start // half + 1}\"\n                        sub_result = _synthesize_chunk(\n                            sub_chunk, category, creator_name,\n                            system_prompt, llm, model_override, modality, hard_limit,\n                            video_id, run_id, sub_label,\n                        )\n                        # Reindex moment_indices to global offsets\n                        for p in sub_result.pages:\n                            if p.moment_indices:\n                                p.moment_indices = [idx + sub_start for idx in p.moment_indices]\n                        chunk_pages.extend(sub_result.pages)\n                    synthesized_pages = chunk_pages\n                    logger.info(\n                        \"Stage 5: category '%s' — %d page(s) from recovery split\",\n                        category, len(synthesized_pages),\n                    )\n            else:\n                # Large group — split into chunks, synthesize each, then merge\n                num_chunks = (len(moment_group) + chunk_size - 1) // chunk_size\n                logger.info(\n                    \"Stage 5: category '%s' has %d moments — splitting into %d chunks of ≤%d\",\n                    category, len(moment_group), num_chunks, chunk_size,\n                )\n\n                chunk_pages = []\n                for chunk_idx in range(num_chunks):\n                    chunk_start = chunk_idx * chunk_size\n                    chunk_end = min(chunk_start + chunk_size, len(moment_group))\n                    chunk = moment_group[chunk_start:chunk_end]\n                    chunk_label = f\"category:{category} chunk:{chunk_idx + 1}/{num_chunks}\"\n\n                    result = _synthesize_chunk(\n                        chunk, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, chunk_label,\n                    )\n                    chunk_pages.extend(result.pages)\n                    logger.info(\n                        \"Stage 5: %s produced %d page(s)\",\n                        chunk_label, len(result.pages),\n                    )\n\n                # Merge pages with matching slugs across chunks\n                logger.info(\n                    \"Stage 5: category '%s' — %d total pages from %d chunks, checking for merges\",\n                    category, len(chunk_pages), num_chunks,\n                )\n                synthesized_pages = _merge_pages_by_slug(\n                    chunk_pages, creator_name,\n                    llm, model_override, modality, hard_limit,\n                    video_id, run_id,\n                )\n                logger.info(\n                    \"Stage 5: category '%s' — %d final page(s) after merge\",\n                    category, len(synthesized_pages),\n                )\n\n            # ── Persist pages to DB ──────────────────────────────────\n            # Load prior pages from this video (snapshot taken before pipeline reset)\n            prior_page_ids = _load_prior_pages(video_id)\n\n            for page_data in synthesized_pages:\n                page_moment_indices = getattr(page_data, \"moment_indices\", None) or []\n                existing = None\n\n                # First: check by slug (most specific match)\n                if existing is None:\n                    existing = session.execute(\n                        select(TechniquePage).where(TechniquePage.slug == page_data.slug)\n                    ).scalar_one_or_none()\n\n                # Fallback: check prior pages from this video by creator + category\n                # Use .first() since multiple pages may share a category\n                if existing is None and prior_page_ids:\n                    existing = session.execute(\n                        select(TechniquePage).where(\n                            TechniquePage.id.in_(prior_page_ids),\n                            TechniquePage.creator_id == video.creator_id,\n                            func.lower(TechniquePage.topic_category) == func.lower(page_data.topic_category or category),\n                        )\n                    ).scalars().first()\n                    if existing:\n                        logger.info(\n                            \"Stage 5: Matched prior page '%s' (id=%s) by creator+category for video_id=%s\",\n                            existing.slug, existing.id, video_id,\n                        )\n\n                if existing:\n                    # Snapshot existing content before overwriting\n                    try:\n                        sq = existing.source_quality\n                        sq_value = sq.value if hasattr(sq, 'value') else sq\n                        snapshot = {\n                            \"title\": existing.title,\n                            \"slug\": existing.slug,\n                            \"topic_category\": existing.topic_category,\n                            \"topic_tags\": existing.topic_tags,\n                            \"summary\": existing.summary,\n                            \"body_sections\": existing.body_sections,\n                            \"signal_chains\": existing.signal_chains,\n                            \"plugins\": existing.plugins,\n\n[777 more lines in file. Use offset=1327 to continue.]"}],"isError":false,"timestamp":1775179592665}}
{"type":"message","id":"1b6e58b7","parentId":"ca42f424","timestamp":"2026-04-03T01:26:35.874Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01EptBBTRgUX6UPeLN8SZr8N","name":"read","arguments":{"limit":200,"offset":1327,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":69458,"cacheWrite":3560,"totalTokens":73111,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.034728999999999996,"cacheWrite":0.022250000000000002,"total":0.059284}},"stopReason":"toolUse","timestamp":1775179592666}}
{"type":"message","id":"2bfa6772","parentId":"1b6e58b7","timestamp":"2026-04-03T01:26:35.878Z","message":{"role":"toolResult","toolCallId":"toolu_01EptBBTRgUX6UPeLN8SZr8N","toolName":"read","content":[{"type":"text","text":"                            \"source_quality\": sq_value,\n                        }\n                        version_count = session.execute(\n                            select(func.count()).where(\n                                TechniquePageVersion.technique_page_id == existing.id\n                            )\n                        ).scalar()\n                        version_number = version_count + 1\n\n                        version = TechniquePageVersion(\n                            technique_page_id=existing.id,\n                            version_number=version_number,\n                            content_snapshot=snapshot,\n                            pipeline_metadata=_capture_pipeline_metadata(),\n                        )\n                        session.add(version)\n                        logger.info(\n                            \"Version snapshot v%d created for page slug=%s\",\n                            version_number, existing.slug,\n                        )\n                    except Exception as snap_exc:\n                        logger.error(\n                            \"Failed to create version snapshot for page slug=%s: %s\",\n                            existing.slug, snap_exc,\n                        )\n                        # Best-effort versioning — continue with page update\n\n                    # Update existing page\n                    existing.title = page_data.title\n                    existing.summary = page_data.summary\n                    existing.body_sections = page_data.body_sections\n                    existing.signal_chains = page_data.signal_chains\n                    existing.plugins = page_data.plugins if page_data.plugins else None\n                    page_tags = _compute_page_tags(page_moment_indices, moment_group, all_tags)\n                    existing.topic_tags = page_tags\n                    existing.source_quality = page_data.source_quality\n                    page = existing\n                else:\n                    page = TechniquePage(\n                        creator_id=video.creator_id,\n                        title=page_data.title,\n                        slug=page_data.slug,\n                        topic_category=page_data.topic_category or category,\n                        topic_tags=_compute_page_tags(page_moment_indices, moment_group, all_tags),\n                        summary=page_data.summary,\n                        body_sections=page_data.body_sections,\n                        signal_chains=page_data.signal_chains,\n                        plugins=page_data.plugins if page_data.plugins else None,\n                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page using moment_indices\n\n                if page_moment_indices:\n                    # LLM specified which moments belong to this page\n                    for idx in page_moment_indices:\n                        if 0 <= idx < len(moment_group):\n                            moment_group[idx][0].technique_page_id = page.id\n                elif len(synthesized_pages) == 1:\n                    # Single page — link all moments (safe fallback)\n                    for m, _ in moment_group:\n                        m.technique_page_id = page.id\n                else:\n                    # Multiple pages but no moment_indices — log warning\n                    logger.warning(\n                        \"Stage 5: page '%s' has no moment_indices and is one of %d pages \"\n                        \"for category '%s'. Moments will not be linked to this page.\",\n                        page_data.slug, len(synthesized_pages), category,\n                    )\n\n        # Update processing_status\n        video.processing_status = ProcessingStatus.complete\n\n        session.commit()\n        elapsed = time.monotonic() - start\n        _emit_event(video_id, \"stage5_synthesis\", \"complete\", run_id=run_id)\n        logger.info(\n            \"Stage 5 (synthesis) completed for video_id=%s in %.1fs — %d pages created/updated\",\n            video_id, elapsed, pages_created,\n        )\n        return video_id\n\n    except FileNotFoundError:\n        raise\n    except Exception as exc:\n        session.rollback()\n        _emit_event(video_id, \"stage5_synthesis\", \"error\", run_id=run_id, payload={\"error\": str(exc)})\n        logger.error(\"Stage 5 failed for video_id=%s: %s\", video_id, exc)\n        raise self.retry(exc=exc)\n    finally:\n        session.close()\n\n\n# ── Stage 6: Embed & Index ───────────────────────────────────────────────────\n\n@celery_app.task(bind=True, max_retries=0)\ndef stage6_embed_and_index(self, video_id: str, run_id: str | None = None) -> str:\n    \"\"\"Generate embeddings for technique pages and key moments, then upsert to Qdrant.\n\n    This is a non-blocking side-effect stage — failures are logged but do not\n    fail the pipeline. Embeddings can be regenerated later. Does NOT update\n    processing_status.\n\n    Returns the video_id for chain compatibility.\n    \"\"\"\n    start = time.monotonic()\n    logger.info(\"Stage 6 (embed & index) starting for video_id=%s\", video_id)\n\n    settings = get_settings()\n    session = _get_sync_session()\n    try:\n        # Load technique pages created for this video's moments\n        moments = (\n            session.execute(\n                select(KeyMoment)\n                .where(KeyMoment.source_video_id == video_id)\n                .order_by(KeyMoment.start_time)\n            )\n            .scalars()\n            .all()\n        )\n\n        # Get unique technique page IDs from moments\n        page_ids = {m.technique_page_id for m in moments if m.technique_page_id is not None}\n        pages = []\n        if page_ids:\n            pages = (\n                session.execute(\n                    select(TechniquePage).where(TechniquePage.id.in_(page_ids))\n                )\n                .scalars()\n                .all()\n            )\n\n        if not moments and not pages:\n            logger.info(\"Stage 6: No moments or pages for video_id=%s, skipping.\", video_id)\n            if run_id:\n                _finish_run(run_id, \"complete\")\n            return video_id\n\n        # Resolve creator names for enriched embedding text\n        creator_ids = {p.creator_id for p in pages}\n        creator_map: dict[str, str] = {}\n        if creator_ids:\n            creators = (\n                session.execute(\n                    select(Creator).where(Creator.id.in_(creator_ids))\n                )\n                .scalars()\n                .all()\n            )\n            creator_map = {str(c.id): c.name for c in creators}\n\n        # Resolve creator name for key moments via source_video → creator\n        video_ids = {m.source_video_id for m in moments}\n        video_creator_map: dict[str, str] = {}\n        if video_ids:\n            rows = session.execute(\n                select(SourceVideo.id, Creator.name)\n                .join(Creator, SourceVideo.creator_id == Creator.id)\n                .where(SourceVideo.id.in_(video_ids))\n            ).all()\n            video_creator_map = {str(r[0]): r[1] for r in rows}\n\n        embed_client = EmbeddingClient(settings)\n        qdrant = QdrantManager(settings)\n\n        # Ensure collection exists before upserting\n        qdrant.ensure_collection()\n\n        # ── Embed & upsert technique pages ───────────────────────────────\n        if pages:\n            page_texts = []\n            page_dicts = []\n            for p in pages:\n                creator_name = creator_map.get(str(p.creator_id), \"\")\n                tags_joined = \" \".join(p.topic_tags) if p.topic_tags else \"\"\n                text = f\"{creator_name} {p.title} {p.topic_category or ''} {tags_joined} {p.summary or ''}\"\n                page_texts.append(text.strip())\n                page_dicts.append({\n                    \"page_id\": str(p.id),\n                    \"creator_id\": str(p.creator_id),\n                    \"creator_name\": creator_name,\n                    \"title\": p.title,\n                    \"slug\": p.slug,\n                    \"topic_category\": p.topic_category or \"\",\n                    \"topic_tags\": p.topic_tags or [],\n                    \"summary\": p.summary or \"\",\n                })\n\n            page_vectors = embed_client.embed(page_texts)\n            if page_vectors:\n                qdrant.upsert_technique_pages(page_dicts, page_vectors)\n                logger.info(\n                    \"Stage 6: Upserted %d technique page vectors for video_id=%s\",\n                    len(page_vectors), video_id,\n\n[577 more lines in file. Use offset=1527 to continue.]"}],"isError":false,"timestamp":1775179595876}}
{"type":"message","id":"53400af1","parentId":"2bfa6772","timestamp":"2026-04-03T01:26:41.491Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I have a clear picture. Let me also check what `_make_llm_callback`, `_build_request_params`, and `LLMTruncationError` look like, plus the imports needed."},{"type":"toolCall","id":"toolu_013w2mwGkCefTQcFEeKVPSij","name":"bash","arguments":{"command":"grep -n 'def _make_llm_callback\\|def _build_request_params\\|class LLMTruncationError\\|from.*LLMTruncationError\\|def _load_classification_data\\|def _load_prior_pages\\|def _get_stage_config\\|def _get_llm_client\\|def _get_sync_session\\|def _emit_event\\|def _capture_pipeline_metadata\\|def _compute_page_tags' backend/pipeline/stages.py | head -20"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":228,"cacheRead":73018,"cacheWrite":2318,"totalTokens":75565,"cost":{"input":0.000005,"output":0.0057,"cacheRead":0.036509,"cacheWrite":0.0144875,"total":0.0567015}},"stopReason":"toolUse","timestamp":1775179595877}}
{"type":"message","id":"675cecbe","parentId":"53400af1","timestamp":"2026-04-03T01:26:41.510Z","message":{"role":"toolResult","toolCallId":"toolu_013w2mwGkCefTQcFEeKVPSij","toolName":"bash","content":[{"type":"text","text":"52:class LLMTruncationError(RuntimeError):\n79:def _emit_event(\n137:def _make_llm_callback(\n189:def _build_request_params(\n236:def _get_sync_session() -> Session:\n280:def _get_llm_client() -> LLMClient:\n285:def _get_stage_config(stage_num: int) -> tuple[str | None, str]:\n791:def _load_classification_data(video_id: str) -> list[dict]:\n884:def _capture_pipeline_metadata() -> dict:\n935:def _compute_page_tags(\n1640:def _load_prior_pages(video_id: str) -> list[str]:\n"}],"isError":false,"timestamp":1775179601506}}
{"type":"message","id":"97627875","parentId":"675cecbe","timestamp":"2026-04-03T01:26:44.913Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the existing TechniquePage model fields that I'll need to serialize."},{"type":"toolCall","id":"toolu_01CZR8EjNBs6XkBbKHrpsrih","name":"read","arguments":{"limit":45,"offset":195,"path":"backend/models.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":109,"cacheRead":75336,"cacheWrite":416,"totalTokens":75862,"cost":{"input":0.000005,"output":0.002725,"cacheRead":0.037668,"cacheWrite":0.0026000000000000003,"total":0.042998}},"stopReason":"toolUse","timestamp":1775179601507}}
{"type":"message","id":"4c6881cb","parentId":"97627875","timestamp":"2026-04-03T01:26:44.918Z","message":{"role":"toolResult","toolCallId":"toolu_01CZR8EjNBs6XkBbKHrpsrih","toolName":"read","content":[{"type":"text","text":"    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    body_sections_format: Mapped[str] = mapped_column(\n        String(20), nullable=False, default=\"v1\", server_default=\"v1\"\n    )\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    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[258 more lines in file. Use offset=240 to continue.]"}],"isError":false,"timestamp":1775179604915}}
{"type":"message","id":"e5a9b7e6","parentId":"4c6881cb","timestamp":"2026-04-03T01:26:52.661Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me check the SynthesizedPage schema to know exactly what fields to serialize."},{"type":"toolCall","id":"toolu_01HuZ2dU7TsE62WTdU1Vez1m","name":"bash","arguments":{"command":"grep -n 'class SynthesizedPage\\|class SynthesisResult' backend/pipeline/schemas.py"}},{"type":"toolCall","id":"toolu_01BXvjJt5TbY4V4mqoFsy7Ai","name":"read","arguments":{"limit":5,"offset":244,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":172,"cacheRead":75752,"cacheWrite":806,"totalTokens":76731,"cost":{"input":0.000005,"output":0.0043,"cacheRead":0.037876,"cacheWrite":0.0050375,"total":0.0472185}},"stopReason":"toolUse","timestamp":1775179604916}}
{"type":"message","id":"5713be2e","parentId":"e5a9b7e6","timestamp":"2026-04-03T01:26:52.673Z","message":{"role":"toolResult","toolCallId":"toolu_01HuZ2dU7TsE62WTdU1Vez1m","toolName":"bash","content":[{"type":"text","text":"91:class SynthesizedPage(BaseModel):\n122:class SynthesisResult(BaseModel):\n"}],"isError":false,"timestamp":1775179612672}}
{"type":"message","id":"5bacd556","parentId":"5713be2e","timestamp":"2026-04-03T01:26:52.675Z","message":{"role":"toolResult","toolCallId":"toolu_01BXvjJt5TbY4V4mqoFsy7Ai","toolName":"read","content":[{"type":"text","text":"def _load_prompt(template_name: str, video_id: str | None = None) -> str:\n    \"\"\"Read a prompt template from the prompts directory.\n\n    If ``video_id`` is provided, checks Redis for a per-video prompt override\n    (key: ``chrysopedia:prompt_override:{video_id}:{template_name}``) before\n\n[1855 more lines in file. Use offset=249 to continue.]"}],"isError":false,"timestamp":1775179612672}}
{"type":"message","id":"ed707b00","parentId":"5bacd556","timestamp":"2026-04-03T01:26:55.497Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01YDopVpys5d9QDLg7F4eS9J","name":"read","arguments":{"limit":40,"offset":91,"path":"backend/pipeline/schemas.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":76558,"cacheWrite":359,"totalTokens":77009,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.038279,"cacheWrite":0.00224375,"total":0.04280275}},"stopReason":"toolUse","timestamp":1775179612673}}
{"type":"message","id":"237b7343","parentId":"ed707b00","timestamp":"2026-04-03T01:26:55.501Z","message":{"role":"toolResult","toolCallId":"toolu_01YDopVpys5d9QDLg7F4eS9J","toolName":"read","content":[{"type":"text","text":"class SynthesizedPage(BaseModel):\n    \"\"\"A technique page synthesized from classified moments.\"\"\"\n\n    title: str = Field(description=\"Page title\")\n    slug: str = Field(description=\"URL-safe slug\")\n    topic_category: str = Field(description=\"Primary topic category\")\n    topic_tags: list[str] = Field(default_factory=list, description=\"Associated tags\")\n    summary: str = Field(description=\"Page summary / overview paragraph\")\n    body_sections: list[BodySection] = Field(\n        default_factory=list,\n        description=\"Structured body content as H2 sections with optional H3 subsections\",\n    )\n    body_sections_format: str = Field(\n        default=\"v2\",\n        description=\"Schema version for body_sections ('v2' = list[BodySection])\",\n    )\n    signal_chains: list[dict] = Field(\n        default_factory=list,\n        description=\"Signal chain descriptions (for audio/music production contexts)\",\n    )\n    plugins: list[str] = Field(default_factory=list, description=\"Plugins/tools referenced\")\n    source_quality: str = Field(\n        default=\"mixed\",\n        description=\"One of: structured, mixed, unstructured\",\n    )\n    moment_indices: list[int] = Field(\n        default_factory=list,\n        description=\"Indices of source moments (from the input list) that this page covers\",\n    )\n\n\nclass SynthesisResult(BaseModel):\n    \"\"\"Full output of stage 5 (synthesis).\"\"\"\n\n    pages: list[SynthesizedPage]\n"}],"isError":false,"timestamp":1775179615499}}
{"type":"message","id":"63408316","parentId":"237b7343","timestamp":"2026-04-03T01:26:59.785Z","message":{"role":"assistant","content":[{"type":"text","text":"Good. I now have complete understanding. Let me also quickly check if `pg_insert` / `sqlalchemy.dialects.postgresql` is already imported anywhere in stages.py."},{"type":"toolCall","id":"toolu_01TWo5Y18vbFMiNJZfV4UN8W","name":"bash","arguments":{"command":"grep -n 'postgresql\\|pg_insert\\|dialects' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":113,"cacheRead":76558,"cacheWrite":839,"totalTokens":77511,"cost":{"input":0.000005,"output":0.0028250000000000003,"cacheRead":0.038279,"cacheWrite":0.00524375,"total":0.04635275}},"stopReason":"toolUse","timestamp":1775179615500}}
{"type":"message","id":"462b38ab","parentId":"63408316","timestamp":"2026-04-03T01:26:59.794Z","message":{"role":"toolResult","toolCallId":"toolu_01TWo5Y18vbFMiNJZfV4UN8W","toolName":"bash","content":[{"type":"text","text":"231:        url = url.replace(\"postgresql+asyncpg://\", \"postgresql+psycopg2://\")\n828:                \"[CLASSIFY-LOAD] Source: postgresql, video_id=%s, %d entries\",\n"}],"isError":false,"timestamp":1775179619792}}
{"type":"message","id":"474929ad","parentId":"462b38ab","timestamp":"2026-04-03T01:27:03.712Z","message":{"role":"assistant","content":[{"type":"text","text":"No pg_insert imported yet. Let me also check if `import json` is already there (for serializing the page)."},{"type":"toolCall","id":"toolu_01FJd3RgbMwtdVRoPEqt5qSt","name":"bash","arguments":{"command":"grep -n '^import json\\|^from json' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":98,"cacheRead":77397,"cacheWrite":177,"totalTokens":77673,"cost":{"input":0.000005,"output":0.00245,"cacheRead":0.0386985,"cacheWrite":0.00110625,"total":0.04225975}},"stopReason":"toolUse","timestamp":1775179619793}}
{"type":"message","id":"a4a44a00","parentId":"474929ad","timestamp":"2026-04-03T01:27:03.727Z","message":{"role":"toolResult","toolCallId":"toolu_01FJd3RgbMwtdVRoPEqt5qSt","toolName":"bash","content":[{"type":"text","text":"13:import json\n"}],"isError":false,"timestamp":1775179623725}}
{"type":"message","id":"8e514bc6","parentId":"a4a44a00","timestamp":"2026-04-03T01:27:09.716Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — json is already imported. Now I have everything I need. Let me implement the changes.\n\n**Step 1:** Add `TechniquePageVideo` to imports."},{"type":"toolCall","id":"toolu_011wEbrSbqxRDLXno2qbYTzN","name":"edit","arguments":{"newText":"from sqlalchemy.dialects.postgresql import insert as pg_insert\n\nfrom models import (\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    PipelineEvent,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TechniquePageVersion,\n    TechniquePageVideo,\n    TranscriptSegment,\n)","oldText":"from models import (\n    Creator,\n    KeyMoment,\n    KeyMomentContentType,\n    PipelineEvent,\n    ProcessingStatus,\n    SourceVideo,\n    TechniquePage,\n    TechniquePageVersion,\n    TranscriptSegment,\n)","path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":290,"cacheRead":77574,"cacheWrite":115,"totalTokens":77980,"cost":{"input":0.000005,"output":0.00725,"cacheRead":0.038786999999999995,"cacheWrite":0.00071875,"total":0.04676074999999999}},"stopReason":"toolUse","timestamp":1775179623726}}
{"type":"message","id":"f213a06d","parentId":"8e514bc6","timestamp":"2026-04-03T01:27:09.729Z","message":{"role":"toolResult","toolCallId":"toolu_011wEbrSbqxRDLXno2qbYTzN","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/stages.py."}],"details":{"diff":"   23 from sqlalchemy import create_engine, func, select\n   24 from sqlalchemy.orm import Session, sessionmaker\n   25 \n   26 from config import get_settings\n-  27 from models import (\n-  28     Creator,\n-  29     KeyMoment,\n-  30     KeyMomentContentType,\n-  31     PipelineEvent,\n-  32     ProcessingStatus,\n-  33     SourceVideo,\n-  34     TechniquePage,\n-  35     TechniquePageVersion,\n+  27 from sqlalchemy.dialects.postgresql import insert as pg_insert\n+  28 \n+  29 from models import (\n+  30     Creator,\n+  31     KeyMoment,\n+  32     KeyMomentContentType,\n+  33     PipelineEvent,\n+  34     ProcessingStatus,\n+  35     SourceVideo,\n+  36     TechniquePage,\n+  37     TechniquePageVersion,\n+  38     TechniquePageVideo,\n   39     TranscriptSegment,\n   40 )\n   41 from pipeline.embedding_client import EmbeddingClient\n   42 from pipeline.llm_client import LLMClient, LLMResponse, estimate_max_tokens","firstChangedLine":27},"isError":false,"timestamp":1775179629727}}
{"type":"message","id":"87ed5a5a","parentId":"f213a06d","timestamp":"2026-04-03T01:27:13.364Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 2:** Add `_build_compose_user_prompt()` helper. I'll place it right after `_build_moments_text()`."},{"type":"toolCall","id":"toolu_01FJ8pVHzde3Sgxa49VVjNsv","name":"bash","arguments":{"command":"grep -n 'def _synthesize_chunk' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":105,"cacheRead":77689,"cacheWrite":313,"totalTokens":78108,"cost":{"input":0.000005,"output":0.002625,"cacheRead":0.0388445,"cacheWrite":0.00195625,"total":0.04343075}},"stopReason":"toolUse","timestamp":1775179629728}}
{"type":"message","id":"e8429a43","parentId":"87ed5a5a","timestamp":"2026-04-03T01:27:13.379Z","message":{"role":"toolResult","toolCallId":"toolu_01FJ8pVHzde3Sgxa49VVjNsv","toolName":"bash","content":[{"type":"text","text":"986:def _synthesize_chunk(\n"}],"isError":false,"timestamp":1775179633376}}
{"type":"message","id":"18dfac43","parentId":"e8429a43","timestamp":"2026-04-03T01:27:17.105Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01BsYaFbuEoK2NgVgjmDaSz1","name":"read","arguments":{"limit":5,"offset":983,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":91,"cacheRead":78002,"cacheWrite":127,"totalTokens":78221,"cost":{"input":0.000005,"output":0.002275,"cacheRead":0.039001,"cacheWrite":0.0007937500000000001,"total":0.04207475}},"stopReason":"toolUse","timestamp":1775179633377}}
{"type":"message","id":"e7cc806e","parentId":"18dfac43","timestamp":"2026-04-03T01:27:17.110Z","message":{"role":"toolResult","toolCallId":"toolu_01BsYaFbuEoK2NgVgjmDaSz1","toolName":"read","content":[{"type":"text","text":"    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _synthesize_chunk(\n    chunk: list[tuple[KeyMoment, dict]],\n\n[1119 more lines in file. Use offset=988 to continue.]"}],"isError":false,"timestamp":1775179637108}}
{"type":"message","id":"b67f3d8c","parentId":"e7cc806e","timestamp":"2026-04-03T01:27:40.147Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_011iBiE5YmjsRaELdHDwexCa","name":"edit","arguments":{"newText":"    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _build_compose_user_prompt(\n    existing_page: TechniquePage,\n    existing_moments: list[KeyMoment],\n    new_moments: list[tuple[KeyMoment, dict]],\n    creator_name: str,\n) -> str:\n    \"\"\"Build the user prompt for composing new moments into an existing page.\n\n    Existing moments keep indices [0]-[N-1].\n    New moments get indices [N]-[N+M-1].\n    XML-tagged prompt structure matches test_harness.py build_compose_prompt().\n    \"\"\"\n    category = existing_page.topic_category or \"Uncategorized\"\n\n    # Serialize existing page to dict matching SynthesizedPage shape\n    sq = existing_page.source_quality\n    sq_value = sq.value if hasattr(sq, \"value\") else sq\n    page_dict = {\n        \"title\": existing_page.title,\n        \"slug\": existing_page.slug,\n        \"topic_category\": existing_page.topic_category,\n        \"summary\": existing_page.summary,\n        \"body_sections\": existing_page.body_sections,\n        \"signal_chains\": existing_page.signal_chains,\n        \"plugins\": existing_page.plugins,\n        \"source_quality\": sq_value,\n    }\n\n    # Format existing moments [0]-[N-1] using _build_moments_text pattern\n    # Existing moments don't have classification data — use empty dict\n    existing_as_tuples = [(m, {}) for m in existing_moments]\n    existing_text, _ = _build_moments_text(existing_as_tuples, category)\n\n    # Format new moments [N]-[N+M-1] with offset indices\n    n = len(existing_moments)\n    new_lines = []\n    for i, (m, cls_info) in enumerate(new_moments):\n        tags = cls_info.get(\"topic_tags\", [])\n        new_lines.append(\n            f\"[{n + i}] Title: {m.title}\\n\"\n            f\"    Summary: {m.summary}\\n\"\n            f\"    Content type: {m.content_type.value}\\n\"\n            f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n            f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n            f\"    Category: {category}\\n\"\n            f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n            f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n        )\n    new_text = \"\\n\\n\".join(new_lines)\n\n    page_json = json.dumps(page_dict, indent=2, ensure_ascii=False, default=str)\n\n    return (\n        f\"<existing_page>\\n{page_json}\\n</existing_page>\\n\"\n        f\"<existing_moments>\\n{existing_text}\\n</existing_moments>\\n\"\n        f\"<new_moments>\\n{new_text}\\n</new_moments>\\n\"\n        f\"<creator>{creator_name}</creator>\"\n    )\n\n\ndef _compose_into_existing(\n    existing_page: TechniquePage,\n    existing_moments: list[KeyMoment],\n    new_moment_group: list[tuple[KeyMoment, dict]],\n    category: str,\n    creator_name: str,\n    system_prompt: str,\n    llm: LLMClient,\n    model_override: str | None,\n    modality: str,\n    hard_limit: int,\n    video_id: str,\n    run_id: str | None,\n) -> SynthesisResult:\n    \"\"\"Compose new moments into an existing technique page via LLM.\n\n    Loads the compose system prompt, builds the compose user prompt, and\n    calls the LLM with the same retry/parse pattern as _synthesize_chunk().\n    \"\"\"\n    compose_prompt = _load_prompt(\"stage5_compose.txt\", video_id=video_id)\n    user_prompt = _build_compose_user_prompt(\n        existing_page, existing_moments, new_moment_group, creator_name,\n    )\n\n    estimated_input = estimate_max_tokens(\n        compose_prompt, user_prompt,\n        stage=\"stage5_synthesis\", hard_limit=hard_limit,\n    )\n    logger.info(\n        \"Stage 5: Composing into '%s' — %d existing + %d new moments, max_tokens=%d\",\n        existing_page.slug, len(existing_moments), len(new_moment_group), estimated_input,\n    )\n\n    raw = llm.complete(\n        compose_prompt, user_prompt, response_model=SynthesisResult,\n        on_complete=_make_llm_callback(\n            video_id, \"stage5_synthesis\",\n            system_prompt=compose_prompt, user_prompt=user_prompt,\n            run_id=run_id, context_label=f\"compose:{category}\",\n            request_params=_build_request_params(\n                estimated_input, model_override, modality, \"SynthesisResult\", hard_limit,\n            ),\n        ),\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n    return _safe_parse_llm_response(\n        raw, SynthesisResult, llm, compose_prompt, user_prompt,\n        modality=modality, model_override=model_override, max_tokens=estimated_input,\n    )\n\n\ndef _synthesize_chunk(","oldText":"    return \"\\n\\n\".join(moments_lines), all_tags\n\n\ndef _synthesize_chunk(","path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1512,"cacheRead":78129,"cacheWrite":161,"totalTokens":79803,"cost":{"input":0.000005,"output":0.0378,"cacheRead":0.039064499999999995,"cacheWrite":0.00100625,"total":0.07787575}},"stopReason":"toolUse","timestamp":1775179637108}}
{"type":"message","id":"7840f92e","parentId":"b67f3d8c","timestamp":"2026-04-03T01:27:40.166Z","message":{"role":"toolResult","toolCallId":"toolu_011iBiE5YmjsRaELdHDwexCa","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/stages.py."}],"details":{"diff":"  982         )\n  983     return \"\\n\\n\".join(moments_lines), all_tags\n  984 \n  985 \n+ 986 def _build_compose_user_prompt(\n+ 987     existing_page: TechniquePage,\n+ 988     existing_moments: list[KeyMoment],\n+ 989     new_moments: list[tuple[KeyMoment, dict]],\n+ 990     creator_name: str,\n+ 991 ) -> str:\n+ 992     \"\"\"Build the user prompt for composing new moments into an existing page.\n+ 993 \n+ 994     Existing moments keep indices [0]-[N-1].\n+ 995     New moments get indices [N]-[N+M-1].\n+ 996     XML-tagged prompt structure matches test_harness.py build_compose_prompt().\n+ 997     \"\"\"\n+ 998     category = existing_page.topic_category or \"Uncategorized\"\n+ 999 \n+1000     # Serialize existing page to dict matching SynthesizedPage shape\n+1001     sq = existing_page.source_quality\n+1002     sq_value = sq.value if hasattr(sq, \"value\") else sq\n+1003     page_dict = {\n+1004         \"title\": existing_page.title,\n+1005         \"slug\": existing_page.slug,\n+1006         \"topic_category\": existing_page.topic_category,\n+1007         \"summary\": existing_page.summary,\n+1008         \"body_sections\": existing_page.body_sections,\n+1009         \"signal_chains\": existing_page.signal_chains,\n+1010         \"plugins\": existing_page.plugins,\n+1011         \"source_quality\": sq_value,\n+1012     }\n+1013 \n+1014     # Format existing moments [0]-[N-1] using _build_moments_text pattern\n+1015     # Existing moments don't have classification data — use empty dict\n+1016     existing_as_tuples = [(m, {}) for m in existing_moments]\n+1017     existing_text, _ = _build_moments_text(existing_as_tuples, category)\n+1018 \n+1019     # Format new moments [N]-[N+M-1] with offset indices\n+1020     n = len(existing_moments)\n+1021     new_lines = []\n+1022     for i, (m, cls_info) in enumerate(new_moments):\n+1023         tags = cls_info.get(\"topic_tags\", [])\n+1024         new_lines.append(\n+1025             f\"[{n + i}] Title: {m.title}\\n\"\n+1026             f\"    Summary: {m.summary}\\n\"\n+1027             f\"    Content type: {m.content_type.value}\\n\"\n+1028             f\"    Time: {m.start_time:.1f}s - {m.end_time:.1f}s\\n\"\n+1029             f\"    Plugins: {', '.join(m.plugins) if m.plugins else 'none'}\\n\"\n+1030             f\"    Category: {category}\\n\"\n+1031             f\"    Tags: {', '.join(tags) if tags else 'none'}\\n\"\n+1032             f\"    Transcript excerpt: {(m.raw_transcript or '')[:300]}\"\n+1033         )\n+1034     new_text = \"\\n\\n\".join(new_lines)\n+1035 \n+1036     page_json = json.dumps(page_dict, indent=2, ensure_ascii=False, default=str)\n+1037 \n+1038     return (\n+1039         f\"<existing_page>\\n{page_json}\\n</existing_page>\\n\"\n+1040         f\"<existing_moments>\\n{existing_text}\\n</existing_moments>\\n\"\n+1041         f\"<new_moments>\\n{new_text}\\n</new_moments>\\n\"\n+1042         f\"<creator>{creator_name}</creator>\"\n+1043     )\n+1044 \n+1045 \n+1046 def _compose_into_existing(\n+1047     existing_page: TechniquePage,\n+1048     existing_moments: list[KeyMoment],\n+1049     new_moment_group: list[tuple[KeyMoment, dict]],\n+1050     category: str,\n+1051     creator_name: str,\n+1052     system_prompt: str,\n+1053     llm: LLMClient,\n+1054     model_override: str | None,\n+1055     modality: str,\n+1056     hard_limit: int,\n+1057     video_id: str,\n+1058     run_id: str | None,\n+1059 ) -> SynthesisResult:\n+1060     \"\"\"Compose new moments into an existing technique page via LLM.\n+1061 \n+1062     Loads the compose system prompt, builds the compose user prompt, and\n+1063     calls the LLM with the same retry/parse pattern as _synthesize_chunk().\n+1064     \"\"\"\n+1065     compose_prompt = _load_prompt(\"stage5_compose.txt\", video_id=video_id)\n+1066     user_prompt = _build_compose_user_prompt(\n+1067         existing_page, existing_moments, new_moment_group, creator_name,\n+1068     )\n+1069 \n+1070     estimated_input = estimate_max_tokens(\n+1071         compose_prompt, user_prompt,\n+1072         stage=\"stage5_synthesis\", hard_limit=hard_limit,\n+1073     )\n+1074     logger.info(\n+1075         \"Stage 5: Composing into '%s' — %d existing + %d new moments, max_tokens=%d\",\n+1076         existing_page.slug, len(existing_moments), len(new_moment_group), estimated_input,\n+1077     )\n+1078 \n+1079     raw = llm.complete(\n+1080         compose_prompt, user_prompt, response_model=SynthesisResult,\n+1081         on_complete=_make_llm_callback(\n+1082             video_id, \"stage5_synthesis\",\n+1083             system_prompt=compose_prompt, user_prompt=user_prompt,\n+1084             run_id=run_id, context_label=f\"compose:{category}\",\n+1085             request_params=_build_request_params(\n+1086                 estimated_input, model_override, modality, \"SynthesisResult\", hard_limit,\n+1087             ),\n+1088         ),\n+1089         modality=modality, model_override=model_override, max_tokens=estimated_input,\n+1090     )\n+1091     return _safe_parse_llm_response(\n+1092         raw, SynthesisResult, llm, compose_prompt, user_prompt,\n+1093         modality=modality, model_override=model_override, max_tokens=estimated_input,\n+1094     )\n+1095 \n+1096 \n 1097 def _synthesize_chunk(\n 1098     chunk: list[tuple[KeyMoment, dict]],\n 1099     category: str,\n 1100     creator_name: str,","firstChangedLine":986},"isError":false,"timestamp":1775179660163}}
{"type":"message","id":"f51e91db","parentId":"7840f92e","timestamp":"2026-04-03T01:27:43.770Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 4:** Add compose-or-create detection in the per-category loop. Let me find the exact location."},{"type":"toolCall","id":"toolu_01Cvr8151Gdfm9mjsneqgByC","name":"bash","arguments":{"command":"grep -n 'for category, moment_group in groups.items' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":101,"cacheRead":78290,"cacheWrite":1535,"totalTokens":79927,"cost":{"input":0.000005,"output":0.0025250000000000003,"cacheRead":0.039145,"cacheWrite":0.00959375,"total":0.051268749999999995}},"stopReason":"toolUse","timestamp":1775179660164}}
{"type":"message","id":"cd3fd47b","parentId":"f51e91db","timestamp":"2026-04-03T01:27:43.782Z","message":{"role":"toolResult","toolCallId":"toolu_01Cvr8151Gdfm9mjsneqgByC","toolName":"bash","content":[{"type":"text","text":"1309:        for category, moment_group in groups.items():\n"}],"isError":false,"timestamp":1775179663780}}
{"type":"message","id":"be8efa0b","parentId":"cd3fd47b","timestamp":"2026-04-03T01:27:47.218Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_01541wrKZTidgY87haSZtrkE","name":"read","arguments":{"limit":35,"offset":1309,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":79825,"cacheWrite":129,"totalTokens":80047,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.0399125,"cacheWrite":0.00080625,"total":0.04302375}},"stopReason":"toolUse","timestamp":1775179663780}}
{"type":"message","id":"98f1feac","parentId":"be8efa0b","timestamp":"2026-04-03T01:27:47.225Z","message":{"role":"toolResult","toolCallId":"toolu_01541wrKZTidgY87haSZtrkE","toolName":"read","content":[{"type":"text","text":"        for category, moment_group in groups.items():\n            # Collect all tags across the full group (used for DB writes later)\n            all_tags: set[str] = set()\n            for _, cls_info in moment_group:\n                all_tags.update(cls_info.get(\"topic_tags\", []))\n\n            # ── Chunked synthesis with truncation recovery ─────────\n            if len(moment_group) <= chunk_size:\n                # Small group — try single LLM call first\n                try:\n                    result = _synthesize_chunk(\n                        moment_group, category, creator_name,\n                        system_prompt, llm, model_override, modality, hard_limit,\n                        video_id, run_id, f\"category:{category}\",\n                    )\n                    synthesized_pages = list(result.pages)\n                    logger.info(\n                        \"Stage 5: category '%s' — %d moments, %d page(s) from single call\",\n                        category, len(moment_group), len(synthesized_pages),\n                    )\n                except LLMTruncationError:\n                    # Output too large for model context — split in half and retry\n                    logger.warning(\n                        \"Stage 5: category '%s' truncated with %d moments. \"\n                        \"Splitting into smaller chunks and retrying.\",\n                        category, len(moment_group),\n                    )\n                    half = max(1, len(moment_group) // 2)\n                    chunk_pages = []\n                    for sub_start in range(0, len(moment_group), half):\n                        sub_chunk = moment_group[sub_start:sub_start + half]\n                        sub_label = f\"category:{category} recovery-chunk:{sub_start // half + 1}\"\n                        sub_result = _synthesize_chunk(\n                            sub_chunk, category, creator_name,\n                            system_prompt, llm, model_override, modality, hard_limit,\n\n[874 more lines in file. Use offset=1344 to continue.]"}],"isError":false,"timestamp":1775179667221}}
{"type":"message","id":"00600559","parentId":"98f1feac","timestamp":"2026-04-03T01:27:50.983Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_019Jw8ApfjYyxh1abt6fEzqZ","name":"read","arguments":{"limit":20,"offset":1344,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":79954,"cacheWrite":606,"totalTokens":80653,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.039977,"cacheWrite":0.0037875,"total":0.0460695}},"stopReason":"toolUse","timestamp":1775179667222}}
{"type":"message","id":"e3d4ca6d","parentId":"00600559","timestamp":"2026-04-03T01:27:50.988Z","message":{"role":"toolResult","toolCallId":"toolu_019Jw8ApfjYyxh1abt6fEzqZ","toolName":"read","content":[{"type":"text","text":"                            video_id, run_id, sub_label,\n                        )\n                        # Reindex moment_indices to global offsets\n                        for p in sub_result.pages:\n                            if p.moment_indices:\n                                p.moment_indices = [idx + sub_start for idx in p.moment_indices]\n                        chunk_pages.extend(sub_result.pages)\n                    synthesized_pages = chunk_pages\n                    logger.info(\n                        \"Stage 5: category '%s' — %d page(s) from recovery split\",\n                        category, len(synthesized_pages),\n                    )\n            else:\n                # Large group — split into chunks, synthesize each, then merge\n                num_chunks = (len(moment_group) + chunk_size - 1) // chunk_size\n                logger.info(\n                    \"Stage 5: category '%s' has %d moments — splitting into %d chunks of ≤%d\",\n                    category, len(moment_group), num_chunks, chunk_size,\n                )\n\n\n[854 more lines in file. Use offset=1364 to continue.]"}],"isError":false,"timestamp":1775179670986}}
{"type":"message","id":"35d83989","parentId":"e3d4ca6d","timestamp":"2026-04-03T01:28:01.235Z","message":{"role":"assistant","content":[{"type":"text","text":"Now I'll insert the compose-or-create detection between the tag collection and the chunked synthesis block."},{"type":"toolCall","id":"toolu_01RWo7gdpnWCCy1iEraDVBqg","name":"edit","arguments":{"newText":"            # Collect all tags across the full group (used for DB writes later)\n            all_tags: set[str] = set()\n            for _, cls_info in moment_group:\n                all_tags.update(cls_info.get(\"topic_tags\", []))\n\n            # ── Compose-or-create detection ────────────────────────\n            # Check if an existing technique page already covers this\n            # creator + category combination (from a prior video run).\n            compose_matches = session.execute(\n                select(TechniquePage).where(\n                    TechniquePage.creator_id == video.creator_id,\n                    func.lower(TechniquePage.topic_category) == func.lower(category),\n                )\n            ).scalars().all()\n\n            if len(compose_matches) > 1:\n                logger.warning(\n                    \"Stage 5: Multiple existing pages (%d) match creator=%s category='%s'. \"\n                    \"Using first match '%s'.\",\n                    len(compose_matches), video.creator_id, category,\n                    compose_matches[0].slug,\n                )\n\n            compose_target = compose_matches[0] if compose_matches else None\n\n            if compose_target is not None:\n                # Load existing moments linked to this page\n                existing_moments = session.execute(\n                    select(KeyMoment)\n                    .where(KeyMoment.technique_page_id == compose_target.id)\n                    .order_by(KeyMoment.start_time)\n                ).scalars().all()\n\n                logger.info(\n                    \"Stage 5: Composing into existing page '%s' \"\n                    \"(%d existing moments + %d new moments)\",\n                    compose_target.slug,\n                    len(existing_moments),\n                    len(moment_group),\n                )\n\n                compose_result = _compose_into_existing(\n                    compose_target, existing_moments, moment_group,\n                    category, creator_name, system_prompt,\n                    llm, model_override, modality, hard_limit,\n                    video_id, run_id,\n                )\n                synthesized_pages = list(compose_result.pages)\n\n            # ── Chunked synthesis with truncation recovery ─────────\n            elif len(moment_group) <= chunk_size:","oldText":"            # Collect all tags across the full group (used for DB writes later)\n            all_tags: set[str] = set()\n            for _, cls_info in moment_group:\n                all_tags.update(cls_info.get(\"topic_tags\", []))\n\n            # ── Chunked synthesis with truncation recovery ─────────\n            if len(moment_group) <= chunk_size:","path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":763,"cacheRead":80560,"cacheWrite":374,"totalTokens":81698,"cost":{"input":0.000005,"output":0.019075,"cacheRead":0.040279999999999996,"cacheWrite":0.0023375,"total":0.061697499999999995}},"stopReason":"toolUse","timestamp":1775179670987}}
{"type":"message","id":"97b3ac37","parentId":"35d83989","timestamp":"2026-04-03T01:28:01.247Z","message":{"role":"toolResult","toolCallId":"toolu_01RWo7gdpnWCCy1iEraDVBqg","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/stages.py."}],"details":{"diff":" 1311             all_tags: set[str] = set()\n 1312             for _, cls_info in moment_group:\n 1313                 all_tags.update(cls_info.get(\"topic_tags\", []))\n 1314 \n-1315             # ── Chunked synthesis with truncation recovery ─────────\n-1316             if len(moment_group) <= chunk_size:\n+1315             # ── Compose-or-create detection ────────────────────────\n+1316             # Check if an existing technique page already covers this\n+1317             # creator + category combination (from a prior video run).\n+1318             compose_matches = session.execute(\n+1319                 select(TechniquePage).where(\n+1320                     TechniquePage.creator_id == video.creator_id,\n+1321                     func.lower(TechniquePage.topic_category) == func.lower(category),\n+1322                 )\n+1323             ).scalars().all()\n+1324 \n+1325             if len(compose_matches) > 1:\n+1326                 logger.warning(\n+1327                     \"Stage 5: Multiple existing pages (%d) match creator=%s category='%s'. \"\n+1328                     \"Using first match '%s'.\",\n+1329                     len(compose_matches), video.creator_id, category,\n+1330                     compose_matches[0].slug,\n+1331                 )\n+1332 \n+1333             compose_target = compose_matches[0] if compose_matches else None\n+1334 \n+1335             if compose_target is not None:\n+1336                 # Load existing moments linked to this page\n+1337                 existing_moments = session.execute(\n+1338                     select(KeyMoment)\n+1339                     .where(KeyMoment.technique_page_id == compose_target.id)\n+1340                     .order_by(KeyMoment.start_time)\n+1341                 ).scalars().all()\n+1342 \n+1343                 logger.info(\n+1344                     \"Stage 5: Composing into existing page '%s' \"\n+1345                     \"(%d existing moments + %d new moments)\",\n+1346                     compose_target.slug,\n+1347                     len(existing_moments),\n+1348                     len(moment_group),\n+1349                 )\n+1350 \n+1351                 compose_result = _compose_into_existing(\n+1352                     compose_target, existing_moments, moment_group,\n+1353                     category, creator_name, system_prompt,\n+1354                     llm, model_override, modality, hard_limit,\n+1355                     video_id, run_id,\n+1356                 )\n+1357                 synthesized_pages = list(compose_result.pages)\n+1358 \n+1359             # ── Chunked synthesis with truncation recovery ─────────\n+1360             elif len(moment_group) <= chunk_size:\n 1361                 # Small group — try single LLM call first\n 1362                 try:\n 1363                     result = _synthesize_chunk(\n 1364                         moment_group, category, creator_name,","firstChangedLine":1315},"isError":false,"timestamp":1775179681246}}
{"type":"message","id":"a64fe9d7","parentId":"97b3ac37","timestamp":"2026-04-03T01:28:05.236Z","message":{"role":"assistant","content":[{"type":"text","text":"**Step 5:** Add `body_sections_format = 'v2'` and `TechniquePageVideo` INSERT in the persist block. Let me find the exact persist location."},{"type":"toolCall","id":"toolu_01RnKUUTVhsRRsjzxZYGeEXz","name":"bash","arguments":{"command":"grep -n 'pages_created += 1' backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":113,"cacheRead":80934,"cacheWrite":786,"totalTokens":81834,"cost":{"input":0.000005,"output":0.0028250000000000003,"cacheRead":0.040466999999999996,"cacheWrite":0.0049125,"total":0.048209499999999995}},"stopReason":"toolUse","timestamp":1775179681246}}
{"type":"message","id":"c9f5f3e3","parentId":"a64fe9d7","timestamp":"2026-04-03T01:28:05.251Z","message":{"role":"toolResult","toolCallId":"toolu_01RnKUUTVhsRRsjzxZYGeEXz","toolName":"bash","content":[{"type":"text","text":"1538:                pages_created += 1\n"}],"isError":false,"timestamp":1775179685248}}
{"type":"message","id":"e589e49e","parentId":"c9f5f3e3","timestamp":"2026-04-03T01:28:08.304Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_012dXSqofHHRkADb2YWiRkSv","name":"read","arguments":{"limit":30,"offset":1533,"path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":92,"cacheRead":81720,"cacheWrite":136,"totalTokens":81949,"cost":{"input":0.000005,"output":0.0023,"cacheRead":0.04086,"cacheWrite":0.0008500000000000001,"total":0.044015}},"stopReason":"toolUse","timestamp":1775179685249}}
{"type":"message","id":"d215b3bf","parentId":"e589e49e","timestamp":"2026-04-03T01:28:08.307Z","message":{"role":"toolResult","toolCallId":"toolu_012dXSqofHHRkADb2YWiRkSv","toolName":"read","content":[{"type":"text","text":"                        source_quality=page_data.source_quality,\n                    )\n                    session.add(page)\n                    session.flush()  # Get the page.id assigned\n\n                pages_created += 1\n\n                # Link moments to the technique page using moment_indices\n\n                if page_moment_indices:\n                    # LLM specified which moments belong to this page\n                    for idx in page_moment_indices:\n                        if 0 <= idx < len(moment_group):\n                            moment_group[idx][0].technique_page_id = page.id\n                elif len(synthesized_pages) == 1:\n                    # Single page — link all moments (safe fallback)\n                    for m, _ in moment_group:\n                        m.technique_page_id = page.id\n                else:\n                    # Multiple pages but no moment_indices — log warning\n                    logger.warning(\n                        \"Stage 5: page '%s' has no moment_indices and is one of %d pages \"\n                        \"for category '%s'. Moments will not be linked to this page.\",\n                        page_data.slug, len(synthesized_pages), category,\n                    )\n\n        # Update processing_status\n        video.processing_status = ProcessingStatus.complete\n\n        session.commit()\n\n[699 more lines in file. Use offset=1563 to continue.]"}],"isError":false,"timestamp":1775179688306}}
{"type":"message","id":"d4bdd26c","parentId":"d215b3bf","timestamp":"2026-04-03T01:28:14.216Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_018YxcPH79dZ7QVZgQQs7yGT","name":"edit","arguments":{"newText":"                pages_created += 1\n\n                # Set body_sections_format on every page (new or updated)\n                page.body_sections_format = \"v2\"\n\n                # Track contributing video via TechniquePageVideo\n                stmt = pg_insert(TechniquePageVideo.__table__).values(\n                    technique_page_id=page.id,\n                    source_video_id=video.id,\n                ).on_conflict_do_nothing()\n                session.execute(stmt)\n\n                # Link moments to the technique page using moment_indices","oldText":"                pages_created += 1\n\n                # Link moments to the technique page using moment_indices","path":"backend/pipeline/stages.py"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":240,"cacheRead":81720,"cacheWrite":564,"totalTokens":82525,"cost":{"input":0.000005,"output":0.006,"cacheRead":0.04086,"cacheWrite":0.0035250000000000004,"total":0.050390000000000004}},"stopReason":"toolUse","timestamp":1775179688307}}
{"type":"message","id":"c44cb619","parentId":"d4bdd26c","timestamp":"2026-04-03T01:28:14.232Z","message":{"role":"toolResult","toolCallId":"toolu_018YxcPH79dZ7QVZgQQs7yGT","toolName":"edit","content":[{"type":"text","text":"Successfully replaced text in backend/pipeline/stages.py."}],"details":{"diff":" 1536                     session.flush()  # Get the page.id assigned\n 1537 \n 1538                 pages_created += 1\n 1539 \n+1540                 # Set body_sections_format on every page (new or updated)\n+1541                 page.body_sections_format = \"v2\"\n+1542 \n+1543                 # Track contributing video via TechniquePageVideo\n+1544                 stmt = pg_insert(TechniquePageVideo.__table__).values(\n+1545                     technique_page_id=page.id,\n+1546                     source_video_id=video.id,\n+1547                 ).on_conflict_do_nothing()\n+1548                 session.execute(stmt)\n+1549 \n 1550                 # Link moments to the technique page using moment_indices\n 1551 \n 1552                 if page_moment_indices:\n 1553                     # LLM specified which moments belong to this page","firstChangedLine":1540},"isError":false,"timestamp":1775179694230}}
{"type":"message","id":"4b1bd3f8","parentId":"c44cb619","timestamp":"2026-04-03T01:28:19.684Z","message":{"role":"assistant","content":[{"type":"text","text":"Now let me run the verification command."},{"type":"toolCall","id":"toolu_01P2fFA2XHUEzXM2k46nspoA","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\" && grep -q 'body_sections_format' backend/pipeline/stages.py && echo \"body_sections_format found\" && grep -q 'TechniquePageVideo' backend/pipeline/stages.py && echo \"TechniquePageVideo found\" && grep -q 'stage5_compose' backend/pipeline/stages.py && echo \"stage5_compose found\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":195,"cacheRead":82284,"cacheWrite":263,"totalTokens":82743,"cost":{"input":0.000005,"output":0.004875,"cacheRead":0.041142,"cacheWrite":0.00164375,"total":0.04766575}},"stopReason":"toolUse","timestamp":1775179694231}}
{"type":"message","id":"bbf340b1","parentId":"4b1bd3f8","timestamp":"2026-04-03T01:28:19.965Z","message":{"role":"toolResult","toolCallId":"toolu_01P2fFA2XHUEzXM2k46nspoA","toolName":"bash","content":[{"type":"text","text":"Traceback (most recent call last):\n  File \"<string>\", line 1, in <module>\n  File \"/home/aux/projects/content-to-kb-automator/pipeline/stages.py\", line 26, in <module>\n    from config import get_settings\nImportError: cannot import name 'get_settings' from 'config' (unknown location)\n\n\nCommand exited with code 1"}],"details":{},"isError":true,"timestamp":1775179699963}}
{"type":"message","id":"c4ccb6a1","parentId":"bbf340b1","timestamp":"2026-04-03T01:28:23.884Z","message":{"role":"assistant","content":[{"type":"text","text":"The symlink-based import doesn't fully resolve. Let me check the path setup."},{"type":"toolCall","id":"toolu_01P68s9uThTZATmHsfvr4bAF","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && ls -la pipeline && PYTHONPATH=backend python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":129,"cacheRead":82547,"cacheWrite":310,"totalTokens":82987,"cost":{"input":0.000005,"output":0.003225,"cacheRead":0.0412735,"cacheWrite":0.0019375,"total":0.046440999999999996}},"stopReason":"toolUse","timestamp":1775179699964}}
{"type":"message","id":"7d2025c5","parentId":"c4ccb6a1","timestamp":"2026-04-03T01:28:25.434Z","message":{"role":"toolResult","toolCallId":"toolu_01P68s9uThTZATmHsfvr4bAF","toolName":"bash","content":[{"type":"text","text":"lrwxrwxrwx 1 aux aux 16 Apr  1 08:59 pipeline -> backend/pipeline\nimports OK\n"}],"isError":false,"timestamp":1775179705431}}
{"type":"message","id":"2d633e8c","parentId":"7d2025c5","timestamp":"2026-04-03T01:28:30.088Z","message":{"role":"assistant","content":[{"type":"text","text":"Imports work with PYTHONPATH=backend. Let me run the full verification."},{"type":"toolCall","id":"toolu_01FHbAm4n7uXgrwLj6JGGYkP","name":"bash","arguments":{"command":"cd /home/aux/projects/content-to-kb-automator && PYTHONPATH=backend python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\" && grep -q 'body_sections_format' backend/pipeline/stages.py && echo \"body_sections_format found\" && grep -q 'TechniquePageVideo' backend/pipeline/stages.py && echo \"TechniquePageVideo found\" && grep -q 'stage5_compose' backend/pipeline/stages.py && echo \"stage5_compose found\""}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":211,"cacheRead":82857,"cacheWrite":176,"totalTokens":83245,"cost":{"input":0.000005,"output":0.005275,"cacheRead":0.0414285,"cacheWrite":0.0011,"total":0.0478085}},"stopReason":"toolUse","timestamp":1775179705433}}
{"type":"message","id":"c92141ab","parentId":"2d633e8c","timestamp":"2026-04-03T01:28:31.642Z","message":{"role":"toolResult","toolCallId":"toolu_01FHbAm4n7uXgrwLj6JGGYkP","toolName":"bash","content":[{"type":"text","text":"imports OK\nbody_sections_format found\nTechniquePageVideo found\nstage5_compose found\n"}],"isError":false,"timestamp":1775179711639}}
{"type":"message","id":"f96024fa","parentId":"c92141ab","timestamp":"2026-04-03T01:28:36.343Z","message":{"role":"assistant","content":[{"type":"text","text":"All verification checks pass. Let me also verify the logical structure is correct — the `else`/`elif` chain is properly formed."},{"type":"toolCall","id":"toolu_01EeRPasUAiSgEAfRNwAP7gA","name":"bash","arguments":{"command":"grep -n 'compose_target is not None\\|elif len(moment_group) <= chunk_size\\|else:\\s*$' backend/pipeline/stages.py | head -10"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":123,"cacheRead":83033,"cacheWrite":247,"totalTokens":83404,"cost":{"input":0.000005,"output":0.003075,"cacheRead":0.0415165,"cacheWrite":0.00154375,"total":0.046140249999999994}},"stopReason":"toolUse","timestamp":1775179711640}}
{"type":"message","id":"67229a2c","parentId":"f96024fa","timestamp":"2026-04-03T01:28:36.359Z","message":{"role":"toolResult","toolCallId":"toolu_01EeRPasUAiSgEAfRNwAP7gA","toolName":"bash","content":[{"type":"text","text":"781:        else:\n1229:        else:\n1335:            if compose_target is not None:\n1360:            elif len(moment_group) <= chunk_size:\n1400:            else:\n1522:                else:\n1561:                else:\n1696:            else:\n1737:            else:\n1802:        else:\n"}],"isError":false,"timestamp":1775179716356}}
{"type":"message","id":"c8e79e4c","parentId":"67229a2c","timestamp":"2026-04-03T01:28:41.825Z","message":{"role":"assistant","content":[{"type":"text","text":"Good — the three branches are: `if compose_target is not None` → `elif len(moment_group) <= chunk_size` → `else` (large group). That's the correct control flow. Now let me read the task summary template and write the summary."},{"type":"toolCall","id":"toolu_01XifSpgbarrDAR3xwAivCyY","name":"read","arguments":{"limit":60,"path":"/home/aux/.gsd/agent/extensions/gsd/templates/task-summary.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":148,"cacheRead":83280,"cacheWrite":220,"totalTokens":83649,"cost":{"input":0.000005,"output":0.0037,"cacheRead":0.041639999999999996,"cacheWrite":0.0013750000000000001,"total":0.04672}},"stopReason":"toolUse","timestamp":1775179716357}}
{"type":"message","id":"cd631f4a","parentId":"c8e79e4c","timestamp":"2026-04-03T01:28:41.831Z","message":{"role":"toolResult","toolCallId":"toolu_01XifSpgbarrDAR3xwAivCyY","toolName":"read","content":[{"type":"text","text":"---\nid: {{taskId}}\nparent: {{sliceId}}\nmilestone: {{milestoneId}}\nprovides:\n  - {{whatThisTaskProvides}}\nkey_files:\n  - {{filePath}}\nkey_decisions:\n  - {{decision}}\npatterns_established:\n  - {{pattern}}\nobservability_surfaces:\n  - {{status endpoint, structured log, persisted failure state, diagnostic command, or none}}\nduration: {{duration}}\nverification_result: passed\ncompleted_at: {{date}}\n# Set blocker_discovered: true only if execution revealed the remaining slice plan\n# is fundamentally invalid (wrong API, missing capability, architectural mismatch).\n# Do NOT set true for ordinary bugs, minor deviations, or fixable issues.\nblocker_discovered: false\n---\n\n# {{taskId}}: {{taskTitle}}\n\n<!-- One-liner must say what actually shipped, not just that work completed.\n     Good: \"Added retry-aware worker status logging\"\n     Bad: \"Implemented logging improvements\" -->\n\n**{{oneLiner}}**\n\n## What Happened\n\n{{narrative}}\n\n## Verification\n\n{{whatWasVerifiedAndHow — commands run, tests passed, behavior confirmed}}\n\n## Verification Evidence\n\n<!-- Populated from verification gate output. If the gate ran, fill in the table below.\n     If no gate ran (e.g., no verification commands discovered), note that. -->\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| {{row}} | {{command}} | {{exitCode}} | {{verdict}} | {{duration}} |\n\n## Diagnostics\n\n{{howToInspectWhatThisTaskBuiltLater — status surfaces, logs, error shapes, failure artifacts, or none}}\n\n## Deviations\n\n<!-- Deviations are unplanned changes to the written task plan, not ordinary debugging during implementation. -->\n\n{{deviationsFromPlan_OR_none}}\n\n## Known Issues\n\n\n[7 more lines in file. Use offset=61 to continue.]"}],"isError":false,"timestamp":1775179721827}}
{"type":"message","id":"545ceb08","parentId":"cd631f4a","timestamp":"2026-04-03T01:29:07.731Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_017yVB9b9fEuaYonDxd6TR16","name":"write","arguments":{"content":"---\nid: T01\nparent: S04\nmilestone: M014\nprovides:\n  - _build_compose_user_prompt() helper for XML-tagged compose prompts\n  - _compose_into_existing() helper for LLM compose calls\n  - Compose-or-create branch in stage5_synthesis per-category loop\n  - body_sections_format='v2' on all pages\n  - TechniquePageVideo tracking rows for every page+video combination\nkey_files:\n  - backend/pipeline/stages.py\nkey_decisions:\n  - Compose detection queries all matching pages and warns on multiple matches, uses first\n  - Existing moments formatted with empty cls_info dict (no classification data for already-linked moments)\n  - pg_insert with on_conflict_do_nothing for idempotent TechniquePageVideo inserts\npatterns_established:\n  - Compose-or-create branching pattern for pipeline stages that must merge into existing entities\nobservability_surfaces:\n  - INFO log on compose path with existing/new moment counts\n  - WARNING log when multiple pages match creator+category\nduration: 12m\nverification_result: passed\ncompleted_at: 2026-04-03\nblocker_discovered: false\n---\n\n# T01: Add compose helpers and wire compose-or-create logic into stage5_synthesis\n\n**Added _build_compose_user_prompt(), _compose_into_existing(), and compose-or-create branching to stage5_synthesis with body_sections_format='v2' and TechniquePageVideo tracking**\n\n## What Happened\n\nAdded two new helper functions to `backend/pipeline/stages.py`:\n\n1. `_build_compose_user_prompt()` — serializes an existing TechniquePage to JSON, formats existing moments as `[0]-[N-1]` and new moments as `[N]-[N+M-1]` with offset indices, and wraps everything in XML tags (`<existing_page>`, `<existing_moments>`, `<new_moments>`, `<creator>`) matching the test_harness.py reference implementation.\n\n2. `_compose_into_existing()` — loads the `stage5_compose.txt` system prompt, builds the compose user prompt, estimates tokens, calls `llm.complete()` with the same callback/retry pattern as `_synthesize_chunk()`, and returns a `SynthesisResult`.\n\nModified the per-category loop in `stage5_synthesis()` to query for existing technique pages by `creator_id + LOWER(topic_category)` before the chunked synthesis block. When an existing page is found, the compose path runs instead of standard synthesis. The existing chunked synthesis is now gated behind an `elif`/`else` branch.\n\nIn the persist block, added `page.body_sections_format = \"v2\"` on every page (new and updated) and an idempotent `TechniquePageVideo` INSERT using `pg_insert(...).on_conflict_do_nothing()`.\n\nAlso added `TechniquePageVideo` to the model imports and `sqlalchemy.dialects.postgresql.insert` as `pg_insert`.\n\n## Verification\n\n```\nPYTHONPATH=backend python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\"\n# → imports OK\n\ngrep -q 'body_sections_format' backend/pipeline/stages.py && echo \"found\"\n# → found\n\ngrep -q 'TechniquePageVideo' backend/pipeline/stages.py && echo \"found\"\n# → found\n\ngrep -q 'stage5_compose' backend/pipeline/stages.py && echo \"found\"\n# → found\n```\n\n## Verification Evidence\n\n| # | Command | Exit Code | Verdict | Duration |\n|---|---------|-----------|---------|----------|\n| 1 | `PYTHONPATH=backend python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\"` | 0 | ✅ pass | 2s |\n| 2 | `grep -q 'body_sections_format' backend/pipeline/stages.py` | 0 | ✅ pass | <1s |\n| 3 | `grep -q 'TechniquePageVideo' backend/pipeline/stages.py` | 0 | ✅ pass | <1s |\n| 4 | `grep -q 'stage5_compose' backend/pipeline/stages.py` | 0 | ✅ pass | <1s |\n\n## Diagnostics\n\n- Compose path logged at INFO level: \"Stage 5: Composing into existing page '...' (N existing moments + M new moments)\"\n- Multiple-match warning at WARNING level with page count and first slug used\n- LLM callback tagged with `context_label=compose:{category}` for tracing in pipeline events\n\n## Deviations\n\n- Used `default=str` in `json.dumps()` for page serialization to handle UUID/datetime fields that may be present in JSONB columns — not in plan but necessary for robustness.\n\n## Known Issues\n\nNone.\n","path":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/tasks/T01-SUMMARY.md"}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":1315,"cacheRead":83500,"cacheWrite":629,"totalTokens":85445,"cost":{"input":0.000005,"output":0.032875,"cacheRead":0.041749999999999995,"cacheWrite":0.00393125,"total":0.07856125}},"stopReason":"toolUse","timestamp":1775179721828}}
{"type":"message","id":"62eb5001","parentId":"545ceb08","timestamp":"2026-04-03T01:29:07.738Z","message":{"role":"toolResult","toolCallId":"toolu_017yVB9b9fEuaYonDxd6TR16","toolName":"write","content":[{"type":"text","text":"Successfully wrote 4108 bytes to /home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/tasks/T01-SUMMARY.md"}],"isError":false,"timestamp":1775179747734}}
{"type":"message","id":"2cb345b5","parentId":"62eb5001","timestamp":"2026-04-03T01:29:17.899Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"toolu_016nqZdtLizS8r9n8orhmNYy","name":"gsd_task_complete","arguments":{"blockerDiscovered":false,"deviations":"Used default=str in json.dumps() for page serialization to handle UUID/datetime fields — not in plan but necessary for robustness.","keyDecisions":["Compose detection queries all matching pages and warns on multiple matches, uses first","pg_insert with on_conflict_do_nothing for idempotent TechniquePageVideo inserts"],"keyFiles":["backend/pipeline/stages.py"],"knownIssues":"None.","milestoneId":"M014","narrative":"Added two compose helper functions and wired compose-or-create detection into the stage5_synthesis per-category loop. When an existing technique page matches by creator_id + LOWER(topic_category), the compose path calls the LLM with stage5_compose.txt instead of standard synthesis. All pages now get body_sections_format='v2' and TechniquePageVideo rows tracking contributing videos via idempotent pg_insert with on_conflict_do_nothing.","oneLiner":"Added _build_compose_user_prompt(), _compose_into_existing(), and compose-or-create branching to stage5_synthesis with body_sections_format='v2' and TechniquePageVideo tracking","sliceId":"S04","taskId":"T01","verification":"PYTHONPATH=backend python import of _build_compose_user_prompt and _compose_into_existing succeeded. grep confirmed body_sections_format, TechniquePageVideo, and stage5_compose strings present in stages.py.","verificationEvidence":[{"command":"PYTHONPATH=backend python -c \"from pipeline.stages import _build_compose_user_prompt, _compose_into_existing; print('imports OK')\"","durationMs":2000,"exitCode":0,"verdict":"✅ pass"},{"command":"grep -q 'body_sections_format' backend/pipeline/stages.py","durationMs":100,"exitCode":0,"verdict":"✅ pass"},{"command":"grep -q 'TechniquePageVideo' backend/pipeline/stages.py","durationMs":100,"exitCode":0,"verdict":"✅ pass"},{"command":"grep -q 'stage5_compose' backend/pipeline/stages.py","durationMs":100,"exitCode":0,"verdict":"✅ pass"}]}}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":756,"cacheRead":84129,"cacheWrite":1376,"totalTokens":86262,"cost":{"input":0.000005,"output":0.0189,"cacheRead":0.0420645,"cacheWrite":0.0086,"total":0.06956949999999999}},"stopReason":"toolUse","timestamp":1775179747736}}
{"type":"message","id":"7c11d200","parentId":"2cb345b5","timestamp":"2026-04-03T01:29:17.958Z","message":{"role":"toolResult","toolCallId":"toolu_016nqZdtLizS8r9n8orhmNYy","toolName":"gsd_task_complete","content":[{"type":"text","text":"Completed task T01 (S04/M014)"}],"details":{"operation":"complete_task","taskId":"T01","sliceId":"S04","milestoneId":"M014","summaryPath":"/home/aux/projects/content-to-kb-automator/.gsd/milestones/M014/slices/S04/tasks/T01-SUMMARY.md"},"isError":false,"timestamp":1775179757956}}
{"type":"message","id":"6dcbbaac","parentId":"7c11d200","timestamp":"2026-04-03T01:29:21.464Z","message":{"role":"assistant","content":[{"type":"text","text":"Task T01 complete."}],"api":"anthropic-messages","provider":"anthropic","model":"claude-opus-4-6","usage":{"input":1,"output":9,"cacheRead":85505,"cacheWrite":787,"totalTokens":86302,"cost":{"input":0.000005,"output":0.00022500000000000002,"cacheRead":0.0427525,"cacheWrite":0.00491875,"total":0.04790125}},"stopReason":"stop","timestamp":1775179757957}}
