62 lines
3.9 KiB
Markdown
62 lines
3.9 KiB
Markdown
# Task 010: Research Fetch And S3 Manifest
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Development description: Implement explicit auditable web research with source retrieval metadata, source-section snapshots, object-storage uploads, and Postgres manifest records.
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## Implementation Details
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- Backend endpoint:
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- `POST /api/articles/{article_id}/research/start`
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- `GET /api/articles/{article_id}/research`
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- Research starts only after approved plan.
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- Research uses an explicit search/fetch script, not only free-form agent browsing.
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- For discovered sources, store in object storage:
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- Relevant content sections.
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- Page metadata.
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- Search path to the article.
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- Agent/source-selection criteria.
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- Page `<head>` metadata.
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- Server IP address.
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- Domain WHOIS owner when available.
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- In Postgres store only:
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- Research run manifest.
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- S3/object keys.
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- Source URLs.
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- Content hashes.
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- Artifact types.
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- Summary metadata required for UI and validation.
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- Retention is indefinite for v1.
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- Provide deterministic fake search/fetch fixtures for tests and demo.
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## Public Interface
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- Editor starts research for an approved article plan.
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- UI shows research run status and discovered source summary.
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- Backend exposes manifest-linked evidence summaries.
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## Acceptance Criteria
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- [x] TDD pre-requirement: before implementation, write one failing behavior test that starts research from an approved plan and verifies a manifest with object keys is produced; proceed one research behavior at a time and record evidence in `Result`.
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- [x] Research cannot start before plan approval.
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- [x] Research run creates an agent/job record and article status `RESEARCH_RUNNING`.
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- [x] Source-section artifacts are uploaded to object storage.
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- [x] Manifest records include object keys, hashes, URLs, artifact types, and metadata references.
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- [x] Re-running research creates a new manifest instead of overwriting old artifacts.
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- [x] UI can show source URL, title, domain, type, summary, retrieval timestamp, and artifact link.
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- [x] Demo stack can run with deterministic fake research results.
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## Verification
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- Run research integration tests with fake search/fetch.
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- Verify object storage contains source-section artifact files.
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- Verify Postgres manifest records point to those files.
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- Run Docker Compose research smoke flow.
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## Result
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- Status: Accepted.
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- TDD plan: Added `apps/backend/tests/integration/test_research_manifest_public_api.py` before implementation. The test builds an approved plan, starts research, verifies a manifest with object keys/hashes/source URLs/artifact types/metadata, checks uploaded local object files, lists research runs, and verifies rerun creates a new manifest prefix.
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- Red evidence: Initial run failed with `201 != 202`; `POST /api/articles/{article_id}/research/start` only returned a queued `RESEARCH` job and no manifest/artifacts.
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- Green evidence: Implemented deterministic fake research fetch, local/S3 object storage adapter, research manifest repository, `ResearchStartResponse`, `ResearchListResponse`, `GET /research`, manifest-linked detail output, and FSD frontend research page at `/articles/[articleId]/research`.
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- Refactor notes: Moved research-start behavior out of the Task 009 placeholder and made it produce an auditable manifest immediately for deterministic demo/tests. `OBJECT_STORAGE_LOCAL_ROOT` enables isolated integration tests; Docker Compose uses the existing S3-compatible MinIO env.
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- Verification output: `PYTHONPATH=/private/tmp/pupline-backend-deps python3 -m unittest apps/backend/tests/integration/test_research_manifest_public_api.py apps/backend/tests/integration/test_plan_generation_review_public_api.py apps/backend/tests/integration/test_schema_storage_contracts.py apps/backend/tests/contracts/test_generated_contract_artifacts.py` passed. `pnpm --filter @pipeline/frontend test:ui` passed. `pnpm --filter @pipeline/frontend typecheck` passed. `tests/smoke/research-flow.sh` and `tests/smoke/plan-flow.sh` passed with Docker Compose after Docker socket escalation.
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