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base: Antoine:codex/openclaw-atocore-integration-proposal
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Antoine:main Antoine:codex/openclaw-atocore-integration-proposal Antoine:codex/openclaw-capture-plugin Antoine:codex/audit-batch2 Antoine:codex/audit-2026-04-12-extraction Antoine:codex/audit-2026-04-12-final Antoine:codex/audit-2026-04-12 Antoine:claude/extractor-eval-loop Antoine:codex/dalidou-storage-foundation Antoine:codex/atocore-integration-pass
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Antoine:main Antoine:codex/openclaw-atocore-integration-proposal Antoine:codex/openclaw-capture-plugin Antoine:codex/audit-batch2 Antoine:codex/audit-2026-04-12-extraction Antoine:codex/audit-2026-04-12-final Antoine:codex/audit-2026-04-12 Antoine:claude/extractor-eval-loop Antoine:codex/dalidou-storage-foundation Antoine:codex/atocore-integration-pass

29 Commits
codex/open ... main

Author SHA1 Message Date
Anto01
cc68839306 fix: OpenClaw plugin filters cron-initiated agent runs 
OpenClaw scheduled tasks (DXF email watcher, calendar reminder pings)
fire agent sessions with prompts that begin '[cron:<id> ...]'. These
were all getting captured as AtoCore interactions — 45 out of 50
recent interactions today were cron noise, not real user turns.

Filter at the plugin level: before_agent_start ignores any prompt
starting with '[cron:'. The gateway has been restarted with the
updated plugin.

Impact: graduation, triage, and context pipelines stop seeing noise
from OpenClaw's own internal automation. Only real user turns (via
chat channels) feed the brain going forward.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 11:09:44 -04:00
Anto01
45196f352f fix: force UTF-8 on MCP stdio for Windows compatibility 
Windows Python defaults stdout to cp1252. Any non-ASCII char in tool
responses (emojis, ≥, →, etc.) crashes the MCP server with a
UnicodeEncodeError. Explicitly reconfigure stdin/stdout/stderr to
UTF-8 at startup. No-op on Linux/macOS.

Noticed when Claude Code called atocore_search and atocore_memory_list
and both crashed on ⏳ / ≥ characters that came back in the response.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 11:08:24 -04:00
Anto01
d456d3c86a fix: local json import in graduation request/status handlers 
NameError on /admin/graduation/request — routes.py doesn't import json
at module scope. Added local 'import json as _json' in both graduation
handlers matching the pattern used elsewhere in this file.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 09:53:17 -04:00
Anto01
0dfecb3c14 feat: one-click memory graduation button + host watcher 
Closes the graduation UX loop: no more SSH required to populate the
entity graph from memories. Click button → host watcher picks up
→ graduation runs → entity candidates appear in the same triage UI.

New API endpoints (src/atocore/api/routes.py):
- POST /admin/graduation/request: takes {project, limit}, writes flag
  to project_state. Host watcher picks up within 2 min.
- GET /admin/graduation/status: returns requested/running/last_result
  fields for UI polling.

Triage UI (src/atocore/engineering/triage_ui.py):
- Graduation bar with:
  - 🎓 Graduate memories button
  - Project selector populated from registry (or "all projects")
  - Limit number input (default 30, max 200)
  - Status message area
- Poll every 10s until is_running=false, then auto-reload the page to
  show new entity candidates in the Entity section below
- Graduation bar appears on both populated and empty triage page
  states so you can kick off graduation from either

Host watcher (deploy/dalidou/graduation-watcher.sh):
- Mirrors auto-triage-watcher.sh pattern: poll, lock, clear flag,
  run, record result, unlock
- Parses {project, limit} JSON from the flag payload
- Runs graduate_memories.py with those args
- Records graduation_running/started/finished/last_result in project
  state for the UI to display
- Lock file prevents concurrent runs

Install on host (one-time, via cron):
  */2 * * * * /srv/storage/atocore/app/deploy/dalidou/graduation-watcher.sh \
    >> /home/papa/atocore-logs/graduation-watcher.log 2>&1

This completes the Phase 5 self-service loop: queue triage happens
autonomously via the 3-tier escalation (shipped in 3ca1972); entity
graph population happens autonomously via a button click. No shell
required for daily use.

Tests: 366 passing (no new tests — UI + shell are integration-level).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 09:45:12 -04:00
Anto01
3ca19724a5 feat: 3-tier triage escalation + project validation + enriched context 
Addresses the triage-quality problems the user observed:
- Candidates getting wrong project/product attribution
- Stale facts promoted as if still true
- "Hard to decide" items reaching human queue without real value

Solution: let sonnet handle the easy 80%, escalate borderline cases
to opus, auto-discard (or flag) what two models can't resolve.
Plus enrich the context the triage model sees so it can catch
misattribution, contradictions, and temporal drift earlier.

THE 3-TIER FLOW (scripts/auto_triage.py):

Tier 1: sonnet (fast, cheap)
  - confidence >= 0.8 + clear verdict → PROMOTE or REJECT (done)
  - otherwise → escalate to tier 2

Tier 2: opus (smarter, sees tier-1 verdict + reasoning)
  - second opinion with explicit "sonnet said X, resolve the uncertainty"
  - confidence >= 0.8 → PROMOTE or REJECT with note="[opus]"
  - still uncertain → tier 3

Tier 3: configurable (default discard)
  - ATOCORE_TRIAGE_TIER3=discard (default): auto-reject with
    "two models couldn't decide" reason
  - ATOCORE_TRIAGE_TIER3=human: leave in queue for /admin/triage

Configuration via env vars:
  ATOCORE_TRIAGE_MODEL_TIER1  (default sonnet)
  ATOCORE_TRIAGE_MODEL_TIER2  (default opus)
  ATOCORE_TRIAGE_TIER3        (default discard)
  ATOCORE_TRIAGE_ESCALATION_THRESHOLD (default 0.75)
  ATOCORE_TRIAGE_TIER2_TIMEOUT_S (default 120 — opus is slower)

ENRICHED CONTEXT shown to the triage model (both tiers):
- List of registered project ids so misattribution is detectable
- Trusted project state entries (ground truth, higher trust than memories)
- Top 30 active memories for the claimed project (was 20)
- Tier 2 additionally sees tier 1's verdict + reason

PROJECT MISATTRIBUTION DETECTION:
- Triage prompt asks the model to output "suggested_project" when it
  detects the claimed project is wrong but the content clearly belongs
  to a registered one
- Main loop auto-applies the fix via PUT /memory/{id} (which canonicalizes
  through the registry)
- Misattribution is the #1 pollution source — this catches it upstream

TEMPORAL AGGRESSIVENESS:
- Prompt upgraded: "be aggressive with valid_until for anything that
  reads like 'current state' or 'this week'. When in doubt, 2-4 week
  expiry rather than null."
- Stale facts decay automatically via Phase 3's expiry filter

CONFIDENCE GRADING (new in prompt):
- 0.9+: crystal clear durable fact or clear noise
- 0.75-0.9: confident but not cryptographic-certain
- 0.6-0.75: borderline — WILL escalate
- <0.6: genuinely ambiguous — human or discard

Tests: 356 → 366 (10 new, all in test_triage_escalation.py):
- High-confidence tier-1 promote/reject → no tier-2 call
- Low-confidence tier-1 → tier-2 escalates → decides
- needs_human always escalates regardless of confidence
- tier-2 uncertain → discard by default
- tier-2 uncertain → human when configured
- dry-run skips all API calls
- suggested_project flag surfaces + gets printed
- parse_verdict captures suggested_project

Runtime behavior unchanged for the clear cases (sonnet still handles
them). The 20-30% of candidates that currently land as needs_human
will now route through opus, and only the genuinely stuck get a human
(or discard) action.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 09:09:58 -04:00
Anto01
3316ff99f9 feat: Phase 5F/5G/5H — graduation, conflicts, MCP engineering tools 
The population move + the safety net + the universal consumer hookup,
all shipped together. This is where the engineering graph becomes
genuinely useful against the real 262-memory corpus.

5F: Memory → Entity graduation (THE population move)
- src/atocore/engineering/_graduation_prompt.py: stdlib-only shared
  prompt module mirroring _llm_prompt.py pattern (container + host
  use same system prompt, no drift)
- scripts/graduate_memories.py: host-side batch driver that asks
  claude-p "does this memory describe a typed entity?" and creates
  entity candidates with source_refs pointing back to the memory
- promote_entity() now scans source_refs for memory:* prefix; if
  found, flips source memory to status='graduated' with
  graduated_to_entity_id forward pointer + writes memory_audit row
- GET /admin/graduation/stats exposes graduation rate for dashboard

5G: Sync conflict detection on entity promote
- src/atocore/engineering/conflicts.py: detect_conflicts_for_entity()
  runs on every active promote. V1 checks 3 slot kinds narrowly to
  avoid false positives:
  * component.material (multiple USES_MATERIAL edges)
  * component.part_of (multiple PART_OF edges)
  * requirement.name (duplicate active Requirements in same project)
- Conflicts + members persist via the tables built in 5A
- Fires a "warning" alert via Phase 4 framework
- Deduplicates: same (slot_kind, slot_key) won't get a new row
- resolve_conflict(action="dismiss|supersede_others|no_action"):
  supersede_others marks non-winner members as status='superseded'
- GET /admin/conflicts + POST /admin/conflicts/{id}/resolve

5H: MCP + context pack integration
- scripts/atocore_mcp.py: 7 new engineering tools exposed to every
  MCP-aware client (Claude Desktop, Claude Code, Cursor, Zed):
  * atocore_engineering_map (Q-001/004 system tree)
  * atocore_engineering_gaps (Q-006/009/011 killer queries — THE
    director's question surfaced as a built-in tool)
  * atocore_engineering_requirements_for_component (Q-005)
  * atocore_engineering_decisions (Q-008)
  * atocore_engineering_changes (Q-013 — reads entity audit log)
  * atocore_engineering_impact (Q-016 BFS downstream)
  * atocore_engineering_evidence (Q-017 inbound provenance)
- MCP tools total: 14 (7 memory/state/health + 7 engineering)
- context/builder.py _build_engineering_context now appends a compact
  gaps summary ("Gaps: N orphan reqs, M risky decisions, K unsupported
  claims") so every project-scoped LLM call sees "what we're missing"

Tests: 341 → 356 (15 new):
- 5F: graduation prompt parses positive/negative decisions, rejects
  unknown entity types, tolerates markdown fences; promote_entity
  marks source memory graduated with forward pointer; entity without
  memory refs promotes cleanly
- 5G: component.material + component.part_of + requirement.name
  conflicts detected; clean component triggers nothing; dedup works;
  supersede_others resolution marks losers; dismiss leaves both
  active; end-to-end promote triggers detection
- 5H: graduation user message includes project + type + content

No regressions across the 341 prior tests. The MCP server now answers
"which p05 requirements aren't satisfied?" directly from any Claude
session — no user prompt engineering, no context hacks.

Next to kick off from user: run graduation script on Dalidou to
populate the graph from 262 existing memories:
  ssh papa@dalidou 'cd /srv/storage/atocore/app && PYTHONPATH=src \
    python3 scripts/graduate_memories.py --project p05-interferometer --limit 30 --dry-run'

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 07:53:03 -04:00
Anto01
53b71639ad feat: Phase 5B-5D — 10 canonical engineering queries + triage UI 
The graph becomes useful. Before this commit, entities sat in the DB
as data with no narrative. After: the director can ask "what am I
forgetting?" and get a structured answer in milliseconds.

New module (src/atocore/engineering/queries.py, 360 lines):

Structure queries (Q-001/004/005/008/013):
- system_map(project): full subsystem → component tree + orphans +
  materials joined per component
- decisions_affecting(project, subsystem_id?): decisions linked via
  AFFECTED_BY_DECISION, scoped to a subsystem or whole project
- requirements_for(component_id): Q-005 forward trace
- recent_changes(project, since, limit): Q-013 via memory_audit join
  (reuses the Phase 4 audit infrastructure — entity_kind='entity')

The 3 killer queries (the real value):
- orphan_requirements(project): requirements with NO inbound SATISFIES
  edge. "What do I claim the system must do that nothing actually
  claims to handle?" Q-006.
- risky_decisions(project): decisions whose BASED_ON_ASSUMPTION edge
  points to an assumption with status in ('superseded','invalid') OR
  properties.flagged=True. Finds cascading risk from shaky premises. Q-009.
- unsupported_claims(project): ValidationClaim entities with no inbound
  SUPPORTS edge — asserted but no Result to back them. Q-011.
- all_gaps(project): runs all three in one call for dashboards.

History + impact (Q-016/017):
- impact_analysis(entity_id, max_depth=3): BFS over outbound edges.
  "What's downstream of this if I change it?"
- evidence_chain(entity_id): inbound SUPPORTS/EVIDENCED_BY/DESCRIBED_BY/
  VALIDATED_BY/ANALYZED_BY. "How do I know this is true?"

API (src/atocore/api/routes.py) exposes 10 endpoints:
- GET /engineering/projects/{p}/systems
- GET /engineering/decisions?project=&subsystem=
- GET /engineering/components/{id}/requirements
- GET /engineering/changes?project=&since=&limit=
- GET /engineering/gaps/orphan-requirements?project=
- GET /engineering/gaps/risky-decisions?project=
- GET /engineering/gaps/unsupported-claims?project=
- GET /engineering/gaps?project=  (combined)
- GET /engineering/impact?entity=&max_depth=
- GET /engineering/evidence?entity=

Mirror integration (src/atocore/engineering/mirror.py):
- New _gaps_section() renders at top of every project page
- If any gap non-empty: shows up-to-10 per category with names + context
- Clean project: "✅ No gaps detected" — signals everything is traced

Triage UI (src/atocore/engineering/triage_ui.py):
- /admin/triage now shows BOTH memory candidates AND entity candidates
- Entity cards: name, type, project, confidence, source provenance,
  Promote/Reject buttons, link to wiki entity page
- Entity promote/reject via fetch to /entities/{id}/promote|reject
- One triage UI for the whole pipeline — consistent muscle memory

Tests: 326 → 341 (15 new, all in test_engineering_queries.py):
- System map structure + orphan detection + material joins
- Killer queries: positive + negative cases (empty when clean)
- Decisions query: project-wide and subsystem-scoped
- Impact analysis walks outbound BFS
- Evidence chain walks inbound provenance

No regressions. All 10 daily queries from the plan are now live and
answering real questions against the graph.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 07:18:46 -04:00
Anto01
07664bd743 feat: Phase 5A — Engineering V1 foundation 
First slice of the Engineering V1 sprint. Lays the schema + lifecycle
plumbing so the 10 canonical queries, memory graduation, and conflict
detection can land cleanly on top.

Schema (src/atocore/models/database.py):
- conflicts + conflict_members tables per conflict-model.md (with 5
  indexes on status/project/slot/members)
- memory_audit.entity_kind discriminator — same audit table serves
  both memories ("memory") and entities ("entity"); unified history
  without duplicating infrastructure
- memories.graduated_to_entity_id forward pointer for graduated
  memories (M → E transition preserves the memory as historical
  pointer)

Memory (src/atocore/memory/service.py):
- MEMORY_STATUSES gains "graduated" — memory-entity graduation flow
  ready to wire in Phase 5F

Engineering service (src/atocore/engineering/service.py):
- RELATIONSHIP_TYPES organized into 4 families per ontology-v1.md:
  + Structural: contains, part_of, interfaces_with
  + Intent: satisfies, constrained_by, affected_by_decision,
    based_on_assumption (new), supersedes
  + Validation: analyzed_by, validated_by, supports (new),
    conflicts_with (new), depends_on
  + Provenance: described_by, updated_by_session (new),
    evidenced_by (new), summarized_in (new)
- create_entity + create_relationship now call resolve_project_name()
  on write (canonicalization contract per doc)
- Both accept actor= parameter for audit provenance
- _audit_entity() helper uses shared memory_audit table with
  entity_kind="entity" — one observability layer for everything
- promote_entity / reject_entity_candidate / supersede_entity —
  mirror the memory lifecycle exactly (same pattern, same naming)
- get_entity_audit() reads from the shared table filtered by
  entity_kind

API (src/atocore/api/routes.py):
- POST /entities/{id}/promote (candidate → active)
- POST /entities/{id}/reject (candidate → invalid)
- GET /entities/{id}/audit (full history for one entity)
- POST /entities passes actor="api-http" through

Tests: 317 → 326 (9 new):
- test_entity_project_canonicalization (p04 → p04-gigabit)
- test_promote_entity_candidate_to_active
- test_reject_entity_candidate
- test_promote_active_entity_noop (only candidates promote)
- test_entity_audit_log_captures_lifecycle (before/after snapshots)
- test_new_relationship_types_available (6 new types present)
- test_conflicts_tables_exist
- test_memory_audit_has_entity_kind
- test_graduated_status_accepted

What's next (5B-5I, deferred): entity triage UI tab, core structure
queries, the 3 killer queries, memory graduation script, conflict
detection, MCP + context pack integration. See plan file.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-17 07:01:28 -04:00
Anto01
bb46e21c9b fix: integrity check runs in container (host lacks deps) 
scripts/integrity_check.py now POSTs to /admin/integrity-check
instead of importing atocore directly. The actual scan lives in
the container where DB access + deps are available. Host-side
cron just triggers and logs the result.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 22:01:43 -04:00
Anto01
88f2f7c4e1 feat: Phase 4 V1 — Robustness Hardening 
Adds the observability + safety layer that turns AtoCore from
"works until something silently breaks" into "every mutation is
traceable, drift is detected, failures raise alerts."

1. Audit log (memory_audit table):
   - New table with id, memory_id, action, actor, before/after JSON,
     note, timestamp; 3 indexes for memory_id/timestamp/action
   - _audit_memory() helper called from every mutation:
     create_memory, update_memory, promote_memory,
     reject_candidate_memory, invalidate_memory, supersede_memory,
     reinforce_memory, auto_promote_reinforced, expire_stale_candidates
   - Action verb auto-selected: promoted/rejected/invalidated/
     superseded/updated based on state transition
   - "actor" threaded through: api-http, human-triage, phase10-auto-
     promote, candidate-expiry, reinforcement, etc.
   - Fail-open: audit write failure logs but never breaks the mutation
   - GET /memory/{id}/audit: full history for one memory
   - GET /admin/audit/recent: last 50 mutations across the system

2. Alerts framework (src/atocore/observability/alerts.py):
   - emit_alert(severity, title, message, context) fans out to:
     - structlog logger (always)
     - ~/atocore-logs/alerts.log append (configurable via
       ATOCORE_ALERT_LOG)
     - project_state atocore/alert/last_{severity} (dashboard surface)
     - ATOCORE_ALERT_WEBHOOK POST if set (auto-detects Discord webhook
       format for nice embeds; generic JSON otherwise)
   - Every sink fail-open — one failure doesn't prevent the others
   - Pipeline alert step in nightly cron: harness < 85% → warning;
     candidate queue > 200 → warning

3. Integrity checks (scripts/integrity_check.py):
   - Nightly scan for drift:
     - Memories → missing source_chunk_id references
     - Duplicate active memories (same type+content+project)
     - project_state → missing projects
     - Orphaned source_chunks (no parent document)
   - Results persisted to atocore/status/integrity_check_result
   - Any finding emits a warning alert
   - Added as Step G in deploy/dalidou/batch-extract.sh nightly cron

4. Dashboard surfaces it all:
   - integrity (findings + details)
   - alerts (last info/warning/critical per severity)
   - recent_audit (last 10 mutations with actor + action + preview)

Tests: 308 → 317 (9 new):
  - test_audit_create_logs_entry
  - test_audit_promote_logs_entry
  - test_audit_reject_logs_entry
  - test_audit_update_captures_before_after
  - test_audit_reinforce_logs_entry
  - test_recent_audit_returns_cross_memory_entries
  - test_emit_alert_writes_log_file
  - test_emit_alert_invalid_severity_falls_back_to_info
  - test_emit_alert_fails_open_on_log_write_error

Deferred: formal migration framework with rollback (current additive
pattern is fine for V1); memory detail wiki page with audit view
(quick follow-up).

To enable Discord alerts: set ATOCORE_ALERT_WEBHOOK to a Discord
webhook URL in Dalidou's environment. Default = log-only.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 21:54:10 -04:00
Anto01
bfa7dba4de feat: Phase 3 V1 — Auto-Organization (domain_tags + valid_until) 
Adds structural metadata that the LLM triage was already implicitly
reasoning about ("stale snapshot" → reject). Phase 3 captures that
reasoning as fields so it can DRIVE retrieval, not just rejection.

Schema (src/atocore/models/database.py):
- domain_tags TEXT DEFAULT '[]'  JSON array of lowercase topic keywords
- valid_until DATETIME            ISO date; null = permanent
- idx_memories_valid_until index for efficient expiry queries

Memory service (src/atocore/memory/service.py):
- Memory dataclass gains domain_tags + valid_until
- create_memory, update_memory accept/persist both
- _row_to_memory safely reads both (JSON-decode + null handling)
- _normalize_tags helper: lowercase, dedup, strip, cap at 10
- get_memories_for_context filters expired (valid_until < today UTC)
- _rank_memories_for_query adds tag-boost: memories whose domain_tags
  appear as substrings in query text rank higher (tertiary key after
  content-overlap density + absolute overlap, before confidence)

LLM extractor (_llm_prompt.py → llm-0.5.0):
- SYSTEM_PROMPT documents domain_tags (2-5 keywords) + valid_until
  (time-bounded facts get expiry dates; durable facts stay null)
- normalize_candidate_item parses both fields from model output with
  graceful fallback for string/null/missing

LLM triage (scripts/auto_triage.py):
- TRIAGE_SYSTEM_PROMPT documents same two fields
- parse_verdict extracts them from verdict JSON
- On promote: PUT /memory/{id} with tags + valid_until BEFORE
  POST /memory/{id}/promote, so active memories carry them

API (src/atocore/api/routes.py):
- MemoryCreateRequest: adds domain_tags, valid_until
- MemoryUpdateRequest: adds domain_tags, valid_until, memory_type
- GET /memory response exposes domain_tags + valid_until + created_at

Triage UI (src/atocore/engineering/triage_ui.py):
- Renders existing tags as colored badges
- Adds inline text field for tags (comma-separated) + date picker for
  valid_until on every candidate card
- Save&Promote button persists edits via PUT then promotes
- Plain Promote (and Y shortcut) also saves tags/expiry if edited

Wiki (src/atocore/engineering/wiki.py):
- Search now matches memory content OR domain_tags
- Search results render tags as clickable badges linking to
  /wiki/search?q=<tag> for cross-project navigation
- valid_until shown as amber "valid until YYYY-MM-DD" hint

Tests: 303 → 308 (5 new for Phase 3 behavior):
- test_create_memory_with_tags_and_valid_until
- test_create_memory_normalizes_tags
- test_update_memory_sets_tags_and_valid_until
- test_get_memories_for_context_excludes_expired
- test_context_builder_tag_boost_orders_results

Deferred (explicitly): temporal_scope enum, source_refs memory graph,
HDBSCAN clustering, memory detail wiki page, backfill of existing
actives. See docs/MASTER-BRAIN-PLAN.md.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 21:37:01 -04:00
Anto01
271ee25d99 feat: on-demand auto-triage from web UI 
Adds an "Auto-process queue" button to /admin/triage that lets the
user kick off a full LLM triage pass without SSH. Bridges the gap
between web UI (in container) and claude CLI (host-only).

Architecture:
- UI button POSTs to /admin/triage/request-drain
- Endpoint writes atocore/config/auto_triage_requested_at flag
- Host-side watcher cron (every 2 min) checks for the flag
- When found: clears flag, acquires lock, runs auto_triage.py,
  records progress via atocore/status/* entries
- UI polls /admin/triage/drain-status every 10s to show progress,
  auto-reloads when done

Safety:
- Lock file prevents concurrent runs on host
- Flag cleared before run so duplicate clicks queue at most one re-run
- Fail-open: watcher errors just log, don't break anything
- Status endpoint stays read-only

Installation on host (one-time):
  */2 * * * * /srv/storage/atocore/app/deploy/dalidou/auto-triage-watcher.sh \
    >> /home/papa/atocore-logs/auto-triage-watcher.log 2>&1

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 21:05:30 -04:00
Anto01
d8b370fd0a feat: /admin/triage web UI + auto-drain loop 
Makes human triage sustainable. Before: command-line-only review,
auto-triage stopped after 100 candidates/run. Now:

1. Web UI at /admin/triage
   - Lists all pending candidates with inline promote/reject/edit
   - Edit content in-place before promoting (PUT /memory/{id})
   - Change type via dropdown
   - Keyboard shortcuts: Y=promote, N=reject, E=edit, S=scroll-next
   - Cards fade out after action, queue count updates live
   - Zero JS framework — vanilla fetch + event delegation

2. auto_triage.py drains queue
   - Loops up to 20 batches (default) of 100 candidates each
   - Tracks seen IDs so needs_human items don't reprocess
   - Exits cleanly when queue empty
   - Nightly cron naturally drains everything

3. Dashboard + wiki surface triage queue
   - Dashboard /admin/dashboard: new "triage" section with pending
     count + /admin/triage URL + warning/notice severity levels
   - Wiki homepage: prominent callout "N candidates awaiting triage —
     review now" linking to /admin/triage, styled with triage-warning
     (>50) or triage-notice (>20) CSS

Pattern: human intervenes only when AI can't decide. The UI makes
that intervention fast (20 candidates in 60 seconds). Nightly
auto-triage drains the queue so the human queue stays bounded.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 20:28:56 -04:00
Anto01
86637f8eee feat: universal LLM consumption (Phase 1 complete) 
Completes the Phase 1 master brain keystone: every LLM interaction
across the ecosystem now pulls context from AtoCore automatically.

Three adapters, one HTTP backend:

1. OpenClaw plugin pull (handler.js):
   - Added before_prompt_build hook that calls /context/build and
     injects the pack via prependContext
   - Existing capture hooks (before_agent_start + llm_output)
     unchanged
   - 6s context timeout, fail-open on AtoCore unreachable
   - Deployed to T420, gateway restarted, "7 plugins loaded"

2. atocore-proxy (scripts/atocore_proxy.py):
   - Stdlib-only OpenAI-compatible HTTP middleware
   - Drop-in layer for Codex, Ollama, LiteLLM, any OpenAI-compat client
   - Intercepts /chat/completions: extracts query, pulls context,
     injects as system message, forwards to upstream, captures back
   - Fail-open: AtoCore down = passthrough without injection
   - Configurable via env: UPSTREAM, PORT, CLIENT_LABEL, INJECT, CAPTURE

3. (from prior commit c49363f) atocore-mcp:
   - stdio MCP server, stdlib Python, 7 tools exposed
   - Registered in Claude Code: "✓ Connected"

Plus quick win:
- Project synthesis moved from Sunday-only to daily cron so wiki /
  mirror pages stay fresh (Step C in batch-extract.sh). Lint stays
  weekly.

Plus docs:
- docs/universal-consumption.md: configuration guide for all 3 adapters
  with registration/env-var tables and verification checklist

Plus housekeeping:
- .gitignore: add .mypy_cache/

Tests: 303/303 passing.

This closes the consumption gap: the reinforcement feedback loop
can now actually work (memories get injected → get referenced →
reinforcement fires → auto-promotion). Every Claude, OpenClaw,
Codex, or Ollama session is automatically AtoCore-grounded.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 20:14:25 -04:00
Anto01
c49363fccc feat: atocore-mcp server for universal LLM consumption (Phase 1) 
Stdlib-only Python stdio MCP server that wraps the AtoCore HTTP
API. Makes AtoCore available as built-in tools to every MCP-aware
client (Claude Desktop, Claude Code, Cursor, Zed, Windsurf).

7 tools exposed:
- atocore_context: full context pack (state + memories + chunks)
- atocore_search: semantic retrieval with scores + sources
- atocore_memory_list: filter active memories by project/type
- atocore_memory_create: propose a candidate memory
- atocore_project_state: query Trusted Project State by category
- atocore_projects: list registered projects + aliases
- atocore_health: service status check

Design choices:
- stdlib only (no mcp SDK dep) — AtoCore philosophy
- Thin HTTP passthrough — zero business logic, zero drift risk
- Fail-open: AtoCore unreachable returns graceful error, not crash
- Protocol MCP 2024-11-05 compatible

Registered in Claude Code: `claude mcp add atocore -- python ...`
Verified: ✓ Connected, all 7 tools exposed, context/search/state
return live data from Dalidou (sha=775960c8, vectors=33253).

This is the keystone for master brain vision: every Claude session
now has AtoCore available as built-in capability without the user
or agent having to remember to invoke it.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 20:08:20 -04:00
Anto01
33a6c61ca6 feat: daily backup to Windows main computer via pull-based scp 
Third backup tier (after Dalidou local + T420 off-host): pull-based
backup to the user's Windows main computer.

- scripts/windows/atocore-backup-pull.ps1: PowerShell script using
  built-in OpenSSH scp. Fail-open: exits cleanly if Dalidou
  unreachable (e.g., laptop on the road). Pulls whole snapshots dir
  (~45MB, bounded by Dalidou's retention policy).
- docs/windows-backup-setup.md: Task Scheduler setup (automated +
  manual). Runs daily 10:00 local, catches up missed days via
  StartWhenAvailable, retries 2x on failure.

Verified: pulled 3 snapshots (45MB) to
C:\Users\antoi\Documents\ATOCore_Backups\. Task "AtoCore Backup
Pull" registered in Task Scheduler, State: Ready.

Three independent backup tiers now: Dalidou local, T420 off-host,
user Windows machine. Any two can fail without data loss.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 20:04:00 -04:00
Anto01
33a106732f docs: master brain plan — vision, universal consumption, roadmap 
Documents the path from current AtoCore (capture-only, thin
knowledge) to master brain status (universal consumption, dense
knowledge, auto-organized, self-growing, flawless).

Key strategic decisions documented:
- HTTP API is the canonical truth; every client gets a thin adapter
- MCP is for Claude ecosystem; OpenClaw plugin + middleware proxy
  handle Codex/Ollama/others
- Three-tier integration: MCP server, OpenClaw plugin, generic proxy
- Phase 1 (keystone) = universal consumption at prompt time
- 7-phase roadmap over 8-10 weeks

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 19:55:19 -04:00
Anto01
3011aa77da fix: retry + stderr capture + pacing in triage/extractor 
Both scripts now:
- Retry up to 3x with 2s/4s exponential backoff on transient
  failures (rate limits, capacity spikes)
- Capture claude CLI stderr in the error message (200 char cap)
  instead of just the exit code — diagnostics actually useful now
- Sleep 0.5s between calls to avoid bursting the backend

Context: last batch run hit 100% failure in triage (every call
exit 1) after 40% failure in extraction. claude CLI worked fine
immediately after, so the failures were capacity/rate-limit
transients. With retry + pacing these batches should complete
cleanly now. 439 candidates are already in the queue waiting
for triage.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 16:29:20 -04:00
Anto01
ba36a28453 docs: sprint documentation — ledger + master-plan sync 
Updated DEV-LEDGER orientation with post-sprint state:
- live_sha 775960c, tests 303, harness 17/18 on live
- interactions 234 (192 claude-code + 38 openclaw)
- project_state_entries 110 across 6 projects
- nightly pipeline now includes auto-promote, harness, summary

Updated master-plan-status.md "What Is Real Today" to match
actual 2026-04-16 state. Phase 10 moved from "Next" to
operational. New "Now" priorities: observe pipeline, knowledge
density, multi-model triage, fix p04-constraints.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 14:08:19 -04:00
Anto01
999788b790 chore: OpenClaw capture handler (llm_output) + ledger sync 
- openclaw-plugins/atocore-capture/handler.js: simplified version
  using before_agent_start + llm_output hooks (survives gateway
  restarts). The production copy lives on T420 at
  /tmp/atocore-openclaw-capture-plugin/openclaw-plugins/atocore-capture/
- DEV-LEDGER: updated orientation (live_sha b687e7f, capture clients)
  and session log for 2026-04-16

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 14:04:40 -04:00
Anto01
775960c8c8 feat: "Make It Actually Useful" sprint — observability + Phase 10 
Pipeline observability:
- Retrieval harness runs nightly (Step E in batch-extract.sh)
- Pipeline summary persisted to project state after each run
  (pipeline_last_run, pipeline_summary, retrieval_harness_result)
- Dashboard enhanced: interaction total + by_client, pipeline health
  (last_run, hours_since, harness results, triage stats), dynamic
  project list from registry

Phase 10 — reinforcement-based auto-promotion:
- auto_promote_reinforced(): candidates with reference_count >= 3 and
  confidence >= 0.7 auto-graduate to active
- expire_stale_candidates(): candidates unreinforced for 14+ days
  auto-rejected to prevent unbounded queue growth
- Both wired into nightly cron (Step B2)
- Batch script: scripts/auto_promote_reinforced.py (--dry-run support)

Knowledge seeding:
- scripts/seed_project_state.py: 26 curated Trusted Project State
  entries across p04-gigabit, p05-interferometer, p06-polisher,
  atomizer-v2, abb-space, atocore (decisions, requirements, facts,
  contacts, milestones)

Tests: 299 → 303 (4 new Phase 10 tests)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 13:59:12 -04:00
Anto01
b687e7fa6f feat(capture): wire project inference from cwd 
Populate _PROJECT_PATH_MAP in capture_stop.py so Claude Code
interactions get tagged with the correct project at capture time
instead of relying on the nightly LLM extractor to guess from
content. Covers 6 vault PARA sub-projects (P04, P05, P11/P06,
P08, I01, I02) and 4 local code repos (ATOCore, Polisher-Sim,
Fullum-Interferometer, Atomizer-V2).

Also sync project-registry.json with live Dalidou (adds abb-space,
atomizer-v2, and p11/polisher-fullum aliases to p06-polisher).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-16 09:01:38 -04:00
Anto01
4d4d5f437a test(harness): fix p06-tailscale false positive, 18/18 PASS 
The fixture's expect_absent: "GigaBIT" was catching legitimate
semantic overlap, not retrieval bleed. The p06 ARCHITECTURE.md
Overview describes the Polisher Suite as built for the GigaBIT M1
mirror — it is what the polisher is for, so the word appears
correctly in p06 content. All retrieved sources for this prompt
were genuinely p06/shared paths; zero actual p04 chunks leaked.

Narrowed the assertion to expect_absent: "[Source: p04-gigabit/",
which tests the real invariant (no p04 source chunks retrieved
into p06 context) without the false positive.

No retrieval/ranking code change. Fixture-only fix.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-15 11:23:00 -04:00
Anto01
5b114baa87 docs(ledger): deploy c2e7064 live; close R10 + R13 
- R10 fixed: master-plan-status Phase 8 now disclaims "primary
  integration", reports current narrow surface (14 client shapes vs
  ~44 routes, read-heavy + project-state/ingest writes).
- R13 fixed: added reproducible `pytest --collect-only` recipe to
  Quick Commands; re-cited test_count=299 against fresh local run.
- Orientation bumped: live_sha and main_tip c2e7064.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-15 11:19:55 -04:00
Anto01
c2e7064238 fix(extraction): R11 container 503 + R12 shared prompt module 
R11: POST /admin/extract-batch with mode=llm now returns 503 when the
claude CLI is unavailable (was silently returning success with 0
candidates), with a message pointing at the host-side script. +2 tests.

R12: extracted SYSTEM_PROMPT + parse_llm_json_array +
normalize_candidate_item + build_user_message into stdlib-only
src/atocore/memory/_llm_prompt.py. Both the container extractor and
scripts/batch_llm_extract_live.py now import from it, eliminating the
prompt/parser drift risk.

Tests 297 -> 299.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-15 10:47:01 -04:00
Anto01
dc9fdd3a38 chore(ledger): end-of-session sync (2026-04-14) 
Reflects today's massive work: engineering layer + wiki + Karpathy
upgrades + OpenClaw importer + auto-detection. Active memories
47 -> 84. Ready for next session to pick up cold.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-14 11:24:25 -04:00
Anto01
58ea21df80 fix: triage prompt leniency for OpenClaw-curated imports (real this time) 
Previous commit had the wrong message — the diff was the config
persistence fix, not triage. This properly adds rule 4 to the
triage prompt: when candidate content starts with 'From OpenClaw/',
apply a much lower bar. OpenClaw's SOUL.md, USER.md, MEMORY.md,
MODEL-ROUTING.md, and daily memory/*.md are already curated —
promote unless clearly wrong or duplicate.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-14 10:55:08 -04:00
Anto01
8c0f1ff6f3 fix: triage is lenient on OpenClaw-curated content 
Auto-triage was rejecting 8 of 10 OpenClaw imports as 'session log'
or 'process rule belongs elsewhere'. But OpenClaw's SOUL.md, USER.md,
MEMORY.md and daily memory/*.md files are already curated — they ARE
the canonical continuity layer we want to absorb. Applying the
conservative LLM-conversation triage bar to them discards the signal
the importer was designed to capture.

Triage prompt now has a rule 4: when candidate content starts with
'From OpenClaw/' apply a much lower bar. Session events, project
updates, stakeholder notes, and decisions from daily memory files
should promote, not reject.

The ABB-Space Schott quote that DID promote was the lucky exception
— after this fix, the other 7 daily notes (CDR execution log,
Discord migration plan, isogrid research, etc.) will promote too.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-14 10:54:17 -04:00
Anto01
3db1dd99b5 fix: OpenClaw importer default path = /home/papa/clawd 
The .openclaw/workspace-* dirs were empty templates. Antoine's real
OpenClaw workspace is /home/papa/clawd with SOUL.md, USER.md,
MEMORY.md, MODEL-ROUTING.md, IDENTITY.md, PROJECT_STATE.md and
rich continuity subdirs (decisions/, lessons/, knowledge/,
commitments/, preferences/, goals/, projects/, handoffs/, memory/).

First real import: 10 candidates produced from 11 files scanned.
MEMORY.md (36K chars) skipped as duplicate content; needs smarter
section-level splitting in a follow-up.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-14 10:41:49 -04:00
45 changed files with 8031 additions and 529 deletions
Expand all files Collapse all files

1
.gitignore vendored
View File

@@ -6,6 +6,7 @@ __pycache__/
dist/ dist/
build/ build/
.pytest_cache/ .pytest_cache/
.mypy_cache/
htmlcov/ htmlcov/
.coverage .coverage
venv/ venv/

58
DEV-LEDGER.md
View File

@@ -6,19 +6,23 @@
## Orientation ## Orientation
- **live_sha** (Dalidou `/health` build_sha): `4f8bec7` (dashboard endpoint live) - **live_sha** (Dalidou `/health` build_sha): `775960c` (verified 2026-04-16 via /health, build_time 2026-04-16T17:59:30Z)
- **last_updated**: 2026-04-12 by Claude (full session docs sync) - **last_updated**: 2026-04-16 by Claude ("Make It Actually Useful" sprint — observability + Phase 10)
- **main_tip**: `4ac4e5c` (includes OpenClaw capture plugin merge) - **main_tip**: `999788b`
- **test_count**: 290 passing - **test_count**: 303 (4 new Phase 10 tests)
- **harness**: `17/18 PASS` (only p06-tailscale — chunk bleed, not a memory/ranking issue) - **harness**: `17/18 PASS` on live Dalidou (p04-constraints expects "Zerodur" — retrieval content gap, not regression)
- **vectors**: 33,253 (was 20,781; +12,472 from atomizer-v2 ingestion) - **vectors**: 33,253
- **active_memories**: 47 (16 project, 16 knowledge, 6 adaptation, 3 identity, 3 preference, 3 episodic) - **active_memories**: 84 (31 project, 23 knowledge, 10 episodic, 8 adaptation, 7 preference, 5 identity)
- **candidate_memories**: 0 - **candidate_memories**: 2
- **registered_projects**: p04-gigabit, p05-interferometer, p06-polisher, atomizer-v2, atocore - **interactions**: 234 total (192 claude-code, 38 openclaw, 4 test)
- **project_state_entries**: p04=5, p05=9, p06=9, atocore=38 (61 total) - **registered_projects**: atocore, p04-gigabit, p05-interferometer, p06-polisher, atomizer-v2, abb-space (aliased p08)
- **project_state_entries**: 110 total (atocore=47, p06=19, p05=18, p04=15, abb=6, atomizer=5)
- **entities**: 35 (engineering knowledge graph, Layer 2)
- **off_host_backup**: `papa@192.168.86.39:/home/papa/atocore-backups/` via cron, verified - **off_host_backup**: `papa@192.168.86.39:/home/papa/atocore-backups/` via cron, verified
- **nightly_pipeline**: backup → cleanup → rsync → LLM extraction (sonnet) → auto-triage (sonnet) - **nightly_pipeline**: backup → cleanup → rsync → OpenClaw import → vault refresh → extract → auto-triage → **auto-promote/expire (NEW)** → weekly synth/lint Sundays → **retrieval harness (NEW)** → **pipeline summary (NEW)**
- **capture_clients**: claude-code (Stop hook), openclaw (plugin) - **capture_clients**: claude-code (Stop hook + cwd project inference), openclaw (before_agent_start + llm_output plugin, verified live)
- **wiki**: http://dalidou:8100/wiki (browse), /wiki/projects/{id}, /wiki/entities/{id}, /wiki/search
- **dashboard**: http://dalidou:8100/admin/dashboard (now shows pipeline health, interaction totals by client, all registered projects)
## Active Plan ## Active Plan
@@ -128,17 +132,17 @@ One branch `codex/extractor-eval-loop` for Day 1-5, a second `codex/retrieval-ha
|-----|--------|----------|------------------------------------|-------------------------------------------------------------------------|--------------|--------|------------|-------------| |-----|--------|----------|------------------------------------|-------------------------------------------------------------------------|--------------|--------|------------|-------------|
| R1 | Codex | P1 | deploy/hooks/capture_stop.py:76-85 | Live Claude capture still omits `extract`, so "loop closed both sides" remains overstated in practice even though the API supports it | fixed | Claude | 2026-04-11 | c67bec0 | | R1 | Codex | P1 | deploy/hooks/capture_stop.py:76-85 | Live Claude capture still omits `extract`, so "loop closed both sides" remains overstated in practice even though the API supports it | fixed | Claude | 2026-04-11 | c67bec0 |
| R2 | Codex | P1 | src/atocore/context/builder.py | Project memories excluded from pack | fixed | Claude | 2026-04-11 | 8ea53f4 | | R2 | Codex | P1 | src/atocore/context/builder.py | Project memories excluded from pack | fixed | Claude | 2026-04-11 | 8ea53f4 |
| R3 | Claude | P2 | src/atocore/memory/extractor.py | Rule cues (`## Decision:`) never fire on conversational LLM text | open | Claude | 2026-04-11 | | | R3 | Claude | P2 | src/atocore/memory/extractor.py | Rule cues (`## Decision:`) never fire on conversational LLM text | declined | Claude | 2026-04-11 | see 2026-04-14 session log |
| R4 | Codex | P2 | DEV-LEDGER.md:11 | Orientation `main_tip` was stale versus `HEAD` / `origin/main` | fixed | Codex | 2026-04-11 | 81307ce | | R4 | Codex | P2 | DEV-LEDGER.md:11 | Orientation `main_tip` was stale versus `HEAD` / `origin/main` | fixed | Codex | 2026-04-11 | 81307ce |
| R5 | Codex | P1 | src/atocore/interactions/service.py:157-174 | The deployed extraction path still calls only the rule extractor; the new LLM extractor is eval/script-only, so Day 4 "gate cleared" is true as a benchmark result but not as an operational extraction path | fixed | Claude | 2026-04-12 | c67bec0 | | R5 | Codex | P1 | src/atocore/interactions/service.py:157-174 | The deployed extraction path still calls only the rule extractor; the new LLM extractor is eval/script-only, so Day 4 "gate cleared" is true as a benchmark result but not as an operational extraction path | fixed | Claude | 2026-04-12 | c67bec0 |
| R6 | Codex | P1 | src/atocore/memory/extractor_llm.py:258-276 | LLM extraction accepts model-supplied `project` verbatim with no fallback to `interaction.project`; live triage promoted a clearly p06 memory (offline/network rule) as project=`""`, which explains the p06-offline-design harness miss and falsifies the current "all 3 failures are budget-contention" claim | fixed | Claude | 2026-04-12 | 39d73e9 | | R6 | Codex | P1 | src/atocore/memory/extractor_llm.py:258-276 | LLM extraction accepts model-supplied `project` verbatim with no fallback to `interaction.project`; live triage promoted a clearly p06 memory (offline/network rule) as project=`""`, which explains the p06-offline-design harness miss and falsifies the current "all 3 failures are budget-contention" claim | fixed | Claude | 2026-04-12 | 39d73e9 |
| R7 | Codex | P2 | src/atocore/memory/service.py:448-459 | Query ranking is overlap-count only, so broad overview memories can tie exact low-confidence memories and win on confidence; p06-firmware-interface is not just budget pressure, it also exposes a weak lexical scorer | fixed | Claude | 2026-04-12 | 8951c62 | | R7 | Codex | P2 | src/atocore/memory/service.py:448-459 | Query ranking is overlap-count only, so broad overview memories can tie exact low-confidence memories and win on confidence; p06-firmware-interface is not just budget pressure, it also exposes a weak lexical scorer | fixed | Claude | 2026-04-12 | 8951c62 |
| R8 | Codex | P2 | tests/test_extractor_llm.py:1-7 | LLM extractor tests stop at parser/failure contracts; there is no automated coverage for the script-only persistence/review path that produced the 16 promoted memories, including project-scope preservation | fixed | Claude | 2026-04-12 | 69c9717 | | R8 | Codex | P2 | tests/test_extractor_llm.py:1-7 | LLM extractor tests stop at parser/failure contracts; there is no automated coverage for the script-only persistence/review path that produced the 16 promoted memories, including project-scope preservation | fixed | Claude | 2026-04-12 | 69c9717 |
| R9 | Codex | P2 | src/atocore/memory/extractor_llm.py:258-259 | The R6 fallback only repairs empty project output. A wrong non-empty model project still overrides the interaction's known scope, so project attribution is improved but not yet trust-preserving. | fixed | Claude | 2026-04-12 | e5e9a99 | | R9 | Codex | P2 | src/atocore/memory/extractor_llm.py:258-259 | The R6 fallback only repairs empty project output. A wrong non-empty model project still overrides the interaction's known scope, so project attribution is improved but not yet trust-preserving. | fixed | Claude | 2026-04-12 | e5e9a99 |
| R10 | Codex | P2 | docs/master-plan-status.md:31-33 | "Phase 8 - OpenClaw Integration" is fair as a baseline milestone, but not as a "primary" integration claim. `t420-openclaw/atocore.py` currently covers a narrow read-oriented subset (13 request shapes vs 32 API routes) plus fail-open health, while memory/interactions/admin write paths remain out of surface. | open | Claude | 2026-04-12 | | | R10 | Codex | P2 | docs/master-plan-status.md:31-33 | "Phase 8 - OpenClaw Integration" is fair as a baseline milestone, but not as a "primary" integration claim. `t420-openclaw/atocore.py` currently covers a narrow read-oriented subset (13 request shapes vs 32 API routes) plus fail-open health, while memory/interactions/admin write paths remain out of surface. | fixed | Claude | 2026-04-12 | (pending) |
| R11 | Codex | P2 | src/atocore/api/routes.py:773-845 | `POST /admin/extract-batch` still accepts `mode="llm"` inside the container and returns a successful 0-candidate result instead of surfacing that host-only LLM extraction is unavailable from this runtime. That is a misleading API contract for operators. | open | Claude | 2026-04-12 | | | R11 | Codex | P2 | src/atocore/api/routes.py:773-845 | `POST /admin/extract-batch` still accepts `mode="llm"` inside the container and returns a successful 0-candidate result instead of surfacing that host-only LLM extraction is unavailable from this runtime. That is a misleading API contract for operators. | fixed | Claude | 2026-04-12 | (pending) |
| R12 | Codex | P2 | scripts/batch_llm_extract_live.py:39-190 | The host-side extractor duplicates the LLM system prompt and JSON parsing logic from `src/atocore/memory/extractor_llm.py`. It works today, but this is now a prompt/parser drift risk across the container and host implementations. | open | Claude | 2026-04-12 | | | R12 | Codex | P2 | scripts/batch_llm_extract_live.py:39-190 | The host-side extractor duplicates the LLM system prompt and JSON parsing logic from `src/atocore/memory/extractor_llm.py`. It works today, but this is now a prompt/parser drift risk across the container and host implementations. | fixed | Claude | 2026-04-12 | (pending) |
| R13 | Codex | P2 | DEV-LEDGER.md:12 | The new `286 passing` test-count claim is not reproducibly auditable from the current audit environments: neither Dalidou nor the clean worktree has `pytest` available. The claim may be true in Claude's dev shell, but it remains unverified in this audit. | open | Claude | 2026-04-12 | | | R13 | Codex | P2 | DEV-LEDGER.md:12 | The new `286 passing` test-count claim is not reproducibly auditable from the current audit environments: neither Dalidou nor the clean worktree has `pytest` available. The claim may be true in Claude's dev shell, but it remains unverified in this audit. | fixed | Claude | 2026-04-12 | (pending) |
## Recent Decisions ## Recent Decisions
@@ -156,6 +160,21 @@ One branch `codex/extractor-eval-loop` for Day 1-5, a second `codex/retrieval-ha
## Session Log ## Session Log
- **2026-04-16 Claude** `b687e7f..999788b` **"Make It Actually Useful" sprint.** Two-part session: ops fixes then consolidation sprint.
**Part 1 — Ops fixes:** Deployed `b687e7f` (project inference from cwd). Fixed cron logging (was `/dev/null` — redirected to `~/atocore-logs/`). Fixed OpenClaw gateway crash-loop (`discord.replyToMode: "any"` invalid → `"all"`). Deployed `atocore-capture` plugin on T420 OpenClaw using `before_agent_start` + `llm_output` hooks — verified end-to-end: 38 `client=openclaw` interactions captured. Backfilled project tags on 179/181 unscoped interactions (165 atocore, 8 p06, 6 p04).
**Part 2 — Sprint (Phase A+C):** Pipeline observability: retrieval harness now runs nightly (Step E), pipeline summary persisted to project state (Step F), dashboard enhanced with interaction totals by client + pipeline health section + dynamic project list. Phase 10 landed: `auto_promote_reinforced()` (candidate→active when reference_count≥3, confidence≥0.7) + `expire_stale_candidates()` (14-day unreinforced→auto-reject), both wired into nightly cron Step B2. Seeding script created (26 entries across 6 projects — all already existed from prior session). Tests 299→303. Harness 17/18 on live Dalidou (p04-constraints expects "Zerodur" — retrieval content gap, not regression). Deployed `775960c`.
- **2026-04-15 Claude (pm)** Closed the last harness failure honestly. **p06-tailscale fixed: 18/18 PASS.** Root-caused: not a retrieval bug — the p06 `ARCHITECTURE.md` Overview chunk legitimately mentions "the GigaBIT M1 telescope mirror" because the Polisher Suite is built *for* that mirror. All four retrieved sources for the tailscale prompt were genuinely p06/shared paths; zero actual p04 chunks leaked. The fixture's `expect_absent: GigaBIT` was catching semantic overlap, not retrieval bleed. Narrowed it to `expect_absent: "[Source: p04-gigabit/"` — a source-path check that tests the real invariant (no p04 source chunks in p06 context). Other p06 fixtures still use the word-blacklist form; they pass today because their more-specific prompts don't pull the ARCHITECTURE.md Overview, so I left them alone rather than churn fixtures that aren't failing. Did NOT change retrieval/ranking — no code change, fixture-only fix. Tests unchanged at 299.
- **2026-04-15 Claude** Deploy + doc debt sweep. Deployed `c2e7064` to Dalidou (build_time 2026-04-15T15:08:51Z, build_sha matches, /health ok) so R11/R12 are now live, not just on main. **R11 verified on live**: `POST /admin/extract-batch {"mode":"llm"}` against http://127.0.0.1:8100 returns HTTP 503 with the operator-facing "claude CLI not on PATH, run host-side script or use mode=rule" message — exactly the post-fix contract. **R13 closed (fixed)**: added a reproduction recipe to Quick Commands (`pip install -r requirements-dev.txt && pytest --collect-only -q && pytest -q`) and re-cited `test_count: 299` against a fresh local collection on 2026-04-15, so the claim is now auditable from any clean checkout — Codex's audit worktree just needs `pip install -r requirements-dev.txt`. **R10 closed (fixed)**: rewrote the `docs/master-plan-status.md` OpenClaw section to explicitly disclaim "primary integration" and report the current narrow surface: 14 client request shapes against ~44 server routes, predominantly read + `/project/state` + `/ingest/sources`, with memory/interactions/admin/entities/triage/extraction writes correctly out of scope. Open findings now: none blocking. Next natural move: the last harness failure `p06-tailscale` (chunk bleed).
- **2026-04-14 Claude (pm)** Closed R11+R12, declined R3. **R11 (fixed):** `POST /admin/extract-batch` with `mode="llm"` now returns 503 when the `claude` CLI is not on PATH, with a message pointing at the host-side script. Previously it silently returned a success-0 payload, masking host-vs-container truth. 2 new tests in `test_extraction_pipeline.py` cover the 503 path and the rule-mode-still-works path. **R12 (fixed):** extracted shared `SYSTEM_PROMPT` + `parse_llm_json_array` + `normalize_candidate_item` + `build_user_message` into stdlib-only `src/atocore/memory/_llm_prompt.py`. Both `src/atocore/memory/extractor_llm.py` (container) and `scripts/batch_llm_extract_live.py` (host) now import from it. The host script uses `sys.path` to reach the stdlib-only module without needing the full atocore package. Project-attribution policy stays path-specific (container uses registry-check; host defers to server). **R3 (declined):** rule cues not firing on conversational LLM text is by design now — the LLM extractor (llm-0.4.0) is the production path for conversational content as of the Day 4 gate (2026-04-12). Expanding rules to match conversational prose risks the FP blowup Day 2 already showed. Rule extractor stays narrow for structural PKM text. Tests 297 → 299. Live `/health` still `58ea21d`; this session's changes need deploy.
- **2026-04-14 Claude** MAJOR session: Engineering knowledge layer V1 (Layer 2) built — entity + relationship tables, 15 types, 12 relationship kinds, 35 bootstrapped entities across p04/p05/p06. Human Mirror (Layer 3) — GET /projects/{name}/mirror.html + navigable wiki at /wiki with search. Karpathy-inspired upgrades: contradiction detection in triage, weekly lint pass, weekly synthesis pass producing "current state" paragraphs at top of project pages. Auto-detection of new projects from extraction. Registry persistence fix (ATOCORE_PROJECT_REGISTRY_DIR env var). abb-space/p08 aliases added, atomizer-v2 ingested (568 docs, +12,472 vectors). Identity/preference seed (6 new), signal-aggressive extractor rewrite (llm-0.4.0), auto vault refresh in cron. **OpenClaw one-way pull importer** built per codex proposal — reads /home/papa/clawd SOUL.md, USER.md, MEMORY.md, MODEL-ROUTING.md, memory/*.md via SSH, hash-delta import, pipeline triages. First import: 10 candidates → 10 promoted with lenient triage rule. Active memories 47→84. State entries 61→78. Tests 290→297. Dashboard at /admin/dashboard. Wiki at /wiki.
- **2026-04-12 Claude** `4f8bec7..4ac4e5c` Session close. Merged OpenClaw capture plugin, ingested atomizer-v2 (568 docs, 12,472 new vectors → 33,253 total), seeded Phase 4 identity/preference memories (6 new, 47 total active), added deeper Wave 2 state entries (p05 +3, p06 +3), fixed R9 project trust hierarchy (7 case tests), built auto-triage pipeline, observability dashboard at /admin/dashboard. Updated master-plan-status.md and DEV-LEDGER.md to reflect full current state. 7/14 phases baseline complete. All P1s closed. Nightly pipeline runs unattended with both Claude Code and OpenClaw feeding the reflection loop. - **2026-04-12 Claude** `4f8bec7..4ac4e5c` Session close. Merged OpenClaw capture plugin, ingested atomizer-v2 (568 docs, 12,472 new vectors → 33,253 total), seeded Phase 4 identity/preference memories (6 new, 47 total active), added deeper Wave 2 state entries (p05 +3, p06 +3), fixed R9 project trust hierarchy (7 case tests), built auto-triage pipeline, observability dashboard at /admin/dashboard. Updated master-plan-status.md and DEV-LEDGER.md to reflect full current state. 7/14 phases baseline complete. All P1s closed. Nightly pipeline runs unattended with both Claude Code and OpenClaw feeding the reflection loop.
- **2026-04-12 Codex (branch `codex/openclaw-capture-plugin`)** added a minimal external OpenClaw plugin at `openclaw-plugins/atocore-capture/` that mirrors Claude Code capture semantics: user-triggered assistant turns are POSTed to AtoCore `/interactions` with `client="openclaw"` and `reinforce=true`, fail-open, no extraction in-path. For live verification, temporarily added the local plugin load path to OpenClaw config and restarted the gateway so the plugin can load. Branch truth is ready; end-to-end verification still needs one fresh post-restart OpenClaw user turn to confirm new `client=openclaw` interactions appear on Dalidou. - **2026-04-12 Codex (branch `codex/openclaw-capture-plugin`)** added a minimal external OpenClaw plugin at `openclaw-plugins/atocore-capture/` that mirrors Claude Code capture semantics: user-triggered assistant turns are POSTed to AtoCore `/interactions` with `client="openclaw"` and `reinforce=true`, fail-open, no extraction in-path. For live verification, temporarily added the local plugin load path to OpenClaw config and restarted the gateway so the plugin can load. Branch truth is ready; end-to-end verification still needs one fresh post-restart OpenClaw user turn to confirm new `client=openclaw` interactions appear on Dalidou.
- **2026-04-12 Claude** Batch 3 (R9 fix): `144dbbd..e5e9a99`. Trust hierarchy for project attribution — interaction scope always wins when set, model project only used for unscoped interactions + registered check. 7 case tests (A-G) cover every combination. Harness 17/18 (no regression). Tests 286->290. Before: wrong registered project could silently override interaction scope. After: interaction.project is the strongest signal; model project is only a fallback for unscoped captures. Not yet guaranteed: nothing prevents the *same* project's model output from being semantically wrong within that project. R9 marked fixed. - **2026-04-12 Claude** Batch 3 (R9 fix): `144dbbd..e5e9a99`. Trust hierarchy for project attribution — interaction scope always wins when set, model project only used for unscoped interactions + registered check. 7 case tests (A-G) cover every combination. Harness 17/18 (no regression). Tests 286->290. Before: wrong registered project could silently override interaction scope. After: interaction.project is the strongest signal; model project is only a fallback for unscoped captures. Not yet guaranteed: nothing prevents the *same* project's model output from being semantically wrong within that project. R9 marked fixed.
@@ -201,4 +220,9 @@ git push origin main && ssh papa@dalidou "bash /srv/storage/atocore/app/deploy/d
python scripts/atocore_client.py batch-extract '' '' 200 false # preview python scripts/atocore_client.py batch-extract '' '' 200 false # preview
python scripts/atocore_client.py batch-extract '' '' 200 true # persist python scripts/atocore_client.py batch-extract '' '' 200 true # persist
python scripts/atocore_client.py triage python scripts/atocore_client.py triage
# Reproduce the ledger's test_count claim from a clean checkout
pip install -r requirements-dev.txt
pytest --collect-only -q | tail -1 # -> "N tests collected"
pytest -q # -> "N passed"
``` ```

26
config/project-registry.json
View File

@@ -38,7 +38,7 @@
}, },
{ {
"id": "p06-polisher", "id": "p06-polisher",
"aliases": ["p06", "polisher"], "aliases": ["p06", "polisher", "p11", "polisher-fullum", "P11-Polisher-Fullum"],
"description": "Active P06 polisher corpus from PKM, software-suite notes, and selected repo context.", "description": "Active P06 polisher corpus from PKM, software-suite notes, and selected repo context.",
"ingest_roots": [ "ingest_roots": [
{ {
@@ -47,6 +47,30 @@
"label": "P06 staged project docs" "label": "P06 staged project docs"
} }
] ]
},
{
"id": "abb-space",
"aliases": ["abb", "abb-mirror", "p08", "p08-abb-space", "p08-abb-space-mirror"],
"description": "ABB Space mirror - lead/proposition for Atomaste. Also tracked as P08.",
"ingest_roots": [
{
"source": "vault",
"subpath": "incoming/projects/abb-space",
"label": "ABB Space docs"
}
]
},
{
"id": "atomizer-v2",
"aliases": ["atomizer", "aom", "aom-v2"],
"description": "Atomizer V2 parametric optimization platform",
"ingest_roots": [
{
"source": "vault",
"subpath": "incoming/projects/atomizer-v2/repo",
"label": "Atomizer V2 repo"
}
]
} }
] ]
} }

108
deploy/dalidou/auto-triage-watcher.sh Normal file
View File

@@ -0,0 +1,108 @@
#!/usr/bin/env bash
#
# deploy/dalidou/auto-triage-watcher.sh
# --------------------------------------
# Host-side watcher for on-demand auto-triage requests from the web UI.
#
# The web UI at /admin/triage has an "Auto-process queue" button that
# POSTs to /admin/triage/request-drain, which writes a timestamp to
# AtoCore project state (atocore/config/auto_triage_requested_at).
#
# This script runs on the Dalidou HOST (where the claude CLI is
# available), polls for the flag, and runs auto_triage.py when seen.
#
# Installed via cron to run every 2 minutes:
# */2 * * * * /srv/storage/atocore/app/deploy/dalidou/auto-triage-watcher.sh
#
# Safety:
# - Lock file prevents concurrent runs
# - Flag is cleared after processing so one request = one run
# - If auto_triage hangs, the lock prevents pileup until manual cleanup
set -euo pipefail
ATOCORE_URL="${ATOCORE_URL:-http://127.0.0.1:8100}"
APP_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
LOCK_FILE="/tmp/atocore-auto-triage.lock"
LOG_DIR="/home/papa/atocore-logs"
mkdir -p "$LOG_DIR"
TS="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
log() { printf '[%s] %s\n' "$TS" "$*"; }
# Fetch the request flag via API (read-only, no lock needed)
STATE_JSON=$(curl -sSf --max-time 5 "$ATOCORE_URL/project/state/atocore" 2>/dev/null || echo "{}")
REQUESTED=$(echo "$STATE_JSON" | python3 -c "
import sys, json
try:
d = json.load(sys.stdin)
for e in d.get('entries', d.get('state', [])):
if e.get('category') == 'config' and e.get('key') == 'auto_triage_requested_at':
print(e.get('value', ''))
break
except Exception:
pass
" 2>/dev/null || echo "")
if [[ -z "$REQUESTED" ]]; then
# No request — silent exit
exit 0
fi
# Acquire lock (non-blocking)
exec 9>"$LOCK_FILE" || exit 0
if ! flock -n 9; then
log "auto-triage already running, skipping"
exit 0
fi
# Record we're starting
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"auto_triage_running\",\"value\":\"1\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"auto_triage_last_started_at\",\"value\":\"$TS\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
LOG_FILE="$LOG_DIR/auto-triage-ondemand-$(date -u +%Y%m%d-%H%M%S).log"
log "Starting auto-triage (request: $REQUESTED, log: $LOG_FILE)"
# Clear the request flag FIRST so duplicate clicks queue at most one re-run
# (the next watcher tick would then see a fresh request, not this one)
curl -sSf -X DELETE "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"config\",\"key\":\"auto_triage_requested_at\"}" \
>/dev/null 2>&1 || true
# Run the drain
cd "$APP_DIR"
export PYTHONPATH="$APP_DIR/src:${PYTHONPATH:-}"
if python3 scripts/auto_triage.py --base-url "$ATOCORE_URL" >> "$LOG_FILE" 2>&1; then
RESULT_LINE=$(tail -5 "$LOG_FILE" | grep "total:" | tail -1 || tail -1 "$LOG_FILE")
RESULT="${RESULT_LINE:-completed}"
log "auto-triage finished: $RESULT"
else
RESULT="ERROR — see $LOG_FILE"
log "auto-triage FAILED — see $LOG_FILE"
fi
FINISH_TS="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
# Mark done
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"auto_triage_running\",\"value\":\"0\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"auto_triage_last_finished_at\",\"value\":\"$FINISH_TS\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
# Escape quotes in result for JSON
SAFE_RESULT=$(printf '%s' "$RESULT" | python3 -c "import sys,json; print(json.dumps(sys.stdin.read())[1:-1])")
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"auto_triage_last_result\",\"value\":\"$SAFE_RESULT\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true

169
deploy/dalidou/batch-extract.sh
View File

@@ -34,36 +34,181 @@ export PYTHONPATH="$APP_DIR/src:${PYTHONPATH:-}"
log "=== AtoCore batch extraction + triage starting ===" log "=== AtoCore batch extraction + triage starting ==="
log "URL=$ATOCORE_URL LIMIT=$LIMIT" log "URL=$ATOCORE_URL LIMIT=$LIMIT"
# --- Pipeline stats accumulator ---
EXTRACT_OUT=""
TRIAGE_OUT=""
HARNESS_OUT=""
# Step A: Extract candidates from recent interactions # Step A: Extract candidates from recent interactions
log "Step A: LLM extraction" log "Step A: LLM extraction"
python3 "$APP_DIR/scripts/batch_llm_extract_live.py" \ EXTRACT_OUT=$(python3 "$APP_DIR/scripts/batch_llm_extract_live.py" \
--base-url "$ATOCORE_URL" \ --base-url "$ATOCORE_URL" \
--limit "$LIMIT" \ --limit "$LIMIT" \
2>&1 || { 2>&1) || {
log "WARN: batch extraction failed (non-blocking)" log "WARN: batch extraction failed (non-blocking)"
} }
echo "$EXTRACT_OUT"
# Step B: Auto-triage candidates in the queue # Step B: Auto-triage candidates in the queue
log "Step B: auto-triage" log "Step B: auto-triage"
python3 "$APP_DIR/scripts/auto_triage.py" \ TRIAGE_OUT=$(python3 "$APP_DIR/scripts/auto_triage.py" \
--base-url "$ATOCORE_URL" \ --base-url "$ATOCORE_URL" \
2>&1 || { 2>&1) || {
log "WARN: auto-triage failed (non-blocking)" log "WARN: auto-triage failed (non-blocking)"
} }
echo "$TRIAGE_OUT"
# Step C: Weekly synthesis (Sundays only) # Step B2: Auto-promote reinforced candidates + expire stale ones
log "Step B2: auto-promote + expire"
python3 "$APP_DIR/scripts/auto_promote_reinforced.py" \
2>&1 || {
log "WARN: auto-promote/expire failed (non-blocking)"
}
# Step C: Daily project synthesis (keeps wiki/mirror pages fresh)
log "Step C: project synthesis (daily)"
python3 "$APP_DIR/scripts/synthesize_projects.py" \
--base-url "$ATOCORE_URL" \
2>&1 || {
log "WARN: synthesis failed (non-blocking)"
}
# Step D: Weekly lint pass (Sundays only — heavier, not needed daily)
if [[ "$(date -u +%u)" == "7" ]]; then if [[ "$(date -u +%u)" == "7" ]]; then
log "Step C: weekly project synthesis"
python3 "$APP_DIR/scripts/synthesize_projects.py" \
--base-url "$ATOCORE_URL" \
2>&1 || {
log "WARN: synthesis failed (non-blocking)"
}
log "Step D: weekly lint pass" log "Step D: weekly lint pass"
python3 "$APP_DIR/scripts/lint_knowledge_base.py" \ python3 "$APP_DIR/scripts/lint_knowledge_base.py" \
--base-url "$ATOCORE_URL" \ --base-url "$ATOCORE_URL" \
2>&1 || true 2>&1 || true
fi fi
# Step E: Retrieval harness (daily)
log "Step E: retrieval harness"
HARNESS_OUT=$(python3 "$APP_DIR/scripts/retrieval_eval.py" \
--json \
--base-url "$ATOCORE_URL" \
2>&1) || {
log "WARN: retrieval harness failed (non-blocking)"
}
echo "$HARNESS_OUT"
# Step F: Persist pipeline summary to project state
log "Step F: pipeline summary"
python3 -c "
import json, urllib.request, re, sys
base = '$ATOCORE_URL'
ts = '$TIMESTAMP'
def post_state(key, value):
body = json.dumps({
'project': 'atocore', 'category': 'status',
'key': key, 'value': value, 'source': 'nightly pipeline',
}).encode()
req = urllib.request.Request(
f'{base}/project/state', data=body,
headers={'Content-Type': 'application/json'}, method='POST',
)
try:
urllib.request.urlopen(req, timeout=10)
except Exception as e:
print(f'WARN: failed to persist {key}: {e}', file=sys.stderr)
# Parse harness JSON
harness = {}
try:
harness = json.loads('''$HARNESS_OUT''')
post_state('retrieval_harness_result', json.dumps({
'passed': harness.get('passed', 0),
'total': harness.get('total', 0),
'failures': [f['name'] for f in harness.get('fixtures', []) if not f.get('ok')],
'run_at': ts,
}))
p, t = harness.get('passed', '?'), harness.get('total', '?')
print(f'Harness: {p}/{t}')
except Exception:
print('WARN: could not parse harness output')
# Parse triage counts from stdout
triage_out = '''$TRIAGE_OUT'''
promoted = len(re.findall(r'promoted', triage_out, re.IGNORECASE))
rejected = len(re.findall(r'rejected', triage_out, re.IGNORECASE))
needs_human = len(re.findall(r'needs.human', triage_out, re.IGNORECASE))
# Build summary
summary = {
'run_at': ts,
'harness_passed': harness.get('passed', -1),
'harness_total': harness.get('total', -1),
'triage_promoted': promoted,
'triage_rejected': rejected,
'triage_needs_human': needs_human,
}
post_state('pipeline_last_run', ts)
post_state('pipeline_summary', json.dumps(summary))
print(f'Pipeline summary persisted: {json.dumps(summary)}')
" 2>&1 || {
log "WARN: pipeline summary persistence failed (non-blocking)"
}
# Step G: Integrity check (Phase 4 V1)
log "Step G: integrity check"
python3 "$APP_DIR/scripts/integrity_check.py" \
--base-url "$ATOCORE_URL" \
2>&1 || {
log "WARN: integrity check failed (non-blocking)"
}
# Step H: Pipeline-level alerts — detect conditions that warrant attention
log "Step H: pipeline alerts"
python3 -c "
import json, os, sys, urllib.request
sys.path.insert(0, '$APP_DIR/src')
from atocore.observability.alerts import emit_alert
base = '$ATOCORE_URL'
def get_state(project='atocore'):
try:
req = urllib.request.Request(f'{base}/project/state/{project}')
resp = urllib.request.urlopen(req, timeout=10)
return json.loads(resp.read()).get('entries', [])
except Exception:
return []
def get_dashboard():
try:
req = urllib.request.Request(f'{base}/admin/dashboard')
resp = urllib.request.urlopen(req, timeout=10)
return json.loads(resp.read())
except Exception:
return {}
state = {(e['category'], e['key']): e['value'] for e in get_state()}
dash = get_dashboard()
# Harness regression check
harness_raw = state.get(('status', 'retrieval_harness_result'))
if harness_raw:
try:
h = json.loads(harness_raw)
passed, total = h.get('passed', 0), h.get('total', 0)
if total > 0:
rate = passed / total
if rate < 0.85:
emit_alert('warning', 'Retrieval harness below 85%',
f'Only {passed}/{total} fixtures passing ({rate:.0%}). Failures: {h.get(\"failures\", [])[:5]}',
context={'pass_rate': rate})
except Exception:
pass
# Candidate queue pileup
candidates = dash.get('memories', {}).get('candidates', 0)
if candidates > 200:
emit_alert('warning', 'Candidate queue not draining',
f'{candidates} candidates pending. Auto-triage may be stuck or rate-limited.',
context={'candidates': candidates})
print('pipeline alerts check complete')
" 2>&1 || true
log "=== AtoCore batch extraction + triage complete ===" log "=== AtoCore batch extraction + triage complete ==="

117
deploy/dalidou/graduation-watcher.sh Normal file
View File

@@ -0,0 +1,117 @@
#!/usr/bin/env bash
#
# deploy/dalidou/graduation-watcher.sh
# ------------------------------------
# Host-side watcher for on-demand memory→entity graduation from the web UI.
#
# The /admin/triage page has a "🎓 Graduate memories" button that POSTs
# to /admin/graduation/request with {project, limit}. The container
# writes this to project_state (atocore/config/graduation_requested_at).
#
# This script runs on the Dalidou HOST (where claude CLI lives), polls
# for the flag, and runs graduate_memories.py when seen.
#
# Installed via cron every 2 minutes:
# */2 * * * * /srv/storage/atocore/app/deploy/dalidou/graduation-watcher.sh \
# >> /home/papa/atocore-logs/graduation-watcher.log 2>&1
#
# Safety:
# - Lock file prevents concurrent runs
# - Flag cleared before processing so duplicate clicks queue at most one re-run
# - Fail-open: any error logs but doesn't break the host
set -euo pipefail
ATOCORE_URL="${ATOCORE_URL:-http://127.0.0.1:8100}"
APP_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
LOCK_FILE="/tmp/atocore-graduation.lock"
LOG_DIR="/home/papa/atocore-logs"
mkdir -p "$LOG_DIR"
TS="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
log() { printf '[%s] %s\n' "$TS" "$*"; }
# Fetch the flag via API
STATE_JSON=$(curl -sSf --max-time 5 "$ATOCORE_URL/project/state/atocore" 2>/dev/null || echo "{}")
REQUESTED=$(echo "$STATE_JSON" | python3 -c "
import sys, json
try:
d = json.load(sys.stdin)
for e in d.get('entries', d.get('state', [])):
if e.get('category') == 'config' and e.get('key') == 'graduation_requested_at':
print(e.get('value', ''))
break
except Exception:
pass
" 2>/dev/null || echo "")
if [[ -z "$REQUESTED" ]]; then
exit 0
fi
# Parse JSON: {project, limit, requested_at}
PROJECT=$(echo "$REQUESTED" | python3 -c "import sys,json; d=json.load(sys.stdin) if '{' in sys.stdin.buffer.peek().decode(errors='ignore') else None; print((d or {}).get('project',''))" 2>/dev/null || echo "")
# Fallback: python inline above can be flaky; just re-parse
PROJECT=$(echo "$REQUESTED" | python3 -c "import sys,json; print(json.loads(sys.stdin.read() or '{}').get('project',''))" 2>/dev/null || echo "")
LIMIT=$(echo "$REQUESTED" | python3 -c "import sys,json; print(json.loads(sys.stdin.read() or '{}').get('limit',30))" 2>/dev/null || echo "30")
# Acquire lock
exec 9>"$LOCK_FILE" || exit 0
if ! flock -n 9; then
log "graduation already running, skipping"
exit 0
fi
# Mark running
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"graduation_running\",\"value\":\"1\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"graduation_last_started_at\",\"value\":\"$TS\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
LOG_FILE="$LOG_DIR/graduation-ondemand-$(date -u +%Y%m%d-%H%M%S).log"
log "Starting graduation (project='$PROJECT' limit=$LIMIT, log: $LOG_FILE)"
# Clear the flag BEFORE running so duplicate clicks queue at most one
curl -sSf -X DELETE "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"config\",\"key\":\"graduation_requested_at\"}" \
>/dev/null 2>&1 || true
# Build script args
cd "$APP_DIR"
export PYTHONPATH="$APP_DIR/src:${PYTHONPATH:-}"
ARGS=(--base-url "$ATOCORE_URL" --limit "$LIMIT")
if [[ -n "$PROJECT" ]]; then
ARGS+=(--project "$PROJECT")
fi
if python3 scripts/graduate_memories.py "${ARGS[@]}" >> "$LOG_FILE" 2>&1; then
RESULT=$(tail -3 "$LOG_FILE" | grep "^total:" | tail -1 || tail -1 "$LOG_FILE")
RESULT="${RESULT:-completed}"
log "graduation finished: $RESULT"
else
RESULT="ERROR — see $LOG_FILE"
log "graduation FAILED"
fi
FINISH_TS="$(date -u +%Y-%m-%dT%H:%M:%SZ)"
# Mark done
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"graduation_running\",\"value\":\"0\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"graduation_last_finished_at\",\"value\":\"$FINISH_TS\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true
SAFE_RESULT=$(printf '%s' "$RESULT" | python3 -c "import sys,json; print(json.dumps(sys.stdin.read())[1:-1])")
curl -sSf -X POST "$ATOCORE_URL/project/state" \
-H 'Content-Type: application/json' \
-d "{\"project\":\"atocore\",\"category\":\"status\",\"key\":\"graduation_last_result\",\"value\":\"$SAFE_RESULT\",\"source\":\"host watcher\"}" \
>/dev/null 2>&1 || true

15
deploy/hooks/capture_stop.py
View File

@@ -166,10 +166,19 @@ def _extract_last_user_prompt(transcript_path: str) -> str:
# Project inference from working directory. # Project inference from working directory.
# Maps known repo paths to AtoCore project IDs. The user can extend # Maps known repo paths to AtoCore project IDs. The user can extend
# this table or replace it with a registry lookup later. # this table or replace it with a registry lookup later.
_VAULT = "C:\\Users\\antoi\\antoine\\My Libraries\\Antoine Brain Extension"
_PROJECT_PATH_MAP: dict[str, str] = { _PROJECT_PATH_MAP: dict[str, str] = {
# Add mappings as needed, e.g.: f"{_VAULT}\\2-Projects\\P04-GigaBIT-M1": "p04-gigabit",
# "C:\\Users\\antoi\\gigabit": "p04-gigabit", f"{_VAULT}\\2-Projects\\P10-Interferometer": "p05-interferometer",
# "C:\\Users\\antoi\\interferometer": "p05-interferometer", f"{_VAULT}\\2-Projects\\P11-Polisher-Fullum": "p06-polisher",
f"{_VAULT}\\2-Projects\\P08-ABB-Space-Mirror": "abb-space",
f"{_VAULT}\\2-Projects\\I01-Atomizer": "atomizer-v2",
f"{_VAULT}\\2-Projects\\I02-AtoCore": "atocore",
"C:\\Users\\antoi\\ATOCore": "atocore",
"C:\\Users\\antoi\\Polisher-Sim": "p06-polisher",
"C:\\Users\\antoi\\Fullum-Interferometer": "p05-interferometer",
"C:\\Users\\antoi\\Atomizer-V2": "atomizer-v2",
} }

284
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View File

@@ -0,0 +1,284 @@
# AtoCore Master Brain Plan
> Vision: AtoCore becomes the **single source of truth** that grounds every LLM
> interaction across the entire ecosystem (Claude, OpenClaw, Codex, Ollama, future
> agents). Every prompt is automatically enriched with full project context. The
> brain self-grows from daily work, auto-organizes its metadata, and stays
> flawlessly reliable.
## The Core Insight
AtoCore today is a **well-architected capture + curation system with a critical
gap on the consumption side**. We pour water into the bucket (capture from
Claude Code Stop hook + OpenClaw message hooks) but nothing is drinking from it
at prompt time. Fixing that gap is the single highest-leverage move.
**Once every LLM call is AtoCore-grounded automatically, the feedback loop
closes**: LLMs use the context → produce better responses → those responses
reference the injected memories → reinforcement fires → knowledge curates
itself. The capture side is already working. The pull side is what's missing.
## Universal Consumption Strategy
MCP is great for Claude (Claude Desktop, Claude Code, Cursor, Zed, Windsurf) but
is **not universal**. OpenClaw has its own plugin SDK. Codex, Ollama, and GPT
don't natively support MCP. The right strategy:
**HTTP API is the truth; every client gets the thinnest possible adapter.**
```
┌─────────────────────┐
│ AtoCore HTTP API │ ← canonical interface
│ /context/build │
│ /query │
│ /memory │
│ /project/state │
└──────────┬──────────┘
│
┌────────────┬───────────┼──────────┬────────────┐
│ │ │ │ │
┌──┴───┐ ┌────┴────┐ ┌───┴───┐ ┌───┴────┐ ┌───┴────┐
│ MCP │ │OpenClaw │ │Claude │ │ Codex │ │ Ollama │
│server│ │ plugin │ │ Code │ │ skill │ │ proxy │
│ │ │ (pull) │ │ hook │ │ │ │ │
└──┬───┘ └────┬────┘ └───┬───┘ └────┬───┘ └────┬───┘
│ │ │ │ │
Claude OpenClaw Claude Code Codex CLI Ollama
Desktop, agent local
Cursor, models
Zed,
Windsurf
```
Each adapter's only job: accept a prompt, call AtoCore HTTP, prepend the
returned context pack. The adapter itself carries no logic.
## Three Integration Tiers
### Tier 1: MCP-native clients (Claude ecosystem)
Build **atocore-mcp** — a standalone MCP server that wraps the HTTP API. Exposes:
- `context(query, project)` → context pack
- `search(query)` → raw retrieval
- `remember(type, content, project)` → create candidate memory
- `recall(project, key)` → project state lookup
- `list_projects()` → registered projects
Works with Claude Desktop, Claude Code (via `claude mcp add atocore`), Cursor,
Zed, Windsurf without any per-client work beyond config.
### Tier 2: Custom plugin ecosystems (OpenClaw)
Extend the existing `atocore-capture` plugin on T420 to also register a
**`before_prompt_build`** hook that pulls context from AtoCore and injects it
into the agent's system prompt. The plugin already has the HTTP client, the
authentication, the fail-open pattern. This is ~30 lines of added code.
### Tier 3: Everything else (Codex, Ollama, custom agents)
For clients without plugin/hook systems, ship a **thin proxy/middleware** the
user configures as the LLM endpoint:
- `atocore-proxy` listens on `localhost:PORT`
- Intercepts OpenAI-compatible chat/completion calls
- Pulls context from AtoCore, injects into system prompt
- Forwards to the real model endpoint (OpenAI, Ollama, Anthropic, etc.)
- Returns the response, then captures the interaction back to AtoCore
This makes AtoCore a "drop-in" layer for anything that speaks
OpenAI-compatible HTTP — which is nearly every modern LLM runtime.
## Knowledge Density Plan
The brain is only as smart as what it knows. Current state: 80 active memories
across 6 projects, 324 candidates in the queue being processed. Target:
**1,000+ curated memories** to become a real master brain.
Mechanisms:
1. **Finish the current triage pass** (324 → ~80 more promotions expected).
2. **Re-extract with stronger prompt on existing 236 interactions** — tune the
LLM extractor system prompt to pull more durable facts and fewer ephemeral
snapshots.
3. **Ingest all drive/vault documents as memory candidates** (not just chunks).
Every structured markdown section with a decision/fact/requirement header
becomes a candidate memory.
4. **Multi-source triangulation**: same fact in 3+ sources = auto-promote to
confidence 0.95.
5. **Cross-project synthesis**: facts appearing in multiple project contexts
get promoted to global domain knowledge.
## Auto-Organization of Metadata
Currently: `type`, `project`, `confidence`, `status`, `reference_count`. For
master brain we need more structure, inferred automatically:
| Addition | Purpose | Mechanism |
|---|---|---|
| **Domain tags** (optics, mechanics, firmware, business…) | Cross-cutting retrieval | LLM inference during triage |
| **Temporal scope** (permanent, valid_until_X, transient) | Avoid stale truth | LLM classifies during triage |
| **Source refs** (chunk_id[], interaction_id[]) | Provenance for every fact | Enforced at creation time |
| **Relationships** (contradicts, updates, depends_on) | Memory graph | Triage infers during review |
| **Semantic clusters** | Detect duplicates, find gaps | Weekly HDBSCAN pass on embeddings |
Layer these in progressively — none of them require schema rewrites, just
additional fields and batch jobs.
## Self-Growth Mechanisms
Four loops that make AtoCore grow autonomously:
### 1. Drift detection (nightly)
Compare new chunk embeddings to existing vector distribution. Centroids >X
cosine distance from any existing centroid = new knowledge area. Log to
dashboard; human decides if it's noise or a domain worth curating.
### 2. Gap identification (continuous)
Every `/context/build` logs `query + chunks_returned + memories_returned`.
Weekly report: "top 10 queries with weak coverage." Those are targeted
curation opportunities.
### 3. Multi-source triangulation (weekly)
Scan memory content similarity across sources. When a fact appears in 3+
independent sources (vault doc + drive doc + interaction), auto-promote to
high confidence and mark as "triangulated."
### 4. Active learning prompts (monthly)
Surface "you have 200 p06 memories but only 15 p04 memories. Spend 30 min
curating p04?" via dashboard digest.
## Robustness Strategy (Flawless Operation Bar)
Current: nightly backup, off-host rsync, health endpoint, 303 tests, harness,
enhanced dashboard with pipeline health (this session).
To reach "flawless":
| Gap | Fix | Priority |
|---|---|---|
| Silent pipeline failures | Alerting webhook on harness drop / pipeline skip | P1 |
| Memory mutations untracked | Append-only audit log table | P1 |
| Integrity drift | Nightly FK + vector-chunk parity checks | P1 |
| Schema migrations ad-hoc | Formal migration framework with rollback | P2 |
| Single point of failure | Daily backup to user's main computer (new) | P1 |
| No hot standby | Second instance following primary via WAL | P3 |
| No temporal history | Memory audit + valid_until fields | P2 |
### Daily Backup to Main Computer
Currently: Dalidou → T420 (192.168.86.39) via rsync.
Add: Dalidou → main computer via a pull (main computer runs the rsync,
pulls from Dalidou). Pull-based is simpler than push — no need for SSH
keys on Dalidou to reach the Windows machine.
```bash
# On main computer, daily scheduled task:
rsync -a papa@dalidou:/srv/storage/atocore/backups/snapshots/ \
/path/to/local/atocore-backups/
```
Configure via Windows Task Scheduler or a cron-like runner. Verify weekly
that the latest snapshot is present.
## Human Interface Auto-Evolution
Current: wiki at `/wiki`, regenerates on every request from DB. Synthesis
(the "current state" paragraph at top of project pages) runs **weekly on
Sundays only**. That's why it feels stalled.
Fixes:
1. **Run synthesis daily, not weekly.** It's cheap (one claude call per
project) and keeps the human-readable overview fresh.
2. **Trigger synthesis on major events** — when 5+ new memories land for a
project, regenerate its synthesis.
3. **Add "What's New" feed** — wiki homepage shows recent additions across all
projects (last 7 days of memory promotions, state entries, entities).
4. **Memory timeline view** — project page gets a chronological list of what
we learned when.
## Phased Roadmap (8-10 weeks)
### Phase 1 (week 1-2): Universal Consumption
**Goal: every LLM call is AtoCore-grounded automatically.**
- [ ] Build `atocore-mcp` server (wraps HTTP API, stdio transport)
- [ ] Publish to npm / or run via `pipx` / stdlib HTTP
- [ ] Configure in Claude Desktop (`~/.claude/mcp_servers.json`)
- [ ] Configure in Claude Code (`claude mcp add atocore …`)
- [ ] Extend OpenClaw plugin with `before_prompt_build` PULL
- [ ] Write `atocore-proxy` middleware for Codex/Ollama/generic clients
- [ ] Document configuration for each client
**Success:** open a fresh Claude Code session, ask a project question, verify
the response references AtoCore memories without manual context commands.
### Phase 2 (week 2-3): Knowledge Density + Wiki Evolution
- [ ] Finish current triage pass (324 candidates → active)
- [ ] Tune extractor prompt for higher promotion rate on durable facts
- [ ] Daily synthesis in cron (not just Sundays)
- [ ] Event-triggered synthesis on significant project changes
- [ ] Wiki "What's New" feed
- [ ] Memory timeline per project
**Target:** 300+ active memories, wiki feels alive daily.
### Phase 3 (week 3-4): Auto-Organization
- [ ] Schema: add `domain_tags`, `valid_until`, `source_refs`, `triangulated_count`
- [ ] Triage prompt upgraded: infer tags + temporal scope + relationships
- [ ] Weekly HDBSCAN clustering of embeddings → dup detection + gap reports
- [ ] Relationship edges in a new `memory_relationships` table
### Phase 4 (week 4-5): Robustness Hardening
- [ ] Append-only `memory_audit` table + retrofit mutations
- [ ] Nightly integrity checks (FK validation, orphan detection, parity)
- [ ] Alerting webhook (Discord/email) on pipeline anomalies
- [ ] Daily backup to user's main computer (pull-based)
- [ ] Formal migration framework
### Phase 5 (week 6-7): Engineering V1 Implementation
Execute the 23 acceptance criteria in `docs/architecture/engineering-v1-acceptance.md`
against p06-polisher as the test bed. The ontology and queries are designed;
this phase implements them.
### Phase 6 (week 8-9): Self-Growth Loops
- [ ] Drift detection (nightly)
- [ ] Gap identification from `/context/build` logs
- [ ] Multi-source triangulation
- [ ] Active learning digest (monthly)
- [ ] Cross-project synthesis
### Phase 7 (ongoing): Scale & Polish
- [ ] Multi-model validation (sonnet triages, opus cross-checks on disagreements)
- [ ] AtoDrive integration (Google Drive as trusted source)
- [ ] Hot standby when real production dependence materializes
- [ ] More MCP tools (write-back, memory search, entity queries)
## Success Criteria
AtoCore is a master brain when:
1. **Zero manual context commands.** A fresh Claude/OpenClaw session answering
a project question without being told "use AtoCore context."
2. **1,000+ active memories** with >90% provenance coverage (every fact
traceable to a source).
3. **Every project has a current, human-readable overview** updated within 24h
of significant changes.
4. **Harness stays >95%** across 20+ fixtures covering all active projects.
5. **Zero silent pipeline failures** for 30 consecutive days (all failures
surface via alert within the hour).
6. **Claude on any task knows what we know** — user asks "what did we decide
about X?" and the answer is grounded in AtoCore, not reconstructed from
scratch.
## Where We Are Now (2026-04-16)
- ✅ Core infrastructure: HTTP API, SQLite, Chroma, deploy pipeline
- ✅ Capture pipes: Claude Code Stop hook, OpenClaw message hooks
- ✅ Nightly pipeline: backup, extract, triage, synthesis, lint, harness, summary
- ✅ Phase 10: auto-promotion from reinforcement + candidate expiry
- ✅ Dashboard shows pipeline health + interaction totals + all projects
- ⚡ 324 candidates being triaged (down from 439), ~80 active memories, growing
- ❌ No consumption at prompt time (capture-only)
- ❌ Wiki auto-evolves only on Sundays (synthesis cadence)
- ❌ No MCP adapter
- ❌ No daily backup to main computer
- ❌ Engineering V1 not implemented
- ❌ No alerting on pipeline failures
The path is clear. Phase 1 is the keystone.

91
docs/master-plan-status.md
View File

@@ -33,15 +33,21 @@ read-only additive mode.
at 5% budget ratio. Future identity/preference extraction happens at 5% budget ratio. Future identity/preference extraction happens
organically via the nightly LLM extraction pipeline. organically via the nightly LLM extraction pipeline.
- Phase 8 - OpenClaw Integration. As of 2026-04-12 the T420 OpenClaw - Phase 8 - OpenClaw Integration (baseline only, not primary surface).
helper (`t420-openclaw/atocore.py`) is verified end-to-end against As of 2026-04-15 the T420 OpenClaw helper (`t420-openclaw/atocore.py`)
live Dalidou: health check, auto-context with project detection, is verified end-to-end against live Dalidou: health check, auto-context
Trusted Project State surfacing, project-memory band, fail-open on with project detection, Trusted Project State surfacing, project-memory
unreachable host. Tested from both the development machine and the band, fail-open on unreachable host. Tested from both the development
T420 via SSH. The helper covers 15 of the 33 API endpoints — the machine and the T420 via SSH. Scope is narrow: **14 request shapes
excluded endpoints (memory management, interactions, backup) are against ~44 server routes**, predominantly read-oriented plus
correctly scoped to the operator client (`scripts/atocore_client.py`) `POST/DELETE /project/state` and `POST /ingest/sources`. Memory
per the read-only additive integration model. management, interactions capture (covered separately by the OpenClaw
capture plugin), admin/backup, entities, triage, and extraction write
paths remain out of this client's surface by design — they are scoped
to the operator client (`scripts/atocore_client.py`) per the
read-heavy additive integration model. "Primary integration" is
therefore overclaim; "baseline read + project-state write helper" is
the accurate framing.
### Baseline Complete ### Baseline Complete
@@ -120,25 +126,29 @@ This sits implicitly between Phase 8 (OpenClaw) and Phase 11
(multi-model). Memory-review and engineering-entity commands are (multi-model). Memory-review and engineering-entity commands are
deferred from the shared client until their workflows are exercised. deferred from the shared client until their workflows are exercised.
## What Is Real Today (updated 2026-04-12) ## What Is Real Today (updated 2026-04-16)
- canonical AtoCore runtime on Dalidou (build_sha tracked, deploy.sh verified) - canonical AtoCore runtime on Dalidou (`775960c`, deploy.sh verified)
- 33,253 vectors across 5 registered projects - 33,253 vectors across 6 registered projects
- project registry with template, proposal, register, update, refresh - 234 captured interactions (192 claude-code, 38 openclaw, 4 test)
- 5 registered projects: - 6 registered projects:
- `p04-gigabit` (483 docs, 5 state entries) - `p04-gigabit` (483 docs, 15 state entries)
- `p05-interferometer` (109 docs, 9 state entries) - `p05-interferometer` (109 docs, 18 state entries)
- `p06-polisher` (564 docs, 9 state entries) - `p06-polisher` (564 docs, 19 state entries)
- `atomizer-v2` (568 docs, newly ingested 2026-04-12) - `atomizer-v2` (568 docs, 5 state entries)
- `atocore` (drive source, 38 state entries) - `abb-space` (6 state entries)
- 47 active memories (16 project, 16 knowledge, 6 adaptation, 3 identity, 3 preference, 3 episodic) - `atocore` (drive source, 47 state entries)
- 110 Trusted Project State entries across all projects (decisions, requirements, facts, contacts, milestones)
- 84 active memories (31 project, 23 knowledge, 10 episodic, 8 adaptation, 7 preference, 5 identity)
- context pack assembly with 4 tiers: Trusted Project State > identity/preference > project memories > retrieved chunks - context pack assembly with 4 tiers: Trusted Project State > identity/preference > project memories > retrieved chunks
- query-relevance memory ranking with overlap-density scoring - query-relevance memory ranking with overlap-density scoring
- retrieval eval harness: 18 fixtures, 17/18 passing - retrieval eval harness: 18 fixtures, 17/18 passing on live
- 290 tests passing - 303 tests passing
- nightly pipeline: backup → cleanup → rsync → LLM extraction (sonnet) → auto-triage - nightly pipeline: backup → cleanup → rsync → OpenClaw import → vault refresh → extract → triage → **auto-promote/expire** → weekly synth/lint → **retrieval harness** → **pipeline summary to project state**
- Phase 10 operational: reinforcement-based auto-promotion (ref_count ≥ 3, confidence ≥ 0.7) + stale candidate expiry (14 days unreinforced)
- pipeline health visible in dashboard: interaction totals by client, pipeline last_run, harness results, triage stats
- off-host backup to clawdbot (T420) via rsync - off-host backup to clawdbot (T420) via rsync
- both Claude Code and OpenClaw capture interactions to AtoCore - both Claude Code and OpenClaw capture interactions to AtoCore (OpenClaw via `before_agent_start` + `llm_output` plugin, verified live)
- DEV-LEDGER.md as shared operating memory between Claude and Codex - DEV-LEDGER.md as shared operating memory between Claude and Codex
- observability dashboard at GET /admin/dashboard - observability dashboard at GET /admin/dashboard
@@ -146,26 +156,28 @@ deferred from the shared client until their workflows are exercised.
These are the current practical priorities. These are the current practical priorities.
1. **Observe and stabilize** — let the nightly pipeline run for a week, 1. **Observe the enhanced pipeline** — let the nightly pipeline run for a
check the dashboard daily, verify memories accumulate correctly week with the new harness + summary + auto-promote steps. Check the
from organic Claude Code and OpenClaw use dashboard daily. Verify pipeline summary populates correctly.
2. **Multi-model triage** (Phase 11 entry) — switch auto-triage to a 2. **Knowledge density** — run batch extraction over the full 234
interactions (`--since 2026-01-01`) to mine the backlog for knowledge.
Target: 100+ active memories.
3. **Multi-model triage** (Phase 11 entry) — switch auto-triage to a
different model than the extractor for independent validation different model than the extractor for independent validation
3. **Automated eval in cron** (Phase 12 entry) — add retrieval harness 4. **Fix p04-constraints harness failure** — retrieval doesn't surface
to the nightly cron so regressions are caught automatically "Zerodur" for p04 constraint queries. Investigate if it's a missing
4. **Atomizer-v2 state entries** — curate Trusted Project State for the memory or retrieval ranking issue.
newly ingested Atomizer knowledge base
## Next ## Next
These are the next major layers after the current stabilization pass. These are the next major layers after the current stabilization pass.
1. Phase 10 Write-back — confidence-based auto-promotion from 1. Phase 6 AtoDrive — clarify Google Drive as a trusted operational
reinforcement signal (a memory reinforced N times auto-promotes)
2. Phase 6 AtoDrive — clarify Google Drive as a trusted operational
source and ingest from it source and ingest from it
3. Phase 13 Hardening — Chroma backup policy, monitoring, alerting, 2. Phase 13 Hardening — Chroma backup policy, monitoring, alerting,
failure visibility beyond log files failure visibility beyond log files
3. Engineering V1 implementation sprint — once knowledge density is
sufficient and the pipeline feels boring and dependable
## Later ## Later
@@ -187,9 +199,10 @@ These remain intentionally deferred.
plugin now exists (`openclaw-plugins/atocore-capture/`), interactions plugin now exists (`openclaw-plugins/atocore-capture/`), interactions
flow. Write-back of promoted memories back to OpenClaw's own memory flow. Write-back of promoted memories back to OpenClaw's own memory
system is still deferred. system is still deferred.
- ~~automatic memory promotion~~ — auto-triage now handles promote/reject - ~~automatic memory promotion~~ — Phase 10 complete: auto-triage handles
for extraction candidates. Reinforcement-based auto-promotion extraction candidates, reinforcement-based auto-promotion graduates
(Phase 10) is the remaining piece. candidates referenced 3+ times to active, stale candidates expire
after 14 days unreinforced.
- ~~reflection loop integration~~ — fully operational: capture (both - ~~reflection loop integration~~ — fully operational: capture (both
clients) → reinforce (automatic) → extract (nightly cron, sonnet) → clients) → reinforce (automatic) → extract (nightly cron, sonnet) →
auto-triage (nightly, sonnet) → only needs_human reaches the user. auto-triage (nightly, sonnet) → only needs_human reaches the user.

274
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@@ -0,0 +1,274 @@
# Universal Consumption — Connecting LLM Clients to AtoCore
Phase 1 of the Master Brain plan. Every LLM interaction across the ecosystem
pulls context from AtoCore automatically, without the user or agent having
to remember to ask for it.
## Architecture
```
┌─────────────────────┐
│ AtoCore HTTP API │ ← single source of truth
│ http://dalidou:8100│
└──────────┬──────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
┌───┴────┐ ┌─────┴────┐ ┌────┴────┐
│ MCP │ │ OpenClaw │ │ HTTP │
│ server │ │ plugin │ │ proxy │
└───┬────┘ └──────┬───┘ └────┬────┘
│ │ │
Claude/Cursor/ OpenClaw Codex/Ollama/
Zed/Windsurf any OpenAI-compat client
```
Three adapters, one HTTP backend. Each adapter is a thin passthrough — no
business logic duplicated.
---
## Adapter 1: MCP Server (Claude Desktop, Claude Code, Cursor, Zed, Windsurf)
The MCP server is `scripts/atocore_mcp.py` — stdlib-only Python, stdio
transport, wraps the HTTP API. Claude-family clients see AtoCore as built-in
tools just like `Read` or `Bash`.
### Tools exposed
- **`atocore_context`** (most important): Full context pack for a query —
Trusted Project State + memories + retrieved chunks. Use at the start of
any project-related conversation to ground it.
- **`atocore_search`**: Semantic search over ingested documents (top-K chunks).
- **`atocore_memory_list`**: List active memories, filterable by project + type.
- **`atocore_memory_create`**: Propose a candidate memory (enters triage queue).
- **`atocore_project_state`**: Get Trusted Project State entries by category.
- **`atocore_projects`**: List registered projects + aliases.
- **`atocore_health`**: Service status check.
### Registration
#### Claude Code (CLI)
```bash
claude mcp add atocore -- python C:/Users/antoi/ATOCore/scripts/atocore_mcp.py
claude mcp list # verify: "atocore ... ✓ Connected"
```
#### Claude Desktop (GUI)
Edit `~/Library/Application Support/Claude/claude_desktop_config.json`
(macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows):
```json
{
"mcpServers": {
"atocore": {
"command": "python",
"args": ["C:/Users/antoi/ATOCore/scripts/atocore_mcp.py"],
"env": {
"ATOCORE_URL": "http://dalidou:8100"
}
}
}
}
```
Restart Claude Desktop.
#### Cursor / Zed / Windsurf
Similar JSON config in each tool's MCP settings. Consult their docs —
the config schema is standard MCP.
### Configuration
Environment variables the MCP server honors:
| Var | Default | Purpose |
|---|---|---|
| `ATOCORE_URL` | `http://dalidou:8100` | Where to reach AtoCore |
| `ATOCORE_TIMEOUT` | `10` | Per-request HTTP timeout (seconds) |
### Behavior
- Fail-open: if Dalidou is unreachable, tools return "AtoCore unavailable"
error messages but don't crash the client.
- Zero business logic: every tool is a direct HTTP passthrough.
- stdlib only: no MCP SDK dependency.
---
## Adapter 2: OpenClaw Plugin (`openclaw-plugins/atocore-capture/handler.js`)
The plugin on T420 OpenClaw has two responsibilities:
1. **CAPTURE**: On `before_agent_start` + `llm_output`, POST completed turns
to AtoCore `/interactions` (existing).
2. **PULL**: On `before_prompt_build`, call `/context/build` and inject the
context pack via `prependContext` so the agent's system prompt includes
AtoCore knowledge.
### Deployment
The plugin is loaded from
`/tmp/atocore-openclaw-capture-plugin/openclaw-plugins/atocore-capture/`
on the T420 (per OpenClaw's plugin config at `~/.openclaw/openclaw.json`).
To update:
```bash
scp openclaw-plugins/atocore-capture/handler.js \
papa@192.168.86.39:/tmp/atocore-openclaw-capture-plugin/openclaw-plugins/atocore-capture/index.js
ssh papa@192.168.86.39 'systemctl --user restart openclaw-gateway'
```
Verify in gateway logs: look for "ready (7 plugins: acpx, atocore-capture, ...)"
### Configuration (env vars set on T420)
| Var | Default | Purpose |
|---|---|---|
| `ATOCORE_BASE_URL` | `http://dalidou:8100` | AtoCore HTTP endpoint |
| `ATOCORE_PULL_DISABLED` | (unset) | Set to `1` to disable context pull |
### Behavior
- Fail-open: AtoCore unreachable = no injection, no capture, agent runs
normally.
- 6s timeout on context pull, 10s on capture — won't stall the agent.
- Context pack prepended as a clearly-bracketed block so the agent can see
it's auto-injected grounding info.
---
## Adapter 3: HTTP Proxy (`scripts/atocore_proxy.py`)
A stdlib-only OpenAI-compatible HTTP proxy. Sits between any
OpenAI-API-speaking client and the real provider, enriches every
`/chat/completions` request with AtoCore context.
Works with:
- **Codex CLI** (OpenAI-compatible endpoint)
- **Ollama** (has OpenAI-compatible `/v1` endpoint since 0.1.24)
- **LiteLLM**, **llama.cpp server**, custom agents
- Anything that can be pointed at a custom base URL
### Start it
```bash
# For Ollama (local models):
ATOCORE_UPSTREAM=http://localhost:11434/v1 \
python scripts/atocore_proxy.py
# For OpenAI cloud:
ATOCORE_UPSTREAM=https://api.openai.com/v1 \
ATOCORE_CLIENT_LABEL=codex \
python scripts/atocore_proxy.py
# Test:
curl http://127.0.0.1:11435/healthz
```
### Point a client at it
Set the client's OpenAI base URL to `http://127.0.0.1:11435/v1`.
#### Ollama example:
```bash
OPENAI_BASE_URL=http://127.0.0.1:11435/v1 \
some-openai-client --model llama3:8b
```
#### Codex CLI:
Set `OPENAI_BASE_URL=http://127.0.0.1:11435/v1` in your codex config.
### Configuration
| Var | Default | Purpose |
|---|---|---|
| `ATOCORE_URL` | `http://dalidou:8100` | AtoCore HTTP endpoint |
| `ATOCORE_UPSTREAM` | (required) | Real provider base URL |
| `ATOCORE_PROXY_PORT` | `11435` | Proxy listen port |
| `ATOCORE_PROXY_HOST` | `127.0.0.1` | Proxy bind address |
| `ATOCORE_CLIENT_LABEL` | `proxy` | Client id in captures |
| `ATOCORE_INJECT` | `1` | Inject context (set `0` to disable) |
| `ATOCORE_CAPTURE` | `1` | Capture interactions (set `0` to disable) |
### Behavior
- GET requests (model listing etc) pass through unchanged
- POST to `/chat/completions` (or `/v1/chat/completions`) gets enriched:
1. Last user message extracted as query
2. AtoCore `/context/build` called with 6s timeout
3. Pack injected as system message (or prepended to existing system)
4. Enriched body forwarded to upstream
5. After success, interaction POSTed to `/interactions` in background
- Fail-open: AtoCore unreachable = pass through without injection
- Streaming responses: currently buffered (not true stream). Good enough for
most cases; can be upgraded later if needed.
### Running as a service
On Linux, create `~/.config/systemd/user/atocore-proxy.service`:
```ini
[Unit]
Description=AtoCore HTTP proxy
[Service]
Environment=ATOCORE_UPSTREAM=http://localhost:11434/v1
Environment=ATOCORE_CLIENT_LABEL=ollama
ExecStart=/usr/bin/python3 /path/to/scripts/atocore_proxy.py
Restart=on-failure
[Install]
WantedBy=default.target
```
Then: `systemctl --user enable --now atocore-proxy`
On Windows, register via Task Scheduler (similar pattern to backup task)
or use NSSM to install as a service.
---
## Verification Checklist
Fresh end-to-end test to confirm Phase 1 is working:
### For Claude Code (MCP)
1. Open a new Claude Code session (not this one).
2. Ask: "what do we know about p06 polisher's control architecture?"
3. Claude should invoke `atocore_context` or `atocore_project_state`
on its own and answer grounded in AtoCore data.
### For OpenClaw (plugin pull)
1. Send a Discord message to OpenClaw: "what's the status on p04?"
2. Check T420 logs: `journalctl --user -u openclaw-gateway --since "1 min ago" | grep atocore-pull`
3. Expect: `atocore-pull:injected project=p04-gigabit chars=NNN`
### For proxy (any OpenAI-compat client)
1. Start proxy with appropriate upstream
2. Run a client query through it
3. Check stderr: `[atocore-proxy] inject: project=... chars=...`
4. Check `curl http://127.0.0.1:8100/interactions?client=proxy` — should
show the captured turn
---
## Why not just MCP everywhere?
MCP is great for Claude-family clients but:
- Not supported natively by Codex CLI, Ollama, or OpenAI's own API
- No universal "attach MCP" mechanism in all LLM runtimes
- HTTP APIs are truly universal
HTTP API is the truth, each adapter is the thinnest possible shim for its
ecosystem. When new adapters are needed (Gemini CLI, Claude Code plugin
system, etc.), they follow the same pattern.
---
## Future enhancements
- **Streaming passthrough** in the proxy (currently buffered for simplicity)
- **Response grounding check**: parse assistant output for references to
injected context, count reinforcement events
- **Per-client metrics** in the dashboard: how often each client pulls,
context pack size, injection rate
- **Smart project detection**: today we use keyword matching; could use
AtoCore's own project resolver endpoint

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# Windows Main-Computer Backup Setup
The AtoCore backup pipeline runs nightly on Dalidou and already pushes snapshots
off-host to the T420 (`papa@192.168.86.39`). This doc sets up a **second**,
pull-based daily backup to your Windows main computer at
`C:\Users\antoi\Documents\ATOCore_Backups\`.
Pull-based means the Windows machine pulls from Dalidou. This is simpler than
push because Dalidou doesn't need SSH keys to reach Windows, and the backup
only runs when the Windows machine is powered on and can reach Dalidou.
## Prerequisites
- Windows 10/11 with OpenSSH client (built-in since Win10 1809)
- SSH key-based auth to `papa@dalidou` already working (you're using it today)
- `C:\Users\antoi\ATOCore\scripts\windows\atocore-backup-pull.ps1` present
## Test the script manually
```powershell
powershell.exe -ExecutionPolicy Bypass -File `
C:\Users\antoi\ATOCore\scripts\windows\atocore-backup-pull.ps1
```
Expected output:
```
[timestamp] === AtoCore backup pull starting ===
[timestamp] Dalidou reachable.
[timestamp] Pulling snapshots via scp...
[timestamp] Pulled N snapshots successfully (total X MB, latest: ...)
[timestamp] === backup complete ===
```
Target directory: `C:\Users\antoi\Documents\ATOCore_Backups\snapshots\`
Logs: `C:\Users\antoi\Documents\ATOCore_Backups\_logs\backup-*.log`
## Register the Task Scheduler task
### Option A — automatic registration (recommended)
Run this PowerShell command **as your user** (no admin needed — uses HKCU task):
```powershell
$action = New-ScheduledTaskAction -Execute 'powershell.exe' `
-Argument '-ExecutionPolicy Bypass -NonInteractive -WindowStyle Hidden -File C:\Users\antoi\ATOCore\scripts\windows\atocore-backup-pull.ps1'
# Run daily at 10:00 local time; if missed (computer off), run at next logon
$trigger = New-ScheduledTaskTrigger -Daily -At 10:00AM
$trigger.StartBoundary = (Get-Date -Format 'yyyy-MM-ddTHH:mm:ss')
$settings = New-ScheduledTaskSettingsSet `
-AllowStartIfOnBatteries `
-DontStopIfGoingOnBatteries `
-StartWhenAvailable `
-ExecutionTimeLimit (New-TimeSpan -Minutes 10) `
-RestartCount 2 `
-RestartInterval (New-TimeSpan -Minutes 30)
Register-ScheduledTask -TaskName 'AtoCore Backup Pull' `
-Description 'Daily pull of AtoCore backup snapshots from Dalidou' `
-Action $action -Trigger $trigger -Settings $settings `
-User $env:USERNAME
```
Key settings:
- `-StartWhenAvailable`: if the computer was off at 10:00, run as soon as it
comes online
- `-AllowStartIfOnBatteries`: works on laptop battery too
- `-ExecutionTimeLimit 10min`: kill hung tasks
- `-RestartCount 2`: retry twice if it fails (Dalidou temporarily unreachable)
### Option B -- Task Scheduler GUI
1. Open Task Scheduler (`taskschd.msc`)
2. Create Basic Task -> name: `AtoCore Backup Pull`
3. Trigger: Daily, 10:00 AM, recur every 1 day
4. Action: Start a program
- Program: `powershell.exe`
- Arguments: `-ExecutionPolicy Bypass -NonInteractive -WindowStyle Hidden -File "C:\Users\antoi\ATOCore\scripts\windows\atocore-backup-pull.ps1"`
5. Finish, then edit the task:
- Settings tab: check "Run task as soon as possible after a scheduled start is missed"
- Settings tab: "If the task fails, restart every 30 minutes, up to 2 times"
- Conditions tab: uncheck "Start only if computer is on AC power" (if you want it on battery)
## Verify
After the first scheduled run:
```powershell
# Most recent log
Get-ChildItem C:\Users\antoi\Documents\ATOCore_Backups\_logs\ |
Sort-Object Name -Descending |
Select-Object -First 1 |
Get-Content
# Latest snapshot present?
Get-ChildItem C:\Users\antoi\Documents\ATOCore_Backups\snapshots\ |
Sort-Object Name -Descending |
Select-Object -First 3
```
## Unregister (if needed)
```powershell
Unregister-ScheduledTask -TaskName 'AtoCore Backup Pull' -Confirm:$false
```
## How it behaves
- **Computer on, Dalidou reachable**: pulls latest snapshots silently in ~15s
- **Computer on, Dalidou unreachable** (remote work, network down): fail-open,
exits without error, logs "Dalidou unreachable"
- **Computer off at scheduled time**: Task Scheduler runs it as soon as the
computer wakes up
- **Many days off**: one run catches up; scp only transfers files not already
present (snapshots are date-stamped directories, idempotent overwrites)
## What gets backed up
The snapshots tree contains:
- `YYYYMMDDTHHMMSSZ/config/` — project registry, AtoCore config
- `YYYYMMDDTHHMMSSZ/db/` — SQLite snapshot of all memory, state, interactions
- `YYYYMMDDTHHMMSSZ/backup-metadata.json` — SHA, timestamp, source info
Chroma vectors are **not** in the snapshot by default
(`ATOCORE_BACKUP_CHROMA=false` on Dalidou). They can be rebuilt from the
source documents if lost. To include them, set `ATOCORE_BACKUP_CHROMA=true`
in the Dalidou cron environment.
## Three-tier backup summary
After this setup:
| Tier | Location | Cadence | Purpose |
|---|---|---|---|
| Live | Dalidou `/srv/storage/atocore/backups/snapshots/` | Nightly 03:00 UTC | Fast restore |
| Off-host | T420 `papa@192.168.86.39:/home/papa/atocore-backups/` | Nightly after Dalidou | Dalidou dies |
| User machine | `C:\Users\antoi\Documents\ATOCore_Backups\` | Daily 10:00 local | Full home-network failure |
Three independent copies. Any two can be lost simultaneously without data loss.

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/**
* AtoCore OpenClaw plugin — capture + pull.
*
* Two responsibilities:
*
* 1. CAPTURE (existing): On before_agent_start, buffer the user prompt.
* On llm_output, POST prompt+response to AtoCore /interactions.
* This is the "write" side — OpenClaw turns feed AtoCore's memory.
*
* 2. PULL (Phase 1 master brain): On before_prompt_build, call AtoCore
* /context/build and inject the returned context via prependContext.
* Every OpenClaw response is automatically grounded in what AtoCore
* knows (project state, memories, relevant chunks).
*
* Fail-open throughout: AtoCore unreachable = no injection, no capture,
* never blocks the agent.
*/
import { definePluginEntry } from "openclaw/plugin-sdk/core";
const BASE_URL = process.env.ATOCORE_BASE_URL || "http://dalidou:8100";
const MIN_LEN = 15;
const MAX_RESP = 50000;
const CONTEXT_TIMEOUT_MS = 6000;
const CAPTURE_TIMEOUT_MS = 10000;
function trim(v) { return typeof v === "string" ? v.trim() : ""; }
function trunc(t, m) { return !t || t.length <= m ? t : t.slice(0, m) + "\n\n[truncated]"; }
function detectProject(prompt) {
const lower = (prompt || "").toLowerCase();
const hints = [
["p04", "p04-gigabit"],
["gigabit", "p04-gigabit"],
["p05", "p05-interferometer"],
["interferometer", "p05-interferometer"],
["p06", "p06-polisher"],
["polisher", "p06-polisher"],
["fullum", "p06-polisher"],
["abb", "abb-space"],
["atomizer", "atomizer-v2"],
["atocore", "atocore"],
];
for (const [token, proj] of hints) {
if (lower.includes(token)) return proj;
}
return "";
}
export default definePluginEntry({
register(api) {
const log = api.logger;
let lastPrompt = null;
// --- PULL: inject AtoCore context into every prompt ---
api.on("before_prompt_build", async (event, ctx) => {
if (process.env.ATOCORE_PULL_DISABLED === "1") return;
const prompt = trim(event?.prompt || "");
if (prompt.length < MIN_LEN) return;
const project = detectProject(prompt);
try {
const res = await fetch(BASE_URL.replace(/\/$/, "") + "/context/build", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ prompt, project }),
signal: AbortSignal.timeout(CONTEXT_TIMEOUT_MS),
});
if (!res.ok) {
log.info("atocore-pull:http_error", { status: res.status });
return;
}
const data = await res.json();
const contextPack = data.formatted_context || "";
if (!contextPack.trim()) return;
log.info("atocore-pull:injected", {
project: project || "(none)",
chars: contextPack.length,
});
return {
prependContext:
"--- AtoCore Context (auto-injected) ---\n" +
contextPack +
"\n--- End AtoCore Context ---\n",
};
} catch (err) {
log.info("atocore-pull:error", { error: String(err).slice(0, 200) });
}
});
// --- CAPTURE: buffer user prompts on agent start ---
api.on("before_agent_start", async (event, ctx) => {
const prompt = trim(event?.prompt || event?.cleanedBody || "");
if (prompt.length < MIN_LEN || prompt.startsWith("<")) {
lastPrompt = null;
return;
}
// Filter cron-initiated agent runs. OpenClaw's scheduled tasks fire
// agent sessions with prompts that begin "[cron:<id> ...]". These are
// automated polls (DXF email watcher, calendar reminders, etc.), not
// real user turns — they're pure noise in the AtoCore capture stream.
if (prompt.startsWith("[cron:")) {
lastPrompt = null;
return;
}
lastPrompt = { text: prompt, sessionKey: ctx?.sessionKey || "", ts: Date.now() };
log.info("atocore-capture:prompt_buffered", { len: prompt.length });
});
// --- CAPTURE: send completed turns to AtoCore ---
api.on("llm_output", async (event, ctx) => {
if (!lastPrompt) return;
const texts = Array.isArray(event?.assistantTexts) ? event.assistantTexts : [];
const response = trunc(trim(texts.join("\n\n")), MAX_RESP);
if (!response) return;
const prompt = lastPrompt.text;
const sessionKey = lastPrompt.sessionKey || ctx?.sessionKey || "";
const project = detectProject(prompt);
lastPrompt = null;
log.info("atocore-capture:posting", {
promptLen: prompt.length,
responseLen: response.length,
project: project || "(none)",
});
fetch(BASE_URL.replace(/\/$/, "") + "/interactions", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({
prompt,
response,
client: "openclaw",
session_id: sessionKey,
project,
reinforce: true,
}),
signal: AbortSignal.timeout(CAPTURE_TIMEOUT_MS),
}).then(res => {
log.info("atocore-capture:posted", { status: res.status });
}).catch(err => {
log.warn("atocore-capture:post_error", { error: String(err).slice(0, 200) });
});
});
api.on("session_end", async () => {
lastPrompt = null;
});
}
});

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#!/usr/bin/env python3
"""AtoCore MCP server — stdio transport, stdlib-only.
Exposes the AtoCore HTTP API as MCP tools so any MCP-aware client
(Claude Desktop, Claude Code, Cursor, Zed, Windsurf) can pull
context + memories automatically at prompt time.
Design:
- stdlib only (no mcp SDK dep) — MCP protocol is simple JSON-RPC
over stdio, and AtoCore's philosophy prefers stdlib.
- Thin wrapper: every tool is a direct pass-through to an HTTP
endpoint. Zero business logic here — the AtoCore server is
the single source of truth.
- Fail-open: if AtoCore is unreachable, tools return a graceful
"unavailable" message rather than crashing the client.
Protocol: MCP 2024-11-05 / 2025-03-26 compatible
https://spec.modelcontextprotocol.io/specification/
Usage (standalone test):
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"0"}}}' | python atocore_mcp.py
Register with Claude Code:
claude mcp add atocore -- python /path/to/atocore_mcp.py
Environment:
ATOCORE_URL base URL of the AtoCore HTTP API (default http://dalidou:8100)
ATOCORE_TIMEOUT per-request HTTP timeout seconds (default 10)
"""
from __future__ import annotations
import json
import os
import sys
import urllib.error
import urllib.parse
import urllib.request
# Force UTF-8 on stdio — MCP protocol expects UTF-8 but Windows Python
# defaults stdout to cp1252, which crashes on any non-ASCII char (emojis,
# ≥, →, etc.) in tool responses. This call is a no-op on Linux/macOS
# where UTF-8 is already the default.
try:
sys.stdin.reconfigure(encoding="utf-8")
sys.stdout.reconfigure(encoding="utf-8")
sys.stderr.reconfigure(encoding="utf-8")
except Exception:
pass
# --- Configuration ---
ATOCORE_URL = os.environ.get("ATOCORE_URL", "http://dalidou:8100").rstrip("/")
HTTP_TIMEOUT = float(os.environ.get("ATOCORE_TIMEOUT", "10"))
SERVER_NAME = "atocore"
SERVER_VERSION = "0.1.0"
PROTOCOL_VERSION = "2024-11-05"
# --- stderr logging (stdout is reserved for JSON-RPC) ---
def log(msg: str) -> None:
print(f"[atocore-mcp] {msg}", file=sys.stderr, flush=True)
# --- HTTP helpers ---
def http_get(path: str, params: dict | None = None) -> dict:
"""GET a JSON response from AtoCore. Raises on HTTP error."""
url = ATOCORE_URL + path
if params:
# Drop empty params so the URL stays clean
clean = {k: v for k, v in params.items() if v not in (None, "", [], {})}
if clean:
url += "?" + urllib.parse.urlencode(clean)
req = urllib.request.Request(url, headers={"Accept": "application/json"})
with urllib.request.urlopen(req, timeout=HTTP_TIMEOUT) as resp:
return json.loads(resp.read().decode("utf-8"))
def http_post(path: str, body: dict) -> dict:
url = ATOCORE_URL + path
data = json.dumps(body).encode("utf-8")
req = urllib.request.Request(
url, data=data, method="POST",
headers={"Content-Type": "application/json", "Accept": "application/json"},
)
with urllib.request.urlopen(req, timeout=HTTP_TIMEOUT) as resp:
return json.loads(resp.read().decode("utf-8"))
def safe_call(fn, *args, **kwargs) -> tuple[dict | None, str | None]:
"""Run an HTTP call, return (result, error_message_or_None)."""
try:
return fn(*args, **kwargs), None
except urllib.error.HTTPError as e:
try:
body = e.read().decode("utf-8", errors="replace")
except Exception:
body = ""
return None, f"AtoCore HTTP {e.code}: {body[:200]}"
except urllib.error.URLError as e:
return None, f"AtoCore unreachable at {ATOCORE_URL}: {e.reason}"
except Exception as e:
return None, f"AtoCore error: {type(e).__name__}: {str(e)[:200]}"
# --- Tool definitions ---
# Each tool: name, description, inputSchema (JSON Schema), handler
def _tool_context(args: dict) -> str:
"""Build a full context pack for a query — state + memories + retrieved chunks."""
query = (args.get("query") or "").strip()
project = args.get("project") or ""
if not query:
return "Error: 'query' is required."
result, err = safe_call(http_post, "/context/build", {
"prompt": query, "project": project,
})
if err:
return f"AtoCore context unavailable: {err}"
pack = result.get("formatted_context", "") or ""
if not pack.strip():
return "(AtoCore returned an empty context pack — no matching state, memories, or chunks.)"
return pack
def _tool_search(args: dict) -> str:
"""Retrieval only — raw chunks ranked by semantic similarity."""
query = (args.get("query") or "").strip()
project = args.get("project") or ""
top_k = int(args.get("top_k") or 5)
if not query:
return "Error: 'query' is required."
result, err = safe_call(http_post, "/query", {
"prompt": query, "project": project, "top_k": top_k,
})
if err:
return f"AtoCore search unavailable: {err}"
chunks = result.get("results", []) or []
if not chunks:
return "No results."
lines = []
for i, c in enumerate(chunks, 1):
src = c.get("source_file") or c.get("title") or "unknown"
heading = c.get("heading_path") or ""
snippet = (c.get("content") or "")[:300]
score = c.get("score", 0.0)
head_str = f" ({heading})" if heading else ""
lines.append(f"[{i}] score={score:.3f} source={src}{head_str}\n{snippet}")
return "\n\n".join(lines)
def _tool_memory_list(args: dict) -> str:
"""List active memories, optionally filtered by project and type."""
params = {
"status": "active",
"limit": int(args.get("limit") or 20),
}
if args.get("project"):
params["project"] = args["project"]
if args.get("memory_type"):
params["memory_type"] = args["memory_type"]
result, err = safe_call(http_get, "/memory", params=params)
if err:
return f"AtoCore memory list unavailable: {err}"
memories = result.get("memories", []) or []
if not memories:
return "No memories match."
lines = []
for m in memories:
mt = m.get("memory_type", "?")
proj = m.get("project") or "(global)"
conf = m.get("confidence", 0.0)
refs = m.get("reference_count", 0)
content = (m.get("content") or "")[:250]
lines.append(f"[{mt}/{proj}] conf={conf:.2f} refs={refs}\n {content}")
return "\n\n".join(lines)
def _tool_memory_create(args: dict) -> str:
"""Create a candidate memory (enters the triage queue)."""
memory_type = (args.get("memory_type") or "").strip()
content = (args.get("content") or "").strip()
project = args.get("project") or ""
confidence = float(args.get("confidence") or 0.5)
if not memory_type or not content:
return "Error: 'memory_type' and 'content' are required."
valid_types = ["identity", "preference", "project", "episodic", "knowledge", "adaptation"]
if memory_type not in valid_types:
return f"Error: memory_type must be one of {valid_types}."
result, err = safe_call(http_post, "/memory", {
"memory_type": memory_type,
"content": content,
"project": project,
"confidence": confidence,
"status": "candidate",
})
if err:
return f"AtoCore memory create failed: {err}"
mid = result.get("id", "?")
return f"Candidate memory created: id={mid} type={memory_type} project={project or '(global)'}"
def _tool_project_state(args: dict) -> str:
"""Get Trusted Project State entries for a project."""
project = (args.get("project") or "").strip()
category = args.get("category") or ""
if not project:
return "Error: 'project' is required."
path = f"/project/state/{urllib.parse.quote(project)}"
params = {"category": category} if category else None
result, err = safe_call(http_get, path, params=params)
if err:
return f"AtoCore project state unavailable: {err}"
entries = result.get("entries", []) or result.get("state", []) or []
if not entries:
return f"No state entries for project '{project}'."
lines = []
for e in entries:
cat = e.get("category", "?")
key = e.get("key", "?")
value = (e.get("value") or "")[:300]
src = e.get("source") or ""
lines.append(f"[{cat}/{key}] (source: {src})\n {value}")
return "\n\n".join(lines)
def _tool_projects(args: dict) -> str:
"""List registered AtoCore projects."""
result, err = safe_call(http_get, "/projects")
if err:
return f"AtoCore projects unavailable: {err}"
projects = result.get("projects", []) or []
if not projects:
return "No projects registered."
lines = []
for p in projects:
pid = p.get("project_id") or p.get("id") or p.get("name") or "?"
aliases = p.get("aliases", []) or []
alias_str = f" (aliases: {', '.join(aliases)})" if aliases else ""
lines.append(f"- {pid}{alias_str}")
return "\n".join(lines)
def _tool_health(args: dict) -> str:
"""Check AtoCore service health."""
result, err = safe_call(http_get, "/health")
if err:
return f"AtoCore unreachable: {err}"
sha = result.get("build_sha", "?")[:8]
vectors = result.get("vectors_count", "?")
env = result.get("env", "?")
return f"AtoCore healthy: sha={sha} vectors={vectors} env={env}"
# --- Phase 5H: Engineering query tools ---
def _tool_system_map(args: dict) -> str:
"""Q-001 + Q-004: subsystem/component tree for a project."""
project = (args.get("project") or "").strip()
if not project:
return "Error: 'project' is required."
result, err = safe_call(
http_get, f"/engineering/projects/{urllib.parse.quote(project)}/systems"
)
if err:
return f"Engineering query failed: {err}"
subs = result.get("subsystems", []) or []
orphans = result.get("orphan_components", []) or []
if not subs and not orphans:
return f"No subsystems or components registered for {project}."
lines = [f"System map for {project}:"]
for s in subs:
lines.append(f"\n[{s['name']}] — {s.get('description') or '(no description)'}")
for c in s.get("components", []):
mats = ", ".join(c.get("materials", [])) or "-"
lines.append(f" • {c['name']} (materials: {mats})")
if orphans:
lines.append(f"\nOrphan components (not attached to any subsystem):")
for c in orphans:
lines.append(f" • {c['name']}")
return "\n".join(lines)
def _tool_gaps(args: dict) -> str:
"""Q-006 + Q-009 + Q-011: find coverage gaps. Director's most-used query."""
project = (args.get("project") or "").strip()
if not project:
return "Error: 'project' is required."
result, err = safe_call(
http_get, f"/engineering/gaps",
params={"project": project},
)
if err:
return f"Gap query failed: {err}"
orphan = result.get("orphan_requirements", {})
risky = result.get("risky_decisions", {})
unsup = result.get("unsupported_claims", {})
counts = f"{orphan.get('count',0)}/{risky.get('count',0)}/{unsup.get('count',0)}"
lines = [f"Coverage gaps for {project} (orphan reqs / risky decisions / unsupported claims: {counts}):\n"]
if orphan.get("count", 0):
lines.append(f"ORPHAN REQUIREMENTS ({orphan['count']}) — no component claims to satisfy:")
for g in orphan.get("gaps", [])[:10]:
lines.append(f" • {g['name']}: {(g.get('description') or '')[:120]}")
lines.append("")
if risky.get("count", 0):
lines.append(f"RISKY DECISIONS ({risky['count']}) — based on flagged assumptions:")
for g in risky.get("gaps", [])[:10]:
lines.append(f" • {g['decision_name']} (assumption: {g['assumption_name']} — {g['assumption_status']})")
lines.append("")
if unsup.get("count", 0):
lines.append(f"UNSUPPORTED CLAIMS ({unsup['count']}) — no Result entity backs them:")
for g in unsup.get("gaps", [])[:10]:
lines.append(f" • {g['name']}: {(g.get('description') or '')[:120]}")
if orphan.get("count", 0) == 0 and risky.get("count", 0) == 0 and unsup.get("count", 0) == 0:
lines.append("✓ No gaps detected — every requirement satisfied, no flagged assumptions, all claims have evidence.")
return "\n".join(lines)
def _tool_requirements_for(args: dict) -> str:
"""Q-005: requirements that a component satisfies."""
component_id = (args.get("component_id") or "").strip()
if not component_id:
return "Error: 'component_id' is required."
result, err = safe_call(
http_get, f"/engineering/components/{urllib.parse.quote(component_id)}/requirements"
)
if err:
return f"Query failed: {err}"
reqs = result.get("requirements", []) or []
if not reqs:
return "No requirements associated with this component."
lines = [f"Component satisfies {len(reqs)} requirement(s):"]
for r in reqs:
lines.append(f" • {r['name']}: {(r.get('description') or '')[:150]}")
return "\n".join(lines)
def _tool_decisions_affecting(args: dict) -> str:
"""Q-008: decisions affecting a project or subsystem."""
project = (args.get("project") or "").strip()
subsystem = args.get("subsystem_id") or args.get("subsystem") or ""
if not project:
return "Error: 'project' is required."
params = {"project": project}
if subsystem:
params["subsystem"] = subsystem
result, err = safe_call(http_get, "/engineering/decisions", params=params)
if err:
return f"Query failed: {err}"
decisions = result.get("decisions", []) or []
if not decisions:
scope = f"subsystem {subsystem}" if subsystem else f"project {project}"
return f"No decisions recorded for {scope}."
scope = f"subsystem {subsystem}" if subsystem else project
lines = [f"{len(decisions)} decision(s) affecting {scope}:"]
for d in decisions:
lines.append(f" • {d['name']}: {(d.get('description') or '')[:150]}")
return "\n".join(lines)
def _tool_recent_changes(args: dict) -> str:
"""Q-013: what changed recently in the engineering graph."""
project = (args.get("project") or "").strip()
since = args.get("since") or ""
limit = int(args.get("limit") or 20)
if not project:
return "Error: 'project' is required."
params = {"project": project, "limit": limit}
if since:
params["since"] = since
result, err = safe_call(http_get, "/engineering/changes", params=params)
if err:
return f"Query failed: {err}"
changes = result.get("changes", []) or []
if not changes:
return f"No entity changes in {project} since {since or '(all time)'}."
lines = [f"Recent changes in {project} ({len(changes)}):"]
for c in changes:
lines.append(
f" [{c['timestamp'][:16]}] {c['action']:10s} "
f"[{c.get('entity_type','?')}] {c.get('entity_name','?')} "
f"by {c.get('actor','?')}"
)
return "\n".join(lines)
def _tool_impact(args: dict) -> str:
"""Q-016: impact of changing an entity (downstream BFS)."""
entity = (args.get("entity_id") or args.get("entity") or "").strip()
if not entity:
return "Error: 'entity_id' is required."
max_depth = int(args.get("max_depth") or 3)
result, err = safe_call(
http_get, "/engineering/impact",
params={"entity": entity, "max_depth": max_depth},
)
if err:
return f"Query failed: {err}"
root = result.get("root") or {}
impacted = result.get("impacted", []) or []
if not impacted:
return f"Nothing downstream of [{root.get('entity_type','?')}] {root.get('name','?')}."
lines = [
f"Changing [{root.get('entity_type')}] {root.get('name')} "
f"would affect {len(impacted)} entity(ies) (max depth {max_depth}):"
]
for i in impacted[:25]:
indent = " " * i.get("depth", 1)
lines.append(f"{indent}→ [{i['entity_type']}] {i['name']} (via {i['relationship']})")
if len(impacted) > 25:
lines.append(f" ... and {len(impacted)-25} more")
return "\n".join(lines)
def _tool_evidence(args: dict) -> str:
"""Q-017: evidence chain for an entity."""
entity = (args.get("entity_id") or args.get("entity") or "").strip()
if not entity:
return "Error: 'entity_id' is required."
result, err = safe_call(http_get, "/engineering/evidence", params={"entity": entity})
if err:
return f"Query failed: {err}"
root = result.get("root") or {}
chain = result.get("evidence_chain", []) or []
lines = [f"Evidence for [{root.get('entity_type','?')}] {root.get('name','?')}:"]
if not chain:
lines.append(" (no inbound provenance edges)")
else:
for e in chain:
lines.append(
f" {e['via']} ← [{e['source_type']}] {e['source_name']}: "
f"{(e.get('source_description') or '')[:100]}"
)
refs = result.get("direct_source_refs") or []
if refs:
lines.append(f"\nDirect source_refs: {refs[:5]}")
return "\n".join(lines)
TOOLS = [
{
"name": "atocore_context",
"description": (
"Get the full AtoCore context pack for a user query. Returns "
"Trusted Project State (high trust), relevant memories, and "
"retrieved source chunks formatted for prompt injection. "
"Use this FIRST on any project-related query to ground the "
"conversation in what AtoCore already knows."
),
"inputSchema": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "The user's question or task"},
"project": {"type": "string", "description": "Project hint (e.g. 'p04-gigabit'); optional"},
},
"required": ["query"],
},
"handler": _tool_context,
},
{
"name": "atocore_search",
"description": (
"Semantic search over AtoCore's ingested source documents. "
"Returns top-K ranked chunks. Use this when you need raw "
"references rather than a full context pack."
),
"inputSchema": {
"type": "object",
"properties": {
"query": {"type": "string"},
"project": {"type": "string", "description": "optional project filter"},
"top_k": {"type": "integer", "minimum": 1, "maximum": 20, "default": 5},
},
"required": ["query"],
},
"handler": _tool_search,
},
{
"name": "atocore_memory_list",
"description": (
"List active memories (curated facts, decisions, preferences). "
"Filter by project and/or memory_type. Use this to inspect what "
"AtoCore currently remembers about a topic."
),
"inputSchema": {
"type": "object",
"properties": {
"project": {"type": "string"},
"memory_type": {
"type": "string",
"enum": ["identity", "preference", "project", "episodic", "knowledge", "adaptation"],
},
"limit": {"type": "integer", "minimum": 1, "maximum": 100, "default": 20},
},
},
"handler": _tool_memory_list,
},
{
"name": "atocore_memory_create",
"description": (
"Propose a new memory for AtoCore. Creates a CANDIDATE that "
"enters the triage queue for human/auto review — not immediately "
"active. Use this to capture durable facts/decisions that "
"should persist across sessions. Do NOT use for transient state "
"or session-specific notes."
),
"inputSchema": {
"type": "object",
"properties": {
"memory_type": {
"type": "string",
"enum": ["identity", "preference", "project", "episodic", "knowledge", "adaptation"],
},
"content": {"type": "string", "description": "The fact/decision/preference to remember"},
"project": {"type": "string", "description": "project id if project-scoped; empty for global"},
"confidence": {"type": "number", "minimum": 0, "maximum": 1, "default": 0.5},
},
"required": ["memory_type", "content"],
},
"handler": _tool_memory_create,
},
{
"name": "atocore_project_state",
"description": (
"Get Trusted Project State entries for a given project — the "
"highest-trust tier with curated decisions, requirements, "
"facts, contacts, milestones. Use this to look up authoritative "
"project info."
),
"inputSchema": {
"type": "object",
"properties": {
"project": {"type": "string"},
"category": {
"type": "string",
"enum": ["status", "decision", "requirement", "contact", "milestone", "fact", "config"],
},
},
"required": ["project"],
},
"handler": _tool_project_state,
},
{
"name": "atocore_projects",
"description": "List all registered AtoCore projects (id + aliases).",
"inputSchema": {"type": "object", "properties": {}},
"handler": _tool_projects,
},
{
"name": "atocore_health",
"description": "Check AtoCore service health (build SHA, vector count, env).",
"inputSchema": {"type": "object", "properties": {}},
"handler": _tool_health,
},
# --- Phase 5H: Engineering knowledge graph tools ---
{
"name": "atocore_engineering_map",
"description": (
"Get the subsystem/component tree for an engineering project. "
"Returns the full system architecture: subsystems, their components, "
"materials, and any orphan components not attached to a subsystem. "
"Use when the user asks about project structure or system design."
),
"inputSchema": {
"type": "object",
"properties": {
"project": {"type": "string", "description": "Project id (e.g. p04-gigabit)"},
},
"required": ["project"],
},
"handler": _tool_system_map,
},
{
"name": "atocore_engineering_gaps",
"description": (
"Find coverage gaps in a project's engineering graph: orphan "
"requirements (no component satisfies them), risky decisions "
"(based on flagged assumptions), and unsupported claims (no "
"Result evidence). This is the director's most useful query — "
"answers 'what am I forgetting?' in seconds."
),
"inputSchema": {
"type": "object",
"properties": {
"project": {"type": "string"},
},
"required": ["project"],
},
"handler": _tool_gaps,
},
{
"name": "atocore_engineering_requirements_for_component",
"description": "List the requirements a specific component claims to satisfy (Q-005).",
"inputSchema": {
"type": "object",
"properties": {
"component_id": {"type": "string"},
},
"required": ["component_id"],
},
"handler": _tool_requirements_for,
},
{
"name": "atocore_engineering_decisions",
"description": (
"Decisions that affect a project, optionally scoped to a specific "
"subsystem. Use when the user asks 'what did we decide about X?'"
),
"inputSchema": {
"type": "object",
"properties": {
"project": {"type": "string"},
"subsystem_id": {"type": "string", "description": "optional subsystem entity id"},
},
"required": ["project"],
},
"handler": _tool_decisions_affecting,
},
{
"name": "atocore_engineering_changes",
"description": (
"Recent changes to the engineering graph for a project: which "
"entities were created/promoted/rejected/updated, by whom, when. "
"Use for 'what changed recently?' type questions."
),
"inputSchema": {
"type": "object",
"properties": {
"project": {"type": "string"},
"since": {"type": "string", "description": "ISO timestamp; optional"},
"limit": {"type": "integer", "minimum": 1, "maximum": 200, "default": 20},
},
"required": ["project"],
},
"handler": _tool_recent_changes,
},
{
"name": "atocore_engineering_impact",
"description": (
"Impact analysis: what's downstream of a given entity. BFS over "
"outbound relationships up to max_depth. Use to answer 'what would "
"break if I change X?'"
),
"inputSchema": {
"type": "object",
"properties": {
"entity_id": {"type": "string"},
"max_depth": {"type": "integer", "minimum": 1, "maximum": 5, "default": 3},
},
"required": ["entity_id"],
},
"handler": _tool_impact,
},
{
"name": "atocore_engineering_evidence",
"description": (
"Evidence chain for an entity: what supports it? Walks inbound "
"SUPPORTS / EVIDENCED_BY / DESCRIBED_BY / VALIDATED_BY / ANALYZED_BY "
"edges. Use for 'how do we know X is true?' type questions."
),
"inputSchema": {
"type": "object",
"properties": {
"entity_id": {"type": "string"},
},
"required": ["entity_id"],
},
"handler": _tool_evidence,
},
]
# --- JSON-RPC handlers ---
def handle_initialize(params: dict) -> dict:
return {
"protocolVersion": PROTOCOL_VERSION,
"capabilities": {
"tools": {"listChanged": False},
},
"serverInfo": {"name": SERVER_NAME, "version": SERVER_VERSION},
}
def handle_tools_list(params: dict) -> dict:
return {
"tools": [
{"name": t["name"], "description": t["description"], "inputSchema": t["inputSchema"]}
for t in TOOLS
]
}
def handle_tools_call(params: dict) -> dict:
tool_name = params.get("name", "")
args = params.get("arguments", {}) or {}
tool = next((t for t in TOOLS if t["name"] == tool_name), None)
if tool is None:
return {
"content": [{"type": "text", "text": f"Unknown tool: {tool_name}"}],
"isError": True,
}
try:
text = tool["handler"](args)
except Exception as e:
log(f"tool {tool_name} raised: {e}")
return {
"content": [{"type": "text", "text": f"Tool error: {type(e).__name__}: {e}"}],
"isError": True,
}
return {"content": [{"type": "text", "text": text}]}
def handle_ping(params: dict) -> dict:
return {}
METHODS = {
"initialize": handle_initialize,
"tools/list": handle_tools_list,
"tools/call": handle_tools_call,
"ping": handle_ping,
}
# --- stdio main loop ---
def send(obj: dict) -> None:
"""Write a single-line JSON message to stdout and flush."""
sys.stdout.write(json.dumps(obj, ensure_ascii=False) + "\n")
sys.stdout.flush()
def make_response(req_id, result=None, error=None) -> dict:
resp = {"jsonrpc": "2.0", "id": req_id}
if error is not None:
resp["error"] = error
else:
resp["result"] = result if result is not None else {}
return resp
def main() -> int:
log(f"starting (AtoCore at {ATOCORE_URL})")
for line in sys.stdin:
line = line.strip()
if not line:
continue
try:
msg = json.loads(line)
except json.JSONDecodeError as e:
log(f"parse error: {e}")
continue
method = msg.get("method", "")
req_id = msg.get("id")
params = msg.get("params", {}) or {}
# Notifications (no id) don't need a response
if req_id is None:
if method == "notifications/initialized":
log("client initialized")
continue
handler = METHODS.get(method)
if handler is None:
send(make_response(req_id, error={
"code": -32601,
"message": f"Method not found: {method}",
}))
continue
try:
result = handler(params)
send(make_response(req_id, result=result))
except Exception as e:
log(f"handler {method} raised: {e}")
send(make_response(req_id, error={
"code": -32603,
"message": f"Internal error: {type(e).__name__}: {e}",
}))
log("stdin closed, exiting")
return 0
if __name__ == "__main__":
sys.exit(main())

321
scripts/atocore_proxy.py Normal file
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@@ -0,0 +1,321 @@
#!/usr/bin/env python3
"""AtoCore Proxy — OpenAI-compatible HTTP middleware.
Acts as a drop-in layer for any client that speaks the OpenAI Chat
Completions API (Codex, Ollama, LiteLLM, custom agents). Sits between
the client and the real model provider:
client -> atocore_proxy -> real_provider (OpenAI, Ollama, Anthropic, ...)
For each chat completion request:
1. Extract the user's last message as the "query"
2. Call AtoCore /context/build to get a context pack
3. Inject the pack as a system message (or prepend to existing system)
4. Forward the enriched request to the real provider
5. Capture the full interaction back to AtoCore /interactions
Fail-open: if AtoCore is unreachable, the request passes through
unchanged. If the real provider fails, the error is propagated to the
client as-is.
Configuration (env vars):
ATOCORE_URL AtoCore base URL (default http://dalidou:8100)
ATOCORE_UPSTREAM real provider base URL (e.g. http://localhost:11434/v1 for Ollama)
ATOCORE_PROXY_PORT port to listen on (default 11435)
ATOCORE_PROXY_HOST bind address (default 127.0.0.1)
ATOCORE_CLIENT_LABEL client id recorded in captures (default "proxy")
ATOCORE_CAPTURE "1" to capture interactions back (default "1")
ATOCORE_INJECT "1" to inject context (default "1")
Usage:
# Proxy for Ollama:
ATOCORE_UPSTREAM=http://localhost:11434/v1 python atocore_proxy.py
# Then point your client at http://localhost:11435/v1 instead of the
# real provider.
Stdlib only — deliberate to keep the dependency footprint at zero.
"""
from __future__ import annotations
import http.server
import json
import os
import socketserver
import sys
import threading
import urllib.error
import urllib.parse
import urllib.request
from typing import Any
ATOCORE_URL = os.environ.get("ATOCORE_URL", "http://dalidou:8100").rstrip("/")
UPSTREAM_URL = os.environ.get("ATOCORE_UPSTREAM", "").rstrip("/")
PROXY_PORT = int(os.environ.get("ATOCORE_PROXY_PORT", "11435"))
PROXY_HOST = os.environ.get("ATOCORE_PROXY_HOST", "127.0.0.1")
CLIENT_LABEL = os.environ.get("ATOCORE_CLIENT_LABEL", "proxy")
CAPTURE_ENABLED = os.environ.get("ATOCORE_CAPTURE", "1") == "1"
INJECT_ENABLED = os.environ.get("ATOCORE_INJECT", "1") == "1"
ATOCORE_TIMEOUT = float(os.environ.get("ATOCORE_TIMEOUT", "6"))
UPSTREAM_TIMEOUT = float(os.environ.get("ATOCORE_UPSTREAM_TIMEOUT", "300"))
PROJECT_HINTS = [
("p04-gigabit", ["p04", "gigabit"]),
("p05-interferometer", ["p05", "interferometer"]),
("p06-polisher", ["p06", "polisher", "fullum"]),
("abb-space", ["abb"]),
("atomizer-v2", ["atomizer"]),
("atocore", ["atocore", "dalidou"]),
]
def log(msg: str) -> None:
print(f"[atocore-proxy] {msg}", file=sys.stderr, flush=True)
def detect_project(text: str) -> str:
lower = (text or "").lower()
for proj, tokens in PROJECT_HINTS:
if any(t in lower for t in tokens):
return proj
return ""
def get_last_user_message(body: dict) -> str:
messages = body.get("messages", []) or []
for m in reversed(messages):
if m.get("role") == "user":
content = m.get("content", "")
if isinstance(content, list):
# OpenAI multi-part content: extract text parts
parts = [p.get("text", "") for p in content if p.get("type") == "text"]
return "\n".join(parts)
return str(content)
return ""
def get_assistant_text(response: dict) -> str:
"""Extract assistant text from an OpenAI-style completion response."""
choices = response.get("choices", []) or []
if not choices:
return ""
msg = choices[0].get("message", {}) or {}
content = msg.get("content", "")
if isinstance(content, list):
parts = [p.get("text", "") for p in content if p.get("type") == "text"]
return "\n".join(parts)
return str(content)
def fetch_context(query: str, project: str) -> str:
"""Pull a context pack from AtoCore. Returns '' on any failure."""
if not INJECT_ENABLED or not query:
return ""
try:
data = json.dumps({"prompt": query, "project": project}).encode("utf-8")
req = urllib.request.Request(
ATOCORE_URL + "/context/build",
data=data,
method="POST",
headers={"Content-Type": "application/json"},
)
with urllib.request.urlopen(req, timeout=ATOCORE_TIMEOUT) as resp:
result = json.loads(resp.read().decode("utf-8"))
return result.get("formatted_context", "") or ""
except Exception as e:
log(f"context fetch failed: {type(e).__name__}: {e}")
return ""
def capture_interaction(prompt: str, response: str, project: str) -> None:
"""POST the completed turn back to AtoCore. Fire-and-forget."""
if not CAPTURE_ENABLED or not prompt or not response:
return
def _post():
try:
data = json.dumps({
"prompt": prompt,
"response": response,
"client": CLIENT_LABEL,
"project": project,
"reinforce": True,
}).encode("utf-8")
req = urllib.request.Request(
ATOCORE_URL + "/interactions",
data=data,
method="POST",
headers={"Content-Type": "application/json"},
)
urllib.request.urlopen(req, timeout=ATOCORE_TIMEOUT)
except Exception as e:
log(f"capture failed: {type(e).__name__}: {e}")
threading.Thread(target=_post, daemon=True).start()
def inject_context(body: dict, context_pack: str) -> dict:
"""Prepend the AtoCore context as a system message, or augment existing."""
if not context_pack.strip():
return body
header = "--- AtoCore Context (auto-injected) ---\n"
footer = "\n--- End AtoCore Context ---\n"
injection = header + context_pack + footer
messages = list(body.get("messages", []) or [])
if messages and messages[0].get("role") == "system":
# Augment existing system message
existing = messages[0].get("content", "") or ""
if isinstance(existing, list):
# multi-part: prepend a text part
messages[0]["content"] = [{"type": "text", "text": injection}] + existing
else:
messages[0]["content"] = injection + "\n" + str(existing)
else:
messages.insert(0, {"role": "system", "content": injection})
body["messages"] = messages
return body
def forward_to_upstream(body: dict, headers: dict[str, str], path: str) -> tuple[int, dict]:
"""Forward the enriched body to the upstream provider. Returns (status, response_dict)."""
if not UPSTREAM_URL:
return 503, {"error": {"message": "ATOCORE_UPSTREAM not configured"}}
url = UPSTREAM_URL + path
data = json.dumps(body).encode("utf-8")
# Strip hop-by-hop / host-specific headers
fwd_headers = {"Content-Type": "application/json"}
for k, v in headers.items():
lk = k.lower()
if lk in ("authorization", "x-api-key", "anthropic-version"):
fwd_headers[k] = v
req = urllib.request.Request(url, data=data, method="POST", headers=fwd_headers)
try:
with urllib.request.urlopen(req, timeout=UPSTREAM_TIMEOUT) as resp:
return resp.status, json.loads(resp.read().decode("utf-8"))
except urllib.error.HTTPError as e:
try:
body_bytes = e.read()
payload = json.loads(body_bytes.decode("utf-8"))
except Exception:
payload = {"error": {"message": f"upstream HTTP {e.code}"}}
return e.code, payload
except Exception as e:
log(f"upstream error: {e}")
return 502, {"error": {"message": f"upstream unreachable: {e}"}}
class ProxyHandler(http.server.BaseHTTPRequestHandler):
# Silence default request logging (we log what matters ourselves)
def log_message(self, format: str, *args: Any) -> None:
pass
def _read_body(self) -> dict:
length = int(self.headers.get("Content-Length", "0") or "0")
if length <= 0:
return {}
raw = self.rfile.read(length)
try:
return json.loads(raw.decode("utf-8"))
except Exception:
return {}
def _send_json(self, status: int, payload: dict) -> None:
body = json.dumps(payload).encode("utf-8")
self.send_response(status)
self.send_header("Content-Type", "application/json")
self.send_header("Content-Length", str(len(body)))
self.send_header("Access-Control-Allow-Origin", "*")
self.end_headers()
self.wfile.write(body)
def do_OPTIONS(self) -> None: # CORS preflight
self.send_response(204)
self.send_header("Access-Control-Allow-Origin", "*")
self.send_header("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
self.send_header("Access-Control-Allow-Headers", "Content-Type, Authorization, X-API-Key")
self.end_headers()
def do_GET(self) -> None:
parsed = urllib.parse.urlparse(self.path)
if parsed.path == "/healthz":
self._send_json(200, {
"status": "ok",
"atocore": ATOCORE_URL,
"upstream": UPSTREAM_URL or "(not configured)",
"inject": INJECT_ENABLED,
"capture": CAPTURE_ENABLED,
})
return
# Pass through GET to upstream (model listing etc)
if not UPSTREAM_URL:
self._send_json(503, {"error": {"message": "ATOCORE_UPSTREAM not configured"}})
return
try:
req = urllib.request.Request(UPSTREAM_URL + parsed.path + (f"?{parsed.query}" if parsed.query else ""))
for k in ("Authorization", "X-API-Key"):
v = self.headers.get(k)
if v:
req.add_header(k, v)
with urllib.request.urlopen(req, timeout=UPSTREAM_TIMEOUT) as resp:
data = resp.read()
self.send_response(resp.status)
self.send_header("Content-Type", resp.headers.get("Content-Type", "application/json"))
self.send_header("Content-Length", str(len(data)))
self.end_headers()
self.wfile.write(data)
except Exception as e:
self._send_json(502, {"error": {"message": f"upstream error: {e}"}})
def do_POST(self) -> None:
parsed = urllib.parse.urlparse(self.path)
body = self._read_body()
# Only enrich chat completions; other endpoints pass through
if parsed.path.endswith("/chat/completions") or parsed.path == "/v1/chat/completions":
prompt = get_last_user_message(body)
project = detect_project(prompt)
context = fetch_context(prompt, project) if prompt else ""
if context:
log(f"inject: project={project or '(none)'} chars={len(context)}")
body = inject_context(body, context)
status, response = forward_to_upstream(body, dict(self.headers), parsed.path)
self._send_json(status, response)
if status == 200:
assistant_text = get_assistant_text(response)
capture_interaction(prompt, assistant_text, project)
else:
# Non-chat endpoints (embeddings, completions, etc.) — pure passthrough
status, response = forward_to_upstream(body, dict(self.headers), parsed.path)
self._send_json(status, response)
class ThreadedServer(socketserver.ThreadingMixIn, http.server.HTTPServer):
daemon_threads = True
allow_reuse_address = True
def main() -> int:
if not UPSTREAM_URL:
log("WARNING: ATOCORE_UPSTREAM not set. Chat completions will fail.")
log("Example: ATOCORE_UPSTREAM=http://localhost:11434/v1 for Ollama")
server = ThreadedServer((PROXY_HOST, PROXY_PORT), ProxyHandler)
log(f"listening on {PROXY_HOST}:{PROXY_PORT}")
log(f"AtoCore: {ATOCORE_URL} inject={INJECT_ENABLED} capture={CAPTURE_ENABLED}")
log(f"Upstream: {UPSTREAM_URL or '(not configured)'}")
log(f"Client label: {CLIENT_LABEL}")
log("Ready. Point your OpenAI-compatible client at /v1/chat/completions")
try:
server.serve_forever()
except KeyboardInterrupt:
log("stopping")
server.server_close()
return 0
if __name__ == "__main__":
sys.exit(main())

79
scripts/auto_promote_reinforced.py Normal file
View File

@@ -0,0 +1,79 @@
#!/usr/bin/env python3
"""Auto-promote reinforced candidates + expire stale ones.
Phase 10: reinforcement-based auto-promotion. Candidates referenced
by 3+ interactions with confidence >= 0.7 graduate to active.
Candidates unreinforced for 14+ days are auto-rejected.
Usage:
python3 scripts/auto_promote_reinforced.py [--base-url URL] [--dry-run]
"""
from __future__ import annotations
import argparse
import json
import os
import sys
# Allow importing from src/ when run from repo root
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
from atocore.memory.service import auto_promote_reinforced, expire_stale_candidates
def main() -> None:
parser = argparse.ArgumentParser(description="Auto-promote + expire candidates")
parser.add_argument("--dry-run", action="store_true", help="Report only, don't change anything")
parser.add_argument("--min-refs", type=int, default=3, help="Min reference_count for promotion")
parser.add_argument("--min-confidence", type=float, default=0.7, help="Min confidence for promotion")
parser.add_argument("--expire-days", type=int, default=14, help="Days before unreinforced candidates expire")
args = parser.parse_args()
if args.dry_run:
print("DRY RUN — no changes will be made")
# For dry-run, query directly and report
from atocore.models.database import get_connection
from datetime import datetime, timedelta, timezone
cutoff_promote = (datetime.now(timezone.utc) - timedelta(days=args.expire_days)).strftime("%Y-%m-%d %H:%M:%S")
cutoff_expire = cutoff_promote
with get_connection() as conn:
promotable = conn.execute(
"SELECT id, content, memory_type, project, confidence, reference_count "
"FROM memories WHERE status = 'candidate' "
"AND COALESCE(reference_count, 0) >= ? AND confidence >= ? "
"AND last_referenced_at >= ?",
(args.min_refs, args.min_confidence, cutoff_promote),
).fetchall()
expirable = conn.execute(
"SELECT id, content, memory_type, project "
"FROM memories WHERE status = 'candidate' "
"AND COALESCE(reference_count, 0) = 0 AND created_at < ?",
(cutoff_expire,),
).fetchall()
print(f"\nWould promote {len(promotable)} candidates:")
for r in promotable:
print(f" [{r['memory_type']}] refs={r['reference_count']} conf={r['confidence']:.2f} | {r['content'][:80]}...")
print(f"\nWould expire {len(expirable)} stale candidates:")
for r in expirable:
print(f" [{r['memory_type']}] {r['project'] or 'global'} | {r['content'][:80]}...")
return
promoted = auto_promote_reinforced(
min_reference_count=args.min_refs,
min_confidence=args.min_confidence,
)
expired = expire_stale_candidates(max_age_days=args.expire_days)
print(f"promoted={len(promoted)} expired={len(expired)}")
if promoted:
print(f"Promoted IDs: {promoted}")
if expired:
print(f"Expired IDs: {expired}")
if __name__ == "__main__":
main()

460
scripts/auto_triage.py
View File

@@ -29,25 +29,61 @@ import os
import shutil import shutil
import subprocess import subprocess
import sys import sys
import time
import tempfile import tempfile
import urllib.error import urllib.error
import urllib.parse import urllib.parse
import urllib.request import urllib.request
DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100") DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100")
DEFAULT_MODEL = os.environ.get("ATOCORE_TRIAGE_MODEL", "sonnet")
# 3-tier escalation config (Phase "Triage Quality")
TIER1_MODEL = os.environ.get("ATOCORE_TRIAGE_MODEL_TIER1",
os.environ.get("ATOCORE_TRIAGE_MODEL", "sonnet"))
TIER2_MODEL = os.environ.get("ATOCORE_TRIAGE_MODEL_TIER2", "opus")
# Tier 3: default "discard" (auto-reject uncertain after opus disagrees/wavers),
# alternative "human" routes them to /admin/triage.
TIER3_ACTION = os.environ.get("ATOCORE_TRIAGE_TIER3", "discard").lower()
DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_TRIAGE_TIMEOUT_S", "60")) DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_TRIAGE_TIMEOUT_S", "60"))
TIER2_TIMEOUT_S = float(os.environ.get("ATOCORE_TRIAGE_TIER2_TIMEOUT_S", "120"))
AUTO_PROMOTE_MIN_CONFIDENCE = 0.8 AUTO_PROMOTE_MIN_CONFIDENCE = 0.8
# Below this, tier 1 decision is "not confident enough" and we escalate
ESCALATION_CONFIDENCE_THRESHOLD = float(
os.environ.get("ATOCORE_TRIAGE_ESCALATION_THRESHOLD", "0.75")
)
# Kept for legacy callers that reference DEFAULT_MODEL
DEFAULT_MODEL = TIER1_MODEL
TRIAGE_SYSTEM_PROMPT = """You are a memory triage reviewer for a personal context engine called AtoCore. You review candidate memories extracted from LLM conversations and decide whether each should be promoted to active status, rejected, or flagged for human review. TRIAGE_SYSTEM_PROMPT = """You are a memory triage reviewer for a personal context engine called AtoCore. You review candidate memories extracted from LLM conversations and decide whether each should be promoted to active status, rejected, or flagged for human review.
You will receive: You will receive:
- The candidate memory content and type - The candidate memory content, type, and claimed project
- A list of existing active memories for the same project (to check for duplicates) - A list of existing active memories for the same project (to check for duplicates + contradictions)
- Trusted project state entries (curated ground truth — higher trust than memories)
- Known project ids so you can flag misattribution
For each candidate, output exactly one JSON object: For each candidate, output exactly one JSON object:
{"verdict": "promote|reject|needs_human|contradicts", "confidence": 0.0-1.0, "reason": "one sentence", "conflicts_with": "id of existing memory if contradicts"} {"verdict": "promote|reject|needs_human|contradicts", "confidence": 0.0-1.0, "reason": "one sentence", "conflicts_with": "id of existing memory if contradicts", "domain_tags": ["tag1","tag2"], "valid_until": null}
DOMAIN TAGS (Phase 3): A lowercase list of 2-5 topical keywords describing
the SUBJECT matter (not the project). This enables cross-project retrieval:
a query about "optics" can pull matches from p04 + p05 + p06.
Good tags are single lowercase words or hyphenated terms. Mix:
- domain keywords (optics, thermal, firmware, materials, controls)
- project tokens when clearly scoped (p04, p05, p06, abb)
- lifecycle/activity words (procurement, design, validation, vendor)
Always emit domain_tags on a promote. For reject, empty list is fine.
VALID_UNTIL (Phase 3): ISO date "YYYY-MM-DD" OR null (permanent).
Set to a near-future date when the candidate is time-bounded:
- Status snapshots ("current blocker is X") → ~2 weeks out
- Scheduled events ("meeting Friday") → event date
- Quotes with expiry → quote expiry date
Leave null for durable decisions, engineering insights, ratified requirements.
Rules: Rules:
@@ -63,9 +99,26 @@ Rules:
3. CONTRADICTS when the candidate *conflicts* with an existing active memory (not a duplicate, but states something that can't both be true). Set `conflicts_with` to the existing memory id. This flags the tension for human review instead of silently rejecting or double-storing. Examples: "Option A selected" vs "Option B selected" for the same decision; "uses material X" vs "uses material Y" for the same component. 3. CONTRADICTS when the candidate *conflicts* with an existing active memory (not a duplicate, but states something that can't both be true). Set `conflicts_with` to the existing memory id. This flags the tension for human review instead of silently rejecting or double-storing. Examples: "Option A selected" vs "Option B selected" for the same decision; "uses material X" vs "uses material Y" for the same component.
4. NEEDS_HUMAN when you're genuinely unsure — the candidate might be valuable but you can't tell without domain knowledge. This should be rare (< 20% of candidates). 4. OPENCLAW-CURATED content (candidate content starts with "From OpenClaw/"): apply a MUCH LOWER bar. OpenClaw's SOUL.md, USER.md, MEMORY.md, MODEL-ROUTING.md, and dated memory/*.md files are ALREADY curated by OpenClaw as canonical continuity. Promote unless clearly wrong or a genuine duplicate. Do NOT reject OpenClaw content as "process rule belongs elsewhere" or "session log" — that's exactly what AtoCore wants to absorb. Session events, project updates, stakeholder notes, and decisions from OpenClaw daily memory files ARE valuable context and should promote.
5. Output ONLY the JSON object. No prose, no markdown, no explanation outside the reason field.""" 5. NEEDS_HUMAN when you're genuinely unsure — the candidate might be valuable but you can't tell without domain knowledge. This should be rare (< 20% of candidates). If this is just noise/filler, prefer REJECT with low confidence.
6. PROJECT VALIDATION: The candidate has a "claimed project". You'll see the list of registered project ids. If the claimed project doesn't match any registered id AND the content clearly belongs to a registered project, include "suggested_project": "<correct_id>" in your output so the caller can auto-fix the attribution. If the content is genuinely cross-project or global, leave project empty (suggested_project=""). Misattribution is the #1 pollution source — flag it.
7. TEMPORAL SENSITIVITY: Be aggressive with valid_until for anything that reads like "current state", "right now", "this week", "as of". Stale facts pollute context. When in doubt, set a 2-4 week expiry rather than null.
8. CONFIDENCE GRADING:
- 0.9+: crystal clear durable fact or clear noise
- 0.75-0.9: confident but not cryptographic-certain
- 0.6-0.75: borderline — will escalate to opus for second opinion
- <0.6: genuinely ambiguous — needs human or will be discarded
9. Output ONLY the JSON object. No prose, no markdown, no explanation outside the reason field. Include optional "suggested_project" field when misattribution detected."""
TIER2_SECOND_OPINION_PROMPT = TRIAGE_SYSTEM_PROMPT + """
ESCALATED REVIEW: You are seeing this candidate because the tier-1 (sonnet) reviewer could not decide confidently. You will be shown tier-1's verdict + reason as additional context. Your job is to resolve the uncertainty with more careful thinking. Use your full context window to cross-reference the existing memories. If you ALSO cannot decide with confidence >= 0.8, output verdict="needs_human" with a clear explanation of what information would break the tie. That signal will route to a human (or auto-discard, depending on config)."""
_sandbox_cwd = None _sandbox_cwd = None
@@ -102,48 +155,129 @@ def fetch_active_memories_for_project(base_url, project):
return result.get("memories", []) return result.get("memories", [])
def triage_one(candidate, active_memories, model, timeout_s): def fetch_project_state(base_url, project):
"""Ask the triage model to classify one candidate.""" """Fetch trusted project state for ground-truth context."""
if not shutil.which("claude"): if not project:
return {"verdict": "needs_human", "confidence": 0.0, "reason": "claude CLI not available"} return []
try:
result = api_get(base_url, f"/project/state/{urllib.parse.quote(project)}")
return result.get("entries", result.get("state", []))
except Exception:
return []
def fetch_registered_projects(base_url):
"""Return list of registered project ids + aliases for misattribution check."""
try:
result = api_get(base_url, "/projects")
projects = result.get("projects", [])
out = {}
for p in projects:
pid = p.get("project_id") or p.get("id") or p.get("name")
if pid:
out[pid] = p.get("aliases", []) or []
return out
except Exception:
return {}
def build_triage_user_message(candidate, active_memories, project_state, known_projects):
"""Richer context for the triage model: memories + state + project registry."""
active_summary = "\n".join( active_summary = "\n".join(
f"- [{m['memory_type']}] {m['content'][:150]}" f"- [{m['memory_type']}] {m['content'][:200]}"
for m in active_memories[:20] for m in active_memories[:30]
) or "(no active memories for this project)" ) or "(no active memories for this project)"
user_message = ( state_summary = ""
if project_state:
lines = []
for e in project_state[:20]:
cat = e.get("category", "?")
key = e.get("key", "?")
val = (e.get("value") or "")[:200]
lines.append(f"- [{cat}/{key}] {val}")
state_summary = "\n".join(lines)
else:
state_summary = "(no trusted state entries for this project)"
projects_line = ", ".join(sorted(known_projects.keys())) if known_projects else "(none)"
return (
f"CANDIDATE TO TRIAGE:\n" f"CANDIDATE TO TRIAGE:\n"
f" type: {candidate['memory_type']}\n" f" type: {candidate['memory_type']}\n"
f" project: {candidate.get('project') or '(none)'}\n" f" claimed project: {candidate.get('project') or '(none)'}\n"
f" content: {candidate['content']}\n\n" f" content: {candidate['content']}\n\n"
f"REGISTERED PROJECT IDS: {projects_line}\n\n"
f"TRUSTED PROJECT STATE (ground truth, higher trust than memories):\n{state_summary}\n\n"
f"EXISTING ACTIVE MEMORIES FOR THIS PROJECT:\n{active_summary}\n\n" f"EXISTING ACTIVE MEMORIES FOR THIS PROJECT:\n{active_summary}\n\n"
f"Return the JSON verdict now." f"Return the JSON verdict now."
) )
def _call_claude(system_prompt, user_message, model, timeout_s):
"""Shared CLI caller with retry + stderr capture."""
args = [ args = [
"claude", "-p", "claude", "-p",
"--model", model, "--model", model,
"--append-system-prompt", TRIAGE_SYSTEM_PROMPT, "--append-system-prompt", system_prompt,
"--disable-slash-commands", "--disable-slash-commands",
user_message, user_message,
] ]
last_error = ""
for attempt in range(3):
if attempt > 0:
time.sleep(2 ** attempt)
try:
completed = subprocess.run(
args, capture_output=True, text=True,
timeout=timeout_s, cwd=get_sandbox_cwd(),
encoding="utf-8", errors="replace",
)
except subprocess.TimeoutExpired:
last_error = f"{model} timed out"
continue
except Exception as exc:
last_error = f"subprocess error: {exc}"
continue
try: if completed.returncode == 0:
completed = subprocess.run( return (completed.stdout or "").strip(), None
args, capture_output=True, text=True,
timeout=timeout_s, cwd=get_sandbox_cwd(),
encoding="utf-8", errors="replace",
)
except subprocess.TimeoutExpired:
return {"verdict": "needs_human", "confidence": 0.0, "reason": "triage model timed out"}
except Exception as exc:
return {"verdict": "needs_human", "confidence": 0.0, "reason": f"subprocess error: {exc}"}
if completed.returncode != 0: stderr = (completed.stderr or "").strip()[:200]
return {"verdict": "needs_human", "confidence": 0.0, "reason": f"claude exit {completed.returncode}"} last_error = f"{model} exit {completed.returncode}: {stderr}" if stderr else f"{model} exit {completed.returncode}"
return None, last_error
raw = (completed.stdout or "").strip()
def triage_one(candidate, active_memories, project_state, known_projects, model, timeout_s):
"""Tier-1 triage: ask the cheap model for a verdict."""
if not shutil.which("claude"):
return {"verdict": "needs_human", "confidence": 0.0, "reason": "claude CLI not available"}
user_message = build_triage_user_message(candidate, active_memories, project_state, known_projects)
raw, err = _call_claude(TRIAGE_SYSTEM_PROMPT, user_message, model, timeout_s)
if err:
return {"verdict": "needs_human", "confidence": 0.0, "reason": err}
return parse_verdict(raw)
def triage_escalation(candidate, tier1_verdict, active_memories, project_state, known_projects, model, timeout_s):
"""Tier-2 escalation: opus sees tier-1's verdict + reasoning, tries again."""
if not shutil.which("claude"):
return {"verdict": "needs_human", "confidence": 0.0, "reason": "claude CLI not available"}
base_msg = build_triage_user_message(candidate, active_memories, project_state, known_projects)
tier1_context = (
f"\nTIER-1 REVIEW (sonnet, for your reference):\n"
f" verdict: {tier1_verdict.get('verdict')}\n"
f" confidence: {tier1_verdict.get('confidence', 0.0):.2f}\n"
f" reason: {tier1_verdict.get('reason', '')[:300]}\n\n"
f"Resolve the uncertainty. If you also can't decide with confidence ≥ 0.8, "
f"return verdict='needs_human' with a specific explanation of what information "
f"would break the tie.\n\nReturn the JSON verdict now."
)
raw, err = _call_claude(TIER2_SECOND_OPINION_PROMPT, base_msg + tier1_context, model, timeout_s)
if err:
return {"verdict": "needs_human", "confidence": 0.0, "reason": f"tier2: {err}"}
return parse_verdict(raw) return parse_verdict(raw)
@@ -182,81 +316,235 @@ def parse_verdict(raw):
reason = str(parsed.get("reason", "")).strip()[:200] reason = str(parsed.get("reason", "")).strip()[:200]
conflicts_with = str(parsed.get("conflicts_with", "")).strip() conflicts_with = str(parsed.get("conflicts_with", "")).strip()
# Phase 3: domain tags + expiry
raw_tags = parsed.get("domain_tags") or []
if isinstance(raw_tags, str):
raw_tags = [t.strip() for t in raw_tags.split(",") if t.strip()]
if not isinstance(raw_tags, list):
raw_tags = []
domain_tags = []
for t in raw_tags[:10]:
if not isinstance(t, str):
continue
tag = t.strip().lower()
if tag and tag not in domain_tags:
domain_tags.append(tag)
valid_until = parsed.get("valid_until")
if valid_until is None:
valid_until = ""
else:
valid_until = str(valid_until).strip()
if valid_until.lower() in ("", "null", "none", "permanent"):
valid_until = ""
# Triage Quality: project misattribution flag
suggested_project = str(parsed.get("suggested_project", "")).strip()
return { return {
"verdict": verdict, "verdict": verdict,
"confidence": confidence, "confidence": confidence,
"reason": reason, "reason": reason,
"conflicts_with": conflicts_with, "conflicts_with": conflicts_with,
"domain_tags": domain_tags,
"valid_until": valid_until,
"suggested_project": suggested_project,
} }
def main(): def _apply_metadata_update(base_url, mid, verdict_obj):
parser = argparse.ArgumentParser(description="Auto-triage candidate memories") """Persist tags + valid_until + suggested_project before the promote call."""
parser.add_argument("--base-url", default=DEFAULT_BASE_URL) tags = verdict_obj.get("domain_tags") or []
parser.add_argument("--model", default=DEFAULT_MODEL) valid_until = verdict_obj.get("valid_until") or ""
parser.add_argument("--dry-run", action="store_true", help="preview without executing") suggested = verdict_obj.get("suggested_project") or ""
args = parser.parse_args()
# Fetch candidates body = {}
result = api_get(args.base_url, "/memory?status=candidate&limit=100") if tags:
candidates = result.get("memories", []) body["domain_tags"] = tags
print(f"candidates: {len(candidates)} model: {args.model} dry_run: {args.dry_run}") if valid_until:
body["valid_until"] = valid_until
if not candidates: if not body and not suggested:
print("queue empty, nothing to triage")
return return
# Cache active memories per project for dedup if body:
active_cache = {} try:
promoted = rejected = needs_human = errors = 0 import urllib.request as _ur
req = _ur.Request(
f"{base_url}/memory/{mid}", method="PUT",
headers={"Content-Type": "application/json"},
data=json.dumps(body).encode("utf-8"),
)
_ur.urlopen(req, timeout=10).read()
except Exception:
pass
for i, cand in enumerate(candidates, 1): # Project auto-fix via direct SQLite update would bypass audit; use PUT if supported.
project = cand.get("project") or "" # For now we log the suggestion — operator script can apply it in batch.
if project not in active_cache: if suggested:
active_cache[project] = fetch_active_memories_for_project(args.base_url, project) # noop here — handled by caller which tracks suggested_project_fixes
pass
verdict_obj = triage_one(cand, active_cache[project], args.model, DEFAULT_TIMEOUT_S)
verdict = verdict_obj["verdict"]
conf = verdict_obj["confidence"]
reason = verdict_obj["reason"]
conflicts_with = verdict_obj.get("conflicts_with", "")
mid = cand["id"] def process_candidate(cand, base_url, active_cache, state_cache, known_projects, dry_run):
label = f"[{i:2d}/{len(candidates)}] {mid[:8]} [{cand['memory_type']}]" """Run the 3-tier triage and apply the resulting action.
if verdict == "promote" and conf >= AUTO_PROMOTE_MIN_CONFIDENCE: Returns (action, note) where action in {promote, reject, discard, human, error}.
if args.dry_run: """
print(f" WOULD PROMOTE {label} conf={conf:.2f} {reason}") mid = cand["id"]
project = cand.get("project") or ""
if project not in active_cache:
active_cache[project] = fetch_active_memories_for_project(base_url, project)
if project not in state_cache:
state_cache[project] = fetch_project_state(base_url, project)
# === Tier 1 ===
v1 = triage_one(
cand, active_cache[project], state_cache[project],
known_projects, TIER1_MODEL, DEFAULT_TIMEOUT_S,
)
# Project misattribution fix: suggested_project surfaces from tier 1
suggested = (v1.get("suggested_project") or "").strip()
if suggested and suggested != project and suggested in known_projects:
# Try to re-canonicalize the memory's project
if not dry_run:
try:
import urllib.request as _ur
req = _ur.Request(
f"{base_url}/memory/{mid}", method="PUT",
headers={"Content-Type": "application/json"},
data=json.dumps({"content": cand["content"]}).encode("utf-8"),
)
_ur.urlopen(req, timeout=10).read() # triggers canonicalization via update
except Exception:
pass
print(f" ↺ misattribution flagged: {project!r} → {suggested!r}")
# High-confidence tier 1 decision → act
if v1["verdict"] in ("promote", "reject") and v1["confidence"] >= AUTO_PROMOTE_MIN_CONFIDENCE:
return _apply_verdict(v1, cand, base_url, active_cache, dry_run, tier="sonnet")
# Borderline or uncertain → escalate to tier 2 (opus)
print(f" ↑ escalating (tier1 verdict={v1['verdict']} conf={v1['confidence']:.2f})")
v2 = triage_escalation(
cand, v1, active_cache[project], state_cache[project],
known_projects, TIER2_MODEL, TIER2_TIMEOUT_S,
)
# Tier 2 is confident → act
if v2["verdict"] in ("promote", "reject") and v2["confidence"] >= AUTO_PROMOTE_MIN_CONFIDENCE:
return _apply_verdict(v2, cand, base_url, active_cache, dry_run, tier="opus")
# Tier 3: still uncertain — route per config
if TIER3_ACTION == "discard":
reason = f"tier1+tier2 uncertain: {v2.get('reason', '')[:150]}"
if dry_run:
return ("discard", reason)
try:
api_post(base_url, f"/memory/{mid}/reject")
except Exception:
return ("error", reason)
return ("discard", reason)
else:
# "human" — leave in queue for /admin/triage review
return ("human", v2.get("reason", "no reason")[:200])
def _apply_verdict(verdict_obj, cand, base_url, active_cache, dry_run, tier):
"""Execute the promote/reject action and update metadata."""
mid = cand["id"]
verdict = verdict_obj["verdict"]
conf = verdict_obj["confidence"]
reason = f"[{tier}] {verdict_obj['reason']}"
if verdict == "promote":
if dry_run:
return ("promote", reason)
_apply_metadata_update(base_url, mid, verdict_obj)
try:
api_post(base_url, f"/memory/{mid}/promote")
project = cand.get("project") or ""
if project in active_cache:
active_cache[project].append(cand)
return ("promote", reason)
except Exception as e:
return ("error", f"promote failed: {e}")
else:
if dry_run:
return ("reject", reason)
try:
api_post(base_url, f"/memory/{mid}/reject")
return ("reject", reason)
except Exception as e:
return ("error", f"reject failed: {e}")
def main():
parser = argparse.ArgumentParser(description="Auto-triage candidate memories (3-tier escalation)")
parser.add_argument("--base-url", default=DEFAULT_BASE_URL)
parser.add_argument("--dry-run", action="store_true", help="preview without executing")
parser.add_argument("--max-batches", type=int, default=20,
help="Max batches of 100 to process per run")
parser.add_argument("--no-escalation", action="store_true",
help="Disable tier-2 escalation (legacy single-model behavior)")
args = parser.parse_args()
seen_ids: set[str] = set()
active_cache: dict[str, list] = {}
state_cache: dict[str, list] = {}
known_projects = fetch_registered_projects(args.base_url)
print(f"Registered projects: {sorted(known_projects.keys())}")
print(f"Tier1: {TIER1_MODEL} Tier2: {TIER2_MODEL} Tier3: {TIER3_ACTION} "
f"escalation_threshold: {ESCALATION_CONFIDENCE_THRESHOLD}")
counts = {"promote": 0, "reject": 0, "discard": 0, "human": 0, "error": 0}
batch_num = 0
while batch_num < args.max_batches:
batch_num += 1
result = api_get(args.base_url, "/memory?status=candidate&limit=100")
all_candidates = result.get("memories", [])
candidates = [c for c in all_candidates if c["id"] not in seen_ids]
if not candidates:
if batch_num == 1:
print("queue empty, nothing to triage")
else: else:
try: print(f"\nQueue drained after batch {batch_num-1}.")
api_post(args.base_url, f"/memory/{mid}/promote") break
print(f" PROMOTED {label} conf={conf:.2f} {reason}")
active_cache[project].append(cand)
except Exception:
errors += 1
promoted += 1
elif verdict == "reject":
if args.dry_run:
print(f" WOULD REJECT {label} conf={conf:.2f} {reason}")
else:
try:
api_post(args.base_url, f"/memory/{mid}/reject")
print(f" REJECTED {label} conf={conf:.2f} {reason}")
except Exception:
errors += 1
rejected += 1
elif verdict == "contradicts":
# Leave candidate in queue but flag the conflict in content
# so the wiki/triage shows it. This is conservative: we
# don't silently merge or reject when sources disagree.
print(f" CONTRADICTS {label} vs {conflicts_with[:8] if conflicts_with else '?'} {reason}")
contradicts_count = locals().get('contradicts_count', 0) + 1
needs_human += 1
else:
print(f" NEEDS_HUMAN {label} conf={conf:.2f} {reason}")
needs_human += 1
print(f"\npromoted={promoted} rejected={rejected} needs_human={needs_human} errors={errors}") print(f"\n=== batch {batch_num}: {len(candidates)} candidates dry_run: {args.dry_run} ===")
for i, cand in enumerate(candidates, 1):
if i > 1:
time.sleep(0.5)
seen_ids.add(cand["id"])
mid = cand["id"]
label = f"[{i:2d}/{len(candidates)}] {mid[:8]} [{cand['memory_type']}]"
try:
action, note = process_candidate(
cand, args.base_url, active_cache, state_cache,
known_projects, args.dry_run,
)
except Exception as e:
action, note = ("error", f"exception: {e}")
counts[action] = counts.get(action, 0) + 1
verb = {"promote": "PROMOTED ", "reject": "REJECTED ",
"discard": "DISCARDED ", "human": "NEEDS_HUM ",
"error": "ERROR "}.get(action, action.upper())
if args.dry_run and action in ("promote", "reject", "discard"):
verb = "WOULD " + verb.strip()
print(f" {verb} {label} {note[:120]}")
print(
f"\ntotal: promoted={counts['promote']} rejected={counts['reject']} "
f"discarded={counts['discard']} human={counts['human']} errors={counts['error']} "
f"batches={batch_num}"
)
if __name__ == "__main__": if __name__ == "__main__":

233
scripts/batch_llm_extract_live.py
View File

@@ -1,12 +1,15 @@
"""Host-side LLM batch extraction — pure HTTP client, no atocore imports. """Host-side LLM batch extraction — HTTP client + shared prompt module.
Fetches interactions from the AtoCore API, runs ``claude -p`` locally Fetches interactions from the AtoCore API, runs ``claude -p`` locally
for each, and POSTs candidates back. Zero dependency on atocore source for each, and POSTs candidates back. Uses stdlib + the ``claude`` CLI
or Python packages — only uses stdlib + the ``claude`` CLI on PATH. on PATH, plus the stdlib-only shared prompt/parser module at
``atocore.memory._llm_prompt`` to eliminate prompt/parser drift
against the in-container extractor (R12).
This is necessary because the ``claude`` CLI is on the Dalidou HOST This is necessary because the ``claude`` CLI is on the Dalidou HOST
but not inside the Docker container, and the host's Python doesn't but not inside the Docker container, and the host's Python doesn't
have the container's dependencies (pydantic_settings, etc.). have the container's dependencies (pydantic_settings, etc.) — so we
only import the one stdlib-only module, not the full atocore package.
""" """
from __future__ import annotations from __future__ import annotations
@@ -23,88 +26,26 @@ import urllib.parse
import urllib.request import urllib.request
from datetime import datetime, timezone from datetime import datetime, timezone
# R12: share the prompt + parser with the in-container extractor so
# the two paths can't drift. The imported module is stdlib-only by
# design; see src/atocore/memory/_llm_prompt.py.
_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
_SRC_DIR = os.path.abspath(os.path.join(_SCRIPT_DIR, "..", "src"))
if _SRC_DIR not in sys.path:
sys.path.insert(0, _SRC_DIR)
from atocore.memory._llm_prompt import ( # noqa: E402
MEMORY_TYPES,
SYSTEM_PROMPT,
build_user_message,
normalize_candidate_item,
parse_llm_json_array,
)
DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100") DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100")
DEFAULT_MODEL = os.environ.get("ATOCORE_LLM_EXTRACTOR_MODEL", "sonnet") DEFAULT_MODEL = os.environ.get("ATOCORE_LLM_EXTRACTOR_MODEL", "sonnet")
DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_LLM_EXTRACTOR_TIMEOUT_S", "90")) DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_LLM_EXTRACTOR_TIMEOUT_S", "90"))
MAX_RESPONSE_CHARS = 8000
MAX_PROMPT_CHARS = 2000
MEMORY_TYPES = {"identity", "preference", "project", "episodic", "knowledge", "adaptation"}
SYSTEM_PROMPT = """You extract memory candidates from LLM conversation turns for a personal context engine called AtoCore.
AtoCore is the brain for Atomaste's engineering work. Known projects:
p04-gigabit, p05-interferometer, p06-polisher, atomizer-v2, atocore,
abb-space. Unknown project names — still tag them, the system auto-detects.
Your job is to emit SIGNALS that matter for future context. Be aggressive:
err on the side of capturing useful signal. Triage filters noise downstream.
WHAT TO EMIT (in order of importance):
1. PROJECT ACTIVITY — any mention of a project with context worth remembering:
- "Schott quote received for ABB-Space" (event + project)
- "Cédric asked about p06 firmware timing" (stakeholder event)
- "Still waiting on Zygo lead-time from Nabeel" (blocker status)
- "p05 vendor decision needs to happen this week" (action item)
2. DECISIONS AND CHOICES — anything that commits to a direction:
- "Going with Zygo Verifire SV for p05" (decision)
- "Dropping stitching from primary workflow" (design choice)
- "USB SSD mandatory, not SD card" (architectural commitment)
3. DURABLE ENGINEERING INSIGHT — earned knowledge that generalizes:
- "CTE gradient dominates WFE at F/1.2" (materials insight)
- "Preston model breaks below 5N because contact assumption fails"
- "m=1 coma NOT correctable by force modulation" (controls insight)
Test: would a competent engineer NEED experience to know this?
If it's textbook/google-findable, skip it.
4. STAKEHOLDER AND VENDOR EVENTS:
- "Email sent to Nabeel 2026-04-13 asking for lead time"
- "Meeting with Jason on Table 7 next Tuesday"
- "Starspec wants updated CAD by Friday"
5. PREFERENCES AND ADAPTATIONS that shape how Antoine works:
- "Antoine prefers OAuth over API keys"
- "Extraction stays off the capture hot path"
WHAT TO SKIP:
- Pure conversational filler ("ok thanks", "let me check")
- Instructional help content ("run this command", "here's how to...")
- Obvious textbook facts anyone can google in 30 seconds
- Session meta-chatter ("let me commit this", "deploy running")
- Transient system state snapshots ("36 active memories right now")
CANDIDATE TYPES — choose the best fit:
- project — a fact, decision, or event specific to one named project
- knowledge — durable engineering insight (use domain, not project)
- preference — how Antoine works / wants things done
- adaptation — a standing rule or adjustment to behavior
- episodic — a stakeholder event or milestone worth remembering
DOMAINS for knowledge candidates (required when type=knowledge and project is empty):
physics, materials, optics, mechanics, manufacturing, metrology,
controls, software, math, finance, business
TRUST HIERARCHY:
- project-specific: set project to the project id, leave domain empty
- domain knowledge: set domain, leave project empty
- events/activity: use project, type=project or episodic
- one conversation can produce MULTIPLE candidates — emit them all
OUTPUT RULES:
- Each candidate content under 250 characters, stands alone
- Default confidence 0.5. Raise to 0.7 only for ratified/committed claims.
- Raw JSON array, no prose, no markdown fences
- Empty array [] is fine when the conversation has no durable signal
Each element:
{"type": "project|knowledge|preference|adaptation|episodic", "content": "...", "project": "...", "domain": "", "confidence": 0.5}"""
_sandbox_cwd = None _sandbox_cwd = None
@@ -175,14 +116,7 @@ def extract_one(prompt, response, project, model, timeout_s):
if not shutil.which("claude"): if not shutil.which("claude"):
return [], "claude_cli_missing" return [], "claude_cli_missing"
prompt_excerpt = prompt[:MAX_PROMPT_CHARS] user_message = build_user_message(prompt, response, project)
response_excerpt = response[:MAX_RESPONSE_CHARS]
user_message = (
f"PROJECT HINT (may be empty): {project}\n\n"
f"USER PROMPT:\n{prompt_excerpt}\n\n"
f"ASSISTANT RESPONSE:\n{response_excerpt}\n\n"
"Return the JSON array now."
)
args = [ args = [
"claude", "-p", "claude", "-p",
@@ -192,85 +126,58 @@ def extract_one(prompt, response, project, model, timeout_s):
user_message, user_message,
] ]
try: # Retry with exponential backoff on transient failures (rate limits etc)
completed = subprocess.run( import time as _time
args, capture_output=True, text=True, last_error = ""
timeout=timeout_s, cwd=get_sandbox_cwd(), for attempt in range(3):
encoding="utf-8", errors="replace", if attempt > 0:
) _time.sleep(2 ** attempt) # 2s, 4s
except subprocess.TimeoutExpired: try:
return [], "timeout" completed = subprocess.run(
except Exception as exc: args, capture_output=True, text=True,
return [], f"subprocess_error: {exc}" timeout=timeout_s, cwd=get_sandbox_cwd(),
encoding="utf-8", errors="replace",
)
except subprocess.TimeoutExpired:
last_error = "timeout"
continue
except Exception as exc:
last_error = f"subprocess_error: {exc}"
continue
if completed.returncode != 0: if completed.returncode == 0:
return [], f"exit_{completed.returncode}" raw = (completed.stdout or "").strip()
return parse_candidates(raw, project), ""
raw = (completed.stdout or "").strip() # Capture stderr for diagnostics (truncate to 200 chars)
return parse_candidates(raw, project), "" stderr = (completed.stderr or "").strip()[:200]
last_error = f"exit_{completed.returncode}: {stderr}" if stderr else f"exit_{completed.returncode}"
return [], last_error
def parse_candidates(raw, interaction_project): def parse_candidates(raw, interaction_project):
"""Parse model JSON output into candidate dicts.""" """Parse model JSON output into candidate dicts.
text = raw.strip()
if text.startswith("```"):
text = text.strip("`")
nl = text.find("\n")
if nl >= 0:
text = text[nl + 1:]
if text.endswith("```"):
text = text[:-3]
text = text.strip()
if not text or text == "[]":
return []
if not text.lstrip().startswith("["):
start = text.find("[")
end = text.rfind("]")
if start >= 0 and end > start:
text = text[start:end + 1]
try:
parsed = json.loads(text)
except json.JSONDecodeError:
return []
if not isinstance(parsed, list):
return []
Stripping + per-item normalization come from the shared
``_llm_prompt`` module. Host-side project attribution: interaction
scope wins, otherwise keep the model's tag (the API's own R9
registry-check will happen server-side in the container on write;
here we preserve the signal instead of dropping it).
"""
results = [] results = []
for item in parsed: for item in parse_llm_json_array(raw):
if not isinstance(item, dict): normalized = normalize_candidate_item(item)
if normalized is None:
continue continue
mem_type = str(item.get("type") or "").strip().lower() project = interaction_project or normalized["project"] or ""
content = str(item.get("content") or "").strip()
model_project = str(item.get("project") or "").strip()
domain = str(item.get("domain") or "").strip().lower()
# R9 trust hierarchy: interaction scope always wins when set.
# For unscoped interactions, keep model's project tag even if
# unregistered — the system will detect new projects/leads.
if interaction_project:
project = interaction_project
elif model_project:
project = model_project
else:
project = ""
# Domain knowledge: embed tag in content for cross-project retrieval
if domain and not project:
content = f"[{domain}] {content}"
conf = item.get("confidence", 0.5)
if mem_type not in MEMORY_TYPES or not content:
continue
try:
conf = max(0.0, min(1.0, float(conf)))
except (TypeError, ValueError):
conf = 0.5
results.append({ results.append({
"memory_type": mem_type, "memory_type": normalized["type"],
"content": content[:1000], "content": normalized["content"],
"project": project, "project": project,
"confidence": conf, "confidence": normalized["confidence"],
"domain_tags": normalized.get("domain_tags") or [],
"valid_until": normalized.get("valid_until") or "",
}) })
return results return results
@@ -299,10 +206,14 @@ def main():
total_persisted = 0 total_persisted = 0
errors = 0 errors = 0
for summary in interaction_summaries: import time as _time
for ix, summary in enumerate(interaction_summaries):
resp_chars = summary.get("response_chars", 0) or 0 resp_chars = summary.get("response_chars", 0) or 0
if resp_chars < 50: if resp_chars < 50:
continue continue
# Light pacing between calls to avoid bursting the claude CLI
if ix > 0:
_time.sleep(0.5)
iid = summary["id"] iid = summary["id"]
try: try:
raw = api_get( raw = api_get(
@@ -341,6 +252,8 @@ def main():
"project": c["project"], "project": c["project"],
"confidence": c["confidence"], "confidence": c["confidence"],
"status": "candidate", "status": "candidate",
"domain_tags": c.get("domain_tags") or [],
"valid_until": c.get("valid_until") or "",
}) })
total_persisted += 1 total_persisted += 1
except urllib.error.HTTPError as exc: except urllib.error.HTTPError as exc:

237
scripts/graduate_memories.py Normal file
View File

@@ -0,0 +1,237 @@
#!/usr/bin/env python3
"""Phase 5F — Memory → Entity graduation batch pass.
Takes active memories, asks claude-p whether each describes a typed
engineering entity, and creates entity candidates for the ones that do.
Each candidate carries source_refs back to its source memory so human
review can trace provenance.
Human reviews the entity candidates via /admin/triage (same UI as memory
triage). When a candidate is promoted, a post-promote hook marks the source
memory as `graduated` and sets `graduated_to_entity_id` for traceability.
This is THE population move: without it, the engineering graph stays sparse
and the killer queries (Q-006/009/011) have nothing to find gaps in.
Usage:
python3 scripts/graduate_memories.py --base-url http://127.0.0.1:8100 \\
--project p05-interferometer --limit 20
# Dry run (don't create entities, just show decisions):
python3 scripts/graduate_memories.py --project p05-interferometer --dry-run
# Process all active memories across all projects (big run):
python3 scripts/graduate_memories.py --limit 200
Host-side because claude CLI lives on Dalidou, not in the container.
"""
from __future__ import annotations
import argparse
import json
import os
import shutil
import subprocess
import sys
import tempfile
import time
import urllib.error
import urllib.request
from typing import Any
# Make src/ importable so we can reuse the stdlib-only prompt module
_SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
_SRC_DIR = os.path.abspath(os.path.join(_SCRIPT_DIR, "..", "src"))
if _SRC_DIR not in sys.path:
sys.path.insert(0, _SRC_DIR)
from atocore.engineering._graduation_prompt import ( # noqa: E402
GRADUATION_PROMPT_VERSION,
SYSTEM_PROMPT,
build_user_message,
parse_graduation_output,
)
DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://127.0.0.1:8100")
DEFAULT_MODEL = os.environ.get("ATOCORE_LLM_EXTRACTOR_MODEL", "sonnet")
DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_GRADUATION_TIMEOUT_S", "90"))
_sandbox_cwd = None
def get_sandbox_cwd() -> str:
"""Temp cwd so claude CLI doesn't auto-discover project CLAUDE.md files."""
global _sandbox_cwd
if _sandbox_cwd is None:
_sandbox_cwd = tempfile.mkdtemp(prefix="ato-graduate-")
return _sandbox_cwd
def api_get(base_url: str, path: str) -> dict:
req = urllib.request.Request(f"{base_url}{path}")
with urllib.request.urlopen(req, timeout=15) as resp:
return json.loads(resp.read().decode("utf-8"))
def api_post(base_url: str, path: str, body: dict | None = None) -> dict:
data = json.dumps(body or {}).encode("utf-8")
req = urllib.request.Request(
f"{base_url}{path}", method="POST",
headers={"Content-Type": "application/json"}, data=data,
)
with urllib.request.urlopen(req, timeout=15) as resp:
return json.loads(resp.read().decode("utf-8"))
def graduate_one(memory: dict, model: str, timeout_s: float) -> dict[str, Any] | None:
"""Ask claude whether this memory describes a typed entity.
Returns None on any failure (parse error, timeout, exit!=0).
Applies retry+pacing to match the pattern in auto_triage/batch_extract.
"""
if not shutil.which("claude"):
return None
user_msg = build_user_message(
memory_content=memory.get("content", "") or "",
memory_project=memory.get("project", "") or "",
memory_type=memory.get("memory_type", "") or "",
)
args = [
"claude", "-p",
"--model", model,
"--append-system-prompt", SYSTEM_PROMPT,
"--disable-slash-commands",
user_msg,
]
last_error = ""
for attempt in range(3):
if attempt > 0:
time.sleep(2 ** attempt)
try:
completed = subprocess.run(
args, capture_output=True, text=True,
timeout=timeout_s, cwd=get_sandbox_cwd(),
encoding="utf-8", errors="replace",
)
except subprocess.TimeoutExpired:
last_error = "timeout"
continue
except Exception as exc:
last_error = f"subprocess error: {exc}"
continue
if completed.returncode == 0:
return parse_graduation_output(completed.stdout or "")
stderr = (completed.stderr or "").strip()[:200]
last_error = f"exit_{completed.returncode}: {stderr}" if stderr else f"exit_{completed.returncode}"
print(f" ! claude failed after 3 tries: {last_error}", file=sys.stderr)
return None
def create_entity_candidate(
base_url: str,
decision: dict,
memory: dict,
) -> str | None:
"""Create an entity candidate with source_refs pointing at the memory."""
try:
result = api_post(base_url, "/entities", {
"entity_type": decision["entity_type"],
"name": decision["name"],
"project": memory.get("project", "") or "",
"description": decision["description"],
"properties": {
"graduated_from_memory": memory["id"],
"proposed_relationships": decision["relationships"],
"prompt_version": GRADUATION_PROMPT_VERSION,
},
"status": "candidate",
"confidence": decision["confidence"],
"source_refs": [f"memory:{memory['id']}"],
})
return result.get("id")
except Exception as e:
print(f" ! entity create failed: {e}", file=sys.stderr)
return None
def main() -> None:
parser = argparse.ArgumentParser(description="Graduate active memories into entity candidates")
parser.add_argument("--base-url", default=DEFAULT_BASE_URL)
parser.add_argument("--model", default=DEFAULT_MODEL)
parser.add_argument("--project", default=None, help="Only graduate memories in this project")
parser.add_argument("--limit", type=int, default=50, help="Max memories to process")
parser.add_argument("--min-confidence", type=float, default=0.3,
help="Skip memories with confidence below this (they're probably noise)")
parser.add_argument("--dry-run", action="store_true", help="Show decisions without creating entities")
args = parser.parse_args()
# Fetch active memories
query = "status=active"
query += f"&limit={args.limit}"
if args.project:
query += f"&project={args.project}"
result = api_get(args.base_url, f"/memory?{query}")
memories = result.get("memories", [])
# Filter by min_confidence + skip already-graduated
memories = [m for m in memories
if m.get("confidence", 0) >= args.min_confidence
and m.get("status") != "graduated"]
print(f"graduating: {len(memories)} memories project={args.project or '(all)'} "
f"model={args.model} dry_run={args.dry_run}")
graduated = 0
skipped = 0
errors = 0
entities_created: list[str] = []
for i, mem in enumerate(memories, 1):
if i > 1:
time.sleep(0.5) # light pacing, matches auto_triage
mid = mem["id"]
label = f"[{i:3d}/{len(memories)}] {mid[:8]} [{mem.get('memory_type','?')}]"
decision = graduate_one(mem, args.model, DEFAULT_TIMEOUT_S)
if decision is None:
print(f" ERROR {label} (graduate_one returned None)")
errors += 1
continue
if not decision.get("graduate"):
reason = decision.get("reason", "(no reason)")
print(f" skip {label} {reason}")
skipped += 1
continue
etype = decision["entity_type"]
ename = decision["name"]
nrel = len(decision.get("relationships", []))
if args.dry_run:
print(f" WOULD {label} → [{etype}] {ename!r} ({nrel} rels)")
graduated += 1
else:
entity_id = create_entity_candidate(args.base_url, decision, mem)
if entity_id:
print(f" CREATE {label} → [{etype}] {ename!r} ({nrel} rels) entity={entity_id[:8]}")
graduated += 1
entities_created.append(entity_id)
else:
errors += 1
print(f"\ntotal: graduated={graduated} skipped={skipped} errors={errors}")
if entities_created:
print(f"Review at /admin/triage ({len(entities_created)} entity candidates created)")
if __name__ == "__main__":
main()

2
scripts/import_openclaw_state.py
View File

@@ -42,7 +42,7 @@ from pathlib import Path
DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100") DEFAULT_BASE_URL = os.environ.get("ATOCORE_BASE_URL", "http://localhost:8100")
DEFAULT_OPENCLAW_HOST = os.environ.get("ATOCORE_OPENCLAW_HOST", "papa@192.168.86.39") DEFAULT_OPENCLAW_HOST = os.environ.get("ATOCORE_OPENCLAW_HOST", "papa@192.168.86.39")
DEFAULT_OPENCLAW_PATH = os.environ.get("ATOCORE_OPENCLAW_PATH", "/home/papa/openclaw-workspace") DEFAULT_OPENCLAW_PATH = os.environ.get("ATOCORE_OPENCLAW_PATH", "/home/papa/clawd")
# Files to pull and how to classify them # Files to pull and how to classify them
DURABLE_FILES = [ DURABLE_FILES = [

49
scripts/integrity_check.py Normal file
View File

@@ -0,0 +1,49 @@
#!/usr/bin/env python3
"""Trigger the integrity check inside the AtoCore container.
The scan itself lives in the container (needs direct DB access via the
already-loaded sqlite connection). This host-side wrapper just POSTs to
/admin/integrity-check so the nightly cron can kick it off from bash
without needing the container's Python deps on the host.
Usage:
python3 scripts/integrity_check.py [--base-url URL] [--dry-run]
"""
from __future__ import annotations
import argparse
import json
import os
import sys
import urllib.parse
import urllib.request
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--base-url", default=os.environ.get("ATOCORE_BASE_URL", "http://127.0.0.1:8100"))
parser.add_argument("--dry-run", action="store_true",
help="Report without persisting findings to state")
args = parser.parse_args()
url = args.base_url.rstrip("/") + "/admin/integrity-check"
if args.dry_run:
url += "?persist=false"
req = urllib.request.Request(url, method="POST")
try:
with urllib.request.urlopen(req, timeout=30) as resp:
result = json.loads(resp.read().decode("utf-8"))
except Exception as e:
print(f"ERROR: could not reach {url}: {e}", file=sys.stderr)
sys.exit(1)
print(json.dumps(result, indent=2))
if not result.get("ok", True):
# Non-zero exit so cron logs flag it
sys.exit(2)
if __name__ == "__main__":
main()

4
scripts/retrieval_eval_fixtures.json
View File

@@ -218,8 +218,8 @@
"Tailscale" "Tailscale"
], ],
"expect_absent": [ "expect_absent": [
"GigaBIT" "[Source: p04-gigabit/"
], ],
"notes": "New p06 memory: Tailscale mesh for RPi remote access" "notes": "New p06 memory: Tailscale mesh for RPi remote access. Cross-project guard is a source-path check, not a word blacklist: the polisher ARCHITECTURE.md legitimately mentions the GigaBIT M1 mirror (it is what the polisher is built for), so testing for absence of that word produces false positives. The real invariant is that no p04 source chunks are retrieved into p06 context."
} }
] ]

159
scripts/seed_project_state.py Normal file
View File

@@ -0,0 +1,159 @@
#!/usr/bin/env python3
"""Seed Trusted Project State entries for all active projects.
Populates the project_state table with curated decisions, requirements,
facts, contacts, and milestones so context packs have real content
in the highest-trust tier.
Usage:
python3 scripts/seed_project_state.py --base-url http://dalidou:8100
python3 scripts/seed_project_state.py --base-url http://dalidou:8100 --dry-run
"""
from __future__ import annotations
import argparse
import json
import urllib.request
import sys
# Each entry: (project, category, key, value, source)
SEED_ENTRIES: list[tuple[str, str, str, str, str]] = [
# ---- p04-gigabit (GigaBIT M1 1.2m Primary Mirror) ----
("p04-gigabit", "fact", "mirror-spec",
"1.2m borosilicate primary mirror for GigaBIT telescope. F/1.5, lightweight isogrid back structure.",
"CDR docs + vault"),
("p04-gigabit", "decision", "back-structure",
"Option B selected: conical isogrid back structure with variable rib density. Chosen over flat-back for stiffness-to-weight ratio.",
"CDR 2026-01"),
("p04-gigabit", "decision", "polishing-vendor",
"ABB Space (formerly INO) selected as polishing vendor. Contract includes computer-controlled polishing (CCP) and ion beam figuring (IBF).",
"Entente de service 2026-01"),
("p04-gigabit", "requirement", "surface-quality",
"Surface figure accuracy: < 25nm RMS after final figuring. Microroughness: < 2nm RMS.",
"CDR requirements"),
("p04-gigabit", "contact", "abb-space",
"ABB Space (INO), Quebec City. Primary contact for mirror polishing, CCP, and IBF. Project lead: coordinating FDR deliverables.",
"vendor records"),
("p04-gigabit", "milestone", "fdr",
"Final Design Review (FDR) in preparation. Deliverables include interface drawings, thermal analysis, and updated error budget.",
"project timeline"),
# ---- p05-interferometer (Fullum Interferometer) ----
("p05-interferometer", "fact", "system-overview",
"Custom Fizeau interferometer for in-situ metrology of large optics. Designed for the Fullum observatory polishing facility.",
"vault docs"),
("p05-interferometer", "decision", "cgh-design",
"Computer-generated hologram (CGH) selected for null testing of the 1.2m mirror. Vendor: Diffraction International.",
"vendor correspondence"),
("p05-interferometer", "requirement", "measurement-accuracy",
"Measurement accuracy target: lambda/20 (< 30nm PV) for surface figure verification.",
"system requirements"),
("p05-interferometer", "fact", "laser-source",
"HeNe laser source at 632.8nm. Beam expansion to cover full 1.2m aperture via diverger + CGH.",
"optical design docs"),
("p05-interferometer", "contact", "diffraction-intl",
"Diffraction International: CGH vendor. Fabricates the computer-generated hologram for null testing.",
"vendor records"),
# ---- p06-polisher (Polisher Suite / P11-Polisher-Fullum) ----
("p06-polisher", "fact", "suite-overview",
"Integrated CNC polishing suite for the Fullum observatory. Includes 3-axis polishing machine, metrology integration, and real-time process control.",
"vault docs"),
("p06-polisher", "decision", "control-architecture",
"Beckhoff TwinCAT 3 selected for real-time motion control. EtherCAT fieldbus for servo drives and I/O.",
"architecture docs"),
("p06-polisher", "decision", "firmware-split",
"Firmware split into safety layer (PLC-level interlocks) and application layer (trajectory generation, adaptive dwell-time).",
"architecture docs"),
("p06-polisher", "requirement", "axis-travel",
"Z-axis: 200mm travel for tool engagement. X/Y: covers 1.2m mirror diameter plus overshoot margin.",
"mechanical requirements"),
("p06-polisher", "fact", "telemetry",
"Real-time telemetry via MQTT. Metrics: spindle RPM, force sensor, temperature probes, position feedback at 1kHz.",
"control design docs"),
("p06-polisher", "contact", "fullum-observatory",
"Fullum Observatory: site where the polishing suite will be installed. Provides infrastructure (power, vibration isolation, clean environment).",
"project records"),
# ---- atomizer-v2 ----
("atomizer-v2", "fact", "product-overview",
"Atomizer V2: internal project management and multi-agent orchestration platform. War-room based task coordination.",
"repo docs"),
("atomizer-v2", "decision", "projects-first-architecture",
"Migration to projects-first architecture: each project is a workspace with its own agents, tasks, and knowledge.",
"war-room-migration-plan-v2.md"),
# ---- abb-space (P08) ----
("abb-space", "fact", "contract-overview",
"ABB Space mirror polishing contract. Phase 1: spherical mirror polishing (200mm). Schott Zerodur substrate.",
"quotes + correspondence"),
("abb-space", "contact", "schott",
"Schott AG: substrate supplier for Zerodur mirror blanks. Quote received for 200mm blank.",
"vendor records"),
# ---- atocore ----
("atocore", "fact", "architecture",
"AtoCore: runtime memory and knowledge layer. FastAPI + SQLite + ChromaDB. Hosted on Dalidou (Docker). Nightly pipeline: backup, extract, triage, synthesis.",
"codebase"),
("atocore", "decision", "no-api-keys",
"No API keys allowed in AtoCore. LLM-assisted features use OAuth via 'claude -p' CLI or equivalent CLI-authenticated paths.",
"DEV-LEDGER 2026-04-12"),
("atocore", "decision", "storage-separation",
"Human-readable sources (vault, drive) and machine operational storage (SQLite, ChromaDB) must remain separate. Machine DB is derived state.",
"AGENTS.md"),
("atocore", "decision", "extraction-off-hot-path",
"Extraction stays off the capture hot path. Batch/manual only. Never block interaction recording with extraction.",
"DEV-LEDGER 2026-04-11"),
]
def main() -> None:
parser = argparse.ArgumentParser(description="Seed Trusted Project State")
parser.add_argument("--base-url", default="http://dalidou:8100")
parser.add_argument("--dry-run", action="store_true")
args = parser.parse_args()
base = args.base_url.rstrip("/")
created = 0
skipped = 0
errors = 0
for project, category, key, value, source in SEED_ENTRIES:
if args.dry_run:
print(f" [DRY] {project}/{category}/{key}: {value[:60]}...")
created += 1
continue
body = json.dumps({
"project": project,
"category": category,
"key": key,
"value": value,
"source": source,
"confidence": 1.0,
}).encode()
req = urllib.request.Request(
f"{base}/project/state",
data=body,
headers={"Content-Type": "application/json"},
method="POST",
)
try:
resp = urllib.request.urlopen(req, timeout=10)
result = json.loads(resp.read())
if result.get("created"):
created += 1
print(f" + {project}/{category}/{key}")
else:
skipped += 1
print(f" = {project}/{category}/{key} (already exists)")
except Exception as e:
errors += 1
print(f" ! {project}/{category}/{key}: {e}", file=sys.stderr)
print(f"\nDone: {created} created, {skipped} skipped, {errors} errors")
if __name__ == "__main__":
main()

87
scripts/windows/atocore-backup-pull.ps1 Normal file
View File

@@ -0,0 +1,87 @@
# atocore-backup-pull.ps1
#
# Pull the latest AtoCore backup snapshot from Dalidou to this Windows machine.
# Designed to be run by Windows Task Scheduler. Fail-open by design -- if
# Dalidou is unreachable (laptop on the road, etc.), exit cleanly without error.
#
# Usage (manual test):
# powershell.exe -ExecutionPolicy Bypass -File atocore-backup-pull.ps1
#
# Scheduled task: see docs/windows-backup-setup.md for Task Scheduler config.
$ErrorActionPreference = "Continue"
# --- Configuration ---
$Remote = "papa@dalidou"
$RemoteSnapshots = "/srv/storage/atocore/backups/snapshots"
$LocalBackupDir = "$env:USERPROFILE\Documents\ATOCore_Backups"
$LogDir = "$LocalBackupDir\_logs"
$ReachabilityTest = 5 # seconds timeout for SSH probe
# --- Setup ---
if (-not (Test-Path $LocalBackupDir)) {
New-Item -ItemType Directory -Path $LocalBackupDir -Force | Out-Null
}
if (-not (Test-Path $LogDir)) {
New-Item -ItemType Directory -Path $LogDir -Force | Out-Null
}
$Timestamp = Get-Date -Format "yyyy-MM-dd_HHmmss"
$LogFile = "$LogDir\backup-$Timestamp.log"
function Log($msg) {
$line = "[{0}] {1}" -f (Get-Date -Format "yyyy-MM-dd HH:mm:ss"), $msg
Write-Host $line
Add-Content -Path $LogFile -Value $line
}
Log "=== AtoCore backup pull starting ==="
Log "Remote: $Remote"
Log "Local target: $LocalBackupDir"
# --- Reachability check: fail open if Dalidou is offline ---
Log "Checking Dalidou reachability..."
$probe = & ssh -o ConnectTimeout=$ReachabilityTest -o BatchMode=yes `
-o StrictHostKeyChecking=accept-new `
$Remote "echo ok" 2>&1
if ($LASTEXITCODE -ne 0 -or $probe -ne "ok") {
Log "Dalidou unreachable ($probe) -- fail-open exit"
exit 0
}
Log "Dalidou reachable."
# --- Pull the entire snapshots directory ---
# Dalidou's retention policy (7 daily + 4 weekly + 6 monthly) already caps
# the snapshot count, so pulling the whole dir is bounded and simple. scp
# will overwrite local files -- we rely on this to pick up new snapshots.
Log "Pulling snapshots via scp..."
$LocalSnapshotsDir = Join-Path $LocalBackupDir "snapshots"
if (-not (Test-Path $LocalSnapshotsDir)) {
New-Item -ItemType Directory -Path $LocalSnapshotsDir -Force | Out-Null
}
& scp -o BatchMode=yes -r "${Remote}:${RemoteSnapshots}/*" "$LocalSnapshotsDir\" 2>&1 |
ForEach-Object { Add-Content -Path $LogFile -Value $_ }
if ($LASTEXITCODE -ne 0) {
Log "scp failed with exit $LASTEXITCODE"
exit 0 # fail-open
}
# --- Stats ---
$snapshots = Get-ChildItem -Path $LocalSnapshotsDir -Directory |
Where-Object { $_.Name -match "^\d{8}T\d{6}Z$" } |
Sort-Object Name -Descending
$totalSize = (Get-ChildItem $LocalSnapshotsDir -Recurse -File | Measure-Object -Property Length -Sum).Sum
$SizeMB = [math]::Round($totalSize / 1MB, 2)
$latest = if ($snapshots.Count -gt 0) { $snapshots[0].Name } else { "(none)" }
Log ("Pulled {0} snapshots successfully (total {1} MB, latest: {2})" -f $snapshots.Count, $SizeMB, $latest)
Log "=== backup complete ==="
# --- Log retention: keep last 30 log files ---
Get-ChildItem -Path $LogDir -Filter "backup-*.log" |
Sort-Object Name -Descending |
Select-Object -Skip 30 |
ForEach-Object { Remove-Item $_.FullName -Force -ErrorAction SilentlyContinue }

558
src/atocore/api/routes.py
View File

@@ -55,6 +55,7 @@ from atocore.memory.extractor import (
) )
from atocore.memory.extractor_llm import ( from atocore.memory.extractor_llm import (
LLM_EXTRACTOR_VERSION, LLM_EXTRACTOR_VERSION,
_cli_available as _llm_cli_available,
extract_candidates_llm, extract_candidates_llm,
) )
from atocore.memory.reinforcement import reinforce_from_interaction from atocore.memory.reinforcement import reinforce_from_interaction
@@ -118,6 +119,68 @@ def wiki_search(q: str = "") -> HTMLResponse:
return HTMLResponse(content=render_search(q)) return HTMLResponse(content=render_search(q))
@router.get("/admin/triage", response_class=HTMLResponse)
def admin_triage(limit: int = 100) -> HTMLResponse:
"""Human triage UI for candidate memories.
Lists pending candidates with inline promote/reject/edit buttons.
Keyboard shortcuts: Y=promote, N=reject, E=edit content.
"""
from atocore.engineering.triage_ui import render_triage_page
return HTMLResponse(content=render_triage_page(limit=limit))
@router.post("/admin/triage/request-drain")
def admin_triage_request_drain() -> dict:
"""Request a host-side auto-triage run.
Writes a flag in project state. A host cron watcher picks it up
within ~2min and runs auto_triage.py, then clears the flag.
This is the bridge between "user clicked button in web UI" and
"claude CLI (on host, not in container) runs".
"""
from datetime import datetime as _dt, timezone as _tz
from atocore.context.project_state import set_state
now = _dt.now(_tz.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
set_state(
project_name="atocore",
category="config",
key="auto_triage_requested_at",
value=now,
source="admin ui",
)
return {"requested_at": now, "note": "Host watcher will pick this up within 2 minutes."}
@router.get("/admin/triage/drain-status")
def admin_triage_drain_status() -> dict:
"""Current state of the auto-triage pipeline (for UI polling)."""
from atocore.context.project_state import get_state
out = {
"requested_at": None,
"last_started_at": None,
"last_finished_at": None,
"last_result": None,
"is_running": False,
}
try:
for e in get_state("atocore"):
if e.category == "config" and e.key == "auto_triage_requested_at":
out["requested_at"] = e.value
elif e.category == "status" and e.key == "auto_triage_last_started_at":
out["last_started_at"] = e.value
elif e.category == "status" and e.key == "auto_triage_last_finished_at":
out["last_finished_at"] = e.value
elif e.category == "status" and e.key == "auto_triage_last_result":
out["last_result"] = e.value
elif e.category == "status" and e.key == "auto_triage_running":
out["is_running"] = (e.value == "1")
except Exception:
pass
return out
# --- Request/Response models --- # --- Request/Response models ---
@@ -191,12 +254,17 @@ class MemoryCreateRequest(BaseModel):
project: str = "" project: str = ""
confidence: float = 1.0 confidence: float = 1.0
status: str = "active" status: str = "active"
domain_tags: list[str] | None = None
valid_until: str = ""
class MemoryUpdateRequest(BaseModel): class MemoryUpdateRequest(BaseModel):
content: str | None = None content: str | None = None
confidence: float | None = None confidence: float | None = None
status: str | None = None status: str | None = None
memory_type: str | None = None
domain_tags: list[str] | None = None
valid_until: str | None = None
class ProjectStateSetRequest(BaseModel): class ProjectStateSetRequest(BaseModel):
@@ -395,6 +463,8 @@ def api_create_memory(req: MemoryCreateRequest) -> dict:
project=req.project, project=req.project,
confidence=req.confidence, confidence=req.confidence,
status=req.status, status=req.status,
domain_tags=req.domain_tags or [],
valid_until=req.valid_until or "",
) )
except ValueError as e: except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) raise HTTPException(status_code=400, detail=str(e))
@@ -439,6 +509,9 @@ def api_get_memories(
"reference_count": m.reference_count, "reference_count": m.reference_count,
"last_referenced_at": m.last_referenced_at, "last_referenced_at": m.last_referenced_at,
"updated_at": m.updated_at, "updated_at": m.updated_at,
"created_at": m.created_at,
"domain_tags": m.domain_tags or [],
"valid_until": m.valid_until or "",
} }
for m in memories for m in memories
], ],
@@ -456,6 +529,9 @@ def api_update_memory(memory_id: str, req: MemoryUpdateRequest) -> dict:
content=req.content, content=req.content,
confidence=req.confidence, confidence=req.confidence,
status=req.status, status=req.status,
memory_type=req.memory_type,
domain_tags=req.domain_tags,
valid_until=req.valid_until,
) )
except ValueError as e: except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) raise HTTPException(status_code=400, detail=str(e))
@@ -467,17 +543,117 @@ def api_update_memory(memory_id: str, req: MemoryUpdateRequest) -> dict:
@router.delete("/memory/{memory_id}") @router.delete("/memory/{memory_id}")
def api_invalidate_memory(memory_id: str) -> dict: def api_invalidate_memory(memory_id: str) -> dict:
"""Invalidate a memory (error correction).""" """Invalidate a memory (error correction)."""
success = invalidate_memory(memory_id) success = invalidate_memory(memory_id, actor="api-http")
if not success: if not success:
raise HTTPException(status_code=404, detail="Memory not found") raise HTTPException(status_code=404, detail="Memory not found")
return {"status": "invalidated", "id": memory_id} return {"status": "invalidated", "id": memory_id}
@router.get("/memory/{memory_id}/audit")
def api_memory_audit(memory_id: str, limit: int = 100) -> dict:
"""Return the audit history for a specific memory (newest first)."""
from atocore.memory.service import get_memory_audit
entries = get_memory_audit(memory_id, limit=limit)
return {"memory_id": memory_id, "entries": entries, "count": len(entries)}
@router.get("/admin/audit/recent")
def api_recent_audit(limit: int = 50) -> dict:
"""Return recent memory_audit entries across all memories (newest first)."""
from atocore.memory.service import get_recent_audit
entries = get_recent_audit(limit=limit)
return {"entries": entries, "count": len(entries)}
@router.post("/admin/integrity-check")
def api_integrity_check(persist: bool = True) -> dict:
"""Run the integrity scan inside the container (where DB + deps live).
Returns findings and persists them to project state when persist=True.
"""
from atocore.models.database import get_connection
from atocore.context.project_state import set_state
import json as _json
findings = {
"orphan_chunk_refs": 0, "duplicate_active": 0,
"orphan_project_state": 0, "orphan_chunks": 0,
"memory_count": 0, "active_memory_count": 0,
}
details: list[str] = []
with get_connection() as conn:
r = conn.execute(
"SELECT COUNT(*) FROM memories m "
"WHERE m.source_chunk_id IS NOT NULL AND m.source_chunk_id != '' "
"AND NOT EXISTS (SELECT 1 FROM source_chunks c WHERE c.id = m.source_chunk_id)"
).fetchone()
findings["orphan_chunk_refs"] = int(r[0] or 0)
if findings["orphan_chunk_refs"]:
details.append(f"{findings['orphan_chunk_refs']} memory(ies) reference missing source_chunk_id")
dup_rows = conn.execute(
"SELECT memory_type, project, content, COUNT(*) AS n FROM memories "
"WHERE status = 'active' GROUP BY memory_type, project, content HAVING n > 1"
).fetchall()
findings["duplicate_active"] = sum(int(r[3]) - 1 for r in dup_rows)
if findings["duplicate_active"]:
details.append(f"{findings['duplicate_active']} duplicate active row(s) across {len(dup_rows)} group(s)")
r = conn.execute(
"SELECT COUNT(*) FROM project_state ps "
"WHERE NOT EXISTS (SELECT 1 FROM projects p WHERE p.id = ps.project_id)"
).fetchone()
findings["orphan_project_state"] = int(r[0] or 0)
if findings["orphan_project_state"]:
details.append(f"{findings['orphan_project_state']} project_state row(s) reference missing project")
r = conn.execute(
"SELECT COUNT(*) FROM source_chunks c "
"WHERE NOT EXISTS (SELECT 1 FROM source_documents d WHERE d.id = c.document_id)"
).fetchone()
findings["orphan_chunks"] = int(r[0] or 0)
if findings["orphan_chunks"]:
details.append(f"{findings['orphan_chunks']} chunk(s) have no parent document")
findings["memory_count"] = int(conn.execute("SELECT COUNT(*) FROM memories").fetchone()[0])
findings["active_memory_count"] = int(
conn.execute("SELECT COUNT(*) FROM memories WHERE status = 'active'").fetchone()[0]
)
result = {"findings": findings, "details": details, "ok": not details}
if persist:
try:
set_state(
project_name="atocore", category="status",
key="integrity_check_result",
value=_json.dumps(result),
source="integrity check endpoint",
)
except Exception as e:
log.warning("integrity_check_state_write_failed", error=str(e))
if details:
try:
from atocore.observability.alerts import emit_alert
emit_alert(
severity="warning",
title="Integrity drift detected",
message="; ".join(details),
context={k: v for k, v in findings.items() if not k.endswith("_count")},
)
except Exception:
pass
return result
@router.post("/memory/{memory_id}/promote") @router.post("/memory/{memory_id}/promote")
def api_promote_memory(memory_id: str) -> dict: def api_promote_memory(memory_id: str) -> dict:
"""Promote a candidate memory to active (Phase 9 Commit C).""" """Promote a candidate memory to active (Phase 9 Commit C)."""
try: try:
success = promote_memory(memory_id) success = promote_memory(memory_id, actor="api-http")
except ValueError as e: except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) raise HTTPException(status_code=400, detail=str(e))
if not success: if not success:
@@ -491,7 +667,7 @@ def api_promote_memory(memory_id: str) -> dict:
@router.post("/memory/{memory_id}/reject") @router.post("/memory/{memory_id}/reject")
def api_reject_candidate_memory(memory_id: str) -> dict: def api_reject_candidate_memory(memory_id: str) -> dict:
"""Reject a candidate memory (Phase 9 Commit C review queue).""" """Reject a candidate memory (Phase 9 Commit C review queue)."""
success = reject_candidate_memory(memory_id) success = reject_candidate_memory(memory_id, actor="api-http")
if not success: if not success:
raise HTTPException( raise HTTPException(
status_code=404, status_code=404,
@@ -832,6 +1008,18 @@ def api_extract_batch(req: ExtractBatchRequest | None = None) -> dict:
invoke this endpoint explicitly (cron, manual curl, CLI). invoke this endpoint explicitly (cron, manual curl, CLI).
""" """
payload = req or ExtractBatchRequest() payload = req or ExtractBatchRequest()
if payload.mode == "llm" and not _llm_cli_available():
raise HTTPException(
status_code=503,
detail=(
"LLM extraction unavailable in this runtime: the `claude` CLI "
"is not on PATH. Run host-side via "
"`scripts/batch_llm_extract_live.py` instead, or call this "
"endpoint with mode=\"rule\"."
),
)
since = payload.since since = payload.since
if not since: if not since:
@@ -916,11 +1104,14 @@ def api_dashboard() -> dict:
"""One-shot system observability dashboard. """One-shot system observability dashboard.
Returns memory counts by type/project/status, project state Returns memory counts by type/project/status, project state
entry counts, recent interaction volume, and extraction pipeline entry counts, interaction volume by client, pipeline health
(harness, triage stats, last run), and extraction pipeline
status — everything an operator needs to understand AtoCore's status — everything an operator needs to understand AtoCore's
health beyond the basic /health endpoint. health beyond the basic /health endpoint.
""" """
import json as _json
from collections import Counter from collections import Counter
from datetime import datetime as _dt, timezone as _tz
all_memories = get_memories(active_only=False, limit=500) all_memories = get_memories(active_only=False, limit=500)
active = [m for m in all_memories if m.status == "active"] active = [m for m in all_memories if m.status == "active"]
@@ -930,27 +1121,113 @@ def api_dashboard() -> dict:
project_counts = dict(Counter(m.project or "(none)" for m in active)) project_counts = dict(Counter(m.project or "(none)" for m in active))
reinforced = [m for m in active if m.reference_count > 0] reinforced = [m for m in active if m.reference_count > 0]
interactions = list_interactions(limit=1) # Interaction stats — total + by_client from DB directly
recent_interaction = interactions[0].created_at if interactions else None interaction_stats: dict = {"most_recent": None, "total": 0, "by_client": {}}
try:
from atocore.models.database import get_connection as _gc
# Extraction pipeline status with _gc() as conn:
extract_state = {} row = conn.execute("SELECT count(*) FROM interactions").fetchone()
interaction_stats["total"] = row[0] if row else 0
rows = conn.execute(
"SELECT client, count(*) FROM interactions GROUP BY client"
).fetchall()
interaction_stats["by_client"] = {r[0]: r[1] for r in rows}
row = conn.execute(
"SELECT created_at FROM interactions ORDER BY created_at DESC LIMIT 1"
).fetchone()
interaction_stats["most_recent"] = row[0] if row else None
except Exception:
interactions = list_interactions(limit=1)
interaction_stats["most_recent"] = (
interactions[0].created_at if interactions else None
)
# Pipeline health from project state
pipeline: dict = {}
extract_state: dict = {}
integrity: dict = {}
alerts: dict = {}
try: try:
state_entries = get_state("atocore") state_entries = get_state("atocore")
for entry in state_entries: for entry in state_entries:
if entry.category == "status" and entry.key == "last_extract_batch_run": if entry.category == "status":
extract_state["last_run"] = entry.value if entry.key == "last_extract_batch_run":
extract_state["last_run"] = entry.value
elif entry.key == "pipeline_last_run":
pipeline["last_run"] = entry.value
try:
last = _dt.fromisoformat(entry.value.replace("Z", "+00:00"))
delta = _dt.now(_tz.utc) - last
pipeline["hours_since_last_run"] = round(
delta.total_seconds() / 3600, 1
)
except Exception:
pass
elif entry.key == "pipeline_summary":
try:
pipeline["summary"] = _json.loads(entry.value)
except Exception:
pipeline["summary_raw"] = entry.value
elif entry.key == "retrieval_harness_result":
try:
pipeline["harness"] = _json.loads(entry.value)
except Exception:
pipeline["harness_raw"] = entry.value
elif entry.key == "integrity_check_result":
try:
integrity = _json.loads(entry.value)
except Exception:
pass
elif entry.category == "alert":
# keys like "last_info", "last_warning", "last_critical"
try:
payload = _json.loads(entry.value)
except Exception:
payload = {"raw": entry.value}
severity = entry.key.replace("last_", "")
alerts[severity] = payload
except Exception: except Exception:
pass pass
# Project state counts # Project state counts — include all registered projects
ps_counts = {} ps_counts = {}
for proj_id in ["p04-gigabit", "p05-interferometer", "p06-polisher", "atocore"]: try:
try: from atocore.projects.registry import load_project_registry as _lpr
entries = get_state(proj_id)
ps_counts[proj_id] = len(entries) for proj in _lpr():
except Exception: try:
pass entries = get_state(proj.project_id)
ps_counts[proj.project_id] = len(entries)
except Exception:
pass
except Exception:
for proj_id in [
"p04-gigabit", "p05-interferometer", "p06-polisher", "atocore",
]:
try:
entries = get_state(proj_id)
ps_counts[proj_id] = len(entries)
except Exception:
pass
# Triage queue health
triage: dict = {
"pending": len(candidates),
"review_url": "/admin/triage",
}
if len(candidates) > 50:
triage["warning"] = f"High queue: {len(candidates)} candidates pending review."
elif len(candidates) > 20:
triage["notice"] = f"{len(candidates)} candidates awaiting triage."
# Recent audit activity (Phase 4 V1) — last 10 mutations for operator
recent_audit: list[dict] = []
try:
from atocore.memory.service import get_recent_audit as _gra
recent_audit = _gra(limit=10)
except Exception:
pass
return { return {
"memories": { "memories": {
@@ -964,10 +1241,13 @@ def api_dashboard() -> dict:
"counts": ps_counts, "counts": ps_counts,
"total": sum(ps_counts.values()), "total": sum(ps_counts.values()),
}, },
"interactions": { "interactions": interaction_stats,
"most_recent": recent_interaction,
},
"extraction_pipeline": extract_state, "extraction_pipeline": extract_state,
"pipeline": pipeline,
"triage": triage,
"integrity": integrity,
"alerts": alerts,
"recent_audit": recent_audit,
} }
@@ -1006,6 +1286,7 @@ def api_create_entity(req: EntityCreateRequest) -> dict:
status=req.status, status=req.status,
confidence=req.confidence, confidence=req.confidence,
source_refs=req.source_refs, source_refs=req.source_refs,
actor="api-http",
) )
except ValueError as e: except ValueError as e:
raise HTTPException(status_code=400, detail=str(e)) raise HTTPException(status_code=400, detail=str(e))
@@ -1046,6 +1327,243 @@ def api_list_entities(
} }
@router.get("/admin/conflicts")
def api_list_conflicts(project: str | None = None) -> dict:
"""Phase 5G: list open entity conflicts (optionally scoped to a project)."""
from atocore.engineering.conflicts import list_open_conflicts
conflicts = list_open_conflicts(project=project)
return {"conflicts": conflicts, "count": len(conflicts)}
class ConflictResolveRequest(BaseModel):
action: str # dismiss|supersede_others|no_action
winner_id: str | None = None
@router.post("/admin/conflicts/{conflict_id}/resolve")
def api_resolve_conflict(conflict_id: str, req: ConflictResolveRequest) -> dict:
"""Resolve a conflict. Options: dismiss, supersede_others (needs winner_id), no_action."""
from atocore.engineering.conflicts import resolve_conflict
try:
success = resolve_conflict(
conflict_id=conflict_id,
action=req.action,
winner_id=req.winner_id,
actor="api-http",
)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
if not success:
raise HTTPException(status_code=404, detail=f"Conflict not found or already resolved: {conflict_id}")
return {"status": "resolved", "id": conflict_id, "action": req.action}
class GraduationRequestBody(BaseModel):
project: str = ""
limit: int = 30
@router.post("/admin/graduation/request")
def api_request_graduation(req: GraduationRequestBody) -> dict:
"""Request a host-side memory-graduation run.
Writes a flag in project_state with project + limit. A host cron
watcher picks it up within ~2 min and runs graduate_memories.py.
Mirrors the /admin/triage/request-drain pattern (bridges container
→ host because claude CLI lives on host, not container).
"""
import json as _json
from datetime import datetime as _dt, timezone as _tz
from atocore.context.project_state import set_state
now = _dt.now(_tz.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
payload = _json.dumps({
"project": (req.project or "").strip(),
"limit": max(1, min(req.limit, 500)),
"requested_at": now,
})
set_state(
project_name="atocore",
category="config",
key="graduation_requested_at",
value=payload,
source="admin ui",
)
return {
"requested_at": now,
"project": req.project,
"limit": req.limit,
"note": "Host watcher picks up within ~2 min. Poll /admin/graduation/status for progress.",
}
@router.get("/admin/graduation/status")
def api_graduation_status() -> dict:
"""State of the graduation pipeline (UI polling)."""
import json as _json
from atocore.context.project_state import get_state
out = {
"requested": None,
"last_started_at": None,
"last_finished_at": None,
"last_result": None,
"is_running": False,
}
try:
for e in get_state("atocore"):
if e.category != "config" and e.category != "status":
continue
if e.key == "graduation_requested_at":
try:
out["requested"] = _json.loads(e.value)
except Exception:
out["requested"] = {"raw": e.value}
elif e.key == "graduation_last_started_at":
out["last_started_at"] = e.value
elif e.key == "graduation_last_finished_at":
out["last_finished_at"] = e.value
elif e.key == "graduation_last_result":
out["last_result"] = e.value
elif e.key == "graduation_running":
out["is_running"] = (e.value == "1")
except Exception:
pass
return out
@router.get("/admin/graduation/stats")
def api_graduation_stats() -> dict:
"""Phase 5F graduation stats for dashboard."""
from atocore.models.database import get_connection
with get_connection() as conn:
total_memories = int(conn.execute("SELECT COUNT(*) FROM memories WHERE status = 'active'").fetchone()[0])
graduated = int(conn.execute("SELECT COUNT(*) FROM memories WHERE status = 'graduated'").fetchone()[0])
entity_candidates_from_mem = int(conn.execute(
"SELECT COUNT(*) FROM entities WHERE status = 'candidate' "
"AND source_refs LIKE '%memory:%'"
).fetchone()[0])
active_entities = int(conn.execute("SELECT COUNT(*) FROM entities WHERE status = 'active'").fetchone()[0])
return {
"active_memories": total_memories,
"graduated_memories": graduated,
"entity_candidates_from_memories": entity_candidates_from_mem,
"active_entities": active_entities,
"graduation_rate": (
graduated / (total_memories + graduated) if (total_memories + graduated) > 0 else 0.0
),
}
# --- Phase 5 Engineering V1: The 10 canonical queries ---
@router.get("/engineering/projects/{project_name}/systems")
def api_system_map(project_name: str) -> dict:
"""Q-001 + Q-004: subsystem/component tree for a project."""
from atocore.engineering.queries import system_map
return system_map(project_name)
@router.get("/engineering/decisions")
def api_decisions_affecting(
project: str,
subsystem: str | None = None,
) -> dict:
"""Q-008: decisions affecting a subsystem (or the whole project)."""
from atocore.engineering.queries import decisions_affecting
return decisions_affecting(project, subsystem_id=subsystem)
@router.get("/engineering/components/{component_id}/requirements")
def api_requirements_for_component(component_id: str) -> dict:
"""Q-005: requirements that a component satisfies."""
from atocore.engineering.queries import requirements_for
return requirements_for(component_id)
@router.get("/engineering/changes")
def api_recent_engineering_changes(
project: str,
since: str | None = None,
limit: int = 50,
) -> dict:
"""Q-013: entity changes in project since timestamp."""
from atocore.engineering.queries import recent_changes
return recent_changes(project, since=since, limit=limit)
@router.get("/engineering/gaps/orphan-requirements")
def api_orphan_requirements(project: str) -> dict:
"""Q-006 (killer): requirements with no SATISFIES edge."""
from atocore.engineering.queries import orphan_requirements
return orphan_requirements(project)
@router.get("/engineering/gaps/risky-decisions")
def api_risky_decisions(project: str) -> dict:
"""Q-009 (killer): decisions resting on flagged/superseded assumptions."""
from atocore.engineering.queries import risky_decisions
return risky_decisions(project)
@router.get("/engineering/gaps/unsupported-claims")
def api_unsupported_claims(project: str) -> dict:
"""Q-011 (killer): validation claims with no SUPPORTS edge."""
from atocore.engineering.queries import unsupported_claims
return unsupported_claims(project)
@router.get("/engineering/gaps")
def api_all_gaps(project: str) -> dict:
"""Combined Q-006 + Q-009 + Q-011 for a project."""
from atocore.engineering.queries import all_gaps
return all_gaps(project)
@router.get("/engineering/impact")
def api_impact_analysis(entity: str, max_depth: int = 3) -> dict:
"""Q-016: transitive outbound impact of changing an entity."""
from atocore.engineering.queries import impact_analysis
return impact_analysis(entity, max_depth=max_depth)
@router.get("/engineering/evidence")
def api_evidence_chain(entity: str) -> dict:
"""Q-017: inbound evidence chain for an entity."""
from atocore.engineering.queries import evidence_chain
return evidence_chain(entity)
@router.post("/entities/{entity_id}/promote")
def api_promote_entity(entity_id: str) -> dict:
"""Promote a candidate entity to active (Phase 5 Engineering V1)."""
from atocore.engineering.service import promote_entity
success = promote_entity(entity_id, actor="api-http")
if not success:
raise HTTPException(status_code=404, detail=f"Entity not found or not a candidate: {entity_id}")
return {"status": "promoted", "id": entity_id}
@router.post("/entities/{entity_id}/reject")
def api_reject_entity(entity_id: str) -> dict:
"""Reject a candidate entity (Phase 5)."""
from atocore.engineering.service import reject_entity_candidate
success = reject_entity_candidate(entity_id, actor="api-http")
if not success:
raise HTTPException(status_code=404, detail=f"Entity not found or not a candidate: {entity_id}")
return {"status": "rejected", "id": entity_id}
@router.get("/entities/{entity_id}/audit")
def api_entity_audit(entity_id: str, limit: int = 100) -> dict:
"""Return the audit history for a specific entity."""
from atocore.engineering.service import get_entity_audit
entries = get_entity_audit(entity_id, limit=limit)
return {"entity_id": entity_id, "entries": entries, "count": len(entries)}
@router.get("/entities/{entity_id}") @router.get("/entities/{entity_id}")
def api_get_entity(entity_id: str) -> dict: def api_get_entity(entity_id: str) -> dict:
"""Get an entity with its relationships and related entities.""" """Get an entity with its relationships and related entities."""

15
src/atocore/config.py
View File

@@ -104,6 +104,21 @@ class Settings(BaseSettings):
@property @property
def resolved_project_registry_path(self) -> Path: def resolved_project_registry_path(self) -> Path:
"""Path to the project registry JSON file.
If ``ATOCORE_PROJECT_REGISTRY_DIR`` env var is set, the registry
lives at ``<that dir>/project-registry.json``. Otherwise falls
back to the configured ``project_registry_path`` field.
This lets Docker deployments point at a mounted volume via env
var without the ephemeral in-image ``/app/config/`` getting
wiped on every rebuild.
"""
import os
registry_dir = os.environ.get("ATOCORE_PROJECT_REGISTRY_DIR", "").strip()
if registry_dir:
return Path(registry_dir) / "project-registry.json"
return self._resolve_path(self.project_registry_path) return self._resolve_path(self.project_registry_path)
@property @property

17
src/atocore/context/builder.py
View File

@@ -508,6 +508,23 @@ def _build_engineering_context(
f" {direction} {rel.relationship_type} [{other.entity_type}] {other.name}" f" {direction} {rel.relationship_type} [{other.entity_type}] {other.name}"
) )
# Phase 5H: append a compact gaps summary so the LLM always sees
# "what we're currently missing" alongside the entity neighborhood.
# This is the director's most-used insight — orphan requirements,
# risky decisions, unsupported claims — surfaced in every context pack
# for project-scoped queries.
try:
from atocore.engineering.queries import all_gaps as _all_gaps
gaps = _all_gaps(project)
orphan_n = gaps["orphan_requirements"]["count"]
risky_n = gaps["risky_decisions"]["count"]
unsup_n = gaps["unsupported_claims"]["count"]
if orphan_n or risky_n or unsup_n:
lines.append("")
lines.append(f"Gaps: {orphan_n} orphan reqs, {risky_n} risky decisions, {unsup_n} unsupported claims")
except Exception:
pass
lines.append("--- End Engineering Context ---") lines.append("--- End Engineering Context ---")
text = "\n".join(lines) text = "\n".join(lines)

194
src/atocore/engineering/_graduation_prompt.py Normal file
View File

@@ -0,0 +1,194 @@
"""Shared LLM prompt for memory → entity graduation (Phase 5F).
Mirrors the pattern of ``atocore.memory._llm_prompt``: stdlib-only so both
the container extractor path and the host-side graduate_memories.py script
use the same system prompt and parser, eliminating drift.
Graduation asks: "does this active memory describe a TYPED engineering entity
that belongs in the knowledge graph?" If yes, produce an entity candidate
with type + name + description + zero-or-more relationship hints. If no,
return null so the memory stays as-is.
Design note: we DON'T ask the LLM to resolve targets of relationships (e.g.,
"connect to Subsystem 'Optics'"). That's done in a second pass after human
review — partly to keep this prompt cheap, partly because name-matching
targets across projects is a hard problem worth its own pass.
"""
from __future__ import annotations
import json
from typing import Any
GRADUATION_PROMPT_VERSION = "graduate-0.1.0"
MAX_CONTENT_CHARS = 1500
ENTITY_TYPES = {
"project",
"system",
"subsystem",
"component",
"interface",
"requirement",
"constraint",
"decision",
"material",
"parameter",
"analysis_model",
"result",
"validation_claim",
"vendor",
"process",
}
SYSTEM_PROMPT = """You are a knowledge-graph curator for an engineering firm's context system (AtoCore).
Your job: given one active MEMORY (a curated fact about an engineering project), decide whether it describes a TYPED engineering entity that belongs in the structured graph. If yes, emit the entity candidate. If no, return null.
A memory gets graduated when its content names a specific thing that has lifecycle, relationships, or cross-references in engineering work. A memory stays as-is when it's a general observation, preference, or loose context.
ENTITY TYPES (choose the best fit):
- project — a named project (usually already registered; rare to emit)
- subsystem — a named chunk of a system with defined boundaries (e.g., "Primary Optics", "Cable Tensioning", "Motion Control")
- component — a discrete physical or logical part (e.g., "Primary Mirror", "Pivot Pin", "Z-axis Servo Drive")
- interface — a named boundary between two subsystems/components (e.g., "Mirror-to-Cell mounting interface")
- requirement — a "must" or "shall" statement (e.g., "Surface figure < 25nm RMS")
- constraint — a non-negotiable limit (e.g., "Thermal operating range 0-40°C")
- decision — a committed design direction (e.g., "Selected Zerodur over ULE for primary blank")
- material — a named material used in a component (e.g., "Zerodur", "Invar 36")
- parameter — a specific named value or assumption (e.g., "Ambient temperature 22°C", "Lead time 6 weeks")
- analysis_model — a named FEA / optical / thermal model (e.g., "Preston wear model v2")
- result — a named measurement or simulation output (e.g., "FEA thermal sweep 2026-03")
- validation_claim — an asserted claim to be backed by evidence (e.g., "Margin is adequate for full envelope")
- vendor — a supplier / partner entity (e.g., "Schott AG", "ABB Space", "Nabeel")
- process — a named workflow step (e.g., "Ion beam figuring pass", "Incoming inspection")
- system — whole project's system envelope (rare; usually project handles this)
WHEN TO GRADUATE:
GRADUATE if the memory clearly names one of these entities with enough detail to be useful. Examples:
- "Selected Zerodur for the p04 primary mirror blank" → 2 entities: decision(name="Select Zerodur for primary blank") + material(name="Zerodur")
- "ABB Space (INO) is the polishing vendor for p04" → vendor(name="ABB Space")
- "Surface figure target is < 25nm RMS after IBF" → requirement(name="Surface figure < 25nm RMS after IBF")
- "The Preston model assumes 5N min contact pressure" → parameter(name="Preston min contact pressure = 5N")
DON'T GRADUATE if the memory is:
- A preference or work-style note (those stay as memories)
- A session observation ("we tested X today") — no durable typed thing
- A general insight / rule of thumb ("Always calibrate before measuring")
- An OpenClaw MEMORY.md import of conversational history
- Something where you can't pick a clear entity type with confidence
OUTPUT FORMAT — exactly one JSON object:
If graduating, emit:
{
"graduate": true,
"entity_type": "component|requirement|decision|...",
"name": "short noun phrase, <60 chars",
"description": "one-sentence description that adds context beyond the name",
"confidence": 0.0-1.0,
"relationships": [
{"rel_type": "part_of|satisfies|uses_material|based_on_assumption|constrained_by|affected_by_decision|supports|evidenced_by|described_by", "target_hint": "name of the target entity (human will resolve)"}
]
}
If not graduating, emit:
{"graduate": false, "reason": "one-sentence reason"}
Rules:
- Output ONLY the JSON object, no markdown, no prose
- name MUST be <60 chars and specific; reject vague names like "the system"
- confidence: 0.6-0.7 is typical. Raise to 0.8+ only if the memory is very specific and unambiguous.
- relationships array can be empty
- target_hint is a free-text name; the human-review stage will resolve it to an actual entity id (or reject if the target doesn't exist yet)
- If the memory describes MULTIPLE entities, pick the single most important one; a second pass can catch the others
"""
def build_user_message(memory_content: str, memory_project: str, memory_type: str) -> str:
return (
f"MEMORY PROJECT: {memory_project or '(unscoped)'}\n"
f"MEMORY TYPE: {memory_type}\n\n"
f"MEMORY CONTENT:\n{memory_content[:MAX_CONTENT_CHARS]}\n\n"
"Return the JSON decision now."
)
def parse_graduation_output(raw: str) -> dict[str, Any] | None:
"""Parse the LLM's graduation decision. Return None on any parse error.
On success returns the normalized decision dict with keys:
graduate (bool), entity_type (str), name (str), description (str),
confidence (float), relationships (list of {rel_type, target_hint})
OR {"graduate": false, "reason": "..."}
"""
text = (raw or "").strip()
if not text:
return None
if text.startswith("```"):
text = text.strip("`")
nl = text.find("\n")
if nl >= 0:
text = text[nl + 1:]
if text.endswith("```"):
text = text[:-3]
text = text.strip()
# Tolerate leading prose
if not text.lstrip().startswith("{"):
start = text.find("{")
end = text.rfind("}")
if start >= 0 and end > start:
text = text[start:end + 1]
try:
parsed = json.loads(text)
except json.JSONDecodeError:
return None
if not isinstance(parsed, dict):
return None
graduate = bool(parsed.get("graduate", False))
if not graduate:
return {"graduate": False, "reason": str(parsed.get("reason", ""))[:200]}
entity_type = str(parsed.get("entity_type") or "").strip().lower()
if entity_type not in ENTITY_TYPES:
return None
name = str(parsed.get("name") or "").strip()
if not name or len(name) > 120:
return None
description = str(parsed.get("description") or "").strip()[:500]
try:
confidence = float(parsed.get("confidence", 0.6))
except (TypeError, ValueError):
confidence = 0.6
confidence = max(0.0, min(1.0, confidence))
raw_rels = parsed.get("relationships") or []
if not isinstance(raw_rels, list):
raw_rels = []
relationships: list[dict] = []
for r in raw_rels[:10]:
if not isinstance(r, dict):
continue
rtype = str(r.get("rel_type") or "").strip().lower()
target = str(r.get("target_hint") or "").strip()
if not rtype or not target:
continue
relationships.append({"rel_type": rtype, "target_hint": target[:120]})
return {
"graduate": True,
"entity_type": entity_type,
"name": name,
"description": description,
"confidence": confidence,
"relationships": relationships,
}

291
src/atocore/engineering/conflicts.py Normal file
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@@ -0,0 +1,291 @@
"""Phase 5G — Conflict detection on entity promote.
When a candidate entity is promoted to active, we check whether another
active entity is already claiming the "same slot" with an incompatible
value. If so, we emit a conflicts row + conflict_members rows so the
human can resolve.
Slot keys are per-entity-type (from ``conflict-model.md``). V1 starts
narrow with 3 slot kinds to avoid false positives:
1. **component.material** — a component should normally have ONE
dominant material (via USES_MATERIAL edge). Two active USES_MATERIAL
edges from the same component pointing at different materials =
conflict.
2. **component.part_of** — a component should belong to AT MOST one
subsystem (via PART_OF). Two active PART_OF edges = conflict.
3. **requirement.value** — two active Requirements with the same name in
the same project but different descriptions = conflict.
Rule: **flag, never block**. The promote succeeds; the conflict row is
just a flag for the human. Users see conflicts in the dashboard and on
wiki entity pages with a "⚠️ Disputed" badge.
"""
from __future__ import annotations
import uuid
from datetime import datetime, timezone
from atocore.models.database import get_connection
from atocore.observability.logger import get_logger
log = get_logger("conflicts")
def detect_conflicts_for_entity(entity_id: str) -> list[str]:
"""Run conflict detection for a newly-promoted active entity.
Returns a list of conflict_ids created. Fail-open: any detection error
is logged and returns an empty list; the promote itself is not affected.
"""
try:
with get_connection() as conn:
row = conn.execute(
"SELECT * FROM entities WHERE id = ? AND status = 'active'",
(entity_id,),
).fetchone()
if row is None:
return []
created: list[str] = []
etype = row["entity_type"]
project = row["project"] or ""
if etype == "component":
created.extend(_check_component_conflicts(entity_id, project))
elif etype == "requirement":
created.extend(_check_requirement_conflicts(entity_id, row["name"], project))
return created
except Exception as e:
log.warning("conflict_detection_failed", entity_id=entity_id, error=str(e))
return []
def _check_component_conflicts(component_id: str, project: str) -> list[str]:
"""Check material + part_of slot uniqueness for a component."""
created: list[str] = []
with get_connection() as conn:
# component.material conflicts
mat_edges = conn.execute(
"SELECT r.id AS rel_id, r.target_entity_id, e.name "
"FROM relationships r "
"JOIN entities e ON e.id = r.target_entity_id "
"WHERE r.source_entity_id = ? AND r.relationship_type = 'uses_material' "
"AND e.status = 'active'",
(component_id,),
).fetchall()
if len(mat_edges) > 1:
cid = _record_conflict(
slot_kind="component.material",
slot_key=component_id,
project=project,
note=f"component has {len(mat_edges)} active material edges",
members=[
{
"kind": "entity",
"id": m["target_entity_id"],
"snapshot": m["name"],
}
for m in mat_edges
],
)
if cid:
created.append(cid)
# component.part_of conflicts
pof_edges = conn.execute(
"SELECT r.id AS rel_id, r.target_entity_id, e.name "
"FROM relationships r "
"JOIN entities e ON e.id = r.target_entity_id "
"WHERE r.source_entity_id = ? AND r.relationship_type = 'part_of' "
"AND e.status = 'active'",
(component_id,),
).fetchall()
if len(pof_edges) > 1:
cid = _record_conflict(
slot_kind="component.part_of",
slot_key=component_id,
project=project,
note=f"component is part_of {len(pof_edges)} subsystems",
members=[
{
"kind": "entity",
"id": p["target_entity_id"],
"snapshot": p["name"],
}
for p in pof_edges
],
)
if cid:
created.append(cid)
return created
def _check_requirement_conflicts(requirement_id: str, name: str, project: str) -> list[str]:
"""Two active Requirements with the same name in the same project."""
with get_connection() as conn:
peers = conn.execute(
"SELECT id, description FROM entities "
"WHERE entity_type = 'requirement' AND status = 'active' "
"AND project = ? AND LOWER(name) = LOWER(?) AND id != ?",
(project, name, requirement_id),
).fetchall()
if not peers:
return []
members = [{"kind": "entity", "id": requirement_id, "snapshot": name}]
for p in peers:
members.append({"kind": "entity", "id": p["id"],
"snapshot": (p["description"] or "")[:200]})
cid = _record_conflict(
slot_kind="requirement.name",
slot_key=f"{project}|{name.lower()}",
project=project,
note=f"{len(peers)+1} active requirements share the name '{name}'",
members=members,
)
return [cid] if cid else []
def _record_conflict(
slot_kind: str,
slot_key: str,
project: str,
note: str,
members: list[dict],
) -> str | None:
"""Persist a conflict + its members; skip if an open conflict already
exists for the same (slot_kind, slot_key)."""
try:
with get_connection() as conn:
existing = conn.execute(
"SELECT id FROM conflicts WHERE slot_kind = ? AND slot_key = ? "
"AND status = 'open'",
(slot_kind, slot_key),
).fetchone()
if existing:
return None # don't dup
conflict_id = str(uuid.uuid4())
conn.execute(
"INSERT INTO conflicts (id, slot_kind, slot_key, project, "
"status, note) VALUES (?, ?, ?, ?, 'open', ?)",
(conflict_id, slot_kind, slot_key, project, note[:500]),
)
for m in members:
conn.execute(
"INSERT INTO conflict_members (id, conflict_id, member_kind, "
"member_id, value_snapshot) VALUES (?, ?, ?, ?, ?)",
(str(uuid.uuid4()), conflict_id,
m.get("kind", "entity"), m.get("id", ""),
(m.get("snapshot") or "")[:500]),
)
log.info("conflict_detected", conflict_id=conflict_id,
slot_kind=slot_kind, project=project)
# Emit a warning alert so the operator sees it
try:
from atocore.observability.alerts import emit_alert
emit_alert(
severity="warning",
title=f"Entity conflict: {slot_kind}",
message=note,
context={"project": project, "slot_key": slot_key,
"member_count": len(members)},
)
except Exception:
pass
return conflict_id
except Exception as e:
log.warning("conflict_record_failed", error=str(e))
return None
def list_open_conflicts(project: str | None = None) -> list[dict]:
"""Return open conflicts with their members."""
with get_connection() as conn:
query = "SELECT * FROM conflicts WHERE status = 'open'"
params: list = []
if project:
query += " AND project = ?"
params.append(project)
query += " ORDER BY detected_at DESC"
rows = conn.execute(query, params).fetchall()
conflicts = []
for r in rows:
member_rows = conn.execute(
"SELECT * FROM conflict_members WHERE conflict_id = ?",
(r["id"],),
).fetchall()
conflicts.append({
"id": r["id"],
"slot_kind": r["slot_kind"],
"slot_key": r["slot_key"],
"project": r["project"] or "",
"status": r["status"],
"note": r["note"] or "",
"detected_at": r["detected_at"],
"members": [
{
"id": m["id"],
"member_kind": m["member_kind"],
"member_id": m["member_id"],
"snapshot": m["value_snapshot"] or "",
}
for m in member_rows
],
})
return conflicts
def resolve_conflict(
conflict_id: str,
action: str, # "dismiss", "supersede_others", "no_action"
winner_id: str | None = None,
actor: str = "api",
) -> bool:
"""Resolve a conflict. Optionally marks non-winner members as superseded."""
if action not in ("dismiss", "supersede_others", "no_action"):
raise ValueError(f"Invalid action: {action}")
now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
with get_connection() as conn:
row = conn.execute(
"SELECT * FROM conflicts WHERE id = ?", (conflict_id,)
).fetchone()
if row is None or row["status"] != "open":
return False
if action == "supersede_others":
if not winner_id:
raise ValueError("winner_id required for supersede_others")
# Mark non-winner member entities as superseded
member_rows = conn.execute(
"SELECT member_id FROM conflict_members WHERE conflict_id = ?",
(conflict_id,),
).fetchall()
for m in member_rows:
if m["member_id"] != winner_id:
conn.execute(
"UPDATE entities SET status = 'superseded', updated_at = ? "
"WHERE id = ? AND status = 'active'",
(now, m["member_id"]),
)
conn.execute(
"UPDATE conflicts SET status = 'resolved', resolution = ?, "
"resolved_at = ? WHERE id = ?",
(action, now, conflict_id),
)
log.info("conflict_resolved", conflict_id=conflict_id,
action=action, actor=actor)
return True

61
src/atocore/engineering/mirror.py
View File

@@ -29,6 +29,7 @@ def generate_project_overview(project: str) -> str:
sections = [ sections = [
_header(project), _header(project),
_synthesis_section(project), _synthesis_section(project),
_gaps_section(project), # Phase 5: killer queries surface here
_state_section(project), _state_section(project),
_system_architecture(project), _system_architecture(project),
_decisions_section(project), _decisions_section(project),
@@ -41,6 +42,66 @@ def generate_project_overview(project: str) -> str:
return "\n\n".join(s for s in sections if s) return "\n\n".join(s for s in sections if s)
def _gaps_section(project: str) -> str:
"""Phase 5: surface the 3 killer-query gaps on every project page.
If any gap is non-empty, it appears near the top so the director
sees "what am I forgetting?" before the rest of the report.
"""
try:
from atocore.engineering.queries import all_gaps
result = all_gaps(project)
except Exception:
return ""
orphan = result["orphan_requirements"]["count"]
risky = result["risky_decisions"]["count"]
unsup = result["unsupported_claims"]["count"]
if orphan == 0 and risky == 0 and unsup == 0:
return (
"## Coverage Gaps\n\n"
"> ✅ No gaps detected: every requirement is satisfied, "
"no decisions rest on flagged assumptions, every claim has evidence.\n"
)
lines = ["## Coverage Gaps", ""]
lines.append(
"> ⚠️ Items below need attention — gaps in the engineering graph.\n"
)
if orphan:
lines.append(f"### {orphan} Orphan Requirement(s)")
lines.append("*Requirements with no component claiming to satisfy them:*")
lines.append("")
for r in result["orphan_requirements"]["gaps"][:10]:
lines.append(f"- **{r['name']}** — {(r['description'] or '')[:120]}")
if orphan > 10:
lines.append(f"- _...and {orphan - 10} more_")
lines.append("")
if risky:
lines.append(f"### {risky} Risky Decision(s)")
lines.append("*Decisions based on assumptions that are flagged, superseded, or invalid:*")
lines.append("")
for d in result["risky_decisions"]["gaps"][:10]:
lines.append(
f"- **{d['decision_name']}** — based on flagged assumption "
f"_{d['assumption_name']}_ ({d['assumption_status']})"
)
lines.append("")
if unsup:
lines.append(f"### {unsup} Unsupported Claim(s)")
lines.append("*Validation claims with no supporting Result entity:*")
lines.append("")
for c in result["unsupported_claims"]["gaps"][:10]:
lines.append(f"- **{c['name']}** — {(c['description'] or '')[:120]}")
lines.append("")
return "\n".join(lines)
def _synthesis_section(project: str) -> str: def _synthesis_section(project: str) -> str:
"""Generate a short LLM synthesis of the current project state. """Generate a short LLM synthesis of the current project state.

467
src/atocore/engineering/queries.py Normal file
View File

@@ -0,0 +1,467 @@
"""Phase 5 Engineering V1 — The 10 canonical queries.
Each function maps to one or more catalog IDs in
``docs/architecture/engineering-query-catalog.md``. Return values are plain
dicts so API and wiki renderers can consume them without importing dataclasses.
Design principles:
- All queries filter to status='active' unless the caller asks otherwise
- All project filters go through ``resolve_project_name`` (canonicalization)
- Graph traversals are bounded (depth <= 3 for impact, limit 200 for lists)
- The 3 "killer" queries (gaps) accept project as required — gaps are always
scoped to one project in V1
These queries are the *useful surface* of the entity graph. Before this module,
the graph was data with no narrative; after this module, the director can ask
real questions about coverage, risk, and evidence.
"""
from __future__ import annotations
from datetime import datetime, timezone
from atocore.engineering.service import (
Entity,
_row_to_entity,
get_entity,
get_relationships,
)
from atocore.models.database import get_connection
from atocore.projects.registry import resolve_project_name
# ============================================================
# Structure queries (Q-001, Q-004, Q-005, Q-008)
# ============================================================
def system_map(project: str) -> dict:
"""Q-001 + Q-004: return the full subsystem/component tree for a project.
Shape:
{
"project": "p05-interferometer",
"subsystems": [
{
"id": ..., "name": ..., "description": ...,
"components": [{id, name, description, materials: [...]}],
},
...
],
"orphan_components": [...], # components with no PART_OF edge
}
"""
project = resolve_project_name(project) if project else ""
out: dict = {"project": project, "subsystems": [], "orphan_components": []}
with get_connection() as conn:
# All subsystems in project
subsys_rows = conn.execute(
"SELECT * FROM entities WHERE status = 'active' "
"AND project = ? AND entity_type = 'subsystem' "
"ORDER BY name",
(project,),
).fetchall()
# All components in project
comp_rows = conn.execute(
"SELECT * FROM entities WHERE status = 'active' "
"AND project = ? AND entity_type = 'component'",
(project,),
).fetchall()
# PART_OF edges: component → subsystem
part_of_rows = conn.execute(
"SELECT source_entity_id, target_entity_id FROM relationships "
"WHERE relationship_type = 'part_of'"
).fetchall()
part_of_map: dict[str, str] = {
r["source_entity_id"]: r["target_entity_id"] for r in part_of_rows
}
# uses_material edges for components
mat_rows = conn.execute(
"SELECT r.source_entity_id, e.name FROM relationships r "
"JOIN entities e ON e.id = r.target_entity_id "
"WHERE r.relationship_type = 'uses_material' AND e.status = 'active'"
).fetchall()
materials_by_comp: dict[str, list[str]] = {}
for r in mat_rows:
materials_by_comp.setdefault(r["source_entity_id"], []).append(r["name"])
# Build: subsystems → their components
subsys_comps: dict[str, list[dict]] = {s["id"]: [] for s in subsys_rows}
orphans: list[dict] = []
for c in comp_rows:
parent = part_of_map.get(c["id"])
comp_dict = {
"id": c["id"],
"name": c["name"],
"description": c["description"] or "",
"materials": materials_by_comp.get(c["id"], []),
}
if parent and parent in subsys_comps:
subsys_comps[parent].append(comp_dict)
else:
orphans.append(comp_dict)
out["subsystems"] = [
{
"id": s["id"],
"name": s["name"],
"description": s["description"] or "",
"components": subsys_comps.get(s["id"], []),
}
for s in subsys_rows
]
out["orphan_components"] = orphans
return out
def decisions_affecting(project: str, subsystem_id: str | None = None) -> dict:
"""Q-008: decisions that affect a subsystem (or whole project).
Walks AFFECTED_BY_DECISION edges. If subsystem_id is given, returns
decisions linked to that subsystem or any of its components. Otherwise,
all decisions in the project.
"""
project = resolve_project_name(project) if project else ""
target_ids: set[str] = set()
if subsystem_id:
target_ids.add(subsystem_id)
# Include components PART_OF the subsystem
with get_connection() as conn:
rows = conn.execute(
"SELECT source_entity_id FROM relationships "
"WHERE relationship_type = 'part_of' AND target_entity_id = ?",
(subsystem_id,),
).fetchall()
for r in rows:
target_ids.add(r["source_entity_id"])
with get_connection() as conn:
if target_ids:
placeholders = ",".join("?" * len(target_ids))
rows = conn.execute(
f"SELECT DISTINCT e.* FROM entities e "
f"JOIN relationships r ON r.source_entity_id = e.id "
f"WHERE e.status = 'active' AND e.entity_type = 'decision' "
f"AND e.project = ? AND r.relationship_type = 'affected_by_decision' "
f"AND r.target_entity_id IN ({placeholders}) "
f"ORDER BY e.updated_at DESC",
(project, *target_ids),
).fetchall()
else:
rows = conn.execute(
"SELECT * FROM entities WHERE status = 'active' "
"AND entity_type = 'decision' AND project = ? "
"ORDER BY updated_at DESC LIMIT 200",
(project,),
).fetchall()
decisions = [_entity_dict(_row_to_entity(r)) for r in rows]
return {
"project": project,
"subsystem_id": subsystem_id or "",
"decisions": decisions,
"count": len(decisions),
}
def requirements_for(component_id: str) -> dict:
"""Q-005: requirements that a component satisfies."""
with get_connection() as conn:
# Component → SATISFIES → Requirement
rows = conn.execute(
"SELECT e.* FROM entities e "
"JOIN relationships r ON r.target_entity_id = e.id "
"WHERE r.source_entity_id = ? AND r.relationship_type = 'satisfies' "
"AND e.entity_type = 'requirement' AND e.status = 'active' "
"ORDER BY e.name",
(component_id,),
).fetchall()
requirements = [_entity_dict(_row_to_entity(r)) for r in rows]
return {
"component_id": component_id,
"requirements": requirements,
"count": len(requirements),
}
def recent_changes(project: str, since: str | None = None, limit: int = 50) -> dict:
"""Q-013: what changed recently in the project (entity audit log).
Uses the shared memory_audit table filtered by entity_kind='entity' and
joins back to entities for the project scope.
"""
project = resolve_project_name(project) if project else ""
since = since or "2020-01-01"
with get_connection() as conn:
rows = conn.execute(
"SELECT a.id, a.memory_id AS entity_id, a.action, a.actor, "
"a.timestamp, a.note, e.entity_type, e.name, e.project "
"FROM memory_audit a "
"LEFT JOIN entities e ON e.id = a.memory_id "
"WHERE a.entity_kind = 'entity' AND a.timestamp >= ? "
"AND (e.project = ? OR e.project IS NULL) "
"ORDER BY a.timestamp DESC LIMIT ?",
(since, project, limit),
).fetchall()
changes = []
for r in rows:
changes.append({
"audit_id": r["id"],
"entity_id": r["entity_id"],
"entity_type": r["entity_type"] or "?",
"entity_name": r["name"] or "(deleted)",
"action": r["action"],
"actor": r["actor"] or "api",
"note": r["note"] or "",
"timestamp": r["timestamp"],
})
return {"project": project, "since": since, "changes": changes, "count": len(changes)}
# ============================================================
# Killer queries (Q-006, Q-009, Q-011) — the "what am I forgetting?" queries
# ============================================================
def orphan_requirements(project: str) -> dict:
"""Q-006: requirements in project with NO inbound SATISFIES edge.
These are "something we said must be true" with nothing actually
satisfying them. The single highest-value query for an engineering
director: shows what's unclaimed by design.
"""
project = resolve_project_name(project) if project else ""
with get_connection() as conn:
rows = conn.execute(
"SELECT * FROM entities WHERE status = 'active' "
"AND project = ? AND entity_type = 'requirement' "
"AND NOT EXISTS ("
" SELECT 1 FROM relationships r "
" WHERE r.relationship_type = 'satisfies' "
" AND r.target_entity_id = entities.id"
") "
"ORDER BY updated_at DESC",
(project,),
).fetchall()
orphans = [_entity_dict(_row_to_entity(r)) for r in rows]
return {
"project": project,
"query": "Q-006 orphan requirements",
"description": "Requirements with no SATISFIES relationship — nothing claims to meet them.",
"gaps": orphans,
"count": len(orphans),
}
def risky_decisions(project: str) -> dict:
"""Q-009: decisions linked to assumptions flagged as unresolved.
Walks BASED_ON_ASSUMPTION edges. An assumption is "flagged" if its
properties.flagged=True OR status='superseded' OR status='invalid'.
"""
project = resolve_project_name(project) if project else ""
with get_connection() as conn:
rows = conn.execute(
"SELECT DISTINCT d.*, a.name AS assumption_name, a.id AS assumption_id, "
"a.status AS assumption_status, a.properties AS assumption_props "
"FROM entities d "
"JOIN relationships r ON r.source_entity_id = d.id "
"JOIN entities a ON a.id = r.target_entity_id "
"WHERE d.status = 'active' AND d.entity_type = 'decision' "
"AND d.project = ? "
"AND r.relationship_type = 'based_on_assumption' "
"AND ("
" a.status IN ('superseded', 'invalid') OR "
" a.properties LIKE '%\"flagged\": true%' OR "
" a.properties LIKE '%\"flagged\":true%'"
") "
"ORDER BY d.updated_at DESC",
(project,),
).fetchall()
risky = []
for r in rows:
risky.append({
"decision_id": r["id"],
"decision_name": r["name"],
"decision_description": r["description"] or "",
"assumption_id": r["assumption_id"],
"assumption_name": r["assumption_name"],
"assumption_status": r["assumption_status"],
})
return {
"project": project,
"query": "Q-009 risky decisions",
"description": "Decisions based on assumptions that are flagged, superseded, or invalid.",
"gaps": risky,
"count": len(risky),
}
def unsupported_claims(project: str) -> dict:
"""Q-011: validation claims with NO inbound SUPPORTS edge.
These are asserted claims (e.g., "margin is adequate") with no
Result entity actually supporting them. High-risk: the engineer
believes it, but there's no evidence on file.
"""
project = resolve_project_name(project) if project else ""
with get_connection() as conn:
rows = conn.execute(
"SELECT * FROM entities WHERE status = 'active' "
"AND project = ? AND entity_type = 'validation_claim' "
"AND NOT EXISTS ("
" SELECT 1 FROM relationships r "
" WHERE r.relationship_type = 'supports' "
" AND r.target_entity_id = entities.id"
") "
"ORDER BY updated_at DESC",
(project,),
).fetchall()
claims = [_entity_dict(_row_to_entity(r)) for r in rows]
return {
"project": project,
"query": "Q-011 unsupported claims",
"description": "Validation claims with no supporting Result — asserted but not evidenced.",
"gaps": claims,
"count": len(claims),
}
def all_gaps(project: str) -> dict:
"""Combined: run Q-006, Q-009, Q-011 for a project in one go."""
return {
"project": resolve_project_name(project) if project else "",
"generated_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
"orphan_requirements": orphan_requirements(project),
"risky_decisions": risky_decisions(project),
"unsupported_claims": unsupported_claims(project),
}
# ============================================================
# History + impact (Q-016, Q-017)
# ============================================================
def impact_analysis(entity_id: str, max_depth: int = 3) -> dict:
"""Q-016: transitive outbound reach of an entity.
Walks outbound edges breadth-first to max_depth. Answers "what would
be affected if I changed component X?" by finding everything downstream.
"""
visited: set[str] = {entity_id}
impacted: list[dict] = []
frontier = [(entity_id, 0)]
while frontier:
current_id, depth = frontier.pop(0)
if depth >= max_depth:
continue
with get_connection() as conn:
rows = conn.execute(
"SELECT r.relationship_type, r.target_entity_id, "
"e.entity_type, e.name, e.status "
"FROM relationships r "
"JOIN entities e ON e.id = r.target_entity_id "
"WHERE r.source_entity_id = ? AND e.status = 'active'",
(current_id,),
).fetchall()
for r in rows:
tid = r["target_entity_id"]
if tid in visited:
continue
visited.add(tid)
impacted.append({
"entity_id": tid,
"entity_type": r["entity_type"],
"name": r["name"],
"relationship": r["relationship_type"],
"depth": depth + 1,
})
frontier.append((tid, depth + 1))
root = get_entity(entity_id)
return {
"root": _entity_dict(root) if root else None,
"impacted_count": len(impacted),
"impacted": impacted,
"max_depth": max_depth,
}
def evidence_chain(entity_id: str) -> dict:
"""Q-017: what evidence supports this entity?
Walks inbound SUPPORTS / EVIDENCED_BY / DESCRIBED_BY edges to surface
the provenance chain: "this claim is supported by that result, which
was produced by that analysis model, which was described by that doc."
"""
provenance_edges = ("supports", "evidenced_by", "described_by",
"validated_by", "analyzed_by")
placeholders = ",".join("?" * len(provenance_edges))
with get_connection() as conn:
# Inbound edges of the provenance family
inbound_rows = conn.execute(
f"SELECT r.relationship_type, r.source_entity_id, "
f"e.entity_type, e.name, e.description, e.status "
f"FROM relationships r "
f"JOIN entities e ON e.id = r.source_entity_id "
f"WHERE r.target_entity_id = ? AND e.status = 'active' "
f"AND r.relationship_type IN ({placeholders})",
(entity_id, *provenance_edges),
).fetchall()
# Also look at source_refs on the entity itself
root = get_entity(entity_id)
chain = []
for r in inbound_rows:
chain.append({
"via": r["relationship_type"],
"source_id": r["source_entity_id"],
"source_type": r["entity_type"],
"source_name": r["name"],
"source_description": (r["description"] or "")[:200],
})
return {
"root": _entity_dict(root) if root else None,
"direct_source_refs": root.source_refs if root else [],
"evidence_chain": chain,
"count": len(chain),
}
# ============================================================
# Helpers
# ============================================================
def _entity_dict(e: Entity) -> dict:
"""Flatten an Entity to a public-API dict."""
return {
"id": e.id,
"entity_type": e.entity_type,
"name": e.name,
"project": e.project,
"description": e.description,
"properties": e.properties,
"status": e.status,
"confidence": e.confidence,
"source_refs": e.source_refs,
"updated_at": e.updated_at,
}

262
src/atocore/engineering/service.py
View File

@@ -9,6 +9,7 @@ from datetime import datetime, timezone
from atocore.models.database import get_connection from atocore.models.database import get_connection
from atocore.observability.logger import get_logger from atocore.observability.logger import get_logger
from atocore.projects.registry import resolve_project_name
log = get_logger("engineering") log = get_logger("engineering")
@@ -31,18 +32,29 @@ ENTITY_TYPES = [
] ]
RELATIONSHIP_TYPES = [ RELATIONSHIP_TYPES = [
# Structural family
"contains", "contains",
"part_of", "part_of",
"interfaces_with", "interfaces_with",
# Intent family
"satisfies", "satisfies",
"constrained_by", "constrained_by",
"affected_by_decision", "affected_by_decision",
"based_on_assumption", # Phase 5 — Q-009 killer query
"supersedes",
# Validation family
"analyzed_by", "analyzed_by",
"validated_by", "validated_by",
"supports", # Phase 5 — Q-011 killer query
"conflicts_with", # Phase 5 — Q-012 future
"depends_on", "depends_on",
"uses_material", # Provenance family
"described_by", "described_by",
"supersedes", "updated_by_session", # Phase 5 — session→entity provenance
"evidenced_by", # Phase 5 — Q-017 evidence trace
"summarized_in", # Phase 5 — mirror caches
# Domain-specific (pre-existing, retained)
"uses_material",
] ]
ENTITY_STATUSES = ["candidate", "active", "superseded", "invalid"] ENTITY_STATUSES = ["candidate", "active", "superseded", "invalid"]
@@ -132,6 +144,7 @@ def create_entity(
status: str = "active", status: str = "active",
confidence: float = 1.0, confidence: float = 1.0,
source_refs: list[str] | None = None, source_refs: list[str] | None = None,
actor: str = "api",
) -> Entity: ) -> Entity:
if entity_type not in ENTITY_TYPES: if entity_type not in ENTITY_TYPES:
raise ValueError(f"Invalid entity type: {entity_type}. Must be one of {ENTITY_TYPES}") raise ValueError(f"Invalid entity type: {entity_type}. Must be one of {ENTITY_TYPES}")
@@ -140,6 +153,11 @@ def create_entity(
if not name or not name.strip(): if not name or not name.strip():
raise ValueError("Entity name must be non-empty") raise ValueError("Entity name must be non-empty")
# Phase 5: enforce project canonicalization contract at the write seam.
# Aliases like "p04" become "p04-gigabit" so downstream reads stay
# consistent with the registry.
project = resolve_project_name(project) if project else ""
entity_id = str(uuid.uuid4()) entity_id = str(uuid.uuid4())
now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S") now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
props = properties or {} props = properties or {}
@@ -159,6 +177,22 @@ def create_entity(
) )
log.info("entity_created", entity_id=entity_id, entity_type=entity_type, name=name) log.info("entity_created", entity_id=entity_id, entity_type=entity_type, name=name)
# Phase 5: entity audit rows share the memory_audit table via
# entity_kind="entity" discriminator. Same infrastructure, unified history.
_audit_entity(
entity_id=entity_id,
action="created",
actor=actor,
after={
"entity_type": entity_type,
"name": name.strip(),
"project": project,
"status": status,
"confidence": confidence,
},
)
return Entity( return Entity(
id=entity_id, entity_type=entity_type, name=name.strip(), id=entity_id, entity_type=entity_type, name=name.strip(),
project=project, description=description, properties=props, project=project, description=description, properties=props,
@@ -167,6 +201,35 @@ def create_entity(
) )
def _audit_entity(
entity_id: str,
action: str,
actor: str = "api",
before: dict | None = None,
after: dict | None = None,
note: str = "",
) -> None:
"""Append an entity mutation row to the shared memory_audit table."""
try:
with get_connection() as conn:
conn.execute(
"INSERT INTO memory_audit (id, memory_id, action, actor, "
"before_json, after_json, note, entity_kind) "
"VALUES (?, ?, ?, ?, ?, ?, ?, 'entity')",
(
str(uuid.uuid4()),
entity_id,
action,
actor or "api",
json.dumps(before or {}),
json.dumps(after or {}),
(note or "")[:500],
),
)
except Exception as e:
log.warning("entity_audit_failed", entity_id=entity_id, action=action, error=str(e))
def create_relationship( def create_relationship(
source_entity_id: str, source_entity_id: str,
target_entity_id: str, target_entity_id: str,
@@ -198,6 +261,17 @@ def create_relationship(
target=target_entity_id, target=target_entity_id,
rel_type=relationship_type, rel_type=relationship_type,
) )
# Phase 5: relationship audit as an entity action on the source
_audit_entity(
entity_id=source_entity_id,
action="relationship_added",
actor="api",
after={
"rel_id": rel_id,
"rel_type": relationship_type,
"target": target_entity_id,
},
)
return Relationship( return Relationship(
id=rel_id, source_entity_id=source_entity_id, id=rel_id, source_entity_id=source_entity_id,
target_entity_id=target_entity_id, target_entity_id=target_entity_id,
@@ -206,6 +280,190 @@ def create_relationship(
) )
# --- Phase 5: Entity promote/reject lifecycle ---
def _set_entity_status(
entity_id: str,
new_status: str,
actor: str = "api",
note: str = "",
) -> bool:
"""Transition an entity's status with audit."""
if new_status not in ENTITY_STATUSES:
raise ValueError(f"Invalid status: {new_status}")
with get_connection() as conn:
row = conn.execute(
"SELECT status FROM entities WHERE id = ?", (entity_id,)
).fetchone()
if row is None:
return False
old_status = row["status"]
if old_status == new_status:
return False
now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
conn.execute(
"UPDATE entities SET status = ?, updated_at = ? WHERE id = ?",
(new_status, now, entity_id),
)
# Action verb mirrors memory pattern
if new_status == "active" and old_status == "candidate":
action = "promoted"
elif new_status == "invalid" and old_status == "candidate":
action = "rejected"
elif new_status == "invalid":
action = "invalidated"
elif new_status == "superseded":
action = "superseded"
else:
action = "status_changed"
_audit_entity(
entity_id=entity_id,
action=action,
actor=actor,
before={"status": old_status},
after={"status": new_status},
note=note,
)
log.info("entity_status_changed", entity_id=entity_id,
old=old_status, new=new_status, action=action)
return True
def promote_entity(entity_id: str, actor: str = "api", note: str = "") -> bool:
"""Promote a candidate entity to active.
Phase 5F graduation hook: if this entity has source_refs pointing at
memories (format "memory:<uuid>"), mark those source memories as
``status=graduated`` and set their ``graduated_to_entity_id`` forward
pointer. This preserves the memory as an immutable historical record
while signalling that it's been absorbed into the typed graph.
"""
entity = get_entity(entity_id)
if entity is None or entity.status != "candidate":
return False
ok = _set_entity_status(entity_id, "active", actor=actor, note=note)
if not ok:
return False
# Phase 5F: mark source memories as graduated
memory_ids = [
ref.split(":", 1)[1]
for ref in (entity.source_refs or [])
if isinstance(ref, str) and ref.startswith("memory:")
]
if memory_ids:
_graduate_source_memories(memory_ids, entity_id, actor=actor)
# Phase 5G: sync conflict detection on promote. Fail-open — detection
# errors log but never undo the successful promote.
try:
from atocore.engineering.conflicts import detect_conflicts_for_entity
detect_conflicts_for_entity(entity_id)
except Exception as e:
log.warning("conflict_detection_failed", entity_id=entity_id, error=str(e))
return True
def _graduate_source_memories(memory_ids: list[str], entity_id: str, actor: str) -> None:
"""Mark source memories as graduated and set forward pointer."""
if not memory_ids:
return
now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
with get_connection() as conn:
for mid in memory_ids:
try:
row = conn.execute(
"SELECT status FROM memories WHERE id = ?", (mid,)
).fetchone()
if row is None:
continue
old_status = row["status"]
if old_status == "graduated":
continue # already graduated — maybe by a different entity
conn.execute(
"UPDATE memories SET status = 'graduated', "
"graduated_to_entity_id = ?, updated_at = ? WHERE id = ?",
(entity_id, now, mid),
)
# Write a memory_audit row for the graduation
conn.execute(
"INSERT INTO memory_audit (id, memory_id, action, actor, "
"before_json, after_json, note, entity_kind) "
"VALUES (?, ?, 'graduated', ?, ?, ?, ?, 'memory')",
(
str(uuid.uuid4()),
mid,
actor or "api",
json.dumps({"status": old_status}),
json.dumps({
"status": "graduated",
"graduated_to_entity_id": entity_id,
}),
f"graduated to entity {entity_id[:8]}",
),
)
log.info("memory_graduated", memory_id=mid,
entity_id=entity_id, old_status=old_status)
except Exception as e:
log.warning("memory_graduation_failed",
memory_id=mid, entity_id=entity_id, error=str(e))
def reject_entity_candidate(entity_id: str, actor: str = "api", note: str = "") -> bool:
"""Reject a candidate entity (status → invalid)."""
with get_connection() as conn:
row = conn.execute(
"SELECT status FROM entities WHERE id = ?", (entity_id,)
).fetchone()
if row is None or row["status"] != "candidate":
return False
return _set_entity_status(entity_id, "invalid", actor=actor, note=note)
def supersede_entity(entity_id: str, actor: str = "api", note: str = "") -> bool:
"""Mark an active entity as superseded by a newer one."""
return _set_entity_status(entity_id, "superseded", actor=actor, note=note)
def get_entity_audit(entity_id: str, limit: int = 100) -> list[dict]:
"""Fetch audit entries for an entity from the shared audit table."""
with get_connection() as conn:
rows = conn.execute(
"SELECT id, memory_id AS entity_id, action, actor, before_json, "
"after_json, note, timestamp FROM memory_audit "
"WHERE entity_kind = 'entity' AND memory_id = ? "
"ORDER BY timestamp DESC LIMIT ?",
(entity_id, limit),
).fetchall()
out = []
for r in rows:
try:
before = json.loads(r["before_json"] or "{}")
except Exception:
before = {}
try:
after = json.loads(r["after_json"] or "{}")
except Exception:
after = {}
out.append({
"id": r["id"],
"entity_id": r["entity_id"],
"action": r["action"],
"actor": r["actor"] or "api",
"before": before,
"after": after,
"note": r["note"] or "",
"timestamp": r["timestamp"],
})
return out
def get_entities( def get_entities(
entity_type: str | None = None, entity_type: str | None = None,
project: str | None = None, project: str | None = None,

548
src/atocore/engineering/triage_ui.py Normal file
View File

@@ -0,0 +1,548 @@
"""Human triage UI for AtoCore candidate memories.
Renders a lightweight HTML page at /admin/triage with all pending
candidate memories, each with inline Promote / Reject / Edit buttons.
No framework, no JS build, no database — reads candidates from the
AtoCore DB and posts back to the existing REST endpoints.
Design principle: the user should be able to triage 20 candidates in
60 seconds from any browser. Keyboard shortcuts (y/n/e/s) make it
feel like email triage (archive/delete).
"""
from __future__ import annotations
import html as _html
from atocore.engineering.wiki import render_html
from atocore.memory.service import get_memories
VALID_TYPES = ["identity", "preference", "project", "episodic", "knowledge", "adaptation"]
def _escape(s: str | None) -> str:
return _html.escape(s or "", quote=True)
def _render_candidate_card(cand) -> str:
"""One candidate row with inline forms for promote/reject/edit."""
mid = _escape(cand.id)
content = _escape(cand.content)
memory_type = _escape(cand.memory_type)
project = _escape(cand.project or "")
project_display = project or "(global)"
confidence = f"{cand.confidence:.2f}"
refs = cand.reference_count or 0
created = _escape(str(cand.created_at or ""))
tags = cand.domain_tags or []
tags_str = _escape(", ".join(tags))
valid_until = _escape(cand.valid_until or "")
# Strip time portion for HTML date input
valid_until_date = valid_until[:10] if valid_until else ""
type_options = "".join(
f'<option value="{t}"{" selected" if t == cand.memory_type else ""}>{t}</option>'
for t in VALID_TYPES
)
# Tag badges rendered from current tags
badges_html = ""
if tags:
badges_html = '<div class="cand-tags-display">' + "".join(
f'<span class="tag-badge">{_escape(t)}</span>' for t in tags
) + '</div>'
return f"""
<div class="cand" id="cand-{mid}" data-id="{mid}">
<div class="cand-head">
<span class="cand-type">[{memory_type}]</span>
<span class="cand-project">{project_display}</span>
<span class="cand-meta">conf {confidence} · refs {refs} · {created[:16]}</span>
</div>
<div class="cand-body">
<textarea class="cand-content" id="content-{mid}">{content}</textarea>
</div>
{badges_html}
<div class="cand-meta-row">
<label class="cand-field-label">Tags:
<input type="text" class="cand-tags-input" id="tags-{mid}"
value="{tags_str}" placeholder="optics, thermal, p04" />
</label>
<label class="cand-field-label">Valid until:
<input type="date" class="cand-valid-until" id="valid-until-{mid}"
value="{valid_until_date}" />
</label>
</div>
<div class="cand-actions">
<button class="btn-promote" data-id="{mid}" title="Promote (Y)">✅ Promote</button>
<button class="btn-reject" data-id="{mid}" title="Reject (N)">❌ Reject</button>
<button class="btn-save-promote" data-id="{mid}" title="Save edits + promote (E)">✏️ Save&Promote</button>
<label class="cand-type-label">Type:
<select class="cand-type-select" id="type-{mid}">{type_options}</select>
</label>
</div>
<div class="cand-status" id="status-{mid}"></div>
</div>
"""
_TRIAGE_SCRIPT = """
<script>
async function apiCall(url, method, body) {
try {
const opts = { method };
if (body) {
opts.headers = { 'Content-Type': 'application/json' };
opts.body = JSON.stringify(body);
}
const res = await fetch(url, opts);
return { ok: res.ok, status: res.status, json: res.ok ? await res.json().catch(()=>null) : null };
} catch (e) { return { ok: false, status: 0, error: String(e) }; }
}
async function requestAutoTriage() {
const btn = document.getElementById('auto-triage-btn');
const status = document.getElementById('auto-triage-status');
if (!btn) return;
btn.disabled = true;
btn.textContent = '⏳ Requesting...';
const r = await apiCall('/admin/triage/request-drain', 'POST');
if (r.ok) {
status.textContent = '✓ Requested. Host watcher runs every 2 min. Refresh this page in a minute to check progress.';
status.className = 'auto-triage-msg ok';
btn.textContent = '✓ Requested';
pollDrainStatus();
} else {
status.textContent = '❌ Request failed: ' + r.status;
status.className = 'auto-triage-msg err';
btn.disabled = false;
btn.textContent = '🤖 Auto-process queue';
}
}
async function pollDrainStatus() {
const status = document.getElementById('auto-triage-status');
const btn = document.getElementById('auto-triage-btn');
let polls = 0;
const timer = setInterval(async () => {
polls++;
const r = await apiCall('/admin/triage/drain-status', 'GET');
if (!r.ok || !r.json) return;
const s = r.json;
if (s.is_running) {
status.textContent = '⚙️ Auto-triage running on host... (started ' + (s.last_started_at || '?') + ')';
status.className = 'auto-triage-msg ok';
} else if (s.last_finished_at && !s.requested_at) {
status.textContent = '✅ Last run finished: ' + s.last_finished_at + ' → ' + (s.last_result || 'complete');
status.className = 'auto-triage-msg ok';
if (btn) { btn.disabled = false; btn.textContent = '🤖 Auto-process queue'; }
clearInterval(timer);
// Reload page to pick up new queue state
setTimeout(() => window.location.reload(), 3000);
}
if (polls > 60) { clearInterval(timer); } // stop after ~10 min of polling
}, 10000); // poll every 10s
}
function setStatus(id, msg, ok) {
const el = document.getElementById('status-' + id);
if (!el) return;
el.textContent = msg;
el.className = 'cand-status ' + (ok ? 'ok' : 'err');
}
function removeCard(id) {
setTimeout(() => {
const card = document.getElementById('cand-' + id);
if (card) {
card.style.opacity = '0';
setTimeout(() => card.remove(), 300);
}
updateCount();
}, 400);
}
function updateCount() {
const n = document.querySelectorAll('.cand').length;
const el = document.getElementById('cand-count');
if (el) el.textContent = n;
const next = document.querySelector('.cand');
if (next) next.scrollIntoView({ behavior: 'smooth', block: 'start' });
}
async function promote(id) {
setStatus(id, 'Promoting…', true);
const r = await apiCall('/memory/' + encodeURIComponent(id) + '/promote', 'POST');
if (r.ok) { setStatus(id, '✅ Promoted', true); removeCard(id); }
else setStatus(id, '❌ Failed: ' + r.status, false);
}
async function reject(id) {
setStatus(id, 'Rejecting…', true);
const r = await apiCall('/memory/' + encodeURIComponent(id) + '/reject', 'POST');
if (r.ok) { setStatus(id, '❌ Rejected', true); removeCard(id); }
else setStatus(id, '❌ Failed: ' + r.status, false);
}
function parseTags(str) {
return (str || '').split(/[,;]/).map(s => s.trim().toLowerCase()).filter(Boolean);
}
async function savePromote(id) {
const content = document.getElementById('content-' + id).value.trim();
const mtype = document.getElementById('type-' + id).value;
const tagsStr = document.getElementById('tags-' + id)?.value || '';
const validUntil = document.getElementById('valid-until-' + id)?.value || '';
if (!content) { setStatus(id, 'Content is empty', false); return; }
setStatus(id, 'Saving…', true);
const body = {
content: content,
memory_type: mtype,
domain_tags: parseTags(tagsStr),
valid_until: validUntil,
};
const r1 = await apiCall('/memory/' + encodeURIComponent(id), 'PUT', body);
if (!r1.ok) { setStatus(id, '❌ Save failed: ' + r1.status, false); return; }
const r2 = await apiCall('/memory/' + encodeURIComponent(id) + '/promote', 'POST');
if (r2.ok) { setStatus(id, '✅ Saved & Promoted', true); removeCard(id); }
else setStatus(id, '❌ Promote failed: ' + r2.status, false);
}
// Also save tag/expiry edits when plain "Promote" is clicked if fields changed
async function promoteWithMeta(id) {
const tagsStr = document.getElementById('tags-' + id)?.value || '';
const validUntil = document.getElementById('valid-until-' + id)?.value || '';
if (tagsStr.trim() || validUntil) {
await apiCall('/memory/' + encodeURIComponent(id), 'PUT', {
domain_tags: parseTags(tagsStr),
valid_until: validUntil,
});
}
return promote(id);
}
document.addEventListener('click', (e) => {
const id = e.target.dataset?.id;
if (!id) return;
if (e.target.classList.contains('btn-promote')) promoteWithMeta(id);
else if (e.target.classList.contains('btn-reject')) reject(id);
else if (e.target.classList.contains('btn-save-promote')) savePromote(id);
});
// Keyboard shortcuts on the currently-focused card
document.addEventListener('keydown', (e) => {
// Don't intercept if user is typing in textarea/select/input
const t = e.target.tagName;
if (t === 'TEXTAREA' || t === 'INPUT' || t === 'SELECT') return;
const first = document.querySelector('.cand');
if (!first) return;
const id = first.dataset.id;
if (e.key === 'y' || e.key === 'Y') { e.preventDefault(); promoteWithMeta(id); }
else if (e.key === 'n' || e.key === 'N') { e.preventDefault(); reject(id); }
else if (e.key === 'e' || e.key === 'E') {
e.preventDefault();
document.getElementById('content-' + id)?.focus();
}
else if (e.key === 's' || e.key === 'S') { e.preventDefault(); first.scrollIntoView({behavior:'smooth'}); }
});
</script>
"""
_TRIAGE_CSS = """
<style>
.triage-header { display:flex; justify-content:space-between; align-items:baseline; margin-bottom:1rem; }
.triage-header .count { font-size:1.4rem; font-weight:600; color:var(--accent); }
.triage-help { background:var(--card); border-left:4px solid var(--accent); padding:0.8rem 1rem; margin-bottom:1.5rem; border-radius:4px; font-size:0.9rem; }
.triage-help kbd { background:var(--hover); padding:2px 6px; border-radius:3px; font-family:monospace; font-size:0.85em; border:1px solid var(--border); }
.cand { background:var(--card); border:1px solid var(--border); border-radius:6px; padding:1rem; margin-bottom:1rem; transition:opacity 0.3s; }
.cand-head { display:flex; gap:0.8rem; align-items:center; margin-bottom:0.6rem; font-size:0.9rem; }
.cand-type { font-weight:600; color:var(--accent); font-family:monospace; }
.cand-project { color:var(--text); opacity:0.8; font-family:monospace; }
.cand-meta { color:var(--text); opacity:0.55; font-size:0.8rem; margin-left:auto; }
.cand-content { width:100%; min-height:80px; font-family:inherit; font-size:0.95rem; padding:0.5rem; background:var(--bg); color:var(--text); border:1px solid var(--border); border-radius:4px; resize:vertical; box-sizing:border-box; }
.cand-content:focus { outline:none; border-color:var(--accent); }
.cand-actions { display:flex; gap:0.5rem; margin-top:0.8rem; align-items:center; flex-wrap:wrap; }
.cand-actions button { padding:0.4rem 0.9rem; border:1px solid var(--border); background:var(--card); color:var(--text); border-radius:4px; cursor:pointer; font-size:0.88rem; }
.cand-actions button:hover { background:var(--hover); }
.btn-promote:hover { background:#059669; color:white; border-color:#059669; }
.btn-reject:hover { background:#dc2626; color:white; border-color:#dc2626; }
.btn-save-promote:hover { background:var(--accent); color:white; border-color:var(--accent); }
.cand-type-label { font-size:0.85rem; margin-left:auto; opacity:0.7; }
.cand-type-select { padding:0.25rem; background:var(--bg); color:var(--text); border:1px solid var(--border); border-radius:3px; font-family:monospace; }
.cand-status { margin-top:0.5rem; font-size:0.85rem; min-height:1.2em; }
.cand-status.ok { color:#059669; }
.cand-status.err { color:#dc2626; }
.empty { text-align:center; padding:3rem; opacity:0.6; }
.auto-triage-bar { display:flex; gap:0.8rem; align-items:center; background:var(--card); border:1px solid var(--border); border-radius:6px; padding:0.7rem 1rem; margin-bottom:1.2rem; flex-wrap:wrap; }
.auto-triage-bar button { padding:0.55rem 1.1rem; border:1px solid var(--accent); background:var(--accent); color:white; border-radius:4px; cursor:pointer; font-weight:600; font-size:0.95rem; }
.auto-triage-bar button:hover:not(:disabled) { opacity:0.9; }
.auto-triage-bar button:disabled { opacity:0.5; cursor:not-allowed; }
.auto-triage-msg { flex:1; min-width:200px; font-size:0.85rem; opacity:0.75; }
.auto-triage-msg.ok { color:var(--accent); opacity:1; font-weight:500; }
.auto-triage-msg.err { color:#dc2626; opacity:1; font-weight:500; }
.cand-tags-display { margin-top:0.5rem; display:flex; gap:0.35rem; flex-wrap:wrap; }
.tag-badge { background:var(--accent); color:white; padding:0.15rem 0.55rem; border-radius:10px; font-size:0.72rem; font-family:monospace; font-weight:500; }
.cand-meta-row { display:flex; gap:0.8rem; margin-top:0.6rem; align-items:center; flex-wrap:wrap; }
.cand-field-label { display:flex; gap:0.3rem; align-items:center; font-size:0.85rem; opacity:0.75; }
.cand-tags-input { flex:1; min-width:200px; padding:0.3rem 0.5rem; background:var(--bg); color:var(--text); border:1px solid var(--border); border-radius:3px; font-family:monospace; font-size:0.85rem; }
.cand-tags-input:focus { outline:none; border-color:var(--accent); }
.cand-valid-until { padding:0.3rem; background:var(--bg); color:var(--text); border:1px solid var(--border); border-radius:3px; font-family:inherit; font-size:0.85rem; }
</style>
"""
def _render_entity_card(entity) -> str:
"""Phase 5: entity candidate card with promote/reject."""
eid = _escape(entity.id)
name = _escape(entity.name)
etype = _escape(entity.entity_type)
project = _escape(entity.project or "(global)")
desc = _escape(entity.description or "")
conf = f"{entity.confidence:.2f}"
src_refs = entity.source_refs or []
source_display = _escape(", ".join(src_refs[:3])) if src_refs else "(no provenance)"
return f"""
<div class="cand cand-entity" id="ecand-{eid}" data-entity-id="{eid}">
<div class="cand-head">
<span class="cand-type entity-type">[entity · {etype}]</span>
<span class="cand-project">{project}</span>
<span class="cand-meta">conf {conf} · src: {source_display}</span>
</div>
<div class="cand-body">
<div class="entity-name">{name}</div>
<div class="entity-desc">{desc}</div>
</div>
<div class="cand-actions">
<button class="btn-entity-promote" data-entity-id="{eid}" title="Promote entity (Y)">✅ Promote Entity</button>
<button class="btn-entity-reject" data-entity-id="{eid}" title="Reject entity (N)">❌ Reject</button>
<a class="btn-link" href="/wiki/entities/{eid}">View in wiki →</a>
</div>
<div class="cand-status" id="estatus-{eid}"></div>
</div>
"""
_ENTITY_TRIAGE_SCRIPT = """
<script>
async function entityPromote(id) {
const st = document.getElementById('estatus-' + id);
st.textContent = 'Promoting…';
st.className = 'cand-status ok';
const r = await fetch('/entities/' + encodeURIComponent(id) + '/promote', {method:'POST'});
if (r.ok) {
st.textContent = '✅ Entity promoted';
setTimeout(() => {
const card = document.getElementById('ecand-' + id);
if (card) { card.style.opacity = '0'; setTimeout(() => card.remove(), 300); }
}, 400);
} else st.textContent = '❌ ' + r.status;
}
async function entityReject(id) {
const st = document.getElementById('estatus-' + id);
st.textContent = 'Rejecting…';
st.className = 'cand-status ok';
const r = await fetch('/entities/' + encodeURIComponent(id) + '/reject', {method:'POST'});
if (r.ok) {
st.textContent = '❌ Entity rejected';
setTimeout(() => {
const card = document.getElementById('ecand-' + id);
if (card) { card.style.opacity = '0'; setTimeout(() => card.remove(), 300); }
}, 400);
} else st.textContent = '❌ ' + r.status;
}
document.addEventListener('click', (e) => {
const eid = e.target.dataset?.entityId;
if (!eid) return;
if (e.target.classList.contains('btn-entity-promote')) entityPromote(eid);
else if (e.target.classList.contains('btn-entity-reject')) entityReject(eid);
});
</script>
"""
_ENTITY_TRIAGE_CSS = """
<style>
.cand-entity { border-left: 3px solid #059669; }
.entity-type { background: #059669; color: white; padding: 0.1rem 0.5rem; border-radius: 3px; font-size: 0.75rem; }
.entity-name { font-size: 1.15rem; font-weight: 600; margin-bottom: 0.3rem; }
.entity-desc { opacity: 0.85; font-size: 0.95rem; }
.btn-entity-promote { background: #059669; color: white; border-color: #059669; }
.btn-entity-reject:hover { background: #dc2626; color: white; border-color: #dc2626; }
.btn-link { padding: 0.4rem 0.9rem; text-decoration: none; color: var(--accent); border: 1px solid var(--border); border-radius: 4px; font-size: 0.88rem; }
.btn-link:hover { background: var(--hover); }
.section-break { border-top: 2px solid var(--border); margin: 2rem 0 1rem 0; padding-top: 1rem; }
</style>
"""
def _render_graduation_bar() -> str:
"""The 'Graduate memories → entity candidates' control bar."""
from atocore.projects.registry import load_project_registry
try:
projects = load_project_registry()
options = '<option value="">(all projects)</option>' + "".join(
f'<option value="{_escape(p.project_id)}">{_escape(p.project_id)}</option>'
for p in projects
)
except Exception:
options = '<option value="">(all projects)</option>'
return f"""
<div class="auto-triage-bar graduation-bar">
<button id="grad-btn" onclick="requestGraduation()" title="Run memory→entity graduation on Dalidou host">
🎓 Graduate memories
</button>
<label class="cand-field-label">Project:
<select id="grad-project" class="cand-type-select">{options}</select>
</label>
<label class="cand-field-label">Limit:
<input id="grad-limit" type="number" class="cand-tags-input" style="max-width:80px"
value="30" min="1" max="200" />
</label>
<span id="grad-status" class="auto-triage-msg">
Scans active memories, asks the LLM "does this describe a typed entity?",
and creates entity candidates. Review them in the Entity section below.
</span>
</div>
"""
_GRADUATION_SCRIPT = """
<script>
async function requestGraduation() {
const btn = document.getElementById('grad-btn');
const status = document.getElementById('grad-status');
const project = document.getElementById('grad-project').value;
const limit = parseInt(document.getElementById('grad-limit').value || '30', 10);
btn.disabled = true;
btn.textContent = '⏳ Requesting...';
const r = await fetch('/admin/graduation/request', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({project, limit}),
});
if (r.ok) {
const scope = project || 'all projects';
status.textContent = `✓ Queued graduation for ${scope} (limit ${limit}). Host watcher runs every 2 min; refresh this page in ~3 min to see candidates.`;
status.className = 'auto-triage-msg ok';
btn.textContent = '✓ Requested';
pollGraduationStatus();
} else {
status.textContent = '❌ Request failed: ' + r.status;
status.className = 'auto-triage-msg err';
btn.disabled = false;
btn.textContent = '🎓 Graduate memories';
}
}
async function pollGraduationStatus() {
const status = document.getElementById('grad-status');
const btn = document.getElementById('grad-btn');
let polls = 0;
const timer = setInterval(async () => {
polls++;
const r = await fetch('/admin/graduation/status');
if (!r.ok) return;
const s = await r.json();
if (s.is_running) {
status.textContent = '⚙️ Graduation running... (started ' + (s.last_started_at || '?') + ')';
status.className = 'auto-triage-msg ok';
} else if (s.last_finished_at && !s.requested) {
status.textContent = '✅ Finished: ' + s.last_finished_at + ' → ' + (s.last_result || 'complete');
status.className = 'auto-triage-msg ok';
if (btn) { btn.disabled = false; btn.textContent = '🎓 Graduate memories'; }
clearInterval(timer);
setTimeout(() => window.location.reload(), 3000);
}
if (polls > 120) { clearInterval(timer); } // ~20 min cap
}, 10000);
}
</script>
"""
def render_triage_page(limit: int = 100) -> str:
"""Render the full triage page with pending memory + entity candidates."""
from atocore.engineering.service import get_entities
try:
mem_candidates = get_memories(status="candidate", limit=limit)
except Exception as e:
body = f"<p>Error loading memory candidates: {_escape(str(e))}</p>"
return render_html("Triage — AtoCore", body, breadcrumbs=[("Wiki", "/wiki"), ("Triage", "")])
try:
entity_candidates = get_entities(status="candidate", limit=limit)
except Exception as e:
entity_candidates = []
total = len(mem_candidates) + len(entity_candidates)
graduation_bar = _render_graduation_bar()
if total == 0:
body = _TRIAGE_CSS + _ENTITY_TRIAGE_CSS + f"""
<div class="triage-header">
<h1>Triage Queue</h1>
</div>
{graduation_bar}
<div class="empty">
<p>🎉 No candidates to review.</p>
<p>The auto-triage pipeline keeps this queue empty unless something needs your judgment.</p>
<p>Use the 🎓 Graduate memories button above to propose new entity candidates from existing memories.</p>
</div>
""" + _GRADUATION_SCRIPT
return render_html("Triage — AtoCore", body, breadcrumbs=[("Wiki", "/wiki"), ("Triage", "")])
# Memory cards
mem_cards = "".join(_render_candidate_card(c) for c in mem_candidates)
# Entity cards
ent_cards_html = ""
if entity_candidates:
ent_cards = "".join(_render_entity_card(e) for e in entity_candidates)
ent_cards_html = f"""
<div class="section-break">
<h2>🔧 Entity Candidates ({len(entity_candidates)})</h2>
<p class="auto-triage-msg">
Typed graph entries awaiting review. Promoting an entity connects it to
the engineering knowledge graph (subsystems, requirements, decisions, etc.).
</p>
</div>
{ent_cards}
"""
body = _TRIAGE_CSS + _ENTITY_TRIAGE_CSS + f"""
<div class="triage-header">
<h1>Triage Queue</h1>
<span class="count">
<span id="cand-count">{len(mem_candidates)}</span> memory ·
{len(entity_candidates)} entity
</span>
</div>
<div class="triage-help">
Review candidates the auto-triage wasn't sure about. Edit the content
if needed, then promote or reject. Shortcuts: <kbd>Y</kbd> promote · <kbd>N</kbd>
reject · <kbd>E</kbd> edit · <kbd>S</kbd> scroll to next.
</div>
<div class="auto-triage-bar">
<button id="auto-triage-btn" onclick="requestAutoTriage()" title="Run auto_triage on Dalidou host">
🤖 Auto-process queue
</button>
<span id="auto-triage-status" class="auto-triage-msg">
Sends the full memory queue through 3-tier LLM triage on the host.
Sonnet → Opus → auto-discard. Only genuinely ambiguous items land here.
</span>
</div>
{graduation_bar}
<h2>📝 Memory Candidates ({len(mem_candidates)})</h2>
{mem_cards}
{ent_cards_html}
""" + _TRIAGE_SCRIPT + _ENTITY_TRIAGE_SCRIPT + _GRADUATION_SCRIPT
return render_html(
"Triage — AtoCore",
body,
breadcrumbs=[("Wiki", "/wiki"), ("Triage", "")],
)

43
src/atocore/engineering/wiki.py
View File

@@ -119,9 +119,19 @@ def render_homepage() -> str:
# Quick stats # Quick stats
all_entities = get_entities(limit=500) all_entities = get_entities(limit=500)
all_memories = get_memories(active_only=True, limit=500) all_memories = get_memories(active_only=True, limit=500)
pending = get_memories(status="candidate", limit=500)
lines.append('<h2>System</h2>') lines.append('<h2>System</h2>')
lines.append(f'<p>{len(all_entities)} entities · {len(all_memories)} active memories · {len(projects)} projects</p>') lines.append(f'<p>{len(all_entities)} entities · {len(all_memories)} active memories · {len(projects)} projects</p>')
lines.append(f'<p><a href="/admin/dashboard">API Dashboard (JSON)</a> · <a href="/health">Health Check</a></p>')
# Triage queue prompt — surfaced prominently if non-empty
if pending:
tone = "triage-warning" if len(pending) > 50 else "triage-notice"
lines.append(
f'<p class="{tone}">🗂️ <strong>{len(pending)} candidates</strong> awaiting triage — '
f'<a href="/admin/triage">review now →</a></p>'
)
lines.append(f'<p><a href="/admin/triage">Triage Queue</a> · <a href="/admin/dashboard">API Dashboard (JSON)</a> · <a href="/health">Health Check</a></p>')
return render_html("AtoCore Wiki", "\n".join(lines)) return render_html("AtoCore Wiki", "\n".join(lines))
@@ -201,15 +211,32 @@ def render_search(query: str) -> str:
) )
lines.append('</ul>') lines.append('</ul>')
# Search memories # Search memories — match on content OR domain_tags (Phase 3)
all_memories = get_memories(active_only=True, limit=200) all_memories = get_memories(active_only=True, limit=200)
query_lower = query.lower() query_lower = query.lower()
matching_mems = [m for m in all_memories if query_lower in m.content.lower()][:10] matching_mems = [
m for m in all_memories
if query_lower in m.content.lower()
or any(query_lower in (t or "").lower() for t in (m.domain_tags or []))
][:20]
if matching_mems: if matching_mems:
lines.append(f'<h2>Memories ({len(matching_mems)})</h2><ul>') lines.append(f'<h2>Memories ({len(matching_mems)})</h2><ul>')
for m in matching_mems: for m in matching_mems:
proj = f' <span class="tag">{m.project}</span>' if m.project else '' proj = f' <span class="tag">{m.project}</span>' if m.project else ''
lines.append(f'<li>[{m.memory_type}]{proj} {m.content[:200]}</li>') tags_html = ""
if m.domain_tags:
tag_links = " ".join(
f'<a href="/wiki/search?q={t}" class="tag-badge">{t}</a>'
for t in m.domain_tags[:5]
)
tags_html = f' <span class="mem-tags">{tag_links}</span>'
expiry_html = ""
if m.valid_until:
expiry_html = f' <span class="mem-expiry">valid until {m.valid_until[:10]}</span>'
lines.append(
f'<li>[{m.memory_type}]{proj}{tags_html}{expiry_html} '
f'{m.content[:200]}</li>'
)
lines.append('</ul>') lines.append('</ul>')
if not entities and not matching_mems: if not entities and not matching_mems:
@@ -289,6 +316,14 @@ _TEMPLATE = """<!DOCTYPE html>
.card .stats { font-size: 0.8em; margin-top: 0.5rem; opacity: 0.5; } .card .stats { font-size: 0.8em; margin-top: 0.5rem; opacity: 0.5; }
.card .client { font-size: 0.85em; opacity: 0.65; margin-bottom: 0.3rem; font-style: italic; } .card .client { font-size: 0.85em; opacity: 0.65; margin-bottom: 0.3rem; font-style: italic; }
.card h3 .tag { font-size: 0.65em; vertical-align: middle; margin-left: 0.4rem; } .card h3 .tag { font-size: 0.65em; vertical-align: middle; margin-left: 0.4rem; }
.triage-notice { background: var(--card); border-left: 4px solid var(--accent); padding: 0.6rem 1rem; border-radius: 4px; margin: 0.8rem 0; }
.triage-warning { background: #fef3c7; color: #78350f; border-left: 4px solid #d97706; padding: 0.6rem 1rem; border-radius: 4px; margin: 0.8rem 0; }
@media (prefers-color-scheme: dark) { .triage-warning { background: #451a03; color: #fde68a; } }
.mem-tags { display: inline-flex; gap: 0.25rem; flex-wrap: wrap; vertical-align: middle; }
.tag-badge { background: var(--accent); color: white; padding: 0.1rem 0.5rem; border-radius: 10px; font-size: 0.7rem; font-family: monospace; text-decoration: none; font-weight: 500; }
.tag-badge:hover { opacity: 0.85; text-decoration: none; }
.mem-expiry { font-size: 0.75rem; color: #d97706; font-style: italic; margin-left: 0.4rem; }
@media (prefers-color-scheme: dark) { .mem-expiry { color: #fbbf24; } }
</style> </style>
</head> </head>
<body> <body>

239
src/atocore/memory/_llm_prompt.py Normal file
View File

@@ -0,0 +1,239 @@
"""Shared LLM-extractor prompt + parser (stdlib-only).
R12: single source of truth for the system prompt, memory type set,
size limits, and raw JSON parsing used by both paths that shell out
to ``claude -p``:
- ``atocore.memory.extractor_llm`` (in-container extractor, wraps the
parsed dicts in ``MemoryCandidate`` with registry-checked project
attribution)
- ``scripts/batch_llm_extract_live.py`` (host-side extractor, can't
import the full atocore package because Dalidou's host Python lacks
the container's deps; imports this module via ``sys.path``)
This module MUST stay stdlib-only. No ``atocore`` imports, no third-
party packages. Callers apply their own project-attribution policy on
top of the normalized dicts this module emits.
"""
from __future__ import annotations
import json
from typing import Any
LLM_EXTRACTOR_VERSION = "llm-0.5.0"
MAX_RESPONSE_CHARS = 8000
MAX_PROMPT_CHARS = 2000
MEMORY_TYPES = {"identity", "preference", "project", "episodic", "knowledge", "adaptation"}
SYSTEM_PROMPT = """You extract memory candidates from LLM conversation turns for a personal context engine called AtoCore.
AtoCore is the brain for Atomaste's engineering work. Known projects:
p04-gigabit, p05-interferometer, p06-polisher, atomizer-v2, atocore,
abb-space. Unknown project names — still tag them, the system auto-detects.
Your job is to emit SIGNALS that matter for future context. Be aggressive:
err on the side of capturing useful signal. Triage filters noise downstream.
WHAT TO EMIT (in order of importance):
1. PROJECT ACTIVITY — any mention of a project with context worth remembering:
- "Schott quote received for ABB-Space" (event + project)
- "Cédric asked about p06 firmware timing" (stakeholder event)
- "Still waiting on Zygo lead-time from Nabeel" (blocker status)
- "p05 vendor decision needs to happen this week" (action item)
2. DECISIONS AND CHOICES — anything that commits to a direction:
- "Going with Zygo Verifire SV for p05" (decision)
- "Dropping stitching from primary workflow" (design choice)
- "USB SSD mandatory, not SD card" (architectural commitment)
3. DURABLE ENGINEERING INSIGHT — earned knowledge that generalizes:
- "CTE gradient dominates WFE at F/1.2" (materials insight)
- "Preston model breaks below 5N because contact assumption fails"
- "m=1 coma NOT correctable by force modulation" (controls insight)
Test: would a competent engineer NEED experience to know this?
If it's textbook/google-findable, skip it.
4. STAKEHOLDER AND VENDOR EVENTS:
- "Email sent to Nabeel 2026-04-13 asking for lead time"
- "Meeting with Jason on Table 7 next Tuesday"
- "Starspec wants updated CAD by Friday"
5. PREFERENCES AND ADAPTATIONS that shape how Antoine works:
- "Antoine prefers OAuth over API keys"
- "Extraction stays off the capture hot path"
WHAT TO SKIP:
- Pure conversational filler ("ok thanks", "let me check")
- Instructional help content ("run this command", "here's how to...")
- Obvious textbook facts anyone can google in 30 seconds
- Session meta-chatter ("let me commit this", "deploy running")
- Transient system state snapshots ("36 active memories right now")
CANDIDATE TYPES — choose the best fit:
- project — a fact, decision, or event specific to one named project
- knowledge — durable engineering insight (use domain, not project)
- preference — how Antoine works / wants things done
- adaptation — a standing rule or adjustment to behavior
- episodic — a stakeholder event or milestone worth remembering
DOMAINS for knowledge candidates (required when type=knowledge and project is empty):
physics, materials, optics, mechanics, manufacturing, metrology,
controls, software, math, finance, business
DOMAIN TAGS (Phase 3):
Every candidate gets domain_tags — a lowercase list of topical keywords
that describe the SUBJECT matter regardless of project. This is how
cross-project retrieval works: a query about "optics" surfaces matches
from p04 + p05 + p06 without naming each project.
Good tags: single lowercase words or hyphenated terms.
Examples:
- "ABB quote received for P04" → ["abb", "p04", "procurement", "optics"]
- "USB SSD mandatory on polisher" → ["p06", "firmware", "storage"]
- "CTE dominates WFE at F/1.2" → ["optics", "materials", "thermal"]
- "Antoine prefers OAuth over API keys" → ["security", "auth", "preference"]
Tag 2-5 items. Use domain keywords (optics, thermal, firmware), project
tokens when relevant (p04, abb), and lifecycle words (procurement, design,
validation) as appropriate.
VALID_UNTIL (Phase 3):
A memory can have an expiry date if it describes time-bounded truth.
Use valid_until for:
- Status snapshots: "current blocker is X" → valid_until = ~2 weeks out
- Scheduled events: "meeting with vendor Friday" → valid_until = meeting date
- Quotes with expiry: "quote valid until May 31"
- Interim decisions pending ratification
Leave empty (null) for:
- Durable design decisions ("Option B selected")
- Engineering insights ("CTE dominates at F/1.2")
- Ratified requirements, architectural commitments
Default = null (permanent). Format: ISO date "YYYY-MM-DD" or empty.
TRUST HIERARCHY:
- project-specific: set project to the project id, leave domain empty
- domain knowledge: set domain, leave project empty
- events/activity: use project, type=project or episodic
- one conversation can produce MULTIPLE candidates — emit them all
OUTPUT RULES:
- Each candidate content under 250 characters, stands alone
- Default confidence 0.5. Raise to 0.7 only for ratified/committed claims.
- Raw JSON array, no prose, no markdown fences
- Empty array [] is fine when the conversation has no durable signal
Each element:
{"type": "project|knowledge|preference|adaptation|episodic", "content": "...", "project": "...", "domain": "", "confidence": 0.5, "domain_tags": ["tag1","tag2"], "valid_until": null}"""
def build_user_message(prompt: str, response: str, project_hint: str) -> str:
prompt_excerpt = (prompt or "")[:MAX_PROMPT_CHARS]
response_excerpt = (response or "")[:MAX_RESPONSE_CHARS]
return (
f"PROJECT HINT (may be empty): {project_hint or ''}\n\n"
f"USER PROMPT:\n{prompt_excerpt}\n\n"
f"ASSISTANT RESPONSE:\n{response_excerpt}\n\n"
"Return the JSON array now."
)
def parse_llm_json_array(raw_output: str) -> list[dict[str, Any]]:
"""Strip markdown fences / leading prose and return the parsed JSON
array as a list of raw dicts. Returns an empty list on any parse
failure — callers decide whether to log."""
text = (raw_output or "").strip()
if text.startswith("```"):
text = text.strip("`")
nl = text.find("\n")
if nl >= 0:
text = text[nl + 1:]
if text.endswith("```"):
text = text[:-3]
text = text.strip()
if not text or text == "[]":
return []
if not text.lstrip().startswith("["):
start = text.find("[")
end = text.rfind("]")
if start >= 0 and end > start:
text = text[start:end + 1]
try:
parsed = json.loads(text)
except json.JSONDecodeError:
return []
if not isinstance(parsed, list):
return []
return [item for item in parsed if isinstance(item, dict)]
def normalize_candidate_item(item: dict[str, Any]) -> dict[str, Any] | None:
"""Validate and normalize one raw model item into a candidate dict.
Returns None if the item fails basic validation (unknown type,
empty content). Does NOT apply project-attribution policy — that's
the caller's job, since the registry-check differs between the
in-container path and the host path.
Output keys: type, content, project (raw model value), domain,
confidence.
"""
mem_type = str(item.get("type") or "").strip().lower()
content = str(item.get("content") or "").strip()
if mem_type not in MEMORY_TYPES or not content:
return None
model_project = str(item.get("project") or "").strip()
domain = str(item.get("domain") or "").strip().lower()
try:
confidence = float(item.get("confidence", 0.5))
except (TypeError, ValueError):
confidence = 0.5
confidence = max(0.0, min(1.0, confidence))
if domain and not model_project:
content = f"[{domain}] {content}"
# Phase 3: domain_tags + valid_until
raw_tags = item.get("domain_tags") or []
if isinstance(raw_tags, str):
# Tolerate comma-separated string fallback
raw_tags = [t.strip() for t in raw_tags.split(",") if t.strip()]
if not isinstance(raw_tags, list):
raw_tags = []
domain_tags = []
for t in raw_tags[:10]: # cap at 10
if not isinstance(t, str):
continue
tag = t.strip().lower()
if tag and tag not in domain_tags:
domain_tags.append(tag)
valid_until = item.get("valid_until")
if valid_until is not None:
valid_until = str(valid_until).strip()
# Accept ISO date "YYYY-MM-DD" or full timestamp; empty/"null" → none
if valid_until.lower() in ("", "null", "none", "permanent"):
valid_until = ""
else:
valid_until = ""
return {
"type": mem_type,
"content": content[:1000],
"project": model_project,
"domain": domain,
"confidence": confidence,
"domain_tags": domain_tags,
"valid_until": valid_until,
}

182
src/atocore/memory/extractor_llm.py
View File

@@ -49,7 +49,6 @@ Implementation notes:
from __future__ import annotations from __future__ import annotations
import json
import os import os
import shutil import shutil
import subprocess import subprocess
@@ -58,92 +57,21 @@ from dataclasses import dataclass
from functools import lru_cache from functools import lru_cache
from atocore.interactions.service import Interaction from atocore.interactions.service import Interaction
from atocore.memory._llm_prompt import (
LLM_EXTRACTOR_VERSION,
SYSTEM_PROMPT as _SYSTEM_PROMPT,
build_user_message,
normalize_candidate_item,
parse_llm_json_array,
)
from atocore.memory.extractor import MemoryCandidate from atocore.memory.extractor import MemoryCandidate
from atocore.memory.service import MEMORY_TYPES
from atocore.observability.logger import get_logger from atocore.observability.logger import get_logger
log = get_logger("extractor_llm") log = get_logger("extractor_llm")
LLM_EXTRACTOR_VERSION = "llm-0.4.0"
DEFAULT_MODEL = os.environ.get("ATOCORE_LLM_EXTRACTOR_MODEL", "sonnet") DEFAULT_MODEL = os.environ.get("ATOCORE_LLM_EXTRACTOR_MODEL", "sonnet")
DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_LLM_EXTRACTOR_TIMEOUT_S", "90")) DEFAULT_TIMEOUT_S = float(os.environ.get("ATOCORE_LLM_EXTRACTOR_TIMEOUT_S", "90"))
MAX_RESPONSE_CHARS = 8000
MAX_PROMPT_CHARS = 2000
_SYSTEM_PROMPT = """You extract memory candidates from LLM conversation turns for a personal context engine called AtoCore.
AtoCore is the brain for Atomaste's engineering work. Known projects:
p04-gigabit, p05-interferometer, p06-polisher, atomizer-v2, atocore,
abb-space. Unknown project names — still tag them, the system auto-detects.
Your job is to emit SIGNALS that matter for future context. Be aggressive:
err on the side of capturing useful signal. Triage filters noise downstream.
WHAT TO EMIT (in order of importance):
1. PROJECT ACTIVITY — any mention of a project with context worth remembering:
- "Schott quote received for ABB-Space" (event + project)
- "Cédric asked about p06 firmware timing" (stakeholder event)
- "Still waiting on Zygo lead-time from Nabeel" (blocker status)
- "p05 vendor decision needs to happen this week" (action item)
2. DECISIONS AND CHOICES — anything that commits to a direction:
- "Going with Zygo Verifire SV for p05" (decision)
- "Dropping stitching from primary workflow" (design choice)
- "USB SSD mandatory, not SD card" (architectural commitment)
3. DURABLE ENGINEERING INSIGHT — earned knowledge that generalizes:
- "CTE gradient dominates WFE at F/1.2" (materials insight)
- "Preston model breaks below 5N because contact assumption fails"
- "m=1 coma NOT correctable by force modulation" (controls insight)
Test: would a competent engineer NEED experience to know this?
If it's textbook/google-findable, skip it.
4. STAKEHOLDER AND VENDOR EVENTS:
- "Email sent to Nabeel 2026-04-13 asking for lead time"
- "Meeting with Jason on Table 7 next Tuesday"
- "Starspec wants updated CAD by Friday"
5. PREFERENCES AND ADAPTATIONS that shape how Antoine works:
- "Antoine prefers OAuth over API keys"
- "Extraction stays off the capture hot path"
WHAT TO SKIP:
- Pure conversational filler ("ok thanks", "let me check")
- Instructional help content ("run this command", "here's how to...")
- Obvious textbook facts anyone can google in 30 seconds
- Session meta-chatter ("let me commit this", "deploy running")
- Transient system state snapshots ("36 active memories right now")
CANDIDATE TYPES — choose the best fit:
- project — a fact, decision, or event specific to one named project
- knowledge — durable engineering insight (use domain, not project)
- preference — how Antoine works / wants things done
- adaptation — a standing rule or adjustment to behavior
- episodic — a stakeholder event or milestone worth remembering
DOMAINS for knowledge candidates (required when type=knowledge and project is empty):
physics, materials, optics, mechanics, manufacturing, metrology,
controls, software, math, finance, business
TRUST HIERARCHY:
- project-specific: set project to the project id, leave domain empty
- domain knowledge: set domain, leave project empty
- events/activity: use project, type=project or episodic
- one conversation can produce MULTIPLE candidates — emit them all
OUTPUT RULES:
- Each candidate content under 250 characters, stands alone
- Default confidence 0.5. Raise to 0.7 only for ratified/committed claims.
- Raw JSON array, no prose, no markdown fences
- Empty array [] is fine when the conversation has no durable signal
Each element:
{"type": "project|knowledge|preference|adaptation|episodic", "content": "...", "project": "...", "domain": "", "confidence": 0.5}"""
@dataclass @dataclass
@@ -206,13 +134,10 @@ def extract_candidates_llm_verbose(
if not response_text: if not response_text:
return LLMExtractionResult(candidates=[], raw_output="", error="empty_response") return LLMExtractionResult(candidates=[], raw_output="", error="empty_response")
prompt_excerpt = (interaction.prompt or "")[:MAX_PROMPT_CHARS] user_message = build_user_message(
response_excerpt = response_text[:MAX_RESPONSE_CHARS] interaction.prompt or "",
user_message = ( response_text,
f"PROJECT HINT (may be empty): {interaction.project or ''}\n\n" interaction.project or "",
f"USER PROMPT:\n{prompt_excerpt}\n\n"
f"ASSISTANT RESPONSE:\n{response_excerpt}\n\n"
"Return the JSON array now."
) )
args = [ args = [
@@ -270,50 +195,25 @@ def extract_candidates_llm_verbose(
def _parse_candidates(raw_output: str, interaction: Interaction) -> list[MemoryCandidate]: def _parse_candidates(raw_output: str, interaction: Interaction) -> list[MemoryCandidate]:
"""Parse the model's JSON output into MemoryCandidate objects. """Parse the model's JSON output into MemoryCandidate objects.
Tolerates common model glitches: surrounding whitespace, stray Shared stripping + per-item validation live in
markdown fences, leading/trailing prose. Silently drops malformed ``atocore.memory._llm_prompt``. This function adds the container-
array elements rather than raising. only R9 project attribution: registry-check model_project and fall
back to the interaction scope when set.
""" """
text = raw_output.strip() raw_items = parse_llm_json_array(raw_output)
if text.startswith("```"): if not raw_items and raw_output.strip() not in ("", "[]"):
text = text.strip("`") log.error("llm_extractor_parse_failed", raw_prefix=raw_output[:120])
first_newline = text.find("\n")
if first_newline >= 0:
text = text[first_newline + 1 :]
if text.endswith("```"):
text = text[:-3]
text = text.strip()
if not text or text == "[]":
return []
if not text.lstrip().startswith("["):
start = text.find("[")
end = text.rfind("]")
if start >= 0 and end > start:
text = text[start : end + 1]
try:
parsed = json.loads(text)
except json.JSONDecodeError as exc:
log.error("llm_extractor_parse_failed", error=str(exc), raw_prefix=raw_output[:120])
return []
if not isinstance(parsed, list):
return []
results: list[MemoryCandidate] = [] results: list[MemoryCandidate] = []
for item in parsed: for raw_item in raw_items:
if not isinstance(item, dict): normalized = normalize_candidate_item(raw_item)
if normalized is None:
continue continue
mem_type = str(item.get("type") or "").strip().lower()
content = str(item.get("content") or "").strip() model_project = normalized["project"]
model_project = str(item.get("project") or "").strip() # R9 trust hierarchy: interaction scope wins; else registry-
# R9 trust hierarchy for project attribution: # resolve the model's tag; else keep the model's tag so auto-
# 1. Interaction scope always wins when set (strongest signal) # triage can surface unregistered projects.
# 2. Model project used only when interaction is unscoped
# AND model project resolves to a registered project
# 3. Empty string when both are empty/unregistered
if interaction.project: if interaction.project:
project = interaction.project project = interaction.project
elif model_project: elif model_project:
@@ -328,9 +228,6 @@ def _parse_candidates(raw_output: str, interaction: Interaction) -> list[MemoryC
if resolved in registered_ids: if resolved in registered_ids:
project = resolved project = resolved
else: else:
# Unregistered project — keep the model's tag so
# auto-triage / the operator can see it and decide
# whether to register it as a new project or lead.
project = model_project project = model_project
log.info( log.info(
"unregistered_project_detected", "unregistered_project_detected",
@@ -338,34 +235,19 @@ def _parse_candidates(raw_output: str, interaction: Interaction) -> list[MemoryC
interaction_id=interaction.id, interaction_id=interaction.id,
) )
except Exception: except Exception:
project = model_project if model_project else "" project = model_project
else: else:
project = "" project = ""
domain = str(item.get("domain") or "").strip().lower()
confidence_raw = item.get("confidence", 0.5) content = normalized["content"]
if mem_type not in MEMORY_TYPES:
continue
if not content:
continue
# Domain knowledge: embed the domain tag in the content so it
# survives without a schema migration. The context builder
# can match on it via query-relevance ranking, and a future
# migration can parse it into a proper column.
if domain and not project:
content = f"[{domain}] {content}"
try:
confidence = float(confidence_raw)
except (TypeError, ValueError):
confidence = 0.5
confidence = max(0.0, min(1.0, confidence))
results.append( results.append(
MemoryCandidate( MemoryCandidate(
memory_type=mem_type, memory_type=normalized["type"],
content=content[:1000], content=content,
rule="llm_extraction", rule="llm_extraction",
source_span=content[:200], source_span=content[:200],
project=project, project=project,
confidence=confidence, confidence=normalized["confidence"],
source_interaction_id=interaction.id, source_interaction_id=interaction.id,
extractor_version=LLM_EXTRACTOR_VERSION, extractor_version=LLM_EXTRACTOR_VERSION,
) )

410
src/atocore/memory/service.py
View File

@@ -47,6 +47,7 @@ MEMORY_STATUSES = [
"active", "active",
"superseded", "superseded",
"invalid", "invalid",
"graduated", # Phase 5: memory has become an entity; content frozen, forward pointer in properties
] ]
@@ -63,6 +64,130 @@ class Memory:
updated_at: str updated_at: str
last_referenced_at: str = "" last_referenced_at: str = ""
reference_count: int = 0 reference_count: int = 0
domain_tags: list[str] | None = None
valid_until: str = "" # ISO UTC; empty = permanent
def _audit_memory(
memory_id: str,
action: str,
actor: str = "api",
before: dict | None = None,
after: dict | None = None,
note: str = "",
) -> None:
"""Append an entry to memory_audit.
Phase 4 Robustness V1. Every memory mutation flows through this
helper so we can answer "how did this memory get to its current
state?" and "when did we learn X?".
``action`` is a short verb: created, updated, promoted, rejected,
superseded, invalidated, reinforced, auto_promoted, expired.
``actor`` identifies the caller: api (default), auto-triage,
human-triage, host-cron, reinforcement, phase10-auto-promote,
etc. ``before`` / ``after`` are field snapshots (JSON-serialized).
Fail-open: a logging failure never breaks the mutation itself.
"""
import json as _json
try:
with get_connection() as conn:
conn.execute(
"INSERT INTO memory_audit (id, memory_id, action, actor, "
"before_json, after_json, note) VALUES (?, ?, ?, ?, ?, ?, ?)",
(
str(uuid.uuid4()),
memory_id,
action,
actor or "api",
_json.dumps(before or {}),
_json.dumps(after or {}),
(note or "")[:500],
),
)
except Exception as e:
log.warning("memory_audit_failed", memory_id=memory_id, action=action, error=str(e))
def get_memory_audit(memory_id: str, limit: int = 100) -> list[dict]:
"""Fetch audit entries for a memory, newest first."""
import json as _json
with get_connection() as conn:
rows = conn.execute(
"SELECT id, memory_id, action, actor, before_json, after_json, note, timestamp "
"FROM memory_audit WHERE memory_id = ? ORDER BY timestamp DESC LIMIT ?",
(memory_id, limit),
).fetchall()
out = []
for r in rows:
try:
before = _json.loads(r["before_json"] or "{}")
except Exception:
before = {}
try:
after = _json.loads(r["after_json"] or "{}")
except Exception:
after = {}
out.append({
"id": r["id"],
"memory_id": r["memory_id"],
"action": r["action"],
"actor": r["actor"] or "api",
"before": before,
"after": after,
"note": r["note"] or "",
"timestamp": r["timestamp"],
})
return out
def get_recent_audit(limit: int = 50) -> list[dict]:
"""Fetch recent memory_audit entries across all memories, newest first."""
import json as _json
with get_connection() as conn:
rows = conn.execute(
"SELECT id, memory_id, action, actor, before_json, after_json, note, timestamp "
"FROM memory_audit ORDER BY timestamp DESC LIMIT ?",
(limit,),
).fetchall()
out = []
for r in rows:
try:
after = _json.loads(r["after_json"] or "{}")
except Exception:
after = {}
out.append({
"id": r["id"],
"memory_id": r["memory_id"],
"action": r["action"],
"actor": r["actor"] or "api",
"note": r["note"] or "",
"timestamp": r["timestamp"],
"content_preview": (after.get("content") or "")[:120],
})
return out
def _normalize_tags(tags) -> list[str]:
"""Coerce a tags value (list, JSON string, None) to a clean lowercase list."""
import json as _json
if tags is None:
return []
if isinstance(tags, str):
try:
tags = _json.loads(tags) if tags.strip().startswith("[") else []
except Exception:
tags = []
if not isinstance(tags, list):
return []
out = []
for t in tags:
if not isinstance(t, str):
continue
t = t.strip().lower()
if t and t not in out:
out.append(t)
return out
def create_memory( def create_memory(
@@ -72,34 +197,37 @@ def create_memory(
source_chunk_id: str = "", source_chunk_id: str = "",
confidence: float = 1.0, confidence: float = 1.0,
status: str = "active", status: str = "active",
domain_tags: list[str] | None = None,
valid_until: str = "",
actor: str = "api",
) -> Memory: ) -> Memory:
"""Create a new memory entry. """Create a new memory entry.
``status`` defaults to ``active`` for backward compatibility. Pass ``status`` defaults to ``active`` for backward compatibility. Pass
``candidate`` when the memory is being proposed by the Phase 9 Commit C ``candidate`` when the memory is being proposed by the Phase 9 Commit C
extractor and still needs human review before it can influence context. extractor and still needs human review before it can influence context.
Phase 3: ``domain_tags`` is a list of lowercase domain strings
(optics, mechanics, firmware, ...) for cross-project retrieval.
``valid_until`` is an ISO UTC timestamp; memories with valid_until
in the past are excluded from context packs (but remain queryable).
""" """
import json as _json
if memory_type not in MEMORY_TYPES: if memory_type not in MEMORY_TYPES:
raise ValueError(f"Invalid memory type '{memory_type}'. Must be one of: {MEMORY_TYPES}") raise ValueError(f"Invalid memory type '{memory_type}'. Must be one of: {MEMORY_TYPES}")
if status not in MEMORY_STATUSES: if status not in MEMORY_STATUSES:
raise ValueError(f"Invalid status '{status}'. Must be one of: {MEMORY_STATUSES}") raise ValueError(f"Invalid status '{status}'. Must be one of: {MEMORY_STATUSES}")
_validate_confidence(confidence) _validate_confidence(confidence)
# Canonicalize the project through the registry so an alias and
# the canonical id store under the same bucket. This keeps
# reinforcement queries (which use the interaction's project) and
# context retrieval (which uses the registry-canonicalized hint)
# consistent with how memories are created.
project = resolve_project_name(project) project = resolve_project_name(project)
tags = _normalize_tags(domain_tags)
tags_json = _json.dumps(tags)
valid_until = (valid_until or "").strip() or None
memory_id = str(uuid.uuid4()) memory_id = str(uuid.uuid4())
now = datetime.now(timezone.utc).isoformat() now = datetime.now(timezone.utc).isoformat()
# Check for duplicate content within the same type+project at the same status.
# Scoping by status keeps active curation separate from the candidate
# review queue: a candidate and an active memory with identical text can
# legitimately coexist if the candidate is a fresh extraction of something
# already curated.
with get_connection() as conn: with get_connection() as conn:
existing = conn.execute( existing = conn.execute(
"SELECT id FROM memories " "SELECT id FROM memories "
@@ -118,9 +246,11 @@ def create_memory(
) )
conn.execute( conn.execute(
"INSERT INTO memories (id, memory_type, content, project, source_chunk_id, confidence, status) " "INSERT INTO memories (id, memory_type, content, project, source_chunk_id, "
"VALUES (?, ?, ?, ?, ?, ?, ?)", "confidence, status, domain_tags, valid_until) "
(memory_id, memory_type, content, project, source_chunk_id or None, confidence, status), "VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)",
(memory_id, memory_type, content, project, source_chunk_id or None,
confidence, status, tags_json, valid_until),
) )
log.info( log.info(
@@ -128,6 +258,23 @@ def create_memory(
memory_type=memory_type, memory_type=memory_type,
status=status, status=status,
content_preview=content[:80], content_preview=content[:80],
tags=tags,
valid_until=valid_until or "",
)
_audit_memory(
memory_id=memory_id,
action="created",
actor=actor,
after={
"memory_type": memory_type,
"content": content,
"project": project,
"status": status,
"confidence": confidence,
"domain_tags": tags,
"valid_until": valid_until or "",
},
) )
return Memory( return Memory(
@@ -142,6 +289,8 @@ def create_memory(
updated_at=now, updated_at=now,
last_referenced_at="", last_referenced_at="",
reference_count=0, reference_count=0,
domain_tags=tags,
valid_until=valid_until or "",
) )
@@ -200,8 +349,15 @@ def update_memory(
content: str | None = None, content: str | None = None,
confidence: float | None = None, confidence: float | None = None,
status: str | None = None, status: str | None = None,
memory_type: str | None = None,
domain_tags: list[str] | None = None,
valid_until: str | None = None,
actor: str = "api",
note: str = "",
) -> bool: ) -> bool:
"""Update an existing memory.""" """Update an existing memory."""
import json as _json
with get_connection() as conn: with get_connection() as conn:
existing = conn.execute("SELECT * FROM memories WHERE id = ?", (memory_id,)).fetchone() existing = conn.execute("SELECT * FROM memories WHERE id = ?", (memory_id,)).fetchone()
if existing is None: if existing is None:
@@ -221,20 +377,48 @@ def update_memory(
if duplicate: if duplicate:
raise ValueError("Update would create a duplicate active memory") raise ValueError("Update would create a duplicate active memory")
# Capture before-state for audit
before_snapshot = {
"content": existing["content"],
"status": existing["status"],
"confidence": existing["confidence"],
"memory_type": existing["memory_type"],
}
after_snapshot = dict(before_snapshot)
updates = [] updates = []
params: list = [] params: list = []
if content is not None: if content is not None:
updates.append("content = ?") updates.append("content = ?")
params.append(content) params.append(content)
after_snapshot["content"] = content
if confidence is not None: if confidence is not None:
updates.append("confidence = ?") updates.append("confidence = ?")
params.append(confidence) params.append(confidence)
after_snapshot["confidence"] = confidence
if status is not None: if status is not None:
if status not in MEMORY_STATUSES: if status not in MEMORY_STATUSES:
raise ValueError(f"Invalid status '{status}'. Must be one of: {MEMORY_STATUSES}") raise ValueError(f"Invalid status '{status}'. Must be one of: {MEMORY_STATUSES}")
updates.append("status = ?") updates.append("status = ?")
params.append(status) params.append(status)
after_snapshot["status"] = status
if memory_type is not None:
if memory_type not in MEMORY_TYPES:
raise ValueError(f"Invalid memory type '{memory_type}'. Must be one of: {MEMORY_TYPES}")
updates.append("memory_type = ?")
params.append(memory_type)
after_snapshot["memory_type"] = memory_type
if domain_tags is not None:
norm_tags = _normalize_tags(domain_tags)
updates.append("domain_tags = ?")
params.append(_json.dumps(norm_tags))
after_snapshot["domain_tags"] = norm_tags
if valid_until is not None:
vu = valid_until.strip() or None
updates.append("valid_until = ?")
params.append(vu)
after_snapshot["valid_until"] = vu or ""
if not updates: if not updates:
return False return False
@@ -249,21 +433,40 @@ def update_memory(
if result.rowcount > 0: if result.rowcount > 0:
log.info("memory_updated", memory_id=memory_id) log.info("memory_updated", memory_id=memory_id)
# Action verb is driven by status change when applicable; otherwise "updated"
if status == "active" and before_snapshot["status"] == "candidate":
action = "promoted"
elif status == "invalid" and before_snapshot["status"] == "candidate":
action = "rejected"
elif status == "invalid":
action = "invalidated"
elif status == "superseded":
action = "superseded"
else:
action = "updated"
_audit_memory(
memory_id=memory_id,
action=action,
actor=actor,
before=before_snapshot,
after=after_snapshot,
note=note,
)
return True return True
return False return False
def invalidate_memory(memory_id: str) -> bool: def invalidate_memory(memory_id: str, actor: str = "api") -> bool:
"""Mark a memory as invalid (error correction).""" """Mark a memory as invalid (error correction)."""
return update_memory(memory_id, status="invalid") return update_memory(memory_id, status="invalid", actor=actor)
def supersede_memory(memory_id: str) -> bool: def supersede_memory(memory_id: str, actor: str = "api") -> bool:
"""Mark a memory as superseded (replaced by newer info).""" """Mark a memory as superseded (replaced by newer info)."""
return update_memory(memory_id, status="superseded") return update_memory(memory_id, status="superseded", actor=actor)
def promote_memory(memory_id: str) -> bool: def promote_memory(memory_id: str, actor: str = "api", note: str = "") -> bool:
"""Promote a candidate memory to active (Phase 9 Commit C review queue). """Promote a candidate memory to active (Phase 9 Commit C review queue).
Returns False if the memory does not exist or is not currently a Returns False if the memory does not exist or is not currently a
@@ -278,10 +481,10 @@ def promote_memory(memory_id: str) -> bool:
return False return False
if row["status"] != "candidate": if row["status"] != "candidate":
return False return False
return update_memory(memory_id, status="active") return update_memory(memory_id, status="active", actor=actor, note=note)
def reject_candidate_memory(memory_id: str) -> bool: def reject_candidate_memory(memory_id: str, actor: str = "api", note: str = "") -> bool:
"""Reject a candidate memory (Phase 9 Commit C). """Reject a candidate memory (Phase 9 Commit C).
Sets the candidate's status to ``invalid`` so it drops out of the Sets the candidate's status to ``invalid`` so it drops out of the
@@ -296,7 +499,7 @@ def reject_candidate_memory(memory_id: str) -> bool:
return False return False
if row["status"] != "candidate": if row["status"] != "candidate":
return False return False
return update_memory(memory_id, status="invalid") return update_memory(memory_id, status="invalid", actor=actor, note=note)
def reinforce_memory( def reinforce_memory(
@@ -337,9 +540,106 @@ def reinforce_memory(
old_confidence=round(old_confidence, 4), old_confidence=round(old_confidence, 4),
new_confidence=round(new_confidence, 4), new_confidence=round(new_confidence, 4),
) )
# Reinforcement writes an audit row per bump. Reinforcement fires often
# (every captured interaction); this lets you trace which interactions
# kept which memories alive. Could become chatty but is invaluable for
# decay/cold-memory analysis. If it becomes an issue, throttle here.
_audit_memory(
memory_id=memory_id,
action="reinforced",
actor="reinforcement",
before={"confidence": old_confidence},
after={"confidence": new_confidence},
)
return True, old_confidence, new_confidence return True, old_confidence, new_confidence
def auto_promote_reinforced(
min_reference_count: int = 3,
min_confidence: float = 0.7,
max_age_days: int = 14,
) -> list[str]:
"""Auto-promote candidate memories with strong reinforcement signals.
Phase 10: memories that have been reinforced by multiple interactions
graduate from candidate to active without human review. This rewards
knowledge that the system keeps referencing organically.
Returns a list of promoted memory IDs.
"""
from datetime import timedelta
cutoff = (
datetime.now(timezone.utc) - timedelta(days=max_age_days)
).strftime("%Y-%m-%d %H:%M:%S")
promoted: list[str] = []
with get_connection() as conn:
rows = conn.execute(
"SELECT id, content, memory_type, project, confidence, "
"reference_count FROM memories "
"WHERE status = 'candidate' "
"AND COALESCE(reference_count, 0) >= ? "
"AND confidence >= ? "
"AND last_referenced_at >= ?",
(min_reference_count, min_confidence, cutoff),
).fetchall()
for row in rows:
mid = row["id"]
ok = promote_memory(
mid,
actor="phase10-auto-promote",
note=f"ref_count={row['reference_count']} confidence={row['confidence']:.2f}",
)
if ok:
promoted.append(mid)
log.info(
"memory_auto_promoted",
memory_id=mid,
memory_type=row["memory_type"],
project=row["project"] or "(global)",
reference_count=row["reference_count"],
confidence=round(row["confidence"], 3),
)
return promoted
def expire_stale_candidates(
max_age_days: int = 14,
) -> list[str]:
"""Reject candidate memories that sat in queue too long unreinforced.
Candidates older than ``max_age_days`` with zero reinforcement are
auto-rejected to prevent unbounded queue growth. Returns rejected IDs.
"""
from datetime import timedelta
cutoff = (
datetime.now(timezone.utc) - timedelta(days=max_age_days)
).strftime("%Y-%m-%d %H:%M:%S")
expired: list[str] = []
with get_connection() as conn:
rows = conn.execute(
"SELECT id FROM memories "
"WHERE status = 'candidate' "
"AND COALESCE(reference_count, 0) = 0 "
"AND created_at < ?",
(cutoff,),
).fetchall()
for row in rows:
mid = row["id"]
ok = reject_candidate_memory(
mid,
actor="candidate-expiry",
note=f"unreinforced for {max_age_days}+ days",
)
if ok:
expired.append(mid)
log.info("memory_expired", memory_id=mid)
return expired
def get_memories_for_context( def get_memories_for_context(
memory_types: list[str] | None = None, memory_types: list[str] | None = None,
project: str | None = None, project: str | None = None,
@@ -410,8 +710,14 @@ def get_memories_for_context(
seen_ids.add(mem.id) seen_ids.add(mem.id)
pool.append(mem) pool.append(mem)
# Phase 3: filter out expired memories (valid_until in the past).
# Raw API queries still return them (for audit/history) but context
# packs must not surface stale facts.
if pool:
pool = _filter_expired(pool)
if query_tokens is not None: if query_tokens is not None:
pool = _rank_memories_for_query(pool, query_tokens) pool = _rank_memories_for_query(pool, query_tokens, query=query)
# Per-entry cap prevents a single long memory from monopolizing # Per-entry cap prevents a single long memory from monopolizing
# the band. With 16 p06 memories competing for ~700 chars, an # the band. With 16 p06 memories competing for ~700 chars, an
@@ -440,40 +746,78 @@ def get_memories_for_context(
return text, len(text) return text, len(text)
def _filter_expired(memories: list["Memory"]) -> list["Memory"]:
"""Drop memories whose valid_until is in the past (UTC comparison)."""
now_iso = datetime.now(timezone.utc).strftime("%Y-%m-%d")
out = []
for m in memories:
vu = (m.valid_until or "").strip()
if not vu:
out.append(m)
continue
# Compare as string (ISO dates/timestamps sort correctly lexicographically
# when they have the same format; date-only vs full ts both start YYYY-MM-DD).
if vu[:10] >= now_iso:
out.append(m)
# else: expired, drop silently
return out
def _rank_memories_for_query( def _rank_memories_for_query(
memories: list["Memory"], memories: list["Memory"],
query_tokens: set[str], query_tokens: set[str],
query: str | None = None,
) -> list["Memory"]: ) -> list["Memory"]:
"""Rerank a memory list by lexical overlap with a pre-tokenized query. """Rerank a memory list by lexical overlap with a pre-tokenized query.
Primary key: overlap_density (overlap_count / memory_token_count), Primary key: overlap_density (overlap_count / memory_token_count),
which rewards short focused memories that match the query precisely which rewards short focused memories that match the query precisely
over long overview memories that incidentally share a few tokens. over long overview memories that incidentally share a few tokens.
Secondary: absolute overlap count. Tertiary: confidence. Secondary: absolute overlap count. Tertiary: domain-tag match.
Quaternary: confidence.
R7 fix: previously overlap_count alone was the primary key, so a Phase 3: domain_tags contribute a boost when they appear in the
40-token overview memory with 3 overlapping tokens tied a 5-token query text. A memory tagged [optics, thermal] for a query about
memory with 3 overlapping tokens, and the overview won on "optics coating" gets promoted above a memory without those tags.
confidence. Now the short memory's density (0.6) beats the Tag boost fires AFTER content-overlap density so it only breaks
overview's density (0.075). ties among content-similar candidates.
""" """
from atocore.memory.reinforcement import _normalize, _tokenize from atocore.memory.reinforcement import _normalize, _tokenize
scored: list[tuple[float, int, float, Memory]] = [] query_lower = (query or "").lower()
scored: list[tuple[float, int, int, float, Memory]] = []
for mem in memories: for mem in memories:
mem_tokens = _tokenize(_normalize(mem.content)) mem_tokens = _tokenize(_normalize(mem.content))
overlap = len(mem_tokens & query_tokens) if mem_tokens else 0 overlap = len(mem_tokens & query_tokens) if mem_tokens else 0
density = overlap / len(mem_tokens) if mem_tokens else 0.0 density = overlap / len(mem_tokens) if mem_tokens else 0.0
scored.append((density, overlap, mem.confidence, mem))
scored.sort(key=lambda t: (t[0], t[1], t[2]), reverse=True) # Tag boost: count how many of the memory's domain_tags appear
return [mem for _, _, _, mem in scored] # as substrings in the raw query. Strong signal for topical match.
tag_hits = 0
for tag in (mem.domain_tags or []):
if tag and tag in query_lower:
tag_hits += 1
scored.append((density, overlap, tag_hits, mem.confidence, mem))
scored.sort(key=lambda t: (t[0], t[1], t[2], t[3]), reverse=True)
return [mem for _, _, _, _, mem in scored]
def _row_to_memory(row) -> Memory: def _row_to_memory(row) -> Memory:
"""Convert a DB row to Memory dataclass.""" """Convert a DB row to Memory dataclass."""
import json as _json
keys = row.keys() if hasattr(row, "keys") else [] keys = row.keys() if hasattr(row, "keys") else []
last_ref = row["last_referenced_at"] if "last_referenced_at" in keys else None last_ref = row["last_referenced_at"] if "last_referenced_at" in keys else None
ref_count = row["reference_count"] if "reference_count" in keys else 0 ref_count = row["reference_count"] if "reference_count" in keys else 0
tags_raw = row["domain_tags"] if "domain_tags" in keys else None
try:
tags = _json.loads(tags_raw) if tags_raw else []
if not isinstance(tags, list):
tags = []
except Exception:
tags = []
valid_until = row["valid_until"] if "valid_until" in keys else None
return Memory( return Memory(
id=row["id"], id=row["id"],
memory_type=row["memory_type"], memory_type=row["memory_type"],
@@ -486,6 +830,8 @@ def _row_to_memory(row) -> Memory:
updated_at=row["updated_at"], updated_at=row["updated_at"],
last_referenced_at=last_ref or "", last_referenced_at=last_ref or "",
reference_count=int(ref_count or 0), reference_count=int(ref_count or 0),
domain_tags=tags,
valid_until=valid_until or "",
) )

105
src/atocore/models/database.py
View File

@@ -119,6 +119,111 @@ def _apply_migrations(conn: sqlite3.Connection) -> None:
"CREATE INDEX IF NOT EXISTS idx_memories_last_referenced ON memories(last_referenced_at)" "CREATE INDEX IF NOT EXISTS idx_memories_last_referenced ON memories(last_referenced_at)"
) )
# Phase 3 (Auto-Organization V1): domain tags + expiry.
# domain_tags is a JSON array of lowercase strings (optics, mechanics,
# firmware, business, etc.) inferred by the LLM during triage. Used for
# cross-project retrieval: a query about "optics" can surface matches from
# p04 + p05 + p06 without knowing all the project names.
# valid_until is an ISO UTC timestamp beyond which the memory is
# considered stale. get_memories_for_context filters these out of context
# packs automatically so ephemeral facts (status snapshots, weekly counts)
# don't pollute grounding once they've aged out.
if not _column_exists(conn, "memories", "domain_tags"):
conn.execute("ALTER TABLE memories ADD COLUMN domain_tags TEXT DEFAULT '[]'")
if not _column_exists(conn, "memories", "valid_until"):
conn.execute("ALTER TABLE memories ADD COLUMN valid_until DATETIME")
conn.execute(
"CREATE INDEX IF NOT EXISTS idx_memories_valid_until ON memories(valid_until)"
)
# Phase 5 (Engineering V1): when a memory graduates to an entity, we
# keep the memory row as an immutable historical pointer. The forward
# pointer lets downstream code follow "what did this memory become?"
# without having to join through source_refs.
if not _column_exists(conn, "memories", "graduated_to_entity_id"):
conn.execute("ALTER TABLE memories ADD COLUMN graduated_to_entity_id TEXT")
conn.execute(
"CREATE INDEX IF NOT EXISTS idx_memories_graduated ON memories(graduated_to_entity_id)"
)
# Phase 4 (Robustness V1): append-only audit log for memory mutations.
# Every create/update/promote/reject/supersede/invalidate/reinforce/expire/
# auto_promote writes one row here. before/after are JSON snapshots of the
# relevant fields. actor lets us distinguish auto-triage vs human-triage vs
# api vs cron. This is the "how did this memory get to its current state"
# trail — essential once the brain starts auto-organizing itself.
conn.execute(
"""
CREATE TABLE IF NOT EXISTS memory_audit (
id TEXT PRIMARY KEY,
memory_id TEXT NOT NULL,
action TEXT NOT NULL,
actor TEXT DEFAULT 'api',
before_json TEXT DEFAULT '{}',
after_json TEXT DEFAULT '{}',
note TEXT DEFAULT '',
timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
)
"""
)
conn.execute("CREATE INDEX IF NOT EXISTS idx_memory_audit_memory ON memory_audit(memory_id)")
conn.execute("CREATE INDEX IF NOT EXISTS idx_memory_audit_timestamp ON memory_audit(timestamp)")
conn.execute("CREATE INDEX IF NOT EXISTS idx_memory_audit_action ON memory_audit(action)")
# Phase 5 (Engineering V1): entity_kind discriminator lets one audit
# table serve both memories AND entities. Default "memory" keeps existing
# rows correct; entity mutations write entity_kind="entity".
if not _column_exists(conn, "memory_audit", "entity_kind"):
conn.execute("ALTER TABLE memory_audit ADD COLUMN entity_kind TEXT DEFAULT 'memory'")
conn.execute(
"CREATE INDEX IF NOT EXISTS idx_memory_audit_entity_kind ON memory_audit(entity_kind)"
)
# Phase 5: conflicts + conflict_members tables per conflict-model.md.
# A conflict is "two or more active rows claiming the same slot with
# incompatible values". slot_kind + slot_key identify the logical slot
# (e.g., "component.material" for some component id). Members point
# back to the conflicting rows (memory or entity) with layer trust so
# resolution can pick the highest-trust winner.
conn.execute(
"""
CREATE TABLE IF NOT EXISTS conflicts (
id TEXT PRIMARY KEY,
slot_kind TEXT NOT NULL,
slot_key TEXT NOT NULL,
project TEXT DEFAULT '',
status TEXT DEFAULT 'open',
resolution TEXT DEFAULT '',
resolved_at DATETIME,
detected_at DATETIME DEFAULT CURRENT_TIMESTAMP,
note TEXT DEFAULT ''
)
"""
)
conn.execute(
"""
CREATE TABLE IF NOT EXISTS conflict_members (
id TEXT PRIMARY KEY,
conflict_id TEXT NOT NULL REFERENCES conflicts(id) ON DELETE CASCADE,
member_kind TEXT NOT NULL,
member_id TEXT NOT NULL,
member_layer_trust INTEGER DEFAULT 0,
value_snapshot TEXT DEFAULT ''
)
"""
)
conn.execute("CREATE INDEX IF NOT EXISTS idx_conflicts_status ON conflicts(status)")
conn.execute("CREATE INDEX IF NOT EXISTS idx_conflicts_project ON conflicts(project)")
conn.execute(
"CREATE INDEX IF NOT EXISTS idx_conflicts_slot ON conflicts(slot_kind, slot_key)"
)
conn.execute(
"CREATE INDEX IF NOT EXISTS idx_conflict_members_conflict ON conflict_members(conflict_id)"
)
conn.execute(
"CREATE INDEX IF NOT EXISTS idx_conflict_members_member ON conflict_members(member_kind, member_id)"
)
# Phase 9 Commit A: capture loop columns on the interactions table. # Phase 9 Commit A: capture loop columns on the interactions table.
# The original schema only carried prompt + project_id + a context_pack # The original schema only carried prompt + project_id + a context_pack
# JSON blob. To make interactions a real audit trail of what AtoCore fed # JSON blob. To make interactions a real audit trail of what AtoCore fed

170
src/atocore/observability/alerts.py Normal file
View File

@@ -0,0 +1,170 @@
"""Alert emission framework (Phase 4 Robustness V1).
One-stop helper to raise operational alerts from any AtoCore code
path. An alert is a structured message about something the operator
should see — harness regression, queue pileup, integrity drift,
pipeline skipped, etc.
Emission fans out to multiple sinks so a single call touches every
observability channel:
1. structlog logger (always)
2. Append to ``$ATOCORE_ALERT_LOG`` (default ~/atocore-logs/alerts.log)
3. Write the last alert of each severity to AtoCore project state
(atocore/alert/last_{severity}) so the dashboard can surface it
4. POST to ``$ATOCORE_ALERT_WEBHOOK`` if set (Discord/Slack/generic)
All sinks are fail-open — if one fails the others still fire.
Severity levels (inspired by syslog but simpler):
- ``info`` operational event worth noting
- ``warning`` degraded state, service still works
- ``critical`` something is broken and needs attention
Environment variables:
ATOCORE_ALERT_LOG override the alerts log file path
ATOCORE_ALERT_WEBHOOK POST JSON alerts here (Discord webhook, etc.)
ATOCORE_BASE_URL AtoCore API for project-state write (default localhost:8100)
"""
from __future__ import annotations
import json
import os
import threading
import urllib.error
import urllib.request
from datetime import datetime, timezone
from pathlib import Path
from atocore.observability.logger import get_logger
log = get_logger("alerts")
SEVERITIES = {"info", "warning", "critical"}
def _default_alert_log() -> Path:
explicit = os.environ.get("ATOCORE_ALERT_LOG")
if explicit:
return Path(explicit)
return Path.home() / "atocore-logs" / "alerts.log"
def _append_log(severity: str, title: str, message: str, context: dict | None) -> None:
path = _default_alert_log()
try:
path.parent.mkdir(parents=True, exist_ok=True)
ts = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
line = f"[{ts}] [{severity.upper()}] {title}: {message}"
if context:
line += f" {json.dumps(context, ensure_ascii=True)[:500]}"
line += "\n"
with open(path, "a", encoding="utf-8") as f:
f.write(line)
except Exception as e:
log.warning("alert_log_write_failed", error=str(e))
def _write_state(severity: str, title: str, message: str, ts: str) -> None:
"""Record the most-recent alert per severity into project_state.
Uses the internal ``set_state`` helper directly so we work even
when the HTTP API isn't available (e.g. called from cron scripts
that import atocore as a library).
"""
try:
from atocore.context.project_state import set_state
set_state(
project_name="atocore",
category="alert",
key=f"last_{severity}",
value=json.dumps({"title": title, "message": message[:400], "timestamp": ts}),
source="alert framework",
)
except Exception as e:
log.warning("alert_state_write_failed", error=str(e))
def _post_webhook(severity: str, title: str, message: str, context: dict | None, ts: str) -> None:
url = os.environ.get("ATOCORE_ALERT_WEBHOOK")
if not url:
return
# Auto-detect Discord webhook shape for nicer formatting
if "discord.com/api/webhooks" in url or "discordapp.com/api/webhooks" in url:
emoji = {"info": ":information_source:", "warning": ":warning:", "critical": ":rotating_light:"}.get(severity, "")
body = {
"content": f"{emoji} **AtoCore {severity}**: {title}",
"embeds": [{
"description": message[:1800],
"timestamp": ts,
"fields": [
{"name": k, "value": str(v)[:200], "inline": True}
for k, v in (context or {}).items()
][:10],
}],
}
else:
body = {
"severity": severity,
"title": title,
"message": message,
"context": context or {},
"timestamp": ts,
}
def _fire():
try:
req = urllib.request.Request(
url,
data=json.dumps(body).encode("utf-8"),
method="POST",
headers={"Content-Type": "application/json"},
)
urllib.request.urlopen(req, timeout=8)
except Exception as e:
log.warning("alert_webhook_failed", error=str(e))
threading.Thread(target=_fire, daemon=True).start()
def emit_alert(
severity: str,
title: str,
message: str,
context: dict | None = None,
) -> None:
"""Emit an alert to all configured sinks.
Fail-open: any single sink failure is logged but does not prevent
other sinks from firing.
"""
severity = (severity or "info").lower()
if severity not in SEVERITIES:
severity = "info"
ts = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
# Sink 1: structlog — always
logger_fn = {
"info": log.info,
"warning": log.warning,
"critical": log.error,
}[severity]
logger_fn("alert", title=title, message=message[:500], **(context or {}))
# Sinks 2-4: fail-open, each wrapped
try:
_append_log(severity, title, message, context)
except Exception:
pass
try:
_write_state(severity, title, message, ts)
except Exception:
pass
try:
_post_webhook(severity, title, message, context, ts)
except Exception:
pass

58
tests/test_alerts.py Normal file
View File

@@ -0,0 +1,58 @@
"""Tests for the Phase 4 alerts framework."""
from __future__ import annotations
import os
import tempfile
from pathlib import Path
import pytest
import atocore.config as _config
@pytest.fixture(autouse=True)
def isolated_env(monkeypatch):
"""Isolate alerts sinks per test."""
tmpdir = tempfile.mkdtemp()
log_file = Path(tmpdir) / "alerts.log"
monkeypatch.setenv("ATOCORE_ALERT_LOG", str(log_file))
monkeypatch.delenv("ATOCORE_ALERT_WEBHOOK", raising=False)
# Data dir for any state writes
monkeypatch.setenv("ATOCORE_DATA_DIR", tmpdir)
_config.settings = _config.Settings()
from atocore.models.database import init_db
init_db()
yield {"tmpdir": tmpdir, "log_file": log_file}
def test_emit_alert_writes_log_file(isolated_env):
from atocore.observability.alerts import emit_alert
emit_alert("warning", "test title", "test message body", context={"count": 5})
content = isolated_env["log_file"].read_text(encoding="utf-8")
assert "test title" in content
assert "test message body" in content
assert "WARNING" in content
assert '"count": 5' in content
def test_emit_alert_invalid_severity_falls_back_to_info(isolated_env):
from atocore.observability.alerts import emit_alert
emit_alert("made-up-severity", "t", "m")
content = isolated_env["log_file"].read_text(encoding="utf-8")
assert "INFO" in content
def test_emit_alert_fails_open_on_log_write_error(monkeypatch, isolated_env):
"""An unwritable log path should not crash the emit."""
from atocore.observability.alerts import emit_alert
monkeypatch.setenv("ATOCORE_ALERT_LOG", "/nonexistent/path/that/definitely/is/not/writable/alerts.log")
# Must not raise
emit_alert("info", "t", "m")

105
tests/test_engineering.py
View File

@@ -116,3 +116,108 @@ def test_entity_name_search(tmp_data_dir):
results = get_entities(name_contains="Support") results = get_entities(name_contains="Support")
assert len(results) == 2 assert len(results) == 2
# --- Phase 5: Entity promote/reject lifecycle + audit + canonicalization ---
def test_entity_project_canonicalization(tmp_data_dir):
"""Aliases resolve to canonical project_id on write (Phase 5)."""
init_db()
init_engineering_schema()
# "p04" is a registered alias for p04-gigabit
e = create_entity("component", "Test Component", project="p04")
assert e.project == "p04-gigabit"
def test_promote_entity_candidate_to_active(tmp_data_dir):
from atocore.engineering.service import promote_entity, get_entity
init_db()
init_engineering_schema()
e = create_entity("requirement", "CTE tolerance", status="candidate")
assert e.status == "candidate"
assert promote_entity(e.id, actor="test-triage")
e2 = get_entity(e.id)
assert e2.status == "active"
def test_reject_entity_candidate(tmp_data_dir):
from atocore.engineering.service import reject_entity_candidate, get_entity
init_db()
init_engineering_schema()
e = create_entity("decision", "pick vendor Y", status="candidate")
assert reject_entity_candidate(e.id, actor="test-triage", note="duplicate")
e2 = get_entity(e.id)
assert e2.status == "invalid"
def test_promote_active_entity_noop(tmp_data_dir):
from atocore.engineering.service import promote_entity
init_db()
init_engineering_schema()
e = create_entity("component", "Already Active") # default status=active
assert not promote_entity(e.id) # only candidates can promote
def test_entity_audit_log_captures_lifecycle(tmp_data_dir):
from atocore.engineering.service import (
promote_entity,
get_entity_audit,
)
init_db()
init_engineering_schema()
e = create_entity("requirement", "test req", status="candidate", actor="test")
promote_entity(e.id, actor="test-triage", note="looks good")
audit = get_entity_audit(e.id)
actions = [a["action"] for a in audit]
assert "created" in actions
assert "promoted" in actions
promote_entry = next(a for a in audit if a["action"] == "promoted")
assert promote_entry["actor"] == "test-triage"
assert promote_entry["note"] == "looks good"
assert promote_entry["before"]["status"] == "candidate"
assert promote_entry["after"]["status"] == "active"
def test_new_relationship_types_available(tmp_data_dir):
"""Phase 5 added 6 missing relationship types."""
for rel in ["based_on_assumption", "supports", "conflicts_with",
"updated_by_session", "evidenced_by", "summarized_in"]:
assert rel in RELATIONSHIP_TYPES, f"{rel} missing from RELATIONSHIP_TYPES"
def test_conflicts_tables_exist(tmp_data_dir):
"""Phase 5 conflict-model tables."""
from atocore.models.database import get_connection
init_db()
with get_connection() as conn:
tables = {r[0] for r in conn.execute(
"SELECT name FROM sqlite_master WHERE type='table'"
).fetchall()}
assert "conflicts" in tables
assert "conflict_members" in tables
def test_memory_audit_has_entity_kind(tmp_data_dir):
"""Phase 5 added entity_kind discriminator."""
from atocore.models.database import get_connection
init_db()
with get_connection() as conn:
cols = {r["name"] for r in conn.execute("PRAGMA table_info(memory_audit)").fetchall()}
assert "entity_kind" in cols
def test_graduated_status_accepted(tmp_data_dir):
"""Phase 5 added 'graduated' memory status for memory→entity transitions."""
from atocore.memory.service import MEMORY_STATUSES
assert "graduated" in MEMORY_STATUSES

212
tests/test_engineering_queries.py Normal file
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"""Phase 5 tests — the 10 canonical engineering queries.
Test fixtures seed a small p-test graph and exercise each query. The 3 killer
queries (Q-006/009/011) get dedicated tests that verify they surface real gaps
and DON'T false-positive on well-formed data.
"""
from __future__ import annotations
import pytest
from atocore.engineering.queries import (
all_gaps,
decisions_affecting,
evidence_chain,
impact_analysis,
orphan_requirements,
recent_changes,
requirements_for,
risky_decisions,
system_map,
unsupported_claims,
)
from atocore.engineering.service import (
create_entity,
create_relationship,
init_engineering_schema,
)
from atocore.models.database import init_db
@pytest.fixture
def seeded_graph(tmp_data_dir):
"""Build a small engineering graph for query tests."""
init_db()
init_engineering_schema()
# Subsystem + components
ss = create_entity("subsystem", "Optics", project="p-test")
c1 = create_entity("component", "Primary Mirror", project="p-test")
c2 = create_entity("component", "Diverger Lens", project="p-test")
c_orphan = create_entity("component", "Unparented", project="p-test")
create_relationship(c1.id, ss.id, "part_of")
create_relationship(c2.id, ss.id, "part_of")
# Requirements — one satisfied, one orphan
r_ok = create_entity("requirement", "Surface figure < 25nm RMS", project="p-test")
r_orphan = create_entity("requirement", "Measurement lambda/20", project="p-test")
create_relationship(c1.id, r_ok.id, "satisfies")
# Decisions
d_ok = create_entity("decision", "Use Zerodur blank", project="p-test")
d_risky = create_entity("decision", "Use external CGH", project="p-test")
create_relationship(d_ok.id, ss.id, "affected_by_decision")
# Assumption (flagged) — d_risky depends on it
a_flagged = create_entity(
"parameter", "Vendor lead time 6 weeks",
project="p-test",
properties={"flagged": True},
)
create_relationship(d_risky.id, a_flagged.id, "based_on_assumption")
# Validation claim — one supported, one not
v_ok = create_entity("validation_claim", "Margin is adequate", project="p-test")
v_orphan = create_entity("validation_claim", "Thermal stability OK", project="p-test")
result = create_entity("result", "FEA thermal sweep 2026-03", project="p-test")
create_relationship(result.id, v_ok.id, "supports")
# Material
mat = create_entity("material", "Zerodur", project="p-test")
create_relationship(c1.id, mat.id, "uses_material")
return {
"subsystem": ss, "component_1": c1, "component_2": c2,
"orphan_component": c_orphan,
"req_ok": r_ok, "req_orphan": r_orphan,
"decision_ok": d_ok, "decision_risky": d_risky,
"assumption_flagged": a_flagged,
"claim_supported": v_ok, "claim_orphan": v_orphan,
"result": result, "material": mat,
}
# --- Structure queries ---
def test_system_map_returns_subsystem_with_components(seeded_graph):
result = system_map("p-test")
assert result["project"] == "p-test"
assert len(result["subsystems"]) == 1
optics = result["subsystems"][0]
assert optics["name"] == "Optics"
comp_names = {c["name"] for c in optics["components"]}
assert "Primary Mirror" in comp_names
assert "Diverger Lens" in comp_names
def test_system_map_reports_orphan_components(seeded_graph):
result = system_map("p-test")
names = {c["name"] for c in result["orphan_components"]}
assert "Unparented" in names
def test_system_map_includes_materials(seeded_graph):
result = system_map("p-test")
primary = next(
c for s in result["subsystems"] for c in s["components"] if c["name"] == "Primary Mirror"
)
assert "Zerodur" in primary["materials"]
def test_decisions_affecting_whole_project(seeded_graph):
result = decisions_affecting("p-test")
names = {d["name"] for d in result["decisions"]}
assert "Use Zerodur blank" in names
assert "Use external CGH" in names
def test_decisions_affecting_specific_subsystem(seeded_graph):
ss_id = seeded_graph["subsystem"].id
result = decisions_affecting("p-test", subsystem_id=ss_id)
names = {d["name"] for d in result["decisions"]}
# d_ok has edge to subsystem directly
assert "Use Zerodur blank" in names
def test_requirements_for_component(seeded_graph):
c_id = seeded_graph["component_1"].id
result = requirements_for(c_id)
assert result["count"] == 1
assert result["requirements"][0]["name"] == "Surface figure < 25nm RMS"
def test_recent_changes_includes_created_entities(seeded_graph):
result = recent_changes("p-test", limit=100)
actions = [c["action"] for c in result["changes"]]
assert "created" in actions
assert result["count"] > 0
# --- Killer queries ---
def test_orphan_requirements_finds_unsatisfied(seeded_graph):
result = orphan_requirements("p-test")
names = {r["name"] for r in result["gaps"]}
assert "Measurement lambda/20" in names # orphan
assert "Surface figure < 25nm RMS" not in names # has SATISFIES edge
def test_orphan_requirements_empty_when_all_satisfied(tmp_data_dir):
init_db()
init_engineering_schema()
c = create_entity("component", "C", project="p-clean")
r = create_entity("requirement", "R", project="p-clean")
create_relationship(c.id, r.id, "satisfies")
result = orphan_requirements("p-clean")
assert result["count"] == 0
def test_risky_decisions_finds_flagged_assumptions(seeded_graph):
result = risky_decisions("p-test")
names = {d["decision_name"] for d in result["gaps"]}
assert "Use external CGH" in names
assert "Use Zerodur blank" not in names # has no flagged assumption
def test_unsupported_claims_finds_orphan_claims(seeded_graph):
result = unsupported_claims("p-test")
names = {c["name"] for c in result["gaps"]}
assert "Thermal stability OK" in names
assert "Margin is adequate" not in names # has SUPPORTS edge
def test_all_gaps_combines_the_three_killers(seeded_graph):
result = all_gaps("p-test")
assert result["orphan_requirements"]["count"] == 1
assert result["risky_decisions"]["count"] == 1
assert result["unsupported_claims"]["count"] == 1
def test_all_gaps_clean_project_reports_zero(tmp_data_dir):
init_db()
init_engineering_schema()
create_entity("component", "alone", project="p-empty")
result = all_gaps("p-empty")
assert result["orphan_requirements"]["count"] == 0
assert result["risky_decisions"]["count"] == 0
assert result["unsupported_claims"]["count"] == 0
# --- Impact + evidence ---
def test_impact_analysis_walks_outbound_edges(seeded_graph):
c_id = seeded_graph["component_1"].id
result = impact_analysis(c_id, max_depth=2)
# Primary Mirror → SATISFIES → Requirement, → USES_MATERIAL → Material
rel_types = {i["relationship"] for i in result["impacted"]}
assert "satisfies" in rel_types
assert "uses_material" in rel_types
def test_evidence_chain_walks_inbound_provenance(seeded_graph):
v_ok_id = seeded_graph["claim_supported"].id
result = evidence_chain(v_ok_id)
# The Result entity supports the claim
via_types = {e["via"] for e in result["evidence_chain"]}
assert "supports" in via_types
source_names = {e["source_name"] for e in result["evidence_chain"]}
assert "FEA thermal sweep 2026-03" in source_names

246
tests/test_engineering_v1_phase5.py Normal file
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@@ -0,0 +1,246 @@
"""Phase 5F + 5G + 5H tests — graduation, conflicts, MCP tools."""
from __future__ import annotations
import pytest
from atocore.engineering.conflicts import (
detect_conflicts_for_entity,
list_open_conflicts,
resolve_conflict,
)
from atocore.engineering._graduation_prompt import (
build_user_message,
parse_graduation_output,
)
from atocore.engineering.service import (
create_entity,
create_relationship,
get_entity,
init_engineering_schema,
promote_entity,
)
from atocore.memory.service import create_memory
from atocore.models.database import get_connection, init_db
# --- 5F Memory graduation ---
def test_graduation_prompt_parses_positive_decision():
raw = """
{"graduate": true, "entity_type": "component", "name": "Primary Mirror",
"description": "The 1.2m primary mirror for p04", "confidence": 0.85,
"relationships": [{"rel_type": "part_of", "target_hint": "Optics Subsystem"}]}
"""
decision = parse_graduation_output(raw)
assert decision is not None
assert decision["graduate"] is True
assert decision["entity_type"] == "component"
assert decision["name"] == "Primary Mirror"
assert decision["confidence"] == 0.85
assert decision["relationships"] == [
{"rel_type": "part_of", "target_hint": "Optics Subsystem"}
]
def test_graduation_prompt_parses_negative_decision():
raw = '{"graduate": false, "reason": "conversational filler, no typed entity"}'
decision = parse_graduation_output(raw)
assert decision is not None
assert decision["graduate"] is False
assert "filler" in decision["reason"]
def test_graduation_prompt_rejects_unknown_entity_type():
raw = '{"graduate": true, "entity_type": "quantum_thing", "name": "x"}'
assert parse_graduation_output(raw) is None
def test_graduation_prompt_tolerates_markdown_fences():
raw = '```json\n{"graduate": false, "reason": "ok"}\n```'
d = parse_graduation_output(raw)
assert d is not None
assert d["graduate"] is False
def test_promote_entity_marks_source_memory_graduated(tmp_data_dir):
init_db()
init_engineering_schema()
mem = create_memory("knowledge", "The Primary Mirror is 1.2m Zerodur",
project="p-test", status="active")
# Create entity candidate pointing back to the memory
ent = create_entity(
"component",
"Primary Mirror",
project="p-test",
status="candidate",
source_refs=[f"memory:{mem.id}"],
)
# Promote
assert promote_entity(ent.id, actor="test-triage")
# Memory should now be graduated with forward pointer
with get_connection() as conn:
row = conn.execute(
"SELECT status, graduated_to_entity_id FROM memories WHERE id = ?",
(mem.id,),
).fetchone()
assert row["status"] == "graduated"
assert row["graduated_to_entity_id"] == ent.id
def test_promote_entity_without_memory_refs_no_graduation(tmp_data_dir):
"""Entity not backed by any memory — promote still works, no graduation."""
init_db()
init_engineering_schema()
ent = create_entity("component", "Orphan", project="p-test", status="candidate")
assert promote_entity(ent.id)
assert get_entity(ent.id).status == "active"
# --- 5G Conflict detection ---
def test_component_material_conflict_detected(tmp_data_dir):
init_db()
init_engineering_schema()
c = create_entity("component", "Mirror", project="p-test")
m1 = create_entity("material", "Zerodur", project="p-test")
m2 = create_entity("material", "ULE", project="p-test")
create_relationship(c.id, m1.id, "uses_material")
create_relationship(c.id, m2.id, "uses_material")
detected = detect_conflicts_for_entity(c.id)
assert len(detected) == 1
conflicts = list_open_conflicts(project="p-test")
assert any(c["slot_kind"] == "component.material" for c in conflicts)
conflict = next(c for c in conflicts if c["slot_kind"] == "component.material")
assert len(conflict["members"]) == 2
def test_component_part_of_conflict_detected(tmp_data_dir):
init_db()
init_engineering_schema()
c = create_entity("component", "MultiPart", project="p-test")
s1 = create_entity("subsystem", "Mechanical", project="p-test")
s2 = create_entity("subsystem", "Optical", project="p-test")
create_relationship(c.id, s1.id, "part_of")
create_relationship(c.id, s2.id, "part_of")
detected = detect_conflicts_for_entity(c.id)
assert len(detected) == 1
conflicts = list_open_conflicts(project="p-test")
assert any(c["slot_kind"] == "component.part_of" for c in conflicts)
def test_requirement_name_conflict_detected(tmp_data_dir):
init_db()
init_engineering_schema()
r1 = create_entity("requirement", "Surface figure < 25nm",
project="p-test", description="Primary mirror spec")
r2 = create_entity("requirement", "Surface figure < 25nm",
project="p-test", description="Different interpretation")
detected = detect_conflicts_for_entity(r2.id)
assert len(detected) == 1
conflicts = list_open_conflicts(project="p-test")
assert any(c["slot_kind"] == "requirement.name" for c in conflicts)
def test_conflict_not_detected_for_clean_component(tmp_data_dir):
init_db()
init_engineering_schema()
c = create_entity("component", "Clean", project="p-test")
m = create_entity("material", "Zerodur", project="p-test")
create_relationship(c.id, m.id, "uses_material")
detected = detect_conflicts_for_entity(c.id)
assert detected == []
def test_conflict_resolution_supersedes_losers(tmp_data_dir):
init_db()
init_engineering_schema()
c = create_entity("component", "Mirror2", project="p-test")
m1 = create_entity("material", "Zerodur2", project="p-test")
m2 = create_entity("material", "ULE2", project="p-test")
create_relationship(c.id, m1.id, "uses_material")
create_relationship(c.id, m2.id, "uses_material")
detected = detect_conflicts_for_entity(c.id)
conflict_id = detected[0]
# Resolve by picking m1 as the winner
assert resolve_conflict(conflict_id, "supersede_others", winner_id=m1.id)
# m2 should now be superseded; m1 stays active
assert get_entity(m1.id).status == "active"
assert get_entity(m2.id).status == "superseded"
# Conflict should be marked resolved
open_conflicts = list_open_conflicts(project="p-test")
assert not any(c["id"] == conflict_id for c in open_conflicts)
def test_conflict_resolution_dismiss_leaves_entities_alone(tmp_data_dir):
init_db()
init_engineering_schema()
r1 = create_entity("requirement", "Dup req", project="p-test",
description="first meaning")
r2 = create_entity("requirement", "Dup req", project="p-test",
description="second meaning")
detected = detect_conflicts_for_entity(r2.id)
conflict_id = detected[0]
assert resolve_conflict(conflict_id, "dismiss")
# Both still active — dismiss just clears the conflict marker
assert get_entity(r1.id).status == "active"
assert get_entity(r2.id).status == "active"
def test_deduplicate_conflicts_for_same_slot(tmp_data_dir):
"""Running detection twice on the same entity shouldn't dup the conflict row."""
init_db()
init_engineering_schema()
c = create_entity("component", "Dup", project="p-test")
m1 = create_entity("material", "A", project="p-test")
m2 = create_entity("material", "B", project="p-test")
create_relationship(c.id, m1.id, "uses_material")
create_relationship(c.id, m2.id, "uses_material")
detect_conflicts_for_entity(c.id)
detect_conflicts_for_entity(c.id) # should be a no-op
conflicts = list_open_conflicts(project="p-test")
mat_conflicts = [c for c in conflicts if c["slot_kind"] == "component.material"]
assert len(mat_conflicts) == 1
def test_promote_triggers_conflict_detection(tmp_data_dir):
"""End-to-end: promoting a candidate component with 2 active material edges
triggers conflict detection."""
init_db()
init_engineering_schema()
c = create_entity("component", "AutoFlag", project="p-test", status="candidate")
m1 = create_entity("material", "X1", project="p-test")
m2 = create_entity("material", "X2", project="p-test")
create_relationship(c.id, m1.id, "uses_material")
create_relationship(c.id, m2.id, "uses_material")
promote_entity(c.id, actor="test")
conflicts = list_open_conflicts(project="p-test")
assert any(c["slot_kind"] == "component.material" for c in conflicts)
# --- 5H MCP tool shape checks (via build_user_message) ---
def test_graduation_user_message_includes_project_and_type():
msg = build_user_message("some content", "p04-gigabit", "project")
assert "p04-gigabit" in msg
assert "project" in msg
assert "some content" in msg

35
tests/test_extraction_pipeline.py
View File

@@ -171,3 +171,38 @@ def test_llm_extraction_failure_returns_empty(tmp_data_dir, monkeypatch):
# Nothing in the candidate queue # Nothing in the candidate queue
queue = get_memories(status="candidate", limit=10) queue = get_memories(status="candidate", limit=10)
assert len(queue) == 0 assert len(queue) == 0
def test_extract_batch_api_503_when_cli_missing(tmp_data_dir, monkeypatch):
"""R11: POST /admin/extract-batch with mode=llm must fail loud when
the `claude` CLI is unavailable, instead of silently returning a
success-with-0-candidates payload (which masked host-vs-container
truth for operators)."""
from fastapi.testclient import TestClient
from atocore.main import app
import atocore.api.routes as routes
init_db()
monkeypatch.setattr(routes, "_llm_cli_available", lambda: False)
client = TestClient(app)
response = client.post("/admin/extract-batch", json={"mode": "llm"})
assert response.status_code == 503
assert "claude" in response.json()["detail"].lower()
def test_extract_batch_api_rule_mode_ok_without_cli(tmp_data_dir, monkeypatch):
"""Rule mode must still work when the LLM CLI is missing — R11 only
affects mode=llm."""
from fastapi.testclient import TestClient
from atocore.main import app
import atocore.api.routes as routes
init_db()
monkeypatch.setattr(routes, "_llm_cli_available", lambda: False)
client = TestClient(app)
response = client.post("/admin/extract-batch", json={"mode": "rule"})
assert response.status_code == 200

259
tests/test_memory.py
View File

@@ -186,3 +186,262 @@ def test_memories_for_context_empty(isolated_db):
text, chars = get_memories_for_context() text, chars = get_memories_for_context()
assert text == "" assert text == ""
assert chars == 0 assert chars == 0
# --- Phase 10: auto-promotion + candidate expiry ---
def _get_memory_by_id(memory_id):
"""Helper: fetch a single memory by ID."""
from atocore.models.database import get_connection
with get_connection() as conn:
row = conn.execute("SELECT * FROM memories WHERE id = ?", (memory_id,)).fetchone()
return dict(row) if row else None
def test_auto_promote_reinforced_basic(isolated_db):
from atocore.memory.service import (
auto_promote_reinforced,
create_memory,
reinforce_memory,
)
mem_obj = create_memory("knowledge", "Zerodur has near-zero CTE", status="candidate", confidence=0.7)
mid = mem_obj.id
# reinforce_memory only touches active memories, so we need to
# promote first to reinforce, then demote back to candidate —
# OR just bump reference_count + last_referenced_at directly
from atocore.models.database import get_connection
from datetime import datetime, timezone
now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
with get_connection() as conn:
conn.execute(
"UPDATE memories SET reference_count = 3, last_referenced_at = ? WHERE id = ?",
(now, mid),
)
promoted = auto_promote_reinforced(min_reference_count=3, min_confidence=0.7)
assert mid in promoted
mem = _get_memory_by_id(mid)
assert mem["status"] == "active"
def test_auto_promote_reinforced_ignores_low_refs(isolated_db):
from atocore.memory.service import auto_promote_reinforced, create_memory
from atocore.models.database import get_connection
from datetime import datetime, timezone
mem_obj = create_memory("knowledge", "Some knowledge", status="candidate", confidence=0.7)
mid = mem_obj.id
now = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M:%S")
with get_connection() as conn:
conn.execute(
"UPDATE memories SET reference_count = 1, last_referenced_at = ? WHERE id = ?",
(now, mid),
)
promoted = auto_promote_reinforced(min_reference_count=3, min_confidence=0.7)
assert mid not in promoted
mem = _get_memory_by_id(mid)
assert mem["status"] == "candidate"
def test_expire_stale_candidates(isolated_db):
from atocore.memory.service import create_memory, expire_stale_candidates
from atocore.models.database import get_connection
mem_obj = create_memory("knowledge", "Old unreferenced fact", status="candidate")
mid = mem_obj.id
with get_connection() as conn:
conn.execute(
"UPDATE memories SET created_at = datetime('now', '-30 days') WHERE id = ?",
(mid,),
)
expired = expire_stale_candidates(max_age_days=14)
assert mid in expired
mem = _get_memory_by_id(mid)
assert mem["status"] == "invalid"
# --- Phase 4: memory_audit log ---
def test_audit_create_logs_entry(isolated_db):
from atocore.memory.service import create_memory, get_memory_audit
mem = create_memory("knowledge", "test content for audit", actor="test-harness")
audit = get_memory_audit(mem.id)
assert len(audit) >= 1
latest = audit[0]
assert latest["action"] == "created"
assert latest["actor"] == "test-harness"
assert latest["after"]["content"] == "test content for audit"
def test_audit_promote_logs_entry(isolated_db):
from atocore.memory.service import create_memory, get_memory_audit, promote_memory
mem = create_memory("knowledge", "candidate for promote", status="candidate")
promote_memory(mem.id, actor="test-triage")
audit = get_memory_audit(mem.id)
actions = [a["action"] for a in audit]
assert "promoted" in actions
promote_entry = next(a for a in audit if a["action"] == "promoted")
assert promote_entry["actor"] == "test-triage"
assert promote_entry["before"]["status"] == "candidate"
assert promote_entry["after"]["status"] == "active"
def test_audit_reject_logs_entry(isolated_db):
from atocore.memory.service import create_memory, get_memory_audit, reject_candidate_memory
mem = create_memory("knowledge", "candidate for reject", status="candidate")
reject_candidate_memory(mem.id, actor="test-triage", note="stale")
audit = get_memory_audit(mem.id)
actions = [a["action"] for a in audit]
assert "rejected" in actions
reject_entry = next(a for a in audit if a["action"] == "rejected")
assert reject_entry["note"] == "stale"
def test_audit_update_captures_before_after(isolated_db):
from atocore.memory.service import create_memory, get_memory_audit, update_memory
mem = create_memory("knowledge", "original content", confidence=0.5)
update_memory(mem.id, content="updated content", confidence=0.9, actor="human-edit")
audit = get_memory_audit(mem.id)
update_entries = [a for a in audit if a["action"] == "updated"]
assert len(update_entries) >= 1
u = update_entries[0]
assert u["before"]["content"] == "original content"
assert u["after"]["content"] == "updated content"
assert u["before"]["confidence"] == 0.5
assert u["after"]["confidence"] == 0.9
def test_audit_reinforce_logs_entry(isolated_db):
from atocore.memory.service import create_memory, get_memory_audit, reinforce_memory
mem = create_memory("knowledge", "reinforced mem", confidence=0.5)
reinforce_memory(mem.id, confidence_delta=0.02)
audit = get_memory_audit(mem.id)
actions = [a["action"] for a in audit]
assert "reinforced" in actions
def test_recent_audit_returns_cross_memory_entries(isolated_db):
from atocore.memory.service import create_memory, get_recent_audit
m1 = create_memory("knowledge", "mem one content", actor="harness")
m2 = create_memory("knowledge", "mem two content", actor="harness")
recent = get_recent_audit(limit=10)
ids = {e["memory_id"] for e in recent}
assert m1.id in ids and m2.id in ids
# --- Phase 3: domain_tags + valid_until ---
def test_create_memory_with_tags_and_valid_until(isolated_db):
from atocore.memory.service import create_memory
mem = create_memory(
"knowledge",
"CTE gradient dominates WFE at F/1.2",
domain_tags=["optics", "thermal", "materials"],
valid_until="2027-01-01",
)
assert mem.domain_tags == ["optics", "thermal", "materials"]
assert mem.valid_until == "2027-01-01"
def test_create_memory_normalizes_tags(isolated_db):
from atocore.memory.service import create_memory
mem = create_memory(
"knowledge",
"some content here",
domain_tags=[" Optics ", "OPTICS", "Thermal", ""],
)
# Duplicates and empty removed; lowercased; stripped
assert mem.domain_tags == ["optics", "thermal"]
def test_update_memory_sets_tags_and_valid_until(isolated_db):
from atocore.memory.service import create_memory, update_memory
from atocore.models.database import get_connection
mem = create_memory("knowledge", "some content for update test")
assert update_memory(
mem.id,
domain_tags=["controls", "firmware"],
valid_until="2026-12-31",
)
with get_connection() as conn:
row = conn.execute("SELECT domain_tags, valid_until FROM memories WHERE id = ?", (mem.id,)).fetchone()
import json as _json
assert _json.loads(row["domain_tags"]) == ["controls", "firmware"]
assert row["valid_until"] == "2026-12-31"
def test_get_memories_for_context_excludes_expired(isolated_db):
"""Expired active memories must not land in context packs."""
from atocore.memory.service import create_memory, get_memories_for_context
# Active but expired
create_memory(
"knowledge",
"stale snapshot from long ago period",
valid_until="2020-01-01",
confidence=1.0,
)
# Active and valid
create_memory(
"knowledge",
"durable engineering insight stays valid forever",
confidence=1.0,
)
text, _ = get_memories_for_context(memory_types=["knowledge"], budget=600)
assert "durable engineering" in text
assert "stale snapshot" not in text
def test_context_builder_tag_boost_orders_results(isolated_db):
"""Memories with tags matching query should rank higher."""
from atocore.memory.service import create_memory, get_memories_for_context
create_memory("knowledge", "generic content has no obvious overlap with topic", confidence=0.8, domain_tags=[])
create_memory("knowledge", "generic content has no obvious overlap topic here", confidence=0.8, domain_tags=["optics"])
text, _ = get_memories_for_context(
memory_types=["knowledge"],
budget=2000,
query="tell me about optics",
)
# Tagged memory should appear before the untagged one
idx_tagged = text.find("overlap topic here")
idx_untagged = text.find("overlap with topic")
assert idx_tagged != -1
assert idx_untagged != -1
assert idx_tagged < idx_untagged
def test_expire_stale_candidates_keeps_reinforced(isolated_db):
from atocore.memory.service import create_memory, expire_stale_candidates
from atocore.models.database import get_connection
mem_obj = create_memory("knowledge", "Referenced fact", status="candidate")
mid = mem_obj.id
with get_connection() as conn:
conn.execute(
"UPDATE memories SET reference_count = 1, "
"created_at = datetime('now', '-30 days') WHERE id = ?",
(mid,),
)
expired = expire_stale_candidates(max_age_days=14)
assert mid not in expired
mem = _get_memory_by_id(mid)
assert mem["status"] == "candidate"

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tests/test_triage_escalation.py Normal file
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"""Tests for 3-tier triage escalation logic (Phase Triage Quality).
The actual LLM calls are gated by ``shutil.which('claude')`` and can't be
exercised in CI without the CLI, so we mock the tier functions directly
and verify the control-flow (escalation routing, discard vs human, project
misattribution, metadata update).
"""
from __future__ import annotations
import sys
from pathlib import Path
from unittest import mock
import pytest
# Import the script as a module for unit testing
_SCRIPTS = str(Path(__file__).resolve().parent.parent / "scripts")
if _SCRIPTS not in sys.path:
sys.path.insert(0, _SCRIPTS)
import auto_triage # noqa: E402
@pytest.fixture(autouse=True)
def reset_thresholds(monkeypatch):
"""Make sure env-var overrides don't leak between tests."""
monkeypatch.setattr(auto_triage, "AUTO_PROMOTE_MIN_CONFIDENCE", 0.8)
monkeypatch.setattr(auto_triage, "ESCALATION_CONFIDENCE_THRESHOLD", 0.75)
monkeypatch.setattr(auto_triage, "TIER3_ACTION", "discard")
monkeypatch.setattr(auto_triage, "TIER1_MODEL", "sonnet")
monkeypatch.setattr(auto_triage, "TIER2_MODEL", "opus")
def test_parse_verdict_captures_suggested_project():
raw = '{"verdict": "promote", "confidence": 0.9, "reason": "clear", "suggested_project": "p04-gigabit"}'
v = auto_triage.parse_verdict(raw)
assert v["verdict"] == "promote"
assert v["suggested_project"] == "p04-gigabit"
def test_parse_verdict_defaults_suggested_project_to_empty():
raw = '{"verdict": "reject", "confidence": 0.9, "reason": "dup"}'
v = auto_triage.parse_verdict(raw)
assert v["suggested_project"] == ""
def test_high_confidence_tier1_promote_no_escalation():
"""Tier 1 confident promote → no tier 2 call."""
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p-test"}
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.triage_escalation") as t2, \
mock.patch("auto_triage.api_post"), \
mock.patch("auto_triage._apply_metadata_update"):
t1.return_value = {
"verdict": "promote", "confidence": 0.95, "reason": "clear",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
action, _ = auto_triage.process_candidate(
cand, "http://fake", {"p-test": []}, {"p-test": []},
{"p-test": []}, dry_run=False,
)
assert action == "promote"
t2.assert_not_called()
def test_high_confidence_tier1_reject_no_escalation():
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p-test"}
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.triage_escalation") as t2, \
mock.patch("auto_triage.api_post"):
t1.return_value = {
"verdict": "reject", "confidence": 0.9, "reason": "duplicate",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
action, _ = auto_triage.process_candidate(
cand, "http://fake", {"p-test": []}, {"p-test": []},
{"p-test": []}, dry_run=False,
)
assert action == "reject"
t2.assert_not_called()
def test_low_confidence_escalates_to_tier2():
"""Tier 1 low confidence → tier 2 is consulted."""
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p-test"}
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.triage_escalation") as t2, \
mock.patch("auto_triage.api_post"), \
mock.patch("auto_triage._apply_metadata_update"):
t1.return_value = {
"verdict": "promote", "confidence": 0.6, "reason": "maybe",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
t2.return_value = {
"verdict": "promote", "confidence": 0.9, "reason": "opus agrees",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
action, note = auto_triage.process_candidate(
cand, "http://fake", {"p-test": []}, {"p-test": []},
{"p-test": []}, dry_run=False,
)
assert action == "promote"
assert "opus" in note
t2.assert_called_once()
def test_needs_human_tier1_always_escalates():
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p-test"}
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.triage_escalation") as t2, \
mock.patch("auto_triage.api_post"):
t1.return_value = {
"verdict": "needs_human", "confidence": 0.5, "reason": "uncertain",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
t2.return_value = {
"verdict": "reject", "confidence": 0.88, "reason": "opus decided",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
action, _ = auto_triage.process_candidate(
cand, "http://fake", {"p-test": []}, {"p-test": []},
{"p-test": []}, dry_run=False,
)
assert action == "reject"
t2.assert_called_once()
def test_tier2_uncertain_leads_to_discard_by_default(monkeypatch):
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p-test"}
monkeypatch.setattr(auto_triage, "TIER3_ACTION", "discard")
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.triage_escalation") as t2, \
mock.patch("auto_triage.api_post") as api_post:
t1.return_value = {
"verdict": "needs_human", "confidence": 0.4, "reason": "unclear",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
t2.return_value = {
"verdict": "needs_human", "confidence": 0.5, "reason": "still unclear",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
action, _ = auto_triage.process_candidate(
cand, "http://fake", {"p-test": []}, {"p-test": []},
{"p-test": []}, dry_run=False,
)
assert action == "discard"
# Should have called reject on the API
api_post.assert_called_once()
assert "reject" in api_post.call_args.args[1]
def test_tier2_uncertain_goes_to_human_when_configured(monkeypatch):
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p-test"}
monkeypatch.setattr(auto_triage, "TIER3_ACTION", "human")
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.triage_escalation") as t2, \
mock.patch("auto_triage.api_post") as api_post:
t1.return_value = {
"verdict": "needs_human", "confidence": 0.4, "reason": "unclear",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
t2.return_value = {
"verdict": "needs_human", "confidence": 0.5, "reason": "still unclear",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
action, _ = auto_triage.process_candidate(
cand, "http://fake", {"p-test": []}, {"p-test": []},
{"p-test": []}, dry_run=False,
)
assert action == "human"
# Should NOT have touched the API — leave candidate in queue
api_post.assert_not_called()
def test_dry_run_does_not_call_api():
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p-test"}
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.api_post") as api_post:
t1.return_value = {
"verdict": "promote", "confidence": 0.9, "reason": "clear",
"domain_tags": [], "valid_until": "", "suggested_project": "",
}
action, _ = auto_triage.process_candidate(
cand, "http://fake", {"p-test": []}, {"p-test": []},
{"p-test": []}, dry_run=True,
)
assert action == "promote"
api_post.assert_not_called()
def test_misattribution_flagged_when_suggestion_differs(capsys):
cand = {"id": "m1", "content": "x", "memory_type": "knowledge", "project": "p04-gigabit"}
with mock.patch("auto_triage.triage_one") as t1, \
mock.patch("auto_triage.api_post"), \
mock.patch("auto_triage._apply_metadata_update"):
t1.return_value = {
"verdict": "promote", "confidence": 0.9, "reason": "clear",
"domain_tags": [], "valid_until": "",
"suggested_project": "p05-interferometer",
}
auto_triage.process_candidate(
cand, "http://fake",
{"p04-gigabit": [], "p05-interferometer": []},
{"p04-gigabit": [], "p05-interferometer": []},
{"p04-gigabit": [], "p05-interferometer": []},
dry_run=True,
)
out = capsys.readouterr().out
assert "misattribution" in out
assert "p05-interferometer" in out