This repository has been archived on 2026-07-19. You can view files and clone it. You cannot open issues or pull requests or push a commit.
Files
chaim 0d678da25f feat(kb): backend for v0.8.0 — tool kind + labels + AI classifier + bulk upload
Phase 6 of the multi-topic KB refactor (backend half). Subsumes the
original Task #19 design discussion (subject vs labels). EspoCRM
extension UI ships separately.

Migrations (4 new, all idempotent, transaction-wrapped):
  004_kind_tool             — adds 'tool' to kb_source/kb_ingest_job CHECK
  005_source_description    — kb_source.description + ai_classified_at
  006_labels                — kb_label + kb_source_label (M:N), GRANTs
  007_ingest_classifier     — awaiting_review status + processing_stage
                              + ai_suggestions JSONB + batch_id

Two-phase ingest (api/services/kb/ingest_jobs.py):
  queued → processing(stage=classifying)
         → awaiting_review                  ← user reviews AI output
         → processing(stage=embedding)      ← after user commits
         → done | failed

  process_classify_stage() pulls the file, runs parse_first_pages
  (3-page text extract), calls classifier.classify (Claude tool-call
  via ai-gateway, 45s timeout, 5-concurrent semaphore, fail-soft on
  any error → empty suggestions), writes ai_suggestions JSONB,
  transitions to awaiting_review.

  commit_job() resolves user-confirmed labels (existing by slug,
  new ones via slugify+ON CONFLICT DO NOTHING), transitions to
  processing(embedding), schedules process_embed_stage.

  process_embed_stage() runs the legacy ingest_source path with
  user-edited metadata + summary as kb_source.description, then
  apply_to_source for labels.

  discard_job() removes a file from a batch (status=failed,
  S3 deleted).

  list_jobs_by_batch() — single-roundtrip review-screen load.

Labels (NEW api/services/kb/labels.py):
  Hebrew → ASCII slugify (letter-by-letter map, no external dep);
  CRUD; lookup_or_create_batch (race-safe); apply_to_source +
  replace_for_source maintain usage_count; merge race-safely
  (UPDATE assignments + ON CONFLICT DO NOTHING).

Classifier (NEW api/services/kb/classifier.py):
  AsyncOpenAI to ai-gateway, Sonnet, OpenAI-style tool calling for
  guaranteed JSON shape, Hebrew system prompt with 5 kind values,
  citation format examples, "do not invent" rule, summary length
  bounds 80-200 chars.

Routes (api/routes/admin_kb.py — 8 new on top of existing 11):
  POST   /admin/kb/upload-batch                  (multi-file, AI classify)
  GET    /admin/kb/batch/{batch_id}              (review-screen load)
  POST   /admin/kb/jobs/{id}/commit              (user confirms metadata)
  POST   /admin/kb/jobs/{id}/discard             (user removes from batch)
  GET    /admin/kb/labels                        (typeahead)
  POST   /admin/kb/labels                        (explicit create)
  POST   /admin/kb/labels/{id}/merge             (admin cleanup)
  DELETE /admin/kb/labels/{id}                   (only if usage=0)

Public /kb/search gains optional label_id filter.
admin_sources.list_sources LEFT JOINs labels into each row's
output. update_source accepts label_ids to replace the set.

End-to-end smoke test passed: synthetic Hebrew circular →
upload-batch → background classify → awaiting_review with kind,
title, subject, summary, identifier, published_at all populated
correctly.

Refs Task Master #20 (espocrm-extensions/KnowledgeBase v0.8.0)
Subsumes: Task #19 (labels for sub-topics)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-25 18:42:15 +00:00

44 lines
1.8 KiB
PL/PgSQL

-- Migration 007: kb_ingest_job gains AI-classification staging.
--
-- Task Master #20 (v0.8.0). The two-phase ingest pipeline introduced in
-- this release runs:
-- queued → processing(stage=classifying)
-- → awaiting_review (user reviews AI suggestions)
-- → processing(stage=embedding) (after user commits)
-- → done | failed
--
-- New columns:
-- • status gains 'awaiting_review' value
-- • processing_stage: 'classifying' | 'embedding' | 'upserting' (audit)
-- • ai_suggestions: JSONB written by the classifier; consumed by
-- the review screen as default form values
-- • batch_id: uuid string assigned by the upload route to all
-- files in one drag-drop drop. Lets the review
-- screen group N files even after a tab refresh.
--
-- Idempotent — wrapped in a single transaction.
--
-- Run as postgres superuser:
-- docker exec -i <pg-container> psql -U postgres -d insurance_kb \
-- -f /tmp/007_ingest_classifier.sql
BEGIN;
ALTER TABLE kb_ingest_job DROP CONSTRAINT IF EXISTS kb_ingest_job_status_check;
ALTER TABLE kb_ingest_job
ADD CONSTRAINT kb_ingest_job_status_check
CHECK (status IN ('queued','processing','awaiting_review','done','failed'));
ALTER TABLE kb_ingest_job
ADD COLUMN IF NOT EXISTS processing_stage TEXT,
ADD COLUMN IF NOT EXISTS ai_suggestions JSONB,
ADD COLUMN IF NOT EXISTS batch_id TEXT;
CREATE INDEX IF NOT EXISTS kb_ingest_job_batch_idx ON kb_ingest_job (batch_id);
-- Partial index keeps it tiny: production should have very few rows in
-- 'awaiting_review' at any moment (it's a transient state).
CREATE INDEX IF NOT EXISTS kb_ingest_job_awaiting_idx
ON kb_ingest_job (created_at DESC) WHERE status = 'awaiting_review';
COMMIT;