The "חלץ מטא-דאטה" / "חלץ הלכות" buttons in the UI were returning 404
for any precedent with `source_kind != 'external_upload'`. The original
restriction was meant to keep LLM extraction off internal-committee
imports (their metadata supposedly came from the case file system),
but the same precedent rows can still need re-extraction when ingest
produces broken data — e.g. the corrupted `subject_tags` value
`['[','"','ה','י',...]` that motivated this change (an early ingest
stored a JSON literal into a TEXT[] column, which Postgres split into
single chars).
Two changes here:
1. db.request_metadata_extraction / request_halacha_extraction:
drop the `AND source_kind='external_upload'` filter. The extractor
already preserves user values (only fills empty fields), so this
is safe.
2. precedent_metadata_extractor.extract_and_apply: detect the
character-by-character corruption above and treat it as empty so
the freshly-extracted tags actually replace the broken ones.
Heuristic: 3+ elements where every element is at most 2 chars
(legitimate tags are multi-character Hebrew words).
Coolify deploy required for the FastAPI container to pick this up.
Observed 2026-05-03: a `precedent_process_pending(halacha)` run that
chained two precedents (1110/20 → 317/10) succeeded for the first
(9 halachot, 129 chunks) and produced status=`no_halachot` for the
second despite it being a 47KB Supreme Court ruling with rich legal
analysis. A manual single-precedent re-run on 317/10 immediately
extracted 53 halachot. Diagnosis: every chunk's claude_session call
in the back-to-back run silently failed (likely Anthropic rate-limit
storm after the 1110/20 token burn), and the empty list was reported
as "Claude looked and found nothing" — same code path as a real
0-halacha ruling. The user couldn't tell the difference.
Three changes:
1. Surface chunk-level failures (halacha_extractor.py)
`_extract_chunk` now returns `(halachot, succeeded)` so the caller
can count how many chunks crashed. `extract()` uses this to
distinguish:
- `no_halachot` — chunks ran cleanly, Claude found nothing
- `extraction_failed` — ≥50% of chunks crashed AND zero halachot
came back (rate limit, subprocess crash, etc.)
When `extraction_failed`, DB status is left as 'processing' so the
request stays in the queue for the caller to retry — instead of
the old behaviour where it got marked 'completed' and silently
dropped from the queue.
2. Inter-precedent cooldown (precedent_library.py)
`process_pending_extractions` now sleeps 30s between precedents.
Anthropic rate-limits per-org, and back-to-back large rulings
(~4M tokens for 1110/20, immediately followed by another 2-3M)
was the empirical trigger. 30s gives the per-minute counter time
to drain.
3. Auto-retry on extraction_failed (precedent_library.py)
When a precedent comes back as `extraction_failed`, retry once
after a 60s cooldown before giving up. Rate-limit storms are
transient — the manual re-run of 317/10 minutes later succeeded
with 53 halachot and zero chunk failures, confirming a single
retry is sufficient. Only retries `extraction_failed`; never
`no_halachot` (Claude looked and there genuinely is no holding).
The DB status now ends up as 'failed' only after retries are
exhausted, matching the UI's terminal-failure chip.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The legacy chunker did not track which PDF page each chunk came from.
Stored chunks had page_number=NULL, which blocked the multimodal
hybrid retriever's text+image boost — it joins (chunk, image) on
(document_id, page_number) and the join could never fire.
This change:
- extractor.extract_text now returns (text, page_count, page_offsets);
page_offsets[i] is the start char offset of page (i+1) in the joined
text. None for non-PDFs.
- chunker.chunk_document accepts an optional page_offsets and tags
each chunk with the page that contains its first character (uses
the existing chunker logic; pages assigned post-hoc by content
search to keep the diff minimal).
- processor.process_document and precedent_library.ingest_precedent
forward page_offsets through the chunker. New uploads now carry
accurate page_number on every chunk.
- Other extract_text callers (tools/documents, tools/workflow,
web/app.py) updated to unpack the third element (ignored).
- scripts/backfill_chunk_pages.py: per-case retrofit. Re-extracts each
PDF (re-OCRs via Google Vision if needed, ~$0.0015/page), computes
page_offsets, and updates page_number on every chunk by content
search. Idempotent; --force re-runs on already-tagged docs.
Forward-only would leave the 419 image embeddings backfilled on
cases 8174-24 + 8137-24 unable to boost their corresponding text
chunks. The retrofit script closes that gap (cost ~$0.60).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage C: per-page image embeddings via voyage-multimodal-3 + hybrid
text+image search. Off by default; enable with MULTIMODAL_ENABLED=true.
- Schema V9: document_image_embeddings + precedent_image_embeddings
(vector(1024), page_number, image_thumbnail_path)
- extractor.render_pages_for_multimodal renders PDF pages at
MULTIMODAL_DPI (144) for embedding + JPEG thumbnails at
MULTIMODAL_THUMB_DPI (96) for UI preview, in one pass
- embeddings.embed_images calls voyage-multimodal-3 in 50-page batches
- services/hybrid_search.py orchestrator: rerank applied to text side
first (rerank-2 is text-only); image side cosine; weighted merge
with text_weight 0.65 (env-tunable); image-only pages surface as
match_type='image' so dense scanned content still appears
- processor.process_document and precedent_library.ingest_precedent
gated by flag — non-fatal on multimodal failure
- scripts/multimodal_backfill.py — idempotent per-case CLI to embed
existing documents without re-extracting text
Validated locally on a 5-page response brief: render 0.31s, embed 8.32s,
hybrid merge surfaces image rows correctly. Production rollout starts
with flag=false (no behavior change), then per-case A/B.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The chair pointed out three UX gaps after uploading a new precedent:
1. The status said "מחלץ הלכות" but nothing was actually running — the
field only meant "halacha_extraction_status != completed", which
includes the post-upload "pending" state where the local MCP worker
hasn't been told to drain anything yet. Misleading.
2. The page didn't refresh on its own. The chair had to F5 to see new
counts after extraction completed.
3. Clicking the trash icon mid-extraction would cascade-delete the row
while the extractor was still using it (FK errors, partial writes).
Fixes:
- ingest_precedent now auto-queues both metadata and halacha extraction
on upload by stamping the request timestamps. The chair (or me) drains
the queue with one `precedent_process_pending` call from chat —
no need to click any button before that.
- StatusPill is now five-state with proper labels:
"נכשל" (extraction_status=failed) — red
"מעבד טקסט" — shimmer (extraction_status=processing)
"בתור" — neutral (chunks queued, not yet running)
"מחלץ הלכות" — shimmer (halacha_extraction_status=processing)
"ממתין לחילוץ" — neutral (queued for local MCP worker)
"לא חולץ" — neutral (pending without queue stamp — shouldn't happen)
"X/Y מאושרות" — gold (done, with halachot count)
The shimmer is a CSS-only sliding-stripe animation defined in globals.
- usePrecedents has a conditional refetchInterval — polls every 5s while
any row is mid-extraction or queued, then stops once everything settles
to completed/failed. New helper isPrecedentActive() centralises the
"is this row mid-something" check so the UI and the destructive-action
guard agree.
- Trash button is disabled (opacity 30%, tooltip explains) while the row
is active. Pencil/edit stays enabled — editing metadata fields during
extraction is safe (last write wins, low-stakes race).
Schema: list_external_case_law now exposes the two *_requested_at
timestamps so the UI can distinguish "queued" from "never asked".
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The chair wanted a one-click "extract metadata" button on the edit sheet.
The constraint stays the same — claude_session needs the local CLI which
the container doesn't have, so the button can't run the extractor itself.
Compromise: button stamps a queue marker; the local MCP server drains the
queue on demand.
DB (V8): two nullable timestamps on case_law,
metadata_extraction_requested_at and halacha_extraction_requested_at,
with partial indexes for cheap "find pending" scans.
API:
POST /api/precedent-library/{id}/request-metadata → stamp the row
POST /api/precedent-library/{id}/request-halachot → same for halacha
GET /api/precedent-library/queue/pending?kind=... → read-only view
UI: Sparkles button in the edit sheet header. Click → toast tells the
chair what to run from Claude Code. The button never triggers the
extractor directly from the container.
MCP tool: precedent_process_pending(kind, limit) — runs from Claude Code
with the local CLI, picks up everything stamped, calls the extractor for
each, clears the timestamp on success. Failures keep the timestamp so the
next invocation retries them.
Architectural rule (claude_session local-only) is preserved end-to-end
and called out in the new endpoint comment + tool docstring.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Architectural correction: every claude_session caller in this project
runs through the local MCP server (~/.claude.json points at
/home/chaim/legal-ai/mcp-server/.venv/bin/python). The Coolify container
has no `claude` CLI and no claude.ai session, so any LLM call originating
from web/ FastAPI fails with "Claude CLI not found" — which is exactly
what we hit on 403-17.
The earlier Anthropic SDK fallback would have made it work, but at
direct API cost. The chair's preference is to stay on the claude.ai
session for everything. So:
- claude_session.py: removed the SDK fallback, restored CLI-only.
The error message now points the next person at the architectural
rule in the module docstring instead of papering over it.
- precedent_library.py:ingest_precedent (called from FastAPI on upload)
now does only the non-LLM half: extract → chunk → embed → store.
Sets halacha_extraction_status='pending' for the chair to act on.
- reextract_halachot / reextract_metadata kept, but lazy-import their
extractors so the FastAPI path can't accidentally pull them in. They
are reachable only via the MCP tools precedent_extract_halachot /
precedent_extract_metadata, which run locally with CLI.
- Removed POST /api/precedent-library/{id}/extract-halachot and
/extract-metadata — they were dead ends from the container.
- Dropped the `anthropic` Python dep that the SDK fallback required.
- UI: removed the "refresh halachot" and "sparkles metadata" buttons
that called those endpoints. Edit sheet now points the chair at the
MCP tool names instead.
Halacha and metadata extraction for an uploaded precedent now happen
when the chair (via Claude Code) runs:
mcp__legal-ai__precedent_extract_metadata <case_law_id>
mcp__legal-ai__precedent_extract_halachot <case_law_id>
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Three improvements to the precedent library based on usage feedback:
1. Auto-fill metadata at upload time. New service
precedent_metadata_extractor reads the ruling's full_text and
suggests case_name (short), summary, headnote, key_quote,
subject_tags, appeal_subtype. The merge policy fills only empty
fields, preserving everything the chair typed in the upload form.
Wired into the ingest pipeline; also exposed as a re-run endpoint
POST /api/precedent-library/{id}/extract-metadata for existing
records.
2. Edit sheet in the UI. Pencil icon on each library row opens a
pre-populated form covering every field. A Sparkles button on the
sheet runs the metadata extractor on demand and refreshes the
form. The case_number is read-only because halachot are FK'd to
it; renaming requires delete + re-upload.
3. Halacha extractor branches on is_binding. Sources marked binding
(Supreme/Administrative) keep the strict halacha prompt. Non-binding
sources (other appeals committees, district courts on planning
matters) get a different prompt that extracts applications,
interpretive principles, and persuasive conclusions — labeled with
new rule_types 'application' and 'persuasive'. The fallback also
widens chunk selection: if the chunker labeled nothing as
legal_analysis/ruling/conclusion, we now run on all chunks rather
than returning zero halachot for a usable ruling.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a third corpus of legal authority distinct from style_corpus
(Daphna's prior decisions for voice) and case_precedents (chair-attached
quotes per case). The new corpus holds chair-uploaded court rulings and
other appeals committee decisions, with binding rules (הלכות) extracted
automatically and queued for chair approval.
Pipeline (web/app.py + services/precedent_library.py):
file → extract → chunk → Voyage embed → halacha_extractor → store +
publish progress over the existing Redis SSE channel.
Schema V7 (services/db.py): extends case_law with source_kind +
extraction status fields under a CHECK constraint pinning practice_area
to the three appeals committee domains (rishuy_uvniya, betterment_levy,
compensation_197). New precedent_chunks (vector(1024)) and halachot
tables (vector(1024) over rule_statement, IVFFlat indexes, gin on
practice_areas/subject_tags). Halachot start as pending_review; only
approved/published rows are visible to search_precedent_library.
Agents: legal-writer, legal-researcher, legal-analyst, legal-ceo,
legal-qa get search_precedent_library. legal-writer prompt explains
the three-corpus distinction and CREAC use; legal-qa now verifies that
every cited halacha resolves to an approved row in the corpus.
UI: /precedents page with four tabs — library / semantic search /
pending review (J/K nav, A/R/E shortcuts, badge count) / stats.
Reuses the existing upload-sheet progress + SSE pattern.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
