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7 Commits

Author SHA1 Message Date
28f49defff LLM session: async, 30min timeout, semantic chunking + parallel
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The claude_session bridge had two structural defects that made any
non-trivial document extraction unreliable:

  1. subprocess.run() blocks the asyncio event loop in the MCP server
     for the full duration of every LLM call (60-180s typical).
  2. The 120-second timeout was below the cold-cache cost of any
     document over ~12K Hebrew characters. Three back-to-back timeouts
     on case 8174-24 dropped 43 appellant claims on the floor.

Phase 1 of the remediation plan — keeps claude_session as the engine
(no Anthropic API switch) and restructures around it:

claude_session.py
  • query / query_json are now async — asyncio.create_subprocess_exec
    instead of subprocess.run, so MCP server can serve other coroutines
    while a call is in flight.
  • DEFAULT_TIMEOUT 120 → 1800 (30 min). High enough that no realistic
    document hits it; bounded so a runaway never zombifies forever.
  • LONG_TIMEOUT 300 → 3600 for opus block writing on full case context.
  • TimeoutError now actually kills the subprocess (asyncio.wait_for
    cancellation alone leaves the child running).

claims_extractor.py
  • _split_by_sections: chunks at numbered sections / Hebrew letter
    headings / "פרק" markers / markdown ##, falls back to paragraph
    breaks, then to hard splits. Targets 12K chars per chunk — small
    enough that each chunk reliably finishes inside the timeout.
  • _extract_chunk: per-chunk retry (1 attempt by default) with
    structured logging on failure. Failed chunks no longer crash the
    overall extraction; they're skipped with a partial-result warning.
  • extract_claims_with_ai now runs chunks in parallel via
    asyncio.gather bounded by a semaphore (CHUNK_CONCURRENCY=3).
    For a 25K-char appeal: was sequential 150-300s, now ~70-90s.

Updated all 9 callers (claims, appraiser facts, block writer, qa
validator, brainstorm, learning loop, style analyzer × 3) to await
the now-async API.

The one-shot scripts/extract_claims_8174.py used to recover 43
appellant claims on case 8174-24 has been moved to .archive/ — phase 1
makes it obsolete. SCRIPTS.md updated.

Phase 2 (background-task wrapper around LLM-bound MCP tools, persistent
llm_tasks table, SSE progress) is the structural follow-up — separate PR.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-30 14:21:35 +00:00
96ea54dc6e Add claim_type field: distinguish claims vs responses vs replies
Legal documents have 3 types of assertions:
- claim: from appeal documents (כתב ערר)
- response: from original responses (כתב תשובה)
- reply: from supplementary responses (תגובה, השלמת טיעון)

DB: added claim_type column to claims table
Extractor: _infer_claim_type() auto-detects from doc_type + title
Updated existing 113 records: 29 claims, 28 responses, 56 replies

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 15:35:16 +00:00
bacb330a2a Replace all Anthropic API calls with Claude Code session (claude -p)
New module claude_session.py provides query() and query_json() that
run prompts via `claude -p` CLI — uses the claude.ai session, zero API cost.

Converted 6 services:
- claims_extractor.py: extract_claims_with_ai
- brainstorm.py: brainstorm_directions
- block_writer.py: write_block (was streaming+thinking, now simple)
- qa_validator.py: claims_coverage check
- style_analyzer.py: 3 API calls (single pass, multi pass, synthesis)
- learning_loop.py: extract_lessons

Only extractor.py still uses Anthropic API (for PDF OCR with Vision).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-04 14:14:08 +00:00
e725f9ecd7 Fix claims parsing: truncated JSON recovery + chunking + compact output
config.py parse_llm_json: Added truncated JSON recovery. When Claude's
output is cut mid-JSON (common with long claim lists), the parser now:
- Finds the last complete JSON item (closing "}")
- Closes the array/object brackets
- Returns partial but valid results instead of None
Tested: recovers 2/3 items from truncated array, all cases pass.

claims_extractor.py:
- Prompt asks for compact output (150 words max per claim, group similar)
- Explicitly requests "no markdown, no explanations, JSON only"
- Long documents split into chunks at paragraph boundaries
- Each chunk processed separately, results merged
- max_tokens already at 8192

This fixes the recurring "0 claims" bug for committee responses and
permit applicant responses where the JSON was getting truncated.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 16:04:34 +00:00
e24e24dac5 Maximize context and output per Anthropic best practices
Per official Anthropic documentation (April 2026):

Output tokens increased to match model capabilities:
- block-yod (discussion): 8K → 32K (Opus supports 128K)
- block-zayin (claims): 4K → 16K
- block-vav (background): 4K → 16K
- claims_extractor: 4K → 8K (fixes truncated JSON)
- qa_validator: 4K → 8K

Source documents sent in full (not truncated):
- Was: 3000 chars per doc, 15K total
- Now: full document text, no truncation
- Reduces hallucinations: "extract word-for-word quotes first"

Prompt structure follows long-context tips:
- Source documents placed FIRST (top of prompt)
- Instructions and query placed LAST
- "Queries at the end improve quality by up to 30%"

Extended thinking uses adaptive mode for Opus 4.6.
Streaming enabled for all requests > 21K tokens.

Unified JSON parsing via parse_llm_json() helper in config.py.
Applied to: classifier, claims_extractor, brainstorm, qa_validator,
learning_loop (5 files).

Also: extractor.py now supports .md files.

Sources:
- https://docs.anthropic.com/en/docs/build-with-claude/extended-thinking
- https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/long-context-tips
- https://docs.anthropic.com/en/docs/minimizing-hallucinations
- https://docs.anthropic.com/en/docs/about-claude/models/overview

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 14:17:43 +00:00
bed9d5c7e9 Improve block-zayin: synthesize claims by topic + fix markdown JSON parsing
block_writer: Rewrote block-zayin prompt to require synthesis by topic
instead of listing each claim separately. Now produces 3 organized
sections (appellants 8, committee 6, permit applicants 3+) instead
of 40 scattered paragraphs. Target: 800-1500 words.

claims_extractor: Fix markdown code block stripping (same bug as
qa_validator had). Enables parsing claims from Claude responses
wrapped in ```json blocks.

Tested on Hecht: block-zayin from 40 paragraphs/1049 words to
17 organized paragraphs/1039 words. Structure now matches Dafna's
original (3 parties, grouped by topic).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 12:54:42 +00:00
d9e5ef0f46 Add full decision writing pipeline: classify, extract, brainstorm, write, QA, export
New services (11 files):
- classifier.py: auto doc-type classification + party identification (Claude Haiku)
- claims_extractor.py: claim extraction from pleadings (Claude Sonnet + regex)
- references_extractor.py: plan/case-law/legislation detection (regex)
- brainstorm.py: direction generation with 2-3 options (Claude Sonnet)
- block_writer.py: 12-block decision writer (template + Claude Sonnet/Opus)
- docx_exporter.py: DOCX export with David font, RTL, headings
- qa_validator.py: 6 QA checks with export blocking on critical failure
- learning_loop.py: draft vs final comparison + lesson extraction
- metrics.py: KPIs dashboard per case and global
- audit.py: action audit log
- cli.py: standalone CLI with 11 commands

Updated pipeline: extract → classify → chunk → embed → store → extract_references
New MCP tools: 29 total (was 16)
New DB tables: audit_log, decisions CRUD, claims CRUD
Config: Infisical support, external service allowlist

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 10:21:47 +00:00