Commit Graph

1 Commits

Author SHA1 Message Date
Devin AI
45fbfdabb7 Phase 5: hybrid LLM extraction (Ollama) for header gaps
Adds a small Ollama HTTP client (httpx-based, no extra runtime deps),
prompt builders, and a hybrid header extractor that runs *after* the
deterministic regex layer. The merger never overwrites a regex-filled
field — the LLM only fills gaps. If LLM_ENABLED=false (the default), or
the Ollama server is unreachable, the pipeline degrades gracefully:

  - LLM_ENABLED=false  ->  no LLM call at all, no flag.
  - LLM_ENABLED=true,
    header complete    ->  no LLM call.
  - LLM_ENABLED=true,
    header has gaps,
    LLM responded ok   ->  merge + LLM_FALLBACK flag (review hint).
  - LLM_ENABLED=true,
    header has gaps,
    LLM unavailable    ->  keep regex result + LLM_UNAVAILABLE flag.

Default model qwen2.5:1.5b on http://localhost:11434 — chosen for CPU
throughput (~5-15s per call) at acceptable accuracy. The LLM only fills
the *header* (nomor, tanggal, satuan, perihal, dasar). Personnel rows
stay with PP-Structure since that's more accurate and doesn't need LLM.

Tests:
 - test_llm_client.py: httpx MockTransport-driven tests for the wire
   format, error paths (HTTP 5xx, malformed JSON, missing envelope,
   ConnectError), and request shape.
 - test_llm_extractor.py: merge policy + None-on-unavailable behaviour.
 - test_orchestrator_llm.py: end-to-end orchestrator wiring with stubs
   for ingest/preprocess/OCR/table — verifies LLM is skipped when
   disabled, skipped when header is complete, called and flagged when
   gaps exist, and marked unavailable when the client returns None.

162 unit tests pass total (was 146).

Co-Authored-By: adrian kuman firmansah <adriancuman@gmail.com>
2026-04-25 16:56:43 +00:00