Async on-prem LLM-powered structured information extraction microservice
Dirk Riemann
565d8d0676
Final pipeline step. Three mechanical transforms: 1. include_ocr_text -> concatenate non-tag line texts, pages joined with \n\n, write to ocr_result.result.text. 2. include_geometries=False (default) -> strip ocr_result.result.pages + ocr_result.meta_data. Geometries are heavy; callers opt in. 3. Delete response.context so the internal accumulator never leaks to the caller (belt-and-braces; Field(exclude=True) already does this). validate() always returns True per spec. 7 unit tests in tests/unit/test_response_handler_step.py cover all three branches + context-not-in-model_dump check. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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| .forgejo/workflows | ||
| docs | ||
| src/ix | ||
| tests | ||
| .env.example | ||
| .gitignore | ||
| .python-version | ||
| AGENTS.md | ||
| pyproject.toml | ||
| README.md | ||
| uv.lock | ||
InfoXtractor (ix)
Async, on-prem, LLM-powered structured information extraction microservice.
Given a document (PDF, image, text) and a named use case, ix returns a structured JSON result whose shape matches the use-case schema — together with per-field provenance (OCR segment IDs, bounding boxes, cross-OCR agreement flags) that let the caller decide how much to trust each extracted value.
Status: design phase. Implementation about to start.
- Full reference spec:
docs/spec-core-pipeline.md(aspirational; MVP is a strict subset) - MVP design:
docs/superpowers/specs/2026-04-18-ix-mvp-design.md - Agent / development notes:
AGENTS.md
Principles
- On-prem always. LLM = Ollama, OCR = local engines (Surya first). No OpenAI / Anthropic / Azure / AWS / cloud.
- Grounded extraction, not DB truth. ix returns best-effort fields + provenance; the caller decides what to trust.
- Transport-agnostic pipeline core. REST + Postgres-queue adapters in parallel on one job store.