Async on-prem LLM-powered structured information extraction microservice
Dirk Riemann
1e340c82fa
Lands the two remaining provenance-subsystem pieces:
mapper.py — map_segment_refs_to_provenance:
- For each LLM SegmentCitation, pick seg-ids per source_type
(`value` vs `value_and_context`), cap at max_sources_per_field,
resolve each via SegmentIndex, track invalid references.
- Resolve field values by dot-path (`result.items[0].name` supported —
`[N]` bracket notation is normalised to `.N` before traversal).
- Skip fields that resolve to zero valid sources (spec §9.4).
- Write quality_metrics with fields_with_provenance / total_fields /
coverage_rate / invalid_references.
verify.py — verify_field + apply_reliability_flags:
- Dispatches per Pydantic field type: date → parse-both-sides compare;
int/float/Decimal → normalize + whole-snippet / numeric-token scan;
IBAN (detected via `iban` in field name) → upper+strip compare;
Literal / None → flags stay None; else string substring.
- _unwrap_optional handles BOTH typing.Union AND types.UnionType so
`Decimal | None` (PEP 604, what get_type_hints emits on 3.12+) resolves
correctly — caught by the integration-style test_writes_flags_and_counters.
- Number comparator scans numeric tokens in the snippet so labels
("Closing balance CHF 1'234.56") don't mask the match.
- apply_reliability_flags mutates the passed ProvenanceData in place and
writes verified_fields / text_agreement_fields to quality_metrics.
Tests cover each comparator, Literal/None skip, short-value skip (strings
and numerics), Decimal via optional union, and end-to-end flag+counter
writing against a Pydantic use-case schema that mirrors bank_statement_header.
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.