Async on-prem LLM-powered structured information extraction microservice
Dirk Riemann
abee9cea7b
Assembles the prompt, picks the structured-output schema, calls the
injected GenAIClient, and maps any emitted segment_citations into
response.provenance. Reliability flags stay None here; ReliabilityStep
fills them in Task 2.7.
- System prompt = use_case.system_prompt + (provenance-on) the verbatim
citation instruction from spec §9.2.
- User text = SegmentIndex.to_prompt_text([p1_l0] style) when provenance
is on, else plain OCR flat text + texts joined.
- Response schema = UseCaseResponse directly, or a runtime
create_model("ProvenanceWrappedResponse", result=(UCR, ...),
segment_citations=(list[SegmentCitation], Field(default_factory=list)))
when provenance is on.
- Model = request override -> use-case default.
- Failure modes: httpx / connection / timeout errors -> IX_002_000;
pydantic.ValidationError -> IX_002_001.
- Writes ix_result.result + ix_result.meta_data (model_name +
token_usage); builds response.provenance via
map_segment_refs_to_provenance when provenance is on.
17 unit tests in tests/unit/test_genai_step.py cover validate
(ocr_only skip, empty -> IX_001_000, text-only, ocr-text path), process
happy path, system-prompt shape with/without citation instruction, user
text tagged vs. plain, response schema plain vs. wrapped, provenance
mapping, error mapping (IX_002_000 + IX_002_001), and model selection
(request override + use-case default).
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.