Async on-prem LLM-powered structured information extraction microservice
Real GenAIClient for the production pipeline. Sends `format=<pydantic JSON schema>`, `stream=false`, and mapped options (`temperature`; drops `reasoning_effort`). Content-parts lists joined to a single string since MVP models don't speak native content-parts. Error mapping per spec: connection/timeout/5xx → IX_002_000, schema violations → IX_002_001. `selfcheck()` probes /api/tags with a fixed 5 s timeout for /healthz. Tests: 10 hermetic pytest-httpx unit tests; 2 live tests gated on IX_TEST_OLLAMA=1 (never run in CI). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
||
|---|---|---|
| .forgejo/workflows | ||
| alembic | ||
| docs | ||
| scripts | ||
| src/ix | ||
| tests | ||
| .env.example | ||
| .gitignore | ||
| .python-version | ||
| AGENTS.md | ||
| alembic.ini | ||
| 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.