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Async on-prem LLM-powered structured information extraction microservice
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fix(deps): pin surya-ocr ^0.17 and drop cu124 index 
Our client code imports surya.foundation (added in 0.17). The earlier
cu124 torch pin forced uv to downgrade surya to 0.14.1, which doesn't
have that module and depends on a transformers version that lacks
QuantizedCacheConfig. Net: ocr: fail at /healthz.

Drop the cu124 index pin. surya 0.17.1 needs torch >= 2.7, which the
default pypi torch (2.11) satisfies. The deploy host's CUDA 12.4
driver doesn't match torch 2.11's cu13 wheels, so CUDA init warns and
the GPU isn't available — torch + Surya transparently fall back to CPU.
Slower than GPU but correct for MVP. A host driver upgrade later will
unlock GPU with no code changes.

Unit suite stays green.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 13:21:40 +02:00
.forgejo/workflows ci: run on every push (not just main) so feat branches also get CI 2026-04-18 10:40:44 +02:00
alembic feat(store): Alembic scaffolding + initial ix_jobs migration (spec §4) 2026-04-18 11:37:21 +02:00
docs fix(deploy): switch to network_mode: host — reach postgis + ollama on loopback 2026-04-18 13:00:02 +02:00
scripts chore(model): switch default IX_DEFAULT_MODEL to qwen3:14b (already on host) 2026-04-18 12:20:23 +02:00
src/ix fix(deploy): switch to network_mode: host — reach postgis + ollama on loopback 2026-04-18 13:00:02 +02:00
tests fix(deploy): switch to network_mode: host — reach postgis + ollama on loopback 2026-04-18 13:00:02 +02:00
.env.example fix(deploy): switch to network_mode: host — reach postgis + ollama on loopback 2026-04-18 13:00:02 +02:00
.gitignore feat(docker): Dockerfile (CUDA+python3.12) + compose with GPU reservation 2026-04-18 12:15:26 +02:00
.python-version feat(scaffold): project skeleton with uv + pytest + forgejo CI 2026-04-18 10:36:43 +02:00
AGENTS.md chore(model): switch default IX_DEFAULT_MODEL to qwen3:14b (already on host) 2026-04-18 12:20:23 +02:00
alembic.ini feat(store): Alembic scaffolding + initial ix_jobs migration (spec §4) 2026-04-18 11:37:21 +02:00
docker-compose.yml fix(deploy): switch to network_mode: host — reach postgis + ollama on loopback 2026-04-18 13:00:02 +02:00
Dockerfile fix(docker): include README.md in the uv sync COPY so hatchling finds it 2026-04-18 12:42:29 +02:00
pyproject.toml fix(deps): pin surya-ocr ^0.17 and drop cu124 index 2026-04-18 13:21:40 +02:00
README.md Initial design: on-prem LLM extraction microservice MVP 2026-04-18 10:23:17 +02:00
uv.lock fix(deps): pin surya-ocr ^0.17 and drop cu124 index 2026-04-18 13:21:40 +02:00

README.md

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.

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.