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Async on-prem LLM-powered structured information extraction microservice
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fix(genai): drop Ollama format flag; extract trailing JSON from response 
qwen3:14b (and deepseek-r1, other reasoning models) wrap their output
in <think>…</think> chains-of-thought before emitting real output.
With format=json the constrained sampler terminated immediately at
`{}` because the thinking block wasn't valid JSON; without format the
model thinks normally and appends the actual JSON at the end.

OllamaClient now omits the format flag and extracts the outermost
balanced `{…}` block from the response (brace depth counter, string-
literal aware). Works for reasoning models, ```json``` code-fenced
outputs, and plain JSON alike.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 14:05:28 +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(genai): drop Ollama format flag; extract trailing JSON from response 2026-04-18 14:05:28 +02:00
tests fix(genai): drop Ollama format flag; extract trailing JSON from response 2026-04-18 14:05:28 +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(compose): persist Surya + HF caches so rebuilds don't redownload models 2026-04-18 13:49:09 +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.