feat: add taxonomy classify service + /classify endpoint

- scripts/taxonomy.py: shared taxonomy with 14 categories, keyword scorer
  and classify_text() function
- scripts/classify_server.py: FastAPI service — forwards to kreuzberg /extract,
  applies taxonomy, returns category/subcategory/confidence alongside full kreuzberg response
- Dockerfile.classify: lightweight Python image for classify service
- classify.nomad: Nomad job → classify.i80.dk
- .gitea/workflows/classify.yml: CI/CD pipeline (build + deploy)
- analyse_familie.py: refactored to import from taxonomy.py (no duplication)
- .gitignore: exclude dokumenter_keywords.* and extract_all.log

scripts/classify_server.py

@@ -0,0 +1,117 @@
+"""classify_server.py — FastAPI service that adds taxonomy classification to kreuzberg /extract.
+
+Exposes POST /classify — same multipart interface as kreuzberg /extract,
+returns the full kreuzberg response plus category/subcategory/confidence fields.
+
+Usage:
+    uvicorn scripts.classify_server:app --host 0.0.0.0 --port 8001
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from contextlib import asynccontextmanager
+from typing import Annotated
+
+import httpx
+from fastapi import FastAPI, File, Form, UploadFile
+from fastapi.responses import JSONResponse
+
+from taxonomy import classify_text
+
+KREUZBERG_URL = os.getenv("KREUZBERG_URL", "https://check.i80.dk")
+YAKE_CONFIG = {"keywords": {"algorithm": "yake", "max_keywords": 15}}
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    app.state.client = httpx.AsyncClient(timeout=60.0)
+    yield
+    await app.state.client.aclose()
+
+
+app = FastAPI(
+    title="kreuzberg-classify",
+    description="Taxonomy classification on top of kreuzberg /extract",
+    version="1.0.0",
+    lifespan=lifespan,
+)
+
+
+@app.get("/health")
+async def health():
+    return {"status": "healthy", "kreuzberg_url": KREUZBERG_URL}
+
+
+@app.post("/classify")
+async def classify(
+    files: Annotated[list[UploadFile], File()],
+    config: Annotated[str | None, Form()] = None,
+    folder: Annotated[str | None, Form()] = None,
+):
+    """Extract text + keywords via kreuzberg, then classify into taxonomy.
+
+    Args:
+        files: One or more document files (PDF, DOCX, etc.)
+        config: Optional JSON config for kreuzberg (merged with YAKE defaults).
+        folder: Optional current folder path for context hint.
+
+    Returns:
+        List of results — one per file — with all kreuzberg fields plus:
+        category, subcategory, confidence, runner_up, runner_up_score.
+    """
+    # Merge caller config with our YAKE defaults
+    merged_config = dict(YAKE_CONFIG)
+    if config:
+        try:
+            caller_cfg = json.loads(config)
+            merged_config.update(caller_cfg)
+        except json.JSONDecodeError:
+            pass
+
+    # Forward files to kreuzberg /extract
+    form_data = [("config", json.dumps(merged_config))]
+    file_contents = []
+    for upload in files:
+        content = await upload.read()
+        file_contents.append((upload.filename, content, upload.content_type or "application/octet-stream"))
+        form_data.append(("files", (upload.filename, content, upload.content_type or "application/octet-stream")))
+
+    try:
+        response = await app.state.client.post(
+            f"{KREUZBERG_URL}/extract",
+            files=[("files", (fn, fc, ct)) for fn, fc, ct in file_contents],
+            data={"config": json.dumps(merged_config)},
+        )
+        response.raise_for_status()
+        kreuzberg_results = response.json()
+    except httpx.HTTPError as exc:
+        return JSONResponse(status_code=502, content={"error": f"kreuzberg error: {exc}"})
+
+    # Ensure list
+    if isinstance(kreuzberg_results, dict):
+        kreuzberg_results = [kreuzberg_results]
+
+    folder_hint = folder or ""
+
+    results = []
+    for i, result in enumerate(kreuzberg_results):
+        content_text = result.get("content", "") or ""
+
+        # Extract keyword strings from kreuzberg response
+        raw_keywords = result.get("keywords", []) or []
+        if raw_keywords and isinstance(raw_keywords[0], dict):
+            kw_strings = [k.get("keyword", "") or k.get("phrase", "") for k in raw_keywords]
+        else:
+            kw_strings = [str(k) for k in raw_keywords]
+
+        classification = classify_text(
+            content=content_text,
+            keywords=kw_strings,
+            folder_hint=folder_hint,
+        )
+
+        results.append({**result, **classification})
+
+    return results