58210207ea
- 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
118 lines
3.7 KiB
Python
118 lines
3.7 KiB
Python
"""classify_server.py — FastAPI service that adds taxonomy classification to kreuzberg /extract.
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Exposes POST /classify — same multipart interface as kreuzberg /extract,
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returns the full kreuzberg response plus category/subcategory/confidence fields.
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Usage:
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uvicorn scripts.classify_server:app --host 0.0.0.0 --port 8001
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"""
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from __future__ import annotations
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import json
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import os
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from contextlib import asynccontextmanager
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from typing import Annotated
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import httpx
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from fastapi import FastAPI, File, Form, UploadFile
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from fastapi.responses import JSONResponse
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from taxonomy import classify_text
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KREUZBERG_URL = os.getenv("KREUZBERG_URL", "https://check.i80.dk")
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YAKE_CONFIG = {"keywords": {"algorithm": "yake", "max_keywords": 15}}
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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app.state.client = httpx.AsyncClient(timeout=60.0)
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yield
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await app.state.client.aclose()
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app = FastAPI(
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title="kreuzberg-classify",
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description="Taxonomy classification on top of kreuzberg /extract",
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version="1.0.0",
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lifespan=lifespan,
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)
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@app.get("/health")
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async def health():
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return {"status": "healthy", "kreuzberg_url": KREUZBERG_URL}
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@app.post("/classify")
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async def classify(
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files: Annotated[list[UploadFile], File()],
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config: Annotated[str | None, Form()] = None,
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folder: Annotated[str | None, Form()] = None,
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):
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"""Extract text + keywords via kreuzberg, then classify into taxonomy.
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Args:
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files: One or more document files (PDF, DOCX, etc.)
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config: Optional JSON config for kreuzberg (merged with YAKE defaults).
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folder: Optional current folder path for context hint.
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Returns:
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List of results — one per file — with all kreuzberg fields plus:
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category, subcategory, confidence, runner_up, runner_up_score.
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"""
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# Merge caller config with our YAKE defaults
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merged_config = dict(YAKE_CONFIG)
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if config:
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try:
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caller_cfg = json.loads(config)
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merged_config.update(caller_cfg)
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except json.JSONDecodeError:
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pass
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# Forward files to kreuzberg /extract
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form_data = [("config", json.dumps(merged_config))]
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file_contents = []
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for upload in files:
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content = await upload.read()
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file_contents.append((upload.filename, content, upload.content_type or "application/octet-stream"))
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form_data.append(("files", (upload.filename, content, upload.content_type or "application/octet-stream")))
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try:
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response = await app.state.client.post(
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f"{KREUZBERG_URL}/extract",
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files=[("files", (fn, fc, ct)) for fn, fc, ct in file_contents],
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data={"config": json.dumps(merged_config)},
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)
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response.raise_for_status()
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kreuzberg_results = response.json()
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except httpx.HTTPError as exc:
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return JSONResponse(status_code=502, content={"error": f"kreuzberg error: {exc}"})
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# Ensure list
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if isinstance(kreuzberg_results, dict):
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kreuzberg_results = [kreuzberg_results]
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folder_hint = folder or ""
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results = []
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for i, result in enumerate(kreuzberg_results):
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content_text = result.get("content", "") or ""
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# Extract keyword strings from kreuzberg response
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raw_keywords = result.get("keywords", []) or []
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if raw_keywords and isinstance(raw_keywords[0], dict):
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kw_strings = [k.get("keyword", "") or k.get("phrase", "") for k in raw_keywords]
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else:
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kw_strings = [str(k) for k in raw_keywords]
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classification = classify_text(
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content=content_text,
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keywords=kw_strings,
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folder_hint=folder_hint,
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)
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results.append({**result, **classification})
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return results
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