landvex: IOL v3 + Identify API + Neo4j-graf + byggpipeline
- FastAPI Identify API (port 8081) - Neo4j propertygraf (64 noder, 80 kanter) - Ingest-pipeline (Python) - Byggscript + integrationstester - Docker Compose setup
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Bernt
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FROM python:3.12-slim
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WORKDIR /app
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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COPY main.py .
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EXPOSE 8080
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
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#!/usr/bin/env python3
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"""
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Landvex Identify API v0
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FastAPI-implementation av pilot-specen.
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"""
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import json
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import hashlib
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from pathlib import Path
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from typing import List, Optional
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from datetime import datetime
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from fastapi import FastAPI, HTTPException, File, UploadFile, Form
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from pydantic import BaseModel, Field
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# ─── ladda data vid startup ─────────────────────────────────────────
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DATA_DIR = Path("/app/data")
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poster = {} # lvx_id -> post
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kanter = [] # grafkanter
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index_slug = {} # slug -> lvx_id
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index_namn = {} # sv+en -> lvx_id
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def ladda():
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global poster, kanter, index_slug, index_namn
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with open(DATA_DIR / "lvx-klassposter-master-v3.json", encoding="utf-8") as f:
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for p in json.load(f)["poster"]:
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poster[p["lvx_id"]] = p
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if p.get("slug"): index_slug[p["slug"]] = p["lvx_id"]
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for lang in ("sv", "en"):
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if p.get("namn", {}).get(lang):
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index_namn[p["namn"][lang].lower()] = p["lvx_id"]
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# modeller
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for fn in ("lvx-produktmodeller-vag2.json", "lvx-produktmodeller-vag3.json", "lvx-produktmodeller-auto.json"):
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fp = DATA_DIR / fn
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if fp.exists():
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with open(fp, encoding="utf-8") as f:
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for p in json.load(f).get("poster", []):
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poster[p["lvx_id"]] = p
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# graf
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with open(DATA_DIR / "lvx-graf-kanter-v3.json", encoding="utf-8") as f:
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kanter = json.load(f)["kanter"]
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ladda()
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# ─── Pydantic-modeller ──────────────────────────────────────────────
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class Kalla(BaseModel):
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titel: str
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url: Optional[str] = None
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class IdentifyKandidat(BaseModel):
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lvx_id: str
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posttyp: str
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namn: dict
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konfidens: float = Field(..., ge=0, le=1)
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verifieringsniva: str
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kannetecken_match: List[str] = []
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ocr_falt: Optional[dict] = None
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class IdentifyResponse(BaseModel):
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bild_hash: str
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kandidater: List[IdentifyKandidat]
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bounty_skapad: Optional[str] = None
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besked: str
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class ObjectResponse(BaseModel):
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lvx_id: str
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posttyp: str
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namn: dict
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doman: str
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verifieringsniva: str
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konfidens: float
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kallor: List[Kalla]
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teknik: Optional[dict] = None
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ai: Optional[dict] = None
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problem: List[dict] = []
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relationer: List[dict] = []
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class FeedbackRequest(BaseModel):
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lvx_id: Optional[str] = None
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bild_hash: Optional[str] = None
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kommentar: str
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position: Optional[str] = None
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class FeedbackResponse(BaseModel):
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bounty_id: str
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status: str
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# ─── helpers ────────────────────────────────────────────────────────
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def relaterade(obj_id: str):
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"""Hämta alla kanter där objektet är subjekt."""
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return [k for k in kanter if k["subjekt"] == obj_id]
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def succession(obj_id: str):
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"""Ersättningskedja framåt och bakåt."""
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ut = {"ersatter": [], "ersatts_av": []}
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for k in kanter:
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if k["subjekt"] == obj_id:
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if k["predikat"] == "ersatter": ut["ersatter"].append(k["objekt"])
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if k["predikat"] == "ersatts_av": ut["ersatts_av"].append(k["objekt"])
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if k["objekt"] == obj_id:
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if k["predikat"] == "ersatter": ut["ersatts_av"].append(k["subjekt"])
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if k["predikat"] == "ersatts_av": ut["ersatter"].append(k["subjekt"])
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return ut
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def hash_bild(data: bytes) -> str:
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return hashlib.sha256(data).hexdigest()[:16]
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# ─── FastAPI app ────────────────────────────────────────────────────
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app = FastAPI(
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title="Landvex Object API",
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description="Identify, query and feedback API for infrastructure objects.",
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version="0.1.0"
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)
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@app.get("/health")
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def health():
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return {"status": "ok", "poster": len(poster), "kanter": len(kanter)}
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@app.post("/v0/identify", response_model=IdentifyResponse)
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async def identify(
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bild: UploadFile = File(...),
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position: Optional[str] = Form(None)
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):
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"""
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Bild in → rankade kandidater med konfidens.
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I piloten: mockad vision — svarar med de mest kompletta posterna.
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"""
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data = await bild.read()
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bh = hash_bild(data)
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# TODO: vision-modell → feature extraction → vektorsök
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# Pilot: returnera alla Tier 1-klasser sorterade efter konfidens
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kandidater = []
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for p in poster.values():
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if p.get("tier") != 1 and p.get("posttyp") != "objektklass":
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continue
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prov = p.get("proveniens", {})
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k = IdentifyKandidat(
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lvx_id=p["lvx_id"],
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posttyp=p["posttyp"],
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namn=p.get("namn", {}),
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konfidens=prov.get("konfidens", 0.5),
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verifieringsniva=prov.get("verifieringsniva", "obekraftad"),
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kannetecken_match=p.get("ai", {}).get("kannetecken", [])[:3],
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)
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kandidater.append(k)
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kandidater.sort(key=lambda x: x.konfidens, reverse=True)
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top = kandidater[:5]
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# Om ingen når modellnivå → bounty
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bounty = None
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besked = "Kandidater hittade"
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if not any(k.konfidens > 0.75 for k in top):
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bounty = f"BNTY-{bh[:8]}"
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besked = "Svag konfidens — bounty skapad för fältverifiering"
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return IdentifyResponse(
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bild_hash=bh,
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kandidater=top,
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bounty_skapad=bounty,
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besked=besked
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)
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@app.get("/v0/objects/{lvx_id}", response_model=ObjectResponse)
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def get_object(lvx_id: str):
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p = poster.get(lvx_id)
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if not p:
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raise HTTPException(status_code=404, detail="Objekt ej funnet")
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prov = p.get("proveniens", {})
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return ObjectResponse(
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lvx_id=p["lvx_id"],
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posttyp=p["posttyp"],
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namn=p.get("namn", {}),
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doman=p.get("doman", ""),
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verifieringsniva=prov.get("verifieringsniva", "obekraftad"),
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konfidens=prov.get("konfidens", 0),
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kallor=[Kalla(**k) for k in prov.get("kallor", [])],
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teknik=p.get("teknik"),
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ai=p.get("ai"),
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problem=p.get("problem", []),
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relationer=relaterade(lvx_id),
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)
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@app.get("/v0/objects/{lvx_id}/succession")
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def get_succession(lvx_id: str):
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if lvx_id not in poster:
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raise HTTPException(status_code=404, detail="Objekt ej funnet")
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return {"lvx_id": lvx_id, **succession(lvx_id)}
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@app.get("/v0/search")
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def search(q: str, doman: Optional[str] = None, verifieringsniva: Optional[str] = None):
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"""Fritextsök över namn och kännetecken."""
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q = q.lower()
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träffar = []
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for p in poster.values():
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namn = " ".join(p.get("namn", {}).values()).lower()
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kann = " ".join(p.get("ai", {}).get("kannetecken", [])).lower()
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if q not in namn and q not in kann:
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continue
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if doman and p.get("doman") != doman:
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continue
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if verifieringsniva and p.get("proveniens", {}).get("verifieringsniva") != verifieringsniva:
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continue
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träffar.append({
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"lvx_id": p["lvx_id"],
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"namn": p.get("namn", {}),
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"verifieringsniva": p.get("proveniens", {}).get("verifieringsniva", "obekraftad"),
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"konfidens": p.get("proveniens", {}).get("konfidens", 0),
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})
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träffar.sort(key=lambda x: x["konfidens"], reverse=True)
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return {"antal": len(träffar), "traffar": träffar[:20]}
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@app.get("/v0/standards/{beteckning}/objects")
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def objects_by_standard(beteckning: str):
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bet = beteckning.upper()
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träffar = []
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for k in kanter:
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if k["predikat"] == "foljer_standard" and bet in k["objekt"].upper():
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träffar.append(k["subjekt"])
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return {"standard": beteckning, "antal": len(träffar), "lvx_ids": sorted(set(träffar))}
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@app.post("/v0/feedback", response_model=FeedbackResponse)
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def feedback(req: FeedbackRequest):
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"""Fel eller okänt objekt → bounty."""
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bid = f"BNTY-FB-{hashlib.sha256(req.kommentar.encode()).hexdigest()[:8]}"
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return FeedbackResponse(bounty_id=bid, status="skapad")
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8080)
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@@ -0,0 +1,4 @@
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fastapi>=0.110
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uvicorn[standard]>=0.29
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python-multipart>=0.0.9
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pydantic>=2.6
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