Rezept: Relationen zwischen Entitäten extrahieren

curl -X POST https://api.fastino.ai/v1/chat/completions \
  -H "Authorization: Bearer $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "fastino/gliner2.5-multi-v1",
  "messages": [
    {
      "role": "user",
      "content": "Lena Vogel works for Siemens in Munich. Siemens acquired Altair Engineering in 2025."
    }
  ],
  "schema": {
    "entities": [
      {
        "name": "person",
        "description": "full name of a person"
      },
      {
        "name": "organization",
        "description": "company or institution"
      },
      {
        "name": "location",
        "description": "city or country"
      }
    ],
    "relations": {
      "works_for": {
        "description": "a person is employed by an organization"
      },
      "located_in": {
        "description": "an organization or person is based in a place"
      },
      "acquired": {
        "description": "one organization bought another"
      }
    }
  },
  "threshold": 0.5,
  "store": false
}'

{
  "entities": {
    "person": [
      {
        "text": "Lena Vogel",
        "confidence": 1.0,
        "start": 0,
        "end": 10
      }
    ],
    "organization": [
      {
        "text": "Siemens",
        "confidence": 0.996,
        "start": 21,
        "end": 28
      },
      {
        "text": "Siemens",
        "confidence": 0.992,
        "start": 40,
        "end": 47
      },
      {
        "text": "Altair Engineering",
        "confidence": 0.973,
        "start": 57,
        "end": 75
      }
    ],
    "location": [
      {
        "text": "Munich",
        "confidence": 1.0,
        "start": 32,
        "end": 38
      }
    ]
  },
  "relation_extraction": {
    "works_for": [
      {
        "head": {
          "text": "Lena Vogel",
          "start": 0,
          "end": 10,
          "confidence": 0.988
        },
        "tail": {
          "text": "Siemens",
          "start": 21,
          "end": 28,
          "confidence": 0.988
        }
      }
    ],
    "located_in": [
      {
        "head": {
          "text": "Siemens",
          "start": 40,
          "end": 47,
          "confidence": 0.977
        },
        "tail": {
          "text": "Munich",
          "start": 32,
          "end": 38,
          "confidence": 0.977
        }
      }
    ],
    "acquired": [
      {
        "head": {
          "text": "Siemens",
          "start": 40,
          "end": 47,
          "confidence": 0.938
        },
        "tail": {
          "text": "Altair Engineering",
          "start": 57,
          "end": 75,
          "confidence": 0.938
        }
      }
    ]
  }
}

import json

result = json.loads(response.json()["choices"][0]["message"]["content"])

facts = [
    (pair["head"]["text"], relation, pair["tail"]["text"])
    for relation, pairs in result["relation_extraction"].items()
    for pair in pairs
    if min(pair["head"]["confidence"], pair["tail"]["confidence"]) >= 0.5
]
for head, relation, tail in facts:
    graph.add_edge(head, tail, type=relation)