Repolode
제출하기
robert-mcdermott

robert-mcdermott/ai-knowledge-graph

Turns unstructured text into an interactive knowledge graph by using an LLM to extract subject-predicate-object triples.

GitHub에서 보기웹사이트
스타
3,124
포크
434
열린 이슈
8
마지막 커밋
지난달

어떤 프로젝트인가요?

AI Knowledge Graph Generator takes text documents such as .txt, .md, .pdf and .docx files and uses an LLM to extract Subject-Predicate-Object triples with entity types. The triples are merged, standardized and optionally enriched with inferred relationships, then rendered as a single self-contained interactive HTML page with search, communities, a path finder and a relationships panel that shows the source sentence for each extracted edge.

The tool works with any OpenAI-compatible endpoint, including local models through Ollama, LM Studio or vLLM, and hosted services such as OpenAI and Gemini. Results can also be exported as JSON, CSV, GraphML for Gephi, Cytoscape or yEd, and a Cypher script for Neo4j.

Optional commands let you ask questions of a generated graph with cited answers (graph-chat) or browse and build graphs from a local web interface (graph-serve). Sample graphs are included so the explorer can be tried without an LLM.

핵심 기능

  • 01Extracts typed Subject-Predicate-Object triples from .txt, .md, .rst, .pdf and .docx inputs
  • 02Standardizes entity name variants and optionally uses an LLM to merge the rest
  • 03Infers extra relationships, marking each inferred edge with its method
  • 04Exports to JSON, CSV, GraphML and Neo4j Cypher
  • 05Produces a self-contained interactive HTML explorer with search, communities and path finding
  • 06Works with any OpenAI-compatible LLM endpoint and caches responses on disk

활용 사례

  • Turning meeting notes, research papers or long reports into a visual map of entities and relationships
  • Exploring the structure of a biography, historical period or supply chain from a text corpus
  • Loading extracted triples into Neo4j or Gephi for further analysis
  • Running entity extraction with a local model so the source text stays on your machine

토픽

#knowledge-graph#llm#information-extraction#visualization#networkx#python#nlp#artificial-intelligence#knowledge-distillation#pyvis

자주 묻는 질문

ai-knowledge-graph은(는) 무엇인가요?
Turns unstructured text into an interactive knowledge graph by using an LLM to extract subject-predicate-object triples. AI Knowledge Graph Generator takes text documents such as .txt, .md, .pdf and .docx files and uses an LLM to extract Subject-Predicate-Object triples with entity types. The triples are merged, standardized and optionally enriched with inferred relationships, then rendered as a single self-contained interactive HTML page with search, communities, a path finder and a relationships panel that shows the source sentence for each extracted edge.
ai-knowledge-graph은(는) 무료로 사용할 수 있나요? 라이선스는 무엇인가요?
ai-knowledge-graph은(는) Apache-2.0 라이선스의 오픈소스입니다. 상업용 제품에 사용하기 전에 라이선스 조건을 확인하세요.
ai-knowledge-graph은(는) 아직 유지보수되고 있나요?
6개월 이내 업데이트. ai-knowledge-graph의 마지막 커밋은 2026년 9월 13일입니다.
ai-knowledge-graph은(는) 어디에 사용할 수 있나요?
Turning meeting notes, research papers or long reports into a visual map of entities and relationships Exploring the structure of a biography, historical period or supply chain from a text corpus Loading extracted triples into Neo4j or Gephi for further analysis Running entity extraction with a local model so the source text stays on your machine
ai-knowledge-graph은(는) 어디서 받을 수 있나요?
소스 코드와 설치 방법은 GitHub에 있습니다: https://github.com/robert-mcdermott/ai-knowledge-graph

요약은 프로젝트 README를 바탕으로 AI가 작성했습니다. GitHub 통계는 매일 갱신됩니다.

관련 프로젝트