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.
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.
Описание создано ИИ на основе README проекта. Статистика GitHub обновляется ежедневно.
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