{"repo":"hanxiao/knowledge-graph-extractor","free":true,"listed":false,"github":"https://github.com/hanxiao/knowledge-graph-extractor","clone":"git clone https://github.com/hanxiao/knowledge-graph-extractor.git","description":"Turn any document or a whole zip into an interactive knowledge graph, using a self-hosted Qwen3.6-35B-A3B-MTP on a single NVIDIA L4","language":"Python","stars":216,"topics":["fastapi","force-graph","gpu","information-extraction","knowledge-graph","llama-cpp","llm","qwen","self-hosted"],"license":"MIT","category":"self-hosted-apps","readme_excerpt":"Knowledge Graph Extractor Turn any document, URL, or a zip of files into an interactive knowledge graph, using a self-hosted LLM (Qwen3.6-35B-A3B-MTP) on a single NVIDIA L4. Live demo: https://hanxiao.io/knowledge-graph Each extracted fact is one graph edge: a (subject) --[predicate]-- (object) triple plus a title, description, evidence span, confidence, tags, and source file. Facts stream into a force-directed graph; hover an edge for the full card. How it works 1. Input — paste text, a URL (fetched to markdown via Jina Reader), or a .zip (txt, md, html, pdf, docx, json, csv, code...). Oversized docs are chunked (not truncated) so the full text is processed. 2. Extract — the LLM emits atomic (subject, predicate, object) triples. The prompt forces canonical entity/value subjects and objects so nodes connect instead of becoming prose dead-ends. 3. Dedup (on by default) — semantic dedup via jina-embeddings-v5-text-nano on CPU, across rounds and across files. 4. Visualize — every unique fact is one edge; node names are normalized so variants merge. Download the result as JSONL. Job queue The L4 has one llama slot, so jobs run one at a time via a single-slot scheduler: a new submission preempts the running job, which is persisted and auto-resumes from where it left off when the slot frees. Jobs (meta + facts.jsonl + input) persist under data/jobs/ so the list, JSONL reload, and resume survive restarts. Stack - llama-server — llama.cpp with CUDA, serves the model over an OpenAI-co","default_branch":null,"files":null,"tree":[],"storefront":"/r/hanxiao","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/hanxiao/knowledge-graph-extractor/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}