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.

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.

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.

• llama-server — llama.cpp with CUDA, serves the model over an OpenAI-compatible API (port 8080).
• app — FastAPI: extraction + scheduler + CPU dedup + UI (port 3000).

Single NVIDIA L4 24GB GPU (e.g. GCP g2-standard-8). Needs Docker + the NVIDIA Container Toolkit.

git clone https://github.com/hanxiao/knowledge-graph-extractor.git
cd knowledge-graph-extractor

cp .env.example .env          # add your JINA_API_KEY (https://jina.ai/api-key)
bash scripts/setup.sh         # downloads the model (~17GB) and starts both services

Then open http://<your-ip>:3000.

Manual model download + run:

mkdir -p models
pip install -q huggingface-hub
python3 -c "from huggingface_hub import hf_hub_download; \
hf_hub_download('unsloth/Qwen3.6-35B-A3B-MTP-GGUF', \
'Qwen3.6-35B-A3B-UD-Q3_K_XL.gguf', local_dir='models')"
docker compose up -d --build

llama-server flags live in docker-compose.yml. Key ones:

UI parameters: rounds per doc, dedup model (on/off), dedup field, dedup threshold. Benchmark notes on quantization and decoding live in autoresearch/.

app.py             FastAPI app: extraction + UI + API
jobs.py            single-slot job scheduler (queue/preempt/backfill/persist)
Dockerfile         app container
docker-compose.yml both services + data volume
scripts/setup.sh   one-shot GCP L4 setup
autoresearch/      throughput benchmark notes
data/              persisted jobs (gitignored)
models/            model files (gitignored)

MIT