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knowledge-graph-extractor

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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

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최종 버전 다운로드 (.zip)

- demo.gif
- favicon-16.png
- favicon-180.png
- favicon-32.png
- favicon-48.png
- favicon.png
- hero.png
- og-banner.png
- - batch1.json
  - batch10.json
  - batch11.json
  - batch2.json
  - batch3.json
  - batch4.json
  - batch5.json
  - batch6.json
  - batch7.json
  - batch8.json
  - batch9.json
  - lfm.json
  - lfm_v2.json
- baseline.json
- candidates.py
- confirm.py
- confirmation.json
- doc_cache.md
- experiments.jsonl
- harness.py
- leaderboard.md
- lfm_ground.py
- plot.py
- program.md
- progress.png
- prompt_eval.json
- prompt_eval.py
- prompt_eval_dump.txt
- README.md
- REPORT.md
- run.py
- spotcheck.py
- strategies.md
- setup.sh
- chat_template.jinja
.env.example
.gitignore
app.py
docker-compose.yml
Dockerfile
favicon.ico
jina-corpus.zip
jobs.py
LICENSE
README.md

// repository documentation

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# 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

[![Knowledge Graph Extractor](https://raw.githubusercontent.com/hanxiao/knowledge-graph-extractor/main/assets/hero.png)](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](https://huggingface.co/jinaai/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](https://github.com/ggml-org/llama.cpp) with
  CUDA, serves the model over an OpenAI-compatible API (port 8080).
- **app** — FastAPI: extraction + scheduler + CPU dedup + UI (port 3000).

## Setup

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

```bash
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:

```bash
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
```

## Configuration

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

| Flag | Value | Why |
|------|-------|-----|
| `--ctx-size` | 16384 | Input capacity vs VRAM |
| `--spec-type draft-mtp` | — | MTP speculative decoding (large speedup on L4) |
| `--cache-reuse` | 256 | KV cache reuse across rounds on the same doc |
| `--flash-attn` | 1 | Flash attention |
| `--n-predict` | 8192 | Max generation length |

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

## Layout

```
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)
```

## License

MIT