@ChenZiHong-Gavin

Backgraound

To establish GraphGen as an essential tool for training and evaluation data synthesis, its development roadmap focuses on two core pillars: implementing a robust, multi-dimensional data quality assessment and filtering system to ensure the reliability of generated knowledge graphs, and expanding its architecture to support multi-modal and multi-omics data inputs.

If you'd like to work on one of these tasks, please comment below to claim it and create an issue for the feature you'll be implementing.

Features

1 GraphGen Framework

• ♻️ Refactor the pipeline around base classes: Reader → KG_Builder → Partitioner → Generator. Data flow: Raw corpus → Reader → Splitter → KG_Builder → Partitioner → Generator → training / evaluation data: refactor: Partitioner & Generator #59 , Kg builder #58 , Refactor KG builder #52
• 🔧 Operator-orchestration backbone: feat: add orchestration engine for GraphGen and tests #85
• 🔍 Data provenance: ensure every record in the final training/evaluation set can be traced back to its original raw corpus through the full pipeline.

2 Multi-Modal & Multi-Omics

• 🧬 Define ImageNode, AudioNode, ProteinNode, etc.
• 👁️‍🗨️ Vision–language fusion extraction: use open VLMs to generate "image–caption–entity" triples and write them into the graph: feat: add vqa pipeline #69
• 🧪 Multi-omics extraction: process genomics/transcriptomics/proteomics with automatic node-property alignment

3 Data Quality & Curation

• 📊 Multi-dimensional quality metrics with a unified scoring API
• 💓 Graph-quality assessment similar to KGHeartBeat: Feat: kg evaluator #135
• 🎯 One-click export of high-quality sub-graphs and high-quality data
• ⚙️ Configurable pipeline: entity disambiguation, fact verification, redundancy removal, schema validation

4 Graph Construction

• 🚀 Incremental & resumable construction: Feat: add trace info & task storage #168
• ⚖️ Automatic data ratio optimization: dynamically adjust the mixing ratio of different data based on quality scores and training feedback to optimize model performance

5 Engineering

• 📂 Support csv, json, jsonl, txt, pickle, parquet, pdf, and various triple input formats: feat: support Reader classes #50 , feat: add pdf_reader & tests for MinerUParser #65 , feat: add ParquetReader #82 , feat: add PickleReader #81
• 🗄️ Switchable unified storage layer: kv_storage(object store, rocksdb), graph_storage(networkx, kuzudb)
• ✅ Data validation powered by Pydantic
• 👓 More inference servers and clients such as Azure and Ollama: feat: add inference backends #74
• 🔍 test cases

6 Community Detection & Data Synthesis

• 🔎 Apply multiple community-detection algorithms; generate data from communities and provide typical samples plus visualizations
• 🧠 Community summary → CoT data: use community summaries as few-shot examples to synthesize high-quality chain-of-thought data
• 💬 Multi-turn dialogue synthesis: random-walk sampling → multi-turn Q&A while maintaining context consistency
• 📈 Complexity grading for curriculum learning
• 🕵️‍♂️ Support comparison with baselines

7 UX, Docs & Community

• 📦 Streamlined pip install and usage
• 📓 Jupyter tutorial suite
• 📚 Comprehensive documentation
• 🗃️ Data & user case library
• 🤝 Contributor guide & roadmap: clear labels, branching strategy, PR template, code of conduct
• 🌐 More user-friendly web interface

8 Others

• 📝 More standardized prompt & post-processing management; post-processing should be bound to prompts
• 🌐 Improve online connectivity
• 🔗 Enhanced coreference resolution during chunking

Further feature ideas are welcome—feel free to suggest and join the plan!

Task