KG-RAG
DeepSeek-Lattice-KG: A Compact Language Model With Knowledge Graph Augmentation for Lattice Structure Design
Drop-in applicable KG-RAG architecture: fine-tunes DeepSeek-R1-14B on domain data via PEFT, then augments at inference with a Neo4j knowledge graph (50K papers, hundreds of thousands of entity-relationship triples) — user queries are converted to Cypher graph queries, retrieve a relevant subgraph of typed entity relationships, and inject structured context into the LLM; raises accuracy 71.7% (base) → 82.9% (fine-tune alone) → 94.8% (fine-tune + KG); the NL→Cypher→subgraph→LLM pipeline is domain-agnostic and directly applicable to any domain-specific production RAG system.
What the demo shows
Gradio demo showing KG-RAG vs plain BM25 on an AI engineering knowledge graph — entity extraction → Cypher-like query → subgraph retrieval with relationship-aware context vs flat text similarity — with subgraph visualization.
Source: Materials Genome Engineering Advances
Integration prompt
Paste it into Claude Code, Cursor or your coding agent, fill in the "My project" section and it will evaluate whether the technique fits your case and integrate it with a before/after measurement. It is in English because that is what coding agents work best with.
I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.
## Technique: KG-RAG
DeepSeek-Lattice-KG: A Compact Language Model With Knowledge Graph Augmentation for Lattice Structure Design
Drop-in applicable KG-RAG architecture: fine-tunes DeepSeek-R1-14B on domain data via PEFT, then augments at inference with a Neo4j knowledge graph (50K papers, hundreds of thousands of entity-relationship triples) — user queries are converted to Cypher graph queries, retrieve a relevant subgraph of typed entity relationships, and inject structured context into the LLM; raises accuracy 71.7% (base) → 82.9% (fine-tune alone) → 94.8% (fine-tune + KG); the NL→Cypher→subgraph→LLM pipeline is domain-agnostic and directly applicable to any domain-specific production RAG system.
What the reference demo shows: Gradio demo showing KG-RAG vs plain BM25 on an AI engineering knowledge graph — entity extraction → Cypher-like query → subgraph retrieval with relationship-aware context vs flat text similarity — with subgraph visualization.
## Reference implementation (Vector Ridge Labs, POC-013)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-013
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/deepseek_lattice_kg
Original source: AlphaSignal newsletter deep-dive · 2026-07-04
## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">
## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures retrieval quality (recall@k / nDCG on a labelled sample of my queries) and latency, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.
Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.Want this in your stack?
You can integrate it yourself with the prompt above, or we do it with you: we map which technique fits your system, implement it and measure the impact on your data.
Talk about integrating it