Enterprise / Life Sciences

Knowledge Graph Q&A AI Architecture

Compare RAG, fine-tuning, long-context, and hybrid approaches for knowledge graph q&a at 25K queries/month.

Top approaches

#1 Best fit

Hybrid / RAFT

Graph traversal + LLM reasoning requires both retrieval and domain adaptation.

#2 Runner-up

RAG

Works for simpler fact-lookup queries over the graph.

#3 Alternative

Fine-Tuning

Needed for SPARQL/Cypher query generation tasks.

Estimated at 25K queries/month

Long-Context$506/mo

RAG$744/mo

Hybrid / RAFT$3,850/mo

Fine-Tuning$5,176/mo

1Combine vector search with graph traversal (GraphRAG) for multi-hop reasoning.
2Entity disambiguation is the hardest problem — invest in a dedicated NER pipeline.
3SPARQL/Cypher query generation fine-tuning yields 30–50% accuracy gains on complex graph queries.

回答 9 个问题 — 用 Knowledge Graph Q&A 默认值预填 — 以获得确定性架构推荐、成本交叉图和 8 页 PDF 报告。