Multi-Modal Frontiers & Vector Architecture
GraphRAG and Knowledge Graphs
GraphRAG combines retrieval with a knowledge graph so answers can follow relationships, not just similarity. It excels on multi-hop questions and structured domains where entities and connections carry meaning. The cost is data modeling and graph maintenance, which are real engineering work.
Головне
- Extract entities and relationships with a consistent schema before building the graph.
- Use graph traversal to answer multi-hop questions that vector search alone misses.
- Combine graph retrieval with vector retrieval for both precision and recall.
- Keep the graph updated as facts change; stale relationships produce confident wrong answers.
- Scope graph queries to prevent explosive traversal on large or dense graphs.
- Measure answer accuracy on relationship-heavy questions, not just fact lookups.
- Document entity identity and deduplication rules to avoid fragmented graphs.
- Start with one domain and expand only when the graph proves its value.
Часті запитання
When is GraphRAG better than standard RAG?
When questions require connecting multiple facts or navigating relationships.
Do I need a graph database?
Not always; in-memory or lightweight graphs can suffice for small domains.
What is the main failure mode?
Incomplete or inconsistent extraction that leaves gaps in the graph.
How much data preparation is needed?
Significant. Schema design and entity resolution dominate the effort.
Допомога Kinetikatech: multi-modal frontiers & vector architecture
Ось у чому річ: How vector stores, multimodal embeddings, and efficient architectures power modern AI systems from edge to cloud. Передайте це службі Адміністрування серверів та Обслуговування сайтів, і все буде закрито: грамотна оцінка, чисте впровадження, цілодобовий нагляд.