Post by Qdrant

Most "Graph RAG" implementations are vector retrieval with extra steps. Data Graphs built something different. Their UK-based knowledge graph platform combines a proprietary graph database engine with Qdrant-powered semantic search, orchestrated by an agentic layer that picks the right retrieval path for each prompt. The insight from CEO Paul Wilton: vector and graph aren't competitors. They're complementary. Vector excels at semantic similarity over unstructured content. Graphs excel at empirical queries: negation, date ranges, mathematical operations on connected data. Force one to do the other's job and the system breaks. What they built: - Real-time embedding from the graph into Qdrant collections via streaming and queuing - Schema-first retrieval where the agent pulls the full graph schema before deciding how to query - Parallel execution across graph queries and vector retrieval, blended for the LLM - Every answer cited back to source for verifiable provenance Why Qdrant specifically: - Hybrid Cloud kept the entire stack inside their own AWS environment - Payload filtering DSL closely matched their existing OpenSearch patterns, so common metadata structures worked across the graph, OpenSearch, and Qdrant without translation - Terraform support and infrastructure automation fit their existing CI/CD workflows 18 months in production. Zero significant issues. The retrieval layer determines the ceiling of your AI's intelligence. Choose the right components. Compose them well. Full case study: https://lnkd.in/gdinjGCU