Pairing Knowledge Graphs with Vector DBs to Fix Enterprise RAG Limitations

Developers building AI agents for enterprise use cases have encountered a recurring limitation called the 'RAG Wall,' where standard vector database retrieval strips relational context from corporate data. Flat semantic RAG systems break documents into token chunks, destroying the chronological and relational links between tools like Slack, Jira, and SharePoint. To address this, the team behind PipesHub built a dual-store architecture that routes raw text to a vector database while sending extracted entity relationships to a knowledge graph. The system uses Kafka-based event streaming and an entity extraction layer to preserve data lineage before any AI model processes a query. The architecture is exposed via a Model Context Protocol server, allowing AI agents to query either store dynamically, reducing hallucinations and token costs.