From Search to Reasoning

The first generation of RAG was about retrieval: finding the right chunk of text. The next generation is about reasoning: understanding how different pieces of information relate to one another.

The Semantic Gap

Vector search is brilliant at finding "things that sound similar." If you search for "pet safety," it will find chunks about cats and dogs. But if you ask, "What is the primary risk factor for the project mentioned in the Q3 board meeting?", vector search might find the meeting notes, but it won't necessarily find the connection to the risk assessment document stored in a different folder.

Enter GraphRAG

Retriqs utilizes a proprietary GraphRAG architecture that builds a dynamic knowledge graph during ingestion.

1. Entity Extraction: Identifying nodes (Projects, People, Risks, Decisions).
2. Relationship Mapping: Establishing edges (Decided-by, Impacts, Supersedes).
3. Graph-Enhanced Retrieval: When a query comes in, we don't just search; we traverse.

By combining the breadth of vector search with the precision of knowledge graphs, Retriqs provides answers that are not just accurate, but logically sound and fully traceable.