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The Evolution of RAG to OmniRAG

RAG stands for Retrieval-Augmented Generation, a design pattern that supplements LLM prompts with external, domain-specific data retrieved at query time. This offers benefits such as:

  • Access to specific and updated information without the need for retraining
  • Specialization without having to train the model on private or proprietary information
  • Model agnostic design allowing you to flexibly switch to the best model for the task.

Most RAG apps rely on vector databases, retrieving high-dimensional vectors based on proximity i.e. semantic similarity. This vector based approach is excellent for exploratory queries, but it has notable limitations:

  • It lacks explicit relationships between data points.
  • It's constrained by context window limits
  • It relies on surface-level similarity, not structured domain logic.

These limitations mean the model's response may be useful, but it isn't deterministically grounded - you can't reliably trace where the information came from or why it was selected. This makes the output non-repeatable and hard to audit, which becomes a critical weakness in domains that require precision, compliance, or transparency.

GraphRAG addresses these weaknesses by replacing vector search with entities and a knowledge graph. The process works like this:

  • An LLM is used to identify key entities and important relationships in the prompt
  • A query is created to retrieve context from the graph based on those relationships.
  • The context is injected into the model prompt.

Knowledge graphs organize entities and their relationship as nodes and edges, allowing relevant context to be retrieved through structured traversal. This means the results are also auditable back to the source entity, ensuring the provided text is factually correct with clear relationships and results in better completions grounded in the source information. For example, consider this prompt:

“What are three themes in client feedback?”

A traditional RAG system might retrieve content similar to "client feedback" and "theme". The answer may "feel" plausible but it lacks deterministic grounding with a structured, auditable path between the question and the retrieved data. In contrast, a GraphRAG app would:

  • Identify "client" as the entity
  • Follow the "client -> feedback" relationship edge.
  • Retrieve all the feedback records

The reports enrich the prompt, resulting in actual themes gleaned from actual client feedback - all traceable to the "client -> feedback" relationship. If you'd like to dive deeper into knowledge graphs, here is an excellent article about the benefits of these graphs and the technology behind generating them by Microsoft.

OmniRAG evolves out of GraphRAG by introducing multi-modal retrieval routing to adapt to the nature of the prompt by not only determining what to retrieve, but from where by having the option to use:

  • Vector databases (exploratory, non-deterministic answers)
  • Knowledge graphs (structured reasoning)
  • Relational databases (transactional and hierarchical queries)

A lightweight model then analyzes the prompt for entities and intent to determine the most appropriate data store to use. A utility like nl2query translates the prompt into the relevant type of query. The results are then used to enrich the prompt, providing traceable, grounded context for the foundational model. The app becomes more flexible, more efficient, and ultimately more trustworthy.

Looking forward, the new Agentic+OmniRAG architecture introduces MCP Servers to dynamically assemble and inject context from appropriate sources into the agent pipeline. Further resources are available on MCP Servers and the underlying tech stack behind OmniRAG.