RF-KG is an open-source framework for building "Inference-Bearing" Knowledge Graphs. While traditional RAG systems treat documents as flat chunks and standard graphs treat relationships as simple strings, RF-KG extracts the semantic reasoning behind every connection. We enable agents to navigate data not just by keyword, but by intent and reasoning - sasisprite/Reasoning-First-Knowledge-Graph-RF-KG
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Everyone knows that simple RAG solution is not efficient, but the Graph RAG is. Does your Graph RAG gives your reasoning or does your graph segregated based on context???
However, within the Graph RAG, have you ever noticed that you don't have any supporting facts for the relations in the graph that you've identified?
This one gives you reasoning for every node and relationship that you identify. It gives you reason sets that will help your retrieval agent go through the reasoning to find out whether these node relationships are valid for your use case or search, or not, before you even go to the full-blown vectors of your document repository.