GitHub user Aryankb added a comment to the discussion: [Discussion] The selection of Agentic/Taskflow frame
Hey @imbajin I was working with agentic graph RAG recently. The project aimed at Document QnA from a story book. It required Agentic graph-RAG approach as in a story, different characters can have different relationships with each other. Also different incidents are linked to each other. So, here agentic graph-RAG worked well if someone asks deep questions from story. So i made graph where centre node was book --> first level nodes were chunks (each chapter of that book) --> second level nodes were atomic_facts from that chunk--> final level nodes were characters. Same characters from different atomic_facts were connected (so as to know that character was present in which parts of the story). Also different characters were connected to each other having some relationships. So for finding the answer of user's question, I used LANGGRAPH. It was really lightweight , Fast and simple to implement it. There were agents - - character finder (to find all characters, user's question contains) - atomic fact finder ( worked at finding the relevant atomic facts for each character, according to user query. Using similarity search) - information validator (checked if information is enough) - atomic fact extractor (if information is not enough, then extract the nearby atomic fact, or nearby events in that story) - final composer (compose final answer using extracted information and user query. It was a fixed agentic system, worked at graphs of one kind only I have a question, regarding what is the requirement ? Do we need to create a Interface, where user can create his own agentic system for his Graph , just using drag-drop or prompts (without code) If yes, then using LANGGRAPH or completely doing it manually would be the best approach, because there will be transparency , and user would know what is happening. Using CrewAI would not be a good idea , as there we don't know what is happening under the hood. Here is the project :- https://github.com/Aryankb/DOC_QNA GitHub link: https://github.com/apache/incubator-hugegraph-ai/discussions/203#discussioncomment-12666593 ---- This is an automatically sent email for dev@hugegraph.apache.org. To unsubscribe, please send an email to: dev-unsubscr...@hugegraph.apache.org
