GitHub user imbajin added a comment to the discussion: [Discussion] The selection of Agentic/Taskflow frame
> Also > [@imbajin](https://github.com/imbajin?rgh-link-date=2025-02-28T07%3A26%3A17.000Z) > sir just wanted to know , what is the actual requirement. > > * Is it direct prompt -->information > (where user just write a prompt, subtasks will be divided and agents gets > created based on the knowledge graph structure) > * or user will have capability to create and orchestrate his own agents ( to > extract specific information from graph) > > And also where we are currently ? Good question in fact, we need to provide both of these abilities at the same time, but with a **focus on the second point**. We can understand that the first one is mainly aimed at **novice users**(novice here means: people who are not completely unfamiliar with **property graphs**, and they don't expect a way where simply throwing a `text/PDF` can make everything goes fine (similar to simple/one-click but casual extraction of RDF/triplets), and we need to simply support this case, but it is not the key (similarly, including visualization/UI/compatibility with different vector-DBs, etc., are not our focus) Our core focus is on devs with basic vector-db(vector-rag/naive/basic-rag) or Agent systems. Assuming they already have Vector-RAG, how can we better integrate GraphRAG to provide more operability and ease of orchestration? This is also why we provide separate `HTTP-API` layer encapsulation, which facilitates developers to call our core query functions directly. For example, suppose the user is already using `AutoGen` to orchestrate their Agent system, and now assuming that the data in the graph has been extracted (skipping the extraction step), how can they better and faster integrate it into their original system, while maintaining high performance and simplicity as much as possible They(Devs) can directly modify our pipeline/workflow code, instead of requiring us to provide a fixed "local/global" mode like Microsoft GraphRAG that is not easy to adjust. Our agentization/selection is about how to better and faster achieve this goal. I'm not sure if the explanation is clear, but if there are any questions, please continue to reply:) BTW, we are currently in a transition from 1 to 2 GitHub link: https://github.com/apache/incubator-hugegraph-ai/discussions/203#discussioncomment-12666602 ---- This is an automatically sent email for dev@hugegraph.apache.org. To unsubscribe, please send an email to: dev-unsubscr...@hugegraph.apache.org
