GitHub user imbajin added a comment to the discussion: [Discussion] The selection of Agentic/Taskflow frame
> [@Aryankb](https://github.com/Aryankb?rgh-link-date=2025-02-28T07%3A12%3A17.000Z) > I think most of the people won't be proficient enough to write their own > queries I have worked quite a bit with graph rag in my intern and at first > even I had a bit trouble in writing those. So, I would suggest that we can > get a description for the knowledge that the user will be providing us if > they don't, we will by default use a LLM to get what the knowledge or text is > about and then make an agent write the query for us and use that query ? > [@imbajin](https://github.com/imbajin?rgh-link-date=2025-02-28T07%3A12%3A17.000Z) > sir what is your opinion on this? @chiruu12 @Aryankb First, regarding the `text2gql` part, it is an independent matter, and I understand that it is not strongly related to the selection of agentic frame or workflow impl. Here is a brief description of the actual situation. Our implementation and approach earlier was to use both model fine-tuning and **user templates** simultaneously. (see it ↓ By default, we use the GQL query template to optimize the effect of text2gql.) <img width="1610" alt="Image" src="https://github.com/user-attachments/assets/fc278898-4cbf-46b4-8d4d-90dcf0e7df6d"; /> General encoder model fine-tuning for `7-14B` can be a significant task, especially when it comes to how to generate GQL corpus (HG uses Gremlin queries by default and is compatible with most of the Cypher syntax), refer [wiki](https://github.com/apache/incubator-hugegraph-ai/wiki/HugeGraph-LLM-Roadmap#4-graph-query-core-1) to get more context GitHub link: https://github.com/apache/incubator-hugegraph-ai/discussions/203#discussioncomment-12666601 ---- This is an automatically sent email for dev@hugegraph.apache.org. To unsubscribe, please send an email to: dev-unsubscr...@hugegraph.apache.org
