chiruu12 commented on issue #183: URL: https://github.com/apache/incubator-hugegraph-ai/issues/183#issuecomment-2691620185
> > 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. > > [@imbajin](https://github.com/imbajin) As per my understanding of 2, we need a way to integrate hugegraph agents with any other existing framework. And we will provide an http api layer abstraction or python SDK for hugegraph agents. For example. Someone working on some projects involving AI agents. They thought that they needs to use GRAPH RAG for some use case. Now for that, they will be able to import HG agentic library say HG_agentic. Now using this library, they can create agents using plane english (what the agent needs to do, given some input and required output format.) Each agent will have the information about graph from which information needs to be extracted. And it will have some inputs - (output from previous agents, like node names , relationship names , similarity search results, etc), the agent will convert txt2gql according to the given prompt and graph structure knowledge. This way (Devs) can create agents and orchestrate them using our library HG_orchestrator by just passing all created agents in a data structure. @imbajin sir instead of developing a dedicated HG-agentic library, I propose that we make a retriever service similar to what Pinecone provides. Our approach would encompass two distinct modes: a beginner-friendly “agentic retriever” that comes pre-fine-tuned with a robust LLM using few-shot or one-shot prompting (can also integrating advanced prompting techniques and dynamic re-ranking for accurate graph query generation) a fully customizable retriever mode aimed at developers. In the customizable mode, users can modify key code placeholders—such as LLM selection, prompt configuration, and integration of additional tools to replace or enhance their existing RAG systems, including those orchestrated by frameworks like AutoGen. Moreover, we can architect this retriever module using a modular “runnables” concept, like we have in LangChain’s design, which ensures framework agnosticism and ease of integration into various vector database ecosystems (e.g., Pinecone, FAISS, Qdrant). By also providing an HTTP API layer for direct access to our core query functions, this solution will not only offer high performance and simplicity but also enables integration with existing agent systems, making it an ideal bridge between novice users and expert developers. I think this solution will be covering both the points and help the people from both the levels Also enabling beginners as they will be able to use our service without much hassle. the end note is I am inclined to making the agent from scratch rather than taking it as it is from an existing framework and as almost all of the frameworks are opensource we can take the core functionality from them and add those to our own structure. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
