Meet KAG: Supercharging RAG Systems with Advanced Reasoning
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Top Comments (10)
The premise of Knowledge Augmented Generation is promising, but the current KAG code bases failed to deliver today. The TLDR version is that ultimately I saw no notes or edges created in Neo4j. Even weirder is that in spite of there being no graph it was still giving me results in the UI. (the UI is not open source and appears to be locked down) Ultimately the config needs to become more solid and consistent -- and there needs to be agreement between the OpenSPG/openspg and OpenSPG/KAG development teams on whether Ollama is supported. An odd mix of Java and Python. Hopefully this gets straightened up soon. Prompt Engineering... Normally love your stuff. What would be helpful as a starting point is a Jupyter Notebook from OpenSPG that walks through (and validates) the pipeline step by step. A follow-up would be a reproducible and well documented evaluation against other solutions - LazyGraphRAG, LightRAG / nanorag, etc.
If you are interested in learning more about Advanced RAG systems, checkout my RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag
nice. wondering if we can use this on groq platform to speed things up?
It is the ReAct prompting with graph backend
Nice can you do the one in python using all local services/llm please.
Thank you for sharing great tutorial …reading from others comments it looks like the model is slow with responses which prevented me trying it but nevertheless informative nonetheless thanks 🙏
Thank you for this video. Please make a follow up on how to build this. 🙏🏻
amazing!
Very nicely explained, congrats mate! 👏👏👏
Hi, First of all, Thanks for your good video. It's knowledgeble video. I tried what you do in the video on my computer, but when I create a task, I always getting a vectorization connection error. I'm using OpenAI API and it's embedding model. My question, is there any document or any video on platforms about that error?
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Top Comments (10)
The premise of Knowledge Augmented Generation is promising, but the current KAG code bases failed to deliver today. The TLDR version is that ultimately I saw no notes or edges created in Neo4j. Even weirder is that in spite of there being no graph it was still giving me results in the UI. (the UI is not open source and appears to be locked down) Ultimately the config needs to become more solid and consistent -- and there needs to be agreement between the OpenSPG/openspg and OpenSPG/KAG development teams on whether Ollama is supported. An odd mix of Java and Python. Hopefully this gets straightened up soon. Prompt Engineering... Normally love your stuff. What would be helpful as a starting point is a Jupyter Notebook from OpenSPG that walks through (and validates) the pipeline step by step. A follow-up would be a reproducible and well documented evaluation against other solutions - LazyGraphRAG, LightRAG / nanorag, etc.
If you are interested in learning more about Advanced RAG systems, checkout my RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag
nice. wondering if we can use this on groq platform to speed things up?
It is the ReAct prompting with graph backend
Nice can you do the one in python using all local services/llm please.
Thank you for sharing great tutorial …reading from others comments it looks like the model is slow with responses which prevented me trying it but nevertheless informative nonetheless thanks 🙏
Thank you for this video. Please make a follow up on how to build this. 🙏🏻
amazing!
Very nicely explained, congrats mate! 👏👏👏
Hi, First of all, Thanks for your good video. It's knowledgeble video. I tried what you do in the video on my computer, but when I create a task, I always getting a vectorization connection error. I'm using OpenAI API and it's embedding model. My question, is there any document or any video on platforms about that error?