Meet KAG: Supercharging RAG Systems with Advanced Reasoning
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Top Comments (10)
Tried it. It's quite slow for answering simple queries. Also indexing about 20 pages in a PDF took about 300k tokens, which is still quite cheap with DeepSeek, but it seems a lot. Indexing also took like an hour or something. User interface is partly chinese, quite a bit of bugs. Seems unfinished. Answers that we're outputted were mostly correct though
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.
Not to be confused with CAG
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
Another day, another -ag 😂
nice. wondering if we can use this on groq platform to speed things up?
It is the ReAct prompting with graph backend
Very nicely explained, congrats mate! 👏👏👏
amazing!
Thank you for this video. Please make a follow up on how to build this. 🙏🏻
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Top Comments (10)
Tried it. It's quite slow for answering simple queries. Also indexing about 20 pages in a PDF took about 300k tokens, which is still quite cheap with DeepSeek, but it seems a lot. Indexing also took like an hour or something. User interface is partly chinese, quite a bit of bugs. Seems unfinished. Answers that we're outputted were mostly correct though
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.
Not to be confused with CAG
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
Another day, another -ag 😂
nice. wondering if we can use this on groq platform to speed things up?
It is the ReAct prompting with graph backend
Very nicely explained, congrats mate! 👏👏👏
amazing!
Thank you for this video. Please make a follow up on how to build this. 🙏🏻