Thanks for sharing those, it's really nice seeing the chapters through the lens of a reader! Regarding the chapter 2 nit. It’s more of a shortcoming of the tokenizer implementation (it’s a very simple one for educational purposes). The reason why I copied it as is from the
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Skills Required for Working with Generative AI Models
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What does it take to work with Generative AI Models >> https://
buff.ly/3DzqTY2 #AI #GenAI #ArtificialIntelligence #AIModels @IIoT_World @CRudinschi via @fogoros -
Docling: Advanced Document Parsing Beyond Excel Files
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Currently this one handles excel files. But docling is capable of parsing complex docs that include tables, charts, figures etc. It has dedicated models for layout detection and extracting tabular data.
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Llama 3.2 Vision Model for Invoice Processing Tasks
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Since you're processing individual invoices, I would recommend trying with Llama 3.2 vision model directly. If there the accuracy is not great, then you can try other methods.
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Python and ML/AI Engineering Tutorials Daily
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That's a wrap, if you're interested in: – Python – ML/AI Engineering Find me →
@akshay_pachaar
Everyday, I share tutorials on above topics! -
Getting Started with RAG for Complex Document Processing
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This is a good place to get started with RAG over complex docs! We'll cover more advanced techniques very soon! Find all the code here: https://
github.com/patchy631/ai-e
ngineering-hub/tree/main/rag-with-dockling
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Streamlit Chat Interface Wrapper Implementation
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I have also wrapped this into a nice chat interface using Streamlit! Check this out
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Create Query Engine to Ask Questions About Excel Data
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Finally, create a query_engine and start asking question about your excel data! Check this out
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Embedding Data and Creating Index for Next Steps
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Next step is to embed the data and create an index. Check this out
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Setting Up LLM and Embedding Model After Knowledge Base
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Once our knowledge base is ready it's time to setup the LLM and embedding model! Check this out