GPT Paper-Reader
summary
A working Gradio app chunks PDFs into sections, embeds them into a vector store, and feeds context-limited chunks to an LLM to produce per-chunk summaries and a consolidated answer set. Implementation uses GPT-2 local model to avoid billing, LangChain for chains and prompt management, and vector DB queries for retrieval. The demo is ~1GB, processes academic papers, maintains cross-chunk context within token limits, and returns concise summaries and Q&A successfully.
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chapters (9)
- 0:00Generative AI fundamentals
- 2:41What is generative AI
- 6:56AI development milestones
- 9:26Early chatbots and neural nets
- 12:17Transformers in NLP
- 15:32Transformers revolutionizing language models
- 17:19LLMs 2018 to 2024
- 20:11Building AI apps from scratch
- 24:04Live demo: GPT paper summarizer
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