Goodbye Text-Based RAG, Hello Vision AI: Introducing LocalGPT Vision!
Summary
The video showcases Local GPT Vision, a project expanding on Local GPT for text-based end-to-end retrieval augmented generation. It features a comparison between text-based and vision-based retrieval systems using a climate change report. Detailed explanation of the architecture steps involved in Local GPT Vision, from image capturing to projections retrieval. Viewers are guided through setting up the project, testing different models like Quin 2 and Lama, and are encouraged to contribute to the project for future enhancements.
Introduction to Local GPT Vision
Introducing the project called Local GPT Vision, an extension of Local GPT focused on text-based end-to-end retrieval augmented generation.
Example of Local GPT Vision
A demonstration of Local GPT Vision using a climate change report page, highlighting the difference between text-based and vision-based retrieval systems.
Architecture of Local GPT Vision
Explanation of the architecture of Local GPT Vision, involving image capturing, vision encoding, projection creation, and retrieval on these projections.
Setting Up Local GPT Vision
A step-by-step guide on setting up Local GPT Vision, including cloning the repository, installing requirements, setting up API, and running the application.
Testing and Using Local GPT Vision
Demonstration of testing and using Local GPT Vision with different models like Quin 2, Lama, Vision-based retrieval, and generation parts to process invoices and extract information.
Recommendations and Conclusion
Recommendations on model selection, testing, and future improvements for Local GPT Vision, concluding with a call for contributions to the project.
FAQ
Q: What is the focus of Local GPT Vision?
A: Local GPT Vision is an extension of Local GPT that is focused on text-based end-to-end retrieval augmented generation.
Q: Can you explain the process of nuclear fusion?
A: Nuclear fusion is the process by which two light atomic nuclei combine to form a single heavier one while releasing massive amounts of energy.
Q: What are the main components of the architecture of Local GPT Vision?
A: The main components include image capturing, vision encoding, projection creation, and retrieval on these projections.
Q: What are the steps involved in setting up Local GPT Vision?
A: The steps involve cloning the repository, installing requirements, setting up API, and running the application.
Q: What are some of the models demonstrated in testing and using Local GPT Vision?
A: Some models demonstrated include Quin 2, Lama, Vision-based retrieval, and generation parts for processing invoices and extracting information.
Q: What recommendations are provided for model selection, testing, and future improvements in Local GPT Vision?
A: Recommendations include guidance on model selection, testing methodologies, and suggestions for future improvements in the project.
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