Selection of LLM for reading emails with JSON output + Python libraries

Hello! I am ready to help with the refinement of your application for integrating LLM with email reading and banner parsing in MIME format. To implement this optimally, it is best to use Python libraries for MIME processing and select LLM considering the specifics of your emails, which will allow for obtaining correct JSON according to the specified template. I have experience working with OpenAI API and customizing prompts for different models, which will help quickly integrate the solution into your current code. I will propose a clear plan for code writing and result verification. I am ready to consider access to the repository and start working soon. I look forward to your questions and details.

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Hello,

I am a developer in the AI/ML field. I can complete your project. Write to me, and we will discuss.

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Hello! 👋

I am ready to try to complete your application:
- integration of Gmail API for reading emails in MIME format;
- parsing text and banners from emails;
- text recognition on banners using pytesseract;
- text and banner analysis using LLaMA3;
- formatting the result in JSON.

… 💡 Note: Tesseract language files (~400 MB) are installed on the server, they are not added to the repository.

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Hello, this can be implemented using LLM Liama Local + RAG, we break photo banners into chunks, embed them and obtain numerical data, after which we will have a response, we also take text from the banners. The prompt will be generated by LLM Liama, how exactly should this be deployed, API, Local command, Telegram Bot, or another interface?

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I have experience in image recognition on local models, face transplantation, using small hardware resources. Also experience working with various APIs, including GPT, and many others. Write, we will discuss. I will be happy to help.

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