Hello! I am a beginner developer who is greatly interested in the rapidly emerging field of LLMs.

The is my another basic starter project, created for the purpose of studying LLM prompting, Python and RAG for my own study.

Thank you for coming by, and please keep an eye out for future updates!

This project is a simple starter project that demonstrates how you can utilize LangChain and LLM model to perform Q&A on Jupyter Notebook.

1. Clone this repository

git clone https://github.com/MIRACLE-cowf/QA-With-Jupyter.git

2. Move to the cloned repository

cd QA-With-Jupyter

3. Inisde the QA_With_Jupyter, fill in the necessary API keys in the .env file.

   • Anthropic
   • OpenAI
   • LangChain

4. Create a new folder that named src in QA_With_Jupyter folder, and put .ipynb file.

5. Install the required libraries

pip install -r requirements.txt

6. Run main.py

python3 -m main

This project is an Agent Assistant that aims to automatically generate high-quality documents in response to user question. It conducts its own web searches and creates documents to provide answers.

Main Features

• THLO(Think-High-Level-Outline) : It deeply 'think' and 'inner monologue' to understands user question and automatically generates high-level-outline or plan for document creation.
• Utilization of Various Information Sources : It integrates multiple search engines, including Tavily, YouTube, arXiv, and Wikipedia, to gather comprehensive and intelligent information.
• Intelligent Information Summarization and Integration : It intelligently summarizes and integrates the collected information to automatically generate documents that are not only optimized for the user's question but also contain richer and high-quality information.

If you're interested, Check it out
I'm actively seeking feedback and discussions!

This A-SQL-A project is similarly as this QA-With-Jupyter.

Main Features

• You can Q&A with your own CSV file.
• Automatically transform .csv to .db.
• Generate SQL query by LLM.

If you're interested, Check it out