Intro to Streamlit
- Simple and complex Streamlit example
- Data and state management in Streamlit apps
- Data widgets for Streamlit apps
- Deploying Streamlit apps
python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('I love you'))"
A prototype tool powered by Large Language Models to make querying your databases as easy as saying the word.
- Introduction to QueryGPT, a tool using Large Language Models (LLMs) for natural language database queries
- Focus on implementing a basic iteration of the system, with potential for significant enhancements
- Aim is to provide the LLM with the database schema and have it answer questions based on that context
- Discussion on prompt engineering, which is designing inputs for generative AI tools to produce optimal results
The llama-cpp-agent framework is a tool designed for easy interaction with Large Language Models (LLMs). It provides a simple yet robust interface using llama-cpp-python, allowing users to chat with LLM models, execute structured function calls and get structured output.
pip install 'ragna builtin » ' # Install ragna with all extensions
ragna config # Initialize configuration
ragna ui # Launch the web app
Ragna is an open source RAG orchestration framework.
With an intuitive API for quick experimentation and built-in tools for creating production-ready application, you can quickly leverage Large Language Models (LLMs) for your work.
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