LLM POCs (Proof of Concepts)

This repository contains various proof of concepts and experiments with Large Language Models (LLMs) and related technologies. The project is organized into different modules, each focusing on specific aspects of LLM applications.

Project Structure

.
├── src/
│   ├── huggingface/         # Hugging Face model experiments
│   ├── groq/                # Groq API integration
│   ├── agents/              # LLM agent implementations
│   ├── rag/                 # Retrieval Augmented Generation examples
│   ├── llm_apis/           # Various LLM API integrations
│   ├── langchain_llm_app/   # LangChain-based applications
│   └── weviate_study/       # Weaviate vector database experiments
├── data/                    # Data files and resources
├── requirements.txt         # Python dependencies
└── .env                     # Environment variables

Components

Hugging Face Experiments

• huggingface_bot.ipynb: Jupyter notebook demonstrating Hugging Face model integration

Groq Integration

• app.py: Application demonstrating Groq API usage

LLM Agents

• agents.ipynb: Jupyter notebook showcasing LLM agent implementations

RAG (Retrieval Augmented Generation)

• rag_basics.ipynb: Basic RAG implementation
• retriver.ipynb: Advanced retrieval techniques

LLM APIs

• client.py: Generic LLM API client
• app.py: Example application using various LLM APIs

LangChain Applications

• app.py: LangChain-based application
• basic_open_ai.py: Basic OpenAI integration with LangChain

Weaviate Studies

• poc_weviate.py: Proof of concept for Weaviate vector database integration

Setup

1. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

• Copy .env.example to .env
• Fill in the required API keys and configuration values

Usage

Each component can be run independently. Navigate to the specific directory and run the corresponding Python file or Jupyter notebook.

For example:

# Run Groq application
cd src/groq
python app.py

# Run Weaviate POC
cd src/weviate_study
python poc_weviate.py

Requirements

See requirements.txt for the complete list of dependencies. Key dependencies include:

• Python 3.8+
• Various LLM-related packages
• Jupyter for notebook-based experiments

Notes

• This is a collection of proof of concepts and experiments
• Each component is designed to be self-contained
• Some components may require specific API keys or credentials
• Check individual component directories for specific setup instructions

License

MIT License

Copyright (c) 2024 [Amol Jagadambe]

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.