povtornyy-ekzamen-graf-s-refleksiey-i-do/README.md

Graph Reflection and Refinement Demo

This repository demonstrates how to integrate LangChain LLMs (OpenAI or Ollama) into a simple Python script that explains graph theory concepts. The project is intentionally minimal to focus on the LLM integration.

Features

• OpenAI LLM support via langchain-openai.
• Ollama LLM support via langchain-ollama.
• Environment variable configuration using .env or system variables.
• Simple prompt chain that explains graph reflection and refinement.

Setup

1. Clone the repository

   git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-graf-s-refleksiey-i-do
   cd povtornyy-ekzamen-graf-s-refleksiey-i-do
   

2. Create a virtual environment (recommended)

   python3 -m venv .venv
   source .venv/bin/activate
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Configure environment variables

   Create a .env file in the project root (or set system variables) with one of the following:

   # For OpenAI
   OPENAI_API_KEY=your_openai_api_key
   OPENAI_MODEL=gpt-3.5-turbo
   OPENAI_TEMPERATURE=0.7
   
   # OR for Ollama
   OLLAMA_HOST=http://localhost:11434
   OLLAMA_MODEL=llama2
   OLLAMA_TEMPERATURE=0.7
   

   Only one of the two configurations is required.

Usage

Run the script:

python src/main.py

You should see an LLM-generated explanation of graph reflection and refinement printed to the console.

Project Structure

povtornyy-ekzamen-graf-s-refleksiey-i-do/
├── src/
│   └── main.py          # Core script with LangChain integration
├── requirements.txt     # All required Python packages
└── README.md            # Project documentation

Notes

• The script automatically selects the LLM based on the presence of environment variables.
• If neither OPENAI_API_KEY nor OLLAMA_HOST is set, the script will raise an error.
• Feel free to extend the prompt or chain logic to suit more complex use cases.

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Happy coding!