80 lines
2.0 KiB
Markdown
80 lines
2.0 KiB
Markdown
# Graph Reflection and Refinement Demo
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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.
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## Features
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- **OpenAI LLM** support via `langchain-openai`.
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- **Ollama LLM** support via `langchain-ollama`.
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- Environment variable configuration using `.env` or system variables.
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- Simple prompt chain that explains graph reflection and refinement.
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## Setup
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1. **Clone the repository**
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```bash
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git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-graf-s-refleksiey-i-do
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cd povtornyy-ekzamen-graf-s-refleksiey-i-do
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```
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2. **Create a virtual environment (recommended)**
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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```
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3. **Install dependencies**
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```bash
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pip install -r requirements.txt
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```
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4. **Configure environment variables**
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Create a `.env` file in the project root (or set system variables) with one of the following:
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```dotenv
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# For OpenAI
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OPENAI_API_KEY=your_openai_api_key
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OPENAI_MODEL=gpt-3.5-turbo
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OPENAI_TEMPERATURE=0.7
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# OR for Ollama
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OLLAMA_HOST=http://localhost:11434
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OLLAMA_MODEL=llama2
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OLLAMA_TEMPERATURE=0.7
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```
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Only one of the two configurations is required.
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## Usage
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Run the script:
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```bash
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python src/main.py
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```
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You should see an LLM-generated explanation of graph reflection and refinement printed to the console.
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## Project Structure
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```
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povtornyy-ekzamen-graf-s-refleksiey-i-do/
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├── src/
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│ └── main.py # Core script with LangChain integration
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├── requirements.txt # All required Python packages
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└── README.md # Project documentation
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```
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## Notes
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- The script automatically selects the LLM based on the presence of environment variables.
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- If neither `OPENAI_API_KEY` nor `OLLAMA_HOST` is set, the script will raise an error.
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- Feel free to extend the prompt or chain logic to suit more complex use cases.
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---
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Happy coding! |