Graph with Reflection and Rewriting Nodes

This project demonstrates a simple data processing graph in Python that uses LangChain with OpenAI or Ollama to perform reflection and rewriting of text.
The graph is built from reusable node classes and can be extended with additional nodes as needed.

Features

ReflectionNode – Generates reflective insights from input text using an LLM.
RewritingNode – Rewrites the reflection in a specified style (e.g., formal, concise).
Graph – Connects nodes and executes them in sequence.
Configurable LLM provider – Switch between OpenAI and Ollama via the LLM_PROVIDER environment variable.
Unit tests – Verify node behavior with mocked LLM responses.

Requirements

Python 3.10+
langchain
openai (for OpenAI provider)
python-dotenv (optional, for loading environment variables)

Install dependencies:

pip install -r requirements.txt

Configuration

Set the LLM provider by defining the LLM_PROVIDER environment variable:

export LLM_PROVIDER=openai   # or ollama

If using OpenAI, ensure that the OPENAI_API_KEY environment variable is set.
If using Ollama, ensure that the Ollama server is running locally and the model name matches the one configured in src/llm_integration.py.

Usage

Run the graph with a text input:

python -m src.main "Your input text goes here."

Or pipe text via stdin:

echo "Some text" | python -m src.main

The output will be the rewritten text produced by the RewritingNode.

Running Tests

Execute the test suite with:

python -m unittest discover tests

Project Structure

src/
├── llm_integration.py   # LLM client factory
├── nodes.py             # Node definitions
├── graph.py            # Graph construction and execution
└── main.py              # CLI entry point
tests/
└── test_nodes.py        # Unit tests for nodes
requirements.txt
README.md

Extending the Graph

To add new nodes:

Create a new class inheriting from BaseNode in src/nodes.py.
Implement the process method.
Add the node to the graph in src/graph.py and connect it with add_edge.

License

MIT License

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