https://github.com/g-hano/readmecreator
ReadmeCreator automates the generation of README.md files using graph-based workflow. Supports Ollama, OpenAI, HuggingFace and Anthropic models.
https://github.com/g-hano/readmecreator
langchain langgraph llama-index llm ollama
Last synced: 9 months ago
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ReadmeCreator automates the generation of README.md files using graph-based workflow. Supports Ollama, OpenAI, HuggingFace and Anthropic models.
- Host: GitHub
- URL: https://github.com/g-hano/readmecreator
- Owner: g-hano
- License: mit
- Created: 2024-08-23T14:20:08.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-03T07:44:20.000Z (about 1 year ago)
- Last Synced: 2025-01-31T06:51:16.939Z (9 months ago)
- Topics: langchain, langgraph, llama-index, llm, ollama
- Language: Python
- Homepage:
- Size: 109 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
This README is also generated by ReadmeCreator program itself.
---
Only human made additions are the images.
**README.md**
=====================================
### Installation
To get started with this project, follow these steps:
```bash
git clone g-hano/ReadmeCreator
pip install -r requirements.txt
```
### Project Description
This project uses a combination of natural language processing (NLP) and graph-based workflows to generate a README.md file based on code summaries. The system prompts users to input a directory name, list of files, and then creates summaries for each file using an LLM model.
### Usage
To use this project, simply follow the prompts in the `main.py` script:
1. Enter a directory name.
2. The system will generate summaries for each file based on the LLM model.
3. The system will decide which summaries to re-write
4. You can use README.md for your projects.
### Contact Information
* Project Lead: [M.Cihan Yalçın](https://www.linkedin.com/in/chanyalcin/)
### License
This project is licensed under the MIT License.
**Graph-Based Workflow**
-------------------------
The graph-based workflow is defined in the `graph.py` script. It uses a StateGraph to represent the workflow, which consists of nodes and edges. The nodes represent different steps in the process, while the edges represent the relationships between them.
### Nodes
* `get_directory`: Asks the user for a directory name.
* `get_all_files`: Lists all files in the specified directory.
* `get_summaries`: Generates summaries for each file using the LLM model.
* `judge`: Evaluates the generated summaries and decides which ones to rewrite or include in the README.md file.
* `rewrite`: Rewrites selected summaries based on the LLM model.
* `readme`: Creates a README.md file based on the generated summaries.
### Edges
The edges represent the relationships between nodes. For example, the edge from `get_directory` to `get_all_files` means that after getting the directory name, the system will list all files in that directory.
**LLM Model**
-------------
The LLM model used for generating summaries is defined in the `agents.py` script. It uses an Ollama instance with a specific model and prompt.
### System Prompts
----------------
The system prompts are defined in the `configs.py` script. They include the LLM model, summary folder, system prompt folder, and LLM prompt folder.
I hope this README.md file meets your requirements! Let me know if you need any further assistance.