https://github.com/chigwell/refactor-llm-analyzer
A new package designed to facilitate structured and reliable analysis of user input related to software refactoring in the context of LLM capabilities. It accepts a user's discussion or question about
https://github.com/chigwell/refactor-llm-analyzer
automated-decision-making automatic-categorization concern-extraction consistent-interpretation free-form-discussion-analysis insights-generation knowledge-extraction llm-capabilities pattern-matching pattern-validation reliable-analysis software-refactoring strategy-extraction structured-analysis structured-summaries text-based-input theme-extraction user-input-processing
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A new package designed to facilitate structured and reliable analysis of user input related to software refactoring in the context of LLM capabilities. It accepts a user's discussion or question about
- Host: GitHub
- URL: https://github.com/chigwell/refactor-llm-analyzer
- Owner: chigwell
- Created: 2025-12-21T13:47:49.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-12-21T13:47:55.000Z (about 2 months ago)
- Last Synced: 2025-12-23T04:54:06.956Z (about 2 months ago)
- Topics: automated-decision-making, automatic-categorization, concern-extraction, consistent-interpretation, free-form-discussion-analysis, insights-generation, knowledge-extraction, llm-capabilities, pattern-matching, pattern-validation, reliable-analysis, software-refactoring, strategy-extraction, structured-analysis, structured-summaries, text-based-input, theme-extraction, user-input-processing
- Language: Python
- Homepage: https://pypi.org/project/refactor-llm-analyzer/
- Size: 4.88 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# refactor-llm-analyzer
[](https://badge.fury.io/py/refactor-llm-analyzer)
[](https://opensource.org/licenses/MIT)
[](https://pepy.tech/project/refactor-llm-analyzer)
[](https://www.linkedin.com/in/eugene-evstafev-716669181/)
refactor-llm-analyzer is a Python package designed to facilitate structured and reliable analysis of user input related to software refactoring in the context of large language models (LLMs). It processes text-based discussions or questions to extract key themes, concerns, or strategies, enabling consistent interpretation and supporting automated decision-making or knowledge extraction. The package uses pattern matching and LLM capabilities to produce structured summaries or insights from user input.
## Installation
Install the package via pip:
```bash
pip install refactor_llm_analyzer
```
## Usage
Here's an example of how to use the package:
```python
from refactor_llm_analyzer import refactor_llm_analyzer
user_input = "How can I improve the readability of my code by refactoring the functions?"
response = refactor_llm_analyzer(user_input)
print(response)
```
### Parameters
- `user_input` (str): The user input text to process.
- `llm` (Optional[BaseChatModel]): An optional LangChain LLM instance to use. If not provided, the default ChatLLM7 will be used.
- `api_key` (Optional[str]): The API key for LLM7. If not provided, it will be retrieved from the environment variable `LLM7_API_KEY`.
### Supporting External Language Models
This package uses `ChatLLM7` from the `langchain_llm7` module by default. Developers can supply their own language model instances for flexibility and customization. Supported integrations include:
- OpenAI GPT models
- Anthropic models
- Google Generative AI
#### Example of using a custom LLM
```python
from langchain_openai import ChatOpenAI
from refactor_llm_analyzer import refactor_llm_analyzer
llm = ChatOpenAI()
response = refactor_llm_analyzer(user_input, llm=llm)
print(response)
```
```python
from langchain_anthropic import ChatAnthropic
from refactor_llm_analyzer import refactor_llm_analyzer
llm = ChatAnthropic()
response = refactor_llm_analyzer(user_input, llm=llm)
print(response)
```
```python
from langchain_google_genai import ChatGoogleGenerativeAI
from refactor_llm_analyzer import refactor_llm_analyzer
llm = ChatGoogleGenerativeAI()
response = refactor_llm_analyzer(user_input, llm=llm)
print(response)
```
## Rate Limits and API Keys
The default rate limits for LLM7’s free tier are sufficient for most use cases. To increase limits, pass your API key via the environment variable `LLM7_API_KEY` or directly when calling the function:
```python
response = refactor_llm_analyzer(user_input, api_key="your_api_key")
```
You can obtain a free API key by registering at [https://token.llm7.io/](https://token.llm7.io/).
## Resources
- The package relies on `ChatLLM7` from [langchain_llm7](https://pypi.org/project/langchain-llm7/)
- Documentation for other supported LLMs:
- OpenAI: https://docs.openai.com/
- Anthropic: https://console.anthropic.com/
- Google Generative AI: https://cloud.google.com/vertex-ai/docs
## Contact and Support
- Developer: Eugene Evstafev
- Email: hi@eugene.plus
- GitHub: [chigwell](https://github.com/chigwell)
- Issues: [GitHub Issues](https://github.com/chigwell/refactor-llm-analyzer/issues)