refactor-llm-analyzer

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:

pip install refactor_llm_analyzer

Usage

Here's an example of how to use the package:

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

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)

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)

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:

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/.

Resources

• The package relies on ChatLLM7 from 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
• Issues: GitHub Issues