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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.\n\n## Installation\n\nInstall the package via pip:\n\n```bash\npip install refactor_llm_analyzer\n```\n\n## Usage\n\nHere's an example of how to use the package:\n\n```python\nfrom refactor_llm_analyzer import refactor_llm_analyzer\n\nuser_input = \"How can I improve the readability of my code by refactoring the functions?\"\nresponse = refactor_llm_analyzer(user_input)\nprint(response)\n```\n\n### Parameters\n\n- `user_input` (str): The user input text to process.\n- `llm` (Optional[BaseChatModel]): An optional LangChain LLM instance to use. If not provided, the default ChatLLM7 will be used.\n- `api_key` (Optional[str]): The API key for LLM7. If not provided, it will be retrieved from the environment variable `LLM7_API_KEY`.\n\n### Supporting External Language Models\n\nThis 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:\n\n- OpenAI GPT models\n- Anthropic models\n- Google Generative AI\n\n#### Example of using a custom LLM\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom refactor_llm_analyzer import refactor_llm_analyzer\n\nllm = ChatOpenAI()\nresponse = refactor_llm_analyzer(user_input, llm=llm)\nprint(response)\n```\n\n```python\nfrom langchain_anthropic import ChatAnthropic\nfrom refactor_llm_analyzer import refactor_llm_analyzer\n\nllm = ChatAnthropic()\nresponse = refactor_llm_analyzer(user_input, llm=llm)\nprint(response)\n```\n\n```python\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom refactor_llm_analyzer import refactor_llm_analyzer\n\nllm = ChatGoogleGenerativeAI()\nresponse = refactor_llm_analyzer(user_input, llm=llm)\nprint(response)\n```\n\n## Rate Limits and API Keys\n\nThe 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:\n\n```python\nresponse = refactor_llm_analyzer(user_input, api_key=\"your_api_key\")\n```\n\nYou can obtain a free API key by registering at [https://token.llm7.io/](https://token.llm7.io/).\n\n## Resources\n\n- The package relies on `ChatLLM7` from [langchain_llm7](https://pypi.org/project/langchain-llm7/)\n- Documentation for other supported LLMs:\n  - OpenAI: https://docs.openai.com/\n  - Anthropic: https://console.anthropic.com/\n  - Google Generative AI: https://cloud.google.com/vertex-ai/docs\n\n## Contact and Support\n\n- Developer: Eugene Evstafev\n- Email: hi@eugene.plus\n- GitHub: [chigwell](https://github.com/chigwell)\n- Issues: [GitHub Issues](https://github.com/chigwell/refactor-llm-analyzer/issues)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Frefactor-llm-analyzer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchigwell%2Frefactor-llm-analyzer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Frefactor-llm-analyzer/lists"}