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This tool leverages the capabilities of `llmatch-messages` to process and extract meaningful insights from user inputs, such as forum posts, comments, or feedback forms. By using pattern matching and retry logic, the package ensures that the extracted data is consistent and formatted correctly, making it easier for moderators to review and respond to user feedback.\n\n## Features\n\n- **Pattern Matching**: Extracts structured data from unstructured user inputs.\n- **Retry Logic**: Ensures consistent and reliable data extraction.\n- **Flexible LLM Integration**: Supports various LLM providers, including LLM7, OpenAI, Anthropic, and Google.\n- **Easy Integration**: Simple API for seamless integration into existing workflows.\n\n## Installation\n\n```bash\npip install feedback_analyzer_mod\n```\n\n## Usage\n\n### Basic Usage\n\n```python\nfrom feedback_analyzer_mod import feedback_analyzer_mod\n\nuser_input = \"Your user input text here\"\nresponse = feedback_analyzer_mod(user_input)\nprint(response)\n```\n\n### Using a Custom LLM\n\n#### OpenAI\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom feedback_analyzer_mod import feedback_analyzer_mod\n\nllm = ChatOpenAI()\nresponse = feedback_analyzer_mod(user_input, llm=llm)\nprint(response)\n```\n\n#### Anthropic\n\n```python\nfrom langchain_anthropic import ChatAnthropic\nfrom feedback_analyzer_mod import feedback_analyzer_mod\n\nllm = ChatAnthropic()\nresponse = feedback_analyzer_mod(user_input, llm=llm)\nprint(response)\n```\n\n#### Google\n\n```python\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom feedback_analyzer_mod import feedback_analyzer_mod\n\nllm = ChatGoogleGenerativeAI()\nresponse = feedback_analyzer_mod(user_input, llm=llm)\nprint(response)\n```\n\n## Parameters\n\n- **user_input** (str): The user input text to process.\n- **llm** (Optional[BaseChatModel]): The 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, the environment variable `LLM7_API_KEY` will be used.\n\n## Default LLM\n\nThe package uses `ChatLLM7` from `langchain_llm7` by default. You can get a free API key by registering at [LLM7](https://token.llm7.io/).\n\n## Rate Limits\n\nThe default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you want higher rate limits, you can pass your own API key via the environment variable `LLM7_API_KEY` or directly via the `api_key` parameter.\n\n## Author\n\n- **Eugene Evstafev**\n- **Email**: hi@eugene.plus\n- **GitHub**: [chigwell](https://github.com/chigwell)\n\n## Issues\n\nFor any issues or suggestions, please open an issue on [GitHub](https://github.com/chigwell/feedback-analyzer-mod/issues).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Ffeedback-analyzer-mod","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchigwell%2Ffeedback-analyzer-mod","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Ffeedback-analyzer-mod/lists"}