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SupportInsightCheck\n[![PyPI version](https://badge.fury.io/py/supportinsightcheck.svg)](https://badge.fury.io/py/supportinsightcheck)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n[![Downloads](https://static.pepy.tech/badge/supportinsightcheck)](https://pepy.tech/project/supportinsightcheck)\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)\n\n\nA Python package that analyzes customer service or technical support interaction descriptions to identify potential red flags that could lead to public complaints or negative exposure. The system evaluates text for issues like poor communication, unprofessional behavior, lack of accountability, or unethical practices, and returns a structured assessment with actionable feedback.\n\n## Installation\n\n```bash\npip install supportinsightcheck\n```\n\n## Usage\n\n### Basic Usage\n\n```python\nfrom supportinsightcheck import supportinsightcheck\n\nuser_input = \"The support agent was rude and refused to help me with my issue...\"\nresults = supportinsightcheck(user_input)\nprint(results)\n```\n\n### Using Custom LLM\n\nYou can use any LangChain-compatible LLM by passing it to the function:\n\n```python\nfrom langchain_openai import ChatOpenAI\nfrom supportinsightcheck import supportinsightcheck\n\nllm = ChatOpenAI()\nuser_input = \"The technician didn't show up for the scheduled appointment...\"\nresponse = supportinsightcheck(user_input, llm=llm)\n```\n\n```python\nfrom langchain_anthropic import ChatAnthropic\nfrom supportinsightcheck import supportinsightcheck\n\nllm = ChatAnthropic()\nuser_input = \"They charged me for services I didn't request...\"\nresponse = supportinsightcheck(user_input, llm=llm)\n```\n\n```python\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom supportinsightcheck import supportinsightcheck\n\nllm = ChatGoogleGenerativeAI()\nuser_input = \"The support representative gave me incorrect information...\"\nresponse = supportinsightcheck(user_input, llm=llm)\n```\n\n### Using Custom API Key\n\n```python\nfrom supportinsightcheck import supportinsightcheck\n\nuser_input = \"They refused to honor their warranty policy...\"\nresponse = supportinsightcheck(user_input, api_key=\"your_llm7_api_key_here\")\n```\n\n## Parameters\n\n- `user_input` (str): The text description of the support interaction to analyze\n- `llm` (Optional[BaseChatModel]): LangChain LLM instance (defaults to ChatLLM7)\n- `api_key` (Optional[str]): API key for LLM7 service (if using default LLM)\n\n## Default LLM Configuration\n\nThe package uses `ChatLLM7` from [langchain-llm7](https://pypi.org/project/langchain-llm7/) by default. The free tier rate limits are sufficient for most use cases. For higher rate limits, you can:\n\n1. Set the `LLM7_API_KEY` environment variable\n2. Pass your API key directly to the function\n3. Get a free API key at [https://token.llm7.io/](https://token.llm7.io/)\n\n## Error Handling\n\nThe function will raise a `RuntimeError` if the LLM call fails or if the response doesn't match the expected format.\n\n## Contributing\n\nFound an issue or have a suggestion? Please open an issue on [GitHub](https://github.com/chigwell/supportinsightcheck/issues).\n\n## Author\n\n**Eugene Evstafev**  \nEmail: hi@euegne.plus  \nGitHub: [chigwell](https://github.com/chigwell)\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Fsupportinsightcheck","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchigwell%2Fsupportinsightcheck","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Fsupportinsightcheck/lists"}