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MindFlow Synth\n[![PyPI version](https://badge.fury.io/py/mindflow-synth.svg)](https://badge.fury.io/py/mindflow-synth)\n[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)\n[![Downloads](https://static.pepy.tech/badge/mindflow-synth)](https://pepy.tech/project/mindflow-synth)\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-blue)](https://www.linkedin.com/in/eugene-evstafev-716669181/)\n\nPackage designed to extract and structure key insights from text about cognitive processes, such as deep focus and flow states.\n\n## Overview\nThe MindFlow Synth package takes text input describing psychological or neurological concepts and returns a structured summary that highlights the main principles, triggers, and benefits of achieving deep focus. It leverages advanced language models to parse and organize information, providing a reliable and repeatable way to distill complex ideas into actionable insights.\n\n## Installation\n```\npip install mindflow_synth\n```\n\n## Usage\n```python\nfrom mindflow_synth import mindflow_synth\n\nuser_input = \"Text about cognitive processes...\"\nresponse = mindflow_synth(user_input)\nprint(response)\n```\n\n## Function Signature\n```python\ndef mindflow_synth(\n    user_input: str,\n    api_key: Optional[str] = None,\n    llm: Optional[BaseChatModel] = None\n) -\u003e List[str]\n```\n- `user_input`: str - the user input text to process\n- `api_key`: Optional[str] - the api key for llm7, if not provided the default ChatLLM7 will be used\n- `llm`: Optional[BaseChatModel] - the langchain llm instance to use, if not provided the default ChatLLM7 will be used\n\n## Default LLM\nThe package uses the ChatLLM7 from `langchain_llm7` by default. You can safely pass your own `llm` instance (based on `langchain`) if you want to use another LLM, for example:\n```python\nfrom langchain_openai import ChatOpenAI\nfrom mindflow_synth import mindflow_synth\nllm = ChatOpenAI()\nresponse = mindflow_synth(... llm=llm)\n```\nor for example to use the anthropic:\n```python\nfrom langchain_anthropic import ChatAnthropic\nfrom mindflow_synth import mindflow_synth\nllm = ChatAnthropic()\nresponse = mindflow_synth(... llm=llm)\n```\nor google:\n```python\nfrom langchain_google_genai import ChatGoogleGenerativeAI\nfrom mindflow_synth import mindflow_synth\nllm = ChatGoogleGenerativeAI()\nresponse = mindflow_synth(... llm=llm)\n```\n\n## LLM7 Rate Limits\nThe default rate limits for LLM7 free tier are sufficient for most use cases of this package. If you need higher rate limits for LLM7, you can pass your own API key via environment variable `LLM7_API_KEY` or via passing it directly like `mindflow_synth(... api_key=\"your_api_key\")`. You can get a free API key by registering at https://token.llm7.io/\n\n## Issues\nFor any issues or feature requests, please submit a pull request to https://github.com/chigwell/mindflow-synth\n\n## Author\nEugene Evstafev (hi@euegne.plus)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Fmindflow-synth","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchigwell%2Fmindflow-synth","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchigwell%2Fmindflow-synth/lists"}