mindflow-synth 2025.12.21184613

mindflow-synth extracts and structures insights on deep focus and flow from text, providing consistent, actionable summaries.

MindFlow Synth

Package designed to extract and structure key insights from text about cognitive processes, such as deep focus and flow states.

Overview

The 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.

Installation

pip install mindflow_synth

Usage

from mindflow_synth import mindflow_synth

user_input = "Text about cognitive processes..."
response = mindflow_synth(user_input)
print(response)

Function Signature

def mindflow_synth(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]

• user_input: str - the user input text to process
• api_key: Optional[str] - the api key for llm7, if not provided the default ChatLLM7 will be used
• llm: Optional[BaseChatModel] - the langchain llm instance to use, if not provided the default ChatLLM7 will be used

Default LLM

The 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:

from langchain_openai import ChatOpenAI
from mindflow_synth import mindflow_synth
llm = ChatOpenAI()
response = mindflow_synth(... llm=llm)

or for example to use the anthropic:

from langchain_anthropic import ChatAnthropic
from mindflow_synth import mindflow_synth
llm = ChatAnthropic()
response = mindflow_synth(... llm=llm)

or google:

from langchain_google_genai import ChatGoogleGenerativeAI
from mindflow_synth import mindflow_synth
llm = ChatGoogleGenerativeAI()
response = mindflow_synth(... llm=llm)

LLM7 Rate Limits

The 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/

Issues

For any issues or feature requests, please submit a pull request to https://github.com/chigwell/mindflow-synth

Author

Eugene Evstafev ([e-mail])