A Python package that helps users reframe their self-perception from static identity labels (nouns) to dynamic actions and processes (verbs). It uses structured LLM interactions to process user-provided text reflections and returns a transformed perspective that emphasizes growth, change, and agency.
pip install noun_verbImport and use the noun_verb function to process user input:
from noun_verb import noun_verb
user_input = "I am a shy person."
result = noun_verb(user_input)
print(result)user_input(str): The user input text to process.llm(Optional[BaseChatModel]): A LangChain LLM instance. If not provided, the defaultChatLLM7is used.api_key(Optional[str]): API key for LLM7. If not provided, the environment variableLLM7_API_KEYis used.
You can use any LangChain-compatible LLM by passing it to the llm parameter:
from langchain_openai import ChatOpenAI
from noun_verb import noun_verb
llm = ChatOpenAI()
response = noun_verb(user_input, llm=llm)from langchain_anthropic import ChatAnthropic
from noun_verb import noun_verb
llm = ChatAnthropic()
response = noun_verb(user_input, llm=llm)from langchain_google_genai import ChatGoogleGenerativeAI
from noun_verb import noun_verb
llm = ChatGoogleGenerativeAI()
response = noun_verb(user_input, llm=llm)To use your own LLM7 API key:
from noun_verb import noun_verb
response = noun_verb(user_input, api_key="your_api_key_here")Or set the environment variable:
export LLM7_API_KEY="your_api_key_here"You can get a free API key by registering at https://token.llm7.io/.
By default, the package uses ChatLLM7 from the langchain_llm7 package (available at PyPI). The free tier rate limits are sufficient for most use cases.
If you encounter any issues or have suggestions, please open an issue on GitHub.
Eugene Evstafev
Email: hi@euegne.plus
GitHub: chigwell