Transform technical descriptions into structured summaries and feature lists
A new package designed to transform technical descriptions of hardware or software solutions into structured summaries and feature lists.
pip install tech_discriptorfrom tech_discriptor import tech_discriptor
user_input = "Romforth—an ultra-portable, small, bare-metal Forth implementation for multiple processors—"
response = tech_discriptor(user_input)
print(response)user_input:str- the user input text to processllm:Optional[BaseChatModel]- the langchain llm instance to use, if not provided the default ChatLLM7 will be used.api_key:Optional[str]- the api key for llm7, if not provided the default ChatLLM7 will be used.
You can safely pass your own llm instance (based on https://docs.langchain.io/docs/guides/get-started-with-a-model) if you want to use another LLM, via passing it like tech_discriptor(user_input, llm=their_llm_instance).
from langchain_openai import ChatOpenAI
from tech_discriptor import tech_discriptor
llm = ChatOpenAI()
response = tech_discriptor(user_input, llm=llm)from langchain_anthropic import ChatAnthropic
from tech_discriptor import tech_discriptor
llm = ChatAnthropic()
response = tech_discriptor(user_input, llm=llm)from langchain_google_genai import ChatGoogleGenerativeAI
from tech_discriptor import tech_discriptor
llm = ChatGoogleGenerativeAI()
response = tech_discriptor(user_input, llm=llm)The default ChatLLM7 LLM is used if no custom LLM is provided. The free tier rate limits are generally sufficient. For higher rate limits with ChatLLM7, you can:
- Set the
LLM7_API_KEYenvironment variable. - Pass the API key directly:
tech_discriptor(user_input, api_key="your_api_key")You can obtain a free API key by registering at https://token.llm7.io/
Contributions are welcome! Please refer to the GitHub repository for details.
This project is licensed under the MIT License.
- Eugene Evstafev (hi@eugene.plus)
For issues or questions, please visit the GitHub issues page: https://github.com/chigwell/tech-discriptor/