Factofiction Analyzer

Overview

Factofiction Analyzer is a Python package that helps users distinguish between factual information and imaginative interpretations. It takes user-provided text as input, analyzes and structures the content using advanced language models, and identifies and separates factual statements from imaginative or speculative ones.

Installation

pip install factofiction_analyzer

Usage

Usage example:

from factofiction_analyzer import factofiction_analyzer

response = factofiction_analyzer(user_input="The cat sat on the mat.")

Input parameters:

• user_input: The user input text to process (str)
• llm: The langchain LLM instance to use (Optional[BaseChatModel]). If not provided, the default ChatLLM7 will be used.
• api_key: The API key for LLM7 (Optional[str]). If not provided, the LLM7 free tier will be used.

Note: If you want to use a custom LLM instance, you can pass it as an argument like this:

from langchain_openai import ChatOpenAI
from factofiction_analyzer import factofiction_analyzer

llm = ChatOpenAI()
response = factofiction_analyzer(user_input="The cat sat on the mat.", llm=llm)

Supported LLMs

The package uses ChatLLM7 from langchain_llm7 by default. You can use other LLMs by passing your own instance:

• OpenAI: langchain_openai.ChatOpenAI
• Anthropic: langchain_anthropic.ChatAnthropic
• Google Generative AI: langchain_google_genai.ChatGoogleGenerativeAI

API Key

If you need higher rate limits for LLM7, you can set the LLM7_API_KEY environment variable or pass your API key directly:

factofiction_analyzer(user_input="The cat sat on the mat.", api_key="your_api_key")

You can obtain a free API key by registering at https://token.llm7.io/

GitHub Issues

Report any issues or bugs to: https://github.com/your-github-nickname/factofiction-analyzer/issues

Author

Written by Eugene Evstafev (hi@euegne.plus)

Acknowledgement

This package uses the llmatch-messages package for consistent output formatting.