gpt-verse 0.1.0

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GPT-Verse 🎩 -- Library under construction

The GPT-Verse library, is an innovative Python package designed for developers and enthusiasts working in the realm of natural language processing and generative pre-trained transformer models. With its dependency specified for Python 3.12, GPT-Verse ensures compatibility with the latest features and improvements of the Python programming language, ensuring users can leverage the most current language benefits.

💪 Features

• Language Generation: The GPT model excels at generating coherent and contextually relevant text based on input prompts. Users can expect GPT-Verse to provide APIs that allow them to generate text for a variety of applications, ranging from automated content creation to chatbots.

• Fine-tuning on Custom Datasets: A critical feature of GPT models is their ability to be fine-tuned on specific datasets. This allows the model to perform well on niche tasks or understand specific domains much better. GPT-Verse is likely to offer facilities to enable this fine-tuning, making it versatile for specialized applications.

• Language Understanding: Beyond generating text, GPT models are capable of understanding and parsing input text to perform tasks like sentiment analysis, summarization, and question answering. GPT-Verse may include functionalities that harness this capability, offering users tools for extracting insights from text or improving interaction with machine learning systems.

• Integration Ready: Given the nature of such libraries, GPT-Verse might be designed with integration in mind, facilitating its use alongside other Python libraries and frameworks in larger applications or systems.

💻 Installation

GPT-Verse requires Python 3.12 or later. It is recommended to use a virtual environment to manage the project dependencies.

To install GPT-Verse and its dependencies, follow these steps:

# Install the library from pipy
pip install gpt_verse==<last-code-version>

⚒️ Usage

1. Use Langchain Agents as Tool

   To start indexing your documents, you need to prepare your documents in the supported formats (PDF, PPTX, XLSX, web pages, YouTube videos).

   Example for indexing PDFs:

   # Initialize your embeddings model
   from langchain_openai import ChatOpenAI
   from langchain_community.tools.tavily_search import TavilySearchResults
   from gpt_verse.personas import SecondPersona, FirstPersona
   
   llm = ChatOpenAI(
      model="gpt-4-turbo",
      max_tokens=4096 
      )
   
   tavily_tool = TavilySearchResults()
   
   # Define the second agent (it will be the contact-center)
   agent = FirstPersona(
      llm=llm,
      tools=[TavilySearchResults()],
      prompt="Help me with my task"
   )
   
   seconda_persona = SecondPersona(
      agent=agent.as_executor(),
      name="Mario",
      scope="search a job online",
      instructions="give detailed instruction"  # instruction will be given from the prima persona to the second persona
   )
   
   # Define the first agent (it will be the one to pass you to the contact-center agent)
   prima_persona = FirstPersona(
      llm=llm,
      tools=[seconda_persona.as_tool()],
      prompt="Help me with my task" # main scope of the agent
   )
   
   result = prima_persona.as_executor().invoke({"input": "can u help me find a job in python coding?"})
   

🤝 Contributing

Contributions are welcome! Feel free to open an issue or pull request if you have suggestions or improvements.

📜 License

Miner AI Beta is licensed under the MIT License. See the MIT file for more information.

Acknowledgements

• We would like to thank all the contributors to the project and the open-source community for their support.
• GPT-Verse is meant to be used for ai data mining over documents and research purposes only. We are not responsible for any misuse of the library.