Welcome to the conversational chatbot repository utilizing HuggingFace's Zephyr 7B Alpha model. This README will guide you through setting up and utilizing the chatbot effectively.

This chatbot is built using Zephyr 7B Alpha, which implements Direct Preference Optimization (DPO) for fine-tuning, resulting in superior conversational performance. It leverages various tools including Google Colab, ChromaDB, Langchain, and Gradio to create a seamless experience for engaging in conversations and extracting information from PDF documents.

• Effortless Conversation: Engage in conversations effortlessly with the chatbot powered by Zephyr 7B Alpha.
• Document Interaction: Upload PDF documents to Google Drive and seamlessly interact with their content through the chat interface.
• RAG Pipeline: Utilize the Retrieval Augmented Generation (RAG) pipeline for generating responses based on embedded document context.
• Gradio UI: Interact with the chatbot via an intuitive Gradio Chat UI, providing a user-friendly experience.

Follow these steps to set up the chatbot environment and start conversing:

• Install Necessary Packages and Import Dependencies: Install required Python packages using pip and import necessary dependencies.
• Download the Model: Download the Zephyr-7B-Alpha model, preferably the sharded version for optimal performance.
• Connect Google Drive: Establish a connection between Google Drive and Google Colab to upload and access PDF documents.
• Upload Documents: Upload PDF files to a designated folder on Google Drive.
• Embed Documents in Vector Database: Load and segment documents into smaller text chunks, embedding them into Chroma DB using HuggingFace Embeddings and Langchain.
• Build RAG Pipeline: Construct a RAG pipeline using HuggingFace and Langchain.
• Create Gradio UI: Build a Gradio Chat UI and launch it, providing access to the chatbot in a new browser tab.

It's crucial to be aware of the following limitations when using the chatbot:

• Hallucinated Responses: The model may produce hallucinated responses, especially with domain-specific vocabulary.
• Response Times: Response times may vary, ranging from 15 to 30 seconds or longer.

To optimize your experience with the chatbot, consider the following tips:

• If you encounter unusual or incomplete responses, adjust the prompt accordingly or append phrases like "explain," "explain in detail," or "elaborate."
• Keep in mind that while the responses may not be perfect, the chatbot adeptly extracts and abstracts information from provided context.

In future updates, the following enhancements are planned:

• Integration of additional sources to the app for locating document chunks used by the model for generating responses.
• Continuous improvements to enhance conversational performance and reduce response times.

Special thanks to HuggingFace for providing the Zephyr 7B Alpha model and the open-source community for developing the tools utilized in this project.