/Dialogix-Intelligent-Customer-Interaction-Bot

A state-of-the-art customer support chatbot! This repo houses code and resources for a conversational agent trained on real-world support interactions. Powered by LangChain, OpenAI Embedder, and Faiss Vector Database, it provides intelligent responses based on historical conversations.

Primary LanguagePython

LangChain_NLP

Project Overview:

This project involves the development of a customer support chatbot implemented in Python. The chatbot facilitates interactions between customers and support agents using a conversation transcript as training data. The key components of the project include:

  1. Conversation Transcript:

    • The chatbot is trained on a conversation transcript between a customer and a support agent. This dataset captures real-world interactions to enhance the chatbot's understanding and responsiveness.

  2. LangChain Framework:

    • LangChain, a powerful framework, is employed for building the chatbot model. It enables the creation of conversational agents by providing a structured approach to handle dialogues.

  3. Palm Model (Language Model):

    • The project incorporates a Language Model (LLM) known as Palm. Palm is a variant of GPT (Generative Pre-trained Transformer) designed for natural language processing tasks. It enhances the chatbot's ability to generate contextually relevant responses.

  4. OpenAI Embedder:

    • OpenAI's Embedder is utilized for embedding text data. This embedding process converts textual information into numerical vectors, facilitating efficient storage and retrieval.

  5. Faiss Vector Database:

    • Faiss, a vector database, is employed to store and retrieve embeddings based on cosine similarity. This enables quick and accurate retrieval of relevant information during user interactions.

  6. GitHub Repository:

    • The project code and related files are organized in a GitHub repository. The repository includes the implementation code, dataset, and documentation for a seamless understanding of the project structure and functionality.

How to Use:

To run the chatbot and explore its capabilities:

  1. Clone the repository to your local machine.
  2. Install the necessary dependencies using the provided requirements.txt file.
  3. Follow the instructions in the documentation to set up and run the chatbot.

Feel free to contribute, report issues, or suggest improvements to enhance the project's functionality.