Welcome to our Humanised Chatbot project repository! This project aims to enhance user experience and cater to user needs using the power of Large Language Models (LLMs), particularly leveraging LPU (Large Pretrained Unsupervised) models.
Our chatbot is designed for business-to-business (B2B) interactions. It incorporates advanced natural language processing techniques and technologies to provide empathetic responses, context-based query responses, and continuously improves its performance through a feedback mechanism integrated with Customer Relationship Management (CRM) systems.
The chatbot employs sentiment analysis techniques to understand the user's emotions and respond accordingly, enhancing the overall user experience. It utilizes pre-trained models to analyze the sentiment of user inputs and generates responses tailored to the detected emotion.
By utilizing retrieval-augmented generation techniques, the chatbot can generate responses based on context, ensuring more relevant and accurate answers to user queries. This approach involves retrieving relevant information from a knowledge base and using it to augment the response generation process, thereby improving the quality of responses.
Integration with Customer Relationship Management (CRM) systems allows the chatbot to collect and analyze feedback from users. This feedback loop helps improve the efficiency of response generation and enhances the user experience with each iteration. The chatbot stores feedback data in the CRM system, analyzes it to identify patterns or areas for improvement, and incorporates these insights into its response generation algorithms.
Visual cues are incorporated into the chatbot's interface to create a more human-like interaction, making the user experience more engaging and intuitive. These visual cues may include avatars, emoticons, or animations that accompany the chatbot's responses, providing users with a sense of interaction with a human-like entity.
The overall model architecture is based on federal learning, enabling decentralized global model generation. This approach involves training multiple local models on distributed datasets and aggregating their knowledge to generate a global model. By decentralizing the training process, federal learning enhances scalability, privacy, and efficiency in training and deploying the chatbot across various platforms and environments.
To set up the chatbot environment locally, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/humanised-chatbot.git