This project introduces a sophisticated chatbot that leverages Large Language Models (LLMs) and a Retrieval-Augmented Generation (RAG) pipeline to provide quick and accurate responses to user queries. The chatbot is designed to handle a wide range of tasks, including answering essential mental health questions, with robust guardrails in place to ensure user safety and data security.
Key Features
- User Data Access: The chatbot has access to user data, enabling it to provide quick and personalized responses to queries.
- Mental Health Support: It can answer essential mental health questions, offering support and guidance to users in need.
- Guardrails Implementation: To ensure the safety and security of users, the chatbot has implemented strict guardrails.
- Role-Based Access Control (RLS): From a security perspective, Row level security (RLS) has been implemented for each user, ensuring that data access is appropriately restricted.
- Availability: The chatbot is available to both new users (before registration) and existing users (after logging in).
Advanced Technology
- LLMs and RAG Pipeline: The chatbot uses advanced LLMs and a RAG pipeline to generate accurate and relevant responses.
- Expert Feedback Loop: A Reinforcement Learning from Human Feedback (RLHF) pipeline is in place, allowing experts to provide feedback that continuously tunes and improves the chatbot's performance.
- Bias and Performance Management: Tools like Giskard are used to eliminate risks of bias, hallucinations, and performance issues, ensuring reliable and fair interactions.
This comprehensive approach makes the chatbot a valuable tool for providing accessible and secure healthcare services to users.
