ClinIQ — Conversational AI for Clinical Knowledge Q&A
ClinIQ is an open-source sample from the Cloud2 Labs Innovation Hub that demonstrates how Retrieval-Augmented Generation (RAG) and large language models can be applied to clinical knowledge exploration.
It showcases a practical, end-to-end pattern for building conversational AI systems that retrieve information from structured and unstructured healthcare documents and respond in natural language.
Designed for developers, architects, and innovation teams, ClinIQ serves as a reference implementation to explore AI-driven knowledge access in regulated domains
Key Capabilities
Hybrid Retrieval Architecture
Implements advanced search by combining semantic (dense) retrieval with keyword-based (sparse) search for improved recall and precision.
Intelligent Reranking Pipeline
Uses cosine similarity–based reranking to refine retrieved results and improve answer relevance.
Conversational Q&A Interface
Provides an interactive chat experience with conversation history, enabling natural, multi-turn clinical knowledge exploration.
Grounded Responses with Source Citations
Each response includes citations back to the originating documents, supporting traceability and transparency.
Optional Reasoning Visibility
Supports an AI thinking / step-by-step reasoning view (configurable) to help developers understand response generation behavior.
Modern Web UI
Features a clean, responsive React-based frontend designed for usability and rapid iteration.
Containerized Deployment
Docker-based setup simplifies local development, experimentation, and environment consistency.