RAG Agent
A focused AI assistant that uses retrieval-augmented generation to answer questions from a defined knowledge source. It retrieves relevant context before generating a response, helping produce answers that are more specific and grounded.
RAG Agent is a simple question-answering assistant built to demonstrate retrieval-augmented generation inside the TechDxon AI experience.
Instead of relying only on a language model's general knowledge, the agent searches an indexed knowledge base for relevant information and uses that context to generate its response.
Technology Stack
The agent uses Python and LangChain to manage the retrieval pipeline, Hugging Face models for language processing, and FAISS for efficient similarity search across indexed content.
Key Features
The core capabilities that shape this project's experience and workflow.
Question answering from a defined knowledge source
Retrieval-augmented generation for grounded responses
FAISS vector similarity search for relevant context
Simple conversational chat interface
Hugging Face model integration
Related voice-based AI interaction demo
User Interface Showcase
A selection of key interfaces from the project, showcasing core user workflows, essential features, and responsive design.
RAG Chat Agent
A focused chat interface for asking questions grounded in TechDxon knowledge.
TechDxon Ecosystem
The company experience that connects web, mobile, design, and AI products under one studio.
Voice Agent
A related AI interface that starts a spoken conversation through one clear action.
Project Workflow
A user submits a question, the system searches the indexed knowledge base for relevant context, and the language model uses that context to produce a grounded response.
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