Agentic RAG Fitness Chatbot

Overview

The Agentic RAG Fitness Chatbot is an AI-powered application designed to provide personalized fitness guidance and workout recommendations. Uses Retrieval-Augmented Generation (RAG), multi-agent systems, and a curated knowledge base, to deliver context-aware and actionable fitness advice. The chatbot focuses on empowering beginners with evidence-based fitness recommendations, real-time video demonstrations, and safety guidelines.

LangGraph Workflow

The LangGraph Workflow outlines the sequence of nodes in the system’s architecture. Each node represents a specific function, from query handling to generating responses. Below is the compiled workflow:

Architecture

The system consists of the following components:

1. User Input: Accepts user queries through a Streamlit-based chat interface.
2. Query Refinement: Transforms user input into an optimized format using an LLM-based query rewriting mechanism.
3. Retrieval System:
   • Encodes user queries using Sentence Transformers.
   • Queries the Pinecone vector database to fetch relevant fitness data.
   • Retrieves video demonstrations and transcripts.
4. Response Generation: Synthesizes retrieved information using GPT-4o, ensuring the response is actionable and grounded in context.
5. Video Recommendations: Displays video thumbnails, titles, and links alongside detailed transcripts.
6. Langsmith Integration: Tracks agent-level decisions and improves overall system reliability.

Langsmith Trace and LangGraph Workflow

To provide transparency and insights into the system's behavior, the Langsmith trace and LangGraph workflow have been visualized:

Langsmith Trace

The Langsmith Trace captures the flow of the chatbot’s decision-making process, including tool calls and their respective responses. Below is an example trace showcasing a user query and the system's response:

Installation

Steps

1. Clone the repository:

   git clone https://github.com/pramod-zillella/AgenticRagChatbot.git
   cd agentic-rag-fitness-chatbot

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables:
   • Create a .env file in the project directory.
   • Add your API keys:

     OPENAI_API_KEY=your_openai_api_key
     PINECONE_API_KEY=your_pinecone_api_key
     LANGCHAIN_API_KEY_V2=your_langchain_api_key
     

4. Run the Streamlit application:

   streamlit run interface.py

Usage

• Predefined Questions: Select from common fitness-related queries or type your own.
• Custom Queries: Ask personalized questions about workouts, nutrition, or injury prevention.
• Interactive Recommendations: View suggested video demonstrations and detailed response within the chat interface.