https://github.com/pramod-zillella/agentic-rag-chatbot
The Agentic RAG Fitness Chatbot is an AI-powered application designed to provide personalized fitness guidance and workout recommendations.
https://github.com/pramod-zillella/agentic-rag-chatbot
agentic-rag langgraph langsmith prompt-engineering
Last synced: 8 months ago
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The Agentic RAG Fitness Chatbot is an AI-powered application designed to provide personalized fitness guidance and workout recommendations.
- Host: GitHub
- URL: https://github.com/pramod-zillella/agentic-rag-chatbot
- Owner: pramod-zillella
- Created: 2024-12-29T04:00:19.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-19T23:00:02.000Z (8 months ago)
- Last Synced: 2025-02-19T23:29:24.819Z (8 months ago)
- Topics: agentic-rag, langgraph, langsmith, prompt-engineering
- Language: Python
- Homepage: https://fitness-coach.streamlit.app/
- Size: 15.3 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```bash
git clone https://github.com/pramod-zillella/AgenticRagChatbot.git
cd agentic-rag-fitness-chatbot
```
2. Install dependencies:
```bash
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:
```bash
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.