Reduce hallucination in RAG| Self reflective Adaptive GraphRAG and LangGraph , Groq

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Adaptive or self reflective RAG Chatbot Streamlit, Llama, LangGraph | Reduce hallucination RAG |Code
Adaptive or self reflective RAG Chatbot explanation and python coding |Reduce hallucination from RAG
Adaptive or self reflective RAG Chatbot explanation and python coding | Reduce hallucination in RAG
Description:
Welcome to our comprehensive tutorial on implementing an advanced Adaptive or Self-Reflective RAG (Retrieval-Augmented Generation) Chatbot using Streamlit, Groq API, Llama, and LangGraph!
In this video, we will deep dive into the concepts and practical steps needed to create a sophisticated chatbot capable of adaptive learning and self-reflection. Here's what you'll learn:
🔍 What is Self-Reflective RAG?
Understanding the core principles of Retrieval-Augmented Generation (RAG)
Exploring the concepts of self-reflective and adaptive learning in chatbots
Detailed flow and architecture of an Adaptive RAG system
💡 Tools and Technologies:
Streamlit: For building a dynamic and interactive web interface
Groq API: Leveraging the power of high-performance machine learning computations
Llama: Utilizing advanced language models for natural and coherent responses
LangGraph: Integrating graph-based approaches to enhance retrieval and generation processes
🛠️ Step-by-Step Python Coding:
Setting up the development environment
Integrating Groq API with your chatbot
Implementing Llama for generating conversational responses
Using LangGraph to optimize retrieval mechanisms
Building an intuitive interface with Streamlit
Testing and refining the chatbot for adaptive learning
By the end of this tutorial, you'll have a fully functional, advanced RAG chatbot that can adapt and improve over time, providing more accurate and contextually relevant responses.
Adaptive RAG Chatbot
Self-Reflective RAG Chatbot
Streamlit Chatbot Tutorial
Llama Chatbot Integration
LangGraph RAG Optimization
Reduce Hallucination in RAG
Advanced RAG Chatbot Tutorial
Groq API Chatbot Implementation
Python RAG Chatbot Coding
Self-Learning Chatbot Tutorial
RAG Chatbot Explanation
Adaptive Learning in Chatbots
AI Chatbot with Streamlit
Llama and LangGraph for Chatbots
RAG Chatbot Python Code
Streamlit and Groq API Chatbot
Advanced Chatbot Development
Adaptive RAG Flow Explanation
Self-Reflective AI Chatbot
Machine Learning Chatbot Tutorial
Integrating Llama in RAG Chatbot
Building Chatbots with LangGraph
Optimizing RAG Chatbots
Full RAG Chatbot Implementation
Interactive Chatbot with Streamlit
Adaptive RAG chatbot with Python coding
Selfreflective RAG with Streamlit
How to reduce hallucination from RAG with python coding
Langgraph implementation with Python
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Advance RAG chatbot with Streamlit for Knowledgegraph with Langgraph and Gorq
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[@TechKnow_WithMe]

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[@nik8830]

The problem is that this process significantly increases costs and response times, imagine if the question is generic or outside the context of application for the system.

[@VLM234]

Great video! But how to break a loop after a few trials if the model gets stuck into an infinite loop during Hallucinations check or answer relevance?

[@TechKnow_WithMe]

Better to have a few iterations with counter on each decision and then call rewrite question to repeat complete process. Then an overall counter to print user some message. We can call web search as well after that. What do you think?

[@VLM234]

@@TechKnow_WithMe thank you for your response. I tried and it's working fine. Thank again 😊