Build a Self-Corrective RAG App with LangGraph Cloud

preview_player
Показать описание
In this tutorial, we create a Self-Corrective RAG application for answering questions about Pandas documentation using LangGraph Cloud. We implement ideas from both self-RAG and corrective RAG to flexibly handle model hallucinations. You'll see how to check for hallucinations after an answer is generated, and check for answer relevancy before returning the user question.

Check out our other resources for self-RAG and corrective RAG below

  1. LangChain
Рекомендации по теме
Build a Self-Corrective 0:30:15

Build a Self-Corrective RAG App with LangGraph Cloud

Building Corrective RAG 0:26:00

Building Corrective RAG from scratch with open-source, local LLMs

Self-Corrective RAG with 0:14:03

Self-Corrective RAG with LangGraph - Agentic RAG Tutorial

How to Build 0:20:43

How to Build a Self-Improving AI with Agentic RAG and Flowise

Self-reflective RAG with 0:17:38

Self-reflective RAG with LangGraph: Self-RAG and CRAG

Build a Corrective 0:13:05

Build a Corrective RAG App with LangGraph and LangChain

Advanced RAG with 0:13:42

Advanced RAG with Self-Correction | LangGraph | No Hallucination | Agents | LangChain | GROQ | AI

'I want Llama3 0:24:02

'I want Llama3 to perform 10x with my private knowledge' - Local Agentic RAG w/ llama3

Building Production-Ready RAG 0:18:35

Building Production-Ready RAG Applications: Jerry Liu

Learn RAG From 2:33:11

Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer

Fully local RAG 0:20:04

Fully local RAG agents with Llama 3.1

Creating a Corrective-RAG 0:17:35

Creating a Corrective-RAG Workflow with LangGraph: Enhance Your AI Models

AI Critique with 0:16:51

AI Critique with SELF-REFLECTION 🤯 (powered RAG)!!!

Reliable, fully local 0:31:04

Reliable, fully local RAG agents with LLaMA3.2-3b

Advance RAG control 0:26:02

Advance RAG control flow with Mistral and LangChain: Corrective RAG, Self-RAG, Adaptive RAG

LangChain - Advanced 0:24:57

LangChain - Advanced RAG Techniques for better Retrieval Performance

Reliable, fully local 0:21:19

Reliable, fully local RAG agents with LLaMA3

I Built an 0:04:55

I Built an Insanely Complex RAG Flow with LangGraph – You Won't Believe How Easy It Is

Corrective RAG using 0:07:14

Corrective RAG using LangGraph

Master RAG in 4:40:32

Master RAG in 5 Hrs | RAG Introduction, Advanced Data Preparation, Advanced RAG Methods, GraphRAG

Self-RAG : Self 0:12:48

Self-RAG : Self Reflective AI for powerful Retrieval Augmented Generation

A gentle introduction 0:50:10

A gentle introduction to RAG (using open-source models)

RAG ipynb: CRAG, 0:19:20

RAG ipynb: CRAG, LlamaIndex, Ollama, ReAct Agent

LangGraph : Self 0:19:03

LangGraph : Self Corrective RAG (CRAG) to Reduce hallucination in RAG Pipeline : Part4

Комментарии
Автор

good content, i appreciate the concept of Self-Corrective RAG.
May i know where exactly this use case is used, i mean for organization chatbot they wouldnt be interested in going for web search, if they are going for web search there is no need for pre-loading couple of urls.. i'm just curios to know where this comes handy..

ai-touch
Автор

hi, love your videos thank you very much for the effort, this title looked good, but you are using chroma thats vector but if i want lang graph and neo4j?

darkmatter