Enhance RAG with Self-Reflection
Self-RAG is a strategy for retrieval-augmented generation that incorporates self-reflection and self-grading on retrieved documents and generations, based on a
Why it matters
Implement Self-RAG to improve the quality and accuracy of your RAG systems by incorporating self-reflection and self-grading on retrieved documents and generated content.
Outcomes
What it gets done
Integrate Self-RAG strategy into your RAG pipeline.
Utilize Pinecone's movie database for retrieval.
Implement structured output for retrieval grading.
Perform standard RAG generation steps with self-reflection.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/lg-langgraphselfragpineconemovies | bash Steps
Steps in the chain
Let's use Pinecone's sample movies database
Create a retrieval grader component for self-reflection and self-grading on retrieved documents
Standard RAG generation step
Define the graph state to manage the workflow
Build the graph following the flow outlined in the Self-RAG figure
Overview
Self RAG
What it does
A Self-RAG implementation demonstrating retrieval-augmented generation with self-reflection and self-grading capabilities, featuring retrieval grading, generation steps, and graph-based orchestration
How it connects
Building RAG systems that explore self-reflection strategies for document and generation assessment
Source README
This directory is retained purely for archival purposes and is no longer updated. Please see the newly consolidated LangChain documentation for the most current information and resources.
Self RAG
Self-RAG is a strategy for RAG that incorporates self-reflection / self-grading on retrieved documents and generations.
Environment
Tracing
Use LangSmith for tracing (shown at bottom)
Retriever
Let's use Pinecone's sample movies database
Structured Output - Retrieval Grader
Generation Step
Standard RAG
Graph
Capture the flow in as a graph.
Graph state
Build Graph
The just follows the flow we outlined in the figure above.
Step 1: Retriever Setup
Let's use Pinecone's sample movies database
Step 2: Structured Output - Retrieval Grader
Create a retrieval grader component for self-reflection and self-grading on retrieved documents
Step 3: Generation Step
Standard RAG generation step
Step 4: Graph State
Define the graph state to manage the workflow
Step 5: Build Graph
Build the graph following the flow outlined in the Self-RAG figure
Discussion
Questions & comments · 0
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