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LangChain으로 배우는 Retrieval Augmented Generation (RAG)

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업데이트됨 2024. 12.

LangChain을 활용한 검색 강화 생성(RAG)으로 외부 데이터를 대규모 언어 모델(LLM)과 통합하는 최신 기법을 익히세요.

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PythonArtificial Intelligence

3시간

12 동영상

38 연습 문제

3,150 XP

17,880

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선수 조건

Developing LLM Applications with LangChain

1

Building RAG Applications with LangChain

Discover how to integrate external data sources into chat models with LangChain. Learn how to load, split, embed, store, and retrieve data for use in LLM applications.

Loading Documents for RAG with LangChain

Loading PDF files for RAG

Loading HTML files for RAG

Text splitting, embeddings, and vector storage

Getting started with text splitting

Recursively splitting documents

Embedding and storing documents

Building an LCEL retrieval chain

Creating the retrieval prompt

Building the retrieval chain

2

Improving the RAG Architecture

Discover state-of-the-art techniques for loading, splitting, and retrieving documents, including loading Python files, splitting semantically, and using MRR and self-query retrieval methods. Learn to evaluate your RAG architecture using robust metrics and frameworks.

Loading and splitting code files

Loading code files

Splitting Python files

Advanced splitting methods

Splitting by tokens

Splitting semantically

Optimizing document retrieval

Sparse vs. dense retrieval

Understanding BM25

Sparse retrieval with BM25

Introduction to RAG evaluation

Ragas context precision evaluation

Ragas faithfulness evaluation

String evaluation

3

Introduction to Graph RAG

Discover how graph databases and retrieval can overcome some of the limitations of traditional vector-based storage and retrieval.

From vectors to graphs

Creating graph documents

Getting to know graphs

Storing and querying documents

Building-up your graph database

Querying your graph database

Creating the Graph RAG chain

A journey through the Graph RAG system

Chaining, Graph RAG style!

Improving graph retrieval

Graph RAG with filtering

Validating Cypher queries

Creating a Cypher few-shot prompt

Congratulations!

LangChain으로 배우는 Retrieval Augmented Generation (RAG)

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