Advanced RAG Tutorial - Include Sources With Langchain LCEL

In this video, you will learn how to quickly and easily build a full rag chain which provides sources along with its answers.

Specifically, you will learn how to :
Use Unstructured.io to OCRize and process your documents into chunks with metadata
Build a RAG chain which keeps the metadata of chunks
Feed data to your LLM in a specific format allowing it to keep track of sources
Use the chunk metadata to link back chunks to their origin document and page.

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Music track: Marshmallow by Lukrembo
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🧠 Resources
Langchain LCEL : https://python.langchain.com/docs/con...
Unstructured.io Documentation : https://docs.unstructured.io/api-refe...

📚 Chapters
00:00 Intro
00:29 Packages
00:47 Local Embedding
01:05 Bedrock LLM
1:30 Sample Data
1:57 Unstructured.io OCR & Chunking
3:45 Qdrant Vectorization
5:24 RAG Intro
5:49 Retrieve Component
6:17 Augment Component
7:20 Generate Component
9:19 Source Component
11:06 Full Chain
12:06 Tests & Discussion

#rag #sources #lcel #langchain #aws #genai #bedrock #llm #python