Project: Full RAG Implementation in LangGraph

Analytics Vidhya · Intermediate ·🔍 RAG & Vector Search ·2mo ago

Skills: RAG Basics90%Vector Stores80%

Key Takeaways

Implements a full Retrieval-Augmented Generation workflow in LangGraph using FAISS vector database and multiple agents

Original Description

Description: The final step! We implement a full Retrieval-Augmented Generation (RAG) workflow. Learn how to ingest documents into a FAISS vector database, normalize embeddings, and coordinate multiple agents to produce high-quality research reports. Chapters: 0:00 Capstone Workflow Walkthrough 1:35 Designing the Project State 2:45 Setting up the Vector Database (FAISS) 4:10 Understanding Embedding Normalization (L2) 5:30 Building the Retrieval Helper Function 7:00 Defining the Summarizer and Writer Nodes 9:15 The Router Agent Logic 10:45 Final Graph Compilation and Multi-Agent Run 13:00 Results: Researching LangGraph & Vector DBs #RAG #FAISS #LangGraph #VectorDatabase #AIAgents #Python

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Chapters (9)

Capstone Workflow Walkthrough

1:35 Designing the Project State

2:45 Setting up the Vector Database (FAISS)

4:10 Understanding Embedding Normalization (L2)

5:30 Building the Retrieval Helper Function

7:00 Defining the Summarizer and Writer Nodes

9:15 The Router Agent Logic

10:45 Final Graph Compilation and Multi-Agent Run

13:00 Results: Researching LangGraph & Vector DBs

RRF vs DBSF with Qdrant: Hybrid Retrieval Fusion for RAG in Python

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