Master Parent Document Retrieval with MongoDB & LangChain for Smarter RAG apps | Live Code Guide
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💡 Struggling with chunk sizes in RAG apps? Learn how MongoDB’s Parent Document Retrieval balances precision and context—no PhD required! Join this deep dive to:
Solve the “chunking paradox” with small embeddings + large context retrieval 🧩
Live-code async ingestion for 40K+ documents (with batch processing hacks!) ⚡
Build a RAG pipeline and AI agent using LangChain & Atlas Vector Search 🤖
FREE GitHub repo included—steal the code for docs chatbots, legal research, or customer support!
⏱️ TIMESTAMPS:
00:00 - What is Parent Document Retrieval? (Spoiler: It’s a game-changer!)
02:35 - MongoDB + LangChain setup: Chunking strategies & metadata tips
10:06 - Async processing: Ingest 25K docs WITHOUT crashing your system
15:04 - Vector search indexes: Optimize for speed & accuracy
20:12 - AI Agent demo: Answer complex questions with context expansion
25:56 - Pro tips: Avoid “tool loops” in agents & access our Generative AI Cookbook
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