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Lessons > Multi-Source QA

Multi-Source QA

Course Content
Day 1 – Introduction to LangChain
0/2
What is LangChain?
00:20:00
LLMs & Prompt Chains
00:20:00
Day 2 – LangChain Basics: LLMChains, PromptTemplates
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PromptTemplates & LLMChains
00:30:00
Chat Models & ConversationChains
00:30:00
Day 3 – Tools, Agents & LangChain Memory
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Tool Integration & Custom Agents
00:30:00
Memory Types in LangChain
00:30:00
Day 4 – Vector Stores & Embeddings
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Intro to Vector Stores
00:30:00
FAISS & OpenAI Embeddings
00:20:00
Day 5 – Document Loaders & Text Splitters
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Loading from PDFs, URLs, Notion, Markdown, etc.
00:30:00
Smart Chunking Techniques
00:30:00
Day 6 – Retrieval-Augmented Generation (RAG) Basics
0/2
What is RAG?
00:30:00
Simple Retrieval QA Chain
00:30:00
Day 7 – Advanced RAG: Refining & Map-Reduce Chains
0/2
Refine, MapReduce & Stuffing Modes
00:30:00
Evaluation Techniques
00:30:00
Day 8 – Building Multi-Document QA Apps
0/2
Multi-Source QA
00:30:00
Metadata Filtering & Custom Retrieval Logic
00:30:00
Day 9 – Deploying LangChain Apps
0/2
Streamlit + LangChain Deployment
00:30:00
LangServe & API Deployment
00:30:00
Day 10 – Capstone Project + Scaling Tips
0/2
Capstone Project Build
00:30:00
Best Practices & Scaling with LangChain
00:30:00
LangChain & RAG Systems for AI Apps

 Combine knowledge from multiple vector stores or websites.

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