What is LangChain? 101 Beginner's Guide Explained with Animations

This is an introduction to LangChain describing its modules: prompts, models, indexes, chains, memory and agents Send an email to [email protected] you're looking to use AI and automation to cut down costs and save time in your business. If you have any questions, need help to build your product, 1:1 or want to learn more. Join my community of AI builders: https://whop.com/ai-builders 🔗 Links Source code: https://github.com/edrickdch/langchain-101 LangChain: https://python.langchain.com/en/latest/index.html Self-Ask Paper: https://ofir.io/self-ask.pdf ReAct Paper: https://arxiv.org/abs/2210.03629 ⏳ Timestamps 00:00 Intro 00:04 What is it? 00:11 Where is it? 00:18 Why is it needed? 00:42 What it provides 01:28 Why connecting LLM to data and making it agentic is useful 01:43 Introducing LangChain modules 01:51 Models - Intro 01:58 Embeddings Models 02:11 Semantic Search 02:18 Open AI Embedding Model 02:35 HuggingFace's Open Source Embedding Model 03:00 Language Models 03:27 Prompts - Intro 03:44 Prompt Templates 04:12 Substitution Engine 04:23 Prompts - Common use cases 04:25 LLM Few shot learning 05:06 LLM Output parsing 06:10 Example Selectors - Motivation 06:24 Example Selectors 06:55 Chat Prompt Template 07:38 Indexes - Intro 07:46 Document Loaders 08:10 Text splitter 08:34 Vector DB PDF Ingestion Example 08:39 Vectorstores 08:58 Retrievers 09:21 Self-querying with Chroma DB 09:34 Recap 09:39 Chains 09:47 Chain with Memory 10:10 Chain use cases 10:17 Chaining Chains together 10:41 Chain 10:45 Agents 10:56 Thank you 💌 Link to the newsletter https://practical-ai-builder.beehiiv.com/