A fast-paced introduction to LangChain describing its modules: prompts, models, indexes, chains, memory and agents. It is packed with examples and animations to get the main points across as simply as possible. The agent section will be covered in more depth in a following video.
🔗 Links
Source code: github.com/edrickdch/langchai...
LangChain: python.langchain.com/en/lates...
Self-Ask Paper: ofir.io/self-ask.pdf
ReAct Paper: 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
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You can reach out by email at edrick@daceflow.ai if you're looking to improve your business workflows using AI or build an AI app.
24 июн 2024
