Video course · 15 chapters · 99 min · certificate
Learn LangChain.js - Build LLM apps with JavaScript and OpenAI
Build a context-aware chatbot with LangChain.js and OpenAI: embeddings, Supabase as a vector store, text splitting, standalone questions, prompt templates, chains and retrieval, output parsers, RunnableSequence and RunnablePassthrough, UI wiring, conversation memory and a performance checklist.
What you'll learn
- Explain LangChain.js and embeddings
- Split text and store vectors in Supabase
- Use prompt templates and chains
- Build retrieval with standalone questions
- Compose with RunnableSequence
- Add conversation memory and tune performance
Chapters
15 chapters · 99:27-
9:04
01Intro Members
Introduction and app overview
Jacob Lee.
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6:16
02Embeddings Members
What are embeddings
Meaning as vectors.
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6:08
03Supabase Members
Supabase setup and splitting text
Vector store.
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6:19
04Splitter Members
Splitting text II
LangChain tool.
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7:44
05Upload Members
Upload and standalone questions
Prepare queries.
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6:37
06Prompts Members
Prompt templates
Reusable prompts.
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4:03
07Chain Members
Adding the first chain
Standalone question.
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6:34
08Retrieval Members
Retrieval
Find matches.
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7:54
09Parsing Members
StringOutputParser and answer template
Clean outputs.
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7:15
10Runnables Members
Serialising docs and RunnableSequence
Composition.
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9:16
11Passthrough Members
RunnablePassthrough and challenge
Refactor.
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4:37
12Solution Members
Super challenge solution
Many ways.
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5:40
13UI Members
Wiring the UI and memory
Submit handler.
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6:24
14Memory Members
Wiring up memory
Conversation history.
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5:36
15Performance Members
Performance checklist and outro
When results disappoint.
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