Uploaded August 2026 | Updated September 2026, 2 weeks ago
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
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Welcome to Day 5 of LangChain bootcamp. Structured output is how you stop an LLM from answering in paragraphs and make it return clean JSON you can load straight into a pandas DataFrame.
In this lesson you will build a Pydantic data model, attach it to your model with with_structured_output, and get the same fields back on every single call. We start with a sentiment example that shows why plain text output falls apart at scale, then work through Field descriptions, Literal for fixed categories, optional fields and what they cost you, and nested Pydantic models for complaints that contain more than one issue. Everything runs on a free OpenRouter model, so you can code along without paying anything. This video is complete on its own. Memory, tools and agents come later in the bootcamp.
📺 Full playlist: youtube.com/playlist?list=PLW4pPr9JCovI
⏱ Chapters:
0:00 Intro and where we are in the bootcamp
0:56 Why you need structured output
4:15 The Pydantic data model: BaseModel and Field
6:47 Literal: force the LLM to pick from a fixed list
8:25 The catch: your LLM must fill every field
10:47 Setup: OpenRouter key, Nemotron 3.5 Lightning, LangSmith
12:41 Plain text output and why it does not scale
14:07 Writing the ReviewScore Pydantic model
15:01 with_structured_output explained
15:51 Inside it: PydanticToolParser and the pipe operator
16:32 Your Pydantic model becomes a tool
17:22 Running it: same fields every time
18:17 LangSmith trace, tokens and required true
20:21 model_dump: Pydantic object to dictionary
21:10 Ticket routing with Literal: category, urgency, summary
24:05 When the input fits none of your categories
25:34 Fixing it with an others category
26:15 Optional fields and what they cost you
29:46 A better model: mentions_lateness and days_late
31:32 Nested Pydantic models, a class inside a class
33:20 One complaint, multiple issues
34:39 Wrap up
🔗 Resources:
Code and slides: github.com/laxmimerit/Langchain-10-Days-Bootcamp
LangChain structured output docs: docs.langchain.com/oss/python/langchain/structured-output
Pydantic docs: https://docs.pydantic.dev/latest/
Free models on OpenRouter (I used NVIDIA Nemotron 3.5 Lightning): openrouter.ai/models
LangSmith for tracing: smith.langchain.com
Install LangChain: docs.langchain.com/oss/python/langchain/install
UV package manager setup: youtube.com/watch?v=pcYaP1lGd3s&list=PLc2rvfiptPSQdX4jeLgW4nf5PId4j1msI&index=8
📺 Watch next:
LangChain Tutorial for Beginners: LCEL Chains and Pipe Operator youtu.be/h9J0vN1d9qk
🎓 Go deeper, my Udemy course:
Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents kgptalkie.com/langchain
Give the video a like if it cleared things up, and drop a comment if your model returned a field you did not expect. Subscribe and hit the bell so you catch the rest of the bootcamp.
#LangChain #Pydantic #StructuredOutput #Python #GenAI
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
----------
Welcome to Day 5 of LangChain bootcamp. Structured output is how you stop an LLM from answering in paragraphs and make it return clean JSON you can load straight into a pandas DataFrame.
In this lesson you will build a Pydantic data model, attach it to your model with with_structured_output, and get the same fields back on every single call. We start with a sentiment example that shows why plain text output falls apart at scale, then work through Field descriptions, Literal for fixed categories, optional fields and what they cost you, and nested Pydantic models for complaints that contain more than one issue. Everything runs on a free OpenRouter model, so you can code along without paying anything. This video is complete on its own. Memory, tools and agents come later in the bootcamp.
📺 Full playlist: youtube.com/playlist?list=PLW4pPr9JCovI
⏱ Chapters:
0:00 Intro and where we are in the bootcamp
0:56 Why you need structured output
4:15 The Pydantic data model: BaseModel and Field
6:47 Literal: force the LLM to pick from a fixed list
8:25 The catch: your LLM must fill every field
10:47 Setup: OpenRouter key, Nemotron 3.5 Lightning, LangSmith
12:41 Plain text output and why it does not scale
14:07 Writing the ReviewScore Pydantic model
15:01 with_structured_output explained
15:51 Inside it: PydanticToolParser and the pipe operator
16:32 Your Pydantic model becomes a tool
17:22 Running it: same fields every time
18:17 LangSmith trace, tokens and required true
20:21 model_dump: Pydantic object to dictionary
21:10 Ticket routing with Literal: category, urgency, summary
24:05 When the input fits none of your categories
25:34 Fixing it with an others category
26:15 Optional fields and what they cost you
29:46 A better model: mentions_lateness and days_late
31:32 Nested Pydantic models, a class inside a class
33:20 One complaint, multiple issues
34:39 Wrap up
🔗 Resources:
Code and slides: github.com/laxmimerit/Langchain-10-Days-Bootcamp
LangChain structured output docs: docs.langchain.com/oss/python/langchain/structured-output
Pydantic docs: https://docs.pydantic.dev/latest/
Free models on OpenRouter (I used NVIDIA Nemotron 3.5 Lightning): openrouter.ai/models
LangSmith for tracing: smith.langchain.com
Install LangChain: docs.langchain.com/oss/python/langchain/install
UV package manager setup: youtube.com/watch?v=pcYaP1lGd3s&list=PLc2rvfiptPSQdX4jeLgW4nf5PId4j1msI&index=8
📺 Watch next:
LangChain Tutorial for Beginners: LCEL Chains and Pipe Operator youtu.be/h9J0vN1d9qk
🎓 Go deeper, my Udemy course:
Master Langchain v1 and Ollama - Chatbot, RAG and AI Agents kgptalkie.com/langchain
Give the video a like if it cleared things up, and drop a comment if your model returned a field you did not expect. Subscribe and hit the bell so you catch the rest of the bootcamp.
#LangChain #Pydantic #StructuredOutput #Python #GenAI
