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Creating a data extraction agent with langchain

6 lessons · 22:24 · updated 9/9/2026

Watch them in order, then explain the topic in your own words below.

Nobody with subject knowledge has checked this course yet. It was put together automatically.

What you will do in this course

  • LangChain ecosystem overview
  • Extract keywords with LangChain
  • Structured output and memory
  • Set up the environment
  • Extract data from reports
  • Build a simple agent
0 / 6 lessons watched

Getting started

3 lessons

1. LangChain Ecosystem

LangChain ecosystem overview

NeuralNine · watch the original · 2:40

2. Environment Setup

Set up the environment

NeuralNine · watch the original · 2:36

3. Simple AI Agent Example

Build a simple agent

NeuralNine · watch the original · 5:19

Now you explain it

Close what you watched and explain the topic in your own words in 30 seconds. What you can explain is what you have learned. You are marked against this course's lessons and nothing beyond them.

Explain how you would do this: Getting started — Build a simple agent.

You are judged on these, and nothing else in the course:

  • LangChain ecosystem overview
  • Set up the environment
  • Build a simple agent

Doing it properly

2 lessons · short section, not a full path on its own

1. Extract Keywords from Text Using LangChain and Ollama Llama3.2 AI Model

Extract keywords with LangChain

OrangeBrackets · watch the original · 1:14

2. Langchain & ChatGPT Extract Financial Report

Extract data from reports

Python In Office · watch the original · 0:42

Now you explain it

Close what you watched and explain the topic in your own words in 30 seconds. What you can explain is what you have learned. You are marked against this course's lessons and nothing beyond them.

Explain how you would do this: Doing it properly — Extract data from reports.

You are judged on these, and nothing else in the course:

  • Extract keywords with LangChain
  • Extract data from reports

Going deeper

1 lessons · short section, not a full path on its own

1. Advanced Agent Example (Context, Memory, Structured Output)

Structured output and memory

NeuralNine · watch the original · 9:53

Now you explain it

Close what you watched and explain the topic in your own words in 30 seconds. What you can explain is what you have learned. You are marked against this course's lessons and nothing beyond them.

Explain how you would do this: Going deeper — Structured output and memory.

You are judged on these, and nothing else in the course:

  • Structured output and memory

Not part of the path

Added by readers, or indexed before this course was sorted. These are not counted as one of the three sections.

1. How LangChain Works to Create AI Agents | Explained Simply

Amine DALY · watch the original · 1:01

2. Agentic AI Tutorial: Build an LLM Agent for Web Scraping & Real-Time Data Extraction

AI in Plain Text | Debabrata Pruseth · watch the original · 0:25

3. Chat with PDF langchain project

Python Code Camp · watch the original · 0:25

4. Build Smarter Data Workflows with LangChain + OpenAI in Python 2025

CodeVisium · watch the original · 0:10 · a taster

5. Build a Retrieval-Augmented Generation (RAG) System with LangChain + OpenAI in Python

CodeVisium · watch the original · 0:10 · a taster

6. Tutorial 21: Creation of a RAG pipeline Example

SHIVA TATVA AI · watch the original · 0:49

7. Learn to build effective Agentic AI systems

DeepLearningAI · watch the original · 1:00

8. LangChain vs. LangGraph: What's the REAL Difference?!

Analytics Vidhya · watch the original · 1:02

9. Build AI Agent to Scrape Amazon Products 2025 | Python + OPENAI + STREAMLIT

Amine DALY · watch the original · 0:58

10. Agentic RAG vs RAGs

Rakesh Gohel · watch the original · 0:05 · a taster

11. Agentic AI Bootcamp: How LLMs Use SQL to Think, Query & Automate Data Tasks

AI in Plain Text | Debabrata Pruseth · watch the original · 0:17 · a taster

12. Project Demo

Tech With Tim · watch the original · 1:28

13. Python Setup/Installation

Tech With Tim · watch the original · 2:31

14. Ollama Setup

Tech With Tim · watch the original · 2:41

15. GitHub Copilot

Tech With Tim · watch the original · 1:08

16. Local LLM Usage

Tech With Tim · watch the original · 6:30

17. Vector Store Database Setup

Tech With Tim · watch the original · 8:32

18. Connecting LLM & Vector Store

Tech With Tim · watch the original · 4:45

19. Standalone Model Inference

NeuralNine · watch the original · 1:55

20. Conversations

NeuralNine · watch the original · 1:24

21. Streaming Responses

NeuralNine · watch the original · 0:56

22. Multimodal Input

NeuralNine · watch the original · 3:34

23. Video outline

Thu Vu · watch the original · 1:15

24. Why RAG is useful

Thu Vu · watch the original · 2:47

25. Setting up project & Get API key

Thu Vu · watch the original · 5:43

26. Define LLM

Thu Vu · watch the original · 1:28

27. Process PDF document

Thu Vu · watch the original · 1:27

28. Split document

Thu Vu · watch the original · 3:28

29. Create text embeddings

Thu Vu · watch the original · 3:07

30. Vector database

Thu Vu · watch the original · 3:25

31. Agent Demo

Tech With Tim · watch the original · 1:03

Turn what you built into work

  1. Find a business with a task it repeats by hand: answering the same messages, copying orders into a spreadsheet, booking by phone.
  2. Adapt what you built in this course to that task and show it working. Your task's proof is your demo.
  3. Do the first setup for a small fee or free, and ask for a review that describes the result in return.

A suggestion, not a promise of earnings.