Uploaded October 2025 | 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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In this tutorial, I'll show you how to build a RAG (Retrieval Augmented Generation) system using OpenAI's new Agent Builder platform without writing any code. The Agent Builder uses a drag-and-drop workflow interface similar to n8n or LangGraph Studio that makes it incredibly easy to create powerful AI agents.
I'll walk you through the complete process of creating a RAG agent that can search through documents and extract information. We'll use Google's financial 10-Q document as an example and build an agent that can answer questions about earnings data by searching through the file system.
Dataset
github.com/laxmimerit/rag-dataset
What you'll learn:
- How to access and use OpenAI's Agent Builder platform
- Understanding the core nodes: Agent, File Search, Guardrails, and more
- Setting up a RAG agent with system prompts and instructions
- Uploading documents and creating vector stores
- Connecting file search tools to your agent
- Testing your workflow and verifying results
- Working with financial documents and extracting specific data
The video includes a live demonstration where we query Google's earning data and verify the results against the actual 10-Q document. This no-code approach makes it accessible for anyone to build sophisticated RAG systems without programming knowledge.
📚 Learn More with My Udemy Courses
🔥 MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
udemy.com/course/mcp-mastery-build-ai-apps-with-claude-langchain-and-ollama/?referralCode=31C17C306A59601B8689
🚀 Agentic RAG with LangChain & LangGraph
udemy.com/course/agentic-rag-with-langchain-and-langgraph/?referralCode=C0BCC208F53AF2C98AC5
🧠 LangGraph with Ollama
udemy.com/course/langgraph-with-ollama/?referralCode=B646DCB44A189BEBC20C
⚡ Ollama and LangChain
udemy.com/course/ollama-and-langchain/?referralCode=7F4C0C7B8CF223BA9327
🔧 Fine-Tuning LLM with Hugging Face Transformers
udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/?referralCode=6DEB3BE17C2644422D8E
📖 NLP with BERT in Python
udemy.com/course/nlp-with-bert-in-python/?referralCode=063516494616C76907CD
🌐 Connect with Me
Website & Blogs: kgptalkie.com
LinkedIn: linkedin.com/in/laxmimerit
GitHub: github.com/laxmimerit
Twitter (X): twitter.com/laxmimerit
📌 Support the Channel
👍 Like the video if it helps you
💬 Comment your doubts & feedback
🔔 Subscribe for free weekly AI & Data Science content
#DataScience #MachineLearning #LangChain #LangGraph #Ollama #Python #AI #DeepLearning #NLP #GenerativeAI #LLM #HuggingFace #BERT
🚀 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
----------
In this tutorial, I'll show you how to build a RAG (Retrieval Augmented Generation) system using OpenAI's new Agent Builder platform without writing any code. The Agent Builder uses a drag-and-drop workflow interface similar to n8n or LangGraph Studio that makes it incredibly easy to create powerful AI agents.
I'll walk you through the complete process of creating a RAG agent that can search through documents and extract information. We'll use Google's financial 10-Q document as an example and build an agent that can answer questions about earnings data by searching through the file system.
Dataset
github.com/laxmimerit/rag-dataset
What you'll learn:
- How to access and use OpenAI's Agent Builder platform
- Understanding the core nodes: Agent, File Search, Guardrails, and more
- Setting up a RAG agent with system prompts and instructions
- Uploading documents and creating vector stores
- Connecting file search tools to your agent
- Testing your workflow and verifying results
- Working with financial documents and extracting specific data
The video includes a live demonstration where we query Google's earning data and verify the results against the actual 10-Q document. This no-code approach makes it accessible for anyone to build sophisticated RAG systems without programming knowledge.
📚 Learn More with My Udemy Courses
🔥 MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
udemy.com/course/mcp-mastery-build-ai-apps-with-claude-langchain-and-ollama/?referralCode=31C17C306A59601B8689
🚀 Agentic RAG with LangChain & LangGraph
udemy.com/course/agentic-rag-with-langchain-and-langgraph/?referralCode=C0BCC208F53AF2C98AC5
🧠 LangGraph with Ollama
udemy.com/course/langgraph-with-ollama/?referralCode=B646DCB44A189BEBC20C
⚡ Ollama and LangChain
udemy.com/course/ollama-and-langchain/?referralCode=7F4C0C7B8CF223BA9327
🔧 Fine-Tuning LLM with Hugging Face Transformers
udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/?referralCode=6DEB3BE17C2644422D8E
📖 NLP with BERT in Python
udemy.com/course/nlp-with-bert-in-python/?referralCode=063516494616C76907CD
🌐 Connect with Me
Website & Blogs: kgptalkie.com
LinkedIn: linkedin.com/in/laxmimerit
GitHub: github.com/laxmimerit
Twitter (X): twitter.com/laxmimerit
📌 Support the Channel
👍 Like the video if it helps you
💬 Comment your doubts & feedback
🔔 Subscribe for free weekly AI & Data Science content
#DataScience #MachineLearning #LangChain #LangGraph #Ollama #Python #AI #DeepLearning #NLP #GenerativeAI #LLM #HuggingFace #BERT
