Build an AI Agent (OpenAI, LlamaIndex, Pinecone & Streamlit) (Skillshare – Engsub)

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Course Introduction: “Build an AI Agent (OpenAI, LlamaIndex, Pinecone & Streamlit)”
Are you ready to dive into the exciting world of AI and create powerful, intelligent agents using the latest cutting-edge tools? This comprehensive course is designed to take you from zero to hero, teaching you how to build interactive, real-world AI applications with
OpenAI
,
LlamaIndex
,
Pinecone
, and
Streamlit
.
Whether you’re a beginner just starting your journey in AI or a seasoned developer looking to expand your skills, this course covers everything you need—from understanding the core concepts of AI agent development to hands-on implementation of dynamic, intelligent systems. Learn to integrate multiple AI technologies and build agents that can analyze, respond, and interact in real time.
and unlock your potential in AI, transforming your ideas into innovative applications that make a real impact!
What you’ll learn:
How to use OpenAI’s API to generate intelligent responses. 
Building and managing knowledge indexes with LlamaIndex. 
Storing and retrieving vector embeddings with Pinecone for efficient AI searches. 
Creating interactive user interfaces for your AI agents with Streamlit. 
Best practices for integrating these tools to build scalable AI solutions.

Link gốc:

https://www.skillshare.com/en/classes/build-an-ai-agent-openai-llamaindex-pinecone-and-streamlit/279222728

Time Course:
2 hours 15 minutes (22 Lectures)

Instructor
: David Armendariz
Total Weight:
322.1 MB
** Note
:  

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Course Content

ROOT

  • 12 – building-and-interacting-with-the-agent.mkv
    09:03
  • 22 – conclusion.mkv
    00:53
  • 21 – deploying-the-app-to-streamlit-community-cloud.mkv
    05:31
  • 20 – using-the-pinecone-index-in-the-streamlit-app.mkv
    02:42
  • 19 – creating-an-index-manager-for-pinecone.mkv
    11:29
  • 18 – getting-an-api-key-from-pinecone.mkv
    03:09
  • 17 – building-a-chat-ui-with-streamlit.mkv
    13:34
  • 16 – building-a-class-to-interact-with-the-agent.mkv
    04:53
  • 15 – building-a-class-to-manage-the-index.mkv
    06:13
  • 14 – enhancing-the-prompt-to-download-files.mkv
    07:39
  • 13 – downloading-the-papers-and-fetching-new-papers.mkv
    05:15
  • 01 – introduction.mkv
    01:46
  • 11 – creating-the-rag-query-engine-tool.mkv
    10:33
  • 10 – building-the-index-and-saving-it-locally.mkv
    11:55
  • 09 – defining-the-embed-and-llm-models.mkv
    03:40
  • 08 – creating-a-tool-to-download-papers.mkv
    03:48
  • 07 – creating-a-tool-to-fetch-papers-from-arxiv.mkv
    08:11
  • 06 – vector-embeddings.mkv
    02:37
  • 05 – what-are-agents.mkv
    05:04
  • 04 – understanding-llamaindex-and-rag.mkv
    03:39
  • 03 – getting-an-openai-api-key.mkv
    03:16
  • 02 – setting-up-the-development-environment.mkv
    15:19

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