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💻   Accessing Utils File and Helper Functions

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Open"

You will be able to see all the notebook files for the lesson, including any helper functions used in the notebook on the left sidebar. See the following image for the steps above.


💻   Downloading Notebooks

In each notebook on the top menu:

1:   Click on "File"

2:   Then, click on "Download as"

3:   Then, click on "Notebook (.ipynb)"


💻   Uploading Your Files

After following the steps shown in the previous section ("File" => "Open"), then click on "Upload" button to upload your files.


📗   See Your Progress

Once you enroll in this course—or any other short course on the DeepLearning.AI platform—and open it, you can click on 'My Learning' at the top right corner of the desktop view. There, you will be able to see all the short courses you have enrolled in and your progress in each one.

Additionally, your progress in each short course is displayed at the bottom-left corner of the learning page for each course (desktop view).


📱   Features to Use

🎞   Adjust Video Speed: Click on the gear icon (⚙) on the video and then from the Speed option, choose your desired video speed.

🗣   Captions (English and Spanish): Click on the gear icon (⚙) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

🔅   Video Quality: If you do not have access to high-speed internet, click on the gear icon (⚙) on the video and then from Quality, choose the quality that works the best for your Internet speed.

🖥   Picture in Picture (PiP): This feature allows you to continue watching the video when you switch to another browser tab or window. Click on the small rectangle shape on the video to go to PiP mode.

√   Hide and Unhide Lesson Navigation Menu: If you do not have a large screen, you may click on the small hamburger icon beside the title of the course to hide the left-side navigation menu. You can then unhide it by clicking on the same icon again.


🧑   Efficient Learning Tips

The following tips can help you have an efficient learning experience with this short course and other courses.

🧑   Create a Dedicated Study Space: Establish a quiet, organized workspace free from distractions. A dedicated learning environment can significantly improve concentration and overall learning efficiency.

📅   Develop a Consistent Learning Schedule: Consistency is key to learning. Set out specific times in your day for study and make it a routine. Consistent study times help build a habit and improve information retention.

Tip: Set a recurring event and reminder in your calendar, with clear action items, to get regular notifications about your study plans and goals.

☕   Take Regular Breaks: Include short breaks in your study sessions. The Pomodoro Technique, which involves studying for 25 minutes followed by a 5-minute break, can be particularly effective.

💬   Engage with the Community: Participate in forums, discussions, and group activities. Engaging with peers can provide additional insights, create a sense of community, and make learning more enjoyable.

✍   Practice Active Learning: Don't just read or run notebooks or watch the material. Engage actively by taking notes, summarizing what you learn, teaching the concept to someone else, or applying the knowledge in your practical projects.


📚   Enroll in Other Short Courses

Keep learning by enrolling in other short courses. We add new short courses regularly. Visit DeepLearning.AI Short Courses page to see our latest courses and begin learning new topics. 👇

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🙂   Let Us Know What You Think

Your feedback helps us know what you liked and didn't like about the course. We read all your feedback and use them to improve this course and future courses. Please submit your feedback by clicking on "Course Feedback" option at the bottom of the lessons list menu (desktop view).

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Welcome to Building Agentic RAG with LlamaIndex. I'm joined by Jerry Liu, who is co-founder and CEO at LlamaIndex and the instructor for this course. Thanks, Andrew. I'm super excited to be here with you. In this course, you learn about a agentic RAG, a framework to help you build research agents capable of events to use reasoning and decision making over your data. For example, one of you have a set of research papers on some topic and you want to pull out the parts relevant to a question you want to ask, and you get a synthesis of what the papers say. This is a complex requests that require multiple steps of processing. Further, various steps of processing, like maybe identifying a theme for one paper, but also change the later steps that are needed, like retrieving additional information from other papers about that theme. In comparison, the standard RAG pipeline, which is very popular, is mostly good for simpler questions over a small set of documents and works by retrieving some context, sticking that into the prompt and then just calling a single time to get a response. This course will take the idea of chatting over your data to the next level, and show you how to build an autonomous research agent. You'll learn a progression of reasoning ingredients to building a full agent. First, routing. We add decision making to route requests to multiple tools. Next, tool use. Where you create an interface for agents, to selected tool, as well as generate the right arguments for that tool. And then finally, multi-step reasoning with tool use. We'll use LLM to perform multi-step reasoning with a range of tools for retaining memory throughout that process. You will learn how to effectively interact with an agent and use its capability for detailed control and oversight. This will allow you to not only create a higher level research assistant over your RAG pipelines, but also give you more effective ways to guide its actions. Specifically, you'll learn how to ensure debug ability of the LLM. We'll look at how to step through what your agent is doing and how to use that to improve your agent. One additional very powerful tool is to let the user optionally inject guidance at intermediate steps. For example, if you see us searching the wrong document, a little nudge to search a different document from human input, much like an experienced manager, that's you, giving a more junior employee a nudge to consider a new piece of information can give much better performance. Many people have worked to create this course. I'd like to thank from LlamaIndex, Logan Markewich and Andrei Fajardo. From DeepLearning.AI, Diala Ezzeddine also contributed to this course. In the first lesson, you will build a router over a single document that can handle both question answering as well as summarization. That sounds great. Let's go on to the next video and get started.
course detail
Next Lesson
Building Agentic RAG with Llamaindex
  • Introduction
    Video
    ・
    2 mins
  • Router Query Engine
    Video with Code Example
    ・
    9 mins
  • Tool Calling
    Video with Code Example
    ・
    10 mins
  • Building an Agent Reasoning Loop
    Video with Code Example
    ・
    11 mins
  • Building a Multi-Document Agent
    Video with Code Example
    ・
    10 mins
  • Conclusion
    Video
    ・
    1 min
  • Course Feedback
  • Community