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๐Ÿ’ป ย  Accessing Utils File and Helper Functions

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  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.


๐Ÿ’ป ย  Downloading Notebooks

In each notebook on the top menu:

1: ย  Click on "File"

2: ย  Then, click on "Download as"

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


๐Ÿ’ป ย  Uploading Your Files

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


๐Ÿ“— ย  See Your Progress

Once you enroll in this courseโ€”or any other short course on the DeepLearning.AI platformโ€”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).


๐Ÿ“ฑ ย  Features to Use

๐ŸŽž ย  Adjust Video Speed: Click on the gear icon (โš™) on the video and then from the Speed option, choose your desired video speed.

๐Ÿ—ฃ ย  Captions (English and Spanish): Click on the gear icon (โš™) on the video and then from the Captions option, choose to see the captions either in English or Spanish.

๐Ÿ”… ย  Video Quality: If you do not have access to high-speed internet, click on the gear icon (โš™) on the video and then from Quality, choose the quality that works the best for your Internet speed.

๐Ÿ–ฅ ย  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.

โˆš ย  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.


๐Ÿง‘ ย  Efficient Learning Tips

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

๐Ÿง‘ ย  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.

๐Ÿ“… ย  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.

โ˜• ย  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.

๐Ÿ’ฌ ย  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.

โœ ย  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.


๐Ÿ“š ย  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. ๐Ÿ‘‡

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๐Ÿ™‚ ย  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).

Also, you are more than welcome to join our community ๐Ÿ‘‰๐Ÿ‘‰ ๐Ÿ”— DeepLearning.AI Forum


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Welcome to Long-Term Agentic Memory with LangGraph built in partnership with LangChain. I'm delighted to welcome back Harrison Chase, co-founder and CEO of LangChain. Thanks, Andrew. Recently we've seen many agentic applications being built, and this has helped us to develop a mental framework that is useful to think about for when you're adding memory to agents. We would like to share that with learners. More and more AI applications persist over time, and this really drives the need for agent memory. An example would be an AI personal assistant. Exactly. Assistants are a great example. The more they learn, the better they are at future tasks. To add memory to an agent, you must first figure out what information to store in long term memory and also when it comes time to use the information, what to retrieve. First, on what to store: Chatbots initially just saw conversational history in the context memory at each turn of a conversation. But agents that act for you over time need long term memory. For example, a calendar agent might need to persist information about meetings over long periods and across multiple invocations of the agent. Then comes retrieval. Retrieval will take information from memory and insert it into the context. Harrison will show you how to figure out when and what to retrieve. And additionally, you also need to decide when to update the stored information. So should it be updated through each iteration of an agent loop or maybe in the background over time? Right. To address these questions, we've found that it's useful to think about three types of memories. Semantic Memories: These are facts like important birthdays for a calendar agent. The next is Episodic Memories: These are experiences that can help an agent remember how to do tasks. And finally, there's Procedural Memories, which are rules for an agent to follow. To help manage memory, we've created a new library, Langmem, that supports a vector database that provides searchable, shareable, persistent storage that can be updated immediately by the agent or in the background by a helper agent. In this course, you'll build a useful email assistant that demonstrates all of these concepts using Langmem. Several people have worked to create this course. I'd like to thank from LangChain, Lance Martin, Will Fu-Hinthorn, and Nuno Campos. And from DeepLearning.AI Geoff Ladwig. All right, let's get started on the first lesson.
course detail
Next Lesson
Long-Term Agentic Memory With LangGraph
  • Introduction
    Video
    ใƒป
    2 mins
  • Introduction to Agent Memory
    Video
    ใƒป
    8 mins
  • Baseline Email Assistant
    Video with Code Example
    ใƒป
    16 mins
  • Email Assistant with Semantic Memory
    Video with Code Example
    ใƒป
    12 mins
  • Email Assistant with Semantic + Episodic Memory
    Video with Code Example
    ใƒป
    9 mins
  • Email Assistant with Semantic + Episodic + Procedural Memory
    Video with Code Example
    ใƒป
    15 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Appendix - Tips and Helps
    Code Example
    ใƒป
    10 mins
  • Course Feedback
  • Community