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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.


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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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Welcome to this short course on LangChain for large language model application development. By prompting an LLM or large language model, it is now possible to develop AI applications much faster than ever before. But an application can require prompting an LLM multiple times and parsing its output, and so there's a lot of glue code that needs to be written. LangChain, created by Harrison Chase makes this development process much easier. I'm thrilled to have Harrison here, who had built this short course in collaboration with DeepLearning.ai to teach how to use this amazing tool. Thanks for having me. I'm really excited to be here. LangChain started as an open source framework for building LLM applications. It came about when I was talking to a bunch of folks in the field who were building more complex applications and saw some common abstractions in terms of how they were being developed. And we've been really thrilled at the community adoption of LangChain so far. And so look forward to sharing it with everyone here and look forward to seeing what people build with it. And in fact, as a sign of LangChain's momentum, not only does it have numerous users, but there are also many hundreds of contributors to the open source, and this has been instrumental for its rapid rate of development. This team really ships code and features at an amazing pace. So hopefully, after this short course, you'll be able to quickly put together some really cool applications using LangChain, and who knows, maybe you even decide to contribute back to the open source LangChain effort. LangChain is an open source development framework for building LLM applications. We have two different packages, a Python one and a JavaScript one. They're focused on composition and modularity. So they have a lot of individual components that can be used in conjunction with each other or by themselves. And so that's one of the key value adds. And then the other key value add is a bunch of different use cases. So chains of ways of combining these modular components into more end-to-end applications making it very easy to get started with those use cases. In this class, we'll cover the common components of LangChain. So we'll talk about models. We'll talk about prompts, which are how you get models to do useful and interesting things. We'll talk about indexes, which are ways of ingesting data so that you can combine it with models. And then we'll talk about chains, which are more end-to-end use cases along with agents, which are a very exciting type of end-to-end use case which uses the model as a reasoning engine. We're also grateful to Ankush Gola, who is a co-founder of LangChain alongside Harrison Chase, for also putting a lot of thought into these materials and helping with the creation of this short course. And on the DeepLearning.AI side, Geoff Ludwig, Eddy Shyu, and Diala Ezzeddine have also contributed to these materials. And so with that, let's go on to the next video, where we'll learn about LangChain's models, prompts, and parsers.
course detail
Next Lesson
LangChain for LLM Application Development
  • Introduction
    Video
    ใƒป
    3 mins
  • Models, Prompts and parsers
    Video with Code Example
    ใƒป
    18 mins
  • Memory
    Video with Code Example
    ใƒป
    17 mins
  • Chains
    Video with Code Example
    ใƒป
    13 mins
  • Question and Answer
    Video with Code Example
    ใƒป
    15 mins
  • Evaluation
    Video with Code Example
    ใƒป
    15 mins
  • Agents
    Video with Code Example
    ใƒป
    14 mins
  • Conclusion
    Video
    ใƒป
    1 min
  • Course Feedback
  • Community