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LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented... show more

Andrew Ng

1,651,652 subscribers

384,270 views • 3 years ago •via X (Twitter)

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9 Comments

Vlad Romanov3 years ago

@hwchase17 That's interesting; I know firsthand that many manufacturing companies struggle to filter through all the data they're collecting.

Dr Shrivastava A(Generative AI Consultant)3 years ago

@hwchase17 Please further add something to try your function for open source LLM like falcon other than OPENAI. @hwchase17

🧩DemoGPT3 years ago

@hwchase17 Great tutorial. @LangChainAI is the easiest way to build code base for LLM. DemoGPT is the easiest way to build LangChain applications. You can easily try it and give star ⭐⭐⭐ on GitHub:

rohit3 years ago

@hwchase17 who learns from courses now? shit like these are dead 💀

Austin Black3 years ago

@hwchase17 Sick, signing up now, thanks @AndrewYNg and @hwchase17 really helpful for me at this stage.

Don't know who3 years ago

@hwchase17 I don't think a course is required for this, I have been doing this for the last 6 months now... Easy to do !!

Michael McLeod 🐙 🐦3 years ago

@hwchase17 LangchainAI making another great move! congrats on the collab guys!

saba | building open-source AI3 years ago

@hwchase17 Love the concept! At Khoj, we're building an app for people to chat with their documents on their own computer, ideally without any technical knowledge. Here's a demo instance where you can chat with @logseq documentation --

Daniel Gallagher3 years ago

@hwchase17 Great course, thanks for putting it together!

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146,200 views • 1 year ago

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,703 views • 1 year ago