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Create AI powered apps with open source LangChain

Introducing LangChain: a Python library for large language models (LLMs). Perfect for conversational AI developers and data scientists to perform process documents, extract data, and create engaging dialogues. Furthermore, deploy your AI app with Docker on IBM Code Engine for easy internet access.

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Guided Project

Artificial Intelligence

210 Enrolled
(20)

At a Glance

Introducing LangChain: a Python library for large language models (LLMs). Perfect for conversational AI developers and data scientists to perform process documents, extract data, and create engaging dialogues. Furthermore, deploy your AI app with Docker on IBM Code Engine for easy internet access.

Discover LangChain, a revolutionary Python library that serves as your portal to the world of Large Language Models (LLM). LangChain is your one-stop solution for integrating with a wide range of LLMs from industry leaders such as OpenAI, Cohere, Huggingface Hub, IBM Watsonx, Azure OpenAI, and more.

But, LangChain offers more than just model access; it equips you with the ability to create interactive dialogues with language models, blurring the boundary between human and machine. With its conversation chains, agents, memory, and prompt templates, LangChain empowers developers and data scientists to build conversational AI and tackle language processing tasks effectively.

In the final step, we will show you step-by-step how to use Docker container and  IBM Code Engine to deploy your AI application on IBM Cloud. IBM Code Engine is a fully managed, serverless platform that provides an abstraction for the underlying infrastructure required to deploy your apps and lets you focus on the source code only (such as the Python code).

A Look at the Project Ahead

You will learn the following LangChain functions:
  1. Universal Interface: Seamless integration with various LLM models.
  2. Prompt Management: Tools for handling, optimizing, and serializing prompts.
  3. Conversation Chains: Enables the creation of complex dialogues with LLMs.
  4. Memory Interface: Facilitates storing and retrieving model information.
  5. Indexes: Utility functions for loading custom text data.
  6. Agents and Tools: Allows setting up agents that can use tools like Google Search, Wikipedia, or a calculator.
You will also learn:
  1. Get familiar with Docker container and learn how to create the container image for App Deployment
  2. Deploy your app using IBM Code Engine
Join us on this exciting journey and unlock the potential of language processing with LangChain.


What You'll Need

The scripts in this guided project are set up to send a request to the API key using the LangChain library. For instance, you can use OpenAI API key.
To get an OpenAI API key for ChatGPT, you visit OpenAI’s official website (https://platform.openai.com/). Once logged in, click on the “View API Keys” icon located in the top-right corner of the screen, and click on “Create an API Key” to generate your ChatGPT API Key.

Estimated Effort

90 Minutes

Level

Intermediate

Industries

Information Technology

Skills You Will Learn

Generative AI, LLM, Embeddable AI, Artificial Intelligence, Chatbots, Python

Language

English

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Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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