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Langchain - Create AI Agents using Streamlit in Langchain
Rating: 4.4 out of 5(74 ratings)
1,966 students

Langchain - Create AI Agents using Streamlit in Langchain

Use Langchain to build a Streamlit Gen AI Chatbot (LLM powered) coded in Python using the latest langchain version (0.3)
Created byDhruv Bais
Last updated 3/2025
English

What you'll learn

  • Building AI-powered customer support agents using LangChain and LLMs, and deploying them as interactive Streamlit applications.
  • Fundamentals of LangChain and LLMs: Covering core concepts, building AI agents, and managing data flow between LangChain components for Streamlit application
  • Utilizing LLMs within LangChain for function calling to design interactive Streamlit dashboards to control and monitor these workflows
  • Deploying AI Agents using Streamlit - letting users chat with your Ecommerce bot for data analytics!
  • How to do Error Handling and Debugging when it comes to AI automations with Langchain and in Streamlit

Course content

4 sections • 12 lectures • 1h 45m total length

  • Introduction to Langchain and Streamlit3:17

    Explore building a data analytics chatbot with LangChain and Streamlit, using a retrieval chain to subset data and an aggregation chain to compute sums, means, or totals.

  • Prepping for Langchain - Using the Streamlit UI to connect to ChatGPT LLM Api12:43
  • Prepping for Langchain - Adding a Sidebar component in Streamlit + Demo Reruns6:19

    Add a Streamlit sidebar with a data source dropdown to choose between tables A and B, display the selected source in the prompt, and demonstrate app reruns and prompt updates.

  • Keeping your ChatGPT LLM Api Key Safe on Github5:39

    Learn to securely store your OpenAI API key in a streamlit app using a secrets.toml file and gitignore to avoid exposing credentials on GitHub.

Requirements

  • A basic understanding of python is necessary to follow along the videos

Description

Ready to dive into the world of AI-driven data insights? This course will guide you through creating an intelligent data analytics agent using LangChain and LLMs, and then deploying it with Streamlit for interactive data visualization and exploration. This course is your perfect starting point! We'll guide you through the essentials of LangChain and Large Language Models (LLMs), empowering you to build practical AI solutions without extensive prior coding experience.

You'll learn how to create your first AI agent, transform it into a powerful data analytics tool, and connect it to various data sources. We'll also explore integrating external data sources, giving your agent access to valuable information. This course provides a hands-on, step-by-step approach, making AI-driven data analysis accessible to everyone.

What You'll Learn in This Course:

Introduction to LangChain and Data Analytics Agents:

  • Get a comprehensive tour of the LangChain framework for data processing.

  • Build your first AI data analytics agent from scratch using LangChain and LLMs.

  • Transform your agent to handle complex data queries and generate insightful reports.

Data Integration and Processing:

  • Learn how to connect your LangChain agent to various data sources (CSV, databases, APIs).

  • Learn basic data processing techniques to format data for analysis with your agent.

  • Make your AI agent adaptable to various data formats for seamless analysis.

Integrating External Data Sources:

  • Learn how to connect to external data sources.

  • Connect your Langchain workflow to Pandas Dataframes. Foundational Skills:

  • Learn how to use LLMs within LangChain for data analysis and interpretation.

  • Understand how to use APIs within LangChain for data retrieval.

  • Learn how to build interactive data visualizations and dashboards with Streamlit.

Join us and embark on your AI-driven data analytics journey!

Who this course is for:

  • Individuals Seeking to Enhance Problem-Solving Skills: Those who wish to learn how to use LangChain and LLMs for AI and automation, building interactive Streamlit applications to tackle complex problems and develop innovative solutions.
  • Aspiring Innovators: Exploring LangChain, LLMs, and Streamlit: Individuals eager to explore and develop cutting-edge AI applications with LangChain, LLMs, and Streamlit, building custom AI agents and automation workflows to solve real-world challenges.
  • Developers and Programmers: LangChain, LLMs, and Streamlit for AI Development: Looking to expand their skill set by mastering LLM integration, LangChain automation frameworks, and AI-driven development using Streamlit to create compelling user interfaces for their AI projects.
  • Startup Founders and Entrepreneurs: LangChain, LLMs, and Streamlit for AI Growth: Aiming to build AI-powered products and services and automate business processes for scalability.
  • Project Managers and Team Leaders: Gaining AI Insights with LangChain, LLMs, and Streamlit: Looking to implement AI-driven solutions to improve project management and gain actionable insights, using LangChain and LLMs for data analysis and Streamlit for interactive reporting.

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