LangGraph + Gemini Pro + Custom Tool + Streamlit = Multi-Agent Application Development

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AI Summary

Video Summary: Building a Chatbot with Langra and Streamlit

• Introduction
  • Tutorial on creating a chatbot using Langra Gemini Pro or a custom model.
  • Langra is used for building stateful multi-actor applications.
  • Lang chain expression language is extended for multiple actors to collaborate over multiple steps.
• Langra and Cycles
  • Lang chain language is not ideal for describing cycles.
  • Langra simplifies the introduction of cycles into LLM applications.
• Setup Instructions
  • Encouragement to follow and subscribe for AI news updates.
  • Instructions to create an ideal environment by installing necessary Python libraries.
  • Libraries include Lang chain, Lang chain Google, Lang chain Community, and others.
• Streamlit Web Application
  • Setting up page title and layout.
  • Defining main function for the application.
  • Creating a text area for user input and a button to execute code.
• Building the Chatbot
  • Setting up the Serer API key.
  • Adding sorting and case toggle tools.
  • Defining custom functions for sorting words and toggling case.
• Agent and Tools Integration
  • Defining a list of tools and their attributes.
  • Creating the LLM class with the Gemini model.
  • Using the react approach to create agents that interact with tools.
• Agent Processing
  • Defining a class to maintain the agent’s internal state.
  • Outlining the agent’s process with functions corresponding to graph nodes.
  • Implementing tool invocation and decision-making for agent processing continuation.
• Graph Definition and Execution
  • Describing the agent processing graph with nodes and edges.
  • Running the graph to get the final result from the agent.
  • Option to run another query and print only the final results.
• Conclusion
  • Recap of using L graph for agent implementation.
  • Acknowledgment of the framework’s potential when building agents.
  • Invitation to access links in the description for further reading.
  • Call to action for likes, subscriptions, and engagement in the comments.

Markdown Outline

## Building a Chatbot with Langra and Streamlit  
  
### Introduction  
- Learn to create a chatbot with Langra Gemini Pro.  
- Langra: For stateful multi-actor applications.  
- Lang chain language extended for actor collaboration.  
  
### Langra and Cycles  
- Lang chain unsuitable for cycles.  
- Langra enables cycle introduction in LLM apps.  
  
### Setup Instructions  
- Follow for AI updates.  
- Install Python libraries: Lang chain, Lang chain Google, etc.  
  
### Streamlit Web Application  
- Configure page title and layout.  
- Define `main` function.  
- Create user input text area and execution button.  
  
### Building the Chatbot  
- Set Serer API key.  
- Add sorting and case toggle tools.  
- Define functions for sorting and case toggling.  
  
### Agent and Tools Integration  
- List tools with attributes.  
- Create LLM class with Gemini model.  
- React approach for agent-tool interaction.  
  
### Agent Processing  
- Class for agent state maintenance.  
- Functions for agent process and tool invocation.  
- Decision-making for process continuation.  
  
### Graph Definition and Execution  
- Define agent processing graph.  
- Execute graph for agent results.  
- Option for additional queries and results.  
  
### Conclusion  
- Recap of L graph agent implementation.  
- Framework's potential highlighted.  
- Links for further reading in the description.  
- Encouragement for likes, subscriptions, and comments.