Summary
This video introduces the process of building agentic AI applications using different frameworks, focusing on creating independent agents and combining them across multiple platforms. It showcases the role of the Fdata framework in developing AI agents for web search and domain-specific problem-solving by utilizing open-source models. The demonstration includes setting up the project from scratch, creating agents for web search and financial analysis, and combining them into a multi-agent system to provide stock recommendations and news summaries. Additionally, it showcases implementing a custom chatbot within the Fdata platform with interactive functionality.
Introduction to Agentic AI Applications
Introduction to building agentic AI applications using different frameworks, focusing on showcasing how to build independent agents and combine them across multiple frameworks.
Overview of Fdata Framework
Explanation of the Fdata framework and its role in building AI agents for web search and domain-specific problem-solving, including the flexibility of using open-source models like G Gro, hugging face, and AMA.
Setting Up the Project
Setting up the project from scratch, including creating a virtual environment, installing required libraries such as F data, fast API, activating the environment, and setting up API keys.
Creating Agents
Creating independent agents for web search and financial analysis, utilizing tools like Grock, y Finance, and DougDug Go to search the web, fetch stock information, and display data using tables.
Combining Agents
Combining the web search agent and financial agent to create a multi-agent system, showcasing the integration of instructions, displaying data using tables, and summarizing stock recommendations and news.
Creating a Custom Chatbot
Implementing a custom chatbot within the Fdata platform, combining agents for web search and financial analysis, and showcasing interactive functionality for fetching stock information and news.
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