Step-by-Step CrewAI Agent Build - Real Use Case! (Part 1)
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Setting up the Environment
We'll start by spinning up a new Conda environment and installing Crew AI and Langchain. We'll then use the crew ai create command to create the skeleton of the app, which will include the necessary files and configurations.
We'll start by using the OpenAI GPT-4 mini model for speed and low cost, and we'll experiment with higher-end, more expensive models later.
Integrating Perplexity
Next, we'll integrate Perplexity's API to handle the research for us, as we don't want to deal with web scraping. We'll define a new language model (LLM) in Crew AI and use the Perplexity API to get the latest information on AI models and developments.
However, we'll run into some issues with the Perplexity API, and we'll need to work through them to get it integrated correctly.
Introducing Serper for Web Search
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Building an Educational AI Portal from Scratch with Anthropic's Crew AI
Introduction
In this video, we're going to build an educational portal from scratch using Anthropic's Crew AI. This is a real-world use case that I need to build, and I'll explain what it is and show you step-by-step how I go about building it. I'll be testing and making mistakes along the way, and I'll share it all with you.
We'll be using OpenAI models, including GPT-4, as well as Perplexity. I have a lot of ideas about how this should go, so let's get started.
The Goal: An Educational AI Portal
The goal is to build an educational portal that provides all the information needed to become proficient in AI. This will include everything from the basics to complex tutorials, and we want to automate the creation of at least the first drafts of the text-based educational content and tutorials. We'll also include images and step-by-step guides, and we want Crew AI to put it all together for
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After resolving the Perplexity API issues, we'll introduce Serper, a web search tool, to Crew AI. This will allow us to get more up-to-date information from the web, as the Perplexity API seems to have some limitations in this area.
We'll install the Crew AI tools and set up the Serper API key, then integrate the Serper tool into the researcher agent.
Refining the Agents and Tasks
With the web search functionality in place, we'll take a closer look at the researcher and reporting analyst agents, and refine their definitions and tasks. We'll make sure the researcher is a seasoned expert at finding the most relevant and comprehensive information, and the reporting analyst is an educational content creator who can turn complex topics into clear and concise educational materials.
Experimenting with Different Models
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Finally, we'll experiment with different models, including the more expensive GPT-4 01 model, to see if we can get even better results for the educational content. We'll monitor the cost and performance of each model and make decisions accordingly.
Conclusion
By the end of this process, we'll have a solid foundation for an educational AI portal that can automatically generate high-quality, comprehensive educational content on a variety of AI-related topics. In the next video, we'll explore additional features and refinements, such as implementing a reviewer agent and generating images to accompany the text-based content.