CrewAI + Groq Tutorial: Crash Course for Beginners
TLDRIn this tutorial, the speaker introduces Gro, an AI startup company that developed a Language Processing Unit (LPU) designed to run large language models faster and more cost-effectively. The video demonstrates how to integrate Gro with Crew AI to build a Cold Outreach Crew that generates personalized emails for customer outreach. The process involves creating an API key, setting up agents for personalization and style, and dynamically generating tasks based on CSV data. The speaker emphasizes the importance of keeping the max iterations low for efficient tasking and staying under Gro's rate limit of 30 requests per minute. The video concludes with a cost comparison, showing significant savings when using Gro over other platforms for similar tasks.
Takeaways
- π **Groq Introduction**: Groq is an AI startup that developed a Language Processing Unit (LPU) specifically designed to run large language models faster and cheaper than competitors like Chatbot or Google's Gemini.
- π **Performance**: Groq's LPU can process 500 tokens per second, which is 25 times faster than Chatbot and 10 times faster than Google's Gemini, leading to almost instantaneous results.
- π‘ **Free for Developers**: Groq is currently free for developers to start using, making it an attractive option for those looking to experiment with AI language models without incurring costs.
- π **Building a Crew**: The tutorial focuses on building a 'Cold Outreach Crew' that uses Groq to generate hyper-personalized emails from a CSV list of customers and a template email.
- π§ **Setup Process**: To start using Groq, one needs to sign up on Groq Cloud, create an API key, and then install the necessary dependencies using package managers like pip or poetry.
- π **Integration with Crew AI**: Groq can be integrated with Crew AI by updating the Crew's code to use Groq instead of OpenAI, and setting up the API key and desired model (Mixel in this case) within the code.
- π **Max Iterations**: To ensure efficiency and reliability, the tutorial recommends setting the max iterations for the Groq agents to a low number (between 2 to 4) to prevent them from getting confused or deviating from the task.
- π§ **Email Personalization**: The Cold Outreach Crew is designed to take recipient information and merge it with an email template to create personalized emails for customers.
- π **Rate Limiting**: Groq has a rate limit of 30 calls per minute, which can be a challenge when running tasks in parallel. The tutorial suggests strategies to manage and avoid hitting this limit.
- π° **Cost Savings**: Using Groq can lead to significant cost savings compared to other platforms like OpenAI's Chatbot, especially when generating a large volume of content.
- π **Community and Support**: The presenter offers a free school community for AI developers to discuss and get support for their projects, emphasizing the importance of community when working with complex AI tools.
Q & A
What is the main topic of the video?
-The main topic of the video is teaching viewers how to use Groq with Crew AI to build faster and cheaper Crews, specifically by creating a cold Outreach Crew optimized for Groq.
What is Groq and what is its main advantage?
-Groq is an AI startup company that developed a new chip called a Language Processing Unit (LPU) designed to run large language models faster and cheaper than other existing technologies.
How does the video guide users to start using Groq?
-The video guides users to start using Groq by first signing up for an account on Groq Cloud, creating an API key, and then using that API key to connect Groq to their Crew AI project.
What is the purpose of the cold Outreach Crew being built in the video?
-The cold Outreach Crew is designed to take in a CSV list of customers and a template email, then merge them together to build hyper-personalized emails that can be sent to future customers.
Why is setting a maximum number of iterations important when working with Groq?
-Setting a maximum number of iterations is important to ensure that the Groq agents focus on the core task at hand and do not get confused or deviate from the main goal, which is to personalize the emails efficiently.
How does the video ensure that the emails generated by the Crew sound personalized and not like a robot?
-The video uses a 'Ghost Rider' agent that styles the email tone and voice to match a given template, ensuring that the personalized emails sound like they were written by a human.
What is the significance of using asynchronous execution in the tasks?
-Asynchronous execution allows for the tasks to run in parallel, which can significantly speed up the process of generating personalized emails for a list of clients, as opposed to running them one at a time.
How does the video handle potential rate limiting by Groq?
-The video discusses setting the maximum requests per minute (RPM) to a value below Groq's rate limit to avoid getting rate limited during the execution of the Crew.
What is the cost advantage of using Groq for this process?
-The video demonstrates that using Groq for the process is completely free, as opposed to using other technologies like OpenAI's Chat GPT Turbo, which would incur costs based on the number of tokens used.
What is the role of the CSV file in the process demonstrated in the video?
-The CSV file contains a list of clients with their information, which is used by the Crew to generate personalized emails for each client.
How does the video provide source code for the Crew being built?
-The video provides a link in the description below where viewers can access all the source code for the Crew being built in the video for free.
Outlines
π Introduction to Gro and Crew AI Integration
The video begins with an introduction to Gro, an AI startup company that developed a Language Processing Unit (LPU) for faster and cheaper operation of large language models. The host expresses excitement about building a cold Outreach Crew using Gro, which will be faster and cheaper than previous methods. The audience is reassured that the process will be explained step by step, and all source code will be provided for free.
π Setting Up Gro and Building the Outreach Crew
The host demonstrates how to set up Gro by creating an API key from Gro Cloud and installing necessary dependencies using package managers like pip or poetry. The process involves updating the Crew AI code to use Gro instead of OpenAI and setting up the personalized email agent and Ghost Rider agent within the Crew framework. The host emphasizes the importance of setting the max iteration to ensure efficiency and reliability in the agent's tasks.
π§ Fine-Tuning Gro Agents for Efficiency
The host shares a crucial trick for optimizing Gro agents, which is setting the max iteration to a small number like two to prevent the agents from getting confused and straying from the task. This approach ensures that the agents focus on creating personalized emails efficiently. The video then moves on to setting up the Ghost Rider agent, which styles the email to sound less robotic and more personalized.
π Linking Tasks and Executing the Crew
The process of creating personalized email tasks and ghostwriting tasks is detailed, showing how to link the output of one task as input to another. The host explains how to dynamically create tasks based on the number of clients listed in a CSV file and how to set up the Crew to execute these tasks. The importance of asynchronous execution for speed and the potential issue of hitting rate limits with Gro are also discussed.
π‘ Tips for Using Gro and Crew AI
The host provides several tips for using Gro effectively with Crew AI. These include keeping the max iteration low, potentially reducing asynchronous execution to avoid rate limiting, and dynamically generating tasks to streamline the process. The host also shares cost calculations to illustrate the savings of using Gro over other services and demonstrates running the process with different data set sizes.
π Conclusion and Future Use of Gro
The video concludes with a summary of key learnings and tips for using Gro with Crew AI. The host expresses a preference for using Gro initially for its speed and cost savings during the testing phase of building Crews. The source code for the video is offered for free, and the host invites viewers to join a school community for further support and engagement with like-minded AI developers.
Mindmap
Keywords
π‘Groq
π‘Crew AI
π‘Language Processing Unit (LPU)
π‘Personalized Emails
π‘API Key
π‘CSV List
π‘Rate Limiting
π‘Dynamic Task Generation
π‘Max Iterations
π‘Async Execution
π‘Token Economy
Highlights
The tutorial introduces Gro, an AI startup company that developed a Language Processing Unit (LPU) for faster and cheaper operations of large language models.
Gro's LPU can process 500 tokens per second, which is significantly faster than other leading models like Chachi BT and Google's Gemini 1.5.
Gro is free for developers to start using, offering almost instantaneous results.
The video demonstrates how to create a Cold Outreach Crew optimized for Gro, which can send personalized emails to a list of customers.
The presenter shares the process of obtaining an API key from Gro Cloud and integrating it with Crew AI.
The tutorial covers the installation of Gro using pip or poetry, and updating the Crew AI code to use Gro instead of OpenAI.
The presenter discusses the importance of setting the max iteration to a low number to ensure efficiency and reliability of the Gro agents.
The video shows how to dynamically create tasks for each recipient in a CSV file to generate personalized emails.
The Ghost Rider agent is introduced to ensure that the personalized emails maintain the user's unique writing style.
The presenter explains how to set up asynchronous execution to speed up the process without hitting rate limits.
The tutorial includes a cost calculation comparison between using Gro and OpenAI's GPT, highlighting the cost savings of using Gro.
The video demonstrates running the Crew with a large dataset and handling rate limits by adjusting the max RPM.
The presenter shares tips and tricks for using Gro effectively, including keeping the max iteration low and handling potential rate limiting issues.
The tutorial concludes with a recap of the key learnings and a reminder that all source code is available for free.
The presenter mentions creating a free school community for AI developers to discuss and get support for Crew AI projects.
The video emphasizes the value of using Gro for its speed and cost-effectiveness in future projects.