TaskingAI is a revolutionary platform that empowers developers to create AI-native applications with ease. By unifying modular functions like inference, retrieval, and assistance, TaskingAI streamlines the development process, allowing you to focus on building innovative solutions.

Key features of the TaskingAI platform include:

1. Flexible Model Selection: TaskingAI provides access to a wide range of large language models from various providers, including Hugging Face and OpenAI. This flexibility allows you to choose the best-suited model for your specific use case.

2. Customizable Assistants: Leverage the "Assistant" feature to create tailored AI entities capable of performing diverse tasks, from customer service to internal training. Customize their functionality by selecting models, adding system prompts, and integrating external tools.

3. Powerful Tools and Retrieval: TaskingAI's tools enable AI assistants to interact with external resources and perform specific actions, such as fetching live information or communicating with other systems. The retrieval feature allows your AI systems to access external knowledge bases, enhancing their ability to provide accurate and context-relevant answers.

4. Serverless Cloud Platform: TaskingAI has recently introduced a serverless cloud platform, allowing you to access and test its capabilities completely for free. This user-friendly UI and framework empower you to work on both the front-end and back-end of your AI-driven applications.

5. Structured Project Management: The "Projects" feature in TaskingAI helps you organize your initiatives and resources, ensuring clear segregation and efficient management of your AI development efforts.

With TaskingAI, you can create high-quality, AI-native applications in minutes, leveraging the power of large language models and a comprehensive set of tools and features. Explore the platform's capabilities and unlock new possibilities in the world of AI-driven application development.

TaskingAI's new serverless cloud platform offers a user-friendly UI and framework for efficient and flexible large language model app development. The platform provides access to hundreds of AI models and unified APIs, expanding your ability to create high-quality AI-native applications.

The key concepts behind TaskingAI's cloud platform include:

1. Models: TaskingAI incorporates various chat completion models from providers like Anthropic, Hugging Face, and OpenAI, allowing you to select and switch between models based on your needs and task complexities.

2. Assistants: TaskingAI's customizable AI entities can perform various tasks, such as customer service or internal training. Their functionality is based on the selected model and the provided tools, enabling access to a broader range of information and capabilities.

3. Tools: TaskingAI's tools enable AI assistants to interact with external resources and perform specific actions, such as fetching live information or communicating with external systems.

4. Retrieval: The retrieval feature allows AI systems to access external knowledge bases, enhancing their ability to provide more accurate and context-relevant answers.

5. Projects: TaskingAI organizes units within the platform, helping to manage different initiatives and brands with clear segregation and information management.

By leveraging these core concepts, you can create practical AI assistants, such as an archive assistant that can find relevant research papers on AI and answer questions about recent AI papers.

Let's dive into a practical application of the TaskingAI platform by creating an AI assistant that can help us find and answer questions about research papers on AI.

First, we'll select the OpenAI GPT-3.5 Turbo model as the language model for our assistant. This model is well-suited for question-answering tasks.

Next, we'll create a new plugin for searching the arXiv archive. This plugin will allow our assistant to search for and retrieve relevant research papers.

With the model and plugin set up, we'll create a new assistant called "Archive Q&A Bot". We'll provide a system prompt to guide the assistant's responses, and we'll integrate the archive search plugin to enable the assistant to find and summarize research papers.

Now, let's test out our new assistant. We'll ask it to find the best trending research papers on "fine-tuning" published in 2024. The assistant will use the archive search tool to find relevant papers and provide summaries. We can then click on the links to view the full papers on the arXiv website.

To further demonstrate the capabilities, we'll refine our search to only include papers in English. The assistant will update the search results accordingly.

This is just a simple example of what you can create with TaskingAI. The platform provides a flexible and powerful framework for building AI-powered applications that can interact with a wide range of external resources and tools. I encourage you to explore the platform further and share your own creations on Twitter and in the comments section.

Before diving into the code, we'll need to create an API key. Copy the API key and paste it into the code block within Google Colab that I've prepared earlier.

To start using the SDK, the first step is to install the TaskingAI packages using pip. After the installation is complete, we'll initialize the TaskingAI client, which will use the API key we copied earlier to connect to the TaskingAI platform.

Next, we'll try listing the assistants available in our account. We can see that our "Archive Q&A Bot" is listed, along with the description we provided earlier in the TaskingAI platform.

Now, let's try having a conversation with the assistant. We'll create a new chat session and send a user message, asking the assistant to find papers on "rag" from 2024. Once the user message is sent, the assistant will utilize the Archive plugin to search for the relevant papers and generate the answer for us.

For more advanced usage of the SDK, you should refer to the Documentation Center at docs.taskingai.com. There, you'll find a more detailed explanation on how to use the TaskingAI product, including the latest integrations of models and plugins.

That's it for today's video on TaskingAI. I hope you found this information useful and got an idea of what you can do with the platform. Don't forget to check out the links in the description below, including the TaskingAI repository, our Patreon page, and our Twitter account. Stay tuned for more AI-related content!

The tasking AI platform provides a comprehensive and user-friendly solution for developing AI-powered applications. Its key features, including the ability to access a wide range of large language models, the integration of various tools and retrieval capabilities, and the organization of projects, make it a powerful platform for efficient and flexible AI development.

The introduction of the serverless cloud platform allows developers to easily test and create AI-native applications, streamlining the development process. The platform's intuitive UI and the ability to work on both the front-end and back-end of applications make it accessible to users of all skill levels.

By exploring the practical application of creating an archive assistant, the video demonstrates the platform's capabilities in action. The assistant's ability to search for relevant research papers and provide answers to questions about AI concepts showcases the platform's potential for a wide range of use cases.

The integration of the Python SDK further enhances the platform's accessibility, allowing developers to interact with the tasking AI platform programmatically. The detailed documentation and the ongoing updates to the platform's integrations ensure that users can stay up-to-date and leverage the latest advancements in the AI ecosystem.

Overall, the tasking AI platform presents a compelling solution for developers looking to create innovative AI-powered applications with ease and flexibility.