If you're not familiar with the DeepSEE R1 model, it is a powerful new open-source AI model designed to perform at the same levels as OpenAI's GPT-3 model. In fact, it actually outperforms GPT-3 on almost every benchmark test, as well as Anthropic's Clauder 3.5 model.

The DeepSEE R1 model offers a significant performance boost, especially with minimal labeled data. It's also cheaper and released under the MIT license, which allows users to freely distill, commercialize, and leverage the model for various applications.

By using the DeepSEE R1 model as the backend for your agents, you can create powerful AI agents that can automate complex tasks in areas like coding, math problem-solving, and many others. It's a smart choice that can provide a significant boost to the capabilities of your AI-powered applications and automations.

To create a new pipeline in Vector Shift, follow these steps:

1. After signing up or logging in, you'll be taken to the main pipeline dashboard. Here, you can manage your chatbots, automations, forms, and voice bots.

2. On the left-hand panel, you'll see different options to create new pipelines, such as chatbots, automations, and more. Click on the option to create a new pipeline from scratch.

3. You'll be taken to the main drag-and-drop builder, where you can start building your pipeline. On the top, you'll see different types of nodes, including large language models, knowledge bases, and various integrations.

4. To make your pipeline operational, you'll need to place an input node and an output node. These nodes will handle the input and output of your workflow.

5. Next, add a knowledge base node to provide context for your chatbot. You can upload files or integrate with various apps to populate the knowledge base.

6. Add a large language model node, and select the open-source Deep Seek R1 model to power your chatbot. Provide a system prompt to define the chatbot's behavior.

7. Connect the input node to the large language model node, and then connect the knowledge base to the large language model node. This will allow the chatbot to reference the knowledge base when processing user inputs.

8. Add a chat memory node to infuse memory into your chatbot, further enhancing its conversational capabilities.

9. Configure the output by connecting the search query to the input node and setting the output to text.

10. Optionally, you can add integrations like email or calendar to automate tasks based on user queries.

11. Once your pipeline is set up, click on "Deploy Changes" to make it live.

12. Finally, you can export your chatbot as a standalone application, complete with customizable styling and integration options.

To create a knowledge base node in Vector Shift, follow these steps:

1. In the main pipeline dashboard, click on the "Create New" button and select "Knowledge Base".
2. Give your knowledge base a name, then click "Create".
3. You can now add data to your knowledge base by uploading files or integrating with various apps and services.
4. Once you've added the necessary data, leave the "Link" field as is, as we'll use it to connect the knowledge base to the large language model node later.

With the knowledge base created, we can now proceed to set up the large language model node to power our chatbot.

To create a powerful AI agent using the Deep Seek R1 model, follow these steps:

1. After signing in to your Vector Shift account, click on the "Create New Pipeline" button to start building your workflow.

2. In the drag-and-drop builder, locate the "Large Language Model" section and select the "Open Source" option. This will allow you to use the Deep Seek R1 model.

3. Give your Large Language Model node a descriptive name, such as "Deep Seek R1 Agent".

4. In the "System Prompt" field, provide a clear description of the agent's purpose and the type of assistance it should provide. For example, you could write: "You are a helpful chatbot for the World of AI YouTube channel. Your goal is to assist users by answering questions about the channel's content and topics."

5. Next, add a "Chat Memory" node to your workflow. This will enable your agent to maintain context and memory throughout the conversation.

6. To connect the nodes, use the output fields in the "Prompt" tab. Place two curly braces {} and select the appropriate nodes (Input, Large Language Model, Knowledge Base, and Chat Memory) to establish the connections.

7. Configure the "Search Query" to use the input from the user, and set the "Output" to be "Text".

8. Finally, connect the output node to the Large Language Model node to complete the workflow.

With these steps, you have successfully added a powerful Deep Seek R1 agent to your Vector Shift workflow. This agent can now assist users with questions, provide context about your YouTube channel, and even integrate with other applications for tasks like email or calendar management.

To connect the nodes together and create a functional chatbot workflow, follow these steps:

1. For the input node, you'll need to connect it to the large language model node. To do this, go to the "Prompt" tab of the large language model node and place two curly braces {}. This will allow you to select the input node to connect to.

2. Next, connect the knowledge base node to the large language model node. Again, in the "Prompt" tab of the large language model node, place two more curly braces {} and select the knowledge base node.

3. Finally, connect the chat memory node to the large language model node. In the "Prompt" tab, add another set of curly braces {} and select the chat memory node.

Now, the input from the user will be processed by the large language model, which will reference the knowledge base and utilize the chat memory to provide a relevant and contextual response.

To handle the output, create a search query node and connect the input to the question asked by the user (from the input node). Set the output to be "text", which will display the response generated by the large language model.

Finally, drag and drop the output node to connect it to the search query node, completing the workflow.

With these connections in place, you can now deploy the changes and export the chatbot, customizing its appearance as needed. The chatbot will be able to provide information about your YouTube channel, answer related questions, and even integrate with other tools like email or calendar to automate various tasks.

Creating a powerful AI-powered chatbot is just the beginning. To further enhance the capabilities of your chatbot, you can integrate additional functionalities using Vector Shift's extensive range of nodes and integrations.

One key integration you can explore is connecting your chatbot to email and calendar services. By integrating with platforms like Gmail or Google Calendar, you can enable your chatbot to perform tasks such as sending emails, scheduling meetings, and managing your calendar. This can greatly streamline your workflow and provide a seamless user experience.

Another valuable integration is connecting your chatbot to various data sources and knowledge bases. By integrating with external APIs, databases, or even your own internal knowledge repositories, you can equip your chatbot with a wealth of information to draw upon, allowing it to provide more comprehensive and contextual responses to user queries.

Furthermore, you can explore integrating your chatbot with popular communication channels like Slack, WhatsApp, or even your own website. This will enable your users to interact with your AI assistant through their preferred channels, making it more accessible and convenient.

The beauty of Vector Shift lies in its modular and extensible nature. By leveraging its diverse range of nodes and integrations, you can continuously expand the capabilities of your chatbot, tailoring it to your specific needs and requirements. The possibilities are endless, allowing you to create truly powerful and versatile AI-powered applications.

Now that we have the chatbot workflow fully created, we can proceed to deploy and export it. Here's how:

1. Deploy the Changes: Click on the "Deploy Changes" button to deploy the chatbot workflow. This will make the chatbot live and ready to use.

2. Export the Chatbot: Go to the "Export" section and give your chatbot a name, e.g., "World of AI". You can then customize the chatbot's appearance by configuring the styling details, such as adding your logo and adjusting the colors.

3. Open the Chatbot: Once you're done with the styling, click on the "Export" button. This will give you a URL that you can open to access your chatbot.

4. Test the Chatbot: Open the chatbot URL and try asking it questions, such as "What is World of AI?" or "What topics are covered in the YouTube channel?". The chatbot will reference the knowledge base and provide the relevant answers.

5. Integrate the Chatbot: Depending on your needs, you can integrate the chatbot with various platforms, such as Slack, WhatsApp, or export the API for further integration.

The key aspects of this process are:

• Deploying the changes to make the chatbot live
• Exporting the chatbot with a customized appearance
• Opening the chatbot URL to test and interact with it
• Integrating the chatbot with other platforms or systems as needed

By following these steps, you can successfully deploy and export your chatbot created using the Vector Shift platform.

Vector shift has made significant advancements with its latest platform update, making AI workflow creation even more intuitive and accessible. The simplified UI, new components, and integration with powerful AI models like DeepSeeR1 have enhanced the capabilities of the platform.

By leveraging the DeepSeeR1 model, users can create highly capable AI agents that can automate complex tasks in areas such as coding, math problem-solving, and more. The ability to seamlessly connect various nodes, including input, output, knowledge base, and chat memory, allows for the creation of comprehensive chatbots and workflow automations.

The platform's flexibility extends beyond just chatbots, as users can also integrate various third-party applications, such as email and calendar services, to further enhance the functionality of their AI agents. This integration enables the automation of tasks like email generation and meeting scheduling, streamlining workflows and improving productivity.

Overall, the latest updates to Vector shift have made it an even more powerful and user-friendly platform for building AI-powered applications and automations without the need for extensive coding knowledge. The combination of intuitive design, powerful AI models, and seamless integration capabilities positions Vector shift as a leading solution for those looking to leverage the power of AI in their workflows and applications.