Lamatic.ai: a hosted platform for rapidly building and deploying AI intelligences

General Introduction

Lamatic.ai is a development platform focused on generative AI (GenAI). It provides an easy-to-use tool that allows users to quickly build, test and deploy AI intelligences. The platform is suitable for developers and non-technical people and reduces the need for complex programming through a low-code approach. It integrates vector databases, model connectivity, and real-time monitoring capabilities to help teams collaborate efficiently.Lamatic.ai's core strength is the ability to rapidly deploy AI applications on edge servers with fast response times and low latency. It is currently being used to create virtual assistants, chatbots, and other scenarios, and is attracting attention from startups and tech teams.

Function List

• Low-code build tools: Design AI workflows through a drag-and-drop interface without in-depth programming.
• Edge Deployment: Deploy AI applications to edge servers with response times as low as 150 milliseconds.
• Built-in vector database: Use Weaviate to store and search data, with support for contextual search.
• real time monitoring: Provides node-level logging and tracking to ensure stable application operation.
• GraphQL API Integration: Access all functionality through a single API endpoint to automatically generate documentation.
• Pre-built templates and models: Off-the-shelf AI models and application templates are provided to accelerate development.
• Multiple Data Source Connectivity: Support for external data integration such as Google Drive, Slack, etc.

Using Help

Lamatic.ai is an online platform that allows users to get started without installing the software, simply by accessing it through a browser. Below is a detailed guide to help you get started quickly.

Register & Login

1. Open your browser and enter the URL https://lamatic.ai/The
2. Click the "Sign Up" button on the top right corner of the homepage.
3. Enter your e-mail address and password to complete the registration. If you already have an account, you can log in directly by clicking "Login".
4. After registering, you'll be taken to the control panel, which is where you'll manage your project.

Creating the First AI Intelligence

1. Go to Studio
   Once logged in, click on the "Studio" option on the left navigation bar. This is the main workspace for building AI intelligences.
2. New Process (Flow)
   • Click on the "New Flow" button and enter the name of the flow, e.g. "My Chatbot".
   • The system will jump to the process editor, with a toolbar and canvas on the interface.
3. Add Node
   • Select "Nodes" in the toolbar, e.g. "Text Input" or "LLM" (Large Language Model).
   • Drag nodes onto the canvas and connect them. For example, put a "Text Input" node and connect it to a "LLM" node.
   • Click on the node to set the parameters. For example, select a model (e.g. GPT-4) in the "LLM" node and adjust the temperature parameters.
4. Connecting to a data source
   • Select "Integrations" in the toolbar and drag in the "Google Drive" or "Slack" node.
   • Click on the node and follow the prompts to authorize the connection and enter your account information.
   • The data source is connected and can be used to provide context or store data.
5. Testing process
   • Click the "Test" button in the upper right corner and enter a test text, such as "What's the weather like today".
   • The system displays the output and checks that it meets expectations.
6. Saving and Debugging
   • After testing, click "Save" to save the process.
   • If the result is not satisfactory, click "Debug" to view the log and adjust the node settings.

Deploying AI Intelligence

1. Deployment to the edge
   • At the top of the editor, click the "Deploy" button.
   • Select "Edge Deployment" and the system will be deployed within 60 seconds.
   • After a successful deployment, you will get an API link for external calls.
2. Embedded in the application
   • In the "Widgets" option, select the Chat or Search widget.
   • Copy the generated code (e.g. HTML snippet) and paste it into your website or application.
   • Users will be able to interact with the AI through widgets.

Real-time monitoring and optimization

1. View Log
   • In the Control Panel, click on the "Monitoring" option.
   • Real-time logs and node operational status, such as response times and error messages, are displayed here.
2. analysis
   • Switch to the "Reports" option to see usage statistics such as number of requests and model performance.
   • Adjust the process based on the data, such as replacing models or optimizing cue words.
3. continual improvement
   • Return to Studio and edit existing processes, add new nodes or adjust logic.
   • Retest and redeploy after each change to ensure better results.

caveat

• Free users have usage restrictions, such as a limited number of requests per month. Upgrading to the paid version unlocks more features.
• If you encounter problems, you can contact the official e-mail hello@lamatic.ai, or join the Slack community for help.
• Ensure that the data source authorization is valid, otherwise the process may be interrupted.

With these steps, you can easily build an AI intelligence, such as a chatbot that answers questions or a tool that extracts information from documents.Lamatic.ai's intuitive and easy-to-use interface is perfect for getting started quickly.

application scenario

1. virtual customer service
   Businesses can use Lamatic.ai to create an intelligent customer service that connects company documents and customer data to automatically answer frequently asked questions, reducing the burden on human labor.
2. Content generation
   Marketing teams can use it to generate articles or social media content, keeping content relevant by connecting to external data sources.
3. Education Assistant
   Teachers or students can use it to build learning aids and integrate lessons

QA

1. Does Lamatic.ai require programming experience?
   No need. It provides low-code tools, and most of the operations can be done by dragging and dropping, which makes it suitable for zero-based users.
2. What does edge deployment mean?
   Edge deployment is where AI applications are run close to the user, rather than on central servers in the cloud. This results in faster response and lower latency.
3. What are the limitations of the free version?
   The free version supports basic features, but has a limited number of requests and storage space per month. The paid version offers more resources and advanced features.
4. What AI models are supported?
   It supports a variety of mainstream models, such as GPT-4, and other open source models, which can be viewed in Studio.