Whenever you create a stack . The first thing you would choose is whether you are building a Chat usecase or a Text Generation usecase.

  • Chat - This use case allows the model to chat with users, answering questions and providing information on a wide range of topics. It's perfect for customer support, tutoring, or just having a conversation.

  • Text Generation - Here, the model generates text based on prompts given by the user. It can be used to create articles, stories, or any written content quickly and efficiently, starting from just a simple idea or sentence.

Similar to the BeyondLLM, GenAI stack also has a component called “Data Loader” which ingests the data from a diverse set of sources (Websites, PDF files, CSV files, YouTube videos, etc.) and serves as a bridge between the data source and the LLM.

Hovering over the small dot on the bottom right of each component reveals the compatible output types. For instance, when using the CSV loader, you can connect it to text splitters, chains, retrievers, vector stores, and outputs.


Input & Output

Inputs and Outputs are essential for defining the start and end of a stack, particularly in Text Generation use cases where you specify the parameters provided to the stack and the outputs received. The Input component can be connected to a Data Connector or a Prompt Template, with the input_type selected based on the target component. For example, when connecting the Input component to a File Loader, the input_type should be set to "File."

Text Generation Output

The TextGenerationOutput component (the only output component) is designed to store and manage text extracted from Document Loaders, images/speech outputs from Multimodal components, and output generated by Chains. It includes a Query field that allows users to input a query. This component's primary function is to ensure that the generated output is made readily accessible to users.

Just like BeyondLLM and other RAG frameworks discussed earlier, GenAI includes Text Splitters, Embedding Models, Vector Stores, and Large Language Models. Each of these have a dedicated component in the platform. The platform also allows users to test and validate the entire pipeline by inputting data, building the stack, and using the "chat with stack" button (located below the build button that looks like a play button) for stacks with a chat interface.

Deployment


Deploying your stack is a crucial step in transitioning your chatbot application from the testing phase to live usage. This process involves setting up the application on a server and making it accessible to users through the web.

Initiate Deployment:


After thoroughly testing your application through the Chat Interface, click on the "Deploy" button.

Deployment Options:

When you click on the "Deploy" button, a dropdown menu will appear with three options:

Free:

The stack will be deployed and will automatically shut down after 2 hours. Suitable for short-term testing and demonstrations.

Premium:

The stack will be deployed and will remain active indefinitely. Ideal for continuous use without worrying about downtime.

Dedicated:

The stack will be deployed on a dedicated server.

You can choose a custom domain. Autoscaling options are available, allowing the application to handle varying loads efficiently.

This option is best for high-traffic applications needing robust performance.



You can read about all the components available for use in the GenAI stack, along with their use cases and customization options, by referring to the documentation - https://docs.aiplanet.com/