Concepts
This page introduces the core concepts and terminology used throughout the Waterflai platform. Understanding these concepts will help you navigate the platform more effectively.
Fundamental Concepts
AI Model
An AI model is a trained machine learning system capable of performing specific tasks, such as generating text, answering questions, or analyzing data. Waterflai supports various types of AI models, with a focus on Large Language Models (LLMs).
Provider
A provider is a service that offers access to one or more AI models. Examples include OpenAI, Mistral AI, Google, and Anthropic. Waterflai allows you to connect to multiple providers, giving you flexibility in choosing the best model for your needs.
Knowledge Base
A knowledge base is a collection of information that can be used to enhance the capabilities of AI models. In Waterflai, you can create and manage knowledge bases to provide context and domain-specific information to your AI applications.
Chatbot
A chatbot is an AI-powered conversational interface that can interact with users in natural language. Waterflai provides tools to create and customize chatbots for various purposes, from customer support to data analysis.
Workflow
A workflow in Waterflai is a series of interconnected steps that define a process or task. Workflows can combine AI model interactions, data processing, and integrations with external systems to create complex, automated processes.
Vector Store
A vector store is a specialized database designed to store and efficiently retrieve high-dimensional vectors. In the context of AI and Waterflai:
Purpose: Vector stores are crucial for managing and searching large collections of embeddings, which are numerical representations of data (text, images, etc.).
Functionality: They allow for fast similarity searches, enabling AI applications to find and retrieve the most relevant information quickly.
Use in Waterflai: Vector stores are often used in conjunction with knowledge bases to enhance the retrieval of context-relevant information for AI models.
Embeddings
Embeddings are dense vector representations of data in a high-dimensional space. In the context of AI and natural language processing:
Definition: An embedding is a way to represent words, sentences, or any piece of data as a list of numbers (a vector).
Properties:
Captures semantic meaning: Similar concepts have similar embeddings.
Enables mathematical operations: You can perform calculations on embeddings to find relationships between concepts.
Applications in Waterflai:
Semantic Search: Improve the relevance of search results in knowledge bases.
Content Recommendation: Suggest related content or responses based on similarity.
Language Understanding: Enhance the AI model's comprehension of user queries and context.
Data Preprocessing: Transform raw text data into a format that AI models can process more effectively.
Importance of Vector stores and embeddings
Understanding vector stores and embeddings is crucial for leveraging the full potential of Waterflai:
Enhanced Retrieval: By using embeddings and vector stores, your AI applications can quickly find and utilize the most relevant information from large datasets.
Improved Accuracy: Embeddings capture nuanced relationships between concepts, leading to more accurate and contextually appropriate AI responses.
Scalability: Vector stores efficiently manage large volumes of embedded data, allowing your applications to scale with growing knowledge bases.
Cross-lingual Capabilities: Some embedding models support multiple languages, enabling AI applications to work across language barriers.
Waterflai Platform Components
Providers Space
The Providers Space is where you manage your connections to various AI model providers. Here, you can set up API keys, select models, and configure provider-specific settings.
Knowledge Space
The Knowledge Space is dedicated to creating, importing, and managing your knowledge bases. This is where you organize information that will be used to augment your AI applications.
Studio Space
The Studio Space is the heart of Waterflai, where you build your AI applications. It includes tools for creating chatbots, designing workflows, and testing your creations.
Analytics Space
The Analytics Space provides insights into your AI applications' performance and usage. Here, you can view dashboards, analyze logs, and gather data to optimize your applications.
Key Features
No-Code Interface
Waterflai's no-code interface allows you to create AI applications using visual tools, without writing complex code. This includes drag-and-drop components, visual workflow builders, and intuitive configuration panels.
Flow Components
Flow components are the building blocks of workflows in Waterflai. These pre-built modules perform specific functions, such as calling an AI model, processing data, or integrating with external services.
Knowledge Connector
A knowledge connector is a tool that allows you to link your knowledge bases to your AI applications. This enables your applications to access and utilize specific information during interactions.
Publishing
Publishing in Waterflai refers to the process of making your AI application available for use. This can involve deploying a chatbot to a website, exposing a workflow as an API, or integrating your application with other systems.
Embedding
Embedding allows you to integrate Waterflai-built applications into your existing websites or applications. This feature enables seamless incorporation of AI capabilities into your digital products.
Administrative Concepts
Organization
An organization in Waterflai represents a company or group that uses the platform. It encompasses all the users, workspaces, and resources associated with that entity.
Workspace
A workspace is a dedicated environment within your organization where specific projects or teams can work on AI applications. Workspaces help organize your work and manage access to resources.
User Roles
Waterflai supports different user roles (e.g., Admin, Creator, User) to manage access and permissions within your organization and workspaces.
Understanding these concepts will provide you with a solid foundation as you explore and use the Waterflai platform. As you progress through the documentation, you'll learn how these concepts come together to help you create powerful AI applications.
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