Projects
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A Project in Lleverage is where all your AI development work takes place. Projects live inside an Organisation and serve as containers for organising your Workflows, Knowledge bases, Connections, and more. Each project is isolated, allowing you to manage resources, configurations, and collaborators on a per-project basis.
Workflows: Design and manage workflows that chain together data, APIs, and AI models to build comprehensive AI features.
Knowledge bases: Upload documents and data and bring them into vector space. Vector stores can be called from within workflows to perform similarity search. This is very handy to enable chat with your documents or search for examples to give LLMs guidance on how something needs to be generated.
Connections: Each project can have its own connections to external resources such as databases, APIs, or model providers. These connections are isolated to the project, so they don’t interfere with other projects. Learn how to manage your connection for and .
Secrets: Securely manage sensitive information like API keys or credentials. Secrets in a project are stored separately from other projects to ensure data security.
Environment variables: Define variables that can be used in your workflows, such as API endpoints or environment-specific configurations (e.g., for dev, staging, and production environments).
Creating a project: When you create a project, you can define its name, add collaborators, and set up initial connections and secrets. Each project will inherit some default settings from the Organisation, but you can customize them as needed.
Inviting users: You can invite team members to collaborate on a project. Each user can have different roles and permissions within the project, allowing for controlled access to specific resources.
Project settings: Within the Settings tab of a project, you can manage details such as the project’s name, connections, secrets, and environment variables.