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Workspaces in Unli.ai ​

What is a Workspace? ​

A workspace is a dedicated environment for organizing and building AI-powered knowledge bases. It provides:

  • Document isolation
  • Focused AI knowledge management
  • Dedicated storage and processing
  • Unique configuration for your specific project or use case

Key Characteristics of Workspaces ​

  • Isolated Environment: Documents and AI configurations are specific to each workspace
  • Standalone Knowledge Base: Each workspace maintains its own set of documents and AI models
  • Independent Configuration: Separate vector stores, embedding models, and settings

Creating a New Workspace ​

Step-by-Step Guide ​

  1. Access Workspaces

    • Open the sidebar menu
    • Click on "Workspaces" section

  2. Create Workspace

    • Click the "New Workspace" button

  3. Configure Workspace

    • Name: Provide a clear, descriptive name
    • Description: (Optional) Add context about the workspace's purpose

  4. Vector Store Configuration

    • Choose your vector store:
      • Managed Vector Store (Free plans)
      • Pinecone Vector Store (Paid plans)
    • Note: This selection cannot be changed after initial setup

  5. Create Workspace

    • Click "Create Workspace" button

create-ws.png

Post-Creation Configuration ​

Pinecone Vector Store ​

  • If you chose Pinecone, you'll be prompted to enter your Pinecone credentials
  • Action Required: Have your Pinecone API key ready
  • Navigate to Pinecone dashboard to obtain your API key if needed

pinecone-config.png

Embedding Model Configuration ​

  • Select an embedding model:
    • OpenAI
    • Anthropic
    • Cohere
  • Note: API keys are required for each embedding model

embedding-model-add.png

Language Model (LLM) Configuration ​

  • Select a Language Model:
    • OpenAI
    • Google
    • Anthropic
  • Requirement: Bring your own API keys

Workspace Use Cases ​

Example Scenarios ​

  • Research Project: Dedicated workspace for academic literature
  • Customer Support: Workspace for support documentation
  • Product Development: Workspace for technical specifications
  • Legal Compliance: Workspace for regulatory documents

Best Practices ​

  1. Focused Scope: Create separate workspaces for distinct projects or knowledge domains
  2. Consistent Naming: Use clear, descriptive names
  3. Regular Maintenance: Periodically review and organize documents
  4. Security Considerations: Use workspaces to maintain document confidentiality

Workspace Limitations ​

  • One vector store per workspace
  • Vector store type cannot be changed after initial setup
  • Document isolation prevents cross-workspace searching

Managing Workspaces ​

Common Actions ​

  • Create new workspaces
  • Switch between existing workspaces
  • Delete workspaces
  • Manage workspace members (in team plans)

Performance and Scaling ​

  • Each workspace has independent computational resources
  • Scaling and performance depend on your selected plan
  • Paid plans offer enhanced workspace capabilities

Security and Access ​

  • Workspaces provide natural document segmentation
  • Team and enterprise plans support granular access controls
  • Ensures sensitive information remains compartmentalized

Getting Started Checklist ​

  • [ ] Determine workspace purpose
  • [ ] Choose an appropriate name
  • [ ] Select vector store
  • [ ] Obtain necessary API keys
  • [ ] Select embedding model
  • [ ] Select language model
  • [ ] Upload initial documents
  • [ ] Configure AI settings