Model Hub
Last updated
Last updated
The Model Hub is a centralized space for users to browse, manage, and deploy AI models. It provides intuitive workflows for creating pipelines, reviewing configurations, and tracking model usage metrics. This documentation outlines the functionalities of the Model Hub, including model selection, pipeline creation, and pipeline management.
Features
Model List (Model Hub Tab):
Displays available AI models optimized for various workflows.
Metadata for each model includes:
Model type (e.g., RAG, Prompt-based).
Brief description.
Current configuration status.
My Models Tab:
Lists all pipelines created by the user.
Actions:
Review Pipelines: Access existing pipeline details.
Configure AI Pipeline: Start a new pipeline configuration.
Search and Filter:
Users can search by model name or filter models by type, use case, or ratings.
Workflow
Navigate to the Model Hub via the Model Hub Tab.
Use search and filters to explore models.
Select a model to access its detailed information and deployment options.
Steps
Initiate:
Go to the My Models Tab.
Click Configure AI Pipeline to open the configuration interface.
Choose Pipeline Type:
Select either:
RAG Template: Uses retrieval-augmented generation and integrates with Sahara Vault.
Prompt Template: Standard prompt-based pipeline.
Configure Pipeline:
Fill in the following details:
Name: Enter a unique pipeline name.
Pipeline Type: Choose RAG or Prompt.
Description: Briefly describe the pipeline’s purpose.
Instruction (Prompt): Define the system’s behavior (e.g., "You are a helpful assistant").
Conversation Starter: Initial text to initiate user interaction.
Select Model: Choose the AI model to power the pipeline.
Sahara Vault Integration (RAG only): Select Vaults for data retrieval.
Publish Pipeline:
Click Publish to finalize the pipeline.
Select a compute instance that matches the pipeline requirements.
Each model will already be matched to compute instances that match the model’s requirements, so only the instances supported will be available to select.
Generate the API endpoint for integration.
My Models Tab:
Access all pipelines created by the user.
Actions:
Chat: Begin Chatting with AI Pipeline
Details: Access pipeline details and Modify configuration.
Delete Pipeline: Remove the pipeline permanently.
Pipeline Details:
Navigate to a pipeline from the My Models Tab.
Tabs within the Pipeline:
AI Model Info:
Displays pipeline configuration details (e.g., name, type, model, instructions).
Configuration:
View or edit compute instance settings.
Manage API keys (generate new keys if needed).
Dashboard
Monitor real-time metrics:
Token usage.
API calls.
System performance.
Features
Model Details:
Comprehensive description of the model’s capabilities.
RAG-specific details for models with retrieval capabilities:
Sahara Vault integration.
File retrieval features.
Performance metrics (e.g., latency, accuracy).
Actions:
Get the Model:
Deploy the model or add it to My Models for further customization.
Steps
Initiate Deployment:
Select a model from My Models Tab.
Click Deploy to configure deployment parameters.
Configure Deployment:
Define:
System Prompts.
RAG Settings (e.g., retrieval options).
Review and Deploy:
Confirm deployment settings.
Click Deploy to initiate.
The system generates a unique API endpoint.
Monitor Deployment:
Track deployment status in real-time.
Access usage instructions for the API endpoint.
Use descriptive names for pipelines to streamline management.
Regularly monitor the Dashboard Tab for pipeline performance insights.
When creating RAG pipelines, ensure that Sahara Vaults are properly integrated.
For large-scale applications, ensure the compute instance selected matches the model's requirements.
