> For the complete documentation index, see [llms.txt](https://docs.saharaai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.saharaai.com/developer-docs-ai-developer-portal/workflow-steps.md).
# Workflow Steps
## Step 1: Adding Data to Vaults
### **Option A: From the Data Hub**
1. **Browse Data Hub:**
* Navigate to the Data Hub within the Sahara platform.
* Explore available datasets by filtering by domain, size, or licensing terms. (Some explore features coming in Beta)
2. **Purchase Dataset:**
* Select a dataset and click Purchase.
* Complete the payment process using the supported token or currency.
3. **Add Dataset to Vault:**
* A Screen will pop up and you will select the Vault you want to import to and click Import.
* Navigate to your Vaults Tab and see the imported data set.
### **Option B: Upload Personal Data**
1. **Navigate to Vaults:**
* Go to the Vaults Tab in the Sahara dashboard.
2. **Create a New Vault:**
* Click Create Vault and provide a name and description.
3. **Upload Data:**
* Click Upload Files and select files from your local storage.
* Supported formats include CSV, JSON, and Parquet.
4. **Configure Metadata:**
* Add metadata tags for better discoverability and provenance tracking.
## Step 2: Setting Up an Endpoint in the Compute Hub
1. **Access My Submissions:**
* Open the My Submissions on the Sahara dashboard after you purchase a model.
2. **Initiate:**
* Click the New Endpoint button on the My Submissions home page.
3. **Configuration:**
* Fill out the form with the following details:
* Select Provider: Select Provider from list (Lepton, Predibase, Sagemaker, Bedrock, OpenAI)
* Select Model: Opens a popup and allows users to select from available models on platform. Search and press “Select” when you have chosen a model.
* Name: Assign a unique name to the instance.
4. **Review and Create:**
* Review the configuration.
* Click Create Instance to launch the instance.
## Step 3: Creating and Deploying Pipelines
### **A. Create a Pipeline in Model Hub**
1. **Open Model Hub:**
* Navigate to the Model Hub Tab.
2. **Click Create Pipeline:**
* Choose between:
* RAG Pipeline: Requires Vault integration for retrieval tasks.
* Prompt-Based Pipeline: For conversational or structured AI tasks.
3. **Configure Pipeline:**
* Upload an avatar for the pipeline.
* Provide the following:
* Pipeline Name.
* Description.
* Instructions (prompt).
* Conversation Starter (optional).
* Select an AI model.
* (RAG only) Choose Vaults for data retrieval.
### **B. Deploy the Pipeline**
1. **Publish Pipeline:**
* Click Publish and select a compute provider matching your pipeline’s requirements.
2. **Generate API Endpoint:**
* Upon deployment, an API endpoint is generated for integration.
## Step 4: Integration and Monitoring
### **A. Test the Endpoint**
1. **Test the API:**
* Use the generated endpoint to send sample requests.
Example payload:
* Review the AI response and refine configurations as needed.
### **B. Monitor Metrics**
1. **Open Dashboard Tab:**
* Navigate to the Metrics Tab in your pipeline’s details page.
2. **Review Usage**:
* Monitor token usage, API calls, and system performance metrics.