# Getting Started with Sync

This quick guide shows how easy it is to upload a document and start querying it with AI. In just three API calls, you'll go from an uploaded file to intelligent question-answering over your content.

## What You'll Do

1. Upload a document to your dataspace
2. Trigger ingestion to make it AI-ready
3. Query your document using natural language


**Time to complete:** ~5 minutes

## Prerequisites

For this quickstart, you'll need:

- **Account ID** (format: `scd-k2j8n4m1`) - provided when you sign up
- **Workspace ID** (format: `sws-x9p3q7r5`) - your compute cluster
- **Dataspace ID** (format: `sds-a1b2c3d4`) - your data storage
- **Ontology configured** - your dataspace should have an ontology with at least one category
- **API token** - get this from the Sync Cloud web app


> **Don't have these yet?** Follow the [Account Setup Guide](/guides/account-setup) to create your workspace and dataspace.


## Step 1: Upload a Document

Let's upload a PDF document to your dataspace. We'll use a sample employee handbook for this example.


```bash
POST https://sws-x9p3q7r5.syncdocs.ai/api/content
Authorization: Bearer <your-token>
Content-Type: multipart/form-data

# Form fields:
file: <your-pdf-file>
dataspaceId: sds-a1b2c3d4
categoryId: 3fa85f64-5717-4562-b3fc-2c963f66afa6
fileName: employee-handbook-2024.pdf
fileFormat: application/pdf
metadata: {}
```

**Response:**


```json
{
  "contentId": "7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d",
  "dataspaceId": "sds-a1b2c3d4",
  "categoryId": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
  "fileName": "employee-handbook-2024.pdf",
  "fileFormat": "application/pdf",
  "metadata": {},
  "createdAt": "2025-01-30T10:00:00Z",
  "updatedAt": "2025-01-30T10:00:00Z"
}
```

✅ **Your document is uploaded!** Save the `contentId` - you'll need it for the next step.

## Step 2: Make It AI-Ready (Ingestion)

Now trigger ingestion to extract text, generate embeddings, and index your document for AI queries.


```bash
POST https://sws-x9p3q7r5.syncdocs.ai/api/content/sds-a1b2c3d4/7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d/ingest?workflowId=456f1234-e89b-12d3-a456-426614174001
Authorization: Bearer <your-token>
Content-Type: application/json
```

Ingestion usually takes 30 seconds to a few minutes depending on document size. Once complete, let's retrieve the content to see what Sync extracted:


```bash
GET https://sws-x9p3q7r5.syncdocs.ai/api/content/sds-a1b2c3d4/7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d
Authorization: Bearer <your-token>
```

**Response:**


```json
{
  "contentId": "7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d",
  "dataspaceId": "sds-a1b2c3d4",
  "categoryId": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
  "fileName": "employee-handbook-2024.pdf",
  "fileFormat": "application/pdf",
  "createdAt": "2025-01-30T10:00:00Z",
  "updatedAt": "2025-01-30T10:02:30Z",  // Updated after ingestion
  "fileSize": 2458624,  // Extracted during ingestion
  "fileUrl": "https://sws-x9p3q7r5.syncdocs.ai/api/content/sds-a1b2c3d4/7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d/employee-handbook-2024.pdf",  // Generated during ingestion
  "metadata": {
    // These fields were extracted by AI during ingestion based on your ontology's metadata queries
    "Document Type": "Employee Handbook",
    "Publication Year": 2024,
    "Department": "Human Resources",
    "Version": "3.2"
  },
  "inferenceTaskExecutions": {
    // Maps each metadata field to the AI task that extracted it (for audit/attribution)
    "Document Type": "9c0d1e2f-3a4b-5c6d-7e8f-9a0b1c2d3e4f",
    "Publication Year": "9c0d1e2f-3a4b-5c6d-7e8f-9a0b1c2d3e4f",
    "Department": "9c0d1e2f-3a4b-5c6d-7e8f-9a0b1c2d3e4f",
    "Version": "9c0d1e2f-3a4b-5c6d-7e8f-9a0b1c2d3e4f"
  }
}
```

✅ **Your document is now AI-ready!** Notice how Sync automatically:

- Extracted structured metadata from the PDF
- Generated a file URL for downloading
- Tracked which AI tasks created each metadata field
- Indexed the content for semantic search


## Step 3: Query Your Document

Now the fun part! Ask questions about your document using natural language.


```bash
POST https://sws-x9p3q7r5.syncdocs.ai/api/content/sds-a1b2c3d4/query
Authorization: Bearer <your-token>
Content-Type: application/json

{
  "query": "What is the vacation policy for new employees?"
}
```

**Response:**


```json
{
  "query": "What is the vacation policy for new employees?",
  "response": "According to the employee handbook, new employees receive the following vacation benefits:\n\n- **Year 1**: 10 days of paid vacation\n- **Years 2-5**: 15 days of paid vacation\n- **Years 6+**: 20 days of paid vacation\n\nVacation days accrue monthly and can be used after completing 90 days of employment. Unused vacation days can be rolled over up to a maximum of 5 days per year【0】.",
  "analyzedDocumentCount": 1,
  "citedContent": [
    {
      "contentId": "7a8b9c0d-1e2f-3a4b-5c6d-7e8f9a0b1c2d",
      "dataspaceId": "sds-a1b2c3d4",
      "fileName": "employee-handbook-2024.pdf",
      "fileFormat": "application/pdf",
      "categoryId": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
      "metadata": {},
      "createdAt": "2025-01-30T10:00:00Z",
      "updatedAt": "2025-01-30T10:02:00Z"
    }
  ],
  "webSearchResults": [],
  "citedLibraryPages": []
}
```

🎉 **That's it!** You've uploaded a document, made it AI-ready, and queried it with natural language. The AI found the relevant information, synthesized an answer, and provided citations.

## What You Just Experienced

In three simple API calls, Sync:

- ✅ **Accepted your document** in its native format (no preprocessing required)
- ✅ **Extracted and indexed** text, tables, and metadata automatically
- ✅ **Generated embeddings** for semantic search
- ✅ **Answered your question** using AI with proper citations
- ✅ **Tracked everything** in audit logs for compliance


All of this happened in your own infrastructure with complete data isolation.

## Try More Queries

Your document is now AI-ready. Try asking follow-up questions:


```bash
# Ask a different question
{
  "query": "What are the requirements for remote work?"
}

# Ask a follow-up question (include conversationId from previous response)
{
  "query": "Are there any restrictions on where I can work remotely?",
  "conversationId": "conv-a1b2c3d4-e5f6-7890-abcd-ef1234567890"
}

# Filter by metadata (if you have multiple documents)
{
  "query": "What changed in the 2024 policy updates?",
  "context": {
    "contentFilters": {
      "metadata": {
        "year": 2024,
        "documentType": "Policy Update"
      }
    }
  }
}
```

## What's Next?

Now that you've seen how easy it is to query documents with Sync, explore these guides to unlock more powerful features:

### [Account Setup](/guides/account-setup)

Learn how to provision workspaces and dataspaces, and configure your account for production use.

### [Automate Metadata Labeling](/guides/automate-metadata-labeling)

Automatically extract structured metadata from documents using AI-powered ontologies. Turn unstructured PDFs into queryable, structured data.

### [Build a Research Agent](/guides/build-research-agent)

Create specialized AI agents with custom instructions, combine private documents with public reference libraries, and implement metadata-based access controls.

## Core Concepts

Want to understand how Sync works under the hood?

- **[Architecture](/concepts/architecture)** - How Sync's three-tier architecture (control plane, data plane, compute plane) enables scalability and isolation
- **[Content](/concepts/content)** - How documents are processed, indexed, and versioned
- **[Dataspaces](/concepts/dataspaces)** - Data organization and isolation strategies
- **[Workspaces](/concepts/workspaces)** - Compute clusters and how they scale
- **[Ontologies](/concepts/ontologies)** - Define categories and metadata schemas for your content
- **[Queries](/concepts/queries)** - Advanced query techniques and filtering strategies
- **[Agents](/concepts/agents)** - Configure specialized AI assistants for different use cases


## API Reference

For complete API documentation with all endpoints and parameters:

- **[Admin & Workspace APIs](/api)** - Manage accounts, dataspaces, workspaces, and ontologies