# Quick Start

This guide will help you get started with using an existing DataBridge server. If you need to set up your own server, see the [Installation Guide](https://databridge.gitbook.io/databridge-docs/getting-started/installation) instead.

## Getting Your Access URI

> **Note**: For local development, you can skip this step and connect directly to `http://localhost:8000` without authentication.

If you need authentication:

1. Visit your DataBridge server's API documentation (<http://localhost:8000/docs>)
2. Find and use the `/local/generate_uri` endpoint to generate your URI
3. Save this URI - you'll need it to connect to the server with authentication

## Ways to Use DataBridge

You can interact with DataBridge in several ways:

### 1. Using the Shell

The shell provides an interactive Python environment for quick testing:

```bash
# Without authentication (connects to localhost):
python shell.py

# With authentication (using your generated URI):
python shell.py "databridge://user:token@localhost:8000"
```

Once connected, you can interactively run commands:

```python
>>> db.ingest_text("Machine learning is transforming industries...", 
...                metadata={"title": "ML Overview"})
{'external_id': 'doc_123', 'metadata': {'title': 'ML Overview'}, ...}

>>> results = db.retrieve_chunks("What are the key findings?")
>>> for r in results:
...     print(f"Score: {r['score']}")
...     print(f"Content: {r['content']}\n")
Score: 0.89
Content: Machine learning is transforming industries...
```

The shell supports all SDK functionality and allows you to write multi-line code just like in a Python script.

### 2. Using the Python SDK

```python
# Without authentication (connects to localhost):
db = DataBridge()

# With authentication (using your generated URI):
db = DataBridge("databridge://user:token@localhost:8000")

# Example usage:
doc = db.ingest_text(
    content="Machine learning is transforming industries...",
    metadata={"title": "ML Overview"}
)
```

### 3. Using the UI Component

The UI component provides a convenient interface for prototyping and testing:

1. Access the UI at <http://localhost:3000>
2. For authenticated access, enter your generated URI in the connection field
3. For local development, just click Connect (no URI needed)

The UI lets you:

* Test different document types and upload workflows
* Experiment with various query prompts
* Monitor document processing and indexing
* Debug and visualize search results

## Next Steps

Learn more about:

* [API Reference](https://databridge.gitbook.io/databridge-docs/api-reference/overview)
* [Python SDK](https://databridge.gitbook.io/databridge-docs/getting-started/broken-reference)
* [Ingestion](https://github.com/databridge-org/databridge-docs/blob/main/user-guides/ingestion.md)
* [Querying](https://github.com/databridge-org/databridge-docs/blob/main/user-guides/querying.md)