> For the complete documentation index, see [llms.txt](https://docs.etiq.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.etiq.ai/quickstart.md).

# Quickstart

## Requirements

Python 3.10 - 3.13

We currently support the VSCode IDE and Jupyter Notebooks with [our extension](/etiq-extension.md)

## Installation

In order to use the Etiq you need to install the Python package to your local environment.

### Python Package

Install the `etiq-copilot` python package from PyPi:

`pip install etiq-copilot`

### Usage

Use `DebuggerCodeScanner` to run a python file under Etiq's instrumentation. Pass the target source code to `scan_code`; Etiq executes the code, captures the runtime trace and observed objects, and returns a `CodeScannerResult` that can be used to inspect lineage outputs.

```python
from pathlib import Path

from etiq_copilot.engine.implementations.scanner.code_scanner import DebuggerCodeScanner
from etiq_copilot.engine.implementations.scanner.scan_results import CodeScannerResult


def scan_file(scan_file_path: Path | str) -> CodeScannerResult:
    scan_file_path = Path(scan_file_path)
    original_code = scan_file_path.read_text(encoding="utf-8")
    scanner = DebuggerCodeScanner()
    return scanner.scan_code(code_str=original_code)
```

Example usage:

```python
scan_results = scan_file("test_repo/iris_lineage_test.py")
```

### Example Target Script

The quickstart uses this iris pipeline as the target script:

```python
from sklearn import datasets
import sklearn.model_selection
from sklearn.ensemble import RandomForestClassifier
import pandas as pd

empty_dataframe = pd.DataFrame(columns=["a", "b"])

iris = datasets.load_iris()

iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)

iris_df["target"] = iris.target

iris_train_df, iris_test_df = sklearn.model_selection.train_test_split(
    iris_df,
    test_size=0.2,
    random_state=31779,
)

amodel = RandomForestClassifier(random_state=0)

iris_training_features = iris_train_df[iris.feature_names].copy()
iris_test_features = iris_test_df[iris.feature_names].copy()
iris_target_training = iris_train_df["target"].copy()

amodel.fit(iris_training_features, iris_target_training)

iris_target_testing = iris_test_df["target"].copy()
preds = amodel.predict(iris_test_features)
```

### Example Scan Output

After scanning the iris pipeline, inspect the scan result:

```python
lineage_json = scan_results.create_full_lineage_graph(graph_format="json")

print("scan_errors:", scan_results.scan_errors)
print("dataframes:", scan_results.list_dataframes())
print("models:", scan_results.list_models())
print("agents:", scan_results.list_agents())
print("lineage_json:", lineage_json[:80] + "...")
```

Example output:

```
scan_errors: None
dataframes: ['iris_target_testing', 'iris_df', 'iris_training_features', 'preds', 'empty_dataframe', 'iris_target_training', 'iris_train_df', 'iris_test_features', 'iris_test_df']
models: ['amodel']
agents: []
lineage_json: {"objects": [{"style": "filled", "fillcolor": "#FFE18E", "shape": "circle"...
```

The full `lineage_json` value contains the generated graph. A shortened excerpt looks like this:

```json
{
  "objects": [
    {
      "label": "preds",
      "shape": "circle",
      "fillcolor": "#FFE18E"
    },
    {
      "label": "amodel.predict",
      "shape": "diamond",
      "fillcolor": "#46A0FF"
    }
  ],
  "edges": [
    {
      "tail": 5,
      "head": 2
    },
    {
      "tail": 2,
      "head": 1
    }
  ]
}
```

The full lineage graph can be exported with `create_full_lineage_graph(graph_format="json")`. Generated node IDs can differ between runs.

### Example Lineage Graph

<figure><img src="/files/RSZTkeFSF9CSVlm0rEk1" alt=""><figcaption></figcaption></figure>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.etiq.ai/quickstart.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.