Synthetic Data Vault

https://docs.sdv.dev/sdv/
Admin
Synthetic Data Vault (SDV) is a Python library designed for creating tabular synthetic data by...
Tokens:161,113
Snippets:2,257
Trust Score:9
Update:2 months ago
Show doc for...
Tokens:
Raw
### Installation Output Example

Source: https://docs.sdv.dev/sdv/explore/sdv-bundles/ai-connectors.md

Example of the terminal output displayed during the bundle installation process.

```text
Username: <email>
License Key: ********************************

Installing Bundles:
bundle-ai-connectors - Installed!

Success! All packages have been installed. You are ready to use SDV Enterprise.
```

--------------------------------

### Example Installation Output

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions.md

This shows the expected console output after a successful installation.

```text
Installing SDV Enterprise:
sdv-enterprise (version 0.30.0) - Installed!

Installing Bundles:
bundle-cag - Installed!
bundle-xsynthesizers - Installed!

Success! All packages have been installed. You are ready to use SDV Enterprise.
```

--------------------------------

### Install All Available Packages

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions.md

Run this command to authenticate and install all packages associated with your license.

```bash
sdv-installer install --upgrade --username user@company.com --license-key 123
```

--------------------------------

### SDV Enterprise Installation Output

Source: https://docs.sdv.dev/sdv/explore/sdv-bundles/cag.md

Example output showing the successful installation of SDV Enterprise and the CAG bundle.

```text
Username: <email>
License Key: ********************************

Installing SDV Enterprise:
sdv-enterprise (version 0.30.0) - Installed!

Installing Bundles:
bundle-cag - Installed!

bundle-xsynthesizers - Installed!

Success! All packages have been installed. You are ready to use SDV Enterprise.
```

--------------------------------

### Install BigQuery Connector

Source: https://docs.sdv.dev/sdv/integration/db/integration/bigquery.md

Install the required SDV bundle and the BigQuery database dependency.

```bash
pip install sdv-installer --upgrade
sdv-installer install --package bundle-ai-connectors --options db-bigquery
```

--------------------------------

### Install PostgreSQL Connector

Source: https://docs.sdv.dev/sdv/integration/db/integration/postgresql.md

Use the sdv-installer to install the AI connectors bundle with the PostgreSQL dependency.

```bash
pip install sdv-installer --upgrade
sdv-installer install --package bundle-ai-connectors --options db-postgresql
```

--------------------------------

### Install AI Connectors Bundle

Source: https://docs.sdv.dev/sdv/explore/sdv-bundles/ai-connectors.md

Use the sdv-installer to upgrade the installer and install the AI connectors package with specific database options.

```bash
% pip install sdv-installer --upgrade
% sdv-installer install --package bundle-ai-connectors --options db-bigquery db-mssql
```

--------------------------------

### Install SDV Community

Source: https://docs.sdv.dev/sdv-enterprise/resources/poc-preparation/step-1.md

Install the SDV library to access utilities for data loading and creation.

```bash
%pip install sdv
```

--------------------------------

### Install or Update the SDV Installer

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions.md

Use this command to install or upgrade the SDV Installer tool.

```bash
pip install sdv-installer --upgrade
```

--------------------------------

### Install using credential file

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions.md

Use the --credentials flag to point the installer to a JSON file containing your authentication details.

```bash
sdv-installer install --upgrade --credentials my_credentials.json
```

--------------------------------

### Install SDV Enterprise from local folder

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions/download-install-separately.md

Executes the installation process using a previously downloaded local directory.

```bash
sdv-installer install --folder sdv-enterprise
```

--------------------------------

### Download and install packages locally

Source: https://docs.sdv.dev/sdv-enterprise/installation/troubleshooting.md

Download packages to a local directory first, then perform an offline installation.

```bash
mkdir sdv_folder
```

```bash
pip download sdv-enterprise --index-url https://pypi.datacebo.com --only-binary=:all: --dest sdv_folder
```

```bash
pip install -U sdv-enterprise --no-index --find-links sdv_folder
```

--------------------------------

### Example MCP Server Configuration

Source: https://docs.sdv.dev/sdv/agents/sdv-agent.md

A concrete example of the MCP server configuration using a specific file path.

```json
{
  "mcpServers": {
    "sdv_agent": {
      "command": "/Users/username/Documents/SDV/env/bin/sdv-agent"
    }
  }
}
```

--------------------------------

### Install SDV Enterprise packages

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions/sdv-installer-reference.md

Basic command to install packages from the server or a local folder.

```bash
sdv-installer install --username user@company.com --license-key 123
```

--------------------------------

### Install Oracle Connector

Source: https://docs.sdv.dev/sdv/integration/db/integration/oracle.md

Install the required AI Connectors bundle with the Oracle database dependency.

```bash
pip install sdv-installer --upgrade
sdv-installer install --package bundle-ai-connectors --options db-oracle
```

--------------------------------

### Install BigQuery Storage API

Source: https://docs.sdv.dev/sdv/integration/db/integration/bigquery.md

Install the optional dependency to enable faster data import and export.

```bash
pip install google-cloud-bigquery-storage
```

--------------------------------

### Install AlloyDB Connector

Source: https://docs.sdv.dev/sdv/integration/db/integration/alloydb.md

Use the sdv-installer to install the AI Connectors bundle with the AlloyDB dependency.

```bash
pip install sdv-installer --upgrade
sdv-installer install --package bundle-ai-connectors --options db-alloydb
```

--------------------------------

### Install Spanner Connector

Source: https://docs.sdv.dev/sdv/integration/db/integration/spanner.md

Use the sdv-installer to install the AI Connectors bundle with the db-spanner dependency.

```bash
pip install sdv-installer --upgrade
sdv-installer install --package bundle-ai-connectors --options db-spanner
```

--------------------------------

### Install AI Connectors Bundle

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions.md

Use this command to install the AI Connectors bundle with specific database options.

```bash
sdv-installer install --username user@company.com --license-key 123 --package bundle-ai-connectors --options db-bigquery db-mssql
```

--------------------------------

### Install SDV Agent and Enterprise

Source: https://docs.sdv.dev/sdv/agents/sdv-agent.md

Use the sdv-installer to upgrade and install the necessary packages for SDV Enterprise and the SDV Agent.

```bash
% pip install sdv-installer --upgrade
% sdv-installer install --upgrade
```

--------------------------------

### Example download output

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions/sdv-installer-reference.md

Shows the expected console output after running the download command.

```text
Downloading SDV Enterprise:
sdv-enterprise (version 0.30.0) - Downloaded!

Downloading Bundles:
bundle-cag - Downloaded!
bundle-xsynthesizers - Downloaded!

Success! All packages have been downloaded. Please install the packages to use SDV Enterprise.
```

--------------------------------

### Install SDV Excel dependencies

Source: https://docs.sdv.dev/sdv/integration/local/excel.md

Install the necessary optional dependencies to enable Excel file support in SDV.

```bash
pip install 'sdv[excel]'
```

--------------------------------

### List SDV packages

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions/sdv-installer-reference.md

Lists installed packages using provided credentials. If credentials are omitted, the installer will prompt for them interactively.

```bash
sdv-installer list-packages --username user@company.com --license-key 123
```

--------------------------------

### GET /sdv/welcome-to-the-sdv.md

Source: https://docs.sdv.dev/sdv/llms.txt

Perform an HTTP GET request on the documentation index to ask a specific question and optionally provide a broader goal for context.

```APIDOC
## GET /sdv/welcome-to-the-sdv.md

### Description
Perform an HTTP GET request on the documentation index with the `ask` parameter, and the optional `goal` parameter to retrieve a direct answer and relevant excerpts.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/welcome-to-the-sdv.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask, written in natural language.
- **goal** (string) - Optional - Describes the broader end goal to help tailor the answer.
```

--------------------------------

### Manually install SDV Enterprise packages

Source: https://docs.sdv.dev/sdv-enterprise/installation/troubleshooting.md

Use these commands to install SDV Enterprise and optional bundles directly via pip.

```bash
pip install -U sdv-enterprise --index-url https://pypi.datacebo.com
```

```bash
pip install -U sdv-enterprise bundle-cag bundle-xsynthesizers --index-url https://pypi.datacebo.com
```

--------------------------------

### Run SDV Installer with debug mode

Source: https://docs.sdv.dev/sdv-enterprise/installation/troubleshooting.md

Use the --debug flag to gather more information when troubleshooting installer failures.

```bash
pip install sdv-installer --upgrade --debug
```

--------------------------------

### Query documentation via HTTP GET

Source: https://docs.sdv.dev/sdv-enterprise/llms.txt

Perform an HTTP GET request to the documentation index with an ask parameter and an optional goal parameter.

```http
GET https://docs.sdv.dev/sdv-enterprise/installation/it-security.md?ask=<question>&goal=<end_goal>
```

--------------------------------

### Query Documentation via HTTP GET

Source: https://docs.sdv.dev/sdv/integration/local/csv.md

Perform an HTTP GET request to the current page URL with 'ask' and 'goal' parameters to retrieve documentation excerpts.

```http
GET https://docs.sdv.dev/sdv/integration/local/csv.md?ask=<question>&goal=<endgoal>
```

--------------------------------

### GET /modeling/single-table-synthesizers/xgcsynthesizer.md

Source: https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/xgcsynthesizer.md

Queries the documentation page for information using a natural language question.

```APIDOC
## GET /modeling/single-table-synthesizers/xgcsynthesizer.md

### Description
Queries the current documentation page to retrieve answers, excerpts, and sources based on a specific question.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/xgcsynthesizer.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The specific, self-contained question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the response.

### Request Example
GET https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/xgcsynthesizer.md?ask=How do I initialize the XGCSynthesizer?&goal=Training a model
```

--------------------------------

### GET /sdv/get-started.md

Source: https://docs.sdv.dev/sdv/get-started.md

Performs a dynamic query against the documentation page to retrieve answers and relevant excerpts based on a specific question and optional goal.

```APIDOC
## GET /sdv/get-started.md

### Description
Perform an HTTP GET request on the current page URL to query the documentation dynamically.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/get-started.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask, written in natural language.
- **goal** (string) - Optional - Describes the broader end goal to help tailor the response.
```

--------------------------------

### Integrate and Use Database Connectors

Source: https://docs.sdv.dev/sdv/integration/db.md

Example of initializing a database connector, configuring import/export settings, and performing data operations.

```python
from sdv.io.database import AlloyDBConnector

# 1. Integration: Create a connector and set your config
connector = AlloyDBConnector()
connector.set_import_config('source_db_config.yaml')
connector.set_export_config('destination_db_config.yaml')

# 2. Usage: Use the API create metadata, import training data, and export synthetic data
metadata = connector.create_metadata(table_names=['users', 'transactions', 'sessions'])
data = connector.import_random_subset(metadata=metadata)
...
connector.export(synthetic_data)
```

--------------------------------

### Initialize DPGCFlexSynthesizer

Source: https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/dpgcflexsynthesizer.md

Example of instantiating the synthesizer with required metadata, privacy budget, and optional constraints.

```python
synthesizer = DPGCFlexSynthesizer(
  metadata,
  epsilon=2.5, # we recommend values in the 0-10 range; 0-1 is the most conservative
  known_min_max_values={
    'age': {'min': 0, 'max': 120 },
    'salary': { 'min': 0 }
  },
  enforce_rounding=True,
  locales=['en_US'],
)
```

--------------------------------

### Verify SDV Installation

Source: https://docs.sdv.dev/sdv/explore/sdv-community.md

Check the installed version of SDV Community within a Python environment.

```python
import sdv
print(sdv.version.community)
```

--------------------------------

### Example output of requirement check

Source: https://docs.sdv.dev/sdv-enterprise/installation/requirements.md

Sample output showing the system configuration details returned by the verification command.

```text
Verifying system requirements for SDV Enterprise ...

Operating system: macOS
OS Version: 14
Architecture: arm64
Python Version: 3.11.13
System bit: 64-bit
Pip version: 24.2

Result: Passed
Your system meets the technical requirements for SDV Enterprise.
```

--------------------------------

### Install AI Connectors with database dependencies

Source: https://docs.sdv.dev/sdv-enterprise/installation/troubleshooting.md

Specify database-specific dependencies when installing the AI Connectors bundle.

```bash
pip install -U bundle-ai-connectors[db-spanner,db-mssql] --index-url https://pypi.datacebo.com
```

--------------------------------

### Initialize Database Connector

Source: https://docs.sdv.dev/sdv/integration/db/integration.md

Create a connector instance and define the paths for import and export configuration files.

```python
from sdv.io.database import AlloyDBConnector

connector = AlloyDBConnector()
connector.set_import_config(filepath='source_db_config.yaml')
connector.set_export_config(filepath='destination_db_config.yaml')
```

--------------------------------

### Use Credentials File for Authentication

Source: https://docs.sdv.dev/sdv-enterprise/installation/troubleshooting.md

Store credentials in a JSON file and reference it during the installation process.

```bash
% sdv-installer install --upgrade --credentials my_credentials.json
```

```json
{
    "username": "user@company.com",
    "license_key": "123"
}
```

--------------------------------

### Install SDV via Package Managers

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/installation.md

Use pip or conda to install the SDV library in your Python environment.

```shell
pip install sdv
```

```shell
conda install -c pytorch -c conda-forge sdv
```

--------------------------------

### Initialize and fit DPGCSynthesizer

Source: https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/dpgcsynthesizer.md

Basic usage showing the initialization of the synthesizer with metadata and epsilon, followed by fitting and sampling.

```python
from sdv.single_table import DPGCSynthesizer

synthesizer = DPGCSynthesizer(metadata, epsilon=2.5)
synthesizer.fit(data)

synthetic_data = synthesizer.sample(num_rows=10)
```

--------------------------------

### Auto-detect and apply constraints to a synthesizer

Source: https://docs.sdv.dev/sdv/modeling/constraint-augmented-generation-cag/auto-detect-constraints.md

Demonstrates the full workflow of initializing a synthesizer, detecting constraints from data, adding them, and fitting the model.

```python
from sdv.multi_table import HSASynthesizer

# create a synthesizer
synthesizer = HSASynthesizer(metadata)

# detect constraints based on the data and add them to the synthesizer
detected_constraints = synthesizer.detect_constraints(data)
synthesizer.add_constraints(detected_constraints)

# now fit and sample the synthesizer like usual
synthesizer.fit(data)
synthetic_data = synthesizer.sample()
```

--------------------------------

### Authenticate SDV installation

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions/sdv-installer-reference.md

Refreshes authentication credentials for an existing installation if the local auth file becomes out-of-sync.

```bash
sdv-installer authenticate --username user@company.com --license-key 123
```

--------------------------------

### Quality report output example

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/evaluation/data-quality.md

Example output showing the progress of the evaluation process and the resulting quality scores.

```text
Generating report ...
(1/4) Evaluating Column Shapes: : 100%|██████████| 13/13 [00:00<00:00, 338.47it/s]
(2/4) Evaluating Column Pair Trends: : 100%|██████████| 22/22 [00:00<00:00, 95.98it/s]
(3/4) Evaluating Cardinality: : 100%|██████████| 2/2 [00:00<00:00, 69.99it/s]
(4/4) Evaluating Intertable Trends: : 100%|██████████| 36/36 [00:00<00:00, 111.46it/s]

Overall Quality Score: 62.49%

Properties:
- Column Shapes: 79.23%
- Column Pair Trends: 42.5%
- Cardinality: 80.0%
- Intertable Trends: 48.24%
```

--------------------------------

### Initialize and use HSASynthesizer

Source: https://docs.sdv.dev/sdv/modeling/multi-table-synthesizers/hsasynthesizer.md

Basic workflow for fitting the synthesizer to data and generating synthetic samples.

```python
from sdv.multi_table import HSASynthesizer

synthesizer = HSASynthesizer(metadata)
synthesizer.fit(data)

synthetic_data = synthesizer.sample()
```

--------------------------------

### Diagnostic Details Output Example

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/evaluation/diagnostic.md

Example output format for the get_details method showing metrics per table and column.

```text
Table     Column	        Metric                   Score
guests    guest_email           KeyUniqueness            1.0
guests    had_rewards	        CategoryAdherence	 1.0
guests    room_type	        CategoryAdherence	 1.0
guests    amenities_fee	        BoundaryAdherence	 1.0
```

--------------------------------

### Initialize Google Cloud CLI for User Authentication

Source: https://docs.sdv.dev/sdv/integration/db/api.md

Commands to initialize the Google Cloud SDK and authenticate the local environment.

```bash
gcloud init
gcloud auth application-default login
```

--------------------------------

### Initialize and fit a GaussianCopulaSynthesizer

Source: https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/gaussiancopulasynthesizer.md

Basic workflow for importing the synthesizer, fitting it to data, and generating synthetic samples.

```python
from sdv.single_table import GaussianCopulaSynthesizer

synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(data)

synthetic_data = synthesizer.sample(num_rows=10)
```

--------------------------------

### Install MSSQL Connector

Source: https://docs.sdv.dev/sdv/integration/db/integration/mssql.md

Use the sdv-installer to add the db-mssql dependency to the AI Connectors bundle.

```bash
pip install sdv-installer --upgrade
sdv-installer install --package bundle-ai-connectors --options db-mssql
```

--------------------------------

### Verify SDV Enterprise Installation

Source: https://docs.sdv.dev/sdv-enterprise/sdv-enterprise/getting-started/part-i-setup.md

Use this snippet to confirm that the SDV Enterprise package is correctly installed and to check the current version.

```python
import sdv
print(sdv.version.enterprise)
```

--------------------------------

### HSASynthesizer.__init__

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/modeling/synthesizers/hsasynthesizer.md

Initializes the HSASynthesizer with metadata and optional configuration parameters.

```APIDOC
## HSASynthesizer(metadata, locales=None, default_num_clusters=None)

### Description
Initializes a new instance of the HSASynthesizer for multi-table data.

### Parameters
- **metadata** (MultiTableMetadata) - Required - The metadata object describing the multi-table structure.
- **locales** (list) - Optional - A list of locale strings for PII generation. Defaults to ['en_US'].
- **default_num_clusters** (int) - Optional - The number of clusters to segment each table into. Defaults to 3.
```

--------------------------------

### sdv-installer list-packages

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions/sdv-installer-reference.md

Lists all packages available to the user, including SDV Enterprise and purchased bundles.

```APIDOC
## sdv-installer list-packages

### Description
Lists all packages that the user has access to. This command does not download or install any packages.

### Parameters
- **--username** (string) - Optional - The username (email) for authentication.
- **--license-key** (string) - Optional - The license key for authentication.
- **--credentials** (string) - Optional - Path to a JSON file containing "username" and "license_key" fields.

### Authentication
If credentials are not provided via flags, the installer will interactively prompt for them.
```

--------------------------------

### GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/welcome-to-the-sdv.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/welcome-to-the-sdv.md

Perform an HTTP GET request on the documentation page URL to query information dynamically using natural language.

```APIDOC
## GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/welcome-to-the-sdv.md

### Description
Queries the documentation dynamically by asking a question. The response provides a direct answer along with relevant excerpts and sources.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/welcome-to-the-sdv.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask, written in natural language.
- **goal** (string) - Optional - Describes the broader end goal to help tailor the answer.
```

--------------------------------

### Query documentation via HTTP GET

Source: https://docs.sdv.dev/sdv/concepts/constraint-augmented-generation-cag/predefined-constraints/compositekeys

Perform an HTTP GET request to the documentation index to retrieve answers to specific questions.

```http
GET https://docs.sdv.dev/sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/mixedscales.md?ask=<question>&goal=<end_goal>
```

--------------------------------

### Initialize and Train Fast ML Synthesizer

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/single-table-data/modeling/synthesizers/fast-ml-preset.md

Basic workflow to import the preset, initialize it with metadata, fit it to data, and generate synthetic samples.

```python
from sdv.lite import SingleTablePreset

synthesizer = SingleTablePreset(metadata, name='FAST_ML')
synthesizer.fit(data)

synthetic_data = synthesizer.sample(num_rows=10)
```

--------------------------------

### DayZSynthesizer.__init__

Source: https://docs.sdv.dev/sdv/modeling/multi-table-synthesizers/dayzsynthesizer.md

Initializes a new instance of the DayZSynthesizer.

```APIDOC
## DayZSynthesizer(metadata)

### Description
Initializes the synthesizer with the provided metadata.

### Parameters
- **metadata** (Metadata) - Required - The metadata object describing the data structure.
```

--------------------------------

### GET /~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/conditional-sampling.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/conditional-sampling.md

Query the documentation dynamically by asking a question.

```APIDOC
## GET /~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/conditional-sampling.md

### Description
Perform an HTTP GET request on the current page URL to query the documentation dynamically.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/conditional-sampling.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask.
- **goal** (string) - Optional - Describes the broader end goal to tailor the answer.
```

--------------------------------

### Initialize and fit DPGCFlexSynthesizer

Source: https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/dpgcflexsynthesizer.md

Instantiate the synthesizer with metadata and a privacy budget, then fit it to the data and generate synthetic samples.

```python
from sdv.single_table import DPGCFlexSynthesizer

synthesizer = DPGCFlexSynthesizer(metadata, epsilon=2.5)
synthesizer.fit(data)

synthetic_data = synthesizer.sample(num_rows=10)
```

--------------------------------

### GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/single-table-data/sampling/sample-realistic-data.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/single-table-data/sampling/sample-realistic-data.md

Queries the documentation dynamically to retrieve information or clarifications.

```APIDOC
## GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/single-table-data/sampling/sample-realistic-data.md

### Description
Performs a query against the documentation to retrieve answers or context.

### Method
GET

### Query Parameters
- **ask** (string) - Required - The specific question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the answer.
```

--------------------------------

### GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/reference/constraint-logic/predefined-constraint-classes/fixedcombinations.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/reference/constraint-logic/predefined-constraint-classes/fixedcombinations.md

Query the documentation dynamically for additional information or clarification.

```APIDOC
## GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/reference/constraint-logic/predefined-constraint-classes/fixedcombinations.md

### Description
Perform a query on the documentation page to retrieve answers to specific questions or context.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/reference/constraint-logic/predefined-constraint-classes/fixedcombinations.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The specific question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the answer.
```

--------------------------------

### GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/sampling.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/sampling.md

Queries the documentation dynamically to retrieve information or clarifications.

```APIDOC
## GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/sampling.md

### Description
Perform a query against the documentation page to retrieve answers or context.

### Method
GET

### Query Parameters
- **ask** (string) - Required - The specific question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the response.
```

--------------------------------

### Initialize with Locales

Source: https://docs.sdv.dev/sdv/modeling/multi-table-synthesizers/independentsynthesizer.md

Configure PII generation by providing a list of locale strings.

```python
synthesizer = IndependentSynthesizer(
    metadata,
    locales=['en_US', 'en_CA', 'fr_CA']
)
```

--------------------------------

### Get Table Parameters

Source: https://docs.sdv.dev/sdv/modeling/multi-table-synthesizers/independentsynthesizer.md

Retrieve the configuration parameters for a specific table.

```python
synthesizer.get_table_parameters(table_name='users')
```

```python
{
    'synthesizer_name': 'GaussianCopulaSynthesizer',
    'synthesizer_parameters': {
        'default_distribution': 'beta',
        ...
    }
}
```

--------------------------------

### GET /sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/primarytoprimarykeysubset.md

Source: https://docs.sdv.dev/sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/primarytoprimarykeysubset.md

Queries the documentation dynamically for information related to this page.

```APIDOC
## GET /sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/primarytoprimarykeysubset.md

### Description
Performs a dynamic query against the documentation to retrieve answers or clarifications regarding the PrimaryToPrimaryKeySubset constraint.

### Method
GET

### Endpoint
/sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/primarytoprimarykeysubset.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The specific question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the response.
```

--------------------------------

### Create and Use Parameters

Source: https://docs.sdv.dev/sdv/modeling/multi-table-synthesizers/dayzsynthesizer.md

Generate parameters from existing data and metadata before initializing the synthesizer.

```python
from sdv.single_table import DayZSynthesizer

my_parameters = DayZSynthesizer.create_parameters(
  data=my_data,
  metadata=my_metadata,
  output_filename='dayz_parameters.json'
)

synthesizer = DayZSynthesizer(
    metadata,
    parameters=my_parameters,
    locales=['en_US', 'en_CA', 'fr_CA']
)
```

--------------------------------

### Initialize and Train XGCSynthesizer

Source: https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/xgcsynthesizer.md

Basic workflow for importing, fitting, and sampling data using the XGCSynthesizer.

```python
from sdv.single_table import XGCSynthesizer

synthesizer = XGCSynthesizer(metadata)
synthesizer.fit(data)

synthetic_data = synthesizer.sample(num_rows=10)
```

--------------------------------

### GET /sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/mixedscales.md

Source: https://docs.sdv.dev/sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/mixedscales.md

Query the documentation dynamically for additional information or clarification.

```APIDOC
## GET /sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/mixedscales.md

### Description
Perform a query against the documentation to retrieve answers or context not explicitly covered in the page.

### Method
GET

### Query Parameters
- **ask** (string) - Required - The specific question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the response.

### Request Example
GET https://docs.sdv.dev/sdv/modeling/constraint-augmented-generation-cag/predefined-constraints/mixedscales.md?ask=How do I handle multi-table constraints?&goal=Synthesizing patient data
```

--------------------------------

### Get modified metadata

Source: https://docs.sdv.dev/sdv/modeling/constraint-augmented-generation-cag/constraints-api.md

Retrieve the metadata object after constraints have been applied.

```python
modified_metadata = synthesizer.get_metadata(version='modified')
```

--------------------------------

### Download demo dataset

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/reference/constraint-logic/custom-logic/example-iftruethenzero.md

Loads the fake_hotel_guests dataset for testing the custom constraint logic.

```python
from sdv.datasets.demo import download_demo

real_data, metadata = download_demo(
    modality='single_table',
    dataset_name='fake_hotel_guests'
)
```

--------------------------------

### Download packages with sdv-installer

Source: https://docs.sdv.dev/sdv-enterprise/installation/instructions/sdv-installer-reference.md

Downloads packages to a specified folder without installing them.

```bash
sdv-installer download --folder sdv-enterprise/ --username user@company.com --license-key 123
```

--------------------------------

### Configure CTGANSynthesizer parameters

Source: https://docs.sdv.dev/sdv/modeling/single-table-synthesizers/ctgansynthesizer.md

Example of customizing the synthesizer behavior during initialization by passing optional arguments.

```python
from sdv.single_table import CTGANSynthesizer

synthesizer = CTGANSynthesizer(
    metadata, # required
    enforce_rounding=False,
    epochs=500,
    verbose=True
)
```

--------------------------------

### GET /~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/sample-realistic-data.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/sample-realistic-data.md

Queries the documentation dynamically by asking a question related to the content.

```APIDOC
## GET /~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/sample-realistic-data.md

### Description
Perform an HTTP GET request on the current page URL to query the documentation dynamically.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/sampling/sample-realistic-data.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask, written in natural language.
- **goal** (string) - Optional - Describes the broader end goal to help tailor the answer.
```

--------------------------------

### GET /sdv/integration/overview.md

Source: https://docs.sdv.dev/sdv/integration/overview.md

Queries the documentation page dynamically to retrieve answers based on a specific question and optional goal.

```APIDOC
## GET /sdv/integration/overview.md

### Description
Perform an HTTP GET request on the current page URL to query the documentation dynamically. This is useful for retrieving information not explicitly present in the page or for clarifying context.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/integration/overview.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask, written in natural language.
- **goal** (string) - Optional - Describes the broader end goal to help tailor the response.
```

--------------------------------

### GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/data-preparation/sequential-metadata-api.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/data-preparation/sequential-metadata-api.md

Queries the documentation dynamically by asking a question related to the content.

```APIDOC
## GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/data-preparation/sequential-metadata-api.md

### Description
Perform an HTTP GET request on the current page URL to query the documentation dynamically.

### Query Parameters
- **ask** (string) - Required - The immediate question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the answer.

### Request Example
GET https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/sequential-data/data-preparation/sequential-metadata-api.md?ask=How+do+I+set+a+sequence+key?&goal=Data+preparation
```

--------------------------------

### GET /

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/modeling/synthesizers/independentsynthesizer.md

Perform a query against the documentation page to receive an answer based on the content.

```APIDOC
## GET /

### Description
Queries the documentation page to retrieve an answer to a specific question, optionally tailored to a broader goal.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/modeling/synthesizers/independentsynthesizer.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The specific, self-contained question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the response.
```

--------------------------------

### GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/modeling/customizations.md

Source: https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/modeling/customizations.md

Perform a query against the documentation to retrieve answers and relevant excerpts.

```APIDOC
## GET /sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/modeling/customizations.md

### Description
Queries the documentation dynamically by providing a natural language question.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/~/changes/T3ZD1DOoRUEqkmrAGBZp/multi-table-data/modeling/customizations.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask.
- **goal** (string) - Optional - The broader end goal to help tailor the answer.
```

--------------------------------

### GET /sdv/sampling/conditional/sequential.md

Source: https://docs.sdv.dev/sdv/sampling/conditional/sequential.md

Queries the documentation dynamically by asking a question related to the content.

```APIDOC
## GET /sdv/sampling/conditional/sequential.md

### Description
Perform an HTTP GET request on the page URL to query the documentation dynamically when information is not explicitly present or clarification is needed.

### Method
GET

### Endpoint
https://docs.sdv.dev/sdv/sampling/conditional/sequential.md

### Parameters
#### Query Parameters
- **ask** (string) - Required - The immediate question to ask, written in natural language.
- **goal** (string) - Optional - The broader end goal to help tailor the response.

### Response
#### Success Response (200)
- (string) - A direct answer to the question along with relevant excerpts and sources from the documentation.
```
Tokens:
Raw