> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Method to load data

> API reference for the DataAnalyzr `get_data` method

```python theme={null}
DataAnalyzr.get_data(
    db_type: Literal["files", "redshift", "postgres", "sqlite"],
    db_config: dict,
    vector_store_config: dict = {},
) -> None
```

This method retrieves data from various types of databases or files based on the provided configuration.
It also creates a vector store for the data. This method must be called before performing any analysis.

1. The required keys in the `config` dictionary depend on the specified `db_type`.
2. The `vector_store_config` dictionary is optional and can be used to configure the vector store.
3. Sets the `df_dict`, `database_connector` and `vector_store` attributes of the `DataAnalyzr` object.
4. The method does not return any value.

## Parameters

<ParamField path="db_type" type="Literal['files', 'redshift', 'postgres', 'sqlite']" required>
  The type of database to connect to.

  ```python theme={null}
  db_type = "postgres"
  ```
</ParamField>

<ParamField path="db_config" type="dictionary" required>
  Configuration dictionary for the database connection.

  When `db_type` is `files`:

  ```python theme={null}
  db_config = {
      "datasets": [
          {
              "name": "dataset1",
              "value": "path/to/dataset1.csv",
              # files can be in .csv, .xlsx, .xls, and .json formats
          },
          {
              "name": "dataset2",
              "value": "path/to/dataset2.xlsx",
              "kwargs": {"sheet_name": "Sheet1"},
              # pass optional keyword arguments for reading the file
          },
          {
              "name": "dataset3",
              "value": pd.read_csv("path/to/dataset3.csv"),
              # you can also pass pandas DataFrame objects
          },
      ],
      "db_path": "path/to/construct/sqlite.db", # optional
  }
  ```

  <Expandable title="details">
    <ParamField path="datasets" type="list">
      List of dictionaries containing the name and value of the datasets to load.
    </ParamField>

    <ParamField path="db_path" type="string">
      Location where a SQLite database must be created.
      Only relevant when `analysis_type` is `sql`. Defaults to `sqlite/<random-path>.db`.
    </ParamField>
  </Expandable>

  When `db_type` is `redshift` or `postgres`:

  ```python theme={null}
  db_config = {
      "host": "localhost",
      "port": 5432,
      "user": "username",
      "password": "password",
      "database": "dbname",
      "schema": ["schema_name1", "schema_name2"], # optional
      "tables": ["table_name1", "table_name2"], # optional
  }
  ```

  <Expandable title="details">
    <ParamField path="host" type="string">
      Hostname of the database server.
    </ParamField>

    <ParamField path="port" type="integer">
      Port number of the database server.
    </ParamField>

    <ParamField path="user" type="string">
      Username for the database connection.
    </ParamField>

    <ParamField path="password" type="string">
      Password for the database connection.
    </ParamField>

    <ParamField path="database" type="string">
      Name of the database to connect to.
    </ParamField>

    <ParamField path="schema" type="list">
      List of schema names to load. Defaults to all schemas not in the `information_schema` and `pg_catalog` schemas.
    </ParamField>

    <ParamField path="tables" type="list">
      List of table names to load. Defaults to all tables in the specified schema.
    </ParamField>
  </Expandable>

  When `db_type` is `sqlite`:

  ```python theme={null}
  db_config = {
      "db_path": "path/to/sqlite.db",
  }
  ```

  <Expandable title="details">
    <ParamField path="db_path" type="string">
      Path to the SQLite database file.
    </ParamField>
  </Expandable>
</ParamField>

<ParamField path="vector_store_config" type="dictionary">
  Configuration dictionary for the vector store.

  ```python theme={null}
  vector_store_config = {
      "path": "path/to/vector_store", # optional
      "remake_store": False # optional
  }
  ```

  For details on vector store usage and configuration, refer to the [Vector Store](/pre-built-agents/data-analyzr/advanced-configuration/vector-store) guide.

  <Expandable title="details">
    <ParamField path="path" type="string">
      Path to the vector store.
      If not vector store is found at the specified path, a new one will be created.
      Defaults to `vector_store/<random-path>`.
    </ParamField>

    <ParamField path="remake_store" type="boolean">
      Whether to recreate the vector store.
      If set to `True`, the vector store will be recreated.
      Defaults to `False`.
    </ParamField>
  </Expandable>
</ParamField>

## Example usage

<CodeGroup>
  ```python files theme={null}
  db_config = {
      "datasets": [
          {
              "name": "dataset1",
              "value": "path/to/dataset1.csv",
              # files can be in .csv, .xlsx, .xls, and .json formats
          },
          {
              "name": "dataset2",
              "value": "path/to/dataset2.xlsx",
              "kwargs": {"sheet_name": "Sheet1"},
              # pass optional keyword arguments for reading the file
          },
          {
              "name": "dataset3",
              "value": pd.read_csv("path/to/dataset3.csv"),
              # you can also pass pandas DataFrame objects
          },
      ],
      "db_path": "path/to/construct/sqlite.db", # optional
  }
  vector_store_config = {
      "path": "path/to/vector_store",
      "remake_store": False,
  }
  data_analyzr.get_data(
      db_type="files",
      db_config=db_config,
      vector_store_config=vector_store_config, # optional
  )
  ```

  ```python redshift theme={null}
  db_config = {
      "host": "localhost",
      "port": 5432,
      "user": "username",
      "password": "password",
      "database": "dbname",
      "schema": ["schema_name1", "schema_name2"], # optional
      "tables": ["table_name1", "table_name2"], # optional
  }
  vector_store_config = {
      "path": "path/to/vector_store",
      "remake_store": False,
  }
  data_analyzr.get_data(
      db_type="redshift",
      db_config=db_config,
      vector_store_config=vector_store_config, # optional
  )
  ```

  ```python postgres theme={null}
  db_config = {
      "host": "localhost",
      "port": 5432,
      "user": "username",
      "password": "password",
      "database": "dbname",
      "schema": ["schema_name1", "schema_name2"], # optional
      "tables": ["table_name1", "table_name2"], # optional
  }
  vector_store_config = {
      "path": "path/to/vector_store",
      "remake_store": False,
  }
  data_analyzr.get_data(
      db_type="postgres",
      db_config=db_config,
      vector_store_config=vector_store_config, # optional
  )
  ```

  ```python sqlite theme={null}
  db_config = {
      "db_path": "path/to/sqlite.db"
  }
  vector_store_config = {
      "path": "path/to/vector_store",
      "remake_store": False,
  }
  data_analyzr.get_data(
      db_type="sqlite",
      db_config=db_config,
      vector_store_config=vector_store_config, # optional
  )
  ```
</CodeGroup>