--- title: "Database Tools" description: "Install and configure built-in database query tools." --- This page covers installation and server-side configuration for built-in database tools. Use this together with: - [Tools](/api-guide/tools) for request examples and runtime usage --- ## Supported database tools Built-in database tools include: - `database_query_v1` - `/v1/tools/database-query` --- ## How to install ### Python extras Choose the smallest extra that matches your database: | Database | Extra | Installs | |---|---|---| | PostgreSQL | `private-gpt[database-postgres]` | `psycopg2-binary`, `asyncpg` | | MySQL | `private-gpt[database-mysql]` | `pymysql` | | SQL Server | `private-gpt[database-mssql]` | `pyodbc` | | DB2 | `private-gpt[database-db2]` | `ibm-db`, `ibm-db-sa` | | All database drivers | `private-gpt[database]` | all of the above | | Database tool bundle | `private-gpt[tool-database]` | `private-gpt[database]` | If you only want the database tool itself, `tool-database` is the simplest entry point. If you want a narrower install, use the driver-specific extra directly. DB2 support is only available on non-`aarch64` platforms in the current package metadata. On arm64 / Apple Silicon, the `database-db2` extra cannot be installed and DB2 is not supported. Examples: ```bash uv sync --extra database-postgres uv sync --extra database-mysql uv sync --extra database-mssql uv sync --extra database-db2 uv sync --extra tool-database ``` `database_query_v1` will also work when you install broader bundles that include database support, such as `private-gpt[database]`, `private-gpt[tool-database]`, `private-gpt[tools]`, or `private-gpt[core]`. --- ### OS libraries required The Python drivers above need database client libraries from the operating system. | Database | What the driver needs | Notes | |---|---|---| | PostgreSQL | No extra OS package in most cases | `psycopg2-binary` and `asyncpg` bundle their own client libraries on common platforms. | | MySQL / MariaDB | MySQL/MariaDB client libraries | On Debian/Ubuntu the Dockerfile installs `libmariadb3`. On other distros install your MySQL/MariaDB client package. | | SQL Server | Microsoft ODBC driver | The Docker image installs `msodbcsql18` from Microsoft’s APT repo. Use the matching ODBC driver for your OS. | | DB2 | IBM DB2 client libraries | `ibm-db` uses IBM’s DB2 client; availability varies by platform and architecture. Not available on arm64 / Apple Silicon. | --- ## Install by environment When you use the published Docker image (`zylonai/private-gpt:latest` and variants), there is nothing extra to install for database tools: the image already contains the Python drivers and required OS libraries for all supported databases. Only if you are **building your own image from source** do you need to think about extras: ```bash docker build \ --build-arg EXTRAS="core tool-database" \ -t private-gpt-db . ``` This mirrors the official image behavior by: - Installing the Python extras from the table above (`tool-database` → all `database-*` extras). - Triggering the Dockerfile logic that installs OS-level libraries (`libmariadb3` for MySQL/MariaDB and `msodbcsql18` for SQL Server). After that, run the image as usual: ```bash docker run -p 8080:8080 private-gpt-db ``` You can still pass additional extras via `EXTRAS` if you also need ingestion, media, or other tools. For local macOS installs, first install PrivateGPT with database support (via `uv` or the package): ```bash # Example with uv source install uv sync --frozen --extra core --extra tool-database ``` Then ensure database client libraries are available: - **PostgreSQL**: Install Postgres (includes client libs), for example: ```bash brew install postgresql ``` - **MySQL / MariaDB** (matches the example setup script): ```bash brew install mysql pkg-config ``` - **SQL Server**: Install the Microsoft ODBC driver for SQL Server, for example: ```bash brew install unixodbc freetds brew tap microsoft/mssql-release https://github.com/Microsoft/homebrew-mssql-release brew update HOMEBREW_ACCEPT_EULA=Y brew install msodbcsql18 mssql-tools18 ``` - **DB2**: (non-arm64 only) Install IBM’s DB2 client for macOS and configure the environment according to IBM’s documentation; `ibm-db` will detect it. DB2 is not available on Apple Silicon. On Linux, install PrivateGPT with database support (for example with uv): ```bash uv sync --frozen --extra core --extra tool-database ``` Then install the OS packages for your distribution. For Debian/Ubuntu-like systems: - **PostgreSQL**: ```bash sudo apt update sudo apt install postgresql-client ``` - **MySQL / MariaDB** (matches the Dockerfile’s `libmariadb3`): ```bash sudo apt update sudo apt install libmariadb3 ``` - **SQL Server** (same approach as the Dockerfile): ```bash curl -sSL -O https://packages.microsoft.com/config/debian/12/packages-microsoft-prod.deb sudo dpkg -i packages-microsoft-prod.deb rm packages-microsoft-prod.deb sudo apt update sudo ACCEPT_EULA=Y apt install msodbcsql18 ``` - **DB2** (non-arm64 only): Install IBM’s DB2 client packages for your distribution and architecture, then configure as documented by IBM. The `database-db2` extra is only published for non-`aarch64` platforms and is not available on arm64. After installing both the Python extras and the OS libraries, restart the server and `database_query_v1` will be able to connect to your configured databases. --- ## Settings reference Database query does not have a global `enabled` flag in `settings.yaml`. Instead, the server uses runtime limits from `database_query`, and each request provides the target database through a `sql_database` artifact. ```yaml database_query: timeout_seconds: 1000 batch_size: 1000 max_mb_result: 150 ``` --- ### Settings | Setting | Description | |---|---| | `database_query.timeout_seconds` | Maximum time allowed for a database query. | | `database_query.batch_size` | Number of rows processed per batch. | | `database_query.max_mb_result` | Maximum response size in MB before truncation or failure. | --- ### Runtime requirement The database connection is not configured globally in `settings.yaml`. Pass it in the request as a `sql_database` artifact. See [Tools](/api-guide/tools) for Messages API and standalone tool endpoint examples.