langchain/libs/community/langchain_community/utilities/sql_database.py

"""SQLAlchemy wrapper around a database."""
from __future__ import annotations

from typing import Any, Dict, Iterable, List, Literal, Optional, Sequence, Union

import sqlalchemy
from langchain_core._api import deprecated
from langchain_core.utils import get_from_env
from sqlalchemy import (
    Executable,
    MetaData,
    Result,
    Table,
    create_engine,
    inspect,
    select,
    text,
)
from sqlalchemy.engine import Engine
from sqlalchemy.exc import ProgrammingError, SQLAlchemyError
from sqlalchemy.schema import CreateTable
from sqlalchemy.types import NullType


def _format_index(index: sqlalchemy.engine.interfaces.ReflectedIndex) -> str:
    return (
        f'Name: {index["name"]}, Unique: {index["unique"]},'
        f' Columns: {str(index["column_names"])}'
    )


def truncate_word(content: Any, *, length: int, suffix: str = "...") -> str:
    """
    Truncate a string to a certain number of words, based on the max string
    length.
    """

    if not isinstance(content, str) or length <= 0:
        return content

    if len(content) <= length:
        return content

    return content[: length - len(suffix)].rsplit(" ", 1)[0] + suffix


class SQLDatabase:
    """SQLAlchemy wrapper around a database."""

    def __init__(
        self,
        engine: Engine,
        schema: Optional[str] = None,
        metadata: Optional[MetaData] = None,
        ignore_tables: Optional[List[str]] = None,
        include_tables: Optional[List[str]] = None,
        sample_rows_in_table_info: int = 3,
        indexes_in_table_info: bool = False,
        custom_table_info: Optional[dict] = None,
        view_support: bool = False,
        max_string_length: int = 300,
    ):
        """Create engine from database URI."""
        self._engine = engine
        self._schema = schema
        if include_tables and ignore_tables:
            raise ValueError("Cannot specify both include_tables and ignore_tables")

        self._inspector = inspect(self._engine)

        # including view support by adding the views as well as tables to the all
        # tables list if view_support is True
        self._all_tables = set(
            self._inspector.get_table_names(schema=schema)
            + (self._inspector.get_view_names(schema=schema) if view_support else [])
        )

        self._include_tables = set(include_tables) if include_tables else set()
        if self._include_tables:
            missing_tables = self._include_tables - self._all_tables
            if missing_tables:
                raise ValueError(
                    f"include_tables {missing_tables} not found in database"
                )
        self._ignore_tables = set(ignore_tables) if ignore_tables else set()
        if self._ignore_tables:
            missing_tables = self._ignore_tables - self._all_tables
            if missing_tables:
                raise ValueError(
                    f"ignore_tables {missing_tables} not found in database"
                )
        usable_tables = self.get_usable_table_names()
        self._usable_tables = set(usable_tables) if usable_tables else self._all_tables

        if not isinstance(sample_rows_in_table_info, int):
            raise TypeError("sample_rows_in_table_info must be an integer")

        self._sample_rows_in_table_info = sample_rows_in_table_info
        self._indexes_in_table_info = indexes_in_table_info

        self._custom_table_info = custom_table_info
        if self._custom_table_info:
            if not isinstance(self._custom_table_info, dict):
                raise TypeError(
                    "table_info must be a dictionary with table names as keys and the "
                    "desired table info as values"
                )
            # only keep the tables that are also present in the database
            intersection = set(self._custom_table_info).intersection(self._all_tables)
            self._custom_table_info = dict(
                (table, self._custom_table_info[table])
                for table in self._custom_table_info
                if table in intersection
            )

        self._max_string_length = max_string_length

        self._metadata = metadata or MetaData()
        # including view support if view_support = true
        self._metadata.reflect(
            views=view_support,
            bind=self._engine,
            only=list(self._usable_tables),
            schema=self._schema,
        )

    @classmethod
    def from_uri(
        cls, database_uri: str, engine_args: Optional[dict] = None, **kwargs: Any
    ) -> SQLDatabase:
        """Construct a SQLAlchemy engine from URI."""
        _engine_args = engine_args or {}
        return cls(create_engine(database_uri, **_engine_args), **kwargs)

    @classmethod
    def from_databricks(
        cls,
        catalog: str,
        schema: str,
        host: Optional[str] = None,
        api_token: Optional[str] = None,
        warehouse_id: Optional[str] = None,
        cluster_id: Optional[str] = None,
        engine_args: Optional[dict] = None,
        **kwargs: Any,
    ) -> SQLDatabase:
        """
        Class method to create an SQLDatabase instance from a Databricks connection.
        This method requires the 'databricks-sql-connector' package. If not installed,
        it can be added using `pip install databricks-sql-connector`.

        Args:
            catalog (str): The catalog name in the Databricks database.
            schema (str): The schema name in the catalog.
            host (Optional[str]): The Databricks workspace hostname, excluding
                'https://' part. If not provided, it attempts to fetch from the
                environment variable 'DATABRICKS_HOST'. If still unavailable and if
                running in a Databricks notebook, it defaults to the current workspace
                hostname. Defaults to None.
            api_token (Optional[str]): The Databricks personal access token for
                accessing the Databricks SQL warehouse or the cluster. If not provided,
                it attempts to fetch from 'DATABRICKS_TOKEN'. If still unavailable
                and running in a Databricks notebook, a temporary token for the current
                user is generated. Defaults to None.
            warehouse_id (Optional[str]): The warehouse ID in the Databricks SQL. If
                provided, the method configures the connection to use this warehouse.
                Cannot be used with 'cluster_id'. Defaults to None.
            cluster_id (Optional[str]): The cluster ID in the Databricks Runtime. If
                provided, the method configures the connection to use this cluster.
                Cannot be used with 'warehouse_id'. If running in a Databricks notebook
                and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the
                cluster the notebook is attached to. Defaults to None.
            engine_args (Optional[dict]): The arguments to be used when connecting
                Databricks. Defaults to None.
            **kwargs (Any): Additional keyword arguments for the `from_uri` method.

        Returns:
            SQLDatabase: An instance of SQLDatabase configured with the provided
                Databricks connection details.

        Raises:
            ValueError: If 'databricks-sql-connector' is not found, or if both
                'warehouse_id' and 'cluster_id' are provided, or if neither
                'warehouse_id' nor 'cluster_id' are provided and it's not executing
                inside a Databricks notebook.
        """
        try:
            from databricks import sql  # noqa: F401
        except ImportError:
            raise ValueError(
                "databricks-sql-connector package not found, please install with"
                " `pip install databricks-sql-connector`"
            )
        context = None
        try:
            from dbruntime.databricks_repl_context import get_context

            context = get_context()
        except ImportError:
            pass

        default_host = context.browserHostName if context else None
        if host is None:
            host = get_from_env("host", "DATABRICKS_HOST", default_host)

        default_api_token = context.apiToken if context else None
        if api_token is None:
            api_token = get_from_env("api_token", "DATABRICKS_TOKEN", default_api_token)

        if warehouse_id is None and cluster_id is None:
            if context:
                cluster_id = context.clusterId
            else:
                raise ValueError(
                    "Need to provide either 'warehouse_id' or 'cluster_id'."
                )

        if warehouse_id and cluster_id:
            raise ValueError("Can't have both 'warehouse_id' or 'cluster_id'.")

        if warehouse_id:
            http_path = f"/sql/1.0/warehouses/{warehouse_id}"
        else:
            http_path = f"/sql/protocolv1/o/0/{cluster_id}"

        uri = (
            f"databricks://token:{api_token}@{host}?"
            f"http_path={http_path}&catalog={catalog}&schema={schema}"
        )
        return cls.from_uri(database_uri=uri, engine_args=engine_args, **kwargs)

    @classmethod
    def from_cnosdb(
        cls,
        url: str = "127.0.0.1:8902",
        user: str = "root",
        password: str = "",
        tenant: str = "cnosdb",
        database: str = "public",
    ) -> SQLDatabase:
        """
        Class method to create an SQLDatabase instance from a CnosDB connection.
        This method requires the 'cnos-connector' package. If not installed, it
        can be added using `pip install cnos-connector`.

        Args:
            url (str): The HTTP connection host name and port number of the CnosDB
                service, excluding "http://" or "https://", with a default value
                of "127.0.0.1:8902".
            user (str): The username used to connect to the CnosDB service, with a
                default value of "root".
            password (str): The password of the user connecting to the CnosDB service,
                with a default value of "".
            tenant (str): The name of the tenant used to connect to the CnosDB service,
                with a default value of "cnosdb".
            database (str): The name of the database in the CnosDB tenant.

        Returns:
            SQLDatabase: An instance of SQLDatabase configured with the provided
            CnosDB connection details.
        """
        try:
            from cnosdb_connector import make_cnosdb_langchain_uri

            uri = make_cnosdb_langchain_uri(url, user, password, tenant, database)
            return cls.from_uri(database_uri=uri)
        except ImportError:
            raise ValueError(
                "cnos-connector package not found, please install with"
                " `pip install cnos-connector`"
            )

    @property
    def dialect(self) -> str:
        """Return string representation of dialect to use."""
        return self._engine.dialect.name

    def get_usable_table_names(self) -> Iterable[str]:
        """Get names of tables available."""
        if self._include_tables:
            return sorted(self._include_tables)
        return sorted(self._all_tables - self._ignore_tables)

    @deprecated("0.0.1", alternative="get_usable_table_name", removal="0.2.0")
    def get_table_names(self) -> Iterable[str]:
        """Get names of tables available."""
        return self.get_usable_table_names()

    @property
    def table_info(self) -> str:
        """Information about all tables in the database."""
        return self.get_table_info()

    def get_table_info(self, table_names: Optional[List[str]] = None) -> str:
        """Get information about specified tables.

        Follows best practices as specified in: Rajkumar et al, 2022
        (https://arxiv.org/abs/2204.00498)

        If `sample_rows_in_table_info`, the specified number of sample rows will be
        appended to each table description. This can increase performance as
        demonstrated in the paper.
        """
        all_table_names = self.get_usable_table_names()
        if table_names is not None:
            missing_tables = set(table_names).difference(all_table_names)
            if missing_tables:
                raise ValueError(f"table_names {missing_tables} not found in database")
            all_table_names = table_names

        meta_tables = [
            tbl
            for tbl in self._metadata.sorted_tables
            if tbl.name in set(all_table_names)
            and not (self.dialect == "sqlite" and tbl.name.startswith("sqlite_"))
        ]

        tables = []
        for table in meta_tables:
            if self._custom_table_info and table.name in self._custom_table_info:
                tables.append(self._custom_table_info[table.name])
                continue

            # Ignore JSON datatyped columns
            for k, v in table.columns.items():
                if type(v.type) is NullType:
                    table._columns.remove(v)

            # add create table command
            create_table = str(CreateTable(table).compile(self._engine))
            table_info = f"{create_table.rstrip()}"
            has_extra_info = (
                self._indexes_in_table_info or self._sample_rows_in_table_info
            )
            if has_extra_info:
                table_info += "\n\n/*"
            if self._indexes_in_table_info:
                table_info += f"\n{self._get_table_indexes(table)}\n"
            if self._sample_rows_in_table_info:
                table_info += f"\n{self._get_sample_rows(table)}\n"
            if has_extra_info:
                table_info += "*/"
            tables.append(table_info)
        tables.sort()
        final_str = "\n\n".join(tables)
        return final_str

    def _get_table_indexes(self, table: Table) -> str:
        indexes = self._inspector.get_indexes(table.name)
        indexes_formatted = "\n".join(map(_format_index, indexes))
        return f"Table Indexes:\n{indexes_formatted}"

    def _get_sample_rows(self, table: Table) -> str:
        # build the select command
        command = select(table).limit(self._sample_rows_in_table_info)

        # save the columns in string format
        columns_str = "\t".join([col.name for col in table.columns])

        try:
            # get the sample rows
            with self._engine.connect() as connection:
                sample_rows_result = connection.execute(command)  # type: ignore
                # shorten values in the sample rows
                sample_rows = list(
                    map(lambda ls: [str(i)[:100] for i in ls], sample_rows_result)
                )

            # save the sample rows in string format
            sample_rows_str = "\n".join(["\t".join(row) for row in sample_rows])

        # in some dialects when there are no rows in the table a
        # 'ProgrammingError' is returned
        except ProgrammingError:
            sample_rows_str = ""

        return (
            f"{self._sample_rows_in_table_info} rows from {table.name} table:\n"
            f"{columns_str}\n"
            f"{sample_rows_str}"
        )

    def _execute(
        self,
        command: Union[str, Executable],
        fetch: Literal["all", "one", "cursor"] = "all",
        *,
        parameters: Optional[Dict[str, Any]] = None,
        execution_options: Optional[Dict[str, Any]] = None,
    ) -> Union[Sequence[Dict[str, Any]], Result]:
        """
        Executes SQL command through underlying engine.

        If the statement returns no rows, an empty list is returned.
        """
        parameters = parameters or {}
        execution_options = execution_options or {}
        with self._engine.begin() as connection:  # type: Connection  # type: ignore[name-defined]
            if self._schema is not None:
                if self.dialect == "snowflake":
                    connection.exec_driver_sql(
                        "ALTER SESSION SET search_path = %s",
                        (self._schema,),
                        execution_options=execution_options,
                    )
                elif self.dialect == "bigquery":
                    connection.exec_driver_sql(
                        "SET @@dataset_id=?",
                        (self._schema,),
                        execution_options=execution_options,
                    )
                elif self.dialect == "mssql":
                    pass
                elif self.dialect == "trino":
                    connection.exec_driver_sql(
                        "USE ?",
                        (self._schema,),
                        execution_options=execution_options,
                    )
                elif self.dialect == "duckdb":
                    # Unclear which parameterized argument syntax duckdb supports.
                    # The docs for the duckdb client say they support multiple,
                    # but `duckdb_engine` seemed to struggle with all of them:
                    # https://github.com/Mause/duckdb_engine/issues/796
                    connection.exec_driver_sql(
                        f"SET search_path TO {self._schema}",
                        execution_options=execution_options,
                    )
                elif self.dialect == "oracle":
                    connection.exec_driver_sql(
                        f"ALTER SESSION SET CURRENT_SCHEMA = {self._schema}",
                        execution_options=execution_options,
                    )
                elif self.dialect == "sqlany":
                    # If anybody using Sybase SQL anywhere database then it should not
                    # go to else condition. It should be same as mssql.
                    pass
                elif self.dialect == "postgresql":  # postgresql
                    connection.exec_driver_sql(
                        "SET search_path TO %s",
                        (self._schema,),
                        execution_options=execution_options,
                    )

            if isinstance(command, str):
                command = text(command)
            elif isinstance(command, Executable):
                pass
            else:
                raise TypeError(f"Query expression has unknown type: {type(command)}")
            cursor = connection.execute(
                command,
                parameters,
                execution_options=execution_options,
            )

            if cursor.returns_rows:
                if fetch == "all":
                    result = [x._asdict() for x in cursor.fetchall()]
                elif fetch == "one":
                    first_result = cursor.fetchone()
                    result = [] if first_result is None else [first_result._asdict()]
                elif fetch == "cursor":
                    return cursor
                else:
                    raise ValueError(
                        "Fetch parameter must be either 'one', 'all', or 'cursor'"
                    )
                return result
        return []

    def run(
        self,
        command: Union[str, Executable],
        fetch: Literal["all", "one", "cursor"] = "all",
        include_columns: bool = False,
        *,
        parameters: Optional[Dict[str, Any]] = None,
        execution_options: Optional[Dict[str, Any]] = None,
    ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]:
        """Execute a SQL command and return a string representing the results.

        If the statement returns rows, a string of the results is returned.
        If the statement returns no rows, an empty string is returned.
        """
        result = self._execute(
            command, fetch, parameters=parameters, execution_options=execution_options
        )

        if fetch == "cursor":
            return result

        res = [
            {
                column: truncate_word(value, length=self._max_string_length)
                for column, value in r.items()
            }
            for r in result
        ]

        if not include_columns:
            res = [tuple(row.values()) for row in res]  # type: ignore[misc]

        if not res:
            return ""
        else:
            return str(res)

    def get_table_info_no_throw(self, table_names: Optional[List[str]] = None) -> str:
        """Get information about specified tables.

        Follows best practices as specified in: Rajkumar et al, 2022
        (https://arxiv.org/abs/2204.00498)

        If `sample_rows_in_table_info`, the specified number of sample rows will be
        appended to each table description. This can increase performance as
        demonstrated in the paper.
        """
        try:
            return self.get_table_info(table_names)
        except ValueError as e:
            """Format the error message"""
            return f"Error: {e}"

    def run_no_throw(
        self,
        command: str,
        fetch: Literal["all", "one"] = "all",
        include_columns: bool = False,
        *,
        parameters: Optional[Dict[str, Any]] = None,
        execution_options: Optional[Dict[str, Any]] = None,
    ) -> Union[str, Sequence[Dict[str, Any]], Result[Any]]:
        """Execute a SQL command and return a string representing the results.

        If the statement returns rows, a string of the results is returned.
        If the statement returns no rows, an empty string is returned.

        If the statement throws an error, the error message is returned.
        """
        try:
            return self.run(
                command,
                fetch,
                parameters=parameters,
                execution_options=execution_options,
                include_columns=include_columns,
            )
        except SQLAlchemyError as e:
            """Format the error message"""
            return f"Error: {e}"

    def get_context(self) -> Dict[str, Any]:
        """Return db context that you may want in agent prompt."""
        table_names = list(self.get_usable_table_names())
        table_info = self.get_table_info_no_throw()
        return {"table_info": table_info, "table_names": ", ".join(table_names)}