Source code for langchain_experimental.sql.base

"""Chain for interacting with SQL Database."""

from __future__ import annotations

import warnings
from typing import Any, Dict, List, Optional

from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT, SQL_PROMPTS
from langchain.schema import BasePromptTemplate
from langchain_community.tools.sql_database.prompt import QUERY_CHECKER
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_core.callbacks.manager import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.prompt import PromptTemplate
from pydantic import ConfigDict, Field, model_validator

INTERMEDIATE_STEPS_KEY = "intermediate_steps"
SQL_QUERY = "SQLQuery:"
SQL_RESULT = "SQLResult:"



[docs]
class SQLDatabaseChain(Chain):
    """Chain for interacting with SQL Database.

    Example:
        .. code-block:: python

            from langchain_experimental.sql import SQLDatabaseChain
            from langchain_community.llms import OpenAI, SQLDatabase
            db = SQLDatabase(...)
            db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)

    *Security note*: Make sure that the database connection uses credentials
        that are narrowly-scoped to only include the permissions this chain needs.
        Failure to do so may result in data corruption or loss, since this chain may
        attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
        The best way to guard against such negative outcomes is to (as appropriate)
        limit the permissions granted to the credentials used with this chain.
        This issue shows an example negative outcome if these steps are not taken:
        https://github.com/langchain-ai/langchain/issues/5923
    """

    llm_chain: LLMChain
    llm: Optional[BaseLanguageModel] = None
    """[Deprecated] LLM wrapper to use."""
    database: SQLDatabase = Field(exclude=True)
    """SQL Database to connect to."""
    prompt: Optional[BasePromptTemplate] = None
    """[Deprecated] Prompt to use to translate natural language to SQL."""
    top_k: int = 5
    """Number of results to return from the query"""
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    return_sql: bool = False
    """Will return sql-command directly without executing it"""
    return_intermediate_steps: bool = False
    """Whether or not to return the intermediate steps along with the final answer."""
    return_direct: bool = False
    """Whether or not to return the result of querying the SQL table directly."""
    use_query_checker: bool = False
    """Whether or not the query checker tool should be used to attempt
    to fix the initial SQL from the LLM."""
    query_checker_prompt: Optional[BasePromptTemplate] = None
    """The prompt template that should be used by the query checker"""

    model_config = ConfigDict(
        arbitrary_types_allowed=True,
        extra="forbid",
    )

    @model_validator(mode="before")
    @classmethod
    def raise_deprecation(cls, values: Dict) -> Any:
        if "llm" in values:
            warnings.warn(
                "Directly instantiating an SQLDatabaseChain with an llm is deprecated. "
                "Please instantiate with llm_chain argument or using the from_llm "
                "class method."
            )
            if "llm_chain" not in values and values["llm"] is not None:
                database = values["database"]
                prompt = values.get("prompt") or SQL_PROMPTS.get(
                    database.dialect, PROMPT
                )
                values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
        return values

    @property
    def input_keys(self) -> List[str]:
        """Return the singular input key.

        :meta private:
        """
        return [self.input_key]

    @property
    def output_keys(self) -> List[str]:
        """Return the singular output key.

        :meta private:
        """
        if not self.return_intermediate_steps:
            return [self.output_key]
        else:
            return [self.output_key, INTERMEDIATE_STEPS_KEY]

    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        input_text = f"{inputs[self.input_key]}\n{SQL_QUERY}"
        _run_manager.on_text(input_text, verbose=self.verbose)
        # If not present, then defaults to None which is all tables.
        table_names_to_use = inputs.get("table_names_to_use")
        table_info = self.database.get_table_info(table_names=table_names_to_use)
        llm_inputs = {
            "input": input_text,
            "top_k": str(self.top_k),
            "dialect": self.database.dialect,
            "table_info": table_info,
            "stop": ["\nSQLResult:"],
        }
        if self.memory is not None:
            for k in self.memory.memory_variables:
                llm_inputs[k] = inputs[k]
        intermediate_steps: List = []
        try:
            intermediate_steps.append(llm_inputs.copy())  # input: sql generation
            sql_cmd = self.llm_chain.predict(
                callbacks=_run_manager.get_child(),
                **llm_inputs,
            ).strip()
            if self.return_sql:
                return {self.output_key: sql_cmd}
            if not self.use_query_checker:
                _run_manager.on_text(sql_cmd, color="green", verbose=self.verbose)
                intermediate_steps.append(
                    sql_cmd
                )  # output: sql generation (no checker)
                intermediate_steps.append({"sql_cmd": sql_cmd})  # input: sql exec
                if SQL_QUERY in sql_cmd:
                    sql_cmd = sql_cmd.split(SQL_QUERY)[1].strip()
                if SQL_RESULT in sql_cmd:
                    sql_cmd = sql_cmd.split(SQL_RESULT)[0].strip()
                result = self.database.run(sql_cmd)
                intermediate_steps.append(str(result))  # output: sql exec
            else:
                query_checker_prompt = self.query_checker_prompt or PromptTemplate(
                    template=QUERY_CHECKER, input_variables=["query", "dialect"]
                )
                query_checker_chain = LLMChain(
                    llm=self.llm_chain.llm, prompt=query_checker_prompt
                )
                query_checker_inputs = {
                    "query": sql_cmd,
                    "dialect": self.database.dialect,
                }
                checked_sql_command: str = query_checker_chain.predict(
                    callbacks=_run_manager.get_child(), **query_checker_inputs
                ).strip()
                intermediate_steps.append(
                    checked_sql_command
                )  # output: sql generation (checker)
                _run_manager.on_text(
                    checked_sql_command, color="green", verbose=self.verbose
                )
                intermediate_steps.append(
                    {"sql_cmd": checked_sql_command}
                )  # input: sql exec
                result = self.database.run(checked_sql_command)
                intermediate_steps.append(str(result))  # output: sql exec
                sql_cmd = checked_sql_command

            _run_manager.on_text("\nSQLResult: ", verbose=self.verbose)
            _run_manager.on_text(str(result), color="yellow", verbose=self.verbose)
            # If return direct, we just set the final result equal to
            # the result of the sql query result, otherwise try to get a human readable
            # final answer
            if self.return_direct:
                final_result = result
            else:
                _run_manager.on_text("\nAnswer:", verbose=self.verbose)
                input_text += f"{sql_cmd}\nSQLResult: {result}\nAnswer:"
                llm_inputs["input"] = input_text
                intermediate_steps.append(llm_inputs.copy())  # input: final answer
                final_result = self.llm_chain.predict(
                    callbacks=_run_manager.get_child(),
                    **llm_inputs,
                ).strip()
                intermediate_steps.append(final_result)  # output: final answer
                _run_manager.on_text(final_result, color="green", verbose=self.verbose)
            chain_result: Dict[str, Any] = {self.output_key: final_result}
            if self.return_intermediate_steps:
                chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
            return chain_result
        except Exception as exc:
            # Append intermediate steps to exception, to aid in logging and later
            # improvement of few shot prompt seeds
            exc.intermediate_steps = intermediate_steps  # type: ignore
            raise exc

    @property
    def _chain_type(self) -> str:
        return "sql_database_chain"


[docs]
    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        db: SQLDatabase,
        prompt: Optional[BasePromptTemplate] = None,
        **kwargs: Any,
    ) -> SQLDatabaseChain:
        """Create a SQLDatabaseChain from an LLM and a database connection.

        *Security note*: Make sure that the database connection uses credentials
            that are narrowly-scoped to only include the permissions this chain needs.
            Failure to do so may result in data corruption or loss, since this chain may
            attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
            The best way to guard against such negative outcomes is to (as appropriate)
            limit the permissions granted to the credentials used with this chain.
            This issue shows an example negative outcome if these steps are not taken:
            https://github.com/langchain-ai/langchain/issues/5923
        """
        prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT)
        llm_chain = LLMChain(llm=llm, prompt=prompt)
        return cls(llm_chain=llm_chain, database=db, **kwargs)





[docs]
class SQLDatabaseSequentialChain(Chain):
    """Chain for querying SQL database that is a sequential chain.

    The chain is as follows:
    1. Based on the query, determine which tables to use.
    2. Based on those tables, call the normal SQL database chain.

    This is useful in cases where the number of tables in the database is large.
    """

    decider_chain: LLMChain
    sql_chain: SQLDatabaseChain
    input_key: str = "query"  #: :meta private:
    output_key: str = "result"  #: :meta private:
    return_intermediate_steps: bool = False


[docs]
    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        db: SQLDatabase,
        query_prompt: BasePromptTemplate = PROMPT,
        decider_prompt: BasePromptTemplate = DECIDER_PROMPT,
        **kwargs: Any,
    ) -> SQLDatabaseSequentialChain:
        """Load the necessary chains."""
        sql_chain = SQLDatabaseChain.from_llm(llm, db, prompt=query_prompt, **kwargs)
        decider_chain = LLMChain(
            llm=llm, prompt=decider_prompt, output_key="table_names"
        )
        return cls(sql_chain=sql_chain, decider_chain=decider_chain, **kwargs)


    @property
    def input_keys(self) -> List[str]:
        """Return the singular input key.

        :meta private:
        """
        return [self.input_key]

    @property
    def output_keys(self) -> List[str]:
        """Return the singular output key.

        :meta private:
        """
        if not self.return_intermediate_steps:
            return [self.output_key]
        else:
            return [self.output_key, INTERMEDIATE_STEPS_KEY]

    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
        _table_names = self.sql_chain.database.get_usable_table_names()
        table_names = ", ".join(_table_names)
        llm_inputs = {
            "query": inputs[self.input_key],
            "table_names": table_names,
        }
        _lowercased_table_names = [name.lower() for name in _table_names]
        table_names_from_chain = self.decider_chain.predict_and_parse(**llm_inputs)
        table_names_to_use = [
            name
            for name in table_names_from_chain
            if name.lower() in _lowercased_table_names
        ]
        _run_manager.on_text("Table names to use:", end="\n", verbose=self.verbose)
        _run_manager.on_text(
            str(table_names_to_use), color="yellow", verbose=self.verbose
        )
        new_inputs = {
            self.sql_chain.input_key: inputs[self.input_key],
            "table_names_to_use": table_names_to_use,
        }
        return self.sql_chain(
            new_inputs, callbacks=_run_manager.get_child(), return_only_outputs=True
        )

    @property
    def _chain_type(self) -> str:
        return "sql_database_sequential_chain"