langchain/langchain/chains/sql_database/base.py

"""Chain for interacting with SQL Database."""
from __future__ import annotations

from typing import Any, Dict, List, Optional

from pydantic import Extra, Field

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.prompts.base import BasePromptTemplate
from langchain.schema import BaseLanguageModel
from langchain.sql_database import SQLDatabase


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

    Example:
        .. code-block:: python

            from langchain import SQLDatabaseChain, OpenAI, SQLDatabase
            db = SQLDatabase(...)
            db_chain = SQLDatabaseChain(llm=OpenAI(), database=db)
    """

    llm: BaseLanguageModel
    """LLM wrapper to use."""
    database: SQLDatabase = Field(exclude=True)
    """SQL Database to connect to."""
    prompt: Optional[BasePromptTemplate] = None
    """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_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."""

    class Config:
        """Configuration for this pydantic object."""

        extra = Extra.forbid
        arbitrary_types_allowed = True

    @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"]

    def _call(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        try:
            prompt = self.prompt or SQL_PROMPTS[self.database.dialect]
        except KeyError:
            # fallback to generic prompt if dialect-specific prompt doesn't exist yet
            prompt = PROMPT
        llm_chain = LLMChain(llm=self.llm, prompt=prompt)
        input_text = f"{inputs[self.input_key]}\nSQLQuery:"
        self.callback_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": self.top_k,
            "dialect": self.database.dialect,
            "table_info": table_info,
            "stop": ["\nSQLResult:"],
        }
        intermediate_steps = []
        sql_cmd = llm_chain.predict(**llm_inputs)
        intermediate_steps.append(sql_cmd)
        self.callback_manager.on_text(sql_cmd, color="green", verbose=self.verbose)
        result = self.database.run(sql_cmd)
        intermediate_steps.append(result)
        self.callback_manager.on_text("\nSQLResult: ", verbose=self.verbose)
        self.callback_manager.on_text(result, color="yellow", verbose=self.verbose)
        # If return direct, we just set the final result equal to the sql query
        if self.return_direct:
            final_result = result
        else:
            self.callback_manager.on_text("\nAnswer:", verbose=self.verbose)
            input_text += f"{sql_cmd}\nSQLResult: {result}\nAnswer:"
            llm_inputs["input"] = input_text
            final_result = llm_chain.predict(**llm_inputs)
            self.callback_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"] = intermediate_steps
        return chain_result

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


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.
    """

    return_intermediate_steps: bool = False

    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel,
        database: SQLDatabase,
        query_prompt: BasePromptTemplate = PROMPT,
        decider_prompt: BasePromptTemplate = DECIDER_PROMPT,
        **kwargs: Any,
    ) -> SQLDatabaseSequentialChain:
        """Load the necessary chains."""
        sql_chain = SQLDatabaseChain(
            llm=llm, database=database, 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)

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

    @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"]

    def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
        _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,
        }
        table_names_to_use = self.decider_chain.predict_and_parse(**llm_inputs)
        self.callback_manager.on_text(
            "Table names to use:", end="\n", verbose=self.verbose
        )
        self.callback_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, return_only_outputs=True)

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