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287 lines
11 KiB
Python
287 lines
11 KiB
Python
"""Chain for interacting with SQL Database."""
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from __future__ import annotations
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import warnings
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from typing import Any, Dict, List, Optional
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from pydantic import Extra, Field, root_validator
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from langchain.base_language import BaseLanguageModel
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from langchain.callbacks.manager import CallbackManagerForChainRun
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT, SQL_PROMPTS
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from langchain.prompts.base import BasePromptTemplate
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from langchain.prompts.prompt import PromptTemplate
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from langchain.sql_database import SQLDatabase
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from langchain.tools.sql_database.prompt import QUERY_CHECKER
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INTERMEDIATE_STEPS_KEY = "intermediate_steps"
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class SQLDatabaseChain(Chain):
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"""Chain for interacting with SQL Database.
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Example:
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.. code-block:: python
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from langchain import SQLDatabaseChain, OpenAI, SQLDatabase
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db = SQLDatabase(...)
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db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
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"""
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llm_chain: LLMChain
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llm: Optional[BaseLanguageModel] = None
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"""[Deprecated] LLM wrapper to use."""
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database: SQLDatabase = Field(exclude=True)
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"""SQL Database to connect to."""
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prompt: Optional[BasePromptTemplate] = None
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"""[Deprecated] Prompt to use to translate natural language to SQL."""
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top_k: int = 5
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"""Number of results to return from the query"""
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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return_intermediate_steps: bool = False
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"""Whether or not to return the intermediate steps along with the final answer."""
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return_direct: bool = False
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"""Whether or not to return the result of querying the SQL table directly."""
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use_query_checker: bool = False
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"""Whether or not the query checker tool should be used to attempt
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to fix the initial SQL from the LLM."""
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query_checker_prompt: Optional[BasePromptTemplate] = None
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"""The prompt template that should be used by the query checker"""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@root_validator(pre=True)
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def raise_deprecation(cls, values: Dict) -> Dict:
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if "llm" in values:
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warnings.warn(
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"Directly instantiating an SQLDatabaseChain with an llm is deprecated. "
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"Please instantiate with llm_chain argument or using the from_llm "
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"class method."
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)
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if "llm_chain" not in values and values["llm"] is not None:
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database = values["database"]
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prompt = values.get("prompt") or SQL_PROMPTS.get(
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database.dialect, PROMPT
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)
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values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
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return values
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@property
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def input_keys(self) -> List[str]:
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"""Return the singular input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return the singular output key.
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:meta private:
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"""
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if not self.return_intermediate_steps:
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return [self.output_key]
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else:
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return [self.output_key, INTERMEDIATE_STEPS_KEY]
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, Any]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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input_text = f"{inputs[self.input_key]}\nSQLQuery:"
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_run_manager.on_text(input_text, verbose=self.verbose)
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# If not present, then defaults to None which is all tables.
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table_names_to_use = inputs.get("table_names_to_use")
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table_info = self.database.get_table_info(table_names=table_names_to_use)
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llm_inputs = {
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"input": input_text,
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"top_k": str(self.top_k),
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"dialect": self.database.dialect,
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"table_info": table_info,
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"stop": ["\nSQLResult:"],
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}
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intermediate_steps: List = []
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try:
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intermediate_steps.append(llm_inputs) # input: sql generation
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sql_cmd = self.llm_chain.predict(
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callbacks=_run_manager.get_child(),
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**llm_inputs,
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).strip()
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if not self.use_query_checker:
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_run_manager.on_text(sql_cmd, color="green", verbose=self.verbose)
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intermediate_steps.append(
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sql_cmd
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) # output: sql generation (no checker)
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intermediate_steps.append({"sql_cmd": sql_cmd}) # input: sql exec
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result = self.database.run(sql_cmd)
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intermediate_steps.append(str(result)) # output: sql exec
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else:
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query_checker_prompt = self.query_checker_prompt or PromptTemplate(
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template=QUERY_CHECKER, input_variables=["query", "dialect"]
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)
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query_checker_chain = LLMChain(
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llm=self.llm_chain.llm, prompt=query_checker_prompt
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)
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query_checker_inputs = {
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"query": sql_cmd,
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"dialect": self.database.dialect,
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}
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checked_sql_command: str = query_checker_chain.predict(
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callbacks=_run_manager.get_child(), **query_checker_inputs
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).strip()
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intermediate_steps.append(
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checked_sql_command
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) # output: sql generation (checker)
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_run_manager.on_text(
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checked_sql_command, color="green", verbose=self.verbose
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)
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intermediate_steps.append(
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{"sql_cmd": checked_sql_command}
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) # input: sql exec
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result = self.database.run(checked_sql_command)
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intermediate_steps.append(str(result)) # output: sql exec
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sql_cmd = checked_sql_command
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_run_manager.on_text("\nSQLResult: ", verbose=self.verbose)
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_run_manager.on_text(result, color="yellow", verbose=self.verbose)
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# If return direct, we just set the final result equal to
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# the result of the sql query result, otherwise try to get a human readable
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# final answer
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if self.return_direct:
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final_result = result
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else:
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_run_manager.on_text("\nAnswer:", verbose=self.verbose)
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input_text += f"{sql_cmd}\nSQLResult: {result}\nAnswer:"
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llm_inputs["input"] = input_text
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intermediate_steps.append(llm_inputs) # input: final answer
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final_result = self.llm_chain.predict(
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callbacks=_run_manager.get_child(),
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**llm_inputs,
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).strip()
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intermediate_steps.append(final_result) # output: final answer
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_run_manager.on_text(final_result, color="green", verbose=self.verbose)
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chain_result: Dict[str, Any] = {self.output_key: final_result}
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if self.return_intermediate_steps:
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chain_result[INTERMEDIATE_STEPS_KEY] = intermediate_steps
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return chain_result
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except Exception as exc:
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# Append intermediate steps to exception, to aid in logging and later
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# improvement of few shot prompt seeds
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exc.intermediate_steps = intermediate_steps # type: ignore
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raise exc
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@property
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def _chain_type(self) -> str:
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return "sql_database_chain"
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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db: SQLDatabase,
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prompt: Optional[BasePromptTemplate] = None,
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**kwargs: Any,
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) -> SQLDatabaseChain:
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prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT)
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llm_chain = LLMChain(llm=llm, prompt=prompt)
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return cls(llm_chain=llm_chain, database=db, **kwargs)
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class SQLDatabaseSequentialChain(Chain):
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"""Chain for querying SQL database that is a sequential chain.
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The chain is as follows:
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1. Based on the query, determine which tables to use.
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2. Based on those tables, call the normal SQL database chain.
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This is useful in cases where the number of tables in the database is large.
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"""
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decider_chain: LLMChain
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sql_chain: SQLDatabaseChain
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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return_intermediate_steps: bool = False
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@classmethod
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def from_llm(
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cls,
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llm: BaseLanguageModel,
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database: SQLDatabase,
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query_prompt: BasePromptTemplate = PROMPT,
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decider_prompt: BasePromptTemplate = DECIDER_PROMPT,
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**kwargs: Any,
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) -> SQLDatabaseSequentialChain:
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"""Load the necessary chains."""
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sql_chain = SQLDatabaseChain(
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llm=llm, database=database, prompt=query_prompt, **kwargs
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)
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decider_chain = LLMChain(
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llm=llm, prompt=decider_prompt, output_key="table_names"
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)
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return cls(sql_chain=sql_chain, decider_chain=decider_chain, **kwargs)
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@property
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def input_keys(self) -> List[str]:
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"""Return the singular input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Return the singular output key.
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:meta private:
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"""
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if not self.return_intermediate_steps:
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return [self.output_key]
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else:
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return [self.output_key, INTERMEDIATE_STEPS_KEY]
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def _call(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, Any]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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_table_names = self.sql_chain.database.get_usable_table_names()
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table_names = ", ".join(_table_names)
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llm_inputs = {
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"query": inputs[self.input_key],
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"table_names": table_names,
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}
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_lowercased_table_names = [name.lower() for name in _table_names]
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table_names_from_chain = self.decider_chain.predict_and_parse(**llm_inputs)
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table_names_to_use = [
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name
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for name in table_names_from_chain
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if name.lower() in _lowercased_table_names
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]
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_run_manager.on_text("Table names to use:", end="\n", verbose=self.verbose)
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_run_manager.on_text(
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str(table_names_to_use), color="yellow", verbose=self.verbose
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)
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new_inputs = {
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self.sql_chain.input_key: inputs[self.input_key],
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"table_names_to_use": table_names_to_use,
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}
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return self.sql_chain(
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new_inputs, callbacks=_run_manager.get_child(), return_only_outputs=True
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)
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@property
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def _chain_type(self) -> str:
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return "sql_database_sequential_chain"
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