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"""Chain for interacting with SQL Database."""
from __future__ import annotations
from typing import Any, Dict, List
from pydantic import BaseModel, Extra, Field
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.sql_database.prompt import DECIDER_PROMPT, PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.schema import BaseLanguageModel
from langchain.sql_database import SQLDatabase
class SQLDatabaseChain(Chain, BaseModel):
"""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: BasePromptTemplate = PROMPT
"""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]:
llm_chain = LLMChain(llm=self.llm, prompt=self.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, BaseModel):
"""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_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"
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