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76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
"""Chain for interacting with SQL Database."""
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from typing import Dict, List, Any
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from pydantic import BaseModel, Extra
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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 PROMPT
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from langchain.input import ChainedInput
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from langchain.llms.base import LLM
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from langchain.sql_database import SQLDatabase
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from langchain.logger import CONTEXT_KEY
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class SQLDatabaseChain(Chain, BaseModel):
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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 = SelfAskWithSearchChain(llm=OpenAI(), database=db)
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"""
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llm: LLM
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"""LLM wrapper to use."""
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database: SQLDatabase
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"""SQL Database to connect to."""
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input_key: str = "query" #: :meta private:
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output_key: str = "result" #: :meta private:
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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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@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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return [self.output_key]
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def _call(self, inputs: Dict[str, Any]) -> Dict[str, str]:
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llm_chain = LLMChain(llm=self.llm, prompt=PROMPT)
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chained_input = ChainedInput(
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inputs[self.input_key] + "\nSQLQuery:", inputs[CONTEXT_KEY], logger=self.logger
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)
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llm_inputs = {
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"input": chained_input.input,
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"dialect": self.database.dialect,
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"table_info": self.database.table_info,
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"stop": ["\nSQLResult:"],
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}
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sql_cmd = llm_chain.predict(**llm_inputs)
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chained_input.add(sql_cmd, color="green")
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result = self.database.run(sql_cmd)
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chained_input.add("\nSQLResult: ")
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chained_input.add(result, color="yellow")
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chained_input.add("\nAnswer:")
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llm_inputs["input"] = chained_input.input
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final_result = llm_chain.predict(**llm_inputs)
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chained_input.add(final_result, color="green")
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return {self.output_key: final_result}
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