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79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
"""Chain for interacting with SQL Database."""
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from typing import Dict, List
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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 print_text
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from langchain.llms.base import BaseLLM
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from langchain.sql_database import SQLDatabase
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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: BaseLLM
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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, str]) -> Dict[str, str]:
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llm_chain = LLMChain(llm=self.llm, prompt=PROMPT)
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input_text = f"{inputs[self.input_key]} \nSQLQuery:"
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if self.verbose:
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print_text(input_text)
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llm_inputs = {
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"input": input_text,
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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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if self.verbose:
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print_text(sql_cmd, color="green")
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result = self.database.run(sql_cmd)
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if self.verbose:
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print_text("\nSQLResult: ")
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print_text(result, color="yellow")
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print_text("\nAnswer:")
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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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final_result = llm_chain.predict(**llm_inputs)
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if self.verbose:
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print_text(final_result, color="green")
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return {self.output_key: final_result}
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