mirror of https://github.com/hwchase17/langchain
remove CVEs (#8092)
This PR aims to move all code with CVEs into `langchain.experimental`. Note that we are NOT yet removing from the core `langchain` package - we will give people a week to migrate here. See MIGRATE.md for how to migrate Zero changes to functionality Vulnerabilities this addresses: PALChain: - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5752409 - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759265 SQLDatabaseChain - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5759268 `load_prompt` (Python files only) - https://security.snyk.io/vuln/SNYK-PYTHON-LANGCHAIN-5725807pull/8096/head
Harrison Chase
11 months ago
committed by
GitHub
parent
08c658d3f8
commit
d353d668e4
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# Migrating to `langchain.experimental`
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We are moving any experimental components of langchain, or components with vulnerability issues, into `langchain.experimental`.
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This guide covers how to migrate.
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## Installation
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Previously:
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`pip install -U langchain`
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Now:
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`pip install -U langchain langchain.experimental`
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## PALChain
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Previously:
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`from langchain.chains import PALChain`
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Now:
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`from langchain.experimental.pal_chain import PALChain`
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## SQLDatabaseChain
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Previously:
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`from langchain.chains import SQLDatabaseChain`
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Now:
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`from langchain.experimental.sql import SQLDatabaseChain`
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## `load_prompt` for Python files
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Note: this only applies if you want to load Python files as prompts.
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If you want to load json/yaml files, no change is needed.
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Previously:
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`from langchain.prompts import load_prompt`
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Now:
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`from langchain.experimental.prompts import load_prompt`
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@ -1,8 +1,9 @@
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from typing import Any, Dict, List
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from pydantic import Field
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from langchain.memory.chat_memory import BaseChatMemory, get_prompt_input_key
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from langchain.vectorstores.base import VectorStoreRetriever
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from pydantic import Field
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class AutoGPTMemory(BaseChatMemory):
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@ -1,4 +1,5 @@
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from langchain import LLMChain, PromptTemplate
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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from langchain.schema.language_model import BaseLanguageModel
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@ -1,4 +1,5 @@
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from langchain import LLMChain, PromptTemplate
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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from langchain.schema.language_model import BaseLanguageModel
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@ -1,4 +1,5 @@
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from langchain import LLMChain, PromptTemplate
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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from langchain.schema.language_model import BaseLanguageModel
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"""Implements Program-Aided Language Models.
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As in https://arxiv.org/pdf/2211.10435.pdf.
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This is vulnerable to arbitrary code execution:
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https://github.com/hwchase17/langchain/issues/5872
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"""
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from langchain.experimental.pal_chain.base import PALChain
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__all__ = ["PALChain"]
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"""Implements Program-Aided Language Models.
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This module implements the Program-Aided Language Models (PAL) for generating code
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solutions. PAL is a technique described in the paper "Program-Aided Language Models"
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(https://arxiv.org/pdf/2211.10435.pdf).
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"""
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from __future__ import annotations
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import ast
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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.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.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
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from langchain.chains.pal.math_prompt import MATH_PROMPT
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from langchain.schema import BasePromptTemplate
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from langchain.schema.language_model import BaseLanguageModel
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from langchain.utilities import PythonREPL
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COMMAND_EXECUTION_FUNCTIONS = ["system", "exec", "execfile", "eval"]
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class PALValidation:
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SOLUTION_EXPRESSION_TYPE_FUNCTION = ast.FunctionDef
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SOLUTION_EXPRESSION_TYPE_VARIABLE = ast.Name
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def __init__(
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self,
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solution_expression_name: Optional[str] = None,
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solution_expression_type: Optional[type] = None,
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allow_imports: bool = False,
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allow_command_exec: bool = False,
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):
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"""Initialize a PALValidation instance.
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Args:
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solution_expression_name (str): Name of the expected solution expression.
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If passed, solution_expression_type must be passed as well.
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solution_expression_type (str): AST type of the expected solution
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expression. If passed, solution_expression_name must be passed as well.
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Must be one of PALValidation.SOLUTION_EXPRESSION_TYPE_FUNCTION,
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PALValidation.SOLUTION_EXPRESSION_TYPE_VARIABLE.
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allow_imports (bool): Allow import statements.
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allow_command_exec (bool): Allow using known command execution functions.
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"""
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self.solution_expression_name = solution_expression_name
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self.solution_expression_type = solution_expression_type
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if solution_expression_name is not None:
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if not isinstance(self.solution_expression_name, str):
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raise ValueError(
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f"Expected solution_expression_name to be str, "
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f"instead found {type(self.solution_expression_name)}"
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)
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if solution_expression_type is not None:
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if (
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self.solution_expression_type
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is not self.SOLUTION_EXPRESSION_TYPE_FUNCTION
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and self.solution_expression_type
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is not self.SOLUTION_EXPRESSION_TYPE_VARIABLE
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):
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raise ValueError(
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f"Expected solution_expression_type to be one of "
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f"({self.SOLUTION_EXPRESSION_TYPE_FUNCTION},"
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f"{self.SOLUTION_EXPRESSION_TYPE_VARIABLE}),"
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f"instead found {self.solution_expression_type}"
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)
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if solution_expression_name is not None and solution_expression_type is None:
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raise TypeError(
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"solution_expression_name "
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"requires solution_expression_type to be passed as well"
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)
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if solution_expression_name is None and solution_expression_type is not None:
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raise TypeError(
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"solution_expression_type "
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"requires solution_expression_name to be passed as well"
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)
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self.allow_imports = allow_imports
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self.allow_command_exec = allow_command_exec
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class PALChain(Chain):
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"""Implements Program-Aided Language Models (PAL).
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This class implements the Program-Aided Language Models (PAL) for generating code
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solutions. PAL is a technique described in the paper "Program-Aided Language Models"
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(https://arxiv.org/pdf/2211.10435.pdf).
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"""
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llm_chain: LLMChain
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llm: Optional[BaseLanguageModel] = None
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"""[Deprecated]"""
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prompt: BasePromptTemplate = MATH_PROMPT
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"""[Deprecated]"""
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stop: str = "\n\n"
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"""Stop token to use when generating code."""
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get_answer_expr: str = "print(solution())"
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"""Expression to use to get the answer from the generated code."""
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python_globals: Optional[Dict[str, Any]] = None
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"""Python globals and locals to use when executing the generated code."""
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python_locals: Optional[Dict[str, Any]] = None
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"""Python globals and locals to use when executing the generated code."""
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output_key: str = "result" #: :meta private:
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return_intermediate_steps: bool = False
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"""Whether to return intermediate steps in the generated code."""
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code_validations: PALValidation = Field(default_factory=PALValidation)
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"""Validations to perform on the generated code."""
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timeout: Optional[int] = 10
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"""Timeout in seconds for the generated code to execute."""
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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 a PALChain with an llm is deprecated. "
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"Please instantiate with llm_chain argument or using one of "
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"the class method constructors from_math_prompt, "
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"from_colored_object_prompt."
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)
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if "llm_chain" not in values and values["llm"] is not None:
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values["llm_chain"] = LLMChain(llm=values["llm"], prompt=MATH_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.prompt.input_variables
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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"]
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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, str]:
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_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
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code = self.llm_chain.predict(
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stop=[self.stop], callbacks=_run_manager.get_child(), **inputs
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)
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_run_manager.on_text(code, color="green", end="\n", verbose=self.verbose)
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PALChain.validate_code(code, self.code_validations)
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repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
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res = repl.run(code + f"\n{self.get_answer_expr}", timeout=self.timeout)
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output = {self.output_key: res.strip()}
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if self.return_intermediate_steps:
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output["intermediate_steps"] = code
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return output
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@classmethod
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def validate_code(cls, code: str, code_validations: PALValidation) -> None:
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try:
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code_tree = ast.parse(code)
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except (SyntaxError, UnicodeDecodeError):
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raise ValueError(f"Generated code is not valid python code: {code}")
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except TypeError:
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raise ValueError(
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f"Generated code is expected to be a string, "
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f"instead found {type(code)}"
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)
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except OverflowError:
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raise ValueError(
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f"Generated code too long / complex to be parsed by ast: {code}"
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)
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found_solution_expr = False
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if code_validations.solution_expression_name is None:
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# Skip validation if no solution_expression_name was given
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found_solution_expr = True
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has_imports = False
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top_level_nodes = list(ast.iter_child_nodes(code_tree))
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for node in top_level_nodes:
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if (
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code_validations.solution_expression_name is not None
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and code_validations.solution_expression_type is not None
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):
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# Check root nodes (like func def)
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if (
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isinstance(node, code_validations.solution_expression_type)
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and hasattr(node, "name")
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and node.name == code_validations.solution_expression_name
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):
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found_solution_expr = True
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# Check assigned nodes (like answer variable)
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if isinstance(node, ast.Assign):
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for target_node in node.targets:
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if (
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isinstance(
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target_node, code_validations.solution_expression_type
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)
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and hasattr(target_node, "id")
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and target_node.id
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== code_validations.solution_expression_name
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):
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found_solution_expr = True
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if isinstance(node, ast.Import) or isinstance(node, ast.ImportFrom):
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has_imports = True
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if not found_solution_expr:
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raise ValueError(
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f"Generated code is missing the solution expression: "
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f"{code_validations.solution_expression_name} of type: "
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f"{code_validations.solution_expression_type}"
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)
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if not code_validations.allow_imports and has_imports:
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raise ValueError(f"Generated code has disallowed imports: {code}")
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if (
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not code_validations.allow_command_exec
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or not code_validations.allow_imports
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):
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for node in ast.walk(code_tree):
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if (
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(not code_validations.allow_command_exec)
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and isinstance(node, ast.Call)
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and (
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(
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hasattr(node.func, "id")
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and node.func.id in COMMAND_EXECUTION_FUNCTIONS
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)
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or (
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isinstance(node.func, ast.Attribute)
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and node.func.attr in COMMAND_EXECUTION_FUNCTIONS
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)
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)
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):
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raise ValueError(
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f"Found illegal command execution function "
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f"{node.func.id} in code {code}"
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)
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if (not code_validations.allow_imports) and (
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isinstance(node, ast.Import) or isinstance(node, ast.ImportFrom)
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):
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raise ValueError(f"Generated code has disallowed imports: {code}")
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@classmethod
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def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
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"""Load PAL from math prompt.
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Args:
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llm (BaseLanguageModel): The language model to use for generating code.
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Returns:
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PALChain: An instance of PALChain.
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"""
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llm_chain = LLMChain(llm=llm, prompt=MATH_PROMPT)
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code_validations = PALValidation(
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solution_expression_name="solution",
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solution_expression_type=PALValidation.SOLUTION_EXPRESSION_TYPE_FUNCTION,
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)
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return cls(
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llm_chain=llm_chain,
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stop="\n\n",
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get_answer_expr="print(solution())",
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code_validations=code_validations,
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**kwargs,
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)
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@classmethod
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def from_colored_object_prompt(
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cls, llm: BaseLanguageModel, **kwargs: Any
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) -> PALChain:
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"""Load PAL from colored object prompt.
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Args:
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llm (BaseLanguageModel): The language model to use for generating code.
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Returns:
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PALChain: An instance of PALChain.
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"""
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llm_chain = LLMChain(llm=llm, prompt=COLORED_OBJECT_PROMPT)
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code_validations = PALValidation(
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solution_expression_name="answer",
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solution_expression_type=PALValidation.SOLUTION_EXPRESSION_TYPE_VARIABLE,
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)
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return cls(
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llm_chain=llm_chain,
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stop="\n\n\n",
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get_answer_expr="print(answer)",
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code_validations=code_validations,
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**kwargs,
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)
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@property
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def _chain_type(self) -> str:
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return "pal_chain"
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# flake8: noqa
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from langchain.prompts.prompt import PromptTemplate
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template = (
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"""
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# Generate Python3 Code to solve problems
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# Q: On the nightstand, there is a red pencil, a purple mug, a burgundy keychain, a fuchsia teddy bear, a black plate, and a blue stress ball. What color is the stress ball?
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# Put objects into a dictionary for quick look up
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objects = dict()
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objects['pencil'] = 'red'
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objects['mug'] = 'purple'
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objects['keychain'] = 'burgundy'
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objects['teddy bear'] = 'fuchsia'
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objects['plate'] = 'black'
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objects['stress ball'] = 'blue'
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# Look up the color of stress ball
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stress_ball_color = objects['stress ball']
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answer = stress_ball_color
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# Q: On the table, you see a bunch of objects arranged in a row: a purple paperclip, a pink stress ball, a brown keychain, a green scrunchiephone charger, a mauve fidget spinner, and a burgundy pen. What is the color of the object directly to the right of the stress ball?
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# Put objects into a list to record ordering
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objects = []
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objects += [('paperclip', 'purple')] * 1
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objects += [('stress ball', 'pink')] * 1
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objects += [('keychain', 'brown')] * 1
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objects += [('scrunchiephone charger', 'green')] * 1
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objects += [('fidget spinner', 'mauve')] * 1
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objects += [('pen', 'burgundy')] * 1
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# Find the index of the stress ball
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stress_ball_idx = None
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for i, object in enumerate(objects):
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if object[0] == 'stress ball':
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stress_ball_idx = i
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break
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# Find the directly right object
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direct_right = objects[i+1]
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# Check the directly right object's color
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direct_right_color = direct_right[1]
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answer = direct_right_color
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# Q: On the nightstand, you see the following items arranged in a row: a teal plate, a burgundy keychain, a yellow scrunchiephone charger, an orange mug, a pink notebook, and a grey cup. How many non-orange items do you see to the left of the teal item?
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# Put objects into a list to record ordering
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objects = []
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objects += [('plate', 'teal')] * 1
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objects += [('keychain', 'burgundy')] * 1
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objects += [('scrunchiephone charger', 'yellow')] * 1
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objects += [('mug', 'orange')] * 1
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objects += [('notebook', 'pink')] * 1
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objects += [('cup', 'grey')] * 1
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# Find the index of the teal item
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teal_idx = None
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for i, object in enumerate(objects):
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if object[1] == 'teal':
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teal_idx = i
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break
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# Find non-orange items to the left of the teal item
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non_orange = [object for object in objects[:i] if object[1] != 'orange']
|
||||
|
||||
# Count number of non-orange objects
|
||||
num_non_orange = len(non_orange)
|
||||
answer = num_non_orange
|
||||
|
||||
|
||||
# Q: {question}
|
||||
""".strip()
|
||||
+ "\n"
|
||||
)
|
||||
|
||||
COLORED_OBJECT_PROMPT = PromptTemplate(input_variables=["question"], template=template)
|
||||
@ -0,0 +1,157 @@
|
||||
# flake8: noqa
|
||||
from langchain.prompts.prompt import PromptTemplate
|
||||
|
||||
template = (
|
||||
'''
|
||||
Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Olivia has $23. She bought five bagels for $3 each. How much money does she have left?"""
|
||||
money_initial = 23
|
||||
bagels = 5
|
||||
bagel_cost = 3
|
||||
money_spent = bagels * bagel_cost
|
||||
money_left = money_initial - money_spent
|
||||
result = money_left
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?"""
|
||||
golf_balls_initial = 58
|
||||
golf_balls_lost_tuesday = 23
|
||||
golf_balls_lost_wednesday = 2
|
||||
golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday
|
||||
result = golf_balls_left
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?"""
|
||||
computers_initial = 9
|
||||
computers_per_day = 5
|
||||
num_days = 4 # 4 days between monday and thursday
|
||||
computers_added = computers_per_day * num_days
|
||||
computers_total = computers_initial + computers_added
|
||||
result = computers_total
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Shawn has five toys. For Christmas, he got two toys each from his mom and dad. How many toys does he have now?"""
|
||||
toys_initial = 5
|
||||
mom_toys = 2
|
||||
dad_toys = 2
|
||||
total_received = mom_toys + dad_toys
|
||||
total_toys = toys_initial + total_received
|
||||
result = total_toys
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Jason had 20 lollipops. He gave Denny some lollipops. Now Jason has 12 lollipops. How many lollipops did Jason give to Denny?"""
|
||||
jason_lollipops_initial = 20
|
||||
jason_lollipops_after = 12
|
||||
denny_lollipops = jason_lollipops_initial - jason_lollipops_after
|
||||
result = denny_lollipops
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""Leah had 32 chocolates and her sister had 42. If they ate 35, how many pieces do they have left in total?"""
|
||||
leah_chocolates = 32
|
||||
sister_chocolates = 42
|
||||
total_chocolates = leah_chocolates + sister_chocolates
|
||||
chocolates_eaten = 35
|
||||
chocolates_left = total_chocolates - chocolates_eaten
|
||||
result = chocolates_left
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""If there are 3 cars in the parking lot and 2 more cars arrive, how many cars are in the parking lot?"""
|
||||
cars_initial = 3
|
||||
cars_arrived = 2
|
||||
total_cars = cars_initial + cars_arrived
|
||||
result = total_cars
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?
|
||||
|
||||
# solution in Python:
|
||||
|
||||
|
||||
def solution():
|
||||
"""There are 15 trees in the grove. Grove workers will plant trees in the grove today. After they are done, there will be 21 trees. How many trees did the grove workers plant today?"""
|
||||
trees_initial = 15
|
||||
trees_after = 21
|
||||
trees_added = trees_after - trees_initial
|
||||
result = trees_added
|
||||
return result
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Q: {question}
|
||||
|
||||
# solution in Python:
|
||||
'''.strip()
|
||||
+ "\n\n\n"
|
||||
)
|
||||
MATH_PROMPT = PromptTemplate(input_variables=["question"], template=template)
|
||||
@ -1,10 +1,11 @@
|
||||
from abc import abstractmethod
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.chains.base import Chain
|
||||
from langchain.experimental.plan_and_execute.schema import StepResponse
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class BaseExecutor(BaseModel):
|
||||
@ -1,10 +1,11 @@
|
||||
from abc import abstractmethod
|
||||
from typing import Any, List, Optional
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
from langchain.callbacks.manager import Callbacks
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.experimental.plan_and_execute.schema import Plan, PlanOutputParser
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class BasePlanner(BaseModel):
|
||||
@ -1,9 +1,10 @@
|
||||
from abc import abstractmethod
|
||||
from typing import List, Tuple
|
||||
|
||||
from langchain.schema import BaseOutputParser
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from langchain.schema import BaseOutputParser
|
||||
|
||||
|
||||
class Step(BaseModel):
|
||||
value: str
|
||||
@ -0,0 +1,3 @@
|
||||
from langchain.experimental.prompts.load import load_prompt
|
||||
|
||||
__all__ = ["load_prompt"]
|
||||
@ -0,0 +1,52 @@
|
||||
# Susceptible to arbitrary code execution: https://github.com/hwchase17/langchain/issues/4849
|
||||
import importlib
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Union
|
||||
|
||||
import yaml
|
||||
|
||||
from langchain.prompts.loading import load_prompt_from_config, try_load_from_hub
|
||||
from langchain.schema.prompts import BasePromptTemplate
|
||||
|
||||
|
||||
def load_prompt(path: Union[str, Path]) -> BasePromptTemplate:
|
||||
"""Unified method for loading a prompt from LangChainHub or local fs."""
|
||||
if hub_result := try_load_from_hub(
|
||||
path, _load_prompt_from_file, "prompts", {"py", "json", "yaml"}
|
||||
):
|
||||
return hub_result
|
||||
else:
|
||||
return _load_prompt_from_file(path)
|
||||
|
||||
|
||||
def _load_prompt_from_file(file: Union[str, Path]) -> BasePromptTemplate:
|
||||
"""Load prompt from file."""
|
||||
# Convert file to a Path object.
|
||||
if isinstance(file, str):
|
||||
file_path = Path(file)
|
||||
else:
|
||||
file_path = file
|
||||
# Load from either json or yaml.
|
||||
if file_path.suffix == ".json":
|
||||
with open(file_path) as f:
|
||||
config = json.load(f)
|
||||
elif file_path.suffix == ".yaml":
|
||||
with open(file_path, "r") as f:
|
||||
config = yaml.safe_load(f)
|
||||
elif file_path.suffix == ".py":
|
||||
spec = importlib.util.spec_from_loader(
|
||||
"prompt", loader=None, origin=str(file_path)
|
||||
)
|
||||
if spec is None:
|
||||
raise ValueError("could not load spec")
|
||||
helper = importlib.util.module_from_spec(spec)
|
||||
with open(file_path, "rb") as f:
|
||||
exec(f.read(), helper.__dict__)
|
||||
if not isinstance(helper.PROMPT, BasePromptTemplate):
|
||||
raise ValueError("Did not get object of type BasePromptTemplate.")
|
||||
return helper.PROMPT
|
||||
else:
|
||||
raise ValueError(f"Got unsupported file type {file_path.suffix}")
|
||||
# Load the prompt from the config now.
|
||||
return load_prompt_from_config(config)
|
||||
@ -0,0 +1,4 @@
|
||||
"""Chain for interacting with SQL Database."""
|
||||
from langchain.experimental.sql.base import SQLDatabaseChain
|
||||
|
||||
__all__ = ["SQLDatabaseChain"]
|
||||
@ -0,0 +1,291 @@
|
||||
"""Chain for interacting with SQL Database."""
|
||||
from __future__ import annotations
|
||||
|
||||
import warnings
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import Extra, Field, root_validator
|
||||
|
||||
from langchain.callbacks.manager import CallbackManagerForChainRun
|
||||
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.prompt import PromptTemplate
|
||||
from langchain.schema import BasePromptTemplate
|
||||
from langchain.schema.language_model import BaseLanguageModel
|
||||
from langchain.tools.sql_database.prompt import QUERY_CHECKER
|
||||
from langchain.utilities.sql_database import SQLDatabase
|
||||
|
||||
INTERMEDIATE_STEPS_KEY = "intermediate_steps"
|
||||
|
||||
|
||||
class SQLDatabaseChain(Chain):
|
||||
"""Chain for interacting with SQL Database.
|
||||
|
||||
Example:
|
||||
.. code-block:: python
|
||||
|
||||
from langchain.experimental.sql import SQLDatabaseChain
|
||||
from langchain import OpenAI, SQLDatabase
|
||||
db = SQLDatabase(...)
|
||||
db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
|
||||
"""
|
||||
|
||||
llm_chain: LLMChain
|
||||
llm: Optional[BaseLanguageModel] = None
|
||||
"""[Deprecated] LLM wrapper to use."""
|
||||
database: SQLDatabase = Field(exclude=True)
|
||||
"""SQL Database to connect to."""
|
||||
prompt: Optional[BasePromptTemplate] = None
|
||||
"""[Deprecated] 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_sql: bool = False
|
||||
"""Will return sql-command directly without executing it"""
|
||||
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."""
|
||||
use_query_checker: bool = False
|
||||
"""Whether or not the query checker tool should be used to attempt
|
||||
to fix the initial SQL from the LLM."""
|
||||
query_checker_prompt: Optional[BasePromptTemplate] = None
|
||||
"""The prompt template that should be used by the query checker"""
|
||||
|
||||
class Config:
|
||||
"""Configuration for this pydantic object."""
|
||||
|
||||
extra = Extra.forbid
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def raise_deprecation(cls, values: Dict) -> Dict:
|
||||
if "llm" in values:
|
||||
warnings.warn(
|
||||
"Directly instantiating an SQLDatabaseChain with an llm is deprecated. "
|
||||
"Please instantiate with llm_chain argument or using the from_llm "
|
||||
"class method."
|
||||
)
|
||||
if "llm_chain" not in values and values["llm"] is not None:
|
||||
database = values["database"]
|
||||
prompt = values.get("prompt") or SQL_PROMPTS.get(
|
||||
database.dialect, PROMPT
|
||||
)
|
||||
values["llm_chain"] = LLMChain(llm=values["llm"], prompt=prompt)
|
||||
return values
|
||||
|
||||
@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_KEY]
|
||||
|
||||
def _call(
|
||||
self,
|
||||
inputs: Dict[str, Any],
|
||||
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, Any]:
|
||||
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||||
input_text = f"{inputs[self.input_key]}\nSQLQuery:"
|
||||
_run_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": str(self.top_k),
|
||||
"dialect": self.database.dialect,
|
||||
"table_info": table_info,
|
||||
"stop": ["\nSQLResult:"],
|
||||
}
|
||||
intermediate_steps: List = []
|
||||
try:
|
||||
intermediate_steps.append(llm_inputs) # input: sql generation
|
||||
sql_cmd = self.llm_chain.predict(
|
||||
callbacks=_run_manager.get_child(),
|
||||
**llm_inputs,
|
||||
).strip()
|
||||
if self.return_sql:
|
||||
return {self.output_key: sql_cmd}
|
||||
if not self.use_query_checker:
|
||||
_run_manager.on_text(sql_cmd, color="green", verbose=self.verbose)
|
||||
intermediate_steps.append(
|
||||
sql_cmd
|
||||
) # output: sql generation (no checker)
|
||||
intermediate_steps.append({"sql_cmd": sql_cmd}) # input: sql exec
|
||||
result = self.database.run(sql_cmd)
|
||||
intermediate_steps.append(str(result)) # output: sql exec
|
||||
else:
|
||||
query_checker_prompt = self.query_checker_prompt or PromptTemplate(
|
||||
template=QUERY_CHECKER, input_variables=["query", "dialect"]
|
||||
)
|
||||
query_checker_chain = LLMChain(
|
||||
llm=self.llm_chain.llm, prompt=query_checker_prompt
|
||||
)
|
||||
query_checker_inputs = {
|
||||
"query": sql_cmd,
|
||||
"dialect": self.database.dialect,
|
||||
}
|
||||
checked_sql_command: str = query_checker_chain.predict(
|
||||
callbacks=_run_manager.get_child(), **query_checker_inputs
|
||||
).strip()
|
||||
intermediate_steps.append(
|
||||
checked_sql_command
|
||||
) # output: sql generation (checker)
|
||||
_run_manager.on_text(
|
||||
checked_sql_command, color="green", verbose=self.verbose
|
||||
)
|
||||
intermediate_steps.append(
|
||||
{"sql_cmd": checked_sql_command}
|
||||
) # input: sql exec
|
||||
result = self.database.run(checked_sql_command)
|
||||
intermediate_steps.append(str(result)) # output: sql exec
|
||||
sql_cmd = checked_sql_command
|
||||
|
||||
_run_manager.on_text("\nSQLResult: ", verbose=self.verbose)
|
||||
_run_manager.on_text(result, color="yellow", verbose=self.verbose)
|
||||
# If return direct, we just set the final result equal to
|
||||
# the result of the sql query result, otherwise try to get a human readable
|
||||
# final answer
|
||||
if self.return_direct:
|
||||
final_result = result
|
||||
else:
|
||||
_run_manager.on_text("\nAnswer:", verbose=self.verbose)
|
||||
input_text += f"{sql_cmd}\nSQLResult: {result}\nAnswer:"
|
||||
llm_inputs["input"] = input_text
|
||||
intermediate_steps.append(llm_inputs) # input: final answer
|
||||
final_result = self.llm_chain.predict(
|
||||
callbacks=_run_manager.get_child(),
|
||||
**llm_inputs,
|
||||
).strip()
|
||||
intermediate_steps.append(final_result) # output: final answer
|
||||
_run_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_KEY] = intermediate_steps
|
||||
return chain_result
|
||||
except Exception as exc:
|
||||
# Append intermediate steps to exception, to aid in logging and later
|
||||
# improvement of few shot prompt seeds
|
||||
exc.intermediate_steps = intermediate_steps # type: ignore
|
||||
raise exc
|
||||
|
||||
@property
|
||||
def _chain_type(self) -> str:
|
||||
return "sql_database_chain"
|
||||
|
||||
@classmethod
|
||||
def from_llm(
|
||||
cls,
|
||||
llm: BaseLanguageModel,
|
||||
db: SQLDatabase,
|
||||
prompt: Optional[BasePromptTemplate] = None,
|
||||
**kwargs: Any,
|
||||
) -> SQLDatabaseChain:
|
||||
prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT)
|
||||
llm_chain = LLMChain(llm=llm, prompt=prompt)
|
||||
return cls(llm_chain=llm_chain, database=db, **kwargs)
|
||||
|
||||
|
||||
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.
|
||||
"""
|
||||
|
||||
decider_chain: LLMChain
|
||||
sql_chain: SQLDatabaseChain
|
||||
input_key: str = "query" #: :meta private:
|
||||
output_key: str = "result" #: :meta private:
|
||||
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.from_llm(
|
||||
llm, 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)
|
||||
|
||||
@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_KEY]
|
||||
|
||||
def _call(
|
||||
self,
|
||||
inputs: Dict[str, Any],
|
||||
run_manager: Optional[CallbackManagerForChainRun] = None,
|
||||
) -> Dict[str, Any]:
|
||||
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
||||
_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,
|
||||
}
|
||||
_lowercased_table_names = [name.lower() for name in _table_names]
|
||||
table_names_from_chain = self.decider_chain.predict_and_parse(**llm_inputs)
|
||||
table_names_to_use = [
|
||||
name
|
||||
for name in table_names_from_chain
|
||||
if name.lower() in _lowercased_table_names
|
||||
]
|
||||
_run_manager.on_text("Table names to use:", end="\n", verbose=self.verbose)
|
||||
_run_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, callbacks=_run_manager.get_child(), return_only_outputs=True
|
||||
)
|
||||
|
||||
@property
|
||||
def _chain_type(self) -> str:
|
||||
return "sql_database_sequential_chain"
|
||||
@ -0,0 +1,263 @@
|
||||
# flake8: noqa
|
||||
from langchain.output_parsers.list import CommaSeparatedListOutputParser
|
||||
from langchain.prompts.prompt import PromptTemplate
|
||||
|
||||
|
||||
PROMPT_SUFFIX = """Only use the following tables:
|
||||
{table_info}
|
||||
|
||||
Question: {input}"""
|
||||
|
||||
_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
|
||||
|
||||
Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
|
||||
|
||||
Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "dialect", "top_k"],
|
||||
template=_DEFAULT_TEMPLATE + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
_DECIDER_TEMPLATE = """Given the below input question and list of potential tables, output a comma separated list of the table names that may be necessary to answer this question.
|
||||
|
||||
Question: {query}
|
||||
|
||||
Table Names: {table_names}
|
||||
|
||||
Relevant Table Names:"""
|
||||
DECIDER_PROMPT = PromptTemplate(
|
||||
input_variables=["query", "table_names"],
|
||||
template=_DECIDER_TEMPLATE,
|
||||
output_parser=CommaSeparatedListOutputParser(),
|
||||
)
|
||||
|
||||
_duckdb_prompt = """You are a DuckDB expert. Given an input question, first create a syntactically correct DuckDB query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per DuckDB. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use today() function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
DUCKDB_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_duckdb_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
_googlesql_prompt = """You are a GoogleSQL expert. Given an input question, first create a syntactically correct GoogleSQL query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per GoogleSQL. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in backticks (`) to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use CURRENT_DATE() function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
GOOGLESQL_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_googlesql_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
_mssql_prompt = """You are an MS SQL expert. Given an input question, first create a syntactically correct MS SQL query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the TOP clause as per MS SQL. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in square brackets ([]) to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use CAST(GETDATE() as date) function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
MSSQL_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_mssql_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
_mysql_prompt = """You are a MySQL expert. Given an input question, first create a syntactically correct MySQL query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per MySQL. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in backticks (`) to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use CURDATE() function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
MYSQL_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_mysql_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
_mariadb_prompt = """You are a MariaDB expert. Given an input question, first create a syntactically correct MariaDB query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per MariaDB. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in backticks (`) to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use CURDATE() function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
MARIADB_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_mariadb_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
_oracle_prompt = """You are an Oracle SQL expert. Given an input question, first create a syntactically correct Oracle SQL query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the FETCH FIRST n ROWS ONLY clause as per Oracle SQL. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use TRUNC(SYSDATE) function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
ORACLE_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_oracle_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
_postgres_prompt = """You are a PostgreSQL expert. Given an input question, first create a syntactically correct PostgreSQL query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per PostgreSQL. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use CURRENT_DATE function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
POSTGRES_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_postgres_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
_sqlite_prompt = """You are a SQLite expert. Given an input question, first create a syntactically correct SQLite query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per SQLite. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use date('now') function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: Question here
|
||||
SQLQuery: SQL Query to run
|
||||
SQLResult: Result of the SQLQuery
|
||||
Answer: Final answer here
|
||||
|
||||
"""
|
||||
|
||||
SQLITE_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_sqlite_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
_clickhouse_prompt = """You are a ClickHouse expert. Given an input question, first create a syntactically correct Clic query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per ClickHouse. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use today() function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: "Question here"
|
||||
SQLQuery: "SQL Query to run"
|
||||
SQLResult: "Result of the SQLQuery"
|
||||
Answer: "Final answer here"
|
||||
|
||||
"""
|
||||
|
||||
CLICKHOUSE_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_clickhouse_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
_prestodb_prompt = """You are a PrestoDB expert. Given an input question, first create a syntactically correct PrestoDB query to run, then look at the results of the query and return the answer to the input question.
|
||||
Unless the user specifies in the question a specific number of examples to obtain, query for at most {top_k} results using the LIMIT clause as per PrestoDB. You can order the results to return the most informative data in the database.
|
||||
Never query for all columns from a table. You must query only the columns that are needed to answer the question. Wrap each column name in double quotes (") to denote them as delimited identifiers.
|
||||
Pay attention to use only the column names you can see in the tables below. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
|
||||
Pay attention to use current_date function to get the current date, if the question involves "today".
|
||||
|
||||
Use the following format:
|
||||
|
||||
Question: "Question here"
|
||||
SQLQuery: "SQL Query to run"
|
||||
SQLResult: "Result of the SQLQuery"
|
||||
Answer: "Final answer here"
|
||||
|
||||
"""
|
||||
|
||||
PRESTODB_PROMPT = PromptTemplate(
|
||||
input_variables=["input", "table_info", "top_k"],
|
||||
template=_prestodb_prompt + PROMPT_SUFFIX,
|
||||
)
|
||||
|
||||
|
||||
SQL_PROMPTS = {
|
||||
"duckdb": DUCKDB_PROMPT,
|
||||
"googlesql": GOOGLESQL_PROMPT,
|
||||
"mssql": MSSQL_PROMPT,
|
||||
"mysql": MYSQL_PROMPT,
|
||||
"mariadb": MARIADB_PROMPT,
|
||||
"oracle": ORACLE_PROMPT,
|
||||
"postgresql": POSTGRES_PROMPT,
|
||||
"sqlite": SQLITE_PROMPT,
|
||||
"clickhouse": CLICKHOUSE_PROMPT,
|
||||
"prestodb": PRESTODB_PROMPT,
|
||||
}
|
||||
@ -1,4 +1,6 @@
|
||||
"""Implements Program-Aided Language Models.
|
||||
|
||||
As in https://arxiv.org/pdf/2211.10435.pdf.
|
||||
|
||||
TODO: deprecate
|
||||
"""
|
||||
|
||||
Loading…
Reference in New Issue