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Update PAL to allow passing local and global context to PythonREPL (#774)

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Passing additional variables to the python environment can be useful for
example if you want to generate code to analyze a dataset.

I also added a tracker for the executed code - `code_history`.
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Nick Furlotte 2023-02-02 08:34:23 -08:00 committed by GitHub
parent 3f952eb597
commit 576609e665
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1 changed files with 4 additions and 2 deletions
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6
langchain/chains/pal/base.py
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@ -4,7 +4,7 @@ As in https://arxiv.org/pdf/2211.10435.pdf.
""" """
from __future__ import annotations from __future__ import annotations
from typing import Any, Dict, List from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra from pydantic import BaseModel, Extra
@ -24,6 +24,8 @@ class PALChain(Chain, BaseModel):
prompt: BasePromptTemplate prompt: BasePromptTemplate
stop: str = "\n\n" stop: str = "\n\n"
get_answer_expr: str = "print(solution())" get_answer_expr: str = "print(solution())"
python_globals: Optional[Dict[str, Any]] = None
python_locals: Optional[Dict[str, Any]] = None
output_key: str = "result" #: :meta private: output_key: str = "result" #: :meta private:
class Config: class Config:
@ -54,7 +56,7 @@ class PALChain(Chain, BaseModel):
self.callback_manager.on_text( self.callback_manager.on_text(
code, color="green", end="\n", verbose=self.verbose code, color="green", end="\n", verbose=self.verbose
) )
repl = PythonREPL() repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
res = repl.run(code + f"\n{self.get_answer_expr}") res = repl.run(code + f"\n{self.get_answer_expr}")
return {self.output_key: res.strip()} return {self.output_key: res.strip()}