mirror of https://github.com/hwchase17/langchain
Deprecate LLMSymbolicMath from langchain core (#11615)
Deprecate LLMSymbolicMath from langchain core package.pull/11614/head^2
parent
59adeaddb3
commit
b56ca0c2a4
@ -1,14 +1,12 @@
|
|||||||
"""Chain that interprets a prompt and executes python code to do math.
|
def raise_on_import() -> None:
|
||||||
|
"""Raise an error on import since is deprecated."""
|
||||||
|
raise ImportError(
|
||||||
|
"This module has been moved to langchain-experimental. "
|
||||||
|
"For more details: https://github.com/langchain-ai/langchain/discussions/11352."
|
||||||
|
"To access this code, install it with `pip install langchain-experimental`."
|
||||||
|
"`from langchain_experimental.llm_symbolic_math.base "
|
||||||
|
"import LLMSymbolicMathChain`"
|
||||||
|
)
|
||||||
|
|
||||||
Heavily borrowed from llm_math, wrapper for SymPy
|
|
||||||
"""
|
|
||||||
from langchain._api import warn_deprecated
|
|
||||||
|
|
||||||
warn_deprecated(
|
raise_on_import()
|
||||||
since="0.0.304",
|
|
||||||
message=(
|
|
||||||
"On 2023-10-06 this module will be moved to langchain-experimental as "
|
|
||||||
"it relies on sympy https://github.com/sympy/sympy/issues/10805"
|
|
||||||
),
|
|
||||||
pending=True,
|
|
||||||
)
|
|
||||||
|
@ -1,156 +0,0 @@
|
|||||||
"""Chain that interprets a prompt and executes python code to do symbolic math."""
|
|
||||||
from __future__ import annotations
|
|
||||||
|
|
||||||
import re
|
|
||||||
from typing import Any, Dict, List, Optional
|
|
||||||
|
|
||||||
from langchain.base_language import BaseLanguageModel
|
|
||||||
from langchain.callbacks.manager import (
|
|
||||||
AsyncCallbackManagerForChainRun,
|
|
||||||
CallbackManagerForChainRun,
|
|
||||||
)
|
|
||||||
from langchain.chains.base import Chain
|
|
||||||
from langchain.chains.llm import LLMChain
|
|
||||||
from langchain.chains.llm_symbolic_math.prompt import PROMPT
|
|
||||||
from langchain.prompts.base import BasePromptTemplate
|
|
||||||
from langchain.pydantic_v1 import Extra
|
|
||||||
|
|
||||||
|
|
||||||
class LLMSymbolicMathChain(Chain):
|
|
||||||
"""Chain that interprets a prompt and executes python code to do symbolic math.
|
|
||||||
|
|
||||||
Example:
|
|
||||||
.. code-block:: python
|
|
||||||
|
|
||||||
from langchain.chains import LLMSymbolicMathChain
|
|
||||||
from langchain.llms import OpenAI
|
|
||||||
llm_symbolic_math = LLMSymbolicMathChain.from_llm(OpenAI())
|
|
||||||
"""
|
|
||||||
|
|
||||||
llm_chain: LLMChain
|
|
||||||
input_key: str = "question" #: :meta private:
|
|
||||||
output_key: str = "answer" #: :meta private:
|
|
||||||
|
|
||||||
class Config:
|
|
||||||
"""Configuration for this pydantic object."""
|
|
||||||
|
|
||||||
extra = Extra.forbid
|
|
||||||
arbitrary_types_allowed = True
|
|
||||||
|
|
||||||
@property
|
|
||||||
def input_keys(self) -> List[str]:
|
|
||||||
"""Expect input key.
|
|
||||||
|
|
||||||
:meta private:
|
|
||||||
"""
|
|
||||||
return [self.input_key]
|
|
||||||
|
|
||||||
@property
|
|
||||||
def output_keys(self) -> List[str]:
|
|
||||||
"""Expect output key.
|
|
||||||
|
|
||||||
:meta private:
|
|
||||||
"""
|
|
||||||
return [self.output_key]
|
|
||||||
|
|
||||||
def _evaluate_expression(self, expression: str) -> str:
|
|
||||||
try:
|
|
||||||
import sympy
|
|
||||||
except ImportError as e:
|
|
||||||
raise ImportError(
|
|
||||||
"Unable to import sympy, please install it with `pip install sympy`."
|
|
||||||
) from e
|
|
||||||
try:
|
|
||||||
output = str(sympy.sympify(expression, evaluate=True))
|
|
||||||
except Exception as e:
|
|
||||||
raise ValueError(
|
|
||||||
f'LLMSymbolicMathChain._evaluate("{expression}") raised error: {e}.'
|
|
||||||
" Please try again with a valid numerical expression"
|
|
||||||
)
|
|
||||||
|
|
||||||
# Remove any leading and trailing brackets from the output
|
|
||||||
return re.sub(r"^\[|\]$", "", output)
|
|
||||||
|
|
||||||
def _process_llm_result(
|
|
||||||
self, llm_output: str, run_manager: CallbackManagerForChainRun
|
|
||||||
) -> Dict[str, str]:
|
|
||||||
run_manager.on_text(llm_output, color="green", verbose=self.verbose)
|
|
||||||
llm_output = llm_output.strip()
|
|
||||||
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
|
|
||||||
if text_match:
|
|
||||||
expression = text_match.group(1)
|
|
||||||
output = self._evaluate_expression(expression)
|
|
||||||
run_manager.on_text("\nAnswer: ", verbose=self.verbose)
|
|
||||||
run_manager.on_text(output, color="yellow", verbose=self.verbose)
|
|
||||||
answer = "Answer: " + output
|
|
||||||
elif llm_output.startswith("Answer:"):
|
|
||||||
answer = llm_output
|
|
||||||
elif "Answer:" in llm_output:
|
|
||||||
answer = "Answer: " + llm_output.split("Answer:")[-1]
|
|
||||||
else:
|
|
||||||
raise ValueError(f"unknown format from LLM: {llm_output}")
|
|
||||||
return {self.output_key: answer}
|
|
||||||
|
|
||||||
async def _aprocess_llm_result(
|
|
||||||
self,
|
|
||||||
llm_output: str,
|
|
||||||
run_manager: AsyncCallbackManagerForChainRun,
|
|
||||||
) -> Dict[str, str]:
|
|
||||||
await run_manager.on_text(llm_output, color="green", verbose=self.verbose)
|
|
||||||
llm_output = llm_output.strip()
|
|
||||||
text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
|
|
||||||
if text_match:
|
|
||||||
expression = text_match.group(1)
|
|
||||||
output = self._evaluate_expression(expression)
|
|
||||||
await run_manager.on_text("\nAnswer: ", verbose=self.verbose)
|
|
||||||
await run_manager.on_text(output, color="yellow", verbose=self.verbose)
|
|
||||||
answer = "Answer: " + output
|
|
||||||
elif llm_output.startswith("Answer:"):
|
|
||||||
answer = llm_output
|
|
||||||
elif "Answer:" in llm_output:
|
|
||||||
answer = "Answer: " + llm_output.split("Answer:")[-1]
|
|
||||||
else:
|
|
||||||
raise ValueError(f"unknown format from LLM: {llm_output}")
|
|
||||||
return {self.output_key: answer}
|
|
||||||
|
|
||||||
def _call(
|
|
||||||
self,
|
|
||||||
inputs: Dict[str, str],
|
|
||||||
run_manager: Optional[CallbackManagerForChainRun] = None,
|
|
||||||
) -> Dict[str, str]:
|
|
||||||
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
|
|
||||||
_run_manager.on_text(inputs[self.input_key])
|
|
||||||
llm_output = self.llm_chain.predict(
|
|
||||||
question=inputs[self.input_key],
|
|
||||||
stop=["```output"],
|
|
||||||
callbacks=_run_manager.get_child(),
|
|
||||||
)
|
|
||||||
return self._process_llm_result(llm_output, _run_manager)
|
|
||||||
|
|
||||||
async def _acall(
|
|
||||||
self,
|
|
||||||
inputs: Dict[str, str],
|
|
||||||
run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
|
|
||||||
) -> Dict[str, str]:
|
|
||||||
_run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager()
|
|
||||||
await _run_manager.on_text(inputs[self.input_key])
|
|
||||||
llm_output = await self.llm_chain.apredict(
|
|
||||||
question=inputs[self.input_key],
|
|
||||||
stop=["```output"],
|
|
||||||
callbacks=_run_manager.get_child(),
|
|
||||||
)
|
|
||||||
return await self._aprocess_llm_result(llm_output, _run_manager)
|
|
||||||
|
|
||||||
@property
|
|
||||||
def _chain_type(self) -> str:
|
|
||||||
return "llm_symbolic_math_chain"
|
|
||||||
|
|
||||||
@classmethod
|
|
||||||
def from_llm(
|
|
||||||
cls,
|
|
||||||
llm: BaseLanguageModel,
|
|
||||||
prompt: BasePromptTemplate = PROMPT,
|
|
||||||
**kwargs: Any,
|
|
||||||
) -> LLMSymbolicMathChain:
|
|
||||||
llm_chain = LLMChain(llm=llm, prompt=prompt)
|
|
||||||
return cls(llm_chain=llm_chain, **kwargs)
|
|
@ -1,51 +0,0 @@
|
|||||||
# flake8: noqa
|
|
||||||
from langchain.prompts.prompt import PromptTemplate
|
|
||||||
|
|
||||||
_PROMPT_TEMPLATE = """Translate a math problem into a expression that can be executed using Python's SymPy library. Use the output of running this code to answer the question.
|
|
||||||
|
|
||||||
Question: ${{Question with math problem.}}
|
|
||||||
```text
|
|
||||||
${{single line sympy expression that solves the problem}}
|
|
||||||
```
|
|
||||||
...sympy.sympify(text, evaluate=True)...
|
|
||||||
```output
|
|
||||||
${{Output of running the code}}
|
|
||||||
```
|
|
||||||
Answer: ${{Answer}}
|
|
||||||
|
|
||||||
Begin.
|
|
||||||
|
|
||||||
Question: What is the limit of sin(x) / x as x goes to 0
|
|
||||||
```text
|
|
||||||
limit(sin(x)/x, x, 0)
|
|
||||||
```
|
|
||||||
...sympy.sympify("limit(sin(x)/x, x, 0)")...
|
|
||||||
```output
|
|
||||||
1
|
|
||||||
```
|
|
||||||
Answer: 1
|
|
||||||
|
|
||||||
Question: What is the integral of e^-x from 0 to infinity
|
|
||||||
```text
|
|
||||||
integrate(exp(-x), (x, 0, oo))
|
|
||||||
```
|
|
||||||
...sympy.sympify("integrate(exp(-x), (x, 0, oo))")...
|
|
||||||
```output
|
|
||||||
1
|
|
||||||
```
|
|
||||||
|
|
||||||
Question: What are the solutions to this equation x**2 - x?
|
|
||||||
```text
|
|
||||||
solveset(x**2 - x, x)
|
|
||||||
```
|
|
||||||
...sympy.sympify("solveset(x**2 - x, x)")...
|
|
||||||
```output
|
|
||||||
[0, 1]
|
|
||||||
```
|
|
||||||
Question: {question}
|
|
||||||
"""
|
|
||||||
|
|
||||||
PROMPT = PromptTemplate(
|
|
||||||
input_variables=["question"],
|
|
||||||
template=_PROMPT_TEMPLATE,
|
|
||||||
)
|
|
@ -1,82 +0,0 @@
|
|||||||
"""Test LLM Math functionality."""
|
|
||||||
|
|
||||||
import pytest
|
|
||||||
|
|
||||||
from langchain.chains.llm_symbolic_math.base import LLMSymbolicMathChain
|
|
||||||
from langchain.chains.llm_symbolic_math.prompt import _PROMPT_TEMPLATE
|
|
||||||
from tests.unit_tests.llms.fake_llm import FakeLLM
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.fixture
|
|
||||||
@pytest.mark.requires("sympy")
|
|
||||||
def fake_llm_symbolic_math_chain() -> LLMSymbolicMathChain:
|
|
||||||
"""Fake LLM Math chain for testing."""
|
|
||||||
queries = {
|
|
||||||
_PROMPT_TEMPLATE.format(question="What is 1 plus 1?"): "Answer: 2",
|
|
||||||
_PROMPT_TEMPLATE.format(
|
|
||||||
question="What is the square root of 2?"
|
|
||||||
): "```text\nsqrt(2)\n```",
|
|
||||||
_PROMPT_TEMPLATE.format(
|
|
||||||
question="What is the limit of sin(x) / x as x goes to 0?"
|
|
||||||
): "```text\nlimit(sin(x)/x,x,0)\n```",
|
|
||||||
_PROMPT_TEMPLATE.format(
|
|
||||||
question="What is the integral of e^-x from 0 to infinity?"
|
|
||||||
): "```text\nintegrate(exp(-x), (x, 0, oo))\n```",
|
|
||||||
_PROMPT_TEMPLATE.format(
|
|
||||||
question="What are the solutions to this equation x**2 - x?"
|
|
||||||
): "```text\nsolveset(x**2 - x, x)\n```",
|
|
||||||
_PROMPT_TEMPLATE.format(question="foo"): "foo",
|
|
||||||
}
|
|
||||||
fake_llm = FakeLLM(queries=queries)
|
|
||||||
return LLMSymbolicMathChain.from_llm(fake_llm, input_key="q", output_key="a")
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.requires("sympy")
|
|
||||||
def test_simple_question(fake_llm_symbolic_math_chain: LLMSymbolicMathChain) -> None:
|
|
||||||
"""Test simple question that should not need python."""
|
|
||||||
question = "What is 1 plus 1?"
|
|
||||||
output = fake_llm_symbolic_math_chain.run(question)
|
|
||||||
assert output == "Answer: 2"
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.requires("sympy")
|
|
||||||
def test_root_question(fake_llm_symbolic_math_chain: LLMSymbolicMathChain) -> None:
|
|
||||||
"""Test irrational number that should need sympy."""
|
|
||||||
import sympy
|
|
||||||
|
|
||||||
question = "What is the square root of 2?"
|
|
||||||
output = fake_llm_symbolic_math_chain.run(question)
|
|
||||||
assert output == f"Answer: {sympy.sqrt(2)}"
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.requires("sympy")
|
|
||||||
def test_limit_question(fake_llm_symbolic_math_chain: LLMSymbolicMathChain) -> None:
|
|
||||||
"""Test question about limits that needs sympy"""
|
|
||||||
question = "What is the limit of sin(x) / x as x goes to 0?"
|
|
||||||
output = fake_llm_symbolic_math_chain.run(question)
|
|
||||||
assert output == "Answer: 1"
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.requires("sympy")
|
|
||||||
def test_integration_question(
|
|
||||||
fake_llm_symbolic_math_chain: LLMSymbolicMathChain,
|
|
||||||
) -> None:
|
|
||||||
"""Test question about integration that needs sympy"""
|
|
||||||
question = "What is the integral of e^-x from 0 to infinity?"
|
|
||||||
output = fake_llm_symbolic_math_chain.run(question)
|
|
||||||
assert output == "Answer: 1"
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.requires("sympy")
|
|
||||||
def test_solver_question(fake_llm_symbolic_math_chain: LLMSymbolicMathChain) -> None:
|
|
||||||
"""Test question about solving algebraic equations that needs sympy"""
|
|
||||||
question = "What are the solutions to this equation x**2 - x?"
|
|
||||||
output = fake_llm_symbolic_math_chain.run(question)
|
|
||||||
assert output == "Answer: {0, 1}"
|
|
||||||
|
|
||||||
|
|
||||||
@pytest.mark.requires("sympy")
|
|
||||||
def test_error(fake_llm_symbolic_math_chain: LLMSymbolicMathChain) -> None:
|
|
||||||
"""Test question that raises error."""
|
|
||||||
with pytest.raises(ValueError):
|
|
||||||
fake_llm_symbolic_math_chain.run("foo")
|
|
Loading…
Reference in New Issue