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Remove pythonrepl from LLM-MathChain (#2943)

Browse Source 
Use numexpr evaluate instead of the python REPL to avoid malicious code
injection.

Tested against the (limited) math dataset and got the same score as
before.

For more permissive tools (like the REPL tool itself), other approaches
ought to be provided (some combination of Sanitizer + Restricted python
+ unprivileged-docker + ...), but for a calculator tool, only
mathematical expressions should be permitted.

See https://github.com/hwchase17/langchain/issues/814
fix_agent_callbacks
vowelparrot 1 year ago committed by GitHub
parent 2a0f65f7af
commit 5ca7ce77cd
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 251 additions and 230 deletions
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187
docs/use_cases/evaluation/agent_benchmarking.ipynb
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@ -14,9 +14,11 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "46bf9205",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Comment this out if you are NOT using tracing\n",
@ -35,32 +37,12 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "5b2d5e98",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Found cached dataset json (/Users/harrisonchase/.cache/huggingface/datasets/LangChainDatasets___json/LangChainDatasets--agent-search-calculator-8a025c0ce5fb99d2/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3a275586643f4ccfba1a8d54be28c351",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.evaluation.loading import load_dataset\n",
"dataset = load_dataset(\"agent-search-calculator\")"
@ -77,9 +59,11 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"id": "c18680b5",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.llms import OpenAI\n",
@ -88,7 +72,7 @@
"from langchain.agents import AgentType\n",
"\n",
"tools = load_tools(['serpapi', 'llm-math'], llm=OpenAI(temperature=0))\n",
"agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)\n"
"agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
]
},
{
@ -103,22 +87,14 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"id": "cbcafc92",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'38,630,316 people live in Canada as of 2023.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"print(dataset[0]['question'])\n",
"agent.run(dataset[0]['question'])"
]
},
@ -133,18 +109,24 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"id": "bbbbb20e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"agent.run(dataset[4]['question'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24b4c66e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Retrying langchain.llms.openai.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI: ('Connection aborted.', ConnectionResetError(54, 'Connection reset by peer')).\n"
]
}
],
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"predictions = []\n",
"predicted_dataset = []\n",
@ -154,7 +136,8 @@
" try:\n",
" predictions.append(agent(new_data))\n",
" predicted_dataset.append(new_data)\n",
" except Exception:\n",
" except Exception as e:\n",
" predictions.append({\"output\": str(e), **new_data})\n",
" error_dataset.append(new_data)"
]
},
@ -169,25 +152,12 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"id": "1d583f03",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': 'How many people live in canada as of 2023?',\n",
" 'answer': 'approximately 38,625,801',\n",
" 'output': '38,630,316 people live in Canada as of 2023.',\n",
" 'intermediate_steps': [(AgentAction(tool='Search', tool_input='Population of Canada 2023', log=' I need to find population data\\nAction: Search\\nAction Input: Population of Canada 2023'),\n",
" '38,630,316')]}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"predictions[0]"
]
@ -202,9 +172,11 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"id": "d0a9341d",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.evaluation.qa import QAEvalChain"
@ -212,9 +184,11 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"id": "1612dec1",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"llm = OpenAI(temperature=0)\n",
@ -232,9 +206,11 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"id": "2a689df5",
"metadata": {},
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"for i, prediction in enumerate(predictions):\n",
@ -243,21 +219,12 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": null,
"id": "27b61215",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Counter({' CORRECT': 4, ' INCORRECT': 6})"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from collections import Counter\n",
"Counter([pred['grade'] for pred in predictions])"
@ -273,7 +240,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": null,
"id": "47c692a1",
"metadata": {},
"outputs": [],
@ -283,38 +250,18 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": null,
"id": "0ef976c1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'input': \"who is dua lipa's boyfriend? what is his age raised to the .43 power?\",\n",
" 'answer': 'her boyfriend is Romain Gravas. his age raised to the .43 power is approximately 4.9373857399466665',\n",
" 'output': \"Isaac Carew, Dua Lipa's boyfriend, is 36 years old and his age raised to the .43 power is 4.6688516567750975.\",\n",
" 'intermediate_steps': [(AgentAction(tool='Search', tool_input=\"Dua Lipa's boyfriend\", log=' I need to find out who Dua Lipa\\'s boyfriend is and then calculate his age raised to the .43 power\\nAction: Search\\nAction Input: \"Dua Lipa\\'s boyfriend\"'),\n",
" 'Dua and Isaac, a model and a chef, dated on and off from 2013 to 2019. The two first split in early 2017, which is when Dua went on to date LANY ...'),\n",
" (AgentAction(tool='Search', tool_input='Isaac Carew age', log=' I need to find out Isaac\\'s age\\nAction: Search\\nAction Input: \"Isaac Carew age\"'),\n",
" '36 years'),\n",
" (AgentAction(tool='Calculator', tool_input='36^.43', log=' I need to calculate 36 raised to the .43 power\\nAction: Calculator\\nAction Input: 36^.43'),\n",
" 'Answer: 4.6688516567750975\\n')],\n",
" 'grade': ' INCORRECT'}"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"incorrect[0]"
"incorrect"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7710401a",
"id": "3eb948cf-f767-4c87-a12d-275b66eef407",
"metadata": {},
"outputs": [],
"source": []
@ -336,7 +283,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
"version": "3.11.2"
}
},
"nbformat": 4,

83
langchain/chains/llm_math/base.py
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@ -1,6 +1,9 @@
"""Chain that interprets a prompt and executes python code to do math."""
import math
import re
from typing import Dict, List
import numexpr
from pydantic import Extra
from langchain.chains.base import Chain
@ -50,34 +53,54 @@ class LLMMathChain(Chain):
"""
return [self.output_key]
def _process_llm_result(self, t: str) -> Dict[str, str]:
python_executor = PythonREPL()
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
if t.startswith("```python"):
code = t[9:-4]
output = python_executor.run(code)
def _evaluate_expression(self, expression: str) -> str:
try:
local_dict = {"pi": math.pi, "e": math.e}
output = str(
numexpr.evaluate(
expression.strip(),
global_dict={}, # restrict access to globals
local_dict=local_dict, # add common mathematical functions
)
)
except Exception as e:
raise ValueError(f"{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) -> Dict[str, str]:
self.callback_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)
self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
self.callback_manager.on_text(output, color="yellow", verbose=self.verbose)
answer = "Answer: " + output
elif t.startswith("Answer:"):
answer = t
elif "Answer:" in t:
answer = "Answer: " + t.split("Answer:")[-1]
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: {t}")
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
async def _aprocess_llm_result(self, t: str) -> Dict[str, str]:
python_executor = PythonREPL()
async def _aprocess_llm_result(self, llm_output: str) -> Dict[str, str]:
if self.callback_manager.is_async:
await self.callback_manager.on_text(t, color="green", verbose=self.verbose)
await self.callback_manager.on_text(
llm_output, color="green", verbose=self.verbose
)
else:
self.callback_manager.on_text(t, color="green", verbose=self.verbose)
t = t.strip()
if t.startswith("```python"):
code = t[9:-4]
output = python_executor.run(code)
self.callback_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)
if self.callback_manager.is_async:
await self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
await self.callback_manager.on_text(
@ -89,12 +112,12 @@ class LLMMathChain(Chain):
output, color="yellow", verbose=self.verbose
)
answer = "Answer: " + output
elif t.startswith("Answer:"):
answer = t
elif "Answer:" in t:
answer = "Answer: " + t.split("Answer:")[-1]
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: {t}")
raise ValueError(f"unknown format from LLM: {llm_output}")
return {self.output_key: answer}
def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
@ -102,8 +125,10 @@ class LLMMathChain(Chain):
prompt=self.prompt, llm=self.llm, callback_manager=self.callback_manager
)
self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = llm_executor.predict(question=inputs[self.input_key], stop=["```output"])
return self._process_llm_result(t)
llm_output = llm_executor.predict(
question=inputs[self.input_key], stop=["```output"]
)
return self._process_llm_result(llm_output)
async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
llm_executor = LLMChain(
@ -115,10 +140,10 @@ class LLMMathChain(Chain):
)
else:
self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
t = await llm_executor.apredict(
llm_output = await llm_executor.apredict(
question=inputs[self.input_key], stop=["```output"]
)
return await self._aprocess_llm_result(t)
return await self._aprocess_llm_result(llm_output)
@property
def _chain_type(self) -> str:

17
langchain/chains/llm_math/prompt.py
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@ -1,12 +1,13 @@
# flake8: noqa
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """Translate a math problem into Python code that can be executed in Python 3 REPL. Use the output of running this code to answer the question.
_PROMPT_TEMPLATE = """Translate a math problem into a expression that can be executed using Python's numexpr library. Use the output of running this code to answer the question.
Question: ${{Question with math problem.}}
```python
${{Code that solves the problem and prints the solution}}
```text
${{single line mathematical expression that solves the problem}}
```
...numexpr.evaluate(text)...
```output
${{Output of running the code}}
```
@ -16,9 +17,10 @@ Begin.
Question: What is 37593 * 67?
```python
print(37593 * 67)
```text
37593 * 67
```
...numexpr.evaluate("37593 * 67")...
```output
2518731
```
@ -27,4 +29,7 @@ Answer: 2518731
Question: {question}
"""
PROMPT = PromptTemplate(input_variables=["question"], template=_PROMPT_TEMPLATE)
PROMPT = PromptTemplate(
input_variables=["question"],
template=_PROMPT_TEMPLATE,
)

189
poetry.lock generated
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@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry and should not be changed by hand.
# This file is automatically @generated by Poetry 1.4.2 and should not be changed by hand.
[[package]]
name = "absl-py"
@ -289,14 +289,14 @@ types = ["mypy", "types-Pillow", "types-requests"]
[[package]]
name = "anthropic"
version = "0.2.6"
version = "0.2.7"
description = "Library for accessing the anthropic API"
category = "main"
optional = true
python-versions = ">=3.8"
files = [
{file = "anthropic-0.2.6-py3-none-any.whl", hash = "sha256:bf4b69a9d25d573162accbe8aa9e022331c67a82519250ac30573ad506a1f663"},
{file = "anthropic-0.2.6.tar.gz", hash = "sha256:253dca8484bca13eab08afa6b1e1d3f3451d6b65cc2be31c5cd4dac1a49a03f7"},
{file = "anthropic-0.2.7-py3-none-any.whl", hash = "sha256:b5d807b54c43ad4812a82b8389412507d62574b06af79c25dc2433233f1ab50f"},
{file = "anthropic-0.2.7.tar.gz", hash = "sha256:dafdd617c79122bb97bc53634519e8dcba17b7e765a5a2372c77f6e3744127e5"},
]
[package.dependencies]
@ -1198,31 +1198,31 @@ toml = ["tomli"]
[[package]]
name = "cryptography"
version = "40.0.1"
version = "40.0.2"
description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
category = "main"
optional = false
python-versions = ">=3.6"
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@ -8085,15 +8128,15 @@ sigopt = ["sigopt"]
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testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf (<=3.20.2)", "psutil", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "timeout-decorator"]
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.12)", "tensorflow-text", "tf2onnx"]
tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.12)", "tensorflow-text", "tf2onnx"]
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx"]
tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx"]
tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
timm = ["timm"]
tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
torch = ["torch (>=1.7,!=1.12.0)"]
torch = ["torch (>=1.9,!=1.12.0)"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow", "torchvision"]
torchhub = ["filelock", "huggingface-hub (>=0.11.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.2)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.7,!=1.12.0)", "tqdm (>=4.27)"]
torchhub = ["filelock", "huggingface-hub (>=0.11.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf (<=3.20.2)", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow"]
@ -8494,14 +8537,14 @@ files = [
[[package]]
name = "weaviate-client"
version = "3.15.5"
version = "3.15.6"
description = "A python native weaviate client"
category = "main"
optional = false
python-versions = ">=3.7"
files = [
{file = "weaviate-client-3.15.5.tar.gz", hash = "sha256:6da7e5d08dc9bb8b7879661d1a457c50af7d73e621a5305efe131160e83da69e"},
{file = "weaviate_client-3.15.5-py3-none-any.whl", hash = "sha256:24d0be614e5494534e758cc67a45e7e15f3929a89bf512afd642de53d08723c7"},
{file = "weaviate-client-3.15.6.tar.gz", hash = "sha256:ef47dcc1fd0d6c7927e6f65779e5d7a6572972e3b41d0f4a4ae7a29260bf4c34"},
{file = "weaviate_client-3.15.6-py3-none-any.whl", hash = "sha256:18cc1b756bffa99e6dd01c64d71c461c784851e785868f66c458ffc2bcf898c9"},
]
[package.dependencies]
@ -9026,13 +9069,13 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\
cffi = ["cffi (>=1.11)"]
[extras]
all = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "jina", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "spacy", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "weaviate-client", "redis", "google-api-python-client", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "pgvector", "psycopg2-binary", "pyowm"]
all = ["aleph-alpha-client", "anthropic", "beautifulsoup4", "cohere", "deeplake", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "huggingface_hub", "jina", "jinja2", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pyowm", "pypdf", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
cohere = ["cohere"]
llms = ["anthropic", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"]
llms = ["anthropic", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "torch", "transformers"]
openai = ["openai"]
qdrant = ["qdrant-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8.1,<4.0"
content-hash = "7e343fa8e31d8fcf1023cbda592f64c05e80015c4e0e23c1d387d2e9671ce995"
content-hash = "47ad0cfafaf5ec6f27bd1713ac237077cd54083960e937ebd005a7c4b25bbe5e"

1
pyproject.toml
Unescape Escape View File

@ -60,6 +60,7 @@ psycopg2-binary = {version = "^2.9.5", optional = true}
pyowm = {version = "^3.3.0", optional = true}
async-timeout = {version = "^4.0.0", python = "<3.11"}
gptcache = {version = ">=0.1.7", optional = true}
numexpr = "^2.8.4"
[tool.poetry.group.docs.dependencies]
autodoc_pydantic = "^1.8.0"

4
tests/unit_tests/chains/test_llm_math.py
Unescape Escape View File

@ -13,7 +13,7 @@ def fake_llm_math_chain() -> LLMMathChain:
complex_question = _PROMPT_TEMPLATE.format(question="What is the square root of 2?")
queries = {
_PROMPT_TEMPLATE.format(question="What is 1 plus 1?"): "Answer: 2",
complex_question: "```python\nprint(2**.5)\n```",
complex_question: "```text\n2**.5\n```",
_PROMPT_TEMPLATE.format(question="foo"): "foo",
}
fake_llm = FakeLLM(queries=queries)
@ -31,7 +31,7 @@ def test_complex_question(fake_llm_math_chain: LLMMathChain) -> None:
"""Test complex question that should need python."""
question = "What is the square root of 2?"
output = fake_llm_math_chain.run(question)
assert output == f"Answer: {2**.5}\n"
assert output == f"Answer: {2**.5}"
def test_error(fake_llm_math_chain: LLMMathChain) -> None:

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