From 5ca7ce77cd536991d04f476e420446a3b21d2a7b Mon Sep 17 00:00:00 2001 From: vowelparrot <130414180+vowelparrot@users.noreply.github.com> Date: Sun, 16 Apr 2023 08:50:32 -0700 Subject: [PATCH] Remove pythonrepl from LLM-MathChain (#2943) 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 --- .../evaluation/agent_benchmarking.ipynb | 187 +++++++---------- langchain/chains/llm_math/base.py | 83 +++++--- langchain/chains/llm_math/prompt.py | 17 +- poetry.lock | 189 +++++++++++------- pyproject.toml | 1 + tests/unit_tests/chains/test_llm_math.py | 4 +- 6 files changed, 251 insertions(+), 230 deletions(-) diff --git a/docs/use_cases/evaluation/agent_benchmarking.ipynb b/docs/use_cases/evaluation/agent_benchmarking.ipynb index 342cfa6f..4c68b9ce 100644 --- a/docs/use_cases/evaluation/agent_benchmarking.ipynb +++ b/docs/use_cases/evaluation/agent_benchmarking.ipynb @@ -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._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, diff --git a/langchain/chains/llm_math/base.py b/langchain/chains/llm_math/base.py index 5c7ef09e..aece80a9 100644 --- a/langchain/chains/llm_math/base.py +++ b/langchain/chains/llm_math/base.py @@ -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: diff --git a/langchain/chains/llm_math/prompt.py b/langchain/chains/llm_math/prompt.py index 3e78b475..a3c346fc 100644 --- a/langchain/chains/llm_math/prompt.py +++ b/langchain/chains/llm_math/prompt.py @@ -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, +) diff --git a/poetry.lock b/poetry.lock index 6b0e37aa..8b7262a1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -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" files = [ - 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"unidic-lite (>=1.0.7)"] +dev-tensorflow = ["GitPython (<3.1.19)", "Pillow", "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", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.11.1,!=0.11.3,<0.14)"] +dev-torch = ["GitPython (<3.1.19)", "Pillow", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf (<=3.20.2)", "psutil", "pyctcdecode (>=0.4.0)", "pytest", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "safetensors (>=0.2.1)", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "timeout-decorator", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"] +docs = ["Pillow", "accelerate (>=0.10.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1)", "hf-doc-builder", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8)", "optuna", "phonemizer", "protobuf (<=3.20.2)", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.4,<2.13)", "tensorflow-text (<2.13)", "tf2onnx", "timm", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "torchaudio", "torchvision"] docs-specific = ["hf-doc-builder"] fairscale = ["fairscale (>0.3)"] flax = ["flax (>=0.4.1)", "jax (>=0.2.8,!=0.3.2,<=0.3.6)", "jaxlib (>=0.1.65,<=0.3.6)", "optax (>=0.0.8)"] @@ -8071,11 +8114,11 @@ ftfy = ["ftfy"] integrations = ["optuna", "ray[tune]", "sigopt"] ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"] modelcreation = ["cookiecutter (==1.7.3)"] -natten = ["natten (>=0.14.4)"] +natten = ["natten (>=0.14.6)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] -quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241)"] +quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241,<=0.0.259)"] ray = ["ray[tune]"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] sagemaker = ["sagemaker (>=2.31.0)"] @@ -8085,15 +8128,15 @@ sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] 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" diff --git a/pyproject.toml b/pyproject.toml index 5ab29855..cf64becf 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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" diff --git a/tests/unit_tests/chains/test_llm_math.py b/tests/unit_tests/chains/test_llm_math.py index b38d89dd..c412436c 100644 --- a/tests/unit_tests/chains/test_llm_math.py +++ b/tests/unit_tests/chains/test_llm_math.py @@ -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: