diff --git a/docs/use_cases/evaluation/agent_benchmarking.ipynb b/docs/use_cases/evaluation/agent_benchmarking.ipynb index 342cfa6fc87fb..4c68b9ce1c88f 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 5c7ef09efc68a..aece80a97aa93 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 @@ def output_keys(self) -> List[str]: """ 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 @@ async def _aprocess_llm_result(self, t: str) -> Dict[str, str]: 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 @@ def _call(self, inputs: Dict[str, str]) -> Dict[str, str]: 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 @@ async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]: ) 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 3e78b475c3b22..a3c346fcc788e 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 @@ Question: What is 37593 * 67? -```python -print(37593 * 67) +```text +37593 * 67 ``` +...numexpr.evaluate("37593 * 67")... ```output 2518731 ``` @@ -27,4 +29,7 @@ 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 6b0e37aa1a55a..8b7262a1c787d 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 5ab29855f8bb9..cf64becfe2d48 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 b38d89dd2a053..c412436c665cb 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: