Update LLM-Math Chain

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<?, ?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,

langchain/chains/llm_math/base.py

@@ -1,13 +1,15 @@
 """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
 from langchain.chains.llm import LLMChain
 from langchain.chains.llm_math.prompt import PROMPT
 from langchain.prompts.base import BasePromptTemplate
-from langchain.python import PythonREPL
 from langchain.schema import BaseLanguageModel
 
 
@@ -50,34 +52,50 @@ 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:
+        local_dict = {"pi": math.pi, "e": math.e}
+        output = str(
+            numexpr.evaluate(
+                expression.strip(),
+                global_dict={},
+                local_dict=local_dict,
+            )
+        )
+        # Remove the 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 +107,12 @@ 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 +120,10 @@ 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 +135,10 @@ 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:

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,
+)

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"
@@ -4328,6 +4328,49 @@ msgpack = ["msgpack"]
 test = ["coverage", "flake8", "pytest", "pytest-cov"]
 zfpy = ["zfpy (>=1.0.0)"]
 
+[[package]]
+name = "numexpr"
+version = "2.8.4"
+description = "Fast numerical expression evaluator for NumPy"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "numexpr-2.8.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a75967d46b6bd56455dd32da6285e5ffabe155d0ee61eef685bbfb8dafb2e484"},
+    {file = "numexpr-2.8.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:db93cf1842f068247de631bfc8af20118bf1f9447cd929b531595a5e0efc9346"},
+    {file = "numexpr-2.8.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7bca95f4473b444428061d4cda8e59ac564dc7dc6a1dea3015af9805c6bc2946"},
+    {file = "numexpr-2.8.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e34931089a6bafc77aaae21f37ad6594b98aa1085bb8b45d5b3cd038c3c17d9"},
+    {file = "numexpr-2.8.4-cp310-cp310-win32.whl", hash = "sha256:f3a920bfac2645017110b87ddbe364c9c7a742870a4d2f6120b8786c25dc6db3"},
+    {file = "numexpr-2.8.4-cp310-cp310-win_amd64.whl", hash = "sha256:6931b1e9d4f629f43c14b21d44f3f77997298bea43790cfcdb4dd98804f90783"},
+    {file = "numexpr-2.8.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9400781553541f414f82eac056f2b4c965373650df9694286b9bd7e8d413f8d8"},
+    {file = "numexpr-2.8.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:6ee9db7598dd4001138b482342b96d78110dd77cefc051ec75af3295604dde6a"},
+    {file = "numexpr-2.8.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ff5835e8af9a212e8480003d731aad1727aaea909926fd009e8ae6a1cba7f141"},
+    {file = "numexpr-2.8.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:655d84eb09adfee3c09ecf4a89a512225da153fdb7de13c447404b7d0523a9a7"},
+    {file = "numexpr-2.8.4-cp311-cp311-win32.whl", hash = "sha256:5538b30199bfc68886d2be18fcef3abd11d9271767a7a69ff3688defe782800a"},
+    {file = "numexpr-2.8.4-cp311-cp311-win_amd64.whl", hash = "sha256:3f039321d1c17962c33079987b675fb251b273dbec0f51aac0934e932446ccc3"},
+    {file = "numexpr-2.8.4-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:c867cc36cf815a3ec9122029874e00d8fbcef65035c4a5901e9b120dd5d626a2"},
+    {file = "numexpr-2.8.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:059546e8f6283ccdb47c683101a890844f667fa6d56258d48ae2ecf1b3875957"},
+    {file = "numexpr-2.8.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:845a6aa0ed3e2a53239b89c1ebfa8cf052d3cc6e053c72805e8153300078c0b1"},
+    {file = "numexpr-2.8.4-cp37-cp37m-win32.whl", hash = "sha256:a38664e699526cb1687aefd9069e2b5b9387da7feac4545de446141f1ef86f46"},
+    {file = "numexpr-2.8.4-cp37-cp37m-win_amd64.whl", hash = "sha256:eaec59e9bf70ff05615c34a8b8d6c7bd042bd9f55465d7b495ea5436f45319d0"},
+    {file = "numexpr-2.8.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b318541bf3d8326682ebada087ba0050549a16d8b3fa260dd2585d73a83d20a7"},
+    {file = "numexpr-2.8.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b076db98ca65eeaf9bd224576e3ac84c05e451c0bd85b13664b7e5f7b62e2c70"},
+    {file = "numexpr-2.8.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:90f12cc851240f7911a47c91aaf223dba753e98e46dff3017282e633602e76a7"},
+    {file = "numexpr-2.8.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c368aa35ae9b18840e78b05f929d3a7b3abccdba9630a878c7db74ca2368339"},
+    {file = "numexpr-2.8.4-cp38-cp38-win32.whl", hash = "sha256:b96334fc1748e9ec4f93d5fadb1044089d73fb08208fdb8382ed77c893f0be01"},
+    {file = "numexpr-2.8.4-cp38-cp38-win_amd64.whl", hash = "sha256:a6d2d7740ae83ba5f3531e83afc4b626daa71df1ef903970947903345c37bd03"},
+    {file = "numexpr-2.8.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:77898fdf3da6bb96aa8a4759a8231d763a75d848b2f2e5c5279dad0b243c8dfe"},
+    {file = "numexpr-2.8.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:df35324666b693f13a016bc7957de7cc4d8801b746b81060b671bf78a52b9037"},
+    {file = "numexpr-2.8.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:17ac9cfe6d0078c5fc06ba1c1bbd20b8783f28c6f475bbabd3cad53683075cab"},
+    {file = "numexpr-2.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:df3a1f6b24214a1ab826e9c1c99edf1686c8e307547a9aef33910d586f626d01"},
+    {file = "numexpr-2.8.4-cp39-cp39-win32.whl", hash = "sha256:7d71add384adc9119568d7e9ffa8a35b195decae81e0abf54a2b7779852f0637"},
+    {file = "numexpr-2.8.4-cp39-cp39-win_amd64.whl", hash = "sha256:9f096d707290a6a00b6ffdaf581ee37331109fb7b6c8744e9ded7c779a48e517"},
+    {file = "numexpr-2.8.4.tar.gz", hash = "sha256:d5432537418d18691b9115d615d6daa17ee8275baef3edf1afbbf8bc69806147"},
+]
+
+[package.dependencies]
+numpy = ">=1.13.3"
+
 [[package]]
 name = "numpy"
 version = "1.24.2"
@@ -7234,7 +7277,7 @@ files = [
 ]
 
 [package.dependencies]
-greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and (platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\")"}
+greenlet = {version = "!=0.4.17", markers = "python_version >= \"3\" and platform_machine == \"aarch64\" or python_version >= \"3\" and platform_machine == \"ppc64le\" or python_version >= \"3\" and platform_machine == \"x86_64\" or python_version >= \"3\" and platform_machine == \"amd64\" or python_version >= \"3\" and platform_machine == \"AMD64\" or python_version >= \"3\" and platform_machine == \"win32\" or python_version >= \"3\" and platform_machine == \"WIN32\""}
 
 [package.extras]
 aiomysql = ["aiomysql", "greenlet (!=0.4.17)"]
@@ -9002,13 +9045,13 @@ 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 = "26b1bbfbc3a228b892b2466af3561b799238a6d379853d325dc3c798776df0d8"
+content-hash = "00ccf95ce658a0f6e60e58520154498eaa9c0180dd19bdedfbbb1d7c4c0ebe39"

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"