Update Tool Input (#3103)

- Remove dynamic model creation in the `args()` property. _Only infer
for the decorator (and add an argument to NOT infer if someone wishes to
only pass as a string)_
- Update the validation example to make it less likely to be
misinterpreted as a "safe" way to run a repl


There is one example of "Multi-argument tools" in the custom_tools.ipynb
from yesterday, but we could add more. The output parsing for the base
MRKL agent hasn't been adapted to handle structured args at this point
in time

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/modules/agents/tools/custom_tools.ipynb

@@ -12,6 +12,7 @@
     "- name (str), is required and must be unique within a set of tools provided to an agent\n",
     "- description (str), is optional but recommended, as it is used by an agent to determine tool use\n",
     "- return_direct (bool), defaults to False\n",
+    "- args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information or validation for expected parameters.\n",
     "\n",
     "The function that should be called when the tool is selected should return a single string.\n",
     "\n",
@@ -91,12 +92,22 @@
     "        func=search.run,\n",
     "        description=\"useful for when you need to answer questions about current events\"\n",
     "    ),\n",
+    "]\n",
+    "# You can also define an args_schema to provide more information about inputs\n",
+    "from pydantic import BaseModel, Field\n",
+    "\n",
+    "class CalculatorInput(BaseModel):\n",
+    "    query: str = Field(description=\"should be a math expression\")\n",
+    "        \n",
+    "\n",
+    "tools.append(\n",
     "    Tool(\n",
     "        name=\"Calculator\",\n",
     "        func=llm_math_chain.run,\n",
-    "        description=\"useful for when you need to answer questions about math\"\n",
+    "        description=\"useful for when you need to answer questions about math\",\n",
+    "        args_schema=CalculatorInput\n",
     "    )\n",
-    "]"
+    ")"
    ]
   },
   {
@@ -130,22 +141,20 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mI draw the lime at going to get a Mohawk, though.\" DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Gigi Hadid's age\n",
-      "Action: Search\n",
-      "Action Input: \"Gigi Hadid age\"\u001b[0m\u001b[36;1m\u001b[1;3m27 years\u001b[0m\u001b[32;1m\u001b[1;3mI need to calculate her age raised to the 0.43 power\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
       "Action: Calculator\n",
-      "Action Input: 27^(0.43)\u001b[0m\n",
+      "Action Input: 25^(0.43)\u001b[0m\n",
       "\n",
       "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
-      "27^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
-      "27**(0.43)\n",
+      "25^(0.43)\u001b[32;1m\u001b[1;3m```text\n",
+      "25**(0.43)\n",
       "```\n",
-      "...numexpr.evaluate(\"27**(0.43)\")...\n",
+      "...numexpr.evaluate(\"25**(0.43)\")...\n",
       "\u001b[0m\n",
-      "Answer: \u001b[33;1m\u001b[1;3m4.125593352125936\u001b[0m\n",
+      "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mAnswer: 4.125593352125936\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
-      "Final Answer: 4.125593352125936\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
+      "Final Answer: 3.991298452658078\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -153,7 +162,7 @@
     {
      "data": {
       "text/plain": [
-       "'4.125593352125936'"
+       "'3.991298452658078'"
       ]
      },
      "execution_count": 5,
@@ -197,6 +206,7 @@
     "class CustomCalculatorTool(BaseTool):\n",
     "    name = \"Calculator\"\n",
     "    description = \"useful for when you need to answer questions about math\"\n",
+    "    args_schema=CalculatorInput\n",
     "\n",
     "    def _run(self, query: str) -> str:\n",
     "        \"\"\"Use the tool.\"\"\"\n",
@@ -248,9 +258,7 @@
       "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
       "\u001b[32;1m\u001b[1;3mI need to find out Leo DiCaprio's girlfriend's name and her age\n",
       "Action: Search\n",
-      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mI draw the lime at going to get a Mohawk, though.\" DiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years. He's since been linked to another famous supermodel – Gigi Hadid.\u001b[0m\u001b[32;1m\u001b[1;3mI now know Leo DiCaprio's girlfriend's name and that he's currently linked to Gigi Hadid. I need to find out Camila Morrone's age.\n",
-      "Action: Search\n",
-      "Action Input: \"Camila Morrone age\"\u001b[0m\u001b[36;1m\u001b[1;3m25 years\u001b[0m\u001b[32;1m\u001b[1;3mI have Camila Morrone's age. I need to calculate her age raised to the 0.43 power.\n",
+      "Action Input: \"Leo DiCaprio girlfriend\"\u001b[0m\u001b[36;1m\u001b[1;3mDiCaprio broke up with girlfriend Camila Morrone, 25, in the summer of 2022, after dating for four years.\u001b[0m\u001b[32;1m\u001b[1;3mI need to find out Camila Morrone's current age\n",
       "Action: Calculator\n",
       "Action Input: 25^(0.43)\u001b[0m\n",
       "\n",
@@ -262,8 +270,8 @@
       "\u001b[0m\n",
       "Answer: \u001b[33;1m\u001b[1;3m3.991298452658078\u001b[0m\n",
       "\u001b[1m> Finished chain.\u001b[0m\n",
-      "\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the answer to the original question.\n",
-      "Final Answer: Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\u001b[0m\n",
+      "\u001b[33;1m\u001b[1;3mAnswer: 3.991298452658078\u001b[0m\u001b[32;1m\u001b[1;3mI now know the final answer\n",
+      "Final Answer: 3.991298452658078\u001b[0m\n",
       "\n",
       "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
@@ -271,7 +279,7 @@
     {
      "data": {
       "text/plain": [
-       "\"Camila Morrone's current age raised to the 0.43 power is approximately 3.99.\""
+       "'3.991298452658078'"
       ]
      },
      "execution_count": 9,
@@ -321,7 +329,7 @@
     {
      "data": {
       "text/plain": [
-       "Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.ArgsModel'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x124346f10>, func=<function search_api at 0x16ad6e020>, coroutine=None)"
+       "Tool(name='search_api', description='search_api(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=False, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd664c0>, coroutine=None)"
       ]
      },
      "execution_count": 11,
@@ -365,7 +373,7 @@
     {
      "data": {
       "text/plain": [
-       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.ArgsModel'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x124346f10>, func=<function search_api at 0x16ad6d3a0>, coroutine=None)"
+       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class 'pydantic.main.SearchApi'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bd66310>, coroutine=None)"
       ]
      },
      "execution_count": 13,
@@ -377,6 +385,51 @@
     "search_api"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "de34a6a3",
+   "metadata": {},
+   "source": [
+    "You can also provide `args_schema` to provide more information about the argument"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f3a5c106",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class SearchInput(BaseModel):\n",
+    "    query: str = Field(description=\"should be a search query\")\n",
+    "        \n",
+    "@tool(\"search\", return_direct=True, args_schema=SearchInput)\n",
+    "def search_api(query: str) -> str:\n",
+    "    \"\"\"Searches the API for the query.\"\"\"\n",
+    "    return \"Results\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7914ba6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Tool(name='search', description='search(query: str) -> str - Searches the API for the query.', args_schema=<class '__main__.SearchInput'>, return_direct=True, verbose=False, callback_manager=<langchain.callbacks.shared.SharedCallbackManager object at 0x12748c4c0>, func=<function search_api at 0x16bcf0ee0>, coroutine=None)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "search_api"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1d0430d6",
@@ -784,7 +837,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/modules/agents/tools/tool_input_validation.ipynb

@@ -19,13 +19,12 @@
    },
    "outputs": [],
    "source": [
-    "from langchain.agents import initialize_agent, Tool\n",
-    "from langchain.agents import AgentType\n",
+    "from typing import Any, Dict\n",
+    "\n",
+    "from langchain.agents import AgentType, initialize_agent\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.tools.python.tool import PythonREPLTool\n",
-    "from pydantic import BaseModel\n",
-    "from pydantic import Field, root_validator\n",
-    "from typing import Dict, Any"
+    "from langchain.tools.requests.tool import RequestsGetTool, TextRequestsWrapper\n",
+    "from pydantic import BaseModel, Field, root_validator\n"
    ]
   },
   {
@@ -42,38 +41,67 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install tldextract > /dev/null"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "class ToolInputModel(BaseModel):\n",
-    "    query: str = Field(...)\n",
+    "import tldextract\n",
+    "\n",
+    "_APPROVED_DOMAINS = {\n",
+    "    \"langchain\",\n",
+    "    \"wikipedia\",\n",
+    "}\n",
+    "\n",
+    "class ToolInputSchema(BaseModel):\n",
+    "\n",
+    "    url: str = Field(...)\n",
     "    \n",
     "    @root_validator\n",
     "    def validate_query(cls, values: Dict[str, Any]) -> Dict:\n",
-    "        # Note: this is NOT a safe REPL! This is used for instructive purposes only\n",
-    "        if \"import os\" in values[\"query\"]:\n",
-    "            raise ValueError(\"'import os' not permitted in this python REPL.\")\n",
+    "        url = values[\"url\"]\n",
+    "        domain = tldextract.extract(url).domain\n",
+    "        if domain not in _APPROVED_DOMAINS:\n",
+    "            raise ValueError(f\"Domain {domain} is not on the approved list:\"\n",
+    "                             f\" {sorted(_APPROVED_DOMAINS)}\")\n",
     "        return values\n",
     "    \n",
-    "tool = PythonREPLTool(args_schema=ToolInputModel)"
+    "tool = RequestsGetTool(args_schema=ToolInputSchema, requests_wrapper=TextRequestsWrapper())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)"
+    "agent = initialize_agent([tool], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "tags": []
    },
@@ -82,80 +110,46 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to define a function that adds two numbers\n",
-      "Action: Python REPL\n",
-      "Action Input: def add_two_numbers(a, b):\n",
-      "    return a + b\u001b[0m\u001b[36;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3m I need to call the function\n",
-      "Action: Python REPL\n",
-      "Action Input: print(add_two_numbers(2, 2))\u001b[0m\u001b[36;1m\u001b[1;3m4\n",
-      "\u001b[0m\u001b[32;1m\u001b[1;3m I now know the final answer\n",
-      "Final Answer: 4\u001b[0m\n",
-      "\n",
-      "\u001b[1m> Finished chain.\u001b[0m\n"
+      "The main title of langchain.com is \"LANG CHAIN 🦜️🔗 Official Home Page\"\n"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'4'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "# This will succeed, since there aren't any arguments that will be triggered during validation\n",
-    "agent.run(\"Run a python function that adds 2 and 2\")"
+    "answer = agent.run(\"What's the main title on langchain.com?\")\n",
+    "print(answer)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "\n",
-      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
-      "\u001b[32;1m\u001b[1;3m I need to import os and then list the dir\n",
-      "Action: Python REPL\n",
-      "Action Input: import os; print(os.listdir())\u001b[0m"
-     ]
-    },
     {
      "ename": "ValidationError",
-     "evalue": "1 validation error for ToolInputModel\n__root__\n  'import os' not  (type=value_error)",
+     "evalue": "1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mRun a python function that imports os and lists the dir\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:213\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    212\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs)[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput_keys[\u001b[38;5;241m0\u001b[39m]]\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:116\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m, \u001b[38;5;167;01mException\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(inputs, outputs, return_only_outputs)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:113\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[1;32m    108\u001b[0m     {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m},\n\u001b[1;32m    109\u001b[0m     inputs,\n\u001b[1;32m    110\u001b[0m     verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[1;32m    111\u001b[0m )\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m, \u001b[38;5;167;01mException\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallback_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:792\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[38;5;66;03m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m    791\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 792\u001b[0m     next_step_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_take_next_step\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    793\u001b[0m \u001b[43m        \u001b[49m\u001b[43mname_to_tool_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor_mapping\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mintermediate_steps\u001b[49m\n\u001b[1;32m    794\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    795\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m    796\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_return(next_step_output, intermediate_steps)\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:695\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)\u001b[0m\n\u001b[1;32m    693\u001b[0m         tool_run_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mllm_prefix\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    694\u001b[0m     \u001b[38;5;66;03m# We then call the tool on the tool input to get an observation\u001b[39;00m\n\u001b[0;32m--> 695\u001b[0m     observation \u001b[38;5;241m=\u001b[39m \u001b[43mtool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    696\u001b[0m \u001b[43m        \u001b[49m\u001b[43magent_action\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtool_input\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    697\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    698\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    699\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mtool_run_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    700\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    701\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    702\u001b[0m     tool_run_kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39magent\u001b[38;5;241m.\u001b[39mtool_run_logging_kwargs()\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:146\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, **kwargs)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\n\u001b[1;32m    138\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    139\u001b[0m     tool_input: Union[\u001b[38;5;28mstr\u001b[39m, Dict],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    143\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    144\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mstr\u001b[39m:\n\u001b[1;32m    145\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 146\u001b[0m     run_input \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_input\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;129;01mand\u001b[39;00m verbose \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m         verbose_ \u001b[38;5;241m=\u001b[39m verbose\n",
-      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:112\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m    110\u001b[0m     tool_input \u001b[38;5;241m=\u001b[39m {field_name: tool_input}\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pydantic_input_type \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpydantic_input_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtool_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    115\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124margs_schema required for tool \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m in order to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    116\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m accept input of type \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(tool_input)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    117\u001b[0m     )\n",
+      "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m agent\u001b[39m.\u001b[39;49mrun(\u001b[39m\"\u001b[39;49m\u001b[39mWhat\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39ms the main title on google.com?\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:213\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    211\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m    212\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39m`run` supports only one positional argument.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 213\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m(args[\u001b[39m0\u001b[39;49m])[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n\u001b[1;32m    215\u001b[0m \u001b[39mif\u001b[39;00m kwargs \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m args:\n\u001b[1;32m    216\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m(kwargs)[\u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutput_keys[\u001b[39m0\u001b[39m]]\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:116\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[0;32m--> 116\u001b[0m     \u001b[39mraise\u001b[39;00m e\n\u001b[1;32m    117\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_end(outputs, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n\u001b[1;32m    118\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprep_outputs(inputs, outputs, return_only_outputs)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/chains/base.py:113\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_start(\n\u001b[1;32m    108\u001b[0m     {\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m},\n\u001b[1;32m    109\u001b[0m     inputs,\n\u001b[1;32m    110\u001b[0m     verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose,\n\u001b[1;32m    111\u001b[0m )\n\u001b[1;32m    112\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 113\u001b[0m     outputs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call(inputs)\n\u001b[1;32m    114\u001b[0m \u001b[39mexcept\u001b[39;00m (\u001b[39mKeyboardInterrupt\u001b[39;00m, \u001b[39mException\u001b[39;00m) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    115\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallback_manager\u001b[39m.\u001b[39mon_chain_error(e, verbose\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:792\u001b[0m, in \u001b[0;36mAgentExecutor._call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    790\u001b[0m \u001b[39m# We now enter the agent loop (until it returns something).\u001b[39;00m\n\u001b[1;32m    791\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_continue(iterations, time_elapsed):\n\u001b[0;32m--> 792\u001b[0m     next_step_output \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_take_next_step(\n\u001b[1;32m    793\u001b[0m         name_to_tool_map, color_mapping, inputs, intermediate_steps\n\u001b[1;32m    794\u001b[0m     )\n\u001b[1;32m    795\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(next_step_output, AgentFinish):\n\u001b[1;32m    796\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_return(next_step_output, intermediate_steps)\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/agents/agent.py:695\u001b[0m, in \u001b[0;36mAgentExecutor._take_next_step\u001b[0;34m(self, name_to_tool_map, color_mapping, inputs, intermediate_steps)\u001b[0m\n\u001b[1;32m    693\u001b[0m         tool_run_kwargs[\u001b[39m\"\u001b[39m\u001b[39mllm_prefix\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    694\u001b[0m     \u001b[39m# We then call the tool on the tool input to get an observation\u001b[39;00m\n\u001b[0;32m--> 695\u001b[0m     observation \u001b[39m=\u001b[39m tool\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m    696\u001b[0m         agent_action\u001b[39m.\u001b[39;49mtool_input,\n\u001b[1;32m    697\u001b[0m         verbose\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbose,\n\u001b[1;32m    698\u001b[0m         color\u001b[39m=\u001b[39;49mcolor,\n\u001b[1;32m    699\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mtool_run_kwargs,\n\u001b[1;32m    700\u001b[0m     )\n\u001b[1;32m    701\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    702\u001b[0m     tool_run_kwargs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39magent\u001b[39m.\u001b[39mtool_run_logging_kwargs()\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:110\u001b[0m, in \u001b[0;36mBaseTool.run\u001b[0;34m(self, tool_input, verbose, start_color, color, **kwargs)\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mrun\u001b[39m(\n\u001b[1;32m    102\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m    103\u001b[0m     tool_input: Union[\u001b[39mstr\u001b[39m, Dict],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    107\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs: Any,\n\u001b[1;32m    108\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mstr\u001b[39m:\n\u001b[1;32m    109\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Run the tool.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 110\u001b[0m     run_input \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parse_input(tool_input)\n\u001b[1;32m    111\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39mand\u001b[39;00m verbose \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    112\u001b[0m         verbose_ \u001b[39m=\u001b[39m verbose\n",
+      "File \u001b[0;32m~/code/lc/lckg/langchain/tools/base.py:71\u001b[0m, in \u001b[0;36mBaseTool._parse_input\u001b[0;34m(self, tool_input)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(input_args, BaseModel):\n\u001b[1;32m     70\u001b[0m     key_ \u001b[39m=\u001b[39m \u001b[39mnext\u001b[39m(\u001b[39miter\u001b[39m(input_args\u001b[39m.\u001b[39m__fields__\u001b[39m.\u001b[39mkeys()))\n\u001b[0;32m---> 71\u001b[0m     input_args\u001b[39m.\u001b[39;49mparse_obj({key_: tool_input})\n\u001b[1;32m     72\u001b[0m \u001b[39m# Passing as a positional argument is more straightforward for\u001b[39;00m\n\u001b[1;32m     73\u001b[0m \u001b[39m# backwards compatability\u001b[39;00m\n\u001b[1;32m     74\u001b[0m \u001b[39mreturn\u001b[39;00m tool_input\n",
       "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:526\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.parse_obj\u001b[0;34m()\u001b[0m\n",
       "File \u001b[0;32m~/code/lc/lckg/.venv/lib/python3.11/site-packages/pydantic/main.py:341\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for ToolInputModel\n__root__\n  'import os' not  (type=value_error)"
+      "\u001b[0;31mValidationError\u001b[0m: 1 validation error for ToolInputSchema\n__root__\n  Domain google is not on the approved list: ['langchain', 'wikipedia'] (type=value_error)"
      ]
     }
    ],
    "source": [
-    "# This will fail, because the attempt to import os will trigger a validation error\n",
-    "agent.run(\"Run a python function that imports os and lists the dir\")"
+    "agent.run(\"What's the main title on google.com?\")"
    ]
   },
   {

langchain/agents/tools.py

@@ -2,9 +2,9 @@
 from inspect import signature
 from typing import Any, Awaitable, Callable, Optional, Type, Union
 
-from pydantic import BaseModel
+from pydantic import BaseModel, validate_arguments
 
-from langchain.tools.base import BaseTool, create_args_schema_model_from_signature
+from langchain.tools.base import BaseTool
 
 
 class Tool(BaseTool):
@@ -16,15 +16,6 @@ class Tool(BaseTool):
     coroutine: Optional[Callable[..., Awaitable[str]]] = None
     """The asynchronous version of the function."""
 
-    @property
-    def args(self) -> Type[BaseModel]:
-        """Generate an input pydantic model."""
-        if self.args_schema is not None:
-            return self.args_schema
-        # Infer the schema directly from the function to add more structured
-        # arguments.
-        return create_args_schema_model_from_signature(self.func)
-
     def _run(self, *args: Any, **kwargs: Any) -> str:
         """Use the tool."""
         return self.func(*args, **kwargs)
@@ -60,9 +51,23 @@ class InvalidTool(BaseTool):
         return f"{tool_name} is not a valid tool, try another one."
 
 
-def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
+def tool(
+    *args: Union[str, Callable],
+    return_direct: bool = False,
+    args_schema: Optional[Type[BaseModel]] = None,
+    infer_schema: bool = True,
+) -> Callable:
     """Make tools out of functions, can be used with or without arguments.
 
+    Args:
+        *args: The arguments to the tool.
+        return_direct: Whether to return directly from the tool rather
+            than continuing the agent loop.
+        args_schema: optional argument schema for user to specify
+        infer_schema: Whether to infer the schema of the arguments from
+            the function's signature. This also makes the resultant tool
+            accept a dictionary input to its `run()` function.
+
     Requires:
         - Function must be of type (str) -> str
         - Function must have a docstring
@@ -87,11 +92,13 @@ def tool(*args: Union[str, Callable], return_direct: bool = False) -> Callable:
             # Description example:
             # search_api(query: str) - Searches the API for the query.
             description = f"{tool_name}{signature(func)} - {func.__doc__.strip()}"
-            args_schema = create_args_schema_model_from_signature(func)
+            _args_schema = args_schema
+            if _args_schema is None and infer_schema:
+                _args_schema = validate_arguments(func).model  # type: ignore
             tool_ = Tool(
                 name=tool_name,
                 func=func,
-                args_schema=args_schema,
+                args_schema=_args_schema,
                 description=description,
                 return_direct=return_direct,
             )

langchain/tools/base.py

@@ -1,75 +1,21 @@
 """Base implementation for tools or skills."""
 
-import inspect
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Dict, Optional, Sequence, Tuple, Type, Union
 
-from pydantic import BaseModel, Extra, Field, create_model, validator
+from pydantic import BaseModel, Extra, Field, validator
 
 from langchain.callbacks import get_callback_manager
 from langchain.callbacks.base import BaseCallbackManager
 
 
-def create_args_schema_model_from_signature(run_func: Callable) -> Type[BaseModel]:
-    """Create a pydantic model type from a function's signature."""
-    signature_ = inspect.signature(run_func)
-    field_definitions: Dict[str, Any] = {}
-
-    for name, param in signature_.parameters.items():
-        if name == "self":
-            continue
-        default_value = (
-            param.default if param.default != inspect.Parameter.empty else None
-        )
-        annotation = (
-            param.annotation if param.annotation != inspect.Parameter.empty else Any
-        )
-        # Handle functions with *args in the signature
-        if param.kind == inspect.Parameter.VAR_POSITIONAL:
-            field_definitions[name] = (
-                Any,
-                Field(default=None, extra={"is_var_positional": True}),
-            )
-        # handle functions with **kwargs in the signature
-        elif param.kind == inspect.Parameter.VAR_KEYWORD:
-            field_definitions[name] = (
-                Any,
-                Field(default=None, extra={"is_var_keyword": True}),
-            )
-        # Handle all other named parameters
-        else:
-            is_keyword_only = param.kind == inspect.Parameter.KEYWORD_ONLY
-            field_definitions[name] = (
-                annotation,
-                Field(
-                    default=default_value, extra={"is_keyword_only": is_keyword_only}
-                ),
-            )
-    return create_model("ArgsModel", **field_definitions)  # type: ignore
-
-
-def _to_args_and_kwargs(model: BaseModel) -> Tuple[Sequence, dict]:
-    args = []
-    kwargs = {}
-    for name, field in model.__fields__.items():
-        value = getattr(model, name)
-        # Handle *args in the function signature
-        if field.field_info.extra.get("extra", {}).get("is_var_positional"):
-            if isinstance(value, str):
-                # Base case for backwards compatability
-                args.append(value)
-            elif value is not None:
-                args.extend(value)
-        # Handle **kwargs in the function signature
-        elif field.field_info.extra.get("extra", {}).get("is_var_keyword"):
-            if value is not None:
-                kwargs.update(value)
-        elif field.field_info.extra.get("extra", {}).get("is_keyword_only"):
-            kwargs[name] = value
-        else:
-            args.append(value)
-
-    return tuple(args), kwargs
+def _to_args_and_kwargs(run_input: Union[str, Dict]) -> Tuple[Sequence, dict]:
+    # For backwards compatability, if run_input is a string,
+    # pass as a positional argument.
+    if isinstance(run_input, str):
+        return (run_input,), {}
+    else:
+        return [], run_input
 
 
 class BaseTool(ABC, BaseModel):
@@ -90,31 +36,28 @@ class BaseTool(ABC, BaseModel):
         arbitrary_types_allowed = True
 
     @property
-    def args(self) -> Type[BaseModel]:
+    def args(self) -> Union[Type[BaseModel], Type[str]]:
         """Generate an input pydantic model."""
-        if self.args_schema is not None:
-            return self.args_schema
-        return create_args_schema_model_from_signature(self._run)
+        return str if self.args_schema is None else self.args_schema
 
     def _parse_input(
         self,
         tool_input: Union[str, Dict],
-    ) -> BaseModel:
+    ) -> None:
         """Convert tool input to pydantic model."""
-        pydantic_input_type = self.args
+        input_args = self.args
         if isinstance(tool_input, str):
-            # For backwards compatibility, a tool that only takes
-            # a single string input will be converted to a dict.
-            # to be validated.
-            field_name = next(iter(pydantic_input_type.__fields__))
-            tool_input = {field_name: tool_input}
-        if pydantic_input_type is not None:
-            return pydantic_input_type.parse_obj(tool_input)
+            if issubclass(input_args, BaseModel):
+                key_ = next(iter(input_args.__fields__.keys()))
+                input_args.validate({key_: tool_input})
         else:
-            raise ValueError(
-                f"args_schema required for tool {self.name} in order to"
-                f" accept input of type {type(tool_input)}"
-            )
+            if issubclass(input_args, BaseModel):
+                input_args.validate(tool_input)
+            else:
+                raise ValueError(
+                    f"args_schema required for tool {self.name} in order to"
+                    f" accept input of type {type(tool_input)}"
+                )
 
     @validator("callback_manager", pre=True, always=True)
     def set_callback_manager(
@@ -143,20 +86,20 @@ class BaseTool(ABC, BaseModel):
         **kwargs: Any,
     ) -> str:
         """Run the tool."""
-        run_input = self._parse_input(tool_input)
+        self._parse_input(tool_input)
         if not self.verbose and verbose is not None:
             verbose_ = verbose
         else:
             verbose_ = self.verbose
         self.callback_manager.on_tool_start(
             {"name": self.name, "description": self.description},
-            str(run_input),
+            tool_input if isinstance(tool_input, str) else str(tool_input),
             verbose=verbose_,
             color=start_color,
             **kwargs,
         )
         try:
-            args, kwargs = _to_args_and_kwargs(run_input)
+            args, kwargs = _to_args_and_kwargs(tool_input)
             observation = self._run(*args, **kwargs)
         except (Exception, KeyboardInterrupt) as e:
             self.callback_manager.on_tool_error(e, verbose=verbose_)
@@ -175,7 +118,7 @@ class BaseTool(ABC, BaseModel):
         **kwargs: Any,
     ) -> str:
         """Run the tool asynchronously."""
-        run_input = self._parse_input(tool_input)
+        self._parse_input(tool_input)
         if not self.verbose and verbose is not None:
             verbose_ = verbose
         else:
@@ -183,7 +126,7 @@ class BaseTool(ABC, BaseModel):
         if self.callback_manager.is_async:
             await self.callback_manager.on_tool_start(
                 {"name": self.name, "description": self.description},
-                str(run_input.dict()),
+                tool_input if isinstance(tool_input, str) else str(tool_input),
                 verbose=verbose_,
                 color=start_color,
                 **kwargs,
@@ -191,14 +134,14 @@ class BaseTool(ABC, BaseModel):
         else:
             self.callback_manager.on_tool_start(
                 {"name": self.name, "description": self.description},
-                str(run_input.dict()),
+                tool_input if isinstance(tool_input, str) else str(tool_input),
                 verbose=verbose_,
                 color=start_color,
                 **kwargs,
             )
         try:
             # We then call the tool on the tool input to get an observation
-            args, kwargs = _to_args_and_kwargs(run_input)
+            args, kwargs = _to_args_and_kwargs(tool_input)
             observation = await self._arun(*args, **kwargs)
         except (Exception, KeyboardInterrupt) as e:
             if self.callback_manager.is_async:

langchain/tools/file_management/write.py

@@ -29,6 +29,6 @@ class WriteFileTool(BaseTool):
         except Exception as e:
             return "Error: " + str(e)
 
-    async def _arun(self, tool_input: str) -> str:
+    async def _arun(self, file_path: str, text: str) -> str:
         # TODO: Add aiofiles method
         raise NotImplementedError

tests/unit_tests/agents/test_tools.py

@@ -1,6 +1,6 @@
 """Test tool utils."""
 from datetime import datetime
-from typing import Any, Optional, Type, Union
+from typing import Optional, Type, Union
 
 import pytest
 from pydantic import BaseModel
@@ -125,21 +125,27 @@ def test_tool_with_kwargs() -> None:
 
     @tool(return_direct=True)
     def search_api(
-        arg_1: float, *args: Any, ping: Optional[str] = None, **kwargs: Any
+        arg_1: float,
+        ping: str = "hi",
     ) -> str:
         """Search the API for the query."""
-        return f"arg_1={arg_1}, foo={args}, ping={ping}, kwargs={kwargs}"
+        return f"arg_1={arg_1}, ping={ping}"
 
     assert isinstance(search_api, Tool)
     result = search_api.run(
         tool_input={
             "arg_1": 3.2,
-            "args": "fam",
-            "kwargs": {"bar": "baz"},
             "ping": "pong",
         }
     )
-    assert result == "arg_1=3.2, foo=('fam',), ping=pong, kwargs={'bar': 'baz'}"
+    assert result == "arg_1=3.2, ping=pong"
+
+    result = search_api.run(
+        tool_input={
+            "arg_1": 3.2,
+        }
+    )
+    assert result == "arg_1=3.2, ping=hi"
 
 
 def test_missing_docstring() -> None: