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"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
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"source": [
"# Import things that are needed generically\n",
"from langchain import LLMMathChain, SerpAPIWrapper\n",
"from langchain.agents import AgentType, initialize_agent\n",
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.tools import BaseTool, StructuredTool, Tool, tool"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"llm = ChatOpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the tool configs that are needed.\n",
"search = SerpAPIWrapper()\n",
"llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
"tools = [\n",
" Tool.from_function(\n",
" func=search.run,\n",
" name=\"Search\",\n",
" description=\"useful for when you need to answer questions about current events\"\n",
" # coroutine= ... <- you can specify an async method if desired as well\n",
" ),\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pydantic import BaseModel, Field\n",
"\n",
"\n",
"class CalculatorInput(BaseModel):\n",
" question: str = Field()\n",
"\n",
"\n",
"tools.append(\n",
" Tool.from_function(\n",
" func=llm_math_chain.run,\n",
" name=\"Calculator\",\n",
" description=\"useful for when you need to answer questions about math\",\n",
" args_schema=CalculatorInput\n",
" # coroutine= ... <- you can specify an async method if desired as well\n",
" )\n",
")\n",
"\n"
]
}
],
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"file_extension": ".py",
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