"This functionality is natively available in the (`structured-chat-zero-shot-react-description` or `AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION`)."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ccc8ff98",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"LANGCHAIN_TRACING\"] = \"true\" # If you want to trace the execution of the program, set to \"true\""
"This notebook shows how to use a tool that requires multiple inputs with an agent.\n",
"This notebook shows how to use a tool that requires multiple inputs with an agent. The recommended way to do so is with the `StructuredTool` class.\n",
"The difficulty in doing so comes from the fact that an agent decides its next step from a language model, which outputs a string. So if that step requires multiple inputs, they need to be parsed from that. Therefore, the currently supported way to do this is to write a smaller wrapper function that parses a string into multiple inputs.\n",
"llm = OpenAI(temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "21623e8f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain.tools import StructuredTool\n",
"\n",
"For a concrete example, let's work on giving an agent access to a multiplication function, which takes as input two integers. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
"def multiplier(a: float, b: float) -> float:\n",
" \"\"\"Multiply the provided floats.\"\"\"\n",
" return a * b\n",
"\n",
"tool = StructuredTool.from_function(multiplier)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 4,
"id": "ae7e8e07",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"# Structured tools are compatible with the STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION agent type. \n",
"Thought:\u001b[32;1m\u001b[1;3m I know what to respond\n",
"Action:\n",
"```\n",
"{\n",
" \"action\": \"Final Answer\",\n",
" \"action_input\": \"3 times 4 is 12\"\n",
"}\n",
"```\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'3 times 4 is 12'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agent_executor.run(\"What is 3 times 4\")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "e643b307",
"metadata": {},
"source": [
"## Multi-Input Tools with a string format\n",
"\n",
"An alternative to the structured tool would be to use the regular `Tool` class and accept a single string. The tool would then have to handle the parsing logic to extract the relavent values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can't reliabl generate structured schema. \n",
"\n",
"Let's take the multiplication function as an example. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
Zander Chase
1 year ago
committed by
GitHub