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docs: update tool use case (langchain-ai#20404)
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baskaryan committed Apr 16, 2024
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87 changes: 56 additions & 31 deletions docs/docs/use_cases/tool_use/agents.ipynb
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"jp-MarkdownHeadingCollapsed": true
},
"source": [
"## Agents\n",
"## Repeated tool use with agents\n",
"\n",
"Chains are great when we know the specific sequence of tool usage needed for any user input. But for certain use cases, how many times we use tools depends on the input. In these cases, we want to let the model itself decide how many times to use tools and in what order. [Agents](/docs/modules/agents/) let us do just this.\n",
"\n",
"LangChain comes with a number of built-in agents that are optimized for different use cases. Read about all the [agent types here](/docs/modules/agents/agent_types/).\n",
"\n",
"As an example, let's try out the OpenAI tools agent, which makes use of the new OpenAI tool-calling API (this is only available in the latest OpenAI models, and differs from function-calling in that the model can return multiple function invocations at once).\n",
"We'll use the [tool calling agent](/docs/modules/agents/agent_types/tool_calling/), which is generally the most reliable kind and the recommended one for most use cases. \"Tool calling\" in this case refers to a specific type of model API that allows for explicitly passing tool definitions to models and getting explicit tool invocations out. For more on tool calling models see [this guide].(/docs/modules/model_io/chat/function_calling/)\n",
"\n",
"![agent](../../../static/img/tool_agent.svg)"
]
Expand All @@ -45,30 +45,28 @@
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"source": [
"%pip install --upgrade --quiet langchain langchain-openai"
"%pip install --upgrade --quiet langchain langchainhub"
]
},
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"And set these environment variables:"
"If you'd like to use LangSmith, set the environment variables below:"
]
},
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"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass()\n",
"\n",
"# If you'd like to use LangSmith, uncomment the below\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
Expand All @@ -85,7 +83,7 @@
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"from langchain import hub\n",
"from langchain.agents import AgentExecutor, create_openai_tools_agent\n",
"from langchain_openai import ChatOpenAI"
"from langchain.agents import AgentExecutor, create_tool_calling_agent"
]
},
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Expand Down Expand Up @@ -173,27 +170,45 @@
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"## Create agent"
"## Create agent\n",
"\n",
"We'll need to use a model with tool calling capabilities. You can see which models support tool calling [here](/docs/integrations/chat/).\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\"/>\n",
"```"
]
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"# Choose the LLM that will drive the agent\n",
"# Only certain models support this\n",
"model = ChatOpenAI(model=\"gpt-3.5-turbo-1106\", temperature=0)\n",
"# | echo: false\n",
"# | output: false\n",
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"# Construct the OpenAI Tools agent\n",
"agent = create_openai_tools_agent(model, tools, prompt)"
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
]
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"source": [
"# Construct the tool calling agent\n",
"agent = create_tool_calling_agent(llm, tools, prompt)"
]
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Expand All @@ -212,7 +227,7 @@
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Expand All @@ -225,21 +240,23 @@
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
"\u001b[32;1m\u001b[1;3m\n",
"Invoking: `exponentiate` with `{'base': 3, 'exponent': 5}`\n",
"\n",
"responded: [{'text': \"Okay, let's break this down step-by-step:\", 'type': 'text'}, {'id': 'toolu_01CjdiDhDmMtaT1F4R7hSV5D', 'input': {'base': 3, 'exponent': 5}, 'name': 'exponentiate', 'type': 'tool_use'}]\n",
"\n",
"\u001b[0m\u001b[38;5;200m\u001b[1;3m243\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `add` with `{'first_int': 12, 'second_int': 3}`\n",
"\n",
"responded: [{'text': '3 to the 5th power is 243.', 'type': 'text'}, {'id': 'toolu_01EKqn4E5w3Zj7bQ8s8xmi4R', 'input': {'first_int': 12, 'second_int': 3}, 'name': 'add', 'type': 'tool_use'}]\n",
"\n",
"\u001b[0m\u001b[33;1m\u001b[1;3m15\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `multiply` with `{'first_int': 243, 'second_int': 15}`\n",
"\n",
"responded: [{'text': '12 + 3 = 15', 'type': 'text'}, {'id': 'toolu_017VZJgZBYbwMo2KGD6o6hsQ', 'input': {'first_int': 243, 'second_int': 15}, 'name': 'multiply', 'type': 'tool_use'}]\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m3645\u001b[0m\u001b[32;1m\u001b[1;3m\n",
"Invoking: `exponentiate` with `{'base': 3645, 'exponent': 2}`\n",
"Invoking: `multiply` with `{'first_int': 3645, 'second_int': 3645}`\n",
"responded: [{'text': '243 * 15 = 3645', 'type': 'text'}, {'id': 'toolu_01RtFCcQgbVGya3NVDgTYKTa', 'input': {'first_int': 3645, 'second_int': 3645}, 'name': 'multiply', 'type': 'tool_use'}]\n",
"\n",
"\u001b[0m\u001b[36;1m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mSo 3645 squared is 13,286,025.\n",
"\n",
"\u001b[0m\u001b[38;5;200m\u001b[1;3m13286025\u001b[0m\u001b[32;1m\u001b[1;3mThe result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.\u001b[0m\n",
"Therefore, the final result of taking 3 to the 5th power (243), multiplying by 12 + 3 (15), and then squaring the whole result is 13,286,025.\u001b[0m\n",
"\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
Expand All @@ -248,10 +265,10 @@
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"{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',\n",
" 'output': 'The result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.'}"
" 'output': 'So 3645 squared is 13,286,025.\\n\\nTherefore, the final result of taking 3 to the 5th power (243), multiplying by 12 + 3 (15), and then squaring the whole result is 13,286,025.'}"
]
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" }\n",
")"
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"You can see the [LangSmith trace here](https://smith.langchain.com/public/92694ff3-71b7-44ed-bc45-04bdf04d4689/r)."
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