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cli: update integration doc template for tools (#30188)

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Chain example -> langgraph agent
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ccurme 2025-03-09 17:14:43 -04:00 committed by GitHub
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libs/cli/langchain_cli/integration_template/docs/tools.ipynb
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@ -181,11 +181,11 @@
"id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd", "id": "659f9fbd-6fcf-445f-aa8c-72d8e60154bd",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Chaining\n", "## Use within an agent\n",
"\n", "\n",
"- TODO: Add user question and run cells\n", "- TODO: Add user question and run cells\n",
"\n", "\n",
"We can use our tool in a chain by first binding it to a [tool-calling model](/docs/how_to/tool_calling/) and then calling it:\n", "We can use our tool in an [agent](/docs/concepts/agents/). For this we will need a LLM with [tool-calling](/docs/how_to/tool_calling/) capabilities:\n",
"\n", "\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n", "import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n", "\n",
@ -208,6 +208,19 @@
"llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")" "llm = init_chat_model(model=\"gpt-4o\", model_provider=\"openai\")"
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"id": "bea35fa1",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"tools = [tool]\n",
"agent = create_react_agent(llm, tools)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@ -215,32 +228,14 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from langchain_core.prompts import ChatPromptTemplate\n", "example_query = \"...\"\n",
"from langchain_core.runnables import RunnableConfig, chain\n",
"\n", "\n",
"prompt = ChatPromptTemplate(\n", "events = agent.stream(\n",
" [\n", " {\"messages\": [(\"user\", example_query)]},\n",
" (\"system\", \"You are a helpful assistant.\"),\n", " stream_mode=\"values\",\n",
" (\"human\", \"{user_input}\"),\n",
" (\"placeholder\", \"{messages}\"),\n",
" ]\n",
")\n", ")\n",
"\n", "for event in events:\n",
"# specifying tool_choice will force the model to call this tool.\n", " event[\"messages\"][-1].pretty_print()"
"llm_with_tools = llm.bind_tools([tool], tool_choice=tool.name)\n",
"\n",
"llm_chain = prompt | llm_with_tools\n",
"\n",
"\n",
"@chain\n",
"def tool_chain(user_input: str, config: RunnableConfig):\n",
" input_ = {\"user_input\": user_input}\n",
" ai_msg = llm_chain.invoke(input_, config=config)\n",
" tool_msgs = tool.batch(ai_msg.tool_calls, config=config)\n",
" return llm_chain.invoke({**input_, \"messages\": [ai_msg, *tool_msgs]}, config=config)\n",
"\n",
"\n",
"tool_chain.invoke(\"...\")"
] ]
}, },
{ {