diff --git a/examples/docs/quickstart.ipynb b/examples/docs/quickstart.ipynb index e8d621a80..84d73c024 100644 --- a/examples/docs/quickstart.ipynb +++ b/examples/docs/quickstart.ipynb @@ -12,30 +12,55 @@ "execution_count": 13, "metadata": {}, "outputs": [], - "source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain-openai"] + "source": [ + "%%capture --no-stderr\n", + "%pip install --quiet -U langgraph langchain-openai" + ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], - "source": ["import getpass\nimport os\n\nif not os.environ.get(\"OPENAI_API_KEY\"):\n os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")"] + "source": [ + "import getpass\n", + "import os\n", + "\n", + "if not os.environ.get(\"OPENAI_API_KEY\"):\n", + " os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")" + ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, "metadata": {}, "outputs": [], - "source": ["from langchain_core.messages import BaseMessage, HumanMessage\nfrom langchain_openai import ChatOpenAI\n\nfrom langgraph.graph import END, MessageGraph\n\nmodel = ChatOpenAI(temperature=0)\n\ngraph = MessageGraph()\n\ngraph.add_node(\"oracle\", model)\ngraph.add_edge(\"oracle\", END)\n\ngraph.add_edge(START, \"oracle\")\n\nrunnable = graph.compile()"] + "source": [ + "from langchain_core.messages import BaseMessage, HumanMessage\n", + "from langchain_openai import ChatOpenAI\n", + "\n", + "from langgraph.graph import START, END, MessageGraph\n", + "\n", + "model = ChatOpenAI(temperature=0)\n", + "\n", + "graph = MessageGraph()\n", + "\n", + "graph.add_node(\"oracle\", model)\n", + "graph.add_edge(\"oracle\", END)\n", + "\n", + "graph.add_edge(START, \"oracle\")\n", + "\n", + "runnable = graph.compile()" + ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/jpeg": 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", "text/plain": [ "" ] @@ -44,42 +69,90 @@ "output_type": "display_data" } ], - "source": ["from IPython.display import Image, display\n\ntry:\n display(Image(runnable.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"] + "source": [ + "from IPython.display import Image, display\n", + "\n", + "try:\n", + " display(Image(runnable.get_graph(xray=True).draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[HumanMessage(content='What is 1 + 1?', id='bb29f237-0e4d-4354-92e1-d46434c67fe7'),\n", - " AIMessage(content='1 + 1 equals 2.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 15, 'total_tokens': 23}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-2ff0112a-9402-44a1-a992-c44fb49fa894-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})]" + "[HumanMessage(content='What is 1 + 1?', id='28f82989-8a35-4c1e-b12d-aa1b54c2b5ea'),\n", + " AIMessage(content='1 + 1 equals 2.', response_metadata={'token_usage': {'completion_tokens': 8, 'prompt_tokens': 15, 'total_tokens': 23}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-aebd1367-b64d-4c25-971e-db7c88d55aac-0', usage_metadata={'input_tokens': 15, 'output_tokens': 8, 'total_tokens': 23})]" ] }, - "execution_count": 17, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], - "source": ["runnable.invoke(HumanMessage(\"What is 1 + 1?\"))"] + "source": [ + "runnable.invoke(HumanMessage(\"What is 1 + 1?\"))" + ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [], - "source": ["from typing import Literal\n\nfrom langchain_core.tools import tool\n\nfrom langgraph.graph import END, START\nfrom langgraph.prebuilt import ToolNode\n\n\n@tool\ndef multiply(first_number: int, second_number: int):\n \"\"\"Multiplies two numbers together.\"\"\"\n return first_number * second_number\n\n\nmodel = ChatOpenAI(temperature=0)\nmodel_with_tools = model.bind_tools(tools=[multiply])\n\ngraph = MessageGraph()\n\ngraph.add_node(\"oracle\", model_with_tools)\n\ntool_node = ToolNode([multiply])\ngraph.add_node(\"multiply\", tool_node)\ngraph.add_edge(START, \"oracle\")\ngraph.add_edge(\"multiply\", END)\n\n\ndef router(state: list[BaseMessage]) -> Literal[\"multiply\", \"__end__\"]:\n tool_calls = state[-1].additional_kwargs.get(\"tool_calls\", [])\n if len(tool_calls):\n return \"multiply\"\n else:\n return END\n\n\ngraph.add_conditional_edges(\"oracle\", router)\nrunnable = graph.compile()"] + "source": [ + "from typing import Literal\n", + "\n", + "from langchain_core.tools import tool\n", + "\n", + "from langgraph.graph import END, START\n", + "from langgraph.prebuilt import ToolNode\n", + "\n", + "\n", + "@tool\n", + "def multiply(first_number: int, second_number: int):\n", + " \"\"\"Multiplies two numbers together.\"\"\"\n", + " return first_number * second_number\n", + "\n", + "\n", + "model = ChatOpenAI(temperature=0)\n", + "model_with_tools = model.bind_tools(tools=[multiply])\n", + "\n", + "graph = MessageGraph()\n", + "\n", + "graph.add_node(\"oracle\", model_with_tools)\n", + "\n", + "tool_node = ToolNode([multiply])\n", + "graph.add_node(\"multiply\", tool_node)\n", + "graph.add_edge(START, \"oracle\")\n", + "graph.add_edge(\"multiply\", END)\n", + "\n", + "\n", + "def router(state: list[BaseMessage]) -> Literal[\"multiply\", \"__end__\"]:\n", + " tool_calls = state[-1].additional_kwargs.get(\"tool_calls\", [])\n", + " if len(tool_calls):\n", + " return \"multiply\"\n", + " else:\n", + " return END\n", + "\n", + "\n", + "graph.add_conditional_edges(\"oracle\", router)\n", + "runnable = graph.compile()" + ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/jpeg": 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", 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", "text/plain": [ "" ] @@ -88,53 +161,63 @@ "output_type": "display_data" } ], - "source": ["try:\n display(Image(runnable.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"] + "source": [ + "try:\n", + " display(Image(runnable.get_graph(xray=True).draw_mermaid_png()))\n", + "except Exception:\n", + " # This requires some extra dependencies and is optional\n", + " pass" + ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[HumanMessage(content='What is 123 * 456?', id='fa2dbb36-c61b-4ce1-892d-c08f3e741035'),\n", - " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_ZpoO6ClLFKkppN9Y8GEelZH1', 'function': {'arguments': '{\"first_number\":123,\"second_number\":456}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 19, 'prompt_tokens': 57, 'total_tokens': 76}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-ee9faec5-3526-45d5-89b1-5292755a6893-0', tool_calls=[{'name': 'multiply', 'args': {'first_number': 123, 'second_number': 456}, 'id': 'call_ZpoO6ClLFKkppN9Y8GEelZH1'}], usage_metadata={'input_tokens': 57, 'output_tokens': 19, 'total_tokens': 76}),\n", - " ToolMessage(content='56088', name='multiply', id='b9992170-ca76-4256-8560-29b329c1b56e', tool_call_id='call_ZpoO6ClLFKkppN9Y8GEelZH1')]" + "[HumanMessage(content='What is 123 * 456?', id='81692a54-acd4-4ef7-9ccf-49b2efc0b9b1'),\n", + " AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_XneG8vpjfal3lKO4q4fmfUYc', 'function': {'arguments': '{\"first_number\": 123, \"second_number\": 456}', 'name': 'multiply'}, 'type': 'function'}]}, response_metadata={'token_usage': {'completion_tokens': 34, 'prompt_tokens': 57, 'total_tokens': 91}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'tool_calls', 'logprobs': None}, id='run-2fe18a05-45bf-4cf6-ba29-c5956fc928bf-0', tool_calls=[{'name': 'multiply', 'args': {'first_number': 123, 'second_number': 456}, 'id': 'call_XneG8vpjfal3lKO4q4fmfUYc', 'type': 'tool_call'}], usage_metadata={'input_tokens': 57, 'output_tokens': 34, 'total_tokens': 91}),\n", + " ToolMessage(content='56088', name='multiply', id='d494f0c9-daa8-4da2-aa4b-e9d4ace87912', tool_call_id='call_XneG8vpjfal3lKO4q4fmfUYc')]" ] }, - "execution_count": 20, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], - "source": ["runnable.invoke(HumanMessage(\"What is 123 * 456?\"))"] + "source": [ + "runnable.invoke(HumanMessage(\"What is 123 * 456?\"))" + ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[HumanMessage(content='What is your name?', id='09f03ac4-ca68-4464-9ec3-2c393699b3bb'),\n", - " AIMessage(content='My name is Assistant. How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 54, 'total_tokens': 67}, 'model_name': 'gpt-3.5-turbo', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-a21b2f58-3fa6-428f-99e5-9c4e071a9319-0', usage_metadata={'input_tokens': 54, 'output_tokens': 13, 'total_tokens': 67})]" + "[HumanMessage(content='What is your name?', id='184ed583-58f1-4d4c-a428-d573b5a56286'),\n", + " AIMessage(content='My name is Assistant. How can I assist you today?', response_metadata={'token_usage': {'completion_tokens': 13, 'prompt_tokens': 54, 'total_tokens': 67}, 'model_name': 'gpt-3.5-turbo-0125', 'system_fingerprint': None, 'finish_reason': 'stop', 'logprobs': None}, id='run-38dcd15d-8bdf-491a-b7ca-771bb64bb824-0', usage_metadata={'input_tokens': 54, 'output_tokens': 13, 'total_tokens': 67})]" ] }, - "execution_count": 21, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], - "source": ["runnable.invoke(HumanMessage(\"What is your name?\"))"] + "source": [ + "runnable.invoke(HumanMessage(\"What is your name?\"))" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [""] + "source": [] } ], "metadata": {