mirror of
https://github.com/langchain-ai/langgraph.git
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This commit is contained in:
Bagatur
@@ -109,26 +109,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"execution_count": 1,
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"id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_community.tools.tavily_search import TavilySearchResults\n",
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"from langchain_core.tools import tool\n",
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"\n",
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"@tool\n",
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"def multiply(x: int, y: int) -> int:\n",
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" \"\"\"Multiply two ints\"\"\"\n",
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" return x * y\n",
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"\n",
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"@tool\n",
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"def add(x: int, y: int) -> int:\n",
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" \"\"\"Add two ints\"\"\"\n",
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" return x + y\n",
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"\n",
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"\n",
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"tools = [multiply, add]"
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"tools = [TavilySearchResults(max_results=1)]"
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]
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},
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{
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@@ -143,7 +131,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"execution_count": 2,
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"id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
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"metadata": {},
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"outputs": [],
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@@ -175,7 +163,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"execution_count": 3,
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"id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
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"metadata": {},
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"outputs": [],
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@@ -199,7 +187,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"execution_count": 4,
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"id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
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"metadata": {},
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"outputs": [],
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@@ -230,7 +218,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 5,
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"id": "ea793afa-2eab-4901-910d-6eed90cd6564",
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"metadata": {},
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"outputs": [],
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@@ -277,7 +265,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"execution_count": 6,
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"id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
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"metadata": {},
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"outputs": [],
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@@ -295,8 +283,6 @@
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" return \"end\"\n",
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" # Otherwise if there is, we continue\n",
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" else:\n",
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" if last_message.additional_kwargs[\"function_call\"][\"name\"] == \"add\":\n",
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" return \"add\"\n",
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" return \"continue\"\n",
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"\n",
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"# Define the function that calls the model\n",
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@@ -337,21 +323,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"execution_count": 7,
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"id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langgraph.graph import StateGraph, END\n",
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"from langgraph.checkpoint.memory import MemorySaver\n",
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"\n",
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"# Define a new graph\n",
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"workflow = StateGraph(AgentState)\n",
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"\n",
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"# Define the two nodes we will cycle between\n",
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"workflow.add_node(\"agent\", call_model)\n",
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"workflow.add_node(\"action\", call_tool)\n",
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"workflow.add_node(\"add\", call_tool)\n",
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"\n",
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"# Set the entrypoint as `agent`\n",
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"# This means that this node is the first one called\n",
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@@ -373,7 +356,6 @@
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" {\n",
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" # If `tools`, then we call the tool node.\n",
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" \"continue\": \"action\",\n",
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" \"add\": \"add\",\n",
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" # Otherwise we finish.\n",
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" \"end\": END\n",
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" }\n",
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@@ -382,14 +364,11 @@
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"# We now add a normal edge from `tools` to `agent`.\n",
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"# This means that after `tools` is called, `agent` node is called next.\n",
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"workflow.add_edge('action', 'agent')\n",
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"workflow.add_edge('add', END)\n",
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"\n",
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"# Finally, we compile it!\n",
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"# This compiles it into a LangChain Runnable,\n",
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"# meaning you can use it as you would any other runnable\n",
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"app = workflow.compile()\n",
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"app.interrupt=[\"agent\"]\n",
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"app.checkpointer=MemorySaver()"
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"app = workflow.compile()"
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]
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},
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{
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@@ -406,40 +385,90 @@
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"id": "81633bc1-b136-40e9-b8be-9961adb38183",
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"execution_count": 10,
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"id": "cfd140f0-a5a6-4697-8115-322242f197b5",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'messages': [AIMessage(content='', additional_kwargs={'function_call': {'arguments': '{\\n \"x\": 5,\\n \"y\": 4\\n}', 'name': 'multiply'}})]}"
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]
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},
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"execution_count": 34,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"content='' additional_kwargs={'function_call': {'arguments': '', 'name': 'tavily_search_results_json'}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': '{\\n', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': ' ', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': ' \"', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': 'query', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': '\":', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': ' \"', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': 'weather', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': ' in', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': ' San', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': ' Francisco', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': '\"\\n', 'name': ''}}\n",
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"content='' additional_kwargs={'function_call': {'arguments': '}', 'name': ''}}\n",
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"content=''\n",
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"content=''\n",
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"content='I'\n",
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"content=\"'m\"\n",
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"content=' sorry'\n",
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"content=','\n",
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"content=' but'\n",
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"content=' I'\n",
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"content=' couldn'\n",
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"content=\"'t\"\n",
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"content=' find'\n",
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"content=' the'\n",
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"content=' current'\n",
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"content=' weather'\n",
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"content=' in'\n",
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"content=' San'\n",
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"content=' Francisco'\n",
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"content='.'\n",
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"content=' However'\n",
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"content=','\n",
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"content=' you'\n",
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"content=' can'\n",
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"content=' check'\n",
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"content=' the'\n",
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"content=' weather'\n",
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"content=' forecast'\n",
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"content=' for'\n",
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"content=' San'\n",
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"content=' Francisco'\n",
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"content=' on'\n",
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"content=' websites'\n",
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"content=' like'\n",
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"content=' Weather'\n",
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"content='.com'\n",
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"content=' or'\n",
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"content=' Acc'\n",
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"content='u'\n",
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"content='Weather'\n",
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"content='.'\n",
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"content=''\n"
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]
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}
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],
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"source": [
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"inputs = {\"messages\": [HumanMessage(content=\"what is 5 times 4\")]}\n",
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"await app.ainvoke(inputs, output_keys=\"agent\", config={\"configurable\": {\"thread_id\": \"foo\"}})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"id": "08ae8246-11d5-40e1-8567-361e5bef8917",
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"metadata": {},
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"outputs": [],
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"source": [
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"await app.ainvoke({\"messages\": [HumanMessage(content=\"how about the sum of those two numbers\")]},config={\"configurable\": {\"thread_id\": \"foo\"}})"
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"from langchain_core.messages import HumanMessage\n",
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"inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n",
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"async for output in app.astream_log(inputs, include_types=[\"llm\"]):\n",
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" # astream_log() yields the requested logs (here LLMs) in JSONPatch format\n",
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" for op in output.ops:\n",
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" if op[\"path\"] == \"/streamed_output/-\":\n",
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" # this is the output from .stream()\n",
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" ...\n",
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" elif op[\"path\"].startswith(\"/logs/\") and op[\"path\"].endswith(\n",
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" \"/streamed_output/-\"\n",
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" ):\n",
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" # because we chose to only include LLMs, these are LLM tokens\n",
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" print(op[\"value\"])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "371cb452-2508-46c8-878f-2249a0dae93a",
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"id": "08ae8246-11d5-40e1-8567-361e5bef8917",
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"metadata": {},
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"outputs": [],
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"source": []
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@@ -447,9 +476,9 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "langgraph",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "langgraph"
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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@@ -461,7 +490,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"version": "3.11.1"
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}
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},
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"nbformat": 4,
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