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[Docs] Update notebooks to use START (#902)

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This commit is contained in:
William FH
2024-07-01 21:36:34 -07:00
committed by GitHub
parent 267f5e5234
commit 727e63c01e
67 changed files with 1059 additions and 20258 deletions
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examples/chat_agent_executor_with_function_calling/human-in-the-loop.ipynb
+13 -183
View File
@@ -30,10 +30,7 @@
"id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install --quiet -U langgraph langchain_community langchain_openai tavily-python"
]
"source": ["%%capture --no-stderr\n%pip install --quiet -U langgraph langchain_community langchain_openai tavily-python"]
},
{
"cell_type": "markdown",
@@ -58,13 +55,7 @@
]
}
],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
"os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"
]
"source": ["import getpass\nimport os\n\nos.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\nos.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"]
},
{
"cell_type": "markdown",
@@ -88,10 +79,7 @@
]
}
],
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"
]
"source": ["os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\nos.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"]
},
{
"cell_type": "markdown",
@@ -111,11 +99,7 @@
"id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"\n",
"tools = [TavilySearchResults(max_results=1)]"
]
"source": ["from langchain_community.tools.tavily_search import TavilySearchResults\n\ntools = [TavilySearchResults(max_results=1)]"]
},
{
"cell_type": "markdown",
@@ -133,11 +117,7 @@
"id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.prebuilt import ToolExecutor\n",
"\n",
"tool_executor = ToolExecutor(tools)"
]
"source": ["from langgraph.prebuilt import ToolExecutor\n\ntool_executor = ToolExecutor(tools)"]
},
{
"cell_type": "markdown",
@@ -161,13 +141,7 @@
"id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
"metadata": {},
"outputs": [],
"source": [
"from langchain_openai import ChatOpenAI\n",
"\n",
"# We will set streaming=True so that we can stream tokens\n",
"# See the streaming section for more information on this.\n",
"model = ChatOpenAI(temperature=0, streaming=True)"
]
"source": ["from langchain_openai import ChatOpenAI\n\n# We will set streaming=True so that we can stream tokens\n# See the streaming section for more information on this.\nmodel = ChatOpenAI(temperature=0, streaming=True)"]
},
{
"cell_type": "markdown",
@@ -185,9 +159,7 @@
"id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
"metadata": {},
"outputs": [],
"source": [
"model = model.bind_tools(tools)"
]
"source": ["model = model.bind_tools(tools)"]
},
{
"cell_type": "markdown",
@@ -213,16 +185,7 @@
"id": "ea793afa-2eab-4901-910d-6eed90cd6564",
"metadata": {},
"outputs": [],
"source": [
"import operator\n",
"from typing import Annotated, Sequence, TypedDict\n",
"\n",
"from langchain_core.messages import BaseMessage\n",
"\n",
"\n",
"class AgentState(TypedDict):\n",
" messages: Annotated[Sequence[BaseMessage], operator.add]"
]
"source": ["import operator\nfrom typing import Annotated, Sequence, TypedDict\n\nfrom langchain_core.messages import BaseMessage\n\n\nclass AgentState(TypedDict):\n messages: Annotated[Sequence[BaseMessage], operator.add]"]
},
{
"cell_type": "markdown",
@@ -257,31 +220,7 @@
"id": "b547109f-f9e8-4e77-a7e7-ed2bae7a72ab",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.messages import ToolMessage\n",
"\n",
"from langgraph.prebuilt import ToolInvocation\n",
"\n",
"\n",
"# Define the function that determines whether to continue or not\n",
"def should_continue(state):\n",
" messages = state[\"messages\"]\n",
" last_message = messages[-1]\n",
" # If there is no function call, then we finish\n",
" if not last_message.tool_calls:\n",
" return \"end\"\n",
" # Otherwise if there is, we continue\n",
" else:\n",
" return \"continue\"\n",
"\n",
"\n",
"# Define the function that calls the model\n",
"def call_model(state):\n",
" messages = state[\"messages\"]\n",
" response = model.invoke(messages)\n",
" # We return a list, because this will get added to the existing list\n",
" return {\"messages\": [response]}"
]
"source": ["from langchain_core.messages import ToolMessage\n\nfrom langgraph.prebuilt import ToolInvocation\n\n\n# Define the function that determines whether to continue or not\ndef should_continue(state):\n messages = state[\"messages\"]\n last_message = messages[-1]\n # If there is no function call, then we finish\n if not last_message.tool_calls:\n return \"end\"\n # Otherwise if there is, we continue\n else:\n return \"continue\"\n\n\n# Define the function that calls the model\ndef call_model(state):\n messages = state[\"messages\"]\n response = model.invoke(messages)\n # We return a list, because this will get added to the existing list\n return {\"messages\": [response]}"]
},
{
"cell_type": "code",
@@ -289,41 +228,7 @@
"id": "73fd6432-42e8-472a-89ca-bb5ddbbcc35a",
"metadata": {},
"outputs": [],
"source": [
"# Define the function to execute tools\n",
"def call_tool(state):\n",
" messages = state[\"messages\"]\n",
" # Based on the continue condition\n",
" # we know the last message involves a function call\n",
" last_message = messages[-1]\n",
" # We construct an ToolInvocation for each tool call\n",
" tool_invocations = []\n",
" for tool_call in last_message.tool_calls:\n",
" action = ToolInvocation(\n",
" tool=tool_call[\"name\"],\n",
" tool_input=tool_call[\"args\"],\n",
" )\n",
" tool_invocations.append(action)\n",
"\n",
" action = ToolInvocation(\n",
" tool=tool_call[\"name\"],\n",
" tool_input=tool_call[\"args\"],\n",
" )\n",
" # We call the tool_executor and get back a response\n",
" responses = tool_executor.batch(tool_invocations, return_exceptions=True)\n",
" # We use the response to create tool messages\n",
" tool_messages = [\n",
" ToolMessage(\n",
" content=str(response),\n",
" name=tc[\"name\"],\n",
" tool_call_id=tc[\"id\"],\n",
" )\n",
" for tc, response in zip(last_message.tool_calls, responses)\n",
" ]\n",
"\n",
" # We return a list, because this will get added to the existing list\n",
" return {\"messages\": tool_messages}"
]
"source": ["# Define the function to execute tools\ndef call_tool(state):\n messages = state[\"messages\"]\n # Based on the continue condition\n # we know the last message involves a function call\n last_message = messages[-1]\n # We construct an ToolInvocation for each tool call\n tool_invocations = []\n for tool_call in last_message.tool_calls:\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n tool_invocations.append(action)\n\n action = ToolInvocation(\n tool=tool_call[\"name\"],\n tool_input=tool_call[\"args\"],\n )\n # We call the tool_executor and get back a response\n responses = tool_executor.batch(tool_invocations, return_exceptions=True)\n # We use the response to create tool messages\n tool_messages = [\n ToolMessage(\n content=str(response),\n name=tc[\"name\"],\n tool_call_id=tc[\"id\"],\n )\n for tc, response in zip(last_message.tool_calls, responses)\n ]\n\n # We return a list, because this will get added to the existing list\n return {\"messages\": tool_messages}"]
},
{
"cell_type": "markdown",
@@ -345,51 +250,7 @@
"id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.graph import END, StateGraph\n",
"\n",
"# Define a new graph\n",
"workflow = StateGraph(AgentState)\n",
"\n",
"# Define the two nodes we will cycle between\n",
"workflow.add_node(\"agent\", call_model)\n",
"workflow.add_node(\"action\", call_tool)\n",
"\n",
"# Set the entrypoint as `agent`\n",
"# This means that this node is the first one called\n",
"workflow.set_entry_point(\"agent\")\n",
"\n",
"# We now add a conditional edge\n",
"workflow.add_conditional_edges(\n",
" # First, we define the start node. We use `agent`.\n",
" # This means these are the edges taken after the `agent` node is called.\n",
" \"agent\",\n",
" # Next, we pass in the function that will determine which node is called next.\n",
" should_continue,\n",
" # Finally we pass in a mapping.\n",
" # The keys are strings, and the values are other nodes.\n",
" # END is a special node marking that the graph should finish.\n",
" # What will happen is we will call `should_continue`, and then the output of that\n",
" # will be matched against the keys in this mapping.\n",
" # Based on which one it matches, that node will then be called.\n",
" {\n",
" # If `tools`, then we call the tool node.\n",
" \"continue\": \"action\",\n",
" # Otherwise we finish.\n",
" \"end\": END,\n",
" },\n",
")\n",
"\n",
"# We now add a normal edge from `tools` to `agent`.\n",
"# This means that after `tools` is called, `agent` node is called next.\n",
"workflow.add_edge(\"action\", \"agent\")\n",
"\n",
"# Finally, we compile it!\n",
"# This compiles it into a LangChain Runnable,\n",
"# meaning you can use it as you would any other runnable\n",
"app = workflow.compile(checkpointer=MemorySaver(), interrupt_before=[\"action\"])"
]
"source": ["from langgraph.checkpoint.memory import MemorySaver\nfrom langgraph.graph import END, StateGraph, START\n\n# Define a new graph\nworkflow = StateGraph(AgentState)\n\n# Define the two nodes we will cycle between\nworkflow.add_node(\"agent\", call_model)\nworkflow.add_node(\"action\", call_tool)\n\n# Set the entrypoint as `agent`\n# This means that this node is the first one called\nworkflow.add_edge(START, \"agent\")\n\n# We now add a conditional edge\nworkflow.add_conditional_edges(\n # First, we define the start node. We use `agent`.\n # This means these are the edges taken after the `agent` node is called.\n \"agent\",\n # Next, we pass in the function that will determine which node is called next.\n should_continue,\n # Finally we pass in a mapping.\n # The keys are strings, and the values are other nodes.\n # END is a special node marking that the graph should finish.\n # What will happen is we will call `should_continue`, and then the output of that\n # will be matched against the keys in this mapping.\n # Based on which one it matches, that node will then be called.\n {\n # If `tools`, then we call the tool node.\n \"continue\": \"action\",\n # Otherwise we finish.\n \"end\": END,\n },\n)\n\n# We now add a normal edge from `tools` to `agent`.\n# This means that after `tools` is called, `agent` node is called next.\nworkflow.add_edge(\"action\", \"agent\")\n\n# Finally, we compile it!\n# This compiles it into a LangChain Runnable,\n# meaning you can use it as you would any other runnable\napp = workflow.compile(checkpointer=MemorySaver(), interrupt_before=[\"action\"])"]
},
{
"cell_type": "code",
@@ -408,15 +269,7 @@
"output_type": "display_data"
}
],
"source": [
"from IPython.display import Image, display\n",
"\n",
"try:\n",
" display(Image(app.get_graph(xray=True).draw_mermaid_png()))\n",
"except Exception:\n",
" # This requires some extra dependencies and is optional\n",
" pass"
]
"source": ["from IPython.display import Image, display\n\ntry:\n display(Image(app.get_graph(xray=True).draw_mermaid_png()))\nexcept Exception:\n # This requires some extra dependencies and is optional\n pass"]
},
{
"cell_type": "markdown",
@@ -460,30 +313,7 @@
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"\n",
"inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n",
"config = {\"configurable\": {\"thread_id\": \"thread-1\"}}\n",
"while True:\n",
" for output in app.stream(inputs, config):\n",
" # stream() yields dictionaries with output keyed by node name\n",
" for key, value in output.items():\n",
" print(f\"Output from node '{key}':\")\n",
" print(\"---\")\n",
" print(value)\n",
" print(\"\\n---\\n\")\n",
" snapshot = app.get_state(config)\n",
" # If \"next\" is present, it means we've interrupted mid-execution\n",
" if not snapshot.next:\n",
" break\n",
" inputs = None\n",
" response = input(\n",
" \"Do you approve the next step? Type y if you do, anything else to stop: \"\n",
" )\n",
" if response != \"y\":\n",
" break"
]
"source": ["from langchain_core.messages import HumanMessage\n\ninputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\nconfig = {\"configurable\": {\"thread_id\": \"thread-1\"}}\nwhile True:\n for output in app.stream(inputs, config):\n # stream() yields dictionaries with output keyed by node name\n for key, value in output.items():\n print(f\"Output from node '{key}':\")\n print(\"---\")\n print(value)\n print(\"\\n---\\n\")\n snapshot = app.get_state(config)\n # If \"next\" is present, it means we've interrupted mid-execution\n if not snapshot.next:\n break\n inputs = None\n response = input(\n \"Do you approve the next step? Type y if you do, anything else to stop: \"\n )\n if response != \"y\":\n break"]
}
],
"metadata": {