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docs: update how-tos to support pydantic v1 and v2 (#1682)

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Vadym Barda
2024-09-11 19:16:00 +00:00
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parent 0fa10d67f5
commit ee28fd3324
15 changed files with 870 additions and 474 deletions
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docs/docs/how-tos/create-react-agent-hitl.ipynb
+351 -333
View File
@@ -1,336 +1,354 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
"metadata": {},
"source": [
"# How to add human-in-the-loop processes to the prebuilt ReAct agent\n",
"\n",
"This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](../create-react-agent) for how to get started with the prebuilt ReAct agent\n",
"\n",
"You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work."
]
},
{
"cell_type": "markdown",
"id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"First, let's install the required packages and set our API keys"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a213e11a-5c62-4ddb-a707-490d91add383",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U langgraph langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23a1885c-04ab-4750-aefa-105891fddf3e",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"\n",
"def _set_env(var: str):\n",
" if not os.environ.get(var):\n",
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
"\n",
"\n",
"_set_env(\"OPENAI_API_KEY\")"
]
},
{
"cell_type": "markdown",
"id": "d4c5c054",
"metadata": {},
"source": [
"<div class=\"admonition tip\">\n",
" <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
" <p style=\"padding-top: 5px;\">\n",
" Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
" </p>\n",
"</div> "
]
},
{
"cell_type": "markdown",
"id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
"metadata": {},
"source": [
"## Code"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "7a154152-973e-4b5d-aa13-48c617744a4c",
"metadata": {},
"outputs": [],
"source": [
"# First we initialize the model we want to use.\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
"\n",
"\n",
"# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
"\n",
"from typing import Literal\n",
"\n",
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def get_weather(location: str):\n",
" \"\"\"Use this to get weather information from a given location.\"\"\"\n",
" if location.lower() in [\"nyc\", \"new york\"]:\n",
" return \"It might be cloudy in nyc\"\n",
" elif location.lower() in [\"sf\", \"san francisco\"]:\n",
" return \"It's always sunny in sf\"\n",
" else:\n",
" raise AssertionError(\"Unknown Location\")\n",
"\n",
"\n",
"tools = [get_weather]\n",
"\n",
"# We need a checkpointer to enable human-in-the-loop patterns\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"\n",
"memory = MemorySaver()\n",
"\n",
"# Define the graph\n",
"\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"graph = create_react_agent(\n",
" model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n",
")"
]
},
{
"cell_type": "markdown",
"id": "00407425-506d-4ffd-9c86-987921d8c844",
"metadata": {},
"source": [
"## Usage\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
"metadata": {},
"outputs": [],
"source": [
"def print_stream(stream):\n",
" for s in stream:\n",
" message = s[\"messages\"][-1]\n",
" if isinstance(message, tuple):\n",
" print(message)\n",
" else:\n",
" message.pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"what is the weather in SF?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" get_weather (call_TcDfLuoCKLmQ7eG71SedxLZ6)\n",
" Call ID: call_TcDfLuoCKLmQ7eG71SedxLZ6\n",
" Args:\n",
" location: San Francisco, CA\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"config = {\"configurable\": {\"thread_id\": \"42\"}}\n",
"inputs = {\"messages\": [(\"user\", \"what is the weather in SF?\")]}\n",
"\n",
"print_stream(graph.stream(inputs, config, stream_mode=\"values\"))"
]
},
{
"cell_type": "markdown",
"id": "ca40a719",
"metadata": {},
"source": [
"We can verify that our graph stopped at the right place:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Next step: ('tools',)\n"
]
}
],
"source": [
"snapshot = graph.get_state(config)\n",
"print(\"Next step: \", snapshot.next)"
]
},
{
"cell_type": "markdown",
"id": "7de6ca78",
"metadata": {},
"source": [
"Now we can either approve or edit the tool call before proceeding to the next node. If we wanted to approve the tool call, we would simply continue streaming the graph with `None` input. If we wanted to edit the tool call we need to update the state to have the correct tool call, and then after the update has been applied we can continue.\n",
"\n",
"We can try resuming and we will see an error arise:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "740bbaeb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
"Name: get_weather\n",
"\n",
"Error: AssertionError('Unknown Location')\n",
" Please fix your mistakes.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"It seems there was an issue with the location provided. Let's try specifying \"San Francisco, California\" more clearly.\n",
"Tool Calls:\n",
" get_weather (call_TZm9HCShGNEreglVJcmUdXqG)\n",
" Call ID: call_TZm9HCShGNEreglVJcmUdXqG\n",
" Args:\n",
" location: San Francisco, California\n"
]
}
],
"source": [
"print_stream(graph.stream(None, config, stream_mode=\"values\"))"
]
},
{
"cell_type": "markdown",
"id": "c1cf5950",
"metadata": {},
"source": [
"This error arose because our tool argument of \"San Francisco, CA\" is not a location our tool recognizes.\n",
"\n",
"Let's show how we would edit the tool call to search for \"San Francisco\" instead of \"San Francisco, CA\" - since our tool as written treats \"San Francisco, CA\" as an unknown location. We will update the state and then resume streaming the graph and should see no errors arise:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1c81ed9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'configurable': {'thread_id': '42',\n",
" 'checkpoint_ns': '',\n",
" 'checkpoint_id': '1ef66368-9772-67ea-8004-07c779869a0a'}}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state = graph.get_state(config)\n",
"\n",
"last_message = state.values['messages'][-1]\n",
"last_message.tool_calls[0]['args'] = {\"location\": \"San Francisco\"}\n",
"\n",
"graph.update_state(config, {\"messages\": [ last_message]})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "83148e08-63e8-49e5-a08b-02dc907bed1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
"Name: get_weather\n",
"\n",
"It's always sunny in sf\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"The weather in San Francisco is currently sunny. Enjoy the sunshine!\n"
]
}
],
"source": [
"print_stream(graph.stream(None, config, stream_mode=\"values\"))"
]
},
{
"cell_type": "markdown",
"id": "8202a5f9",
"metadata": {},
"source": [
"Fantastic! Our graph updated properly to query the weather in San Francisco and got the correct \"It's always sunny in sf\" response from the tool, and then responded to the user accordingly."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
"cells": [
{
"cell_type": "markdown",
"id": "992c4695-ec4f-428d-bd05-fb3b5fbd70f4",
"metadata": {},
"source": [
"# How to add human-in-the-loop processes to the prebuilt ReAct agent\n",
"\n",
"This tutorial will show how to add human-in-the-loop processes to the prebuilt ReAct agent. Please see [this tutorial](../create-react-agent) for how to get started with the prebuilt ReAct agent\n",
"\n",
"You can add a a breakpoint before tools are called by passing `interrupt_before=[\"tools\"]` to `create_react_agent`. Note that you need to be using a checkpointer for this to work."
]
},
"nbformat": 4,
"nbformat_minor": 5
{
"cell_type": "markdown",
"id": "7be3889f-3c17-4fa1-bd2b-84114a2c7247",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"First, let's install the required packages and set our API keys"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "a213e11a-5c62-4ddb-a707-490d91add383",
"metadata": {},
"outputs": [],
"source": [
"%%capture --no-stderr\n",
"%pip install -U langgraph langchain-openai"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "23a1885c-04ab-4750-aefa-105891fddf3e",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"OPENAI_API_KEY: ········\n"
]
}
],
"source": [
"import getpass\n",
"import os\n",
"\n",
"\n",
"def _set_env(var: str):\n",
" if not os.environ.get(var):\n",
" os.environ[var] = getpass.getpass(f\"{var}: \")\n",
"\n",
"\n",
"_set_env(\"OPENAI_API_KEY\")"
]
},
{
"cell_type": "markdown",
"id": "d4c5c054",
"metadata": {},
"source": [
"<div class=\"admonition tip\">\n",
" <p class=\"admonition-title\">Set up <a href=\"https://smith.langchain.com\">LangSmith</a> for LangGraph development</p>\n",
" <p style=\"padding-top: 5px;\">\n",
" Sign up for LangSmith to quickly spot issues and improve the performance of your LangGraph projects. LangSmith lets you use trace data to debug, test, and monitor your LLM apps built with LangGraph — read more about how to get started <a href=\"https://docs.smith.langchain.com\">here</a>. \n",
" </p>\n",
"</div> "
]
},
{
"cell_type": "markdown",
"id": "03c0f089-070c-4cd4-87e0-6c51f2477b82",
"metadata": {},
"source": [
"## Code"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7a154152-973e-4b5d-aa13-48c617744a4c",
"metadata": {},
"outputs": [],
"source": [
"# First we initialize the model we want to use.\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"model = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n",
"\n",
"\n",
"# For this tutorial we will use custom tool that returns pre-defined values for weather in two cities (NYC & SF)\n",
"\n",
"from typing import Literal\n",
"\n",
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def get_weather(location: str):\n",
" \"\"\"Use this to get weather information from a given location.\"\"\"\n",
" if location.lower() in [\"nyc\", \"new york\"]:\n",
" return \"It might be cloudy in nyc\"\n",
" elif location.lower() in [\"sf\", \"san francisco\"]:\n",
" return \"It's always sunny in sf\"\n",
" else:\n",
" raise AssertionError(\"Unknown Location\")\n",
"\n",
"\n",
"tools = [get_weather]\n",
"\n",
"# We need a checkpointer to enable human-in-the-loop patterns\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"\n",
"memory = MemorySaver()\n",
"\n",
"# Define the graph\n",
"\n",
"from langgraph.prebuilt import create_react_agent\n",
"\n",
"graph = create_react_agent(\n",
" model, tools=tools, interrupt_before=[\"tools\"], checkpointer=memory\n",
")"
]
},
{
"cell_type": "markdown",
"id": "00407425-506d-4ffd-9c86-987921d8c844",
"metadata": {},
"source": [
"## Usage\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "16636975-5f2d-4dc7-ab8e-d0bea0830a28",
"metadata": {},
"outputs": [],
"source": [
"def print_stream(stream):\n",
" for s in stream:\n",
" message = s[\"messages\"][-1]\n",
" if isinstance(message, tuple):\n",
" print(message)\n",
" else:\n",
" message.pretty_print()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9ffff6c3-a4f5-47c9-b51d-97caaee85cd6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================\u001b[1m Human Message \u001b[0m=================================\n",
"\n",
"what is the weather in SF, CA?\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" get_weather (call_uCtiELl4MERM1BSzvGQNVNIO)\n",
" Call ID: call_uCtiELl4MERM1BSzvGQNVNIO\n",
" Args:\n",
" location: SF, CA\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"config = {\"configurable\": {\"thread_id\": \"42\"}}\n",
"inputs = {\"messages\": [(\"user\", \"what is the weather in SF, CA?\")]}\n",
"\n",
"print_stream(graph.stream(inputs, config, stream_mode=\"values\"))"
]
},
{
"cell_type": "markdown",
"id": "ca40a719",
"metadata": {},
"source": [
"We can verify that our graph stopped at the right place:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "3decf001-7228-4ed5-8779-2b9ed98a74ea",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Next step: ('tools',)\n"
]
}
],
"source": [
"snapshot = graph.get_state(config)\n",
"print(\"Next step: \", snapshot.next)"
]
},
{
"cell_type": "markdown",
"id": "7de6ca78",
"metadata": {},
"source": [
"Now we can either approve or edit the tool call before proceeding to the next node. If we wanted to approve the tool call, we would simply continue streaming the graph with `None` input. If we wanted to edit the tool call we need to update the state to have the correct tool call, and then after the update has been applied we can continue.\n",
"\n",
"We can try resuming and we will see an error arise:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "740bbaeb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" get_weather (call_uCtiELl4MERM1BSzvGQNVNIO)\n",
" Call ID: call_uCtiELl4MERM1BSzvGQNVNIO\n",
" Args:\n",
" location: SF, CA\n",
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
"Name: get_weather\n",
"\n",
"Error: AssertionError('Unknown Location')\n",
" Please fix your mistakes.\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" get_weather (call_CS02EQchFuqotH3gAiKcABx1)\n",
" Call ID: call_CS02EQchFuqotH3gAiKcABx1\n",
" Args:\n",
" location: San Francisco, CA\n"
]
}
],
"source": [
"print_stream(graph.stream(None, config, stream_mode=\"values\"))"
]
},
{
"cell_type": "markdown",
"id": "c1cf5950",
"metadata": {},
"source": [
"This error arose because our tool argument of \"San Francisco, CA\" is not a location our tool recognizes.\n",
"\n",
"Let's show how we would edit the tool call to search for \"San Francisco\" instead of \"San Francisco, CA\" - since our tool as written treats \"San Francisco, CA\" as an unknown location. We will update the state and then resume streaming the graph and should see no errors arise:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1c81ed9f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'configurable': {'thread_id': '42',\n",
" 'checkpoint_ns': '',\n",
" 'checkpoint_id': '1ef706ce-e7a4-6740-8004-0bf23a8d9eb8'}}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"state = graph.get_state(config)\n",
"\n",
"last_message = state.values['messages'][-1]\n",
"last_message.tool_calls[0]['args'] = {\"location\": \"San Francisco\"}\n",
"\n",
"graph.update_state(config, {\"messages\": [ last_message]})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "83148e08-63e8-49e5-a08b-02dc907bed1d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"Tool Calls:\n",
" get_weather (call_CS02EQchFuqotH3gAiKcABx1)\n",
" Call ID: call_CS02EQchFuqotH3gAiKcABx1\n",
" Args:\n",
" location: San Francisco\n",
"=================================\u001b[1m Tool Message \u001b[0m=================================\n",
"Name: get_weather\n",
"\n",
"It's always sunny in sf\n",
"==================================\u001b[1m Ai Message \u001b[0m==================================\n",
"\n",
"The weather in San Francisco is currently sunny.\n"
]
}
],
"source": [
"print_stream(graph.stream(None, config, stream_mode=\"values\"))"
]
},
{
"cell_type": "markdown",
"id": "8202a5f9",
"metadata": {},
"source": [
"Fantastic! Our graph updated properly to query the weather in San Francisco and got the correct \"It's always sunny in sf\" response from the tool, and then responded to the user accordingly."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/docs/how-tos/human_in_the_loop/wait-user-input.ipynb
+1 -1
View File
@@ -379,7 +379,7 @@
"# Since `bind_tools` takes in tools but also just tool definitions,\n",
"# We can define a tool definition for `ask_human`\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"from pydantic import BaseModel\n",
"\n",
"\n",
"class AskHuman(BaseModel):\n",
docs/docs/how-tos/many-tools.ipynb
+2 -2
View File
@@ -351,7 +351,7 @@
"outputs": [],
"source": [
"from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from pydantic import BaseModel, Field\n",
"\n",
"\n",
"class QueryForTools(BaseModel):\n",
@@ -524,7 +524,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
"version": "3.11.9"
}
},
"nbformat": 4,
docs/docs/how-tos/map-reduce.ipynb
+1 -1
View File
@@ -94,7 +94,7 @@
"import operator\n",
"from typing import Annotated, TypedDict\n",
"\n",
"from langchain_core.pydantic_v1 import BaseModel, Field\n",
"from pydantic import BaseModel, Field\n",
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"from langgraph.constants import Send\n",
docs/docs/how-tos/memory/delete-messages.ipynb
+1 -3
View File
@@ -118,9 +118,7 @@
" \"\"\"Call to surf the web.\"\"\"\n",
" # This is a placeholder for the actual implementation\n",
" # Don't let the LLM know this though 😊\n",
" return [\n",
" \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
" ]\n",
" return \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
"\n",
"\n",
"tools = [search]\n",
docs/docs/how-tos/memory/manage-conversation-history.ipynb
+2 -4
View File
@@ -109,9 +109,7 @@
" \"\"\"Call to surf the web.\"\"\"\n",
" # This is a placeholder for the actual implementation\n",
" # Don't let the LLM know this though 😊\n",
" return [\n",
" \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
" ]\n",
" return \"It's sunny in San Francisco, but you better look out if you're a Gemini 😈.\"\n",
"\n",
"\n",
"tools = [search]\n",
@@ -358,7 +356,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
docs/docs/how-tos/pass-run-time-values-to-tools.ipynb
+1 -1
View File
@@ -98,7 +98,7 @@
"from typing_extensions import Annotated\n",
"\n",
"from langchain_core.documents import Document\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"from pydantic import BaseModel\n",
"from langchain_core.tools import tool\n",
"\n",
"from langgraph.prebuilt import InjectedState\n",
docs/docs/how-tos/recursion-limit.ipynb
+2 -2
View File
@@ -183,7 +183,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -201,5 +201,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
docs/docs/how-tos/state-context-key.ipynb
+19 -39
View File
@@ -105,7 +105,7 @@
" \"\"\"Call to surf the web.\"\"\"\n",
" # This is a placeholder for the actual implementation\n",
" # Don't let the LLM know this though 😊\n",
" return [\"The answer to your question lies within.\"]\n",
" return \"The answer to your question lies within.\"\n",
"\n",
"\n",
"tools = [search]"
@@ -116,10 +116,7 @@
"id": "01885785-b71a-44d1-b1d6-7b5b14d53b58",
"metadata": {},
"source": [
"We can now wrap these tools in a simple [ToolExecutor](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolexecutor).\n",
"This is a real simple class that takes in a [ToolInvocation](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolinvocation) and calls that tool, returning the output.\n",
"\n",
"A ToolInvocation is any dict-like class with `tool` and `tool_input` attributes."
"We can now wrap these tools in a simple [tool-calling node](https://langchain-ai.github.io/langgraph/reference/prebuilt/#toolnode)."
]
},
{
@@ -129,9 +126,9 @@
"metadata": {},
"outputs": [],
"source": [
"from langgraph.prebuilt import ToolExecutor\n",
"from langgraph.prebuilt import ToolNode\n",
"\n",
"tool_executor = ToolExecutor(tools)"
"tool_node = ToolNode(tools)"
]
},
{
@@ -201,9 +198,20 @@
"source": [
"import httpx\n",
"from contextlib import contextmanager\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"from langchain_core.runnables import RunnableConfig\n",
"\n",
"# NOTE:\n",
"# - if you're using langchain-core >= 0.3, you need to use pydantic v2\n",
"# - if you're using langchain-core >= 0.2,<0.3, you need to use pydantic v1\n",
"from langchain_core import __version__ as core_version\n",
"from packaging import version\n",
"\n",
"core_version = version.parse(core_version)\n",
"if (core_version.major, core_version.minor) < (0, 3):\n",
" from pydantic.v1 import BaseModel\n",
"else:\n",
" from pydantic import BaseModel\n",
"\n",
"\n",
"class AgentContext(BaseModel):\n",
" class Config:\n",
@@ -253,7 +261,6 @@
"from typing import Annotated, Sequence\n",
"\n",
"from langchain_core.messages import BaseMessage\n",
"from langchain_core.pydantic_v1 import BaseModel\n",
"from langgraph.channels.context import Context\n",
"\n",
"\n",
@@ -300,11 +307,6 @@
"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",
@@ -326,29 +328,7 @@
" 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]}\n",
"\n",
"\n",
"# 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 from the function_call\n",
" tool_call = last_message.tool_calls[0]\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",
" response = tool_executor.invoke(action)\n",
" # We use the response to create a ToolMessage\n",
" tool_message = ToolMessage(\n",
" content=str(response), name=action.tool, tool_call_id=tool_call[\"id\"]\n",
" )\n",
" # We return a list, because this will get added to the existing list\n",
" return {\"messages\": [tool_message]}"
" return {\"messages\": [response]}"
]
},
{
@@ -375,7 +355,7 @@
"\n",
"# Define the two nodes we will cycle between\n",
"workflow.add_node(\"agent\", call_model)\n",
"workflow.add_node(\"action\", call_tool)\n",
"workflow.add_node(\"action\", tool_node)\n",
"\n",
"# Set the entrypoint as `agent`\n",
"# This means that this node is the first one called\n",
@@ -477,7 +457,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
"version": "3.11.9"
}
},
"nbformat": 4,
docs/docs/how-tos/state-model.ipynb
+12 -1
View File
@@ -215,7 +215,18 @@
"from typing import Annotated, Sequence\n",
"\n",
"from langchain_core.messages import BaseMessage\n",
"from pydantic.v1 import BaseModel\n",
"\n",
"# NOTE:\n",
"# - if you're using langchain-core >= 0.3, you need to use pydantic v2\n",
"# - if you're using langchain-core >= 0.2,<0.3, you need to use pydantic v1\n",
"from langchain_core import __version__ as core_version\n",
"from packaging import version\n",
"\n",
"core_version = version.parse(core_version)\n",
"if (core_version.major, core_version.minor) < (0, 3):\n",
" from pydantic.v1 import BaseModel\n",
"else:\n",
" from pydantic import BaseModel\n",
"\n",
"\n",
"class AgentState(BaseModel):\n",
docs/docs/how-tos/streaming-subgraphs.ipynb
+4 -3
View File
@@ -68,7 +68,8 @@
"metadata": {},
"outputs": [],
"source": [
"from typing import TypedDict, Optional, Annotated\n",
"from typing import Optional, Annotated\n",
"from typing_extensions import TypedDict\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.graph import StateGraph, START, END\n",
"\n",
@@ -354,7 +355,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -372,5 +373,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
docs/docs/how-tos/subgraph.ipynb
+2 -1
View File
@@ -78,7 +78,8 @@
"metadata": {},
"outputs": [],
"source": [
"from typing import TypedDict, Optional, Annotated\n",
"from typing import Optional, Annotated\n",
"from typing_extensions import TypedDict\n",
"from langgraph.checkpoint.memory import MemorySaver\n",
"from langgraph.graph import StateGraph, START, END\n",
"\n",
docs/docs/how-tos/tool-calling-errors.ipynb
+14 -7
View File
@@ -279,11 +279,22 @@
],
"source": [
"from langchain_core.output_parsers import StrOutputParser\n",
"from langchain.pydantic_v1 import BaseModel, conlist\n",
"\n",
"# NOTE:\n",
"# - if you're using langchain-core >= 0.3, you need to use pydantic v2\n",
"# - if you're using langchain-core >= 0.2,<0.3, you need to use pydantic v1\n",
"from langchain_core import __version__ as core_version\n",
"from packaging import version\n",
"\n",
"class HaikuRequest(BaseModel):\n",
" topic: conlist(str, min_items=3, max_items=3)\n",
"core_version = version.parse(core_version)\n",
"if (core_version.major, core_version.minor) < (0, 3):\n",
" from pydantic.v1 import BaseModel, conlist\n",
" class HaikuRequest(BaseModel):\n",
" topic: conlist(str, min_items=3, max_items=3)\n",
"else:\n",
" from pydantic import BaseModel, conlist\n",
" class HaikuRequest(BaseModel):\n",
" topic: conlist(str, min_length=3, max_length=3)\n",
"\n",
"\n",
"@tool\n",
@@ -362,10 +373,6 @@
"from langchain_core.messages.modifier import RemoveMessage\n",
"\n",
"\n",
"class HaikuRequest(BaseModel):\n",
" topic: conlist(str, min_items=3, max_items=3)\n",
"\n",
"\n",
"@tool\n",
"def master_haiku_generator(request: HaikuRequest):\n",
" \"\"\"Generates a haiku based on the provided topics.\"\"\"\n",
poetry.lock
Generated
+456 -76
View File
@@ -29,6 +29,31 @@ files = [
{file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
]
[[package]]
name = "anthropic"
version = "0.34.2"
description = "The official Python library for the anthropic API"
optional = false
python-versions = ">=3.7"
files = [
{file = "anthropic-0.34.2-py3-none-any.whl", hash = "sha256:f50a628eb71e2c76858b106c8cbea278c45c6bd2077cb3aff716a112abddc9fc"},
{file = "anthropic-0.34.2.tar.gz", hash = "sha256:808ea19276f26646bfde9ee535669735519376e4eeb301a2974fc69892be1d6e"},
]
[package.dependencies]
anyio = ">=3.5.0,<5"
distro = ">=1.7.0,<2"
httpx = ">=0.23.0,<1"
jiter = ">=0.4.0,<1"
pydantic = ">=1.9.0,<3"
sniffio = "*"
tokenizers = ">=0.13.0"
typing-extensions = ">=4.7,<5"
[package.extras]
bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
vertex = ["google-auth (>=2,<3)"]
[[package]]
name = "anyio"
version = "4.4.0"
@@ -512,6 +537,17 @@ files = [
{file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
]
[[package]]
name = "distro"
version = "1.9.0"
description = "Distro - an OS platform information API"
optional = false
python-versions = ">=3.6"
files = [
{file = "distro-1.9.0-py3-none-any.whl", hash = "sha256:7bffd925d65168f85027d8da9af6bddab658135b840670a223589bc0c8ef02b2"},
{file = "distro-1.9.0.tar.gz", hash = "sha256:2fa77c6fd8940f116ee1d6b94a2f90b13b5ea8d019b98bc8bafdcabcdd9bdbed"},
]
[[package]]
name = "exceptiongroup"
version = "1.2.1"
@@ -570,6 +606,45 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1
testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
typing = ["typing-extensions (>=4.8)"]
[[package]]
name = "fsspec"
version = "2024.9.0"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
{file = "fsspec-2024.9.0-py3-none-any.whl", hash = "sha256:a0947d552d8a6efa72cc2c730b12c41d043509156966cca4fb157b0f2a0c574b"},
{file = "fsspec-2024.9.0.tar.gz", hash = "sha256:4b0afb90c2f21832df142f292649035d80b421f60a9e1c027802e5a0da2b04e8"},
]
[package.extras]
abfs = ["adlfs"]
adl = ["adlfs"]
arrow = ["pyarrow (>=1)"]
dask = ["dask", "distributed"]
dev = ["pre-commit", "ruff"]
doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
dropbox = ["dropbox", "dropboxdrivefs", "requests"]
full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
fuse = ["fusepy"]
gcs = ["gcsfs"]
git = ["pygit2"]
github = ["requests"]
gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
sftp = ["paramiko"]
smb = ["smbprotocol"]
ssh = ["paramiko"]
test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask-expr", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
tqdm = ["tqdm"]
[[package]]
name = "ghp-import"
version = "2.1.0"
@@ -710,6 +785,40 @@ files = [
{file = "httpx_sse-0.4.0-py3-none-any.whl", hash = "sha256:f329af6eae57eaa2bdfd962b42524764af68075ea87370a2de920af5341e318f"},
]
[[package]]
name = "huggingface-hub"
version = "0.24.6"
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
optional = false
python-versions = ">=3.8.0"
files = [
{file = "huggingface_hub-0.24.6-py3-none-any.whl", hash = "sha256:a990f3232aa985fe749bc9474060cbad75e8b2f115f6665a9fda5b9c97818970"},
{file = "huggingface_hub-0.24.6.tar.gz", hash = "sha256:cc2579e761d070713eaa9c323e3debe39d5b464ae3a7261c39a9195b27bb8000"},
]
[package.dependencies]
filelock = "*"
fsspec = ">=2023.5.0"
packaging = ">=20.9"
pyyaml = ">=5.1"
requests = "*"
tqdm = ">=4.42.1"
typing-extensions = ">=3.7.4.3"
[package.extras]
all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
cli = ["InquirerPy (==0.3.4)"]
dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
hf-transfer = ["hf-transfer (>=0.1.4)"]
inference = ["aiohttp", "minijinja (>=1.0)"]
quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
tensorflow = ["graphviz", "pydot", "tensorflow"]
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
torch = ["safetensors[torch]", "torch"]
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
[[package]]
name = "idna"
version = "3.7"
@@ -846,6 +955,76 @@ MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
[[package]]
name = "jiter"
version = "0.5.0"
description = "Fast iterable JSON parser."
optional = false
python-versions = ">=3.8"
files = [
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{file = "jiter-0.5.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c22541f0b672f4d741382a97c65609332a783501551445ab2df137ada01e019e"},
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{file = "tokenizers-0.20.0-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:3e14cdef1efa96ecead6ea64a891828432c3ebba128bdc0596e3059fea104ef3"},
{file = "tokenizers-0.20.0.tar.gz", hash = "sha256:39d7acc43f564c274085cafcd1dae9d36f332456de1a31970296a6b8da4eac8d"},
]
[package.dependencies]
huggingface-hub = ">=0.16.4,<1.0"
[package.extras]
dev = ["tokenizers[testing]"]
docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests", "ruff"]
[[package]]
name = "tomli"
version = "2.0.1"
@@ -2849,6 +3209,26 @@ files = [
{file = "tornado-6.4.1.tar.gz", hash = "sha256:92d3ab53183d8c50f8204a51e6f91d18a15d5ef261e84d452800d4ff6fc504e9"},
]
[[package]]
name = "tqdm"
version = "4.66.5"
description = "Fast, Extensible Progress Meter"
optional = false
python-versions = ">=3.7"
files = [
{file = "tqdm-4.66.5-py3-none-any.whl", hash = "sha256:90279a3770753eafc9194a0364852159802111925aa30eb3f9d85b0e805ac7cd"},
{file = "tqdm-4.66.5.tar.gz", hash = "sha256:e1020aef2e5096702d8a025ac7d16b1577279c9d63f8375b63083e9a5f0fcbad"},
]
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"]
telegram = ["requests"]
[[package]]
name = "traitlets"
version = "5.14.3"
@@ -2987,4 +3367,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
[metadata]
lock-version = "2.0"
python-versions = "^3.9"
content-hash = "c607242d7052d59a7b230439cada9b4ee5fed7319ff6427d9d81b0bdc7b951f5"
content-hash = "11a13afccfb79769e9081875fdd2be1f2a9e627a921c3727ee56597f554839cb"
pyproject.toml
+2
View File
@@ -10,6 +10,8 @@ readme = "README.md"
python = "^3.9"
[tool.poetry.group.docs.dependencies]
langchain-openai = "^0.1.23"
langchain-anthropic = "^0.1.23"
langgraph = { path = "libs/langgraph/", develop = true }
langgraph-checkpoint = { path = "libs/checkpoint/", develop = true }
langgraph-checkpoint-sqlite = { path = "libs/checkpoint-sqlite", develop = true }