mirror of
https://github.com/langchain-ai/langgraph.git
synced 2026-09-07 10:17:50 +02:00
docs: reduce number of cells in streaming how-tos (#997)
This commit is contained in:
Vadym Barda
@@ -42,7 +42,7 @@
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"metadata": {},
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"outputs": [
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{
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"name": "stdin",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"OPENAI_API_KEY: ········\n"
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@@ -80,7 +80,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"id": "d59234f9-173e-469d-a725-c13e0979663e",
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"metadata": {},
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"outputs": [],
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@@ -90,16 +90,7 @@
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"from langchain_core.messages import AIMessageChunk\n",
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"from langchain_core.runnables.config import ensure_config, get_callback_manager_for_config\n",
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"\n",
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"openai_client = AsyncOpenAI()"
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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": 4,
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"id": "b29a083e-b753-4392-807d-3625ac85f08f",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai_client = AsyncOpenAI()\n",
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"# define tool schema for openai tool calling\n",
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"\n",
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"tool = {\n",
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@@ -119,16 +110,7 @@
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" ]\n",
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" }\n",
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" }\n",
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"}"
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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": 5,
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"id": "7af98437-f0d8-4110-a33b-ae5d1331d509",
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"metadata": {},
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"outputs": [],
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"source": [
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"}\n",
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"async def call_model(state, config=None):\n",
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" config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
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" callback_manager = get_callback_manager_for_config(config)\n",
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@@ -199,11 +181,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": null,
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"id": "b90941d8-afe4-42ec-9262-9c3b87c3b1ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"from langchain_core.runnables import RunnableLambda\n",
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"\n",
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"async def get_items(place: str) -> str:\n",
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@@ -225,39 +208,13 @@
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" tool_logger.invoke(token)\n",
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" tokens.append(token)\n",
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"\n",
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" return \", \".join(tokens)"
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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": 7,
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"id": "746129da-e926-4844-8da7-bd3bac8276c3",
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"metadata": {},
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"outputs": [],
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"source": [
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" return \", \".join(tokens)\n",
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"\n",
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"# define mapping to look up functions when running tools\n",
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"function_name_to_function = {\n",
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" \"get_items\": get_items\n",
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"}"
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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": 8,
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"id": "6829bd73-8a8e-4726-b73d-393a897b42d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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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": 9,
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"id": "8fc0fbe8-691f-45b1-b506-1222c797d588",
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"metadata": {},
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"outputs": [],
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"source": [
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"}\n",
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"\n",
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"async def call_tools(state):\n",
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" messages = state[\"messages\"]\n",
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"\n",
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@@ -288,7 +245,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": null,
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"id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
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"metadata": {},
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"outputs": [],
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@@ -296,57 +253,24 @@
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"import operator\n",
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"from typing import Annotated, TypedDict, Literal\n",
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"\n",
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"from langgraph.graph import StateGraph, END"
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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": 11,
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"id": "09a7c707-e088-4814-9d2f-eff68ab88771",
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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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"\n",
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"class State(TypedDict):\n",
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" messages: Annotated[list, operator.add]"
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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": 12,
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"id": "d36f2d38-024c-4be0-a79a-96cfd23f9fe5",
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"metadata": {},
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"outputs": [],
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"source": [
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" messages: Annotated[list, operator.add]\n",
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"\n",
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"def should_continue(state) -> Literal[\"tools\", END]:\n",
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" messages = state['messages']\n",
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" last_message = messages[-1]\n",
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" if last_message[\"tool_calls\"]:\n",
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" return \"tools\"\n",
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" return END"
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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": 13,
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"id": "a53aab90-9206-46ef-be83-81c2de2c007a",
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"metadata": {},
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"outputs": [],
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"source": [
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" return END\n",
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"\n",
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"workflow = StateGraph(State)\n",
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"workflow.set_entry_point(\"model\")\n",
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"workflow.add_node(\"model\", call_model) # i.e. our \"agent\"\n",
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"workflow.add_node(\"tools\", call_tools)\n",
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"workflow.add_conditional_edges(\"model\", should_continue)\n",
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"workflow.add_edge(\"tools\", \"model\")"
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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": 14,
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"id": "5aac9d6a-f182-4ddd-af77-3e26fdf4170b",
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"metadata": {},
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"outputs": [],
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"source": [
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"workflow.add_edge(\"tools\", \"model\")\n",
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"graph = workflow.compile()"
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]
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},
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@@ -80,7 +80,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"id": "d59234f9-173e-469d-a725-c13e0979663e",
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"metadata": {},
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"outputs": [],
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@@ -90,16 +90,7 @@
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"from langchain_core.messages import AIMessageChunk\n",
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"from langchain_core.runnables.config import ensure_config, get_callback_manager_for_config\n",
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"\n",
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"openai_client = AsyncOpenAI()"
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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": 3,
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"id": "b29a083e-b753-4392-807d-3625ac85f08f",
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"metadata": {},
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"outputs": [],
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"source": [
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"openai_client = AsyncOpenAI()\n",
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"# define tool schema for openai tool calling\n",
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"\n",
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"tool = {\n",
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@@ -119,16 +110,7 @@
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" ]\n",
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" }\n",
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" }\n",
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"}"
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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": 4,
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"id": "7af98437-f0d8-4110-a33b-ae5d1331d509",
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"metadata": {},
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"outputs": [],
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"source": [
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"}\n",
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"async def call_model(state, config=None):\n",
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" config = ensure_config(config | {\"tags\": [\"agent_llm\"]})\n",
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" callback_manager = get_callback_manager_for_config(config)\n",
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@@ -199,11 +181,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "2cb38dd9-74d8-456d-9e39-4655f2bf3f37",
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"execution_count": null,
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"id": "b756ea32",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"async def get_items(place: str) -> str:\n",
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" \"\"\"Use this tool to look up which items are in the given place.\"\"\"\n",
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" if \"bed\" in place: # For under the bed\n",
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@@ -211,39 +195,13 @@
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" if \"shelf\" in place: # For 'shelf'\n",
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" return \"books, penciles and pictures\"\n",
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" else: # if the agent decides to ask about a different place\n",
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" return \"cat snacks\""
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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": 6,
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"id": "746129da-e926-4844-8da7-bd3bac8276c3",
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"metadata": {},
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"outputs": [],
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"source": [
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" return \"cat snacks\"\n",
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"\n",
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"# define mapping to look up functions when running tools\n",
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"function_name_to_function = {\n",
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" \"get_items\": get_items\n",
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"}"
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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": 7,
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"id": "6829bd73-8a8e-4726-b73d-393a897b42d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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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": 8,
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"id": "8fc0fbe8-691f-45b1-b506-1222c797d588",
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"metadata": {},
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"outputs": [],
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"source": [
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"}\n",
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"\n",
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"async def call_tools(state):\n",
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" messages = state[\"messages\"]\n",
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"\n",
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@@ -274,7 +232,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": null,
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"id": "228260be-1f9a-4195-80e0-9604f8a5dba6",
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"metadata": {},
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"outputs": [],
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@@ -282,57 +240,24 @@
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"import operator\n",
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"from typing import Annotated, TypedDict, Literal\n",
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"\n",
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"from langgraph.graph import StateGraph, END"
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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": 10,
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"id": "09a7c707-e088-4814-9d2f-eff68ab88771",
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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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"\n",
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"class State(TypedDict):\n",
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" messages: Annotated[list, operator.add]"
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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": 11,
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"id": "d36f2d38-024c-4be0-a79a-96cfd23f9fe5",
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"metadata": {},
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"outputs": [],
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"source": [
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" messages: Annotated[list, operator.add]\n",
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"\n",
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"def should_continue(state) -> Literal[\"tools\", END]:\n",
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" messages = state['messages']\n",
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" last_message = messages[-1]\n",
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" if last_message[\"tool_calls\"]:\n",
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" return \"tools\"\n",
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" return END"
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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": 12,
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"id": "a53aab90-9206-46ef-be83-81c2de2c007a",
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"metadata": {},
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"outputs": [],
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"source": [
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" return END\n",
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"\n",
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"workflow = StateGraph(State)\n",
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"workflow.set_entry_point(\"model\")\n",
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"workflow.add_node(\"model\", call_model) # i.e. our \"agent\"\n",
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"workflow.add_node(\"tools\", call_tools)\n",
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"workflow.add_conditional_edges(\"model\", should_continue)\n",
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"workflow.add_edge(\"tools\", \"model\")"
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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": 13,
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"id": "5aac9d6a-f182-4ddd-af77-3e26fdf4170b",
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"metadata": {},
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"outputs": [],
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"source": [
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"workflow.add_edge(\"tools\", \"model\")\n",
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"graph = workflow.compile()"
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]
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},
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