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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# *Workflows* dans LlamaIndex\n",
"\n",
"\n",
"Ce notebook fait parti du cours <a href=\"https://huggingface.co/learn/agents-course/fr\">sur les agents d'Hugging Face</a>, un cours gratuit qui vous guidera, du **niveau débutant à expert**, pour comprendre, utiliser et construire des agents.\n",
"\n",
"\n",
"## Installons les dépendances\n",
"\n",
"Nous allons installer les dépendances pour cette unité."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install llama-index llama-index-vector-stores-chroma llama-index-utils-workflow llama-index-llms-huggingface-api pyvis -U -q"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous allons également nous connecter au Hugging Face Hub pour avoir accès à l'API d'inférence."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"\n",
"login()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Création de *Workflow* de base\n",
"\n",
"Nous pouvons commencer par créer un *workflow* simple. Nous utilisons les classes `StartEvent` et `StopEvent` pour définir le début et la fin de celui-ci."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Hello, world!'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index.core.workflow import StartEvent, StopEvent, Workflow, step\n",
"\n",
"\n",
"class MyWorkflow(Workflow):\n",
" @step\n",
" async def my_step(self, ev: StartEvent) -> StopEvent:\n",
" # faire quelque chose ici\n",
" return StopEvent(result=\"Hello, world!\")\n",
"\n",
"\n",
"w = MyWorkflow(timeout=10, verbose=False)\n",
"result = await w.run()\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Connecter plusieurs étapes\n",
"\n",
"Nous pouvons également créer des *workflows* à plusieurs étapes. Ici, nous transmettons les informations relatives à l'événement entre les étapes. Notez que nous pouvons utiliser l'indication de type pour spécifier le type d'événement et le flux du *workflow*."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Finished processing: Step 1 complete'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index.core.workflow import Event\n",
"\n",
"\n",
"class ProcessingEvent(Event):\n",
" intermediate_result: str\n",
"\n",
"\n",
"class MultiStepWorkflow(Workflow):\n",
" @step\n",
" async def step_one(self, ev: StartEvent) -> ProcessingEvent:\n",
" # Traitement des données initiales\n",
" return ProcessingEvent(intermediate_result=\"Step 1 complete\")\n",
"\n",
" @step\n",
" async def step_two(self, ev: ProcessingEvent) -> StopEvent:\n",
" # Utiliser le résultat intermédiaire\n",
" final_result = f\"Finished processing: {ev.intermediate_result}\"\n",
" return StopEvent(result=final_result)\n",
"\n",
"\n",
"w = MultiStepWorkflow(timeout=10, verbose=False)\n",
"result = await w.run()\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boucles et branches\n",
"\n",
"Nous pouvons également utiliser l'indication de type pour créer des branches et des boucles. Notez que nous pouvons utiliser l'opérateur `|` pour spécifier que l'étape peut renvoyer plusieurs types."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bad thing happened\n",
"Bad thing happened\n",
"Bad thing happened\n",
"Good thing happened\n"
]
},
{
"data": {
"text/plain": [
"'Finished processing: First step complete.'"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index.core.workflow import Event\n",
"import random\n",
"\n",
"\n",
"class ProcessingEvent(Event):\n",
" intermediate_result: str\n",
"\n",
"\n",
"class LoopEvent(Event):\n",
" loop_output: str\n",
"\n",
"\n",
"class MultiStepWorkflow(Workflow):\n",
" @step\n",
" async def step_one(self, ev: StartEvent | LoopEvent) -> ProcessingEvent | LoopEvent:\n",
" if random.randint(0, 1) == 0:\n",
" print(\"Bad thing happened\")\n",
" return LoopEvent(loop_output=\"Back to step one.\")\n",
" else:\n",
" print(\"Good thing happened\")\n",
" return ProcessingEvent(intermediate_result=\"First step complete.\")\n",
"\n",
" @step\n",
" async def step_two(self, ev: ProcessingEvent) -> StopEvent:\n",
" # Utiliser le résultat intermédiaire\n",
" final_result = f\"Finished processing: {ev.intermediate_result}\"\n",
" return StopEvent(result=final_result)\n",
"\n",
"\n",
"w = MultiStepWorkflow(verbose=False)\n",
"result = await w.run()\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dessiner des *Workflows*\n",
"\n",
"Nous pouvons également dessiner des *workflows* avec la fonction `draw_all_possible_flows`."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'NoneType'>\n",
"<class '__main__.ProcessingEvent'>\n",
"<class '__main__.LoopEvent'>\n",
"<class 'llama_index.core.workflow.events.StopEvent'>\n",
"workflow_all_flows.html\n"
]
}
],
"source": [
"from llama_index.utils.workflow import draw_all_possible_flows\n",
"\n",
"draw_all_possible_flows(w)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gestion d'état\n",
"\n",
"Au lieu de passer l'information de l'événement entre les étapes, nous pouvons utiliser l'indice de type `Context` pour passer l'information entre les étapes. \n",
"Cela peut être utile pour les *workflows* de plus longue durée, où l'on souhaite stocker des informations entre les étapes."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Query: What is the capital of France?\n"
]
},
{
"data": {
"text/plain": [
"'Finished processing: Step 1 complete'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index.core.workflow import Event, Context\n",
"from llama_index.core.agent.workflow import ReActAgent\n",
"\n",
"\n",
"class ProcessingEvent(Event):\n",
" intermediate_result: str\n",
"\n",
"\n",
"class MultiStepWorkflow(Workflow):\n",
" @step\n",
" async def step_one(self, ev: StartEvent, ctx: Context) -> ProcessingEvent:\n",
" # Traitement des données initiales\n",
" await ctx.store.set(\"query\", \"What is the capital of France?\")\n",
" return ProcessingEvent(intermediate_result=\"Step 1 complete\")\n",
"\n",
" @step\n",
" async def step_two(self, ev: ProcessingEvent, ctx: Context) -> StopEvent:\n",
" # Utiliser le résultat intermédiaire\n",
" query = await ctx.store.get(\"query\")\n",
" print(f\"Query: {query}\")\n",
" final_result = f\"Finished processing: {ev.intermediate_result}\"\n",
" return StopEvent(result=final_result)\n",
"\n",
"\n",
"w = MultiStepWorkflow(timeout=10, verbose=False)\n",
"result = await w.run()\n",
"result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Multi-Agent Workflows*\n",
"\n",
"Nous pouvons également créer des flux de travail multi-agents. Ici, nous définissons deux agents, l'un qui multiplie deux entiers et l'autre qui ajoute deux entiers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AgentOutput(response=ChatMessage(role=<MessageRole.ASSISTANT: 'assistant'>, additional_kwargs={}, blocks=[TextBlock(block_type='text', text='5 and 3 add up to 8.')]), tool_calls=[ToolCallResult(tool_name='handoff', tool_kwargs={'to_agent': 'add_agent', 'reason': 'The user wants to add two numbers, and the add_agent is better suited for this task.'}, tool_id='831895e7-3502-4642-92ea-8626e21ed83b', tool_output=ToolOutput(content='Agent add_agent is now handling the request due to the following reason: The user wants to add two numbers, and the add_agent is better suited for this task..\n",
"Please continue with the current request.', tool_name='handoff', raw_input={'args': (), 'kwargs': {'to_agent': 'add_agent', 'reason': 'The user wants to add two numbers, and the add_agent is better suited for this task.'}}, raw_output='Agent add_agent is now handling the request due to the following reason: The user wants to add two numbers, and the add_agent is better suited for this task..\n",
"Please continue with the current request.', is_error=False), return_direct=True), ToolCallResult(tool_name='add', tool_kwargs={'a': 5, 'b': 3}, tool_id='c29dc3f7-eaa7-4ba7-b49b-90908f860cc5', tool_output=ToolOutput(content='8', tool_name='add', raw_input={'args': (), 'kwargs': {'a': 5, 'b': 3}}, raw_output=8, is_error=False), return_direct=False)], raw=ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(role='assistant', content='.', tool_call_id=None, tool_calls=None), index=0, finish_reason=None, logprobs=None)], created=1744553546, id='', model='Qwen/Qwen2.5-Coder-32B-Instruct', system_fingerprint='3.2.1-sha-4d28897', usage=None, object='chat.completion.chunk'), current_agent_name='add_agent')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index.core.agent.workflow import AgentWorkflow, ReActAgent\n",
"from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
"from llama_index.core.agent.workflow import AgentWorkflow\n",
"\n",
"# Définir quelques outils\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Add two numbers.\"\"\"\n",
" return a + b\n",
"\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiply two numbers.\"\"\"\n",
" return a * b\n",
"\n",
"llm = HuggingFaceInferenceAPI(model_name=\"Qwen/Qwen2.5-Coder-32B-Instruct\")\n",
"\n",
"# nous pouvons passer des fonctions directement sans FunctionTool -- les fn/docstring sont analysés pour le nom/description\n",
"multiply_agent = ReActAgent(\n",
" name=\"multiply_agent\",\n",
" description=\"Is able to multiply two integers\",\n",
" system_prompt=\"A helpful assistant that can use a tool to multiply numbers.\",\n",
" tools=[multiply], \n",
" llm=llm,\n",
")\n",
"\n",
"addition_agent = ReActAgent(\n",
" name=\"add_agent\",\n",
" description=\"Is able to add two integers\",\n",
" system_prompt=\"A helpful assistant that can use a tool to add numbers.\",\n",
" tools=[add], \n",
" llm=llm,\n",
")\n",
"\n",
"# Créer le workflow\n",
"workflow = AgentWorkflow(\n",
" agents=[multiply_agent, addition_agent],\n",
" root_agent=\"multiply_agent\"\n",
")\n",
"\n",
"# Exécuter le système\n",
"response = await workflow.run(user_msg=\"Can you add 5 and 3?\")\n",
"response"
]
}
],
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