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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"vscode": {
"languageId": "plaintext"
}
},
"source": [
"# Agents dans LlamaIndex\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",
"\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-llms-huggingface-api llama-index-embeddings-huggingface -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": {
"vscode": {
"languageId": "plaintext"
}
},
"source": [
"## Initialisation des agents\n",
"\n",
"Commençons par initialiser un agent. Nous allons utiliser la classe de base `AgentWorkflow` pour créer un agent."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
"from llama_index.core.agent.workflow import AgentWorkflow, ToolCallResult, AgentStream\n",
"\n",
"\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Add two numbers\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"def subtract(a: int, b: int) -> int:\n",
" \"\"\"Subtract two numbers\"\"\"\n",
" return a - b\n",
"\n",
"\n",
"def multiply(a: int, b: int) -> int:\n",
" \"\"\"Multiply two numbers\"\"\"\n",
" return a * b\n",
"\n",
"\n",
"def divide(a: int, b: int) -> int:\n",
" \"\"\"Divide two numbers\"\"\"\n",
" return a / b\n",
"\n",
"\n",
"llm = HuggingFaceInferenceAPI(model_name=\"Qwen/Qwen2.5-Coder-32B-Instruct\")\n",
"\n",
"agent = AgentWorkflow.from_tools_or_functions(\n",
" tools_or_functions=[subtract, multiply, divide, add],\n",
" llm=llm,\n",
" system_prompt=\"You are a math agent that can add, subtract, multiply, and divide numbers using provided tools.\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ensuite, nous pouvons exécuter l'agent et obtenir la réponse et le raisonnement qui sous-tend les appels à l'outil."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"handler = agent.run(\"What is (2 + 2) * 2?\")\n",
"async for ev in handler.stream_events():\n",
" if isinstance(ev, ToolCallResult):\n",
" print(\"\")\n",
" print(\"Called tool: \", ev.tool_name, ev.tool_kwargs, \"=>\", ev.tool_output)\n",
" elif isinstance(ev, AgentStream): # montrer le processus de réflexion\n",
" print(ev.delta, end=\"\", flush=True)\n",
"\n",
"resp = await handler\n",
"resp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"De la même manière, nous pouvons transmettre l'état et le contexte à l'agent."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AgentOutput(response=ChatMessage(role=<MessageRole.ASSISTANT: 'assistant'>, additional_kwargs={}, blocks=[TextBlock(block_type='text', text='Your name is Bob.')]), tool_calls=[], raw={'id': 'chatcmpl-B5sDHfGpSwsVyzvMVH8EWokYwdIKT', 'choices': [{'delta': {'content': None, 'function_call': None, 'refusal': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'stop', 'index': 0, 'logprobs': None}], 'created': 1740739735, 'model': 'gpt-4o-2024-08-06', 'object': 'chat.completion.chunk', 'service_tier': 'default', 'system_fingerprint': 'fp_eb9dce56a8', 'usage': None}, current_agent_name='Agent')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index.core.workflow import Context\n",
"\n",
"ctx = Context(agent)\n",
"\n",
"response = await agent.run(\"My name is Bob.\", ctx=ctx)\n",
"response = await agent.run(\"What was my name again?\", ctx=ctx)\n",
"response"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Création d'agents de RAG avec QueryEngineTools\n",
"\n",
"Réutilisons maintenant le `QueryEngine` que nous avons défini dans [l'unité précédente sur les outils](/tools.ipynb) et convertissons-le en un `QueryEngineTool`. Nous allons le passer à la classe `AgentWorkflow` pour créer un agent de RAG."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"import chromadb\n",
"\n",
"from llama_index.core import VectorStoreIndex\n",
"from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
"from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
"from llama_index.core.tools import QueryEngineTool\n",
"from llama_index.vector_stores.chroma import ChromaVectorStore\n",
"\n",
"# Créer un vector store\n",
"db = chromadb.PersistentClient(path=\"./alfred_chroma_db\")\n",
"chroma_collection = db.get_or_create_collection(\"alfred\")\n",
"vector_store = ChromaVectorStore(chroma_collection=chroma_collection)\n",
"\n",
"# Créer un moteur de recherche\n",
"embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")\n",
"llm = HuggingFaceInferenceAPI(model_name=\"Qwen/Qwen2.5-Coder-32B-Instruct\")\n",
"index = VectorStoreIndex.from_vector_store(\n",
" vector_store=vector_store, embed_model=embed_model\n",
")\n",
"query_engine = index.as_query_engine(llm=llm)\n",
"query_engine_tool = QueryEngineTool.from_defaults(\n",
" query_engine=query_engine,\n",
" name=\"personas\",\n",
" description=\"descriptions for various types of personas\",\n",
" return_direct=False,\n",
")\n",
"\n",
"# Créer un agent de RAG\n",
"query_engine_agent = AgentWorkflow.from_tools_or_functions(\n",
" tools_or_functions=[query_engine_tool],\n",
" llm=llm,\n",
" system_prompt=\"You are a helpful assistant that has access to a database containing persona descriptions. \",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Et nous pouvons une fois de plus obtenir la réponse et le raisonnement derrière les appels d'outils."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"handler = query_engine_agent.run(\n",
" \"Search the database for 'science fiction' and return some persona descriptions.\"\n",
")\n",
"async for ev in handler.stream_events():\n",
" if isinstance(ev, ToolCallResult):\n",
" print(\"\")\n",
" print(\"Called tool: \", ev.tool_name, ev.tool_kwargs, \"=>\", ev.tool_output)\n",
" elif isinstance(ev, AgentStream): # montrer le processus de réflexion\n",
" print(ev.delta, end=\"\", flush=True)\n",
"\n",
"resp = await handler\n",
"resp"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Créer des systèmes multi-agents\n",
"\n",
"Nous pouvons également créer des systèmes multi-agents en passant plusieurs agents à la classe `AgentWorkflow`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from llama_index.core.agent.workflow import (\n",
" AgentWorkflow,\n",
" ReActAgent,\n",
")\n",
"\n",
"\n",
"# Définir quelques outils\n",
"def add(a: int, b: int) -> int:\n",
" \"\"\"Add two numbers.\"\"\"\n",
" return a + b\n",
"\n",
"\n",
"def subtract(a: int, b: int) -> int:\n",
" \"\"\"Subtract two numbers.\"\"\"\n",
" return a - b\n",
"\n",
"\n",
"# Créer les configurations de l'agent\n",
"# NOTE : nous pouvons utiliser FunctionAgent ou ReActAgent ici.\n",
"# FunctionAgent fonctionne pour les LLM avec une API d'appel de fonction.\n",
"# ReActAgent fonctionne pour n'importe quel LLM.\n",
"calculator_agent = ReActAgent(\n",
" name=\"calculator\",\n",
" description=\"Performs basic arithmetic operations\",\n",
" system_prompt=\"You are a calculator assistant. Use your tools for any math operation.\",\n",
" tools=[add, subtract],\n",
" llm=llm,\n",
")\n",
"\n",
"query_agent = ReActAgent(\n",
" name=\"info_lookup\",\n",
" description=\"Looks up information about XYZ\",\n",
" system_prompt=\"Use your tool to query a RAG system to answer information about XYZ\",\n",
" tools=[query_engine_tool],\n",
" llm=llm,\n",
")\n",
"\n",
"# Créer et exécuter le workflow\n",
"agent = AgentWorkflow(agents=[calculator_agent, query_agent], root_agent=\"calculator\")\n",
"\n",
"# Exécuter le système\n",
"handler = agent.run(user_msg=\"Can you add 5 and 3?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"async for ev in handler.stream_events():\n",
" if isinstance(ev, ToolCallResult):\n",
" print(\"\")\n",
" print(\"Called tool: \", ev.tool_name, ev.tool_kwargs, \"=>\", ev.tool_output)\n",
" elif isinstance(ev, AgentStream): # showing the thought process\n",
" print(ev.delta, end=\"\", flush=True)\n",
"\n",
"resp = await handler\n",
"resp"
]
}
],
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