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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Workflows in LlamaIndex\n",
"\n",
"\n",
"This notebook is part of the [Hugging Face Agents Course](https://www.hf.co/learn/agents-course), a free Course from beginner to expert, where you learn to build Agents.\n",
"\n",
"\n",
"\n",
"## Let's install the dependencies\n",
"\n",
"We will install the dependencies for this unit."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"!pip install llama-index datasets llama-index-callbacks-arize-phoenix llama-index-vector-stores-chroma llama-index-utils-workflow llama-index-llms-huggingface-api pyvis -U -q"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And, let's log in to Hugging Face to use serverless Inference APIs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import login\n",
"\n",
"login()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Workflow Creation\n",
"\n",
"We can start by creating a simple workflow. We use the `StartEvent` and `StopEvent` classes to define the start and stop of the workflow."
]
},
{
"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",
" # do something here\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": [
"## Connecting Multiple Steps\n",
"\n",
"We can also create multi-step workflows. Here we pass the event information between steps. Note that we can use type hinting to specify the event type and the flow of the 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",
" # Process initial data\n",
" return ProcessingEvent(intermediate_result=\"Step 1 complete\")\n",
"\n",
" @step\n",
" async def step_two(self, ev: ProcessingEvent) -> StopEvent:\n",
" # Use the intermediate result\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": [
"## Loops and Branches\n",
"\n",
"We can also use type hinting to create branches and loops. Note that we can use the `|` operator to specify that the step can return multiple types."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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) -> 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 | LoopEvent) -> StopEvent:\n",
" # Use the intermediate result\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": [
"## Drawing Workflows\n",
"\n",
"We can also draw workflows using the `draw_all_possible_flows` function.\n"
]
},
{
"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": [
"### State Management\n",
"\n",
"Instead of passing the event information between steps, we can use the `Context` type hint to pass information between steps. \n",
"This might be useful for long running workflows, where you want to store information between steps."
]
},
{
"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",
" # Process initial data\n",
" await ctx.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",
" # Use the intermediate result\n",
" query = await ctx.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",
"We can also create multi-agent workflows. Here we define two agents, one that multiplies two integers and one that adds two integers."
]
},
{
"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='I have handed off the request to an agent who can help you with adding 5 and 3. Please wait for their response.')]), tool_calls=[ToolCallResult(tool_name='handoff', tool_kwargs={'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}, tool_id='call_F97vcIcsvZjfAAOBzzIifW3y', tool_output=ToolOutput(content='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', tool_name='handoff', raw_input={'args': (), 'kwargs': {'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}}, raw_output='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', is_error=False), return_direct=True), ToolCallResult(tool_name='handoff', tool_kwargs={'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}, tool_id='call_jf49ktFRs09xYdOsnApAk2zz', tool_output=ToolOutput(content='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', tool_name='handoff', raw_input={'args': (), 'kwargs': {'to_agent': 'addition_agent', 'reason': 'Add 5 and 3'}}, raw_output='Agent addition_agent is now handling the request due to the following reason: Add 5 and 3.\\nPlease continue with the current request.', is_error=False), return_direct=True)], raw={'id': 'chatcmpl-B6Cy54VQkvlG3VOrmdzCzgwcJmVOc', 'choices': [{'delta': {'content': None, 'function_call': None, 'refusal': None, 'role': None, 'tool_calls': None}, 'finish_reason': 'stop', 'index': 0, 'logprobs': None}], 'created': 1740819517, 'model': 'gpt-3.5-turbo-0125', 'object': 'chat.completion.chunk', 'service_tier': 'default', 'system_fingerprint': None, 'usage': None}, current_agent_name='addition_agent')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n",
"\n",
"# Define some tools\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",
"# we can pass functions directly without FunctionTool -- the fn/docstring are parsed for the name/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",
"# Create the workflow\n",
"workflow = AgentWorkflow(\n",
" agents=[multiply_agent, addition_agent],\n",
" root_agent=\"multiply_agent\"\n",
")\n",
"\n",
"# Run the system\n",
"response = await workflow.run(user_msg=\"Can you add 5 and 3?\")"
]
}
],
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