class MyWorkflow(Workflow):
    @step
    async def step_one(self, ev: StartEvent) -> FirstEvent:
        print(ev.first_input)
        return FirstEvent(first_output="First step complete.")

    @step
    async def step_two(self, ev: FirstEvent) -> SecondEvent:
        print(ev.first_output)
        return SecondEvent(second_output="Second step complete.")

    @step
    async def step_three(self, ev: SecondEvent) -> StopEvent:
        print(ev.second_output)
        return StopEvent(result="Workflow complete.")

def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the result."""
    return a * b

multiply_tool = FunctionTool.from_defaults(fn=multiply)

agent = FunctionCallingAgent.from_tools(
    [multiply_tool],
    llm=llm,
)
response = agent.chat("What is 3 times 4?")

research_agent = FunctionAgent(
    name="ResearchAgent",
    description="Useful for searching the web for information on a given topic and recording notes on the topic.",
    system_prompt=(
        "You are the ResearchAgent that can search the web for information on a given topic and record notes on the topic. "
        "Once notes are recorded and you are satisfied, you should hand off control to the WriteAgent to write a report on the topic."
    ),
    llm=llm,
    tools=[search_web, record_notes],
    can_handoff_to=["WriteAgent"],
)

agent_workflow = AgentWorkflow(
    agents=[research_agent, write_agent, review_agent],
    root_agent=research_agent.name,
    initial_state={
        "research_notes": {},
        "report_content": "Not written yet.",
        "review": "Review required.",
    },
)

documents = LlamaParse().load_data("./myfile.pdf")

index = VectorStoreIndex.from_documents(documents)

query_engine = index.as_query_engine()

response = query_engine.query("What did the author do growing up?")

print(response)

pip install llama-index-llms-deepseek

from llama_index.llms.deepseek import DeepSeek
llm = DeepSeek(model="deepseek-chat", api_key="your_api_key")

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

embed_model = HuggingFaceEmbedding(model_name="./bge-small-en-v1.5-model")

index = VectorStoreIndex.from_documents(documents,embed_model=embed_model)

llm = Groq(
  model="deepseek-r1-distill-llama-70b",
  api_key="your_api_key"
)