Agnoを、はじめるのー

from agno.agent import Agent
from agno.models.openai import OpenAIChat

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are an enthusiastic news reporter with a flair for storytelling!",
    markdown=True
)
agent.print_response("Tell me about a breaking news story from New York.", stream=True)

 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
 from agno.tools.duckduckgo import DuckDuckGoTools

 agent = Agent(
     model=OpenAIChat(id="gpt-4o"),
     description="You are an enthusiastic news reporter with a flair for storytelling!",
+    tools=[DuckDuckGoTools()],
     show_tool_calls=True,
     markdown=True
 )
 agent.print_response("Tell me about a breaking news story from New York.", stream=True)

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.embedder.openai import OpenAIEmbedder
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.knowledge.pdf_url import PDFUrlKnowledgeBase
from agno.vectordb.lancedb import LanceDb, SearchType

agent = Agent(
    model=OpenAIChat(id="gpt-4o"),
    description="You are a Thai cuisine expert!",
    instructions=[
        "Search your knowledge base for Thai recipes.",
        "If the question is better suited for the web, search the web to fill in gaps.",
        "Prefer the information in your knowledge base over the web results."
    ],
    knowledge=PDFUrlKnowledgeBase(
        urls=["https://agno-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
        vector_db=LanceDb(
            uri="tmp/lancedb",
            table_name="recipes",
            search_type=SearchType.hybrid,
            embedder=OpenAIEmbedder(id="text-embedding-3-small"),
        ),
    ),
    tools=[DuckDuckGoTools()],
    show_tool_calls=True,
    markdown=True
)

# Comment out after the knowledge base is loaded
if agent.knowledge is not None:
    agent.knowledge.load()

agent.print_response("How do I make chicken and galangal in coconut milk soup", stream=True)
agent.print_response("What is the history of Thai curry?", stream=True)

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.yfinance import YFinanceTools

web_agent = Agent(
    name="Web Agent",
    role="Search the web for information",
    model=OpenAIChat(id="gpt-4o"),
    tools=[DuckDuckGoTools()],
    instructions="Always include sources",
    show_tool_calls=True,
    markdown=True,
)

finance_agent = Agent(
    name="Finance Agent",
    role="Get financial data",
    model=OpenAIChat(id="gpt-4o"),
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, company_info=True)],
    instructions="Use tables to display data",
    show_tool_calls=True,
    markdown=True,
)

agent_team = Agent(
    team=[web_agent, finance_agent],
    model=OpenAIChat(id="gpt-4o"),
    instructions=["Always include sources", "Use tables to display data"],
    show_tool_calls=True,
    markdown=True,
)

agent_team.print_response("What's the market outlook and financial performance of AI semiconductor companies?", stream=True)

from agno.agent import Agent
from agno.tools.file import FileTools

agent = Agent(tools=[FileTools()], show_tool_calls=True)
agent.print_response("What is the most advanced LLM currently? Save the answer to a file.", markdown=True)

from agno.tools.file import FileTools

agent = Agent(tools=[FileTools()], show_tool_calls=True)

import subprocess
import time
import os

# vLLM サーバーをバックグラウンドで起動
# ログの作成
log_file = open("vllm_output.log", "w")

#model_name = "qwen/Qwen1.5-0.5B-Chat" # Hugging Faceのモデル名を使う場合
model_path = "./qwen_model" # ローカルにダウンロードしたモデルのパス

# --host, --port, --model, --tensor-parallel-size を適切に設定
process = subprocess.Popen([
    "python", "-m", "vllm.entrypoints.openai.api_server",
    "--host", "0.0.0.0",  # Colab 内でアクセス可能にする
    "--port", "8000",
    "--model", model_path, #model_name,
    "--tensor-parallel-size", "1", # ColabのGPU (T4) では1が無難
    "--dtype", "half",
    "--gpu-memory-utilization", "0.99"
    # 必要であれば、他オプション（gpu-memory-utilization など)も追加
], stdout=log_file, stderr=log_file)

from os import getenv
from agno.agent import Agent, RunResponse
from agno.models.openai.like import OpenAILike

agent = Agent(
    model=OpenAILike(
        id=model_path,
        api_key="anything",
        base_url="http://localhost:8000/v1",
    )
)

# Print the response in the terminal
agent.print_response("Share a 2 sentence horror story.")