from gbrain import LLM

# 切换模型只需改一行配置
llm = LLM("gpt-4o")        # OpenAI
llm = LLM("claude-3-5-sonnet")  # Anthropic
llm = LLM("llama-3.1-70b") # Groq
llm = LLM("qwen-2.5-72b")   # Ollama 本地

# 统一接口，相同调用方式
response = llm.chat("用 Python 写一个快速排序")

from gbrain import Agent, Crew

# 创建专业 Agent
researcher = Agent(
    name="研究助手",
    role="信息收集与总结",
    backstory="你是一个专业的研究员，擅长从多个来源收集信息",
    tools=["tavily_search", "browser"]
)

writer = Agent(
    name="写作助手", 
    role="内容创作",
    backstory="你是一个资深编辑，擅长撰写清晰、有说服力的文章",
    tools=["document_writer"]
)

# 编排协作
crew = Crew(agents=[researcher, writer])
result = crew.kickoff("写一篇关于 AI Agent 的博客文章")

from gbrain import RAG, ChromaDB

# 初始化 RAG
rag = RAG(
    vectorstore="chroma",  # 支持: chroma, faiss, qdrant, milvus
    embedding_model="text-embedding-3-small"
)

# 添加文档
rag.add_documents(
    documents=["技术文档...", "产品手册..."],
    metadata=[{"source": "docs"}, {"source": "manual"}]
)

# 语义搜索
results = rag.search("如何配置 SSO？", top_k=5)

from gbrain import Agent

agent = Agent(
    name="研究助手",
    tools=[
        "tavily_search",      # 搜索引擎
        "firecrawl_scrape",    # 网页抓取
        "github_repo",         # GitHub 操作
        "slack_message",       # 发送消息
        "notion_create",      # Notion 创建
        "linear_issue",       # Linear 工单
        "airtable_record",     # Airtable 记录
        "stripe_payment",      # 支付处理
        # ... 50+ 更多
    ]
)

from gbrain import tool

@tool(name="天气查询", description="查询指定城市的天气")
def get_weather(city: str) -> str:
    """这是一个自定义工具示例"""
    import requests
    response = requests.get(f"https://api.weather.com?q={city}")
    return response.json()

# 在 Agent 中使用
agent = Agent(name="助手", tools=[get_weather])

from gbrain import Enterprise

enterprise = Enterprise(
    sso_enabled=True,        # SSO 单点登录
    sso_provider="okta",     # 支持: okta, azure, google
    
    rbac_enabled=True,       # 基于角色的访问控制
    roles={
        "admin": ["*"],      # 所有权限
        "user": ["agent:run", "tool:use"],
        "viewer": ["agent:read"]
    },
    
    audit_enabled=True,       # 审计日志
    ssl_enabled=True,         # SSL 加密
)

from gbrain import observe

# 集成 LangSmith
observe.langsmith(
    api_key="your-api-key",
    project="production-agents"
)

# 集成 OpenTelemetry
observe.otel(
    endpoint="http://otel-collector:4317",
    service_name="gbrain-agent"
)

# pip 安装
pip install gbrain

# 或 uv (更快)
uv add gbrain

# 可选依赖
pip install gbrain[enterprise]  # 企业功能
pip install gbrain[all]        # 所有功能

from gbrain import Agent

# 创建简单 Agent
assistant = Agent(
    name="助手",
    role="通用助手",
    backstory="你是一个有用的人工智能助手"
)

# 运行
result = assistant.run("用 Python 写一个 Hello World")
print(result)

from gbrain import Crew, Agent, Workflow

# 定义工作流
workflow = Workflow(
    name="博客写作流程",
    steps=[
        {"agent": "researcher", "task": "收集 AI Agent 最新动态"},
        {"agent": "writer", "task": "撰写博客文章"},
        {"agent": "editor", "task": "校对和发布"}
    ]
)

# 执行
result = workflow.execute()

# gbrain.yaml
llm:
  default_provider: openai
  models:
    gpt-4o:
      provider: openai
      api_key: ${OPENAI_API_KEY}
    claude-3-5-sonnet:
      provider: anthropic
      api_key: ${ANTHROPIC_API_KEY}

vectorstore:
  type: chroma
  persist_directory: ./data/chroma

enterprise:
  sso_enabled: true
  rbac_enabled: true

observability:
  langsmith_enabled: true
  otel_enabled: true

# .env
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
LANGCHAIN_API_KEY=ls-...

# 向量数据库
CHROMA_HOST=localhost
CHROMA_PORT=8000

from gbrain import Agent, Memory

agent = Agent(
    name="客服助手",
    role="客户支持",
    backstory="你是一个耐心的客服，擅长解决客户问题",
    tools=["search_kb", "create_ticket", "send_email"],
    memory=Memory(
        max_turns=10,  # 保留最近 10 轮对话
        summary=True   # 自动摘要
    )
)

from gbrain import Crew, Agent

# 使用并行执行加速
crew = Crew(
    agents=[researcher1, researcher2, researcher3],
    execution_mode="parallel"  # 并行而非顺序
)

# 使用条件路由
workflow = Workflow(
    steps=[
        {"agent": "classifier", "task": "分类输入"},
        {"agent": "technical", "condition": "type=='技术'"},
        {"agent": "business", "condition": "type=='商务'"}
    ]
)

pip install gbrain