工具

from google.adk.agents import Agent
from google.adk.tools import FunctionTool
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types

APP_NAME="weather_sentiment_agent"
USER_ID="user1234"
SESSION_ID="1234"
MODEL_ID="gemini-2.0-flash"

# Tool 1
def get_weather_report(city: str) -> dict:
    """Retrieves the current weather report for a specified city.

    Returns:
        dict: A dictionary containing the weather information with a 'status' key ('success' or 'error') and a 'report' key with the weather details if successful, or an 'error_message' if an error occurred.
    """
    if city.lower() == "london":
        return {"status": "success", "report": "The current weather in London is cloudy with a temperature of 18 degrees Celsius and a chance of rain."}
    elif city.lower() == "paris":
        return {"status": "success", "report": "The weather in Paris is sunny with a temperature of 25 degrees Celsius."}
    else:
        return {"status": "error", "error_message": f"Weather information for '{city}' is not available."}

weather_tool = FunctionTool(func=get_weather_report)


# Tool 2
def analyze_sentiment(text: str) -> dict:
    """Analyzes the sentiment of the given text.

    Returns:
        dict: A dictionary with 'sentiment' ('positive', 'negative', or 'neutral') and a 'confidence' score.
    """
    if "good" in text.lower() or "sunny" in text.lower():
        return {"sentiment": "positive", "confidence": 0.8}
    elif "rain" in text.lower() or "bad" in text.lower():
        return {"sentiment": "negative", "confidence": 0.7}
    else:
        return {"sentiment": "neutral", "confidence": 0.6}

sentiment_tool = FunctionTool(func=analyze_sentiment)


# Agent
weather_sentiment_agent = Agent(
    model=MODEL_ID,
    name='weather_sentiment_agent',
    instruction="""You are a helpful assistant that provides weather information and analyzes the sentiment of user feedback.
**If the user asks about the weather in a specific city, use the 'get_weather_report' tool to retrieve the weather details.**
**If the 'get_weather_report' tool returns a 'success' status, provide the weather report to the user.**
**If the 'get_weather_report' tool returns an 'error' status, inform the user that the weather information for the specified city is not available and ask if they have another city in mind.**
**After providing a weather report, if the user gives feedback on the weather (e.g., 'That's good' or 'I don't like rain'), use the 'analyze_sentiment' tool to understand their sentiment.** Then, briefly acknowledge their sentiment.
You can handle these tasks sequentially if needed.""",
    tools=[weather_tool, sentiment_tool]
)

# Session and Runner
session_service = InMemorySessionService()
session = session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)
runner = Runner(agent=weather_sentiment_agent, app_name=APP_NAME, session_service=session_service)


# Agent Interaction
def call_agent(query):
    content = types.Content(role='user', parts=[types.Part(text=query)])
    events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)

    for event in events:
        if event.is_final_response():
            final_response = event.content.parts[0].text
            print("Agent Response: ", final_response)

call_agent("weather in london?")

from google.adk.tools import ToolContext, FunctionTool

def update_user_preference(preference: str, value: str, tool_context: ToolContext):
    """Updates a user-specific preference."""
    user_prefs_key = "user:preferences"
    # Get current preferences or initialize if none exist
    preferences = tool_context.state.get(user_prefs_key, {})
    preferences[preference] = value
    # Write the updated dictionary back to the state
    tool_context.state[user_prefs_key] = preferences
    print(f"Tool: Updated user preference '{preference}' to '{value}'")
    return {"status": "success", "updated_preference": preference}

pref_tool = FunctionTool(func=update_user_preference)

# In an Agent:
# my_agent = Agent(..., tools=[pref_tool])

# When the LLM calls update_user_preference(preference='theme', value='dark', ...):
# The tool_context.state will be updated, and the change will be part of the
# resulting tool response event's actions.state_delta.

from google.adk.agents import Agent
from google.adk.tools import FunctionTool
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.adk.tools import ToolContext
from google.genai import types

APP_NAME="customer_support_agent"
USER_ID="user1234"
SESSION_ID="1234"


def check_and_transfer(query: str, tool_context: ToolContext) -> str:
    """Checks if the query requires escalation and transfers to another agent if needed."""
    if "urgent" in query.lower():
        print("Tool: Detected urgency, transferring to the support agent.")
        tool_context.actions.transfer_to_agent = "support_agent"
        return "Transferring to the support agent..."
    else:
        return f"Processed query: '{query}'. No further action needed."

escalation_tool = FunctionTool(func=check_and_transfer)

main_agent = Agent(
    model='gemini-2.0-flash',
    name='main_agent',
    instruction="""You are the first point of contact for customer support of an analytics tool. Answer general queries. If the user indicates urgency, use the 'check_and_transfer' tool.""",
    tools=[check_and_transfer]
)

support_agent = Agent(
    model='gemini-2.0-flash',
    name='support_agent',
    instruction="""You are the dedicated support agent. Mentioned you are a support handler and please help the user with their urgent issue."""
)

main_agent.sub_agents = [support_agent]

# Session and Runner
session_service = InMemorySessionService()
session = session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)
runner = Runner(agent=main_agent, app_name=APP_NAME, session_service=session_service)


# Agent Interaction
def call_agent(query):
    content = types.Content(role='user', parts=[types.Part(text=query)])
    events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)

    for event in events:
        if event.is_final_response():
            final_response = event.content.parts[0].text
            print("Agent Response: ", final_response)

call_agent("this is urgent, i cant login")

from google.adk.tools import ToolContext, FunctionTool
from google.genai import types

def process_document(document_name: str, analysis_query: str, tool_context: ToolContext) -> dict:
    """Analyzes a document using context from memory."""

    # 1. Load the artifact
    print(f"Tool: Attempting to load artifact: {document_name}")
    document_part = tool_context.load_artifact(document_name)

    if not document_part:
        return {"status": "error", "message": f"Document '{document_name}' not found."}

    document_text = document_part.text # Assuming it's text for simplicity
    print(f"Tool: Loaded document '{document_name}' ({len(document_text)} chars).")

    # 2. Search memory for related context
    print(f"Tool: Searching memory for context related to: '{analysis_query}'")
    memory_response = tool_context.search_memory(f"Context for analyzing document about {analysis_query}")
    memory_context = "\n".join([m.events[0].content.parts[0].text for m in memory_response.memories if m.events and m.events[0].content]) # Simplified extraction
    print(f"Tool: Found memory context: {memory_context[:100]}...")

    # 3. Perform analysis (placeholder)
    analysis_result = f"Analysis of '{document_name}' regarding '{analysis_query}' using memory context: [Placeholder Analysis Result]"
    print("Tool: Performed analysis.")

    # 4. Save the analysis result as a new artifact
    analysis_part = types.Part.from_text(text=analysis_result)
    new_artifact_name = f"analysis_{document_name}"
    version = tool_context.save_artifact(new_artifact_name, analysis_part)
    print(f"Tool: Saved analysis result as '{new_artifact_name}' version {version}.")

    return {"status": "success", "analysis_artifact": new_artifact_name, "version": version}

doc_analysis_tool = FunctionTool(func=process_document)

# In an Agent:
# Assume artifact 'report.txt' was previously saved.
# Assume memory service is configured and has relevant past data.
# my_agent = Agent(..., tools=[doc_analysis_tool], artifact_service=..., memory_service=...)