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from typing import Annotated

async def get_weather(
    ctx,
    city: Annotated[str, "The city to check weather for"],
    units: Annotated[str, "celsius or fahrenheit"] = "fahrenheit"
):
    """
    Look up the current weather in a given city.
    """
    return f"72°F and sunny in {city}"

from line.llm_agent import loopback_tool

@loopback_tool
async def get_account_balance(ctx, account_id: Annotated[str, "The account ID"]):
    """Look up the balance for a customer account."""
    balance = await api.get_balance(account_id)
    return f"${balance:.2f}"

from line.llm_agent import passthrough_tool
from line.events import AgentSendText, AgentEndCall

@passthrough_tool
async def end_call_with_message(ctx, message: Annotated[str, "Goodbye message"]):
    """End the call with a custom goodbye message."""
    yield AgentSendText(text=message)
    yield AgentEndCall()

from typing import Annotated
from line.llm_agent import handoff_tool
from line.events import AgentHandedOff, AgentSendText, UserTurnEnded, AgentEndCall

@handoff_tool
async def run_satisfaction_survey(
    ctx,
    customer_name: Annotated[str, "The customer's name"],
    event
):
    """Hand off to a customer satisfaction survey at the end of the call."""
    if isinstance(event, AgentHandedOff):
        # First call - send introduction
        yield AgentSendText(
            text=f"Thank you for your call, {customer_name}. "
            "Please stay on the line for a brief satisfaction survey. "
            "On a scale of 1 to 5, how would you rate your experience today?"
        )
        return

    # Subsequent calls - handle survey responses
    if isinstance(event, UserTurnEnded):
        user_response = event.content[0].content if event.content else ""
        yield AgentSendText(text=f"You rated us {user_response}. Thank you for your feedback!")
        yield AgentEndCall()

import os

from line.llm_agent import LlmAgent, LlmConfig, end_call, knowledge_base, send_dtmf, transfer_call, web_search

agent = LlmAgent(
    model="anthropic/claude-haiku-4-5-20251001",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    tools=[end_call, send_dtmf, transfer_call, web_search, knowledge_base],
    config=LlmConfig(...),
)

# End call: Let the LLM decide when conversation is complete
tools=[end_call]  # LLM calls this when user says "thanks, bye!"

# Transfer: Route to human support
tools=[transfer_call]  # LLM calls transfer_call(target_phone_number="+18005551234")

# Web search with custom context size
tools=[web_search(search_context_size="high")]  # More context for complex queries

# Knowledge base: look up the agent's attached documents
tools=[knowledge_base]  # LLM calls this to answer from uploaded docs

# Knowledge base scoped to a metadata filter with a tailored prompt
tools=[knowledge_base(
    filters={"category": "billing"},
    top_k=10,
    description="Look up billing and payment policy details.",
)]

from line.llm_agent import LlmAgent, LlmConfig, end_call

agent = LlmAgent(
    model="anthropic/claude-haiku-4-5-20251001",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    tools=[end_call],
    config=LlmConfig(...),
)

from line.llm_agent import end_call

# Restaurant reservation agent
tools=[end_call(description="""Ends the call and disconnects.

Call when ALL of the following are true:
- The reservation is confirmed with date, time, party size, and name.
- You have repeated the reservation details back to the guest.
- The guest confirms the details are correct or says goodbye.

Do not call when:
- The guest asks to modify the reservation.
- Details are missing or unconfirmed.
- The guest says 'okay' or 'thanks' without an explicit goodbye.

If unsure, ask: 'Is there anything else I can help you with for your reservation?'
""")]

# Order confirmation agent
tools=[end_call(description="""Ends the call and disconnects.

Call when ALL of the following are true:
- The order is placed and confirmed.
- You have provided the order number and estimated delivery time.
- The customer acknowledges with a goodbye phrase.

Do not call when:
- The customer has questions about their order.
- Payment has not been confirmed.
- The customer says 'got it' without saying goodbye.
""")]

from line.llm_agent import LlmAgent, LlmConfig, agent_as_handoff, end_call, UpdateCallConfig

spanish_agent = LlmAgent(
    model="gpt-5-nano",
    api_key=os.getenv("OPENAI_API_KEY"),
    tools=[end_call],
    config=LlmConfig(
        system_prompt="You speak only in Spanish.",
        introduction="¡Hola! ¿Cómo puedo ayudarte?",
    ),
)

main_agent = LlmAgent(
    model="anthropic/claude-haiku-4-5-20251001",
    api_key=os.getenv("ANTHROPIC_API_KEY"),
    tools=[
        end_call,
        agent_as_handoff(
            spanish_agent,
            handoff_message="Transferring to Spanish support...",
            update_call=UpdateCallConfig(
                voice_id="spanish-voice-id",
                pronunciation_dict_id="spanish-pronunciation-dict-id"
            ),
            name="transfer_to_spanish",
            description="Use when user requests Spanish.",
        ),
    ],
    config=LlmConfig(...),
)

from typing import Annotated
from line.llm_agent import loopback_tool

@loopback_tool(is_background=True)
async def search_database(ctx, query: Annotated[str, "Search query"]) -> str:
    """Search the database - may take several seconds."""
    results = await slow_database_search(query)
    return format_results(results)

@loopback_tool(is_background=True)
async def generate_report(ctx, report_type: Annotated[str, "Type of report"]) -> str:
    """Generate a detailed report - runs in background."""
    report = await compile_report(report_type)
    return report

from line.llm_agent import knowledge_base

# Knowledge base lookups run in the background
tools=[knowledge_base(is_background=True, top_k=10)]