# pip install signalwire-sdk   →   python sigmond.py
# Sigmond: a voice AI that answers questions about SignalWire.
# His persona is a prompt. His knowledge is a tool he calls. Your code owns both.

from signalwire import AgentBase, FunctionResult

# A tiny stand-in knowledge base so this runs with zero setup. In production you
# swap this for the native_vector_search skill (see the note at the bottom).
DOCS = {
    "swaig": "SWAIG is SignalWire's AI Gateway, the tool-calling system that lets the AI in.",
    "latency": "Conversational turns land around 1.2 seconds because the AI kernel runs inside the media engine.",
    "puc": "Programmable Unified Communications: the platform owns the call and your code programs it.",
}


class Sigmond(AgentBase):
    def __init__(self):
        super().__init__(name="sigmond", route="/sigmond")
        self.add_language(name="English", code="en-US", voice="inworld.Mark")

        # Persona: who Sigmond is. The prompt is the ONE place the model gets latitude.
        self.prompt_add_section("Personality", body="You are Sigmond, a warm, concise voice guide to SignalWire.")
        self.prompt_add_section("Goal", body="Answer the caller's SignalWire questions accurately.")
        self.prompt_add_section("Rules", bullets=[
            "Call search_docs before you state a fact.",
            "If the docs do not cover it, say so. Never invent product details.",
        ])

    # A SWAIG function is a tool the AI calls mid-call. It runs on YOUR server.
    # The model decides WHEN to search; your code decides WHAT comes back.
    # The type hint (query: str) becomes the tool's schema. No JSON to hand-write.
    @AgentBase.tool(name="search_docs")
    def search_docs(self, query: str, raw_data=None):
        """Search SignalWire documentation to answer a caller's question."""
        for key, fact in DOCS.items():
            if key in query.lower():
                return FunctionResult(fact)
        return FunctionResult("I don't have that in the docs, but I can connect you with someone who does.")


# In production, delete the tool above and let the built-in skill do real vector
# search over an index you build from your docs with the sw-search CLI.
# (Needs the search extras: pip install "signalwire-sdk[search-queryonly]")
#
#   self.add_skill("native_vector_search", {
#       "tool_name": "search_docs",
#       "description": "Search SignalWire documentation.",
#       "index_file": "./signalwire-docs.swsearch",
#   })

# To talk to Sigmond from a browser, a separate @signalwire/js front-end dials his address (WebRTC). Not shown here.

if __name__ == "__main__":
    Sigmond().run()   # serve it, then route a phone number or browser (WebRTC) call to Sig