Vonage Video Connector Transport

examples/README.md

@@ -55,6 +55,20 @@ Then, run the example with:
 uv run getting-started/06-voice-agent.py -t twilio -x NGROK_HOST_NAME
 ```
 
+### Vonage
+
+It is also possible to run the example through a Vonage session. Just provide the values for the following variables in
+the `.env` file:
+* VONAGE_APPLICATION_ID
+* VONAGE_SESSION_ID
+* VONAGE_TOKEN
+
+Then, run the example with:
+
+```bash
+uv run getting-started/06-voice-agent.py -t vonage
+```
+
 ## Directory Structure
 
 ### [`getting-started/`](./getting-started/)

examples/transports/transports-vonage.py

@@ -0,0 +1,108 @@
+#
+# Copyright (c) 2024-2026, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+"""Example of using AWS Nova Sonic LLM service with Vonage Video Connector transport."""
+
+import asyncio
+import os
+import sys
+from collections.abc import Callable
+from typing import Any
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import (
+    LLMContextAggregatorPair,
+    LLMUserAggregatorParams,
+)
+from pipecat.runner.vonage import configure
+from pipecat.services.aws.nova_sonic.llm import AWSNovaSonicLLMService
+from pipecat.transports.vonage.video_connector import (
+    VonageVideoConnectorTransport,
+    VonageVideoConnectorTransportParams,
+)
+
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main() -> None:
+    """Main entry point for the nova sonic vonage video connector example."""
+    (application_id, session_id, token) = await configure()
+
+    system_instruction = (
+        "You are a friendly assistant. The user and you will engage in a spoken dialog exchanging "
+        "the transcripts of a natural real-time conversation. Keep your responses short, generally "
+        "two or three sentences for chatty scenarios. "
+        f"{AWSNovaSonicLLMService.AWAIT_TRIGGER_ASSISTANT_RESPONSE_INSTRUCTION}"
+    )
+    transport = VonageVideoConnectorTransport(
+        application_id,
+        session_id,
+        token,
+        VonageVideoConnectorTransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            publisher_name="Bot",
+        ),
+    )
+
+    llm = AWSNovaSonicLLMService(
+        secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY", ""),
+        access_key_id=os.getenv("AWS_ACCESS_KEY_ID", ""),
+        region=os.getenv("AWS_REGION", ""),
+        session_token=os.getenv("AWS_SESSION_TOKEN", ""),
+        voice_id="tiffany",
+    )
+    context = LLMContext(
+        messages=[
+            {"role": "system", "content": f"{system_instruction}"},
+            {
+                "role": "user",
+                "content": "Tell me a fun fact!",
+            },
+        ],
+    )
+    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(
+        context, user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer())
+    )
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            user_aggregator,
+            llm,
+            transport.output(),
+            assistant_aggregator,
+        ]
+    )
+
+    task = PipelineTask(pipeline)
+
+    # Handle client connection event
+    event_handler: Callable[[str], Callable[[Any], Any]] = transport.event_handler
+
+    @event_handler("on_client_connected")
+    async def on_client_connected(transport: VonageVideoConnectorTransport, client: object) -> None:
+        logger.info(f"Client connected")
+        await task.queue_frames([LLMRunFrame()])
+
+    runner = PipelineRunner()
+
+    await asyncio.gather(runner.run(task))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())