BentoML Cloud Deployment Configuration

Prepare your LangChain application for deployment using BentoML. Create a `bentofile.yaml` and a `service.py` file that defines your LangChain agent as a BentoML service. Detail the necessary dependencies and how to expose an API endpoint for voice input, which will trigger the agent and return speech output. Explain how BentoML Cloud would manage the serving infrastructure and scaling for this application.

```python
# service.py
import bentoml
from langchain.agents import AgentExecutor # ... and other LangChain imports

# class MyLangChainAgent(bentoml.Service):
#    # ... (load LLM, tools, agent executor)

#    @bentoml.api
#    def generate_playlist(self, audio_data: bytes) -> bytes:
#        # Transcribe audio, invoke agent, synthesize speech
#        pass

# bentofile.yaml
# service: "service:my_langchain_agent"
# labels:
#    owner: your_name
#    project: playlist_generator
# python:
#    packages:
#        - langchain
#        - vapi_sdk
#        - ernie_llm
#        - alibi-detect
#        - pydub # for audio processing
```