Back to Prompt Library
implementation

Integrating Deepgram for Voice Interaction

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Build Proactive Personalized Assistant with AutoGen & Gemini 2.5 Pro

Format
Code-aware
Lines
17
Sections
6
Linked challenge
Build Proactive Personalized Assistant with AutoGen & Gemini 2.5 Pro

Prompt source

Original prompt text with formatting preserved for inspection.

17 lines
6 sections
No variables
1 code block
Implement a component that uses Deepgram's real-time Speech-to-Text to convert spoken user queries into text input for the `user_proxy` agent. Then, integrate Deepgram's Text-to-Speech to vocalize the `user_proxy`'s responses. Describe the architectural flow and provide relevant code snippets for a simple voice interaction loop.

```python
# Deepgram initialization (example, needs actual implementation for streaming)
# from deepgram import DeepgramClient, LiveTranscriptionEvents
# import asyncio

# DEEPGRAM_API_KEY = "YOUR_DEEPGRAM_API_KEY"
# client = DeepgramClient(DEEPGRAM_API_KEY)

# async def get_transcript_from_mic():
#    # This would involve actual microphone input and streaming to Deepgram
#    pass

# async def speak_text(text):
#    # This would involve converting text to speech and playing audio
#    pass

# Modify user_proxy to use voice input/output
# user_proxy.initiate_chat(action_planner, message=await get_transcript_from_mic())
# await speak_text(response_from_agents)
```

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.