Integrate Hume AI with Aembit for Secure Emotional Analysis

Develop a concrete LlamaIndex `FunctionTool` that wraps Hume AI's emotion recognition capabilities. This tool should securely process an audio file (or its transcript) representing developer feedback and return a structured JSON output of detected emotions and overall sentiment. Crucially, use Aembit to manage and secure the API credentials and access to the Hume AI service, demonstrating a robust enterprise-grade integration.
```python
import os
# import hume_sdk # Hypothetical Hume AI SDK
# import aembit_sdk # Hypothetical Aembit SDK
from llama_index.core.tools import FunctionTool # Mock Hume AI client for demonstration
class MockHumeClient: def __init__(self, api_key: str): print(f"MockHumeClient initialized with API Key: {api_key[:4]}...") def recognize_audio(self, audio_path: str) -> dict: # Simulate Hume AI's response for emotional analysis if "game-changer" in audio_path.lower(): # Simulate from transcript return {"emotions": [{"name": "excitement", "score": 0.95}], "overall_sentiment": "positive"} return {"emotions": [{"name": "neutral", "score": 0.7}], "overall_sentiment": "neutral"} # Secure function for Hume AI interaction via Aembit
# @aembit_sdk.secure_call('hume_ai_service_id') # Hypothetical Aembit decorator
def secure_hume_ai_analysis(audio_transcript: str) -> str: """Analyzes audio transcript for emotions and sentiment using Hume AI, with Aembit security.""" # In a real scenario, Aembit would inject the credentials or manage access token. # For this example, we assume API key is securely retrieved or managed. # hume_api_key = aembit_sdk.get_secret('HUME_API_KEY') # Hypothetical hume_api_key = os.getenv("HUME_AI_API_KEY", "mock_hume_key") # Use env var for mock hume_client = MockHumeClient(api_key=hume_api_key) # In a real scenario, `audio_transcript` would come from an ASR step on an audio file. # We'll use it as direct input for mock purposes. result = hume_client.recognize_audio(audio_transcript) return str(result) # LlamaIndex tools expect string output # Create the LlamaIndex FunctionTool
hume_sentiment_tool = FunctionTool.from_defaults( fn=secure_hume_ai_analysis, name="hume_ai_sentiment_analyzer", description="Analyzes an audio transcript for sentiment and emotional cues using Hume AI, with secure access managed by Aembit. Input should be the audio content as a string."
) # Example of how an agent might use this tool:
# agent_response = agent.chat("Analyze the emotional sentiment of the following developer feedback: 'I'm genuinely thrilled about the potential of multi-chip AI inference. It sounds like a game-changer!'")
# print(agent_response)
```