Setup

1. Install the SonnyLabs library.

   pip install git+https://github.com/SonnyLabs/sonnylabs_py
   

2. Install the python-dotenv library to load the necessary secrets from a .env file.

   pip install python-dotenv
   

3. Create a .env file and set the API token and analysis id created earlier.

   SONNYLABS_API_TOKEN=[your_api_token]
   SONNYLABS_ANALYSIS_ID=[your_analysis_id]
   

4. Initialize the SonnyLabs client in your code.

   from sonnylabs_py import SonnyLabsClient
   
   sonnylabs_client = SonnyLabsClient(
       base_url="https://sonnylabs-service.onrender.com",
       api_token=os.environ.get("SONNYLABS_API_TOKEN"),
       analysis_id=os.environ.get("SONNYLABS_ANALYSIS_ID")
   )
   

5. Analyze the input and output text.

   # Analyze the user message before sending it to the LLM
   analysis_result = sonnylabs_client.analyze_text(user_message, scan_type="input")
   
   # Check for prompt injections
   prompt_injection = sonnylabs_client.get_prompt_injections(analysis_result)
   if prompt_injection and prompt_injection["score"] > 0.65:  # Threshold can be adjusted, recommended of over 0.65
       return "I detected potential prompt injection in your message. Please try again."
   
   # Check for PII on the input message
   input_pii_items = sonnylabs_client.get_pii(analysis_result)
   if input_pii_items:
       pii_types = [item["label"] for item in input_pii_items]
       return f"Personal information detected in the user message ({', '.join(pii_types)})."
   
   # If no security issues are found, process the message normally
   llm_response = [call to LLM here]
   
   # Scan the outgoing message for PII
   output_analysis = sonnylabs_client.analyze_text(llm_response, scan_type="output")
   
   # repeat the PII test on the LLM response 
   output_pii_items = sonnylabs_client.get_pii(output_analysis)
   if output_pii_items:
       pii_types = [item["label"] for item in output_pii_items]
       return f"Personal information detected in the LLM response ({', '.join(pii_types)})."