fix(pii-redaction): add a strands specific example, reformat context (#177)

* fix(pii-redaction): add strands specific example, reformat context

* add navigation for pii-redaction

---------

Co-authored-by: Jack Yuan <jackypc@amazon.com>

Author: JackYPCOnline

docs/user-guide/safety-security/pii-redaction.md

@@ -18,16 +18,22 @@ Integrating PII redaction is crucial for:
 ## How to implement PII Redaction
 
 Strands SDK does not natively perform PII redaction within its core telemetry generation but recommends two effective ways to achieve PII masking:
+
 ### Option 1: Using Third-Party Specialized Libraries [Recommended]
 Leverage specialized external libraries like Langfuse, LLM Guard, Presidio, or AWS Comprehend for high-quality PII detection and redaction:
+
 #### Step-by-Step Integration Guide
+
 ##### Step 1: Install your chosen PII Redaction Library.
 Example with [LLM Guard](https://protectai.com/llm-guard):
-````
+
+````bash
 pip install llm-guard
 ````
-##### Step2: Import necessary modules and initialize the Vault and Anonymize scanner.
-````
+
+##### Step 2: Import necessary modules and initialize the Vault and Anonymize scanner.
+
+```python
 from llm_guard.vault import Vault
 from llm_guard.input_scanners import Anonymize
 from llm_guard.input_scanners.anonymize_helpers import BERT_LARGE_NER_CONF
@@ -42,24 +48,31 @@ def create_anonymize_scanner():
         language="en"
     )
     return scanner
-````
-##### Step3: Define a masking function using the anonymize scanner.
-````
+```
+##### Step 3: Define a masking function using the anonymize scanner.
+
+```python
 def masking_function(data, **kwargs):
     if isinstance(data, str):
         scanner = create_anonymize_scanner()
+        # Scan and redact the data
         sanitized_data, is_valid, risk_score = scanner.scan(data)
         return sanitized_data
     return data
-````
-##### Step4: Configure the masking function in Observability platform, eg., Langfuse.
-````
-from langfuse import Langfuse, observe
+```
+
+##### Step 4: Configure the masking function in Observability platform, eg., Langfuse.
+
+```python
+from langfuse import Langfuse
 
 langfuse = Langfuse(mask=masking_function)
-````
-##### Step5: Create a sample function with PII.
-````
+```
+
+##### Step 5: Create a sample function with PII.
+
+```python
+from langfuse import observe
 @observe()
 def generate_report():
     report = "John Doe met with Jane Smith to discuss the project."
@@ -70,14 +83,94 @@ print(result)
 # Output: [REDACTED_PERSON] met with [REDACTED_PERSON] to discuss the project.
 
 langfuse.flush()
-````
+```
+
+#### Complete example with a Strands Agent
+
+```python
+from strands import Agent
+from llm_guard.vault import Vault
+from llm_guard.input_scanners import Anonymize
+from llm_guard.input_scanners.anonymize_helpers import BERT_LARGE_NER_CONF
+from langfuse import Langfuse, observe
+
+vault = Vault()
+
+def create_anonymize_scanner():
+    """Creates a reusable anonymize scanner."""
+    return Anonymize(vault, recognizer_conf=BERT_LARGE_NER_CONF, language="en")
+
+def masking_function(data, **kwargs):
+    """Langfuse masking function to recursively redact PII."""
+    if isinstance(data, str):
+        scanner = create_anonymize_scanner()
+        sanitized_data, _, _ = scanner.scan(data)
+        return sanitized_data
+    elif isinstance(data, dict):
+        return {k: masking_function(v) for k, v in data.items()}
+    elif isinstance(data, list):
+        return [masking_function(item) for item in data]
+    return data
+
+langfuse = Langfuse(mask=masking_function)
+
+
+class CustomerSupportAgent:
+    def __init__(self):
+        self.agent = Agent(
+            system_prompt="You are a helpful customer service agent. Respond professionally to customer inquiries."
+        )
+
+    @observe
+    def process_sanitized_message(self, sanitized_payload):
+        """Processes a pre-sanitized payload and expects sanitized input."""
+        sanitized_content = sanitized_payload.get("prompt", "empty input")
+
+        conversation = f"Customer: {sanitized_content}"
+
+        response = self.agent(conversation)
+        return response
+
+
+def process():
+    support_agent = CustomerSupportAgent()
+    scanner = create_anonymize_scanner()
+
+    raw_payload = {
+        "prompt": "Hi, I'm Jonny Test. My phone number is 123-456-7890 and my email is john@example.com. I need help with my order #123456789."
+    }
+
+    sanitized_prompt, _, _ = scanner.scan(raw_payload["prompt"])
+    sanitized_payload = {"prompt": sanitized_prompt}
 
+    response = support_agent.process_sanitized_message(sanitized_payload)
+
+    print(f"Response: {response}")
+    langfuse.flush()
+    
+    #Example input: prompt:
+        # "Hi, I'm [REDACTED_PERSON_1]. My phone number is [REDACTED_PHONE_NUMBER_1] and my email is [REDACTED_EMAIL_ADDRESS_1]. I need help with my order #123456789."
+    #Example output: 
+        # #Hello! I'd be happy to help you with your order #123456789. 
+        # To better assist you, could you please let me know what specific issue you're experiencing with this order? For example:
+        # - Are you looking for a status update?
+        # - Need to make changes to the order?
+        # - Having delivery issues?
+        # - Need to process a return or exchange?
+        # 
+        # Once I understand what you need help with, I'll be able to provide you with the most relevant assistance."
+
+if __name__ == "__main__":
+    process()
+```
 
 ### Option 2: Using OpenTelemetry Collector Configuration [Collector-level Masking]
 Implement PII masking directly at the collector level, which is ideal for centralized control.
+
 #### Example code:
 1. Edit your collector configuration (eg., otel-collector-config.yaml):
-````
+
+```yaml
 processors:
   attributes/pii:
     actions:
@@ -92,22 +185,28 @@ service:
   pipelines:
     traces:
       processors: [attributes/pii]
-````
+```
+
 2. Deploy or restart your OTEL collector with the updated configuration.
+
 #### Example:
+
 ##### Before:
-````
+
+```json
 {
-"user.email": "user@example.com",
-"http.url": "https://example.com?token=abc123"
+  "user.email": "user@example.com",
+  "http.url": "https://example.com?token=abc123"
 }
-````
+```
+
 #### After:
-````
+
+```json
 {
   "http.url": "https://example.com?token=[REDACTED]"
 }
-````
+```
 
 ## Additional Resources
 * [PII definition](https://www.dol.gov/general/ppii)

mkdocs.yml

@@ -110,6 +110,7 @@ nav:
       - Responsible AI: user-guide/safety-security/responsible-ai.md
       - Guardrails: user-guide/safety-security/guardrails.md
       - Prompt Engineering: user-guide/safety-security/prompt-engineering.md
+      - PII Redaction: user-guide/safety-security/pii-redaction.md
     - Observability & Evaluation:
       - Observability: user-guide/observability-evaluation/observability.md
       - Metrics: user-guide/observability-evaluation/metrics.md