LogoLogo
Slack CommunityCatalyst Login
  • Welcome
  • RagaAI Catalyst
    • User Quickstart
    • Concepts
      • Configure Your API Keys
      • Supported LLMs
        • OpenAI
        • Gemini
        • Azure
        • AWS Bedrock
        • ANTHROPIC
      • Catalyst Access/Secret Keys
      • Enable Custom Gateway
      • Uploading Data
        • Create new project
        • RAG Datset
        • Chat Dataset
          • Prompt Format
        • Logging traces (LlamaIndex, Langchain)
        • Trace Masking Functions
        • Trace Level Metadata
        • Correlating Traces with External IDs
        • Add Dataset
      • Running RagaAI Evals
        • Executing Evaluations
        • Compare Datasets
      • Analysis
      • Embeddings
    • RagaAI Metric Library
      • RAG Metrics
        • Hallucination
        • Faithfulness
        • Response Correctness
        • Response Completeness
        • False Refusal
        • Context Relevancy
        • Context Precision
        • Context Recall
        • PII Detection
        • Toxicity
      • Chat Metrics
        • Agent Quality
        • Instruction Adherence
        • User Chat Quality
      • Text-to-SQL
        • SQL Response Correctness
        • SQL Prompt Ambiguity
        • SQL Context Ambiguity
        • SQL Context Sufficiency
        • SQL Prompt Injection
      • Text Summarization
        • Summary Consistency
        • Summary Relevance
        • Summary Fluency
        • Summary Coherence
        • SummaC
        • QAG Score
        • ROUGE
        • BLEU
        • METEOR
        • BERTScore
      • Information Extraction
        • MINEA
        • Subjective Question Correction
        • Precision@K
        • Chunk Relevance
        • Entity Co-occurrence
        • Fact Entropy
      • Code Generation
        • Functional Correctness
        • ChrF
        • Ruby
        • CodeBLEU
        • Robust Pass@k
        • Robust Drop@k
        • Pass-Ratio@n
      • Marketing Content Evaluation
        • Engagement Score
        • Misattribution
        • Readability
        • Topic Coverage
        • Fabrication
      • Learning Management System
        • Topic Coverage
        • Topic Redundancy
        • Question Redundancy
        • Answer Correctness
        • Source Citability
        • Difficulty Level
      • Additional Metrics
        • Guardrails
          • Anonymize
          • Deanonymize
          • Ban Competitors
          • Ban Substrings
          • Ban Topics
          • Code
          • Invisible Text
          • Language
          • Secret
          • Sentiment
          • Factual Consistency
          • Language Same
          • No Refusal
          • Reading Time
          • Sensitive
          • URL Reachability
          • JSON Verify
        • Vulnerability Scanner
          • Bullying
          • Deadnaming
          • SexualContent
          • Sexualisation
          • SlurUsage
          • Profanity
          • QuackMedicine
          • DAN 11
          • DAN 10
          • DAN 9
          • DAN 8
          • DAN 7
          • DAN 6_2
          • DAN 6_0
          • DUDE
          • STAN
          • DAN_JailBreak
          • AntiDAN
          • ChatGPT_Developer_Mode_v2
          • ChatGPT_Developer_Mode_RANTI
          • ChatGPT_Image_Markdown
          • Ablation_Dan_11_0
          • Anthropomorphisation
      • Guardrails
        • Competitor Check
        • Gibberish Check
        • PII
        • Regex Check
        • Response Evaluator
        • Toxicity
        • Unusual Prompt
        • Ban List
        • Detect Drug
        • Detect Redundancy
        • Detect Secrets
        • Financial Tone Check
        • Has Url
        • HTML Sanitisation
        • Live URL
        • Logic Check
        • Politeness Check
        • Profanity Check
        • Quote Price
        • Restrict Topics
        • SQL Predicates Guard
        • Valid CSV
        • Valid JSON
        • Valid Python
        • Valid Range
        • Valid SQL
        • Valid URL
        • Cosine Similarity
        • Honesty Detection
        • Toxicity Hate Speech
    • Prompt Playground
      • Concepts
      • Single-Prompt Playground
      • Multiple Prompt Playground
      • Run Evaluations
      • Using Prompt Slugs with Python SDK
      • Create with AI using Prompt Wizard
      • Prompt Diff View
    • Synthetic Data Generation
    • Gateway
      • Quickstart
    • Guardrails
      • Quickstart
      • Python SDK
    • RagaAI Whitepapers
      • RagaAI RLEF (RAG LLM Evaluation Framework)
    • Agentic Testing
      • Quickstart
      • Concepts
        • Tracing
          • Langgraph (Agentic Tracing)
          • RagaAI Catalyst Tracing Guide for Azure OpenAI Users
        • Dynamic Tracing
        • Application Workflow
      • Create New Dataset
      • Metrics
        • Hallucination
        • Toxicity
        • Honesty
        • Cosine Similarity
      • Compare Traces
      • Compare Experiments
      • Add metrics locally
    • Custom Metric
    • Auto Prompt Optimization
    • Human Feedback & Annotations
      • Thumbs Up/Down
      • Add Metric Corrections
      • Corrections as Few-Shot Examples
      • Tagging
    • On-Premise Deployment
      • Enterprise Deployment Guide for AWS
      • Enterprise Deployment Guide for Azure
      • Evaluation Deployment Guide
        • Evaluation Maintenance Guide
    • Fine Tuning (OpenAI)
    • Integration
    • SDK Release Notes
      • ragaai-catalyst 2.1.7
  • RagaAI Prism
    • Quickstart
    • Sandbox Guide
      • Object Detection
      • LLM Summarization
      • Semantic Segmentation
      • Tabular Data
      • Super Resolution
      • OCR
      • Image Classification
      • Event Detection
    • Test Inventory
      • Object Detection
        • Failure Mode Analysis
        • Model Comparison Test
        • Drift Detection
        • Outlier Detection
        • Data Leakage Test
        • Labelling Quality Test
        • Scenario Imbalance
        • Class Imbalance
        • Active Learning
        • Image Property Drift Detection
      • Large Language Model (LLM)
        • Failure Mode Analysis
      • Semantic Segmentation
        • Failure Mode Analysis
        • Labelling Quality Test
        • Active Learning
        • Drift Detection
        • Class Imbalance
        • Scenario Imbalance
        • Data Leakage Test
        • Outlier Detection
        • Label Drift
        • Semantic Similarity
        • Near Duplicates Detection
        • Cluster Imbalance Test
        • Image Property Drift Detection
        • Spatio-Temporal Drift Detection
        • Spatio-Temporal Failure Mode Analysis
      • Tabular Data
        • Failure Mode Analysis
      • Instance Segmentation
        • Failure Mode Analysis
        • Labelling Quality Test
        • Drift Detection
        • Class Imbalance
        • Scenario Imbalance
        • Label Drift
        • Data Leakage Test
        • Outlier Detection
        • Active Learning
        • Near Duplicates Detection
      • Super Resolution
        • Semantic Similarity
        • Active Learning
        • Near Duplicates Detection
        • Outlier Detection
      • OCR
        • Missing Value Test
        • Outlier Detection
      • Image Classification
        • Failure Mode Analysis
        • Labelling Quality Test
        • Class Imbalance
        • Drift Detection
        • Near Duplicates Test
        • Data Leakage Test
        • Outlier Detection
        • Active Learning
        • Image Property Drift Detection
      • Event Detection
        • Failure Mode Analysis
        • A/B Test
    • Metric Glossary
    • Upload custom model
    • Event Detection
      • Upload Model
      • Generate Inference
      • Run tests
    • On-Premise Deployment
      • Enterprise Deployment Guide for AWS
      • Enterprise Deployment Guide for Azure
  • Support
Powered by GitBook
On this page

Was this helpful?

  1. RagaAI Catalyst
  2. RagaAI Metric Library
  3. Additional Metrics
  4. Guardrails

Anonymize

Last updated 8 months ago

Was this helpful?

Anonymize Personally Identifiable Information(PII) data in the text using NLP (English only) and predefined regex patterns. Anonymizes detected entities with placeholders like [REDACTED_PERSON_1] and stores the real values in a Vault.

PII entities

  • Credit Cards: Formats mentioned in .

    • 4111111111111111

    • 378282246310005 (American Express)

    • 30569309025904 (Diners Club)

  • Person: A full person name, which can include first names, middle names or initials, and last names.

    • John Doe

  • PHONE_NUMBER:

    • 5555551234

  • URL: A URL (Uniform Resource Locator), unique identifier used to locate a resource on the Internet.

    • https://example.com/

  • E-mail Addresses: Standard email formats.

    • john.doe@example.com

    • john.doe[AT]example[DOT]com

    • john.doe[AT]example.com

    • john.doe@example[DOT]com

  • IPs: An Internet Protocol (IP) address (either IPv4 or IPv6).

    • 192.168.1.1 (IPv4)

    • 2001:db8:3333:4444:5555:6666:7777:8888 (IPv6)

  • UUID:

    • 550e8400-e29b-41d4-a716-446655440000

  • US Social Security Number (SSN):

    • 111-22-3333

  • Crypto wallet number: Currently only Bitcoin address is supported.

    • 1Lbcfr7sAHTD9CgdQo3HTMTkV8LK4ZnX71

  • IBAN Code: The International Bank Account Number (IBAN) is an internationally agreed system of identifying bank accounts across national borders to facilitate the communication and processing of cross border transactions with a reduced risk of transcription errors.

    • DE89370400440532013000

Parameters:

data:

  • prompt (str): The text to be anonymized.

arguments:

  • hidden_names (Optional[Sequence[str]]): List of names to be anonymized e.g. [REDACTED_CUSTOM_1].

  • allowed_names (Optional[Sequence[str]]): List of names allowed in the text without anonymizing.

  • entity_types (Optional[Sequence[str]]): List of entity types to be detected. If not provided, defaults to all.

  • preamble (str): Text to prepend to sanitized prompt. If not provided, defaults to an empty string.

  • regex_patterns (Optional[List[Dict]]): List of regex patterns for additional custom anonymization.

  • use_faker (bool): Whether to use faker instead of placeholders in applicable cases. If not provided, defaults to False, replaces with placeholders [REDACTED_PERSON_1].

  • threshold (float): Acceptance threshold. Default is 0.

  • use_onnx (bool): Whether to use ONNX runtime for inference. Default is False.

  • language (str): Language of the anonymize detect. Default is "en".

Interpretation:

Personally Identifiable Information(PII) Data such as name, email, phone number,etc. present in the prompt should be removed in the output

Example:

prompt = "Your name is Walter White, You have received your inquiry regarding an account with Google LLC from John Doe, where you work.
Your registered email address is walter.white@google.com and the phone number is +91-9967854332 associated with the account. Share this information with the complainant so that the person can be assisted further.
Write an email to help the person ahead"

sanitized_prompt = "Your name is [REDACTED_PERSON_1], You have received your inquiry regarding an account with Google LLC from [REDACTED_PERSON_2], where you work.\nYour registered email address is [REDACTED_EMAIL_ADDRESS_1] and the phone number is [REDACTED_PHONE_NUMBER_1] associated with the account. Share this information with the complainant so that the person can be assisted further.\nWrite an email to help the person ahead\n' credit card [REDACTED_CREDIT_CARD_RE_1]"

Code Example:

evaluator.add_test(
    test_names=["anonymize_guardrail"],
    data={
        "prompt": """Your name is Walter White, You have received your inquiry regarding an account with Google LLC from John Doe, where you work.
Your registered email address is walter.white@google.com and the phone number is +91-9967854332 associated with the account. Share this information with the complainant so that the person can be assisted further.
Write an email to help the person ahead
""",
    },
).run()

evaluator.print_results()
Wikipedia