A condition or rule you never want your application to break. It’s composed of three ingredients:
  • A metric
  • An operator
  • A target value
Your Rules should evaluate to False for the base case, and to True for unwanted scenarios. In the example above, the “input/output shall never contain PII” is encoded into a Rule like below: 
gp.Rule(
    metric=gp.RuleMetrics.pii,
    operator=gp.RuleOperator.contains,
    target_value="ssn"
)

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Metrics and Operators supported

We support several metrics within Protect rules. Because each metric can have different output values (e.g. float metrics, categorical, etc.), the Operators and Target values differ by metric. Below is a list of all supported metric and their available configurations:

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Prompt Injection

Used to detect and stop prompt injections in the input (Read more about Prompt Injection). Metric Constants:
  • gp.RuleMetrics.prompt_injection
Payload Field: input Potential Categories:
  • impersonation
  • obfuscation
  • simple_instruction
  • few_shot
  • new_context
Operators and Target Value Supported:
OperatorTarget Value
Any (gp.RuleOperator.any)A list of categories (e.g. [“obfuscation”, “impersonation”])
All (gp.RuleOperator.all)A list of categories (e.g. [“obfuscation”, “impersonation”])
Contains (gp.RuleOperator.contains)A single category (e.g. “impersonation”)
Equal (gp.RuleOperator.eq)A single category (e.g. “impersonation”)
Not equal (gp.RuleOperator.neq)A single category (e.g. “impersonation”)
Empty (gp.RuleOperator.empty)-
Not Empty (gp.RuleOperator.not_empty)-
Example: 
gp.Rule(
    metric=gp.RuleMetrics.prompt_injection,
    operator=gp.RuleOperator.any,
    target_value=["impersonation", "obfuscation"]
)

​
PII (Personal Identifiable Information)

Used to detect and stop Personal Identifiable Information (PII). When applied on the input, it can be used to stop the user or company PII from being included in API calls to external services. When applied on the output, it can be used to prevent data leakage or PII being shown back to the user. Read more about PII classes and their definitions. Metric Constants:
  • gp.RuleMetrics.piifor output PII
  • gp.RuleMetrics.input_pii for input PII
Payload Field: input (for input PII) or output (for output PII) Potential Categories:
  • account_info
  • address
  • credit_card_info
  • date_of_birth
  • email
  • name
  • network_info
  • password
  • phone_number
  • ssn
  • username
Operators and Target Value Supported:
OperatorTarget Value
Any (gp.RuleOperator.any)A list of categories (e.g. [“ssn”, “address”])
All (gp.RuleOperator.all)A list of categories (e.g. [“ssn”, “address”])
Contains (gp.RuleOperator.contains)A single category (e.g. “ssn”)
Equal (gp.RuleOperator.eq)A single category (e.g. “ssn”)
Not equal (gp.RuleOperator.neq)A single category (e.g. “ssn”)
Empty (gp.RuleOperator.empty)-
Not Empty (gp.RuleOperator.not_empty)-
Example: 
gp.Rule(
    metric=gp.RuleMetrics.pii,
    operator=gp.RuleOperator.any,
    target_value=["ssn", "address"]
)

​
Context Adherence

Measures whether your model’s response was purely based on the context provided. It can be used to stop hallucinations from reaching your end users. Powered by Context Adherence Luna. Metric Constant: gp.RuleMetrics.context_adherence_luna Payload Field: Both input and output must be included in the payload Potential Values: 0.00 to 1.00. Generally, we see 0.1 as a good threshold below which we’re confident the response is not adhering to the context. Operators Supported:
  • Greater than (gp.RuleOperator.gt)
  • Less than (gp.RuleOperator.lt)
  • Greater than or equal (gp.RuleOperator.gte)
  • Less than or equal (gp.RuleOperator.lte)
Example: 
gp.Rule(
    metric=gp.RuleMetrics.context_adherence_luna,
    operator=gp.RuleOperator.lt,
    target_value=0.90
)

​
Toxicity

Used to detect and stop toxic or foul language in the input (user query) or output (response shown to the user). Read more about Toxicity. Metric Constants:
  • gp.RuleMetrics.toxicityfor output Toxicity
  • gp.RuleMetrics.input_toxicity for input Toxicity
Payload Field: input or output Potential Values: 0.00 to 1.00 (higher values indicate higher toxicity) Operators Supported:
  • Greater than (gp.RuleOperator.gt)
  • Less than (gp.RuleOperator.lt)
  • Greater than or equal (gp.RuleOperator.gte)
  • Less than or equal (gp.RuleOperator.lte)
Example: 
gp.Rule(
    metric=gp.RuleMetrics.toxicity,
    operator=gp.RuleOperator.gt,
    target_value=0.95
)

​
Sexism

Detect sexist or biased language. When applied on the input, it can be used to detect sexist remarks in user queries. When applied on the output, it can be used to prevent your application from using an making biased or sexist comments in its responses. Metric Constants:
  • gp.RuleMetrics.sexistfor output Sexism
  • gp.RuleMetrics.input_sexist for input Sexism
Payload Field: input or output Potential Values: 0.00 to 1.00 (higher values indicate higher toxicity) Operators Supported:
  • Greater than (gp.RuleOperator.gt)
  • Less than (gp.RuleOperator.lt)
  • Greater than or equal (gp.RuleOperator.gte)
  • Less than or equal (gp.RuleOperator.lte)
Example: 
{
    "metric": "sexist",
    "operator": "gt",
    "target_value": 0.95,
},

​
Tone

Primary tone detected from the text. When applied on the input, it can be used to detect negative tones in user queries. When applied on the output, it can be used to prevent your application from using an undesired tone in its responses. Metric Constants:
  • gp.RuleMetrics.tonefor output Tone
  • gp.RuleMetrics.input_tone for input Tone
Payload Field: input (for input Tone) or output (for output Tone) Potential Categories:
  • anger
  • annoyance
  • confusion
  • fear
  • joy
  • love
  • sadness
  • surprise
  • neutral
Operators and Target Value Supported:
OperatorTarget Value
Equal (gp.RuleOperator.eq)A single category (e.g. “anger”)
Not equal (gp.RuleOperator.neq)A single category (e.g. “neutral”)
Example: 
gp.Rule(
    metric=gp.RuleMetrics.tone,
    operator=gp.RuleOperator.neq,
    target_value="neutral"
)

​
Registered Scorers

If you have a registered scorer, it can also be used in your Galileo Protect rulesets. Example: 
gp.Rule(
    metric=<registered-metric-name>,
    operator=<operator>,
    target_value=<target>,
)
The operators and target values here should match the type of data that the registered scorer is expected to produce.