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feat(runner): classify privacy through llm targets #930

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165 changes: 145 additions & 20 deletions crates/switchyard-runner/src/config.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -94,10 +94,20 @@ struct PrivacyConfig {
#[derive(Debug, Deserialize)]
#[serde(deny_unknown_fields)]
struct PrivacyClassifierConfig {
#[serde(default, rename = "type")]
kind: PrivacyClassifierKind,
target: String,
clear_threshold: f64,
}

#[derive(Clone, Copy, Debug, Default, Deserialize)]
#[serde(rename_all = "snake_case")]
enum PrivacyClassifierKind {
#[default]
Decision,
Llm,
}

#[derive(Debug, Deserialize)]
#[serde(deny_unknown_fields)]
struct DeterministicConfig {
Expand Down Expand Up @@ -396,7 +406,9 @@ impl DeploymentConfig {
let classifier = self.build_privacy_classifier(
route_name,
config.classifier.as_ref(),
standard,
standard_decision,
clients,
decision_clients,
)?;
let (restricted, _) = self.build_lane(
Expand All @@ -422,7 +434,9 @@ impl DeploymentConfig {
&self,
route_name: &str,
classifier: Option<&PrivacyClassifierConfig>,
standard: &LaneTargets<'_>,
standard_decision: Option<ResolvedDecisionTarget<'_>>,
clients: &BTreeMap<String, Arc<TranslatingLlmClient>>,
decision_clients: &BTreeMap<String, Arc<dyn RoutedDecisionClient>>,
) -> RunnerResult<Option<SemanticPrivacyClassifier>> {
let Some(classifier) = classifier else {
Expand All @@ -434,21 +448,80 @@ impl DeploymentConfig {
classifier.clear_threshold
)));
}
let target =
self.resolve_decision_target(route_name, &classifier.target, decision_clients)?;
if standard_decision.is_some_and(|standard| {
standard.model == target.model && Arc::ptr_eq(standard.client, target.client)
}) {
return Err(RunnerError::configuration(format!(
"route {route_name} privacy classifier target {} overlaps the standard execution lane",
classifier.target
)));
}
Ok(Some(SemanticPrivacyClassifier::new(
target.model.clone(),
Arc::clone(target.client),
classifier.clear_threshold,
)))
let classifier = match classifier.kind {
PrivacyClassifierKind::Decision => {
let target =
self.resolve_decision_target(route_name, &classifier.target, decision_clients)?;
if standard_decision.is_some_and(|standard| {
standard.model == target.model && Arc::ptr_eq(standard.client, target.client)
}) {
return Err(RunnerError::configuration(format!(
"route {route_name} privacy classifier target {} overlaps the standard execution lane",
classifier.target
)));
}
SemanticPrivacyClassifier::decision(
target.model.clone(),
Arc::clone(target.client),
classifier.clear_threshold,
)
}
PrivacyClassifierKind::Llm => {
let target = self.targets.get(&classifier.target).ok_or_else(|| {
RunnerError::configuration(format!(
"route {route_name} privacy classifier references unknown target {}",
classifier.target
))
})?;
if standard.values().any(|candidate| {
candidate.config.id == target.id
&& candidate.config.llm_client == target.llm_client
}) {
return Err(RunnerError::configuration(format!(
"route {route_name} privacy classifier target {} overlaps the standard execution lane",
classifier.target
)));
}
if target.system_prompt.is_some()
|| !target.extra_body.is_empty()
|| !target.omit_body_fields.is_empty()
{
return Err(RunnerError::configuration(format!(
"route {route_name} privacy classifier target {} cannot configure request modifiers",
classifier.target
)));
}
let client_config = self.llm_clients.get(&target.llm_client).ok_or_else(|| {
RunnerError::configuration(format!(
"target {} references unknown llm client {}",
classifier.target, target.llm_client
))
})?;
if client_config.forward_auth {
return Err(RunnerError::configuration(format!(
"route {route_name} privacy classifier cannot use forward_auth"
)));
}
if client_config.timeout_ms.is_none() {
return Err(RunnerError::configuration(format!(
"route {route_name} privacy classifier requires an llm client timeout_ms"
)));
}
let client = Arc::clone(clients.get(&target.llm_client).ok_or_else(|| {
RunnerError::configuration(format!(
"target {} has no constructed llm client",
classifier.target
))
})?);
let client: Arc<dyn RoutedLlmClient> = client;
SemanticPrivacyClassifier::llm(
target.id.clone(),
client,
classifier.clear_threshold,
)
}
};
Ok(Some(classifier))
}

fn uses_forward_auth(&self, targets: &LaneTargets<'_>) -> bool {
Expand Down Expand Up @@ -1352,6 +1425,30 @@ clear_threshold = 0.9
)
}

fn llm_semantic_privacy_config() -> String {
format!(
r#"{VALID_CONFIG}

[llm_clients.privacy_classifier]
format = "openai_chat"
base_url = "https://example.test/v1"
timeout_ms = 30000

[targets.privacy_classifier]
id = "privacy/model"
llm_client = "privacy_classifier"

[routes.passthrough.privacy.restricted_targets]
weak = "strong"

[routes.passthrough.privacy.classifier]
type = "llm"
target = "privacy_classifier"
clear_threshold = 0.9
"#
)
}

#[test]
fn public_runner_from_toml_builds_a_deployment() -> RunnerResult<()> {
let runner = Runner::from_toml(VALID_CONFIG)?;
Expand Down Expand Up @@ -1452,17 +1549,19 @@ clear_threshold = 0.9
}

#[test]
fn semantic_privacy_classifier_config_is_bounded_and_isolated() -> RunnerResult<()> {
let configured = semantic_privacy_config();
Runner::from_toml(&configured)?;
fn semantic_privacy_classifier_configs_are_bounded_and_isolated() -> RunnerResult<()> {
let decision = semantic_privacy_config();
let llm = llm_semantic_privacy_config();
Runner::from_toml(&decision)?;
Runner::from_toml(&llm)?;

for (invalid, expected) in [
(
configured.replace("target = \"privacy_judge\"", "target = \"missing\""),
decision.replace("target = \"privacy_judge\"", "target = \"missing\""),
"requires unknown decision target missing",
),
(
configured.replace("clear_threshold = 0.9", "clear_threshold = 1.1"),
decision.replace("clear_threshold = 0.9", "clear_threshold = 1.1"),
"clear_threshold must be between 0 and 1",
),
(
Expand All @@ -1473,6 +1572,32 @@ clear_threshold = 0.9
),
"overlaps the standard execution lane",
),
(
llm.replace("target = \"privacy_classifier\"", "target = \"missing\""),
"privacy classifier references unknown target missing",
),
(
llm.replace("target = \"privacy_classifier\"", "target = \"weak\""),
"overlaps the standard execution lane",
),
(
llm.replace(
"llm_client = \"privacy_classifier\"",
"llm_client = \"privacy_classifier\"\nomit_body_fields = [\"store\"]",
),
"cannot configure request modifiers",
),
(
llm.replace("timeout_ms = 30000\n", ""),
"requires an llm client timeout_ms",
),
(
llm.replace(
"timeout_ms = 30000",
"timeout_ms = 30000\nforward_auth = true",
),
"cannot use forward_auth",
),
] {
assert!(error_message(&invalid).contains(expected));
}
Expand Down
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