feat- control lakera ai per llm call

litellm/proxy/guardrails/guardrail_helpers.py

@@ -0,0 +1,46 @@
+from litellm._logging import verbose_proxy_logger
+from litellm.proxy.guardrails.init_guardrails import guardrail_name_config_map
+from litellm.types.guardrails import *
+
+
+async def should_proceed_based_on_metadata(data: dict, guardrail_name: str) -> bool:
+    """
+    checks if this guardrail should be applied to this call
+    """
+    if "metadata" in data and isinstance(data["metadata"], dict):
+        if "guardrails" in data["metadata"]:
+            # expect users to pass
+            # guardrails: { prompt_injection: true, rail_2: false }
+            request_guardrails = data["metadata"]["guardrails"]
+            verbose_proxy_logger.debug(
+                "Guardrails %s passed in request - checking which to apply",
+                request_guardrails,
+            )
+
+            requested_callback_names = []
+
+            # get guardrail configs from `init_guardrails.py`
+            # for all requested guardrails -> get their associated callbacks
+            for _guardrail_name, should_run in request_guardrails.items():
+                if should_run is False:
+                    verbose_proxy_logger.debug(
+                        "Guardrail %s skipped because request set to False",
+                        _guardrail_name,
+                    )
+                    continue
+
+                # lookup the guardrail in guardrail_name_config_map
+                guardrail_item: GuardrailItem = guardrail_name_config_map[
+                    _guardrail_name
+                ]
+
+                guardrail_callbacks = guardrail_item.callbacks
+                requested_callback_names.extend(guardrail_callbacks)
+
+            verbose_proxy_logger.debug(
+                "requested_callback_names %s", requested_callback_names
+            )
+            if guardrail_name in requested_callback_names:
+                return True
+
+    return False