added output parameter

backend/app/services/llm/guardrails.py

@@ -17,17 +17,20 @@ def run_guardrails_validation(
     project_id: int | None,
     organization_id: int | None,
     suppress_pass_logs: bool = True,
+    output_text: str | None = None,
 ) -> dict[str, Any]:
     """
     Call the Kaapi guardrails service to validate and process input text.
 
     Args:
-        input_text: Text to validate and process.
+        input_text: User query text, maps to payload["input"].
         guardrail_config: List of validator configurations to apply.
         job_id: Unique identifier for the request.
         project_id: Project identifier expected by guardrails API.
         organization_id: Organization identifier expected by guardrails API.
         suppress_pass_logs: Whether to suppress successful validation logs in guardrails service.
+        output_text: LLM response text, maps to payload["output"]. Required for validators
+            that evaluate input/output pairs.
 
     Returns:
         JSON response from the guardrails service with validation results.
@@ -39,14 +42,17 @@ def run_guardrails_validation(
         for validator in guardrail_config
     ]
 
-    payload = {
+    payload: dict[str, Any] = {
         "request_id": str(job_id),
         "project_id": project_id,
         "organization_id": organization_id,
         "input": input_text,
         "validators": validators,
     }
 
+    if output_text is not None:
+        payload["output"] = output_text
+
     headers = {
         "accept": "application/json",
         "Authorization": f"Bearer {settings.KAAPI_GUARDRAILS_AUTH}",

backend/app/services/llm/jobs.py

@@ -425,6 +425,7 @@ def apply_output_guardrails(
     job_id: UUID,
     project_id: int,
     organization_id: int,
+    input_text: str | None = None,
 ) -> tuple[BlockResult, str | None]:
     """Apply output guardrails from a config_blob. Shared by /llm/call and /llm/chain.
 
@@ -451,14 +452,15 @@ def apply_output_guardrails(
     if not output_guardrails:
         return result, None
 
-    output_text = result.response.response.output.content.value
+    llm_output = result.response.response.output.content.value
     safe = run_guardrails_validation(
-        output_text,
+        input_text or "",
         output_guardrails,
         job_id,
         project_id,
         organization_id,
         suppress_pass_logs=True,
+        output_text=llm_output,
     )
 
     logger.info(
@@ -956,6 +958,7 @@ def execute_llm_call(
                     job_id=job_id,
                     project_id=project_id,
                     organization_id=organization_id,
+                    input_text=original_input_value,
                 )
                 if output_error:
                     out_guard_span.set_status(

backend/app/tests/services/llm/test_guardrails.py

@@ -136,6 +136,54 @@ def test_run_guardrails_validation_serializes_validator_models(mock_client_cls)
     assert kwargs["json"]["validators"] == [{"validator_config_id": str(vid)}]
 
 
+@patch("app.services.llm.guardrails.httpx.Client")
+def test_run_guardrails_validation_includes_output_in_payload(mock_client_cls) -> None:
+    mock_response = MagicMock()
+    mock_response.raise_for_status.return_value = None
+    mock_response.json.return_value = {"success": True}
+
+    mock_client = MagicMock()
+    mock_client.post.return_value = mock_response
+    mock_client_cls.return_value.__enter__.return_value = mock_client
+
+    run_guardrails_validation(
+        TEST_TEXT,
+        TEST_CONFIG,
+        TEST_JOB_ID,
+        TEST_PROJECT_ID,
+        TEST_ORGANIZATION_ID,
+        output_text="some llm response",
+    )
+
+    _, kwargs = mock_client.post.call_args
+    assert kwargs["json"]["input"] == TEST_TEXT
+    assert kwargs["json"]["output"] == "some llm response"
+
+
+@patch("app.services.llm.guardrails.httpx.Client")
+def test_run_guardrails_validation_omits_output_from_payload_by_default(
+    mock_client_cls,
+) -> None:
+    mock_response = MagicMock()
+    mock_response.raise_for_status.return_value = None
+    mock_response.json.return_value = {"success": True}
+
+    mock_client = MagicMock()
+    mock_client.post.return_value = mock_response
+    mock_client_cls.return_value.__enter__.return_value = mock_client
+
+    run_guardrails_validation(
+        TEST_TEXT,
+        TEST_CONFIG,
+        TEST_JOB_ID,
+        TEST_PROJECT_ID,
+        TEST_ORGANIZATION_ID,
+    )
+
+    _, kwargs = mock_client.post.call_args
+    assert "output" not in kwargs["json"]
+
+
 @patch("app.services.llm.guardrails.httpx.Client")
 def test_run_guardrails_validation_allows_disable_suppress_pass_logs(
     mock_client_cls,

backend/app/tests/services/llm/test_jobs.py

@@ -995,6 +995,103 @@ def test_guardrails_sanitize_output_after_provider(
 
         assert "REDACTED" in result["data"]["response"]["output"]["content"]["value"]
 
+    def test_guardrails_output_validation_sends_input_output_pair(
+        self, db, job_env, job_for_execution
+    ):
+        env = job_env
+        user_query = "What is my Aadhar number?"
+        llm_output = "Your Aadhar number is 1234-5678-9012"
+
+        env["mock_llm_response"].response.output.content.value = llm_output
+        env["provider"].execute.return_value = (env["mock_llm_response"], None)
+
+        with (
+            patch("app.services.llm.jobs.run_guardrails_validation") as mock_guardrails,
+            patch("app.services.llm.jobs.list_validators_config") as mock_fetch_configs,
+        ):
+            mock_guardrails.return_value = {
+                "success": True,
+                "bypassed": False,
+                "data": {
+                    "safe_text": "Your Aadhar number is [REDACTED]",
+                    "rephrase_needed": False,
+                },
+            }
+            mock_fetch_configs.return_value = (
+                [],
+                [{"type": "pii_remover", "stage": "output"}],
+            )
+
+            request_data = {
+                "query": {"input": user_query},
+                "config": {
+                    "blob": {
+                        "completion": {
+                            "provider": "openai-native",
+                            "type": "text",
+                            "params": {"model": "gpt-4o"},
+                        },
+                        "input_guardrails": [],
+                        "output_guardrails": [
+                            {"validator_config_id": VALIDATOR_CONFIG_ID_2}
+                        ],
+                    }
+                },
+            }
+            self._execute_job(job_for_execution, db, request_data)
+
+        mock_guardrails.assert_called_once()
+        _, kwargs = mock_guardrails.call_args
+        assert kwargs.get("output_text") == llm_output
+        assert mock_guardrails.call_args[0][0] == user_query
+
+    def test_guardrails_bypass_does_not_modify_output(
+        self, db, job_env, job_for_execution
+    ):
+        env = job_env
+        original_llm_output = "Your Aadhar number is 1234-5678-9012"
+
+        env["mock_llm_response"].response.output.content.value = original_llm_output
+        env["provider"].execute.return_value = (env["mock_llm_response"], None)
+
+        with (
+            patch("app.services.llm.jobs.run_guardrails_validation") as mock_guardrails,
+            patch("app.services.llm.jobs.list_validators_config") as mock_fetch_configs,
+        ):
+            mock_guardrails.return_value = {
+                "success": False,
+                "bypassed": True,
+                "data": {"safe_text": original_llm_output, "rephrase_needed": False},
+            }
+            mock_fetch_configs.return_value = (
+                [],
+                [{"type": "pii_remover", "stage": "output"}],
+            )
+
+            request_data = {
+                "query": {"input": "some question"},
+                "config": {
+                    "blob": {
+                        "completion": {
+                            "provider": "openai-native",
+                            "type": "text",
+                            "params": {"model": "gpt-4o"},
+                        },
+                        "input_guardrails": [],
+                        "output_guardrails": [
+                            {"validator_config_id": VALIDATOR_CONFIG_ID_2}
+                        ],
+                    }
+                },
+            }
+            result = self._execute_job(job_for_execution, db, request_data)
+
+        assert result["success"] is True
+        assert (
+            result["data"]["response"]["output"]["content"]["value"]
+            == original_llm_output
+        )
+
     def test_guardrails_skip_output_validation_for_audio_output(
         self, db, job_env, job_for_execution
     ):