fix(rlsapi): improve exception handling and prevent sensitive data leakage

Add missing exception handlers for RuntimeError (context_length → 413)
and OpenAIAPIStatusError. Use handle_known_apistatus_errors() for smarter
status code mapping instead of generic 500s. Replace raw str(e) in
client-facing cause fields with safe generic messages while preserving
full details in server-side logs. Extract common error bookkeeping into
_record_inference_failure() helper to reduce duplication.

Signed-off-by: Major Hayden <major@redhat.com>

Author: major

src/app/endpoints/rlsapi_v1.py

@@ -10,6 +10,7 @@
 from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Request
 from llama_stack_api.openai_responses import OpenAIResponseObject
 from llama_stack_client import APIConnectionError, APIStatusError, RateLimitError
+from openai._exceptions import APIStatusError as OpenAIAPIStatusError
 
 import constants
 import metrics
@@ -23,6 +24,7 @@
 from models.responses import (
     ForbiddenResponse,
     InternalServerErrorResponse,
+    PromptTooLongResponse,
     QuotaExceededResponse,
     ServiceUnavailableResponse,
     UnauthorizedResponse,
@@ -31,6 +33,7 @@
 from models.rlsapi.requests import RlsapiV1InferRequest, RlsapiV1SystemInfo
 from models.rlsapi.responses import RlsapiV1InferData, RlsapiV1InferResponse
 from observability import InferenceEventData, build_inference_event, send_splunk_event
+from utils.query import handle_known_apistatus_errors
 from utils.responses import extract_text_from_response_output_item, get_mcp_tools
 from utils.suid import get_suid
 from log import get_logger
@@ -73,6 +76,7 @@ def _get_rh_identity_context(request: Request) -> tuple[str, str]:
         examples=["missing header", "missing token"]
     ),
     403: ForbiddenResponse.openapi_response(examples=["endpoint"]),
+    413: PromptTooLongResponse.openapi_response(),
     422: UnprocessableEntityResponse.openapi_response(),
     429: QuotaExceededResponse.openapi_response(),
     500: InternalServerErrorResponse.openapi_response(examples=["generic"]),
@@ -229,6 +233,41 @@ def _queue_splunk_event(  # pylint: disable=too-many-arguments,too-many-position
     background_tasks.add_task(send_splunk_event, event, sourcetype)
 
 
+def _record_inference_failure(  # pylint: disable=too-many-arguments,too-many-positional-arguments
+    background_tasks: BackgroundTasks,
+    infer_request: RlsapiV1InferRequest,
+    request: Request,
+    request_id: str,
+    error: Exception,
+    start_time: float,
+) -> float:
+    """Record metrics and queue Splunk event for an inference failure.
+
+    Args:
+        background_tasks: FastAPI background tasks for async event sending.
+        infer_request: The original inference request.
+        request: The FastAPI request object.
+        request_id: Unique identifier for the request.
+        error: The exception that caused the failure.
+        start_time: Monotonic clock time when inference started.
+
+    Returns:
+        The total inference time in seconds.
+    """
+    inference_time = time.monotonic() - start_time
+    metrics.llm_calls_failures_total.inc()
+    _queue_splunk_event(
+        background_tasks,
+        infer_request,
+        request,
+        request_id,
+        str(error),
+        inference_time,
+        "infer_error",
+    )
+    return inference_time
+
+
 @router.post("/infer", responses=infer_responses)
 @authorize(Action.RLSAPI_V1_INFER)
 async def infer_endpoint(
@@ -265,6 +304,7 @@ async def infer_endpoint(
 
     input_source = infer_request.get_input_source()
     instructions = _build_instructions(infer_request.context.systeminfo)
+    model_id = _get_default_model_id()
     mcp_tools = get_mcp_tools(configuration.mcp_servers)
     logger.debug(
         "Request %s: Combined input source length: %d", request_id, len(input_source)
@@ -276,58 +316,44 @@ async def infer_endpoint(
             input_source, instructions, tools=mcp_tools
         )
         inference_time = time.monotonic() - start_time
+    except RuntimeError as e:
+        if "context_length" in str(e).lower():
+            _record_inference_failure(
+                background_tasks, infer_request, request, request_id, e, start_time
+            )
+            logger.error("Prompt too long for request %s: %s", request_id, e)
+            error_response = PromptTooLongResponse(model=model_id)
+            raise HTTPException(**error_response.model_dump()) from e
+        raise
     except APIConnectionError as e:
-        inference_time = time.monotonic() - start_time
-        metrics.llm_calls_failures_total.inc()
+        _record_inference_failure(
+            background_tasks, infer_request, request, request_id, e, start_time
+        )
         logger.error(
             "Unable to connect to Llama Stack for request %s: %s", request_id, e
         )
-        _queue_splunk_event(
-            background_tasks,
-            infer_request,
-            request,
-            request_id,
-            str(e),
-            inference_time,
-            "infer_error",
-        )
-        response = ServiceUnavailableResponse(
+        error_response = ServiceUnavailableResponse(
             backend_name="Llama Stack",
-            cause=str(e),
+            cause="Unable to connect to the inference backend",
         )
-        raise HTTPException(**response.model_dump()) from e
+        raise HTTPException(**error_response.model_dump()) from e
     except RateLimitError as e:
-        inference_time = time.monotonic() - start_time
-        metrics.llm_calls_failures_total.inc()
+        _record_inference_failure(
+            background_tasks, infer_request, request, request_id, e, start_time
+        )
         logger.error("Rate limit exceeded for request %s: %s", request_id, e)
-        _queue_splunk_event(
-            background_tasks,
-            infer_request,
-            request,
-            request_id,
-            str(e),
-            inference_time,
-            "infer_error",
+        error_response = QuotaExceededResponse(
+            response="The quota has been exceeded",
+            cause="Rate limit exceeded, please try again later",
         )
-        response = QuotaExceededResponse(
-            response="The quota has been exceeded", cause=str(e)
+        raise HTTPException(**error_response.model_dump()) from e
+    except (APIStatusError, OpenAIAPIStatusError) as e:
+        _record_inference_failure(
+            background_tasks, infer_request, request, request_id, e, start_time
         )
-        raise HTTPException(**response.model_dump()) from e
-    except APIStatusError as e:
-        inference_time = time.monotonic() - start_time
-        metrics.llm_calls_failures_total.inc()
         logger.exception("API error for request %s: %s", request_id, e)
-        _queue_splunk_event(
-            background_tasks,
-            infer_request,
-            request,
-            request_id,
-            str(e),
-            inference_time,
-            "infer_error",
-        )
-        response = InternalServerErrorResponse.generic()
-        raise HTTPException(**response.model_dump()) from e
+        error_response = handle_known_apistatus_errors(e, model_id)
+        raise HTTPException(**error_response.model_dump()) from e
 
     if not response_text:
         logger.warning("Empty response from LLM for request %s", request_id)