diff --git a/src/codegate/api/v1_processing.py b/src/codegate/api/v1_processing.py
index 6606a882..cb2c0c9b 100644
--- a/src/codegate/api/v1_processing.py
+++ b/src/codegate/api/v1_processing.py
@@ -16,6 +16,7 @@
     PartialQuestionAnswer,
     PartialQuestions,
     QuestionAnswer,
+    QuestionType,
     TokenUsageAggregate,
     TokenUsageByModel,
 )
@@ -384,8 +385,13 @@ async def match_conversations(
                     selected_partial_qa = partial_qa
                     break
 
-            #  check if we have a question and answer, otherwise do not add it
-            if selected_partial_qa and selected_partial_qa.answer is not None:
+            # check if we have a question and answer, otherwise do not add it
+            # if the question is a FIM question, we should add it even if there is no answer
+            # not add Chat questions without answers
+            if selected_partial_qa and (
+                selected_partial_qa.answer is not None
+                or selected_partial_qa.partial_questions.type == QuestionType.fim
+            ):
                 # if we don't have a first question, set it. We will use it
                 # to set the conversation timestamp and provider
                 first_partial_qa = first_partial_qa or selected_partial_qa
@@ -396,7 +402,7 @@ async def match_conversations(
                 alerts.extend(deduped_alerts)
                 token_usage_agg.add_model_token_usage(selected_partial_qa.model_token_usage)
 
-        # only add conversation if we have some answers
+        # if we have a conversation with at least one question and answer
         if len(questions_answers) > 0 and first_partial_qa is not None:
             if token_usage_agg.token_usage.input_tokens == 0:
                 token_usage_agg = None
@@ -435,7 +441,6 @@ async def parse_messages_in_conversations(
     Get all the messages from the database and return them as a list of conversations.
     """
     partial_question_answers = await _process_prompt_output_to_partial_qa(prompts_outputs)
-
     conversations, map_q_id_to_conversation = await match_conversations(partial_question_answers)
     return conversations, map_q_id_to_conversation
 
@@ -510,9 +515,6 @@ async def remove_duplicate_alerts(alerts: List[v1_models.Alert]) -> List[v1_mode
     for alert in sorted(
         alerts, key=lambda x: x.timestamp, reverse=True
     ):  # Sort alerts by timestamp descending
-        if alert.trigger_type != "codegate-secrets":
-            unique_alerts.append(alert)
-            continue
 
         # Extract trigger string content until "Context"
         trigger_string_content = alert.trigger_string.split("Context")[0]
diff --git a/src/codegate/db/connection.py b/src/codegate/db/connection.py
index 78a2d607..2d56fccd 100644
--- a/src/codegate/db/connection.py
+++ b/src/codegate/db/connection.py
@@ -610,7 +610,7 @@ async def get_prompts_with_output_alerts_usage_by_workspace_id(
             LEFT JOIN outputs o ON p.id = o.prompt_id
             LEFT JOIN alerts a ON p.id = a.prompt_id
             WHERE p.workspace_id = :workspace_id
-            AND a.trigger_category LIKE :trigger_category
+            AND (a.trigger_category = :trigger_category OR a.trigger_category is NULL)
             ORDER BY o.timestamp DESC, a.timestamp DESC
             """  # noqa: E501
         )
@@ -622,7 +622,6 @@ async def get_prompts_with_output_alerts_usage_by_workspace_id(
                 IntermediatePromptWithOutputUsageAlerts, sql, conditions, should_raise=True
             )
         )
-
         prompts_dict: Dict[str, GetPromptWithOutputsRow] = {}
         for row in rows:
             prompt_id = row.prompt_id
diff --git a/src/codegate/providers/base.py b/src/codegate/providers/base.py
index d22afcc0..452fe08b 100644
--- a/src/codegate/providers/base.py
+++ b/src/codegate/providers/base.py
@@ -96,6 +96,24 @@ def _get_base_url(self) -> str:
         config = Config.get_config()
         return config.provider_urls.get(self.provider_route_name) if config else ""
 
+    async def process_stream_no_pipeline(
+        self, stream: AsyncIterator[ModelResponse], context: PipelineContext
+    ) -> AsyncIterator[ModelResponse]:
+        """
+        Process a stream when there is no pipeline.
+        This is needed to record the output stream chunks for FIM.
+        """
+        try:
+            async for chunk in stream:
+                context.add_output(chunk)
+                yield chunk
+        except Exception as e:
+            # Log exception and stop processing
+            logger.error(f"Error processing stream: {e}")
+            raise e
+        finally:
+            await self._db_recorder.record_context(context)
+
     async def _run_output_stream_pipeline(
         self,
         input_context: PipelineContext,
@@ -121,7 +139,7 @@ async def _run_output_stream_pipeline(
             and self.provider_route_name != "anthropic"
         ):
             logger.info("No output pipeline steps configured, passing through")
-            return model_stream
+            return self.process_stream_no_pipeline(model_stream, input_context)
 
         normalized_stream = self._output_normalizer.normalize_streaming(model_stream)