Update core/pipeline_1/logic.py

core/pipeline_1/logic.py

@@ -43,12 +43,10 @@ class PipelineLLMOnly:
         """Streaming version of Pipeline 1"""
         start_time = time.time()
         
-        # 1. Filter
+        yield {"type": "status", "data": "APPLYING METADATA FILTERS..."}
         relevant_metadata = self.filter.filter_papers(query, top_n=self.top_n)
-        sources = [p['title'] for p in relevant_metadata]
-        yield {"type": "sources", "data": sources}
         
-        # 2. Fetch Context
+        yield {"type": "status", "data": f"FOUND {len(relevant_metadata)} PAPERS. FETCHING CONTEXT..."}
         context_parts = []
         abstracts = []
         for i, paper in enumerate(relevant_metadata):
@@ -66,8 +64,9 @@ class PipelineLLMOnly:
             context_parts.append(f"--- PAPER {i+1} ---\n{content}\n")
 
         full_context = "\n".join(context_parts)
+        yield {"type": "sources", "data": [p['title'] for p in relevant_metadata]}
         
-        # 3. Prompt
+        yield {"type": "status", "data": "GENERATING BASELINE RESPONSE..."}
         system_prompt = (
             "You are an AI research assistant. Answer the user query based ONLY on the provided papers. "
             "If the answer isn't there, say so. Cite sources like [Paper 1].\n\n"
@@ -95,6 +94,7 @@ class PipelineLLMOnly:
                     last_usage = chunk.usage_metadata
 
             # 5. Process Metrics (after stream finishes)
+            yield {"type": "status", "data": "GENERATION COMPLETE. CALCULATING METRICS..."}
             stats = self.metrics.process_metrics(
                 client=self.client,
                 query=query,
@@ -107,6 +107,7 @@ class PipelineLLMOnly:
                 model_name=self.model_name
             )
             yield {"type": "metrics", "data": stats}
+            yield {"type": "status", "data": "BASELINE PIPELINE SUCCESSFUL."}
             
         except Exception as e:
             logger.error(f"Stream error: {e}")