improved qan to use context aw

Author: Ncode01

agents/__pycache__/orchestrator.cpython-312.pyc

@@ -103,7 +103,7 @@ def run(self, query: str, chat_history: list = None) -> dict:
         # 1. Analyze Query
         analysis_start_time = time.time()
         logger.info("Step 1: Analyzing query...")
-        query_analysis = self.query_analyzer.run(query=query)
+        query_analysis = self.query_analyzer.run(query=query, chat_history=chat_history)  # Pass chat_history
         refined_query = query_analysis.get("refined_query", query)
         analysis_duration = time.time() - analysis_start_time
         logger.info(f"Step 1: Query analysis complete ({analysis_duration:.4f}s). Result: {query_analysis}")

agents/__pycache__/query_analyzer.cpython-312.pyc

@@ -18,9 +18,9 @@
 
 class QueryAnalyzerAgent(BaseAgent):
     """Agent responsible for analyzing the user query."""
-    def run(self, query: str) -> dict:
+    def run(self, query: str, chat_history: list = None) -> dict: # Add chat_history parameter
         start_time = time.time()
-        logger.debug(f"Analyzing query: '{query}'")
+        logger.debug(f"Analyzing query: '{query}' with history: {chat_history is not None}") # Log if history is present
         if not nlp:
             logger.warning("spaCy model not loaded, falling back to basic analysis.")
             # Fallback basic extraction (similar to previous web.py logic)
@@ -29,7 +29,8 @@ def run(self, query: str) -> dict:
             keywords = list(set([k.strip().lower() for k in keywords if k]))
             entities = list(set([e.strip() for e in entities if len(e.split()) > 1 or e in keywords]))
         else:
-            # Use spaCy for extraction
+            # TODO: Incorporate chat_history into spaCy analysis if needed
+            # For now, just process the current query
             doc = nlp(query)
 
             # Extract Named Entities (GPE, PERSON, ORG, LOC, EVENT, DATE etc.)
@@ -60,7 +61,9 @@ def run(self, query: str) -> dict:
             "original_query": query, # Add the original query here
             "keywords": keywords,
             "entities": entities,
-            "query_type": query_type
+            "query_type": query_type,
+            # Optionally include history info if used
+            # "history_considered": chat_history is not None 
         }
 
         end_time = time.time()