Implement fallback filtering in RecommendationService for mentor queries

- Added logic to handle cases where no mentors are found with strict filters (rating/availability) by introducing a fallback mechanism that relaxes these constraints.
- Enhanced logging to provide detailed information about the filtering process and fallback attempts, improving traceability and debugging capabilities.
- Updated the filter construction to include logging for each filter applied based on mentee data.

services/recommendation_service.py

@@ -105,8 +105,41 @@ class RecommendationService:
             pinecone_time = time.perf_counter() - pinecone_start
             logger.info(f"[RECOMMEND] Pinecone query completed in {pinecone_time:.3f}s: found {len(similar_mentors)} candidates")
             
+            if not similar_mentors and filter_dict:
+                has_strict_filters = "rating" in filter_dict or "has_availability" in filter_dict
+                
+                if has_strict_filters:
+                    logger.warning(f"[RECOMMEND] No results with strict filter (rating/availability), trying fallback with relaxed filter")
+                    logger.info(f"[RECOMMEND] Original filter: {filter_dict}")
+                    
+                    fallback_filter = {}
+                    if mentee_data.get("skill_ids"):
+                        fallback_filter["skill_ids"] = {"$in": [str(int(id)) for id in mentee_data["skill_ids"]]}
+                    if mentee_data.get("domain_ids"):
+                        fallback_filter["domain_ids"] = {"$in": [str(int(id)) for id in mentee_data["domain_ids"]]}
+                    if mentee_data.get("career_id"):
+                        fallback_filter["career_id"] = int(mentee_data["career_id"])
+                    if mentee_data.get("status"):
+                        fallback_filter["status"] = str(mentee_data["status"])
+                    else:
+                        fallback_filter["status"] = "ACTIVATED"
+                    
+                    logger.info(f"[RECOMMEND] Fallback filter (removed rating/availability constraints): {fallback_filter}")
+                    
+                    fallback_start = time.perf_counter()
+                    similar_mentors = self.pinecone_service.query_similar(
+                        query_vector=query_embedding,
+                        top_k=top_k,
+                        filter=fallback_filter if fallback_filter else None,
+                        include_metadata=True
+                    )
+                    fallback_time = time.perf_counter() - fallback_start
+                    logger.info(f"[RECOMMEND] Fallback query completed in {fallback_time:.3f}s: found {len(similar_mentors)} candidates")
+                else:
+                    logger.warning(f"[RECOMMEND] No results found even without strict filters")
+            
             if not similar_mentors:
-                logger.warning("[RECOMMEND] No similar mentors found, returning empty list")
+                logger.warning("[RECOMMEND] No similar mentors found after fallback, returning empty list")
                 return []
             
             metadata_start = time.perf_counter()
@@ -162,24 +195,35 @@ class RecommendationService:
         filter_dict = {}
         
         if mentee_data.get("min_rating"):
-            filter_dict["rating"] = {"$gte": float(mentee_data["min_rating"])}
+            min_rating = float(mentee_data["min_rating"])
+            filter_dict["rating"] = {"$gte": min_rating}
+            logger.info(f"[RECOMMEND] Adding rating filter: >= {min_rating}")
         
         if mentee_data.get("require_availability"):
             filter_dict["has_availability"] = True
+            logger.info(f"[RECOMMEND] Adding availability filter: has_availability=True")
         
         if mentee_data.get("skill_ids"):
-            filter_dict["skill_ids"] = {"$in": [str(int(id)) for id in mentee_data["skill_ids"]]}
+            skill_ids = [str(int(id)) for id in mentee_data["skill_ids"]]
+            filter_dict["skill_ids"] = {"$in": skill_ids}
+            logger.info(f"[RECOMMEND] Adding skill_ids filter: {skill_ids}")
         
         if mentee_data.get("domain_ids"):
-            filter_dict["domain_ids"] = {"$in": [str(int(id)) for id in mentee_data["domain_ids"]]}
+            domain_ids = [str(int(id)) for id in mentee_data["domain_ids"]]
+            filter_dict["domain_ids"] = {"$in": domain_ids}
+            logger.info(f"[RECOMMEND] Adding domain_ids filter: {domain_ids}")
         
         if mentee_data.get("career_id"):
-            filter_dict["career_id"] = int(mentee_data["career_id"])
+            career_id = int(mentee_data["career_id"])
+            filter_dict["career_id"] = career_id
+            logger.info(f"[RECOMMEND] Adding career_id filter: {career_id}")
         
         if mentee_data.get("status"):
             filter_dict["status"] = str(mentee_data["status"])
+            logger.info(f"[RECOMMEND] Adding status filter: {mentee_data['status']}")
         else:
             filter_dict["status"] = "ACTIVATED"
+            logger.info(f"[RECOMMEND] Adding default status filter: ACTIVATED")
         
         return filter_dict if filter_dict else None