Refactor mentor availability handling across services

- Removed the `available_slots` and `has_availability` fields from the MentorUpsertRequest and RecommendationRequest models, streamlining the data structure.
- Updated the PineconeService and RecommendationService to eliminate references to availability metrics in logging and processing.
- Simplified scoring logic in the rerank_mentors function by removing the availability score calculation, enhancing performance and clarity.
- Adjusted the mentor text building process to exclude available slots information, improving the output format.

models/schemas.py

@@ -45,13 +45,11 @@ class MentorUpsertRequest(BaseModel):
     rating: Optional[float] = Field(None, ge=0.0, le=5.0)
     total_ratings: Optional[int] = Field(None, ge=0)
     session_count: Optional[int] = Field(None, ge=0)
-    available_slots: Optional[int] = Field(None, ge=0)
     schedules: Optional[List[Union[ScheduleDto, Dict[str, Any]]]] = None
     career_id: Optional[int] = None
     skill_ids: Optional[List[int]] = None
     domain_ids: Optional[List[int]] = None
     status: Optional[str] = "ACTIVATED"
-    has_availability: Optional[bool] = False
 
 class MentorUpsertResponse(BaseModel):
     success: bool
@@ -67,7 +65,6 @@ class RecommendationRequest(BaseModel):
     availability: Optional[str] = None
     preferred_availability: Optional[str] = None
     min_rating: Optional[float] = Field(None, ge=0.0, le=5.0)
-    require_availability: Optional[bool] = False
     skill_ids: Optional[List[int]] = None
     domain_ids: Optional[List[int]] = None
     career_id: Optional[int] = None

services/pinecone_service.py

@@ -135,9 +135,7 @@ class PineconeService:
                         f"score={match.score:.4f}, "
                         f"rating={metadata.get('rating', 'N/A')}, "
                         f"total_ratings={metadata.get('total_ratings', 0)}, "
-                        f"available_slots={metadata.get('available_slots', 0)}, "
                         f"session_count={metadata.get('session_count', 0)}, "
-                        f"has_availability={metadata.get('has_availability', False)}, "
                         f"status={metadata.get('status', 'N/A')}, "
                         f"career_id={metadata.get('career_id', 'N/A')}, "
                         f"skill_ids={metadata.get('skill_ids', [])}, "

services/recommendation_service.py

@@ -45,8 +45,6 @@ class RecommendationService:
                 "rating": safe_float(mentor_data.get("rating"), 0.0),
                 "total_ratings": safe_int(mentor_data.get("total_ratings"), 0),
                 "session_count": safe_int(mentor_data.get("session_count"), 0),
-                "available_slots": safe_int(mentor_data.get("available_slots"), 0),
-                "has_availability": bool(mentor_data.get("has_availability", False)),
                 "career_id": safe_int(mentor_data.get("career_id")) if mentor_data.get("career_id") else None,
                 "status": str(mentor_data.get("status") or "ACTIVATED"),
                 "mentor_text": mentor_text

test_api.py

@@ -18,7 +18,6 @@ def test_recommend():
             {"id": 1, "name": "Web Development"}
         ],
         "min_rating": 4.0,
-        "require_availability": True,
         "skill_ids": [1, 2],
         "domain_ids": [1],
         "top_k": 30,
@@ -63,7 +62,6 @@ def test_recommend():
                         if "metadata" in mentor:
                             meta = mentor["metadata"]
                             print(f"   Rating: {meta.get('rating', 'N/A')}")
-                            print(f"   Available Slots: {meta.get('available_slots', 0)}")
                 
                 if "query_text" in result:
                     print(f"\nQuery Text: {result['query_text']}")

utils/scoring.py

@@ -28,12 +28,8 @@ def rerank_mentors(
         semantic_score = reranker_score * settings.SEMANTIC_WEIGHT
         
         rating_score = _calculate_rating_score(metadata.get("rating", 0.0))
-        availability_score = _calculate_availability_score(metadata.get("available_slots", 0))
         
-        rule_based_score = (
-            rating_score * 0.5 +
-            availability_score * 0.5
-        ) * settings.RULE_BASED_WEIGHT
+        rule_based_score = rating_score * settings.RULE_BASED_WEIGHT
         
         final_score = semantic_score + rule_based_score
         
@@ -43,8 +39,7 @@ def rerank_mentors(
             reranker_score,
             metadata,
             mentee_data,
-            rating_score,
-            availability_score
+            rating_score
         )
         
         scored_mentors.append({
@@ -75,20 +70,12 @@ def _calculate_rating_score(rating: float) -> float:
         return 0.0
     return min(rating / 5.0, 1.0)
 
-def _calculate_availability_score(available_slots: int) -> float:
-    if available_slots <= 0:
-        return 0.0
-    if available_slots >= 10:
-        return 1.0
-    return min(available_slots / 10.0, 1.0)
-
 
 def _generate_reason(
     reranker_score: float,
     metadata: Dict[str, Any],
     mentee_data: Dict[str, Any],
-    rating_score: float,
-    availability_score: float
+    rating_score: float
 ) -> str:
     reasons = []
     
@@ -103,10 +90,6 @@ def _generate_reason(
     elif rating >= 4.0:
         reasons.append("High ratings")
     
-    available_slots = metadata.get("available_slots", 0)
-    if available_slots > 0:
-        reasons.append("Has available slots")
-    
     if not reasons:
         reasons.append("Good overall match")
     

utils/text_builder.py

@@ -99,10 +99,6 @@ def build_mentor_text(mentor_data: Dict[str, Any]) -> str:
     if session_count:
         parts.append(f"Sessions Conducted: {session_count}")
     
-    available_slots = mentor_data.get("available_slots", 0)
-    if available_slots:
-        parts.append(f"Available Slots: {available_slots}")
-    
     schedules = mentor_data.get("schedules", [])
     if schedules:
         schedule_summary = _build_schedule_summary(schedules)