Update retriever.py

retriever.py

@@ -16,7 +16,7 @@ def format_test_type(test_types):
     return str(test_types)
 
 def get_relevant_passages(query, df, top_k=20):
-    """Find most relevant and diverse assessments using semantic search with diversity enhancement."""
+    """Find most relevant assessments using semantic search."""
     # Create a copy to avoid modifying the original dataframe
     df_copy = df.copy()
     
@@ -36,14 +36,6 @@ def get_relevant_passages(query, df, top_k=20):
                     ([x] if not isinstance(x, list) else x)
         )
     
-    # Extract keywords from query for better matching
-    # This helps target specific skills mentioned in the job description
-    keywords = extract_job_keywords(query)
-    print(f"Extracted keywords from query: {keywords}")
-    
-    # Expand query with keywords for better semantic search
-    expanded_query = f"{query} {' '.join(keywords)}"
-    
     # Concatenate all fields into a single string per row for embedding
     corpus = []
     for _, row in df_copy.iterrows():
@@ -54,9 +46,8 @@ def get_relevant_passages(query, df, top_k=20):
             remote = row['remote_support'] if 'remote_support' in row else "Unknown"
             duration = f"{row['duration']} minutes" if pd.notna(row.get('duration')) else "Unknown duration"
             
-            # Enhanced representation with more weight on description and test types
-            text = (f"{description} {description} "  # Repeat description for more weight
-                   f"Test types: {test_types} {test_types}. "  # Repeat test types for more weight
+            text = (f"{description} "
+                   f"Test types: {test_types}. "
                    f"Adaptive support: {adaptive}. "
                    f"Remote support: {remote}. "
                    f"Duration: {duration}.")
@@ -69,7 +60,7 @@ def get_relevant_passages(query, df, top_k=20):
     
     # Generate embeddings
     corpus_embeddings = model.encode(corpus, convert_to_tensor=True)
-    query_embedding = model.encode(expanded_query, convert_to_tensor=True)
+    query_embedding = model.encode(query, convert_to_tensor=True)
     
     # Find most similar
     hits = util.semantic_search(query_embedding, corpus_embeddings, top_k=min(top_k, len(corpus)))[0]
@@ -81,77 +72,4 @@ def get_relevant_passages(query, df, top_k=20):
     # Add score for debugging
     result['score'] = [hit['score'] for hit in hits]
     
-    # Apply diversity enhancement - get a mix of test types if possible
-    if len(result) > top_k / 2:
-        primary_results = result.head(int(top_k * 0.7))  # Keep top 70% based on relevance
-        
-        # Try to find diverse test types for remaining slots
-        test_types_seen = set()
-        for _, row in primary_results.iterrows():
-            if isinstance(row['test_type'], list):
-                for t in row['test_type']:
-                    test_types_seen.add(t)
-            elif isinstance(row['test_type'], str):
-                test_types_seen.add(row['test_type'])
-        
-        # Find assessments with different test types
-        remaining = result.iloc[int(top_k * 0.7):]
-        diverse_picks = []
-        
-        for _, row in remaining.iterrows():
-            if len(diverse_picks) >= (top_k - len(primary_results)):
-                break
-                
-            new_type_found = False
-            if isinstance(row['test_type'], list):
-                for t in row['test_type']:
-                    if t not in test_types_seen:
-                        new_type_found = True
-                        test_types_seen.add(t)
-            elif isinstance(row['test_type'], str) and row['test_type'] not in test_types_seen:
-                new_type_found = True
-                test_types_seen.add(row['test_type'])
-                
-            if new_type_found:
-                diverse_picks.append(row)
-        
-        # Combine primary results with diverse picks
-        if diverse_picks:
-            diverse_df = pd.DataFrame(diverse_picks)
-            result = pd.concat([primary_results, diverse_df]).reset_index(drop=True)
-    
-    return result
-
-def extract_job_keywords(query):
-    """Extract relevant keywords from job description for better matching."""
-    # Common job skill categories that might appear in descriptions
-    skill_categories = [
-        "competencies", "ability", "aptitude", "personality", "behavior", 
-        "leadership", "management", "technical", "analytical", "problem-solving",
-        "communication", "teamwork", "situational", "judgment", "cognitive",
-        "verbal", "numerical", "programming", "coding", "development",
-        "sales", "customer service", "administrative", "executive", "professional",
-        "entry-level", "senior", "mid-level", "assessment", "test"
-    ]
-    
-    # Look for these keywords in the query
-    found_keywords = []
-    query_lower = query.lower()
-    
-    for keyword in skill_categories:
-        if keyword in query_lower:
-            found_keywords.append(keyword)
-    
-    # Add any job titles found
-    job_titles = [
-        "manager", "director", "analyst", "developer", "engineer", "administrator",
-        "assistant", "coordinator", "specialist", "supervisor", "consultant",
-        "executive", "officer", "associate", "representative", "technician",
-        "accountant", "designer", "sales", "support", "professional"
-    ]
-    
-    for title in job_titles:
-        if title in query_lower:
-            found_keywords.append(title)
-    
-    return found_keywords
+    return result