Improve prompt with semantic names

evaluation/synthetic_mentee_evaluation.json

@@ -0,0 +1,3646 @@
+[
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+    "goals": "I want to learn about Figma UI Design, Prototyping in the fields of E-commerce.",
+    "career_id": 4,
+    "domain_ids": [
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+    ],
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+  },
+  {
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+    "career_id": 3,
+    "domain_ids": [
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+    ],
+    "skill_ids": [
+      108,
+      107
+    ],
+    "ground_truth_mentors": [
+      3,
+      18,
+      27,
+      32,
+      43,
+      48,
+      52,
+      63,
+      87,
+      113,
+      138,
+      143,
+      162,
+      198,
+      213,
+      237,
+      267,
+      303,
+      318,
+      327
+    ]
+  },
+  {
+    "mentee_id": 93,
+    "goals": "I want to learn about Agile/Scrum, Stakeholder Management in the fields of SaaS, Logistics.",
+    "career_id": 5,
+    "domain_ids": [
+      55,
+      54
+    ],
+    "mentor_domain_ids": [
+      55,
+      54
+    ],
+    "skill_ids": [
+      501,
+      502
+    ],
+    "ground_truth_mentors": [
+      13,
+      40,
+      69,
+      85,
+      93,
+      120,
+      125,
+      129,
+      160,
+      173,
+      178,
+      184,
+      188,
+      203,
+      210,
+      218,
+      228,
+      233,
+      243,
+      248
+    ]
+  },
+  {
+    "mentee_id": 94,
+    "goals": "I want to learn about Tableau, SQL in the fields of Banking.",
+    "career_id": 4,
+    "domain_ids": [
+      7
+    ],
+    "mentor_domain_ids": [
+      7
+    ],
+    "skill_ids": [
+      403,
+      401
+    ],
+    "ground_truth_mentors": [
+      14,
+      39,
+      68,
+      84,
+      94,
+      119,
+      123,
+      127,
+      159,
+      174,
+      179,
+      183,
+      189,
+      194,
+      204,
+      209,
+      219,
+      229,
+      234,
+      244
+    ]
+  },
+  {
+    "mentee_id": 95,
+    "goals": "I want to learn about Wireframing, Figma/Adobe XD in the fields of IT Outsourcing.",
+    "career_id": 5,
+    "domain_ids": [
+      209
+    ],
+    "mentor_domain_ids": [
+      209
+    ],
+    "skill_ids": [
+      115,
+      113
+    ],
+    "ground_truth_mentors": [
+      5,
+      20,
+      29,
+      34,
+      45,
+      50,
+      54,
+      65,
+      89,
+      115,
+      140,
+      145,
+      164,
+      200,
+      215,
+      239,
+      269,
+      305,
+      320,
+      329
+    ]
+  },
+  {
+    "mentee_id": 96,
+    "goals": "I want to learn about Growth Hacking, SEO/SEM, Content Strategy in the fields of Digital Banking.",
+    "career_id": 2,
+    "domain_ids": [
+      13
+    ],
+    "mentor_domain_ids": [
+      13
+    ],
+    "skill_ids": [
+      202,
+      201,
+      203
+    ],
+    "ground_truth_mentors": [
+      12,
+      37,
+      67,
+      82,
+      92,
+      117,
+      122,
+      128,
+      157,
+      172,
+      177,
+      182,
+      187,
+      192,
+      202,
+      207,
+      217,
+      227,
+      232,
+      242
+    ]
+  },
+  {
+    "mentee_id": 97,
+    "goals": "I want to learn about Node.js, React.js, System Design in the fields of Fintech.",
+    "career_id": 1,
+    "domain_ids": [
+      201
+    ],
+    "mentor_domain_ids": [
+      201
+    ],
+    "skill_ids": [
+      102,
+      101,
+      103
+    ],
+    "ground_truth_mentors": [
+      1,
+      11,
+      16,
+      26,
+      31,
+      36,
+      41,
+      46,
+      51,
+      61,
+      66,
+      81,
+      86,
+      91,
+      111,
+      116,
+      121,
+      126,
+      136,
+      141
+    ]
+  },
+  {
+    "mentee_id": 98,
+    "goals": "I want to learn about System Design in the fields of Fintech, Thương mại điện tử.",
+    "career_id": 1,
+    "domain_ids": [
+      201,
+      202
+    ],
+    "mentor_domain_ids": [
+      201,
+      202
+    ],
+    "skill_ids": [
+      103
+    ],
+    "ground_truth_mentors": [
+      1,
+      11,
+      16,
+      26,
+      31,
+      36,
+      41,
+      46,
+      51,
+      61,
+      66,
+      81,
+      86,
+      91,
+      111,
+      116,
+      121,
+      126,
+      136,
+      141
+    ]
+  },
+  {
+    "mentee_id": 99,
+    "goals": "I want to learn about Python, Tableau in the fields of E-commerce, SaaS.",
+    "career_id": 4,
+    "domain_ids": [
+      2,
+      5
+    ],
+    "mentor_domain_ids": [
+      2,
+      5
+    ],
+    "skill_ids": [
+      31,
+      32
+    ],
+    "ground_truth_mentors": [
+      8,
+      23,
+      148,
+      168,
+      293,
+      298,
+      323,
+      333,
+      348,
+      398
+    ]
+  },
+  {
+    "mentee_id": 100,
+    "goals": "I want to learn about Tableau in the fields of IT Services, Advertising.",
+    "career_id": 4,
+    "domain_ids": [
+      16,
+      15
+    ],
+    "mentor_domain_ids": [
+      16,
+      15
+    ],
+    "skill_ids": [
+      403
+    ],
+    "ground_truth_mentors": [
+      14,
+      39,
+      68,
+      84,
+      94,
+      119,
+      123,
+      127,
+      159,
+      174,
+      179,
+      183,
+      189,
+      194,
+      204,
+      209,
+      219,
+      229,
+      234,
+      244
+    ]
+  }
+]

evaluation/upsert_mentors.py

@@ -123,13 +123,13 @@ def main():
     parser.add_argument(
         "--mentors-file",
         type=str,
-        default=os.path.join(script_dir, "sample_mentor_upsert.json"),
+        default=os.path.join(script_dir, "mentor_profiles_1000.json"),
         help="Path to JSON file containing mentors data"
     )
     parser.add_argument(
         "--base-url",
         type=str,
-        default="https://mentorme666-mentorme.hf.space/api/v1",
+        default="https://jakenb-mentorme858.hf.space/api/v1",
         help="Base URL of the API"
     )
     parser.add_argument(

scripts/generate_evaluation_data.py

@@ -0,0 +1,102 @@
+import json
+import random
+import os
+import argparse
+from typing import List, Dict, Any
+
+def load_json(file_path: str) -> List[Dict[str, Any]]:
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+def generate_synthetic_data(mentors: List[Dict[str, Any]], num_mentees: int = 50) -> List[Dict[str, Any]]:
+    synthetic_mentees = []
+    
+    # Collect all unique skills, domains, careers for random sampling if needed
+    all_skills = {} # id -> name
+    all_domains = {} # id -> name
+    for m in mentors:
+        for s in m.get('skills', []):
+            if isinstance(s, dict): all_skills[s['id']] = s['name']
+        for d in m.get('domains', []):
+            if isinstance(d, dict): all_domains[d['id']] = d['name']
+
+    print(f"Generating {num_mentees} synthetic mentees...")
+
+    for i in range(num_mentees):
+        # Pick a random "persona" based on a random mentor (to ensure matches exist)
+        target_mentor = random.choice(mentors)
+        
+        # Create a mentee that looks somewhat like this mentor's target audience
+        # e.g., wants to learn the skills this mentor has
+        
+        mentor_skills = target_mentor.get('skill_ids', [])
+        mentor_domains = target_mentor.get('domain_ids', [])
+        career_id = target_mentor.get('career_id')
+        
+        # Randomly pick a subset of skills/domains to form the query
+        if not mentor_skills: continue
+        
+        num_skills = random.randint(1, min(len(mentor_skills), 3))
+        selected_skills = random.sample(mentor_skills, num_skills)
+        
+        num_domains = random.randint(1, min(len(mentor_domains), 2)) if mentor_domains else 0
+        selected_domains = random.sample(mentor_domains, num_domains) if num_domains > 0 else []
+        
+        # Build goal string
+        skill_names = [all_skills.get(sid, str(sid)) for sid in selected_skills]
+        domain_names = [all_domains.get(did, str(did)) for did in selected_domains]
+        
+        goals = f"I want to learn about {', '.join(skill_names)}"
+        if domain_names:
+            goals += f" in the fields of {', '.join(domain_names)}"
+        goals += "."
+        
+        # STRICT MATCHING LOGIC for GROUND TRUTH
+        # A mentor is considered "relevant" if they have coverage of the requested skills
+        relevant_mentors = []
+        for m in mentors:
+            m_skills = set(m.get('skill_ids', []))
+            # Calculate overlap
+            overlap = m_skills.intersection(set(selected_skills))
+            # Score: percentage of requested skills covered
+            match_score = len(overlap) / len(selected_skills)
+            
+            if match_score >= 0.5: # At least 50% skill match
+                relevant_mentors.append(m['mentor_id'])
+                
+        # Limit ground truth to top 20 to avoid huge lists
+        ground_truth = relevant_mentors[:20]
+        
+        mentee = {
+            "mentee_id": i + 1,
+            "goals": goals,
+            "career_id": career_id,
+            "domain_ids": selected_domains,
+            "mentor_domain_ids": selected_domains, # Simulate preference
+            "skill_ids": selected_skills,
+            "ground_truth_mentors": ground_truth
+        }
+        synthetic_mentees.append(mentee)
+        
+    return synthetic_mentees
+
+def main():
+    parser = argparse.ArgumentParser(description="Generate synthetic evaluation data")
+    parser.add_argument("mentors_file", help="Path to input mentors JSON file")
+    parser.add_argument("output_file", help="Path to output evaluation JSON file")
+    parser.add_argument("--count", type=int, default=50, help="Number of mentees to generate")
+    
+    args = parser.parse_args()
+    
+    print(f"Loading mentors from {args.mentors_file}...")
+    mentors = load_json(args.mentors_file)
+    
+    data = generate_synthetic_data(mentors, args.count)
+    
+    print(f"Writing {len(data)} mentees to {args.output_file}...")
+    with open(args.output_file, 'w', encoding='utf-8') as f:
+        json.dump(data, f, ensure_ascii=False, indent=2)
+    print("Done.")
+
+if __name__ == "__main__":
+    main()