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a6ebe06 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | #!/usr/bin/env python3
"""
Debug script to test missing skills recomputation
"""
import sys
import os
sys.path.insert(0, '/home/ec2-user/environment/coachable-course-agent')
from coachable_course_agent.memory_store import load_user_profile, save_updated_goal
from langchain_community.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
import json
def test_missing_skills_recomputation():
print("=== Testing Missing Skills Recomputation ===")
# First, check what users we have
memory_dir = "/home/ec2-user/environment/coachable-course-agent/data/memory"
users = [f.replace('.json', '') for f in os.listdir(memory_dir) if f.endswith('.json')]
print(f"Available users: {users}")
if not users:
print("No users found!")
return
# Use the first user for testing
user_id = users[0]
print(f"Testing with user: {user_id}")
# Load their current profile
profile = load_user_profile(user_id)
if not profile:
print("Failed to load profile!")
return
print(f"Current profile keys: {list(profile.keys())}")
print(f"Current goal: {profile.get('goal', 'None')}")
print(f"Current missing skills count: {len(profile.get('missing_skills', []))}")
# Show some missing skills if they exist
missing_skills = profile.get('missing_skills', [])
if missing_skills:
print("Current missing skills (first 3):")
for i, skill in enumerate(missing_skills[:3]):
if isinstance(skill, dict):
print(f" {i+1}. {skill.get('preferredLabel', skill)}")
else:
print(f" {i+1}. {skill}")
else:
print("No missing skills currently!")
# Load ESCO vectorstore
print("\nLoading ESCO vectorstore...")
try:
embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
esco_vectorstore = Chroma(
persist_directory="data/esco_chroma",
embedding_function=embedding_model
)
print("ESCO vectorstore loaded successfully")
except Exception as e:
print(f"Failed to load ESCO vectorstore: {e}")
return
# Test updating goal with the same goal to trigger recomputation
old_goal = profile.get('goal', '')
if not old_goal:
old_goal = "I want to learn data science and machine learning skills"
print(f"No existing goal, using test goal: {old_goal}")
print(f"\nTesting goal update with: {old_goal}")
print("This should trigger missing skills recomputation...")
# Call save_updated_goal which should recompute missing skills
try:
result = save_updated_goal(user_id, old_goal, esco_vectorstore)
print(f"Result: {result}")
except Exception as e:
print(f"Error during goal update: {e}")
import traceback
traceback.print_exc()
return
# Check the profile again
updated_profile = load_user_profile(user_id)
updated_missing_skills = updated_profile.get('missing_skills', [])
print(f"\nAfter goal update:")
print(f"Missing skills count: {len(updated_missing_skills)}")
if updated_missing_skills:
print("Updated missing skills (first 5):")
for i, skill in enumerate(updated_missing_skills[:5]):
if isinstance(skill, dict):
print(f" {i+1}. {skill.get('preferredLabel', skill)}")
else:
print(f" {i+1}. {skill}")
else:
print("Still no missing skills after recomputation!")
print("\n=== Test Complete ===")
if __name__ == "__main__":
test_missing_skills_recomputation()
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