test_docker_qdrant.py•10.1 kB
#!/usr/bin/env python3
"""
Test MongoDB-style operators with Docker Qdrant (no indexes required!)
"""
import asyncio
from datetime import datetime
from api.db.qdrant_store import QdrantStore
async def test_docker_qdrant():
"""Test with Docker Qdrant - no index setup needed!"""
print("🐳 Connecting to Docker Qdrant...")
print(" URL: http://localhost:6333")
print(" Collection: claude_slack_test\n")
try:
# Connect to Docker Qdrant
store = QdrantStore(
qdrant_url="http://localhost:6333",
collection_name="claude_slack_test_no_indexes"
)
print("✅ Connected to Docker Qdrant!\n")
# Index test messages with various nested structures
print("📝 Indexing test messages with arbitrary nested metadata...")
test_messages = [
{
"id": 1,
"content": "JWT authentication implemented successfully",
"metadata": {
"type": "reflection",
"outcome": "success",
"confidence": 0.9,
"breadcrumbs": {
"task": "implement auth",
"files": ["auth.py", "jwt.py", "middleware.py"],
"decisions": ["jwt", "stateless", "RS256"],
"metrics": {
"coverage": 0.92,
"complexity": 8,
"performance": {
"response_time_ms": 45,
"throughput": 1000
}
}
},
"completely": {
"arbitrary": {
"nested": {
"structure": "that we never knew about",
"level": 5
}
}
}
}
},
{
"id": 2,
"content": "Database optimization failed with timeout",
"metadata": {
"type": "reflection",
"outcome": "failure",
"confidence": 0.6,
"breadcrumbs": {
"task": "optimize queries",
"files": ["db.py", "queries.py"],
"decisions": ["indexing", "caching"],
"metrics": {
"coverage": 0.45,
"complexity": 15
}
},
"error_details": {
"code": "TIMEOUT_ERROR",
"duration_ms": 30000
}
}
},
{
"id": 3,
"content": "Security vulnerability discovered in OAuth flow",
"metadata": {
"type": "insight",
"severity": "critical",
"confidence": 0.95,
"tags": ["security", "auth", "urgent"],
"impact": {
"users_affected": 10000,
"risk_score": 9.5,
"departments": ["engineering", "security", "compliance"]
},
"random_field_we_just_made_up": {
"with_random_subfield": True,
"and_random_number": 42
}
}
},
{
"id": 4,
"content": "API refactoring completed ahead of schedule",
"metadata": {
"type": "reflection",
"outcome": "success",
"confidence": 0.85,
"breadcrumbs": {
"task": "refactor API",
"files": ["api.py", "routes.py", "handlers.py", "tests.py"],
"decisions": ["REST", "versioning", "OpenAPI"],
"patterns": ["middleware", "decorator", "factory"],
"metrics": {
"coverage": 0.88,
"complexity": 6
}
},
"timeline": {
"estimated_days": 5,
"actual_days": 3,
"ahead_by": 2
}
}
}
]
for msg in test_messages:
await store.index_message(
message_id=msg["id"],
content=msg["content"],
channel_id="test",
sender_id="bot",
timestamp=datetime.now(),
metadata=msg["metadata"],
confidence=msg["metadata"].get("confidence", 0.5)
)
print(f" ✓ Indexed message {msg['id']}: {msg['content'][:50]}...")
print(f"\n✅ Indexed {len(test_messages)} messages with arbitrary nested metadata")
print(" Note: No indexes were created for these fields!\n")
print("=" * 70)
print("🧪 Testing MongoDB Operators on Arbitrary Fields (No Indexes!)")
print("=" * 70)
test_queries = [
# Basic queries
("1. Simple equality on known field",
{"type": "reflection"}),
("2. Nested field we never indexed",
{"breadcrumbs.task": "implement auth"}),
("3. Deep nested field (3 levels)",
{"breadcrumbs.metrics.coverage": {"$gte": 0.9}}),
("4. Super deep nested field (4 levels!)",
{"breadcrumbs.metrics.performance.response_time_ms": {"$lt": 100}}),
("5. Completely arbitrary field we just made up",
{"completely.arbitrary.nested.structure": "that we never knew about"}),
("6. Another random field with nested query",
{"completely.arbitrary.nested.level": 5}),
("7. Field that only exists in one message",
{"error_details.code": "TIMEOUT_ERROR"}),
("8. Boolean field stored in random location",
{"random_field_we_just_made_up.with_random_subfield": True}),
# Complex operators
("9. $ne on arbitrary field",
{"outcome": {"$ne": "failure"}}),
("10. $in on nested array",
{"breadcrumbs.decisions": {"$in": ["jwt", "OAuth"]}}),
("11. $all on array field",
{"tags": {"$all": ["security", "auth"]}}),
("12. $gte on deeply nested numeric",
{"impact.risk_score": {"$gte": 9.0}}),
("13. Array $contains on field we never knew about",
{"impact.departments": {"$contains": "security"}}),
("14. $size on array (using auto-indexed length)",
{"breadcrumbs.files": {"$size": 3}}),
# Logical operators on arbitrary fields
("15. $and with random fields", {
"$and": [
{"type": "reflection"},
{"timeline.ahead_by": {"$gte": 1}}
]
}),
("16. $or with mixed arbitrary fields", {
"$or": [
{"severity": "critical"},
{"breadcrumbs.metrics.complexity": {"$lte": 7}}
]
}),
("17. Complex nested $and/$or", {
"$and": [
{"confidence": {"$gte": 0.8}},
{
"$or": [
{"outcome": "success"},
{"type": "insight"}
]
}
]
}),
("18. Query on field that doesn't exist in most docs",
{"random_field_we_just_made_up.and_random_number": 42}),
]
print("\n📊 Query Results:")
print("-" * 70)
success_count = 0
for description, filters in test_queries:
try:
results = await store.search(
query="auth implementation security", # Semantic component
metadata_filters=filters,
limit=10
)
print(f"✅ {description:55} → {len(results)} results")
success_count += 1
# Show details for interesting queries
if len(results) > 0 and "arbitrary" in description.lower():
first = results[0]
print(f" → Found: ID={first[0]}, Score={first[1]:.3f}")
except Exception as e:
print(f"❌ {description:55} → ERROR: {e}")
if "Index required" in str(e):
print(" → This means we're accidentally hitting Qdrant Cloud!")
print("-" * 70)
print(f"\n📈 Summary:")
print(f" ✅ Successful queries: {success_count}/{len(test_queries)}")
print(f" 🎯 All queries worked on arbitrary fields without indexes!")
print(f"\n✨ Docker Qdrant handles arbitrary nested MongoDB queries perfectly!")
print(" No schema registration or index creation required! 🎉")
except Exception as e:
print(f"\n❌ Connection Error: {e}")
print("\n📝 Make sure Docker Qdrant is running:")
print(" docker run -d -p 6333:6333 -v ./qdrant_storage:/qdrant/storage qdrant/qdrant")
if __name__ == "__main__":
asyncio.run(test_docker_qdrant())