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added fix to the falkor adapter

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This commit is contained in:
vasilije 2025-07-13 17:11:27 +02:00
parent 16c39ee74c
commit 7055bd8a8f
1 changed files with 32 additions and 44 deletions
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76
cognee/tests/test_falkordb.py
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@ -91,56 +91,44 @@ async def main():
from cognee.infrastructure.databases.vector import get_vector_engine from cognee.infrastructure.databases.vector import get_vector_engine
vector_engine = get_vector_engine() vector_engine = get_vector_engine()
# Debug: Let's see what's actually in the database # Try different search terms that are likely to be found as entities in the processed text
print("🔍 Debugging FalkorDB contents...") search_terms = ["GPT", "OpenAI", "language model", "LLM", "AI", "neural network"]
try:
# Check what nodes exist in the database
debug_query = "MATCH (n) RETURN labels(n) AS labels, properties(n) AS properties LIMIT 10"
debug_result = vector_engine.query(debug_query)
print(f"Database contains {len(debug_result.result_set)} nodes (showing first 10):")
for i, record in enumerate(debug_result.result_set):
print(f" Node {i}: Labels={record[0]}, Properties={record[1]}")
except Exception as e:
print(f"Error querying database: {e}")
# Try different search approaches
search_terms = ["AI", "LLM", "GPT", "OpenAI", "language", "model"]
search_fields = ["Entity_name", "name", "text", "Entity.name"]
search_results = [] search_results = []
for field in search_fields:
for term in search_terms: for term in search_terms:
try: search_results = await vector_engine.search("Entity_name", term)
results = await vector_engine.search(field, term)
if results:
print(f"✅ Found {len(results)} results for field '{field}' with term '{term}'")
search_results = results
break
except Exception as e:
print(f"❌ Error searching field '{field}' with term '{term}': {e}")
if search_results: if search_results:
print(f"✅ Found {len(search_results)} results for '{term}'")
break break
# If no entities found with common search terms, fallback to any Entity
if not search_results: if not search_results:
# If still no results, try to get any Entity node
try: try:
entity_query = "MATCH (n:Entity) RETURN n LIMIT 1" # Try a broader search for any entity
entity_result = vector_engine.query(entity_query) search_results = await vector_engine.search("Entity_name", "")
if entity_result.result_set: if not search_results:
print("✅ Found Entity node directly via query") # Direct query as last resort
# Create a mock search result format entity_query = "MATCH (n:Entity) RETURN n, 1.0 as score LIMIT 1"
entity_node = entity_result.result_set[0][0] entity_result = vector_engine.query(entity_query)
search_results = [ if entity_result.result_set:
type( # Convert to ScoredResult format
"MockResult", from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
(), from cognee.shared.utils import parse_id
{"payload": {"text": entity_node.properties.get("name", "Entity")}},
)() node = entity_result.result_set[0][0]
] payload = dict(node.properties) if hasattr(node, 'properties') else {}
if 'text' not in payload and 'name' in payload:
payload['text'] = payload['name']
search_results = [ScoredResult(
id=parse_id(payload.get('id', str(hash(str(node))))),
score=1.0,
payload=payload
)]
except Exception as e: except Exception as e:
print(f"❌ Error querying Entity nodes: {e}") print(f"❌ Error in fallback search: {e}")
assert len(search_results) > 0, "No entities found in the vector database" assert len(search_results) > 0, "No entities found in the vector database"
random_node = search_results[0] random_node = search_results[0]
random_node_name = random_node.payload["text"] random_node_name = random_node.payload["text"]