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remove debug logs

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This commit is contained in:
Manoj Aggarwal 2026-01-20 17:48:40 -08:00
parent 92aa3f4eab
commit e3fe4b86df
1 changed files with 0 additions and 25 deletions
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surfsense_backend/app/agents/new_chat/tools/user_memory.py
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@ -158,10 +158,6 @@ def create_save_memory_tool(
Returns:
A dictionary with the save status and memory details
"""
# Log at the very start
logger.info(f">>> SAVE_MEMORY TOOL CALLED: content='{content}', category='{category}'")
print(f">>> SAVE_MEMORY TOOL CALLED: content='{content}', category='{category}'")
# Normalize and validate category (LLMs may send uppercase)
category = category.lower() if category else "fact"
valid_categories = ["preference", "fact", "instruction", "context"]
@ -169,26 +165,19 @@ def create_save_memory_tool(
category = "fact"
try:
logger.info(f"save_memory called: user_id={user_id}, search_space_id={search_space_id}, content={content[:50]}...")
# Convert user_id to UUID
uuid_user_id = _to_uuid(user_id)
logger.info(f"UUID conversion successful: {uuid_user_id}")
# Check if we've hit the memory limit
memory_count = await get_user_memory_count(
db_session, user_id, search_space_id
)
logger.info(f"Current memory count: {memory_count}")
if memory_count >= MAX_MEMORIES_PER_USER:
# Delete oldest memory to make room
await delete_oldest_memory(db_session, user_id, search_space_id)
# Generate embedding for the memory
logger.info("Generating embedding...")
embedding = config.embedding_model_instance.embed(content)
logger.info(f"Embedding generated, type: {type(embedding)}, len: {len(embedding) if hasattr(embedding, '__len__') else 'N/A'}")
# Convert numpy array to list of Python floats for PostgreSQL
import numpy as np
@ -223,10 +212,7 @@ def create_save_memory_tool(
}
)
new_memory_id = result.scalar_one()
logger.info("Committing...")
await db_session.commit()
logger.info(f"Memory saved successfully with id: {new_memory_id}")
return {
"status": "saved",
@ -295,15 +281,10 @@ def create_recall_memory_tool(
A dictionary containing relevant memories and formatted context
"""
top_k = min(max(top_k, 1), 20) # Clamp between 1 and 20
# Log at the very start
logger.info(f">>> RECALL_MEMORY TOOL CALLED: query='{query}', category='{category}', top_k={top_k}")
print(f">>> RECALL_MEMORY TOOL CALLED: query='{query}', category='{category}', top_k={top_k}")
try:
# Convert user_id to UUID
uuid_user_id = _to_uuid(user_id)
logger.info(f"Recall memory for user: {uuid_user_id}, search_space: {search_space_id}")
if query:
# Semantic search using embeddings
@ -347,8 +328,6 @@ def create_recall_memory_tool(
result = await db_session.execute(stmt)
memories = result.scalars().all()
logger.info(f"Found {len(memories)} memories")
# Format memories for response
memory_list = [
@ -360,12 +339,8 @@ def create_recall_memory_tool(
}
for m in memories
]
logger.info(f"Formatted memory list: {memory_list}")
formatted_context = format_memories_for_context(memory_list)
logger.info(f"Returning {len(memory_list)} memories")
return {
"status": "success",