Enhanced metadata filtering is available in Mem0 1.0.0 Beta and later versions. This feature provides powerful filtering capabilities with logical operators and comparison functions.
Overview
Mem0 1.0.0 Beta introduces enhanced metadata filtering that allows you to perform complex queries on your memory metadata. You can now use logical operators, comparison functions, and advanced filtering patterns to retrieve exactly the memories you need.
Basic Filtering
Simple Key-Value Filtering
from mem0 import Memory
m = Memory()
# Search with simple metadata filters
results = m.search(
"What are my preferences?",
user_id="alice",
filters={"category": "preferences"}
)
Exact Match Filtering
# Multiple exact match filters
results = m.search(
"movie recommendations",
user_id="alice",
filters={
"category": "entertainment",
"type": "recommendation",
"priority": "high"
}
)
Advanced Filtering with Operators
Comparison Operators
# Greater than / Less than
results = m.search(
"recent activities",
user_id="alice",
filters={
"score": {"gt": 0.8}, # score > 0.8
"priority": {"gte": 5}, # priority >= 5
"confidence": {"lt": 0.9}, # confidence < 0.9
"rating": {"lte": 3} # rating <= 3
}
)
# Equality operators
results = m.search(
"specific content",
user_id="alice",
filters={
"status": {"eq": "active"}, # status == "active"
"archived": {"ne": True} # archived != True
}
)
List-based Operators
# In / Not in operators
results = m.search(
"multi-category search",
user_id="alice",
filters={
"category": {"in": ["food", "travel", "entertainment"]},
"status": {"nin": ["deleted", "archived"]}
}
)
String Operators
# Text matching operators
results = m.search(
"content search",
user_id="alice",
filters={
"title": {"contains": "meeting"}, # case-sensitive contains
"description": {"icontains": "important"}, # case-insensitive contains
"tags": {"contains": "urgent"}
}
)
Wildcard Matching
# Match any value for a field
results = m.search(
"all with category",
user_id="alice",
filters={
"category": "*" # Any memory that has a category field
}
)
Logical Operators
AND Operations
# Logical AND - all conditions must be true
results = m.search(
"complex query",
user_id="alice",
filters={
"AND": [
{"category": "work"},
{"priority": {"gte": 7}},
{"status": {"ne": "completed"}}
]
}
)
OR Operations
# Logical OR - any condition can be true
results = m.search(
"flexible query",
user_id="alice",
filters={
"OR": [
{"category": "urgent"},
{"priority": {"gte": 9}},
{"deadline": {"contains": "today"}}
]
}
)
NOT Operations
# Logical NOT - exclude matches
results = m.search(
"exclusion query",
user_id="alice",
filters={
"NOT": [
{"category": "archived"},
{"status": "deleted"}
]
}
)
Complex Nested Logic
# Combine multiple logical operators
results = m.search(
"advanced query",
user_id="alice",
filters={
"AND": [
{
"OR": [
{"category": "work"},
{"category": "personal"}
]
},
{"priority": {"gte": 5}},
{
"NOT": [
{"status": "archived"}
]
}
]
}
)
Real-world Examples
Project Management Filtering
# Find high-priority active tasks
results = m.search(
"What tasks need attention?",
user_id="project_manager",
filters={
"AND": [
{"project": {"in": ["alpha", "beta"]}},
{"priority": {"gte": 8}},
{"status": {"ne": "completed"}},
{
"OR": [
{"assignee": "alice"},
{"assignee": "bob"}
]
}
]
}
)
Customer Support Filtering
# Find recent unresolved tickets
results = m.search(
"pending support issues",
agent_id="support_bot",
filters={
"AND": [
{"ticket_status": {"ne": "resolved"}},
{"priority": {"in": ["high", "critical"]}},
{"created_date": {"gte": "2024-01-01"}},
{
"NOT": [
{"category": "spam"}
]
}
]
}
)
Content Recommendation Filtering
# Personalized content filtering
results = m.search(
"recommend content",
user_id="reader123",
filters={
"AND": [
{
"OR": [
{"genre": {"in": ["sci-fi", "fantasy"]}},
{"author": {"contains": "favorite"}}
]
},
{"rating": {"gte": 4.0}},
{"read_status": {"ne": "completed"}},
{"language": "english"}
]
}
)
Indexing Strategy
# Ensure your vector store supports indexing on filtered fields
config = {
"vector_store": {
"provider": "qdrant",
"config": {
"host": "localhost",
"port": 6333,
# Enable indexing on frequently filtered fields
"indexed_fields": ["category", "priority", "status", "user_id"]
}
}
}
Filter Optimization
# More efficient: Filter on indexed fields first
good_filters = {
"AND": [
{"user_id": "alice"}, # Indexed field first
{"category": "work"}, # Then other indexed fields
{"content": {"contains": "meeting"}} # Text search last
]
}
# Less efficient: Complex operations first
avoid_filters = {
"AND": [
{"description": {"icontains": "complex text search"}}, # Expensive first
{"user_id": "alice"} # Indexed field last
]
}
Vector Store Compatibility
Different vector stores support different filtering capabilities:
Qdrant
- Full support for all operators
- Efficient nested logical operations
- Indexed field optimization
Chroma
- Basic operators (eq, ne, gt, lt, gte, lte)
- Simple logical operations
- � Limited nested operations
Pinecone
- Good support for comparison operators
- In/nin operations
- � Limited text operations
Weaviate
- Full operator support
- Advanced text operations
- Efficient filtering
Error Handling
try:
results = m.search(
"test query",
user_id="alice",
filters={
"invalid_operator": {"unknown": "value"}
}
)
except ValueError as e:
print(f"Filter error: {e}")
# Fallback to simple filtering
results = m.search(
"test query",
user_id="alice",
filters={"category": "general"}
)
Migration from Simple Filters
Before (v0.x)
# Simple key-value filtering only
results = m.search(
"query",
user_id="alice",
filters={"category": "work", "status": "active"}
)
After (v1.0.0 Beta)
# Enhanced filtering with operators
results = m.search(
"query",
user_id="alice",
filters={
"AND": [
{"category": "work"},
{"status": {"ne": "archived"}},
{"priority": {"gte": 5}}
]
}
)
Best Practices
- Use Indexed Fields: Filter on indexed fields for better performance
- Combine Operators: Use logical operators to create precise queries
- Test Filter Performance: Benchmark complex filters with your data
- Graceful Degradation: Implement fallbacks for unsupported operations
- Validate Filters: Check filter syntax before executing queries
Enhanced metadata filtering provides powerful capabilities for precise memory retrieval. Start with simple filters and gradually adopt more complex patterns as needed.
