Documentation Index
Fetch the complete documentation index at: https://mintlify.com/MemoriLabs/Memori/llms.txt
Use this file to discover all available pages before exploring further.
Overview
Attribution in Memori allows you to associate memories with specific entities (users, customers, agents) and processes (applications, workflows). This enables you to:
- Isolate memories per user or customer
- Track memories across different applications
- Organize memories by context or workflow
attribution()
Set entity and process identifiers for memory attribution.
Parameters
entity_id
str | int | None
default:"None"
Unique identifier for the entity. Can be a user ID, customer ID, agent ID, or any unique identifier.Constraints:
- Automatically converted to string
- Maximum length: 100 characters
- Raises
RuntimeError if length exceeds 100 characters
Examples:
"user-123""customer-abc-456""agent-support-001"12345 (converted to "12345")
process_id
str | int | None
default:"None"
Identifier for the process or application context. Use this to distinguish between different applications or workflows.Constraints:
- Automatically converted to string
- Maximum length: 100 characters
- Raises
RuntimeError if length exceeds 100 characters
Examples:
"my-app""customer-support""sales-agent""onboarding-flow"
Returns
Returns the Memori instance for method chaining
Usage Examples
Basic Attribution
from memori import Memori
mem = Memori()
mem.attribution(entity_id="user-123", process_id="my-app")
Method Chaining
mem = Memori().attribution(
entity_id="customer-456",
process_id="support-agent"
).new_session()
Customer Support Context
from memori import Memori
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
engine = create_engine("postgresql://localhost/memori")
Session = sessionmaker(bind=engine)
mem = Memori(conn=Session)
# Set attribution for a specific customer and support process
mem.attribution(
entity_id="customer-789",
process_id="technical-support"
)
# All memories will be associated with this customer
facts = mem.recall("What issues has this customer reported?")
Multi-Tenant Application
def get_memori_for_user(user_id: str, tenant_id: str):
mem = Memori(conn=get_db_session())
mem.attribution(
entity_id=f"{tenant_id}:{user_id}",
process_id="saas-platform"
)
return mem
# Usage
user_mem = get_memori_for_user("user-123", "tenant-acme")
facts = user_mem.recall("user preferences")
Numeric IDs
# Numeric IDs are automatically converted to strings
mem.attribution(
entity_id=12345, # Converted to "12345"
process_id=67890 # Converted to "67890"
)
Updating Attribution
mem = Memori()
# Initial attribution
mem.attribution(entity_id="user-123", process_id="app-1")
# Update attribution for a different context
mem.attribution(entity_id="user-456", process_id="app-2")
# Update only entity_id
mem.attribution(entity_id="user-789")
Attribution and Recall
When you set attribution, the entity_id is used to filter memories during recall:
mem = Memori(conn=Session)
mem.attribution(entity_id="user-123", process_id="my-app")
# This recall only returns memories associated with "user-123"
facts = mem.recall("What are my preferences?")
Attribution and Cloud Storage
When using Memori’s cloud storage, attribution is sent with API requests:
import os
os.environ["MEMORI_API_KEY"] = "your-api-key"
mem = Memori() # Uses cloud storage
mem.attribution(entity_id="user-123", process_id="my-app")
# Attribution is included in cloud recall requests
facts = mem.recall("user preferences")
Best Practices
1. Set Attribution Early
Always set attribution before registering LLMs or performing recall:
mem = Memori(conn=Session)
mem.attribution(entity_id="user-123", process_id="my-app")
mem.llm.register(openai_client)
2. Use Consistent Entity IDs
Use the same entity_id format across your application:
# Good: Consistent format
mem.attribution(entity_id="user-123")
mem.attribution(entity_id="user-456")
# Avoid: Inconsistent formats
mem.attribution(entity_id="user-123")
mem.attribution(entity_id="customer_456") # Different format
3. Use Process ID for Context
Use process_id to distinguish between different application contexts:
# Support agent
support_mem = Memori().attribution(
entity_id="customer-123",
process_id="support-chat"
)
# Sales agent
sales_mem = Memori().attribution(
entity_id="customer-123",
process_id="sales-inquiry"
)
4. Validate ID Length
Ensure IDs don’t exceed 100 characters:
def safe_attribution(mem: Memori, entity_id: str, process_id: str):
if len(entity_id) > 100:
raise ValueError("entity_id too long")
if len(process_id) > 100:
raise ValueError("process_id too long")
mem.attribution(entity_id=entity_id, process_id=process_id)
Error Handling
try:
mem.attribution(
entity_id="a" * 101, # Too long!
process_id="my-app"
)
except RuntimeError as e:
print(f"Error: {e}") # "entity_id cannot be greater than 100 characters"
Internal Behavior
The attribution() method stores IDs in the config:
mem = Memori()
mem.attribution(entity_id="user-123", process_id="my-app")
# Accessible via config
print(mem.config.entity_id) # "user-123"
print(mem.config.process_id) # "my-app"
See Also