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Documentation Index

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RAG scoping enables precise filtering of retrieval results using scope identifiers, allowing multi-tenant isolation and session-specific knowledge retrieval.

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Quick Start

1

Single Scope Usage

Filter knowledge by user, agent, or session:
from praisonaiagents import Agent

# User-specific knowledge
agent = Agent(
    name="PersonalAssistant",
    instructions="You are a personal assistant.",
    knowledge=["./documents/"],
    user_id="alice",
)

agent.start("Find my recent notes about the project")
2

Combined Scopes

Combine multiple scopes for fine-grained isolation:
from praisonaiagents import Agent

# Multi-tenant SaaS support bot
agent = Agent(
    name="SupportBot",
    instructions="Answer customer questions using their session history.",
    knowledge=["./support_docs/"],
    user_id="customer_42",
    agent_id="support_bot_v1", 
    run_id="session_2026_05_30",
)

agent.start("What was the status of my refund request?")

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How It Works

Scope TypePurposeExample Use Case
user_idPer-user isolationPersonal documents, user-specific data
agent_idPer-agent isolationDifferent bot types sharing infrastructure
run_idPer-session isolationConversation-specific temporary context

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Configuration Options

Knowledge Backend Configuration

Learn about different vector store backends and their scoping support

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Common Patterns

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Multi-Tenant SaaS Application

from praisonaiagents import Agent

def create_customer_agent(customer_id, session_id):
    return Agent(
        name="CustomerSupportBot",
        instructions="Provide customer support using account history.",
        knowledge=["./knowledge_base/"],
        user_id=customer_id,           # Isolate per customer
        agent_id="support_bot_v2",     # Shared bot knowledge
        run_id=session_id,             # Session-specific context
    )

# Each customer gets isolated knowledge
agent = create_customer_agent("cust_12345", "sess_abc")

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Department-Specific Knowledge

# Sales team agent
sales_agent = Agent(
    name="SalesAssistant",
    instructions="Help with sales inquiries and product information.",
    knowledge=["./sales_materials/"],
    agent_id="sales_assistant",
    user_id="sales_team",
)

# Support team agent  
support_agent = Agent(
    name="SupportAgent",
    instructions="Resolve technical support issues.",
    knowledge=["./support_docs/"], 
    agent_id="support_assistant",
    user_id="support_team",
)

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Session-Based Memory

from praisonaiagents import Agent

# Create conversation-specific memory
def start_conversation(user_id):
    import uuid
    session_id = str(uuid.uuid4())
    
    agent = Agent(
        name="ChatBot",
        instructions="Remember our conversation context.",
        knowledge=[],  # Will accumulate during conversation
        user_id=user_id,
        run_id=session_id,
    )
    
    return agent, session_id

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Best Practices

Single scope for simple use cases:
  • Use user_id for personal documents
  • Use agent_id for shared knowledge bases
  • Use run_id for temporary session data
Combined scopes for complex multi-tenant applications:
  • All three scopes provide maximum isolation
  • Omit scopes to broaden access as needed
Use consistent, readable naming patterns:
# Good - descriptive and consistent
user_id = "customer_alice_123"
agent_id = "support_bot_v2"
run_id = "session_2026_05_30_14_30"

# Avoid - unclear or inconsistent
user_id = "u123"
agent_id = "bot1"
run_id = "abc"
mem0 backend requires at least one scope identifier. ChromaDB backend supports all scoping patterns and automatically combines multiple scopes with $and operator. Internal backend has limited scoping support.Choose your backend based on scoping requirements.
More specific scopes = faster queries:
  • Narrow scopes reduce the search space
  • Combined scopes create more selective filters
  • Consider indexing strategies for your vector database
Monitor query performance with different scope combinations.

Knowledge Backends

Configure different vector store backends with scoping support

Agents

Learn how agents use knowledge and memory systems