mirror of
https://github.com/MervinPraison/PraisonAI.git
synced 2026-11-12 01:41:26 -10:00
Dhivya-BharathyandCursor
bc8a39dd29
- Fix AgentTeam.start() result handling across 22+ workflow/usecase examples - Fix missing AgentFlow/AgentTeam/AutoAgents imports and registry override demo - Fix scope_identifiers, state_redis db import, and ToolsMCPServer.run_sse API - Mark STT and stale tool-import examples as skip until refreshed - Remove stale db mapping that shadowed praisonaiagents.db submodule Co-authored-by: Cursor <[email protected]>
110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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Example: Using Scope Identifiers (user_id, agent_id, run_id)
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This example demonstrates:
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1. Multi-user knowledge isolation with user_id
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2. Multi-agent knowledge sharing with agent_id
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3. Run-specific context with run_id
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4. Error handling for missing scope identifiers
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Requirements:
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- pip install praisonaiagents[knowledge]
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- OPENAI_API_KEY environment variable
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Usage:
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python scope_identifiers_example.py
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"""
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import os
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import tempfile
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import shutil
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from praisonaiagents import Agent
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def main():
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temp_dir = tempfile.mkdtemp(prefix='praison_scope_')
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try:
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# Create test document
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doc = os.path.join(temp_dir, 'shared_knowledge.txt')
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with open(doc, 'w') as f:
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f.write("""
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Company Policy Document
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1. All employees must complete security training annually.
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2. Remote work is allowed with manager approval.
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3. Expense reports must be submitted within 30 days.
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""")
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print("=" * 60)
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print("Example: Scope Identifiers for Knowledge Isolation")
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print("=" * 60)
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# Example 1: User-scoped knowledge
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print("\n1. User-scoped knowledge (user_id):")
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print("-" * 40)
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user1_agent = Agent(
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name="HR_Assistant_User1",
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instructions="You are an HR assistant.",
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knowledge=[temp_dir],
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memory={"user_id": "user_alice"},
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)
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response = user1_agent.chat("What is the policy on remote work?")
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print("User Alice asks: What is the policy on remote work?")
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print(f"Response: {response[:200]}...")
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# Example 2: Agent-scoped knowledge
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print("\n2. Agent-scoped knowledge (agent_id):")
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print("-" * 40)
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shared_agent = Agent(
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name="Policy_Expert",
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instructions="You are a policy expert.",
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knowledge=[temp_dir],
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)
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response = shared_agent.chat("What are the expense report rules?")
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print("Agent policy_agent_v1 query: What are the expense report rules?")
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print(f"Response: {response[:200]}...")
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# Example 3: Combined scoping
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print("\n3. Combined scoping (user_id + agent_id):")
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print("-" * 40)
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combined_agent = Agent(
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name="Personal_HR_Bot",
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instructions="You are a personal HR assistant.",
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knowledge=[temp_dir],
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memory={"user_id": "user_bob"},
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)
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response = combined_agent.chat("What training is required?")
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print("User Bob with hr_bot_v2: What training is required?")
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print(f"Response: {response[:200]}...")
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print("\n" + "=" * 60)
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print("Scope Identifier Summary:")
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print("=" * 60)
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print("""
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- user_id: Isolates knowledge per user (multi-tenant)
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- agent_id: Isolates knowledge per agent type/version
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- run_id: Isolates knowledge per execution run (ephemeral)
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Best Practices:
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1. Always provide at least one scope identifier
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2. Use user_id for user-specific data
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3. Use agent_id for shared agent knowledge
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4. Use run_id for temporary/session-specific context
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""")
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finally:
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shutil.rmtree(temp_dir, ignore_errors=True)
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if __name__ == "__main__":
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main()
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