mirror of
https://github.com/MervinPraison/PraisonAI.git
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claude[bot]andMervinPraison
79c4ef1de3
- Fix SQLite query compatibility in session state restoration - Add type safety to increment_state and improve append_to_state - Change API from session.create_agent() to session.Agent() (backward compatible) - Clean up code style: remove unused imports, trailing whitespace, fix f-strings - Fix missing limit parameter in search_knowledge method - Update examples to use new API 🤖 Generated with [Claude Code](https://claude.ai/code) Co-authored-by: MervinPraison <[email protected]>
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
#!/usr/bin/env python3
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"""
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Session Management Example
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Demonstrates how to use the Session class for stateful agent interactions
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with persistent memory and state management.
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"""
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from praisonaiagents import Session
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def main():
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print("🔄 Session Management Example")
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print("=" * 50)
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# Create a session with persistent storage
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session = Session(
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session_id="demo_chat_001",
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user_id="demo_user"
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)
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print(f"📝 Created session: {session}")
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# Create an agent within the session context
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agent = session.Agent(
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name="Personal Assistant",
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role="Helpful AI Assistant",
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instructions="Remember user preferences and provide personalized assistance",
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memory=True
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)
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print(f"🤖 Created agent: {agent.name}")
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# Initial conversation with preference setting
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print("\n--- Initial Conversation ---")
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response1 = agent.chat("Hi! I prefer brief, technical explanations. Please remember this.")
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print(f"Assistant: {response1}")
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# Save some session state
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session.save_state({
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"conversation_style": "brief_technical",
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"topics_discussed": ["preferences"],
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"session_start": "2024-01-15 10:00:00"
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})
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print("\n💾 Saved session state")
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# Continue conversation - agent should remember preference
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print("\n--- Continued Conversation ---")
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response2 = agent.chat("Can you explain how neural networks work?")
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print(f"Assistant: {response2}")
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# Update session state
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session.set_state("topics_discussed", ["preferences", "neural_networks"])
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session.increment_state("message_count", 2, default=0)
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# Add some knowledge to the session
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print("\n📚 Adding knowledge to session...")
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session.add_memory(
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"User prefers brief technical explanations for complex topics",
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memory_type="long"
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)
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# Simulate session restart - restore state
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print("\n🔄 Simulating session restart...")
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restored_state = session.restore_state()
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print(f"Restored state: {restored_state}")
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# Get specific state values
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style = session.get_state("conversation_style", "default")
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topics = session.get_state("topics_discussed", [])
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msg_count = session.get_state("message_count", 0)
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print(f"Conversation style: {style}")
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print(f"Topics discussed: {topics}")
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print(f"Message count: {msg_count}")
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# Search session memory
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print("\n🔍 Searching session memory...")
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memory_results = session.search_memory("preferences", memory_type="long")
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if memory_results:
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print(f"Found memory: {memory_results[0]['text'][:100]}...")
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# Build context for new conversation
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print("\n🧠 Building context for new conversation...")
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context = session.get_context("machine learning concepts")
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if context:
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print(f"Context preview: {context[:200]}...")
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# Final conversation using built context
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print("\n--- Final Conversation with Context ---")
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response3 = agent.chat("What about deep learning? Keep it technical but concise.")
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print(f"Assistant: {response3}")
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print("\n✅ Session example completed!")
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print("The agent maintained conversation context and user preferences across the session.")
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if __name__ == "__main__":
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main() |