mirror of
https://github.com/MervinPraison/PraisonAI.git
synced 2026-11-08 08:11:07 -10:00
cfd7d7c8a9
* fix(examples): resolve syntax errors and align with consolidated-params API * fix: correct TypeError in 3 example files by using proper parameter names - Fix MemoryConfig(memory=...) → user_id=... in agent_centric_api.py - Fix Session(memory=...) → user_id=... in session-example.py - Fix build_context_for_task(memory=...) → user_id=... in memory-quality-example.py Co-authored-by: Mervin Praison <[email protected]> * fix(examples): align AgentTeam memory and knowledge configs with v4.6.52 API * fix(examples): phase-3 API alignment for consolidated params on v4.6.52 * fix(examples): phase-4 API alignment for 40 validation failures on v4.6.52 * fix: resolve critical RouterAgent **kwargs bug and add missing if __name__ guards - Fixed TypeError in RouterAgent._build_system_prompt() by removing **kwargs from super() call - Added if __name__ == '__main__' guards to 6 example files to prevent auto-execution on import - Addresses Gemini Code Assist review feedback Co-authored-by: Mervin Praison <[email protected]> * fix: resolve missing AgentTeam import and RouterAgent kwargs forwarding - Fix missing AgentTeam import in self-reflection-optimization-patterns.py - Forward kwargs properly in RouterAgent._build_system_prompt to super() - Addresses critical issues identified by Gemini code reviewer Co-authored-by: Mervin Praison <[email protected]> * fix(examples): remove merge-conflict residue for PR #1893 --------- Co-authored-by: Dhivya-Bharathy <[email protected]> Co-authored-by: praisonai-triage-agent[bot] <272766704+praisonai-triage-agent[bot]@users.noreply.github.com> Co-authored-by: Mervin Praison <[email protected]> Co-authored-by: Mervin Praison <[email protected]>
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
"""
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Advanced Memory Search and Optimization Example
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This example demonstrates memory search optimization using PraisonAI's
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built-in memory capabilities with semantic search and quality filtering.
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"""
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from praisonaiagents import Agent, Task, AgentTeam
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from praisonaiagents.tools import internet_search
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print("=== Advanced Memory Search and Optimization Example ===\n")
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# Configure optimized memory settings
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memory_config = {
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"config": {
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"provider": "mem0",
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"vector_store": {
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"provider": "chroma",
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"config": {
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"path": ".praison/optimized_memory"
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}
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}
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}
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}
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# Create research agent with optimized memory
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research_agent = Agent(
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name="Memory Research Agent",
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role="Information Researcher with Memory Optimization",
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goal="Research and demonstrate optimized memory search patterns",
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backstory="Expert at storing and retrieving information efficiently with memory optimization",
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tools=[internet_search],
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memory=True)
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# Task to build searchable memory
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build_memory_task = Task(
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description="""Research and store information about these topics for later search:
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1. Artificial intelligence market trends and growth data
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2. Machine learning technologies and applications
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3. Tech industry partnerships and investments
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4. Cloud computing adoption statistics
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Store detailed information that can be searched later.""",
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expected_output="Comprehensive research stored in optimized memory",
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agent=research_agent
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)
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# Task to demonstrate memory search optimization
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search_task = Task(
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description="""Using the stored memory, perform optimized searches for:
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1. "AI market growth trends" - find relevant stored information
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2. "technology partnerships" - retrieve partnership data
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3. "cloud adoption statistics" - search for cloud computing info
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Demonstrate efficient memory retrieval and search optimization.""",
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expected_output="Results from optimized memory searches",
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agent=research_agent
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)
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# Run with memory optimization
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agents_system = AgentTeam(
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agents=[research_agent],
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tasks=[build_memory_task, search_task],
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memory=memory_config,
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output="verbose",
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)
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print("Starting memory search optimization demonstration...")
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result = agents_system.start()
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print(f"\nMemory Search Result: {result[:200]}...")
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print("\n✅ Memory search optimization complete!")
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print("Agent demonstrated efficient memory storage and optimized search retrieval.") |