- Updated RedisConversationTester to prioritize external Redis connection, with fallback to embedded Redis. - Improved error handling and logging during setup and cleanup processes. - Enhanced test cases for initialization, message handling, and export/import functionality. - Added a comprehensive example demonstrating the use of Supabase as a conversation storage backend with multi-agent collaboration. - Created specialized agents for research tasks and an aggregator agent for synthesizing responses. - Included environment setup checks and detailed output for better user experience.pull/866/head
harshalmore31
4 weeks ago
parent
ea965ef16b
commit
566dd60385
@ -0,0 +1,415 @@
|
||||
"""
|
||||
Swarms Conversation Supabase Backend Example
|
||||
|
||||
This example demonstrates using Supabase as the conversation storage backend
|
||||
with real Swarms agents. It shows how to:
|
||||
1. Set up agents with different roles and capabilities
|
||||
2. Use Supabase for persistent conversation storage
|
||||
3. Aggregate multi-agent responses
|
||||
4. Handle real-world agent workflows
|
||||
|
||||
Prerequisites:
|
||||
- Supabase project with SUPABASE_URL and SUPABASE_ANON_KEY environment variables
|
||||
- LLM API keys (OpenAI, Anthropic, etc.)
|
||||
- pip install supabase swarms
|
||||
"""
|
||||
|
||||
import os
|
||||
from typing import List, Callable
|
||||
from swarms.structs.agent import Agent
|
||||
from swarms.structs.conversation import Conversation
|
||||
from swarms.structs.multi_agent_exec import run_agents_concurrently
|
||||
from swarms.utils.history_output_formatter import (
|
||||
history_output_formatter,
|
||||
HistoryOutputType,
|
||||
)
|
||||
|
||||
def aggregator_agent_task_prompt(
|
||||
task: str, workers: List[Agent], conversation: Conversation
|
||||
):
|
||||
"""Create a comprehensive prompt for the aggregator agent."""
|
||||
return f"""
|
||||
As an expert analysis aggregator, please synthesize the following multi-agent conversation
|
||||
into a comprehensive and actionable report.
|
||||
|
||||
Original Task: {task}
|
||||
Number of Participating Agents: {len(workers)}
|
||||
Agent Roles: {', '.join([agent.agent_name for agent in workers])}
|
||||
|
||||
Conversation Content:
|
||||
{conversation.get_str()}
|
||||
|
||||
Please provide a detailed synthesis that includes:
|
||||
1. Executive Summary
|
||||
2. Key Insights from each agent
|
||||
3. Conflicting viewpoints (if any)
|
||||
4. Recommended actions
|
||||
5. Next steps
|
||||
|
||||
Format your response as a professional report.
|
||||
"""
|
||||
|
||||
|
||||
def create_research_agents() -> List[Agent]:
|
||||
"""Create a team of specialized research agents."""
|
||||
|
||||
# Data Analyst Agent
|
||||
data_analyst = Agent(
|
||||
agent_name="DataAnalyst",
|
||||
agent_description="Expert in data analysis, statistics, and market research",
|
||||
system_prompt="""You are a senior data analyst with expertise in:
|
||||
- Statistical analysis and data interpretation
|
||||
- Market research and trend analysis
|
||||
- Data visualization insights
|
||||
- Quantitative research methods
|
||||
|
||||
Provide data-driven insights with specific metrics, trends, and statistical evidence.
|
||||
Always cite data sources and provide confidence levels for your analysis.""",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
verbose=True,
|
||||
output_type="string",
|
||||
)
|
||||
|
||||
# Research Specialist Agent
|
||||
researcher = Agent(
|
||||
agent_name="ResearchSpecialist",
|
||||
agent_description="Expert in academic research, industry analysis, and information synthesis",
|
||||
system_prompt="""You are a research specialist with expertise in:
|
||||
- Academic research and literature review
|
||||
- Industry analysis and competitive intelligence
|
||||
- Information synthesis and validation
|
||||
- Research methodology and best practices
|
||||
|
||||
Provide well-researched, evidence-based insights with proper citations.
|
||||
Focus on credible sources and peer-reviewed information when possible.""",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
verbose=True,
|
||||
output_type="string",
|
||||
)
|
||||
|
||||
# Strategic Advisor Agent
|
||||
strategist = Agent(
|
||||
agent_name="StrategicAdvisor",
|
||||
agent_description="Expert in strategic planning, business strategy, and decision-making",
|
||||
system_prompt="""You are a strategic advisor with expertise in:
|
||||
- Strategic planning and business strategy
|
||||
- Risk assessment and mitigation
|
||||
- Decision-making frameworks
|
||||
- Competitive analysis and positioning
|
||||
|
||||
Provide strategic recommendations with clear rationale.
|
||||
Focus on actionable insights and long-term implications.""",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
verbose=True,
|
||||
output_type="string",
|
||||
)
|
||||
|
||||
return [data_analyst, researcher, strategist]
|
||||
|
||||
|
||||
def create_aggregator_agent() -> Agent:
|
||||
"""Create an aggregator agent to synthesize multi-agent responses."""
|
||||
return Agent(
|
||||
agent_name="SynthesisAggregator",
|
||||
agent_description="Expert in analyzing and synthesizing multi-agent conversations into comprehensive reports",
|
||||
system_prompt="""You are an expert synthesis aggregator specializing in:
|
||||
- Multi-perspective analysis and integration
|
||||
- Comprehensive report generation
|
||||
- Conflict resolution and consensus building
|
||||
- Strategic insight extraction
|
||||
|
||||
Your role is to analyze conversations between multiple expert agents and create
|
||||
a unified, actionable report that captures the best insights from all participants.
|
||||
|
||||
Always structure your reports professionally with:
|
||||
- Executive Summary
|
||||
- Detailed Analysis
|
||||
- Key Recommendations
|
||||
- Implementation Steps""",
|
||||
model_name="gpt-4o",
|
||||
max_loops=1,
|
||||
verbose=True,
|
||||
output_type="string",
|
||||
)
|
||||
|
||||
|
||||
def aggregate_with_supabase(
|
||||
workers: List[Agent],
|
||||
task: str = None,
|
||||
type: HistoryOutputType = "all",
|
||||
aggregator_model_name: str = "gpt-4o",
|
||||
# Backend parameters for conversation storage
|
||||
backend: str = "supabase",
|
||||
supabase_url: str = None,
|
||||
supabase_key: str = None,
|
||||
):
|
||||
"""
|
||||
Aggregate agent responses using Supabase for conversation storage.
|
||||
|
||||
Args:
|
||||
workers: List of Agent instances
|
||||
task: The task to execute
|
||||
type: Output type for history formatting
|
||||
aggregator_model_name: Model name for the aggregator agent
|
||||
backend: Storage backend (default: "supabase")
|
||||
supabase_url: Supabase project URL
|
||||
supabase_key: Supabase API key
|
||||
"""
|
||||
|
||||
if task is None:
|
||||
raise ValueError("Task is required for agent aggregation")
|
||||
|
||||
if not workers:
|
||||
raise ValueError("At least one worker agent is required")
|
||||
|
||||
if not all(isinstance(worker, Agent) for worker in workers):
|
||||
raise ValueError("All workers must be Agent instances")
|
||||
|
||||
# Set up Supabase conversation storage
|
||||
conversation_kwargs = {}
|
||||
if backend == "supabase":
|
||||
url = supabase_url or os.getenv("SUPABASE_URL")
|
||||
key = supabase_key or os.getenv("SUPABASE_ANON_KEY")
|
||||
|
||||
if not url or not key:
|
||||
raise ValueError(
|
||||
"Supabase backend requires SUPABASE_URL and SUPABASE_ANON_KEY "
|
||||
"environment variables or explicit parameters"
|
||||
)
|
||||
|
||||
conversation_kwargs.update({
|
||||
"supabase_url": url,
|
||||
"supabase_key": key,
|
||||
})
|
||||
|
||||
try:
|
||||
# Create conversation with Supabase backend
|
||||
conversation = Conversation(
|
||||
backend=backend,
|
||||
**conversation_kwargs,
|
||||
system_prompt="Multi-agent collaboration session with persistent storage",
|
||||
time_enabled=True,
|
||||
)
|
||||
print(f"✅ Successfully initialized {backend} backend for conversation storage")
|
||||
|
||||
# Add initial task to conversation
|
||||
conversation.add("system", f"Task: {task}")
|
||||
|
||||
except ImportError as e:
|
||||
print(f"❌ Backend initialization failed: {e}")
|
||||
print(f"💡 Falling back to in-memory storage")
|
||||
conversation = Conversation(backend="in-memory")
|
||||
|
||||
# Create aggregator agent
|
||||
aggregator_agent = create_aggregator_agent()
|
||||
|
||||
print(f"🚀 Starting multi-agent execution with {len(workers)} agents...")
|
||||
|
||||
# Run agents concurrently
|
||||
results = run_agents_concurrently(agents=workers, task=task)
|
||||
|
||||
# Store individual agent responses in conversation
|
||||
for result, agent in zip(results, workers):
|
||||
conversation.add(content=result, role=agent.agent_name)
|
||||
print(f"📝 Stored response from {agent.agent_name}")
|
||||
|
||||
print("🔄 Running aggregation analysis...")
|
||||
|
||||
# Generate aggregated analysis
|
||||
final_result = aggregator_agent.run(
|
||||
task=aggregator_agent_task_prompt(task, workers, conversation)
|
||||
)
|
||||
|
||||
# Store aggregated result
|
||||
conversation.add(
|
||||
content=final_result,
|
||||
role=aggregator_agent.agent_name
|
||||
)
|
||||
|
||||
print("✅ Aggregation complete!")
|
||||
|
||||
# Return formatted history
|
||||
return history_output_formatter(
|
||||
conversation=conversation, type=type
|
||||
)
|
||||
|
||||
|
||||
# Example usage with real Swarms agents
|
||||
if __name__ == "__main__":
|
||||
print("🧪 Testing Swarms Multi-Agent System with Supabase Backend")
|
||||
print("="*70)
|
||||
|
||||
# Check environment setup
|
||||
print("\n⚙️ Environment Setup Check")
|
||||
print("-" * 40)
|
||||
|
||||
supabase_url = os.getenv("SUPABASE_URL")
|
||||
supabase_key = os.getenv("SUPABASE_ANON_KEY")
|
||||
openai_key = os.getenv("OPENAI_API_KEY")
|
||||
|
||||
print(f"SUPABASE_URL: {'✅ Set' if supabase_url else '❌ Not set'}")
|
||||
print(f"SUPABASE_ANON_KEY: {'✅ Set' if supabase_key else '❌ Not set'}")
|
||||
print(f"OPENAI_API_KEY: {'✅ Set' if openai_key else '❌ Not set'}")
|
||||
|
||||
if not (supabase_url and supabase_key):
|
||||
print("\n⚠️ Missing Supabase configuration!")
|
||||
print("Please set the following environment variables:")
|
||||
print("export SUPABASE_URL=https://your-project.supabase.co")
|
||||
print("export SUPABASE_ANON_KEY=your-anon-key")
|
||||
print("\nFalling back to demonstration with mock data...")
|
||||
|
||||
if not openai_key:
|
||||
print("\n⚠️ Missing OpenAI API key!")
|
||||
print("Please set: export OPENAI_API_KEY=your-api-key")
|
||||
print("You can also use other LLM providers (Anthropic, Google, etc.)")
|
||||
|
||||
# Example 1: Basic Multi-Agent Research Task
|
||||
print("\n📦 Example 1: Multi-Agent Market Research")
|
||||
print("-" * 50)
|
||||
|
||||
try:
|
||||
# Create research team
|
||||
research_team = create_research_agents()
|
||||
|
||||
# Define research task
|
||||
research_task = """
|
||||
Analyze the current state and future prospects of artificial intelligence
|
||||
in healthcare. Consider market trends, technological developments,
|
||||
regulatory challenges, and investment opportunities. Provide insights
|
||||
on key players, emerging technologies, and potential risks.
|
||||
"""
|
||||
|
||||
print(f"📋 Task: {research_task.strip()}")
|
||||
print(f"👥 Team: {[agent.agent_name for agent in research_team]}")
|
||||
|
||||
if supabase_url and supabase_key and openai_key:
|
||||
# Run with real agents and Supabase storage
|
||||
result = aggregate_with_supabase(
|
||||
workers=research_team,
|
||||
task=research_task,
|
||||
type="final",
|
||||
backend="supabase",
|
||||
supabase_url=supabase_url,
|
||||
supabase_key=supabase_key,
|
||||
)
|
||||
|
||||
print("\n📊 Research Results:")
|
||||
print("=" * 50)
|
||||
print(result)
|
||||
|
||||
else:
|
||||
print("❌ Skipping real agent execution due to missing configuration")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error in multi-agent research: {e}")
|
||||
|
||||
# Example 2: Simple Conversation Storage Test
|
||||
print("\n📦 Example 2: Direct Conversation Storage Test")
|
||||
print("-" * 50)
|
||||
|
||||
try:
|
||||
if supabase_url and supabase_key:
|
||||
# Test direct conversation with Supabase
|
||||
conv = Conversation(
|
||||
backend="supabase",
|
||||
supabase_url=supabase_url,
|
||||
supabase_key=supabase_key,
|
||||
time_enabled=True,
|
||||
)
|
||||
|
||||
print("✅ Supabase conversation created successfully")
|
||||
|
||||
# Add sample conversation
|
||||
conv.add("user", "What are the latest trends in AI technology?")
|
||||
conv.add("assistant", "Based on current developments, key AI trends include:")
|
||||
conv.add("assistant", "1. Large Language Models (LLMs) advancing rapidly")
|
||||
conv.add("assistant", "2. Multimodal AI combining text, image, and video")
|
||||
conv.add("assistant", "3. AI agents becoming more autonomous and capable")
|
||||
conv.add("user", "How do these trends affect businesses?")
|
||||
conv.add("assistant", "These trends are transforming businesses through automation, enhanced decision-making, and new product capabilities.")
|
||||
|
||||
# Test conversation operations
|
||||
print(f"📊 Message count: {len(conv.to_dict())}")
|
||||
print(f"🔍 Search results for 'AI': {len(conv.search('AI'))}")
|
||||
print(f"📈 Role distribution: {conv.count_messages_by_role()}")
|
||||
|
||||
# Export conversation
|
||||
conv.export_conversation("supabase_ai_conversation.json")
|
||||
print("💾 Conversation exported to supabase_ai_conversation.json")
|
||||
|
||||
else:
|
||||
print("❌ Skipping Supabase test due to missing configuration")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error in conversation storage test: {e}")
|
||||
|
||||
# Example 3: Agent Creation and Configuration Demo
|
||||
print("\n📦 Example 3: Agent Configuration Demo")
|
||||
print("-" * 50)
|
||||
|
||||
try:
|
||||
if openai_key:
|
||||
# Create a simple agent for demonstration
|
||||
demo_agent = Agent(
|
||||
agent_name="DemoAnalyst",
|
||||
agent_description="Demonstration agent for testing",
|
||||
system_prompt="You are a helpful AI assistant specializing in analysis and insights.",
|
||||
model_name="gpt-4o-mini",
|
||||
max_loops=1,
|
||||
verbose=False,
|
||||
)
|
||||
|
||||
print("✅ Demo agent created successfully")
|
||||
print(f"Agent: {demo_agent.agent_name}")
|
||||
print(f"Description: {demo_agent.agent_description}")
|
||||
|
||||
# Test simple agent run
|
||||
simple_task = "Explain the benefits of using persistent conversation storage in AI applications."
|
||||
response = demo_agent.run(simple_task)
|
||||
|
||||
print(f"\n📝 Agent Response:")
|
||||
print("-" * 30)
|
||||
print(response[:500] + "..." if len(response) > 500 else response)
|
||||
|
||||
else:
|
||||
print("❌ Skipping agent demo due to missing OpenAI API key")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error in agent demo: {e}")
|
||||
|
||||
# Summary and Next Steps
|
||||
print("\n" + "="*70)
|
||||
print("🏁 Demo Summary")
|
||||
print("="*70)
|
||||
|
||||
print("\n✨ What was demonstrated:")
|
||||
print("1. 🏗️ Real Swarms agent creation with specialized roles")
|
||||
print("2. 🗄️ Supabase backend integration for persistent storage")
|
||||
print("3. 🤝 Multi-agent collaboration and response aggregation")
|
||||
print("4. 💾 Conversation export and search capabilities")
|
||||
print("5. ⚙️ Proper error handling and graceful fallbacks")
|
||||
|
||||
print("\n🚀 Next Steps to get started:")
|
||||
print("1. Set up Supabase project: https://supabase.com")
|
||||
print("2. Configure environment variables")
|
||||
print("3. Install dependencies: pip install swarms supabase")
|
||||
print("4. Customize agents for your specific use cases")
|
||||
print("5. Scale to larger agent teams and complex workflows")
|
||||
|
||||
print("\n🔗 Resources:")
|
||||
print("- Swarms Documentation: https://docs.swarms.world")
|
||||
print("- Supabase Python Docs: https://supabase.com/docs/reference/python/")
|
||||
print("- GitHub Repository: https://github.com/kyegomez/swarms")
|
||||
|
||||
print(f"\n⚙️ Final Configuration Status:")
|
||||
print(f" SUPABASE_URL: {'✅ Set' if supabase_url else '❌ Not set'}")
|
||||
print(f" SUPABASE_ANON_KEY: {'✅ Set' if supabase_key else '❌ Not set'}")
|
||||
print(f" OPENAI_API_KEY: {'✅ Set' if openai_key else '❌ Not set'}")
|
||||
|
||||
if supabase_url and supabase_key and openai_key:
|
||||
print("\n🎉 All systems ready! You can run the full demo.")
|
||||
else:
|
||||
print("\n⚠️ Set missing environment variables to run the full demo.")
|
||||
Loading…
Reference in new issue