Multi-Agent System Quick Start

🚀 5-Minute Setup

Step 1: Install Dependencies (2 min)

cd /Users/moshesmanihuruk/Downloads/PSANEW/psa/HACKATHON_SOLUTION
pip install -r requirements_agents.txt

Step 2: Configure Database (1 min)

Edit

config/db_config.json

{
  "host": "localhost",
  "user": "root",
  "password": "your_password",
  "database": "appdb"
}

Step 3: Build FAISS Index (1 min)

cd python
python agent_kb_search.py

Output:

🔍 Initializing KnowledgeBaseAgent...
   Building FAISS index from Knowledge Base...
   Extracted 313 KB entries
   Generating embeddings...
   ✅ FAISS index built with 313 vectors
   💾 Index saved to kb_faiss.index

Step 4: Test the System (1 min)

python orchestrator_langgraph.py

Expected output:

🤖 INITIALIZING MULTI-AGENT ORCHESTRATOR
======================================================================
🔍 Initializing KnowledgeBaseAgent...
   ✅ Loaded 313 KB entries
🔍 Initializing HistoricalIncidentAgent...
   ✅ Connected to database: appdb
🔍 Initializing LogsSearchAgent...
   ✅ Connected to database: appdb
✅ Orchestrator initialized

🚀 PROCESSING INCIDENT THROUGH MULTI-AGENT SYSTEM
======================================================================

STEP 1: Knowledge Base Search
======================================================================
🔍 KnowledgeBaseAgent - Searching Knowledge Base
   ✅ Found 3 relevant KB articles
   Best Match: EDI Timeout Issues
   Similarity: 85%

STEP 2: Historical Incident Search
======================================================================
📚 HistoricalIncidentAgent - Searching Historical Data
   ✅ Found 5 similar incidents
   Average Resolution Time: 18.5 minutes

STEP 3: Logs Analysis
======================================================================
📝 LogsSearchAgent - Analyzing Recent Logs
   ✅ Found 12 recent changes
   Critical Changes: 3

STEP 4: Decision Making
======================================================================
📊 CONFIDENCE SCORES:
   KB Agent:         85%
   Historical Agent: 72%
   Logs Agent:       65%
   Combined:         76%

🎯 FINAL DECISION: RESOLVE
✅ Resolution Steps: 5
✅ Verification Steps: 3

📖 Usage Examples

Example 1: Complete Pipeline

from Source_Parser import EmailParser
from orchestrator_langgraph import IncidentResolutionOrchestrator

# Parse alert
parser = EmailParser()
alert = """
Subject: URGENT - COARRI Timeout
COARRI EDI message for vessel MSC GEMMA failed with timeout.
"""
parsed = parser.parse_email(alert)

# Run agents
orchestrator = IncidentResolutionOrchestrator()
result = orchestrator.process_incident(parsed)

# Get recommendation
print(f"Decision: {result['decision']}")
print(f"Steps: {len(result['resolution']['steps'])}")

orchestrator.close()

Example 2: Test Single Agent

from agent_kb_search import KnowledgeBaseAgent

agent = KnowledgeBaseAgent()

incident = {
    'problem_statement': 'BAPLIE position mismatch detected',
    'module': 'Vessel Operations'
}

result = agent.execute(incident)
print(f"Confidence: {result['confidence']:.1%}")
print(f"Steps: {result['resolution_steps']}")

Example 3: Custom Decision Logic

from orchestrator_langgraph import IncidentResolutionOrchestrator

orchestrator = IncidentResolutionOrchestrator()

# Override decision threshold
def custom_decision(state):
    if state['combined_confidence'] > 0.9:
        return "RESOLVE"
    return "ESCALATE"

# Process with custom logic
result = orchestrator.process_incident(parsed_incident)

🎯 Decision Flow

Alert/Email
    ↓
Parser (Objective)
    ↓
Orchestrator
    ↓
┌───────────┬───────────┬───────────┐
│  KB       │ Historical│   Logs    │
│  Agent    │   Agent   │   Agent   │
└─────┬─────┴─────┬─────┴─────┬─────┘
      └───────────┴───────────┘
               ↓
        Decision Engine
               ↓
        ┌──────┴──────┐
        ↓             ↓
    RESOLVE       ESCALATE

📊 Output Interpretation

RESOLVE Decision

{
  "decision": "RESOLVE",
  "combined_confidence": 0.85,
  "resolution": {
    "steps": ["Step 1", "Step 2", "..."],
    "verification": ["Check 1", "Check 2"],
    "root_cause": "Connection pool exhausted"
  }
}

Action: Execute resolution steps, verify, mark resolved

ESCALATE Decision

{
  "decision": "ESCALATE",
  "combined_confidence": 0.35,
  "escalation": {
    "required": true,
    "reason": "Novel incident, requires expert analysis"
  }
}

Action: Create escalation ticket, notify product team

🔧 Configuration

Adjust Confidence Weights

orchestrator_langgraph.py

combined_confidence = (
    kb_confidence * 0.4 +          # Change to 0.5 for more KB weight
    historical_confidence * 0.4 +   # Change to 0.3
    logs_confidence * 0.2           # Keep at 0.2
)

Change Escalation Threshold

# In _make_decision() method
if severity == 'CRITICAL' and combined_confidence < 0.8:  # Change to 0.9
    decision = "ESCALATE"

Add Custom Agent

Create new agent file:

# agent_custom.py
class CustomAgent:
    def execute(self, parsed_incident):
        # Your logic
        return {'confidence': 0.8, 'result': '...'}

Add to orchestrator:

# In orchestrator_langgraph.py
self.custom_agent = CustomAgent()

# Add node
workflow.add_node("custom_search", self._execute_custom_agent)
workflow.add_edge("logs_search", "custom_search")

📝 Troubleshooting

Issue	Solution
ImportError: faiss	`pip install faiss-cpu`
Database connection failed	Check `db_config.json` credentials
No KB entries	Run `python agent_kb_search.py` first
LangGraph not found	`pip install langgraph`
Low confidence scores	Rebuild FAISS index, check KB data

🎓 Understanding Confidence Scores

Score	Meaning	Action
90-100%	Exact match found	Auto-resolve
70-89%	Strong match	Resolve with monitoring
50-69%	Moderate match	Resolve cautiously
30-49%	Weak match	Escalate (novel case)
0-29%	No match	Escalate immediately

🚦 Status Indicators

result['decision'] == 'RESOLVE' and result['combined_confidence'] > 0.8
# ✅ Green: High confidence resolution

result['decision'] == 'RESOLVE' and result['combined_confidence'] < 0.7
# ⚠️  Yellow: Cautious resolution, monitor closely

result['decision'] == 'ESCALATE'
# 🔴 Red: Escalation required

📈 Performance Tips

FAISS Index: Rebuild weekly to include new KB articles
Database: Add indexes on
```
category
```
,
```
status
```
,
```
reported_at
```
Caching: Cache frequent queries for faster response
Parallel: Run agents in true parallel (requires async)

🔗 Integration Points

With Ticketing System (Django)

# In Django view
from orchestrator_langgraph import IncidentResolutionOrchestrator

def handle_incident(request):
    alert_text = request.POST['alert']
    parsed = parser.parse_email(alert_text)
    result = orchestrator.process_incident(parsed)
    
    if result['decision'] == 'RESOLVE':
        # Create ticket with resolution steps
        Ticket.objects.create(
            status='IN_PROGRESS',
            resolution_steps=result['resolution']['steps']
        )
    else:
        # Escalate
        Escalation.objects.create(
            reason=result['escalation']['reason']
        )

With Real-time Dashboard

# WebSocket update
result = orchestrator.process_incident(parsed)
websocket.send({
    'type': 'incident_analyzed',
    'decision': result['decision'],
    'confidence': result['combined_confidence'],
    'steps': result['resolution']['steps']
})

🎯 Next Steps

✅ You are here: Multi-agent system working
🔄 Integrate with parser: Connect parser → orchestrator
🎨 Build UI: Dashboard showing agent decisions
📊 Add metrics: Track resolution success rate
🚀 Deploy: Production deployment with monitoring

📞 Quick Reference

# Test parser only
python Source_Parser.py

# Test KB agent
python agent_kb_search.py

# Test historical agent
python agent_historical_search.py

# Test logs agent
python agent_logs_search.py

# Test full orchestrator
python orchestrator_langgraph.py

# Rebuild FAISS index
python -c "from agent_kb_search import KnowledgeBaseAgent; KnowledgeBaseAgent().rebuild_index()"

Ready to start? Run

python orchestrator_langgraph.py

🚀

Multi-Agent System Quick Start

🚀 5-Minute Setup

Step 1: Install Dependencies (2 min)

cd /Users/moshesmanihuruk/Downloads/PSANEW/psa/HACKATHON_SOLUTION
pip install -r requirements_agents.txt

Step 2: Configure Database (1 min)

Edit

config/db_config.json

{
  "host": "localhost",
  "user": "root",
  "password": "your_password",
  "database": "appdb"
}

Step 3: Build FAISS Index (1 min)

cd python
python agent_kb_search.py

Output:

🔍 Initializing KnowledgeBaseAgent...
   Building FAISS index from Knowledge Base...
   Extracted 313 KB entries
   Generating embeddings...
   ✅ FAISS index built with 313 vectors
   💾 Index saved to kb_faiss.index

Step 4: Test the System (1 min)

python orchestrator_langgraph.py

Expected output:

🤖 INITIALIZING MULTI-AGENT ORCHESTRATOR
======================================================================
🔍 Initializing KnowledgeBaseAgent...
   ✅ Loaded 313 KB entries
🔍 Initializing HistoricalIncidentAgent...
   ✅ Connected to database: appdb
🔍 Initializing LogsSearchAgent...
   ✅ Connected to database: appdb
✅ Orchestrator initialized

🚀 PROCESSING INCIDENT THROUGH MULTI-AGENT SYSTEM
======================================================================

STEP 1: Knowledge Base Search
======================================================================
🔍 KnowledgeBaseAgent - Searching Knowledge Base
   ✅ Found 3 relevant KB articles
   Best Match: EDI Timeout Issues
   Similarity: 85%

STEP 2: Historical Incident Search
======================================================================
📚 HistoricalIncidentAgent - Searching Historical Data
   ✅ Found 5 similar incidents
   Average Resolution Time: 18.5 minutes

STEP 3: Logs Analysis
======================================================================
📝 LogsSearchAgent - Analyzing Recent Logs
   ✅ Found 12 recent changes
   Critical Changes: 3

STEP 4: Decision Making
======================================================================
📊 CONFIDENCE SCORES:
   KB Agent:         85%
   Historical Agent: 72%
   Logs Agent:       65%
   Combined:         76%

🎯 FINAL DECISION: RESOLVE
✅ Resolution Steps: 5
✅ Verification Steps: 3

📖 Usage Examples

Example 1: Complete Pipeline

from Source_Parser import EmailParser
from orchestrator_langgraph import IncidentResolutionOrchestrator

# Parse alert
parser = EmailParser()
alert = """
Subject: URGENT - COARRI Timeout
COARRI EDI message for vessel MSC GEMMA failed with timeout.
"""
parsed = parser.parse_email(alert)

# Run agents
orchestrator = IncidentResolutionOrchestrator()
result = orchestrator.process_incident(parsed)

# Get recommendation
print(f"Decision: {result['decision']}")
print(f"Steps: {len(result['resolution']['steps'])}")

orchestrator.close()

Example 2: Test Single Agent

from agent_kb_search import KnowledgeBaseAgent

agent = KnowledgeBaseAgent()

incident = {
    'problem_statement': 'BAPLIE position mismatch detected',
    'module': 'Vessel Operations'
}

result = agent.execute(incident)
print(f"Confidence: {result['confidence']:.1%}")
print(f"Steps: {result['resolution_steps']}")

Example 3: Custom Decision Logic

from orchestrator_langgraph import IncidentResolutionOrchestrator

orchestrator = IncidentResolutionOrchestrator()

# Override decision threshold
def custom_decision(state):
    if state['combined_confidence'] > 0.9:
        return "RESOLVE"
    return "ESCALATE"

# Process with custom logic
result = orchestrator.process_incident(parsed_incident)

🎯 Decision Flow

Alert/Email
    ↓
Parser (Objective)
    ↓
Orchestrator
    ↓
┌───────────┬───────────┬───────────┐
│  KB       │ Historical│   Logs    │
│  Agent    │   Agent   │   Agent   │
└─────┬─────┴─────┬─────┴─────┬─────┘
      └───────────┴───────────┘
               ↓
        Decision Engine
               ↓
        ┌──────┴──────┐
        ↓             ↓
    RESOLVE       ESCALATE

📊 Output Interpretation

RESOLVE Decision

{
  "decision": "RESOLVE",
  "combined_confidence": 0.85,
  "resolution": {
    "steps": ["Step 1", "Step 2", "..."],
    "verification": ["Check 1", "Check 2"],
    "root_cause": "Connection pool exhausted"
  }
}

Action: Execute resolution steps, verify, mark resolved

ESCALATE Decision

{
  "decision": "ESCALATE",
  "combined_confidence": 0.35,
  "escalation": {
    "required": true,
    "reason": "Novel incident, requires expert analysis"
  }
}

Action: Create escalation ticket, notify product team

🔧 Configuration

Adjust Confidence Weights

orchestrator_langgraph.py

combined_confidence = (
    kb_confidence * 0.4 +          # Change to 0.5 for more KB weight
    historical_confidence * 0.4 +   # Change to 0.3
    logs_confidence * 0.2           # Keep at 0.2
)

Change Escalation Threshold

# In _make_decision() method
if severity == 'CRITICAL' and combined_confidence < 0.8:  # Change to 0.9
    decision = "ESCALATE"

Add Custom Agent

Create new agent file:

# agent_custom.py
class CustomAgent:
    def execute(self, parsed_incident):
        # Your logic
        return {'confidence': 0.8, 'result': '...'}

Add to orchestrator:

# In orchestrator_langgraph.py
self.custom_agent = CustomAgent()

# Add node
workflow.add_node("custom_search", self._execute_custom_agent)
workflow.add_edge("logs_search", "custom_search")

📝 Troubleshooting

Issue	Solution
ImportError: faiss	`pip install faiss-cpu`
Database connection failed	Check `db_config.json` credentials
No KB entries	Run `python agent_kb_search.py` first
LangGraph not found	`pip install langgraph`
Low confidence scores	Rebuild FAISS index, check KB data

🎓 Understanding Confidence Scores

Score	Meaning	Action
90-100%	Exact match found	Auto-resolve
70-89%	Strong match	Resolve with monitoring
50-69%	Moderate match	Resolve cautiously
30-49%	Weak match	Escalate (novel case)
0-29%	No match	Escalate immediately

🚦 Status Indicators

result['decision'] == 'RESOLVE' and result['combined_confidence'] > 0.8
# ✅ Green: High confidence resolution

result['decision'] == 'RESOLVE' and result['combined_confidence'] < 0.7
# ⚠️  Yellow: Cautious resolution, monitor closely

result['decision'] == 'ESCALATE'
# 🔴 Red: Escalation required

📈 Performance Tips

FAISS Index: Rebuild weekly to include new KB articles
Database: Add indexes on
```
category
```
,
```
status
```
,
```
reported_at
```
Caching: Cache frequent queries for faster response
Parallel: Run agents in true parallel (requires async)

🔗 Integration Points

With Ticketing System (Django)

# In Django view
from orchestrator_langgraph import IncidentResolutionOrchestrator

def handle_incident(request):
    alert_text = request.POST['alert']
    parsed = parser.parse_email(alert_text)
    result = orchestrator.process_incident(parsed)
    
    if result['decision'] == 'RESOLVE':
        # Create ticket with resolution steps
        Ticket.objects.create(
            status='IN_PROGRESS',
            resolution_steps=result['resolution']['steps']
        )
    else:
        # Escalate
        Escalation.objects.create(
            reason=result['escalation']['reason']
        )

With Real-time Dashboard

# WebSocket update
result = orchestrator.process_incident(parsed)
websocket.send({
    'type': 'incident_analyzed',
    'decision': result['decision'],
    'confidence': result['combined_confidence'],
    'steps': result['resolution']['steps']
})

🎯 Next Steps

✅ You are here: Multi-agent system working
🔄 Integrate with parser: Connect parser → orchestrator
🎨 Build UI: Dashboard showing agent decisions
📊 Add metrics: Track resolution success rate
🚀 Deploy: Production deployment with monitoring

📞 Quick Reference

# Test parser only
python Source_Parser.py

# Test KB agent
python agent_kb_search.py

# Test historical agent
python agent_historical_search.py

# Test logs agent
python agent_logs_search.py

# Test full orchestrator
python orchestrator_langgraph.py

# Rebuild FAISS index
python -c "from agent_kb_search import KnowledgeBaseAgent; KnowledgeBaseAgent().rebuild_index()"

Ready to start? Run

python orchestrator_langgraph.py

🚀

Multi-Agent System Quick Start

Related Skills

Nano Banana Pro

Markdown Converter

1password