feat: add advanced technical use case examples for real-world scenarios

- Implemented `market_system.py` for financial market analysis
- Added `enterprise_doc_processor.py` for document classification and processing
- Created `diagnostic_system.py` for healthcare diagnostics using agent architecture

examples/advanced_market_analysis/market_system.py

@@ -0,0 +1,71 @@
+
+from swarms.structs.agent import Agent
+from swarms.prompts.finance_agent_sys_prompt import FINANCIAL_AGENT_SYS_PROMPT
+
+# Technical Analysis Specialist
+technical_analyst = Agent(
+    agent_name="Technical-Analysis-Expert",
+    agent_description="Advanced technical analysis specialist focusing on complex market patterns",
+    system_prompt="""You are an expert Technical Analyst specializing in:
+    1. Advanced Pattern Recognition (Elliot Wave, Wyckoff Method)
+    2. Multi-timeframe Analysis
+    3. Volume Profile Analysis
+    4. Market Structure Analysis
+    5. Intermarket Analysis""",
+    max_loops=3,
+    model_name="gpt-4"
+)
+
+# Fundamental Analysis Expert
+fundamental_analyst = Agent(
+    agent_name="Fundamental-Analysis-Expert",
+    agent_description="Deep fundamental analysis specialist",
+    system_prompt="""You are a Fundamental Analysis expert focusing on:
+    1. Advanced Financial Statement Analysis
+    2. Economic Indicator Impact Assessment
+    3. Industry Competitive Analysis
+    4. Global Macro Trends
+    5. Corporate Governance Evaluation""",
+    max_loops=3,
+    model_name="gpt-4"
+)
+
+# Risk Management Specialist
+risk_analyst = Agent(
+    agent_name="Risk-Management-Expert",
+    agent_description="Complex risk analysis and management specialist",
+    system_prompt="""You are a Risk Management expert specializing in:
+    1. Portfolio Risk Assessment
+    2. Value at Risk (VaR) Analysis
+    3. Stress Testing Scenarios
+    4. Correlation Analysis
+    5. Risk-Adjusted Performance Metrics""",
+    max_loops=3,
+    model_name="gpt-4"
+)
+
+class MarketAnalysisSystem:
+    def __init__(self):
+        self.agents = [technical_analyst, fundamental_analyst, risk_analyst]
+        
+    def comprehensive_analysis(self, asset_data):
+        analysis_results = []
+        for agent in self.agents:
+            analysis = agent.run(f"Analyze this asset data: {asset_data}")
+            analysis_results.append({
+                "analyst": agent.agent_name,
+                "analysis": analysis
+            })
+        
+        # Synthesize results through risk analyst for final recommendation
+        final_analysis = risk_analyst.run(
+            f"Synthesize these analyses and provide a final recommendation: {analysis_results}"
+        )
+        
+        return {
+            "detailed_analysis": analysis_results,
+            "final_recommendation": final_analysis
+        }
+
+# Usage
+analysis_system = MarketAnalysisSystem()

examples/document_processing/enterprise_doc_processor.py

@@ -0,0 +1,67 @@
+
+from swarms.structs.agent import Agent
+from swarms.utils.pdf_to_text import pdf_to_text
+import asyncio
+
+class DocumentProcessingPipeline:
+    def __init__(self):
+        self.document_analyzer = Agent(
+            agent_name="Document-Analyzer",
+            agent_description="Enterprise document analysis specialist",
+            system_prompt="""You are an expert document analyzer specializing in:
+            1. Complex Document Structure Analysis
+            2. Key Information Extraction
+            3. Compliance Verification
+            4. Document Classification
+            5. Content Validation""",
+            max_loops=2,
+            model_name="gpt-4"
+        )
+        
+        self.legal_reviewer = Agent(
+            agent_name="Legal-Reviewer",
+            agent_description="Legal compliance and risk assessment specialist",
+            system_prompt="""You are a legal review expert focusing on:
+            1. Regulatory Compliance Check
+            2. Legal Risk Assessment
+            3. Contractual Obligation Analysis
+            4. Privacy Requirement Verification
+            5. Legal Term Extraction""",
+            max_loops=2,
+            model_name="gpt-4"
+        )
+        
+        self.data_extractor = Agent(
+            agent_name="Data-Extractor",
+            agent_description="Structured data extraction specialist",
+            system_prompt="""You are a data extraction expert specializing in:
+            1. Named Entity Recognition
+            2. Relationship Extraction
+            3. Tabular Data Processing
+            4. Metadata Extraction
+            5. Data Standardization""",
+            max_loops=2,
+            model_name="gpt-4"
+        )
+
+    async def process_document(self, document_path):
+        # Convert document to text
+        document_text = pdf_to_text(document_path)
+        
+        # Parallel processing tasks
+        tasks = [
+            self.document_analyzer.arun(f"Analyze this document: {document_text}"),
+            self.legal_reviewer.arun(f"Review legal aspects: {document_text}"),
+            self.data_extractor.arun(f"Extract structured data: {document_text}")
+        ]
+        
+        results = await asyncio.gather(*tasks)
+        
+        return {
+            "document_analysis": results[0],
+            "legal_review": results[1],
+            "extracted_data": results[2]
+        }
+
+# Usage
+processor = DocumentProcessingPipeline()

examples/healthcare/diagnostic_system.py

@@ -0,0 +1,69 @@
+
+from swarms.structs.agent import Agent
+from typing import Dict, List
+
+class HealthcareDiagnosticSystem:
+    def __init__(self):
+        self.primary_diagnostician = Agent(
+            agent_name="Primary-Diagnostician",
+            agent_description="Primary diagnostic analysis specialist",
+            system_prompt="""You are a primary diagnostician expert in:
+            1. Initial Symptom Analysis
+            2. Patient History Evaluation
+            3. Preliminary Diagnosis Formation
+            4. Risk Factor Assessment
+            5. Treatment Priority Determination""",
+            max_loops=3,
+            model_name="gpt-4"
+        )
+        
+        self.specialist_consultant = Agent(
+            agent_name="Specialist-Consultant",
+            agent_description="Specialized medical consultation expert",
+            system_prompt="""You are a medical specialist focusing on:
+            1. Complex Case Analysis
+            2. Specialized Treatment Planning
+            3. Comorbidity Assessment
+            4. Treatment Risk Evaluation
+            5. Advanced Diagnostic Interpretation""",
+            max_loops=3,
+            model_name="gpt-4"
+        )
+        
+        self.treatment_coordinator = Agent(
+            agent_name="Treatment-Coordinator",
+            agent_description="Treatment planning and coordination specialist",
+            system_prompt="""You are a treatment coordination expert specializing in:
+            1. Treatment Plan Development
+            2. Care Coordination
+            3. Resource Allocation
+            4. Recovery Timeline Planning
+            5. Follow-up Protocol Design""",
+            max_loops=3,
+            model_name="gpt-4"
+        )
+
+    def process_case(self, patient_data: Dict) -> Dict:
+        # Initial diagnosis
+        primary_assessment = self.primary_diagnostician.run(
+            f"Perform initial diagnosis: {patient_data}"
+        )
+        
+        # Specialist consultation
+        specialist_review = self.specialist_consultant.run(
+            f"Review case with initial assessment: {primary_assessment}"
+        )
+        
+        # Treatment planning
+        treatment_plan = self.treatment_coordinator.run(
+            f"Develop treatment plan based on: Primary: {primary_assessment}, Specialist: {specialist_review}"
+        )
+        
+        return {
+            "initial_assessment": primary_assessment,
+            "specialist_review": specialist_review,
+            "treatment_plan": treatment_plan
+        }
+
+# Usage
+diagnostic_system = HealthcareDiagnosticSystem()