[CLEANUP]

README.md

@@ -46,12 +46,12 @@ pip install -r requirements.txt
 
 3. **Configure Environment Variables**
 ```bash
-# Copy example env file
-cp .env.example .env
+# Create .env file
 
 # Edit .env file with your credentials
 GROQ_API_KEY="your-api-key-here"
 WORKSPACE_DIR="agent_workspace"
+OPENAI_API_KEY="your-openai-api-key-here"
 ```
 
 4. **Run the Example**
@@ -97,6 +97,62 @@ In `main.py`, update the `flow` parameter in the `AgentRearrange` initialization
 flow=f"{agent1.agent_name} -> {agent2.agent_name} -> {new_agent.agent_name}"
 ```
 
+## Integrating RAG
+
+- The `memory_system` parameter in the `AgentRearrange` initialization is used to configure the RAG system.
+- The `memory_system` parameter is an instance of `LlamaIndexDB`, which is a database class for storing and retrieving medical documents.
+- The `memory_system` class must have a `query(query: str)` method that returns a string for the agent to use it.
+
+```python
+
+# Import the AgentRearrange class for coordinating multiple agents
+from swarms import AgentRearrange
+
+# Import specialized medical agents for different aspects of patient care
+from multi_agent_rag.agents import (
+    diagnostic_specialist,      # Agent for diagnostic analysis
+    medical_data_extractor,    # Agent for extracting medical data
+    patient_care_coordinator,  # Agent for coordinating patient care
+    specialist_consultant,     # Agent for specialist consultation
+    treatment_planner,        # Agent for treatment planning
+)
+
+# Import database class for storing and retrieving medical documents
+from multi_agent_rag.memory import LlamaIndexDB
+
+# Initialize the SwarmRouter to coordinate the medical agents
+router = AgentRearrange(
+    name="medical-diagnosis-treatment-swarm",
+    description="Collaborative medical team for comprehensive patient diagnosis and treatment planning",
+    max_loops=1,  # Limit to one iteration through the agent flow
+    agents=[
+        medical_data_extractor,    # First agent to extract medical data
+        diagnostic_specialist,      # Second agent to analyze and diagnose
+        treatment_planner,         # Third agent to plan treatment
+        specialist_consultant,      # Fourth agent to provide specialist input
+        patient_care_coordinator,  # Final agent to coordinate care plan
+    ],
+    # Configure the document storage and retrieval system
+    memory_system=LlamaIndexDB(
+        data_dir="docs",           # Directory containing medical documents
+        filename_as_id=True,       # Use filenames as document identifiers
+        recursive=True,            # Search subdirectories
+        # required_exts=[".txt", ".pdf", ".docx"],  # Supported file types
+        similarity_top_k=10,       # Return top 10 most relevant documents
+    ),
+    # Define the sequential flow of information between agents
+    flow=f"{medical_data_extractor.agent_name} -> {diagnostic_specialist.agent_name} -> {treatment_planner.agent_name} -> {specialist_consultant.agent_name} -> {patient_care_coordinator.agent_name}",
+)
+
+# Example usage
+if __name__ == "__main__":
+    # Run a comprehensive medical analysis task for patient Lucas Brown
+    router.run(
+        "Analyze this Lucas Brown's medical data to provide a diagnosis and treatment plan"
+    )
+
+```
+
 
 
 ## 📚 Documentation