Merge pull request #104 from Madhuvod/r1-tooluse-langroid

Added new demo: AI System Architect Agent: r1 demo with structured outputs

ai_agent_tutorials/ai_system_architect_r1/README.md

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+# 🤖 AI System Architect Advisor with R1
+
+An Agno agentic system that provides expert software architecture analysis and recommendations using a dual-model approach combining DeepSeek R1's Reasoning and Claude. The system provides detailed technical analysis, implementation roadmaps, and architectural decisions for complex software systems.
+
+## Features
+
+- **Dual AI Model Architecture**
+  - **DeepSeek Reasoner**: Provides initial technical analysis and structured reasoning about architecture patterns, tools, and implementation strategies
+  - **Claude-3.5**: Generates detailed explanations, implementation roadmaps, and technical specifications based on DeepSeek's analysis
+
+- **Comprehensive Analysis Components**
+  - Architecture Pattern Selection
+  - Infrastructure Resource Planning
+  - Security Measures and Compliance
+  - Database Architecture
+  - Performance Requirements
+  - Cost Estimation
+  - Risk Assessment
+
+- **Analysis Types**
+  - Real-time Event Processing Systems
+  - Healthcare Data Platforms
+  - Financial Trading Platforms
+  - Multi-tenant SaaS Solutions
+  - Digital Content Delivery Networks
+  - Supply Chain Management Systems
+
+## How to Run
+
+1. **Setup Environment**
+   ```bash
+   # Clone the repository
+   git clone https://github.com/Shubhamsaboo/awesome-llm-apps.git
+   cd awesome-llm-apps/ai_agent_tutorials/ai_system_architect_r1
+
+   # Install dependencies
+   pip install -r requirements.txt
+   ```
+
+2. **Configure API Keys**
+   - Get DeepSeek API key from DeepSeek platform
+   - Get Anthropic API key from [Anthropic Platform](https://www.anthropic.com)
+
+3. **Run the Application**
+   ```bash
+   streamlit run ai_system_architect_r1.py
+   ```
+
+4. **Use the Interface**
+   - Enter API credentials in sidebar
+   - Structure your prompt with:
+     - Project Context
+     - Requirements
+     - Constraints
+     - Scale
+     - Security/Compliance needs
+   - View detailed analysis results
+
+## Example Test Prompts:
+
+### 1. Financial Trading Platform
+"We need to build a high-frequency trading platform that processes market data streams, executes trades with sub-millisecond latency, maintains audit trails, and handles complex risk calculations. The system needs to be globally distributed, handle 100,000 transactions per second, and have robust disaster recovery capabilities."
+### 2. Multi-tenant SaaS Platform
+"Design a multi-tenant SaaS platform for enterprise resource planning that needs to support customization per tenant, handle different data residency requirements, support offline capabilities, and maintain performance isolation between tenants. The system should scale to 10,000 concurrent users and support custom integrations."
+
+## Notes
+
+- Requires both DeepSeek and Anthropic API keys
+- Provides real-time analysis with detailed explanations
+- Supports chat-based interaction
+- Includes clear reasoning for all architectural decisions
+- API usage costs apply
+
+

ai_agent_tutorials/ai_system_architect_r1/ai_system_architect_r1.py

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+from typing import Optional, List, Dict, Any, Union
+import os
+import time
+import streamlit as st
+from openai import OpenAI
+import anthropic
+from dotenv import load_dotenv
+from pydantic import BaseModel, Field
+from enum import Enum
+import json
+from agno.agent import Agent, RunResponse
+from agno.models.anthropic import Claude
+
+# Model Constants
+DEEPSEEK_MODEL: str = "deepseek-reasoner"
+CLAUDE_MODEL: str = "claude-3-5-sonnet-20241022"
+
+class ArchitecturePattern(str, Enum):
+    """Architectural patterns for system design."""
+    MICROSERVICES = "microservices"  # Decomposed into small, independent services
+    MONOLITHIC = "monolithic"  # Single, unified codebase
+    SERVERLESS = "serverless"  # Function-as-a-Service architecture
+    EVENT_DRIVEN = "event_driven"  # Asynchronous event-based communication
+
+class DatabaseType(str, Enum):
+    """Types of database systems."""
+    SQL = "sql"  # Relational databases with ACID properties
+    NOSQL = "nosql"  # Non-relational databases for flexible schemas
+    HYBRID = "hybrid"  # Combined SQL and NoSQL approach
+
+class ComplianceStandard(str, Enum):
+    """Regulatory compliance standards."""
+    HIPAA = "hipaa"  # Healthcare data protection
+    GDPR = "gdpr"  # EU data privacy regulation
+    SOC2 = "soc2"  # Service organization security controls
+    ISO27001 = "iso27001"  # Information security management
+
+class ArchitectureDecision(BaseModel):
+    """Represents architectural decisions and their justifications."""
+    pattern: ArchitecturePattern
+    rationale: str = Field(..., min_length=50)  # Detailed explanation for the choice
+    trade_offs: Dict[str, List[str]] = Field(..., alias="trade_offs")  # Pros and cons
+    estimated_cost: Dict[str, float]  # Cost breakdown
+
+class SecurityMeasure(BaseModel):
+    """Security controls and implementation details."""
+    measure_type: str  # Type of security measure
+    implementation_priority: int = Field(..., ge=1, le=5)  # Priority level 1-5
+    compliance_standards: List[ComplianceStandard]  # Applicable standards
+    data_classification: str  # Data sensitivity level
+
+class InfrastructureResource(BaseModel):
+    """Infrastructure components and specifications."""
+    resource_type: str  # Type of infrastructure resource
+    specifications: Dict[str, str]  # Technical specifications
+    scaling_policy: Dict[str, str]  # Scaling rules and thresholds
+    estimated_cost: float  # Estimated cost per resource
+
+class TechnicalAnalysis(BaseModel):
+    """Complete technical analysis of the system architecture."""
+    architecture_decision: ArchitectureDecision  # Core architecture choices
+    infrastructure_resources: List[InfrastructureResource]  # Required resources
+    security_measures: List[SecurityMeasure]  # Security controls
+    database_choice: DatabaseType  # Database architecture
+    compliance_requirements: List[ComplianceStandard] = []  # Required standards
+    performance_requirements: List[Dict[str, Union[str, float]]] = []  # Performance metrics
+    risk_assessment: Dict[str, str] = {}  # Identified risks and mitigations
+
+
+class ModelChain:
+    def __init__(self, deepseek_api_key: str, anthropic_api_key: str) -> None:
+        self.client = OpenAI(
+            api_key=deepseek_api_key,
+            base_url="https://api.deepseek.com" 
+        )
+        self.claude_client = anthropic.Anthropic(api_key=anthropic_api_key)
+
+        # Create Claude model with system prompt
+        claude_model = Claude(
+            id="claude-3-5-sonnet-20241022", 
+            api_key=anthropic_api_key,
+            system_prompt="""Given the user's query and the DeepSeek reasoning:
+            1. Provide a detailed analysis of the architecture decisions
+            2. Generate a project implementation roadmap
+            3. Create a comprehensive technical specification document
+            4. Format the output in clean markdown with proper sections
+            5. Include diagrams descriptions in mermaid.js format"""
+        )
+
+        # Initialize agent with configured model
+        self.agent = Agent(
+            model=claude_model,
+            markdown=True
+        )
+
+        self.deepseek_messages: List[Dict[str, str]] = []
+        self.claude_messages: List[Dict[str, Any]] = []
+        self.current_model: str = CLAUDE_MODEL
+    def get_deepseek_reasoning(self, user_input: str) -> tuple[str, str]:    
+        start_time = time.time()
+
+        system_prompt = """You are an expert software architect and technical advisor. Analyze the user's project requirements 
+        and provide structured reasoning about architecture, tools, and implementation strategies. 
+
+        IMPORTANT: Reason why you are choosing a particular architecture pattern, database type, etc. for user understanding in your reasoning.
+        
+        IMPORTANT: Your response must be a valid JSON object (not a string or any other format) that matches the schema provided below.
+        Do not include any explanatory text, markdown formatting, or code blocks - only return the JSON object.
+        
+        Schema:
+        {
+            "architecture_decision": {
+                "pattern": "one of: microservices|monolithic|serverless|event_driven|layered",
+                "rationale": "string",
+                "trade_offs": {"advantage": ["list of strings"], "disadvantage": ["list of strings"]},
+                "estimated_cost": {"implementation": float, "maintenance": float}
+            },
+            "infrastructure_resources": [{
+                "resource_type": "string",
+                "specifications": {"key": "value"},
+                "scaling_policy": {"key": "value"},
+                "estimated_cost": float
+            }],
+            "security_measures": [{
+                "measure_type": "string",
+                "implementation_priority": "integer 1-5",
+                "compliance_standards": ["hipaa", "gdpr", "soc2", "hitech", "iso27001", "pci_dss"],
+                "estimated_setup_time_days": "integer",
+                "data_classification": "one of: protected_health_information|personally_identifiable_information|confidential|public",
+                "encryption_requirements": {"key": "value"},
+                "access_control_policy": {"role": ["permissions"]},
+                "audit_requirements": ["list of strings"]
+            }],
+            "database_choice": "one of: sql|nosql|graph|time_series|hybrid",
+            "ml_capabilities": [{
+                "model_type": "string",
+                "training_frequency": "string",
+                "input_data_types": ["list of strings"],
+                "performance_requirements": {"metric": float},
+                "hardware_requirements": {"resource": "specification"},
+                "regulatory_constraints": ["list of strings"]
+            }],
+            "data_integrations": [{
+                "integration_type": "one of: hl7|fhir|dicom|rest|soap|custom",
+                "data_format": "string",
+                "frequency": "string",
+                "volume": "string",
+                "security_requirements": {"key": "value"}
+            }],
+            "performance_requirements": [{
+                "metric_name": "string",
+                "target_value": float,
+                "measurement_unit": "string",
+                "priority": "integer 1-5"
+            }],
+            "audit_config": {
+                "log_retention_period": "integer",
+                "audit_events": ["list of strings"],
+                "compliance_mapping": {"standard": ["requirements"]}
+            },
+            "api_config": {
+                "version": "string",
+                "auth_method": "string",
+                "rate_limits": {"role": "requests_per_minute"},
+                "documentation_url": "string"
+            },
+            "error_handling": {
+                "retry_policy": {"key": "value"},
+                "fallback_strategies": ["list of strings"],
+                "notification_channels": ["list of strings"]
+            },
+            "estimated_team_size": "integer",
+            "critical_path_components": ["list of strings"],
+            "risk_assessment": {"risk": "mitigation"},
+            "maintenance_considerations": ["list of strings"],
+            "compliance_requirements": ["list of compliance standards"],
+            "data_retention_policy": {"data_type": "retention_period"},
+            "disaster_recovery": {"key": "value"},
+            "interoperability_standards": ["list of strings"]
+        }
+
+        Consider scalability, security, maintenance, and technical debt in your analysis.
+        Focus on practical, modern solutions while being mindful of trade-offs."""
+
+        try:
+            deepseek_response = self.client.chat.completions.create(
+                model="deepseek-reasoner",
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_input}
+                ],
+                max_tokens=3000,
+                stream=False   
+            )
+
+            reasoning_content = deepseek_response.choices[0].message.reasoning_content
+            normal_content = deepseek_response.choices[0].message.content
+
+            # Display the reasoning separately
+            with st.expander("DeepSeek Reasoning", expanded=True):
+                st.markdown(reasoning_content)
+
+
+            with st.expander("💭 Technical Analysis", expanded=True):
+                st.markdown(normal_content)
+                elapsed_time = time.time() - start_time
+                time_str = f"{elapsed_time/60:.1f} minutes" if elapsed_time >= 60 else f"{elapsed_time:.1f} seconds"
+                st.caption(f"⏱️ Analysis completed in {time_str}")
+
+                # Return both reasoning and normal content
+                return reasoning_content, normal_content
+
+        except Exception as e:
+            st.error(f"Error in DeepSeek analysis: {str(e)}")
+            return "Error occurred while analyzing", ""
+
+    def get_claude_response(self, user_input: str, deepseek_output: tuple[str, str]) -> str:
+        try:
+            reasoning_content, normal_content = deepseek_output
+
+            # Create expander for Claude's response
+            with st.expander("🤖 Claude's Response", expanded=True):
+                response_placeholder = st.empty()
+
+                # Prepare the message with user input, reasoning and normal output
+                message = f"""User Query: {user_input}
+
+                DeepSeek Reasoning: {reasoning_content}
+
+                DeepSeek Technical Analysis: {normal_content}
+                Give detailed explanation for each key value pair in brief in the JSON object, and why we chose it clearly. Dont use your own opinions, use the reasoning and the structured output to explain the choices."""
+
+                # Use Phi Agent to get response
+                response: RunResponse = self.agent.run(
+                    message=message
+                )
+
+                dub = response.content
+                st.markdown(dub)
+                return dub
+
+        except Exception as e:
+            st.error(f"Error in Claude response: {str(e)}")
+            return "Error occurred while getting response"
+
+def main() -> None:
+    """Main function to run the Streamlit app."""
+    st.title("🤖 AI System Architect Advisor with R1")
+
+    # Add prompt guidance
+    st.info("""
+    📝 For best results, structure your prompt with:
+    
+    1. **Project Context**: Brief description of your project/system
+    2. **Requirements**: Key functional and non-functional requirements
+    3. **Constraints**: Any technical, budget, or time constraints
+    4. **Scale**: Expected user base and growth projections
+    5. **Security/Compliance**: Any specific security or regulatory needs
+    
+    Example:
+    ```
+    I need to build a healthcare data management system that:
+    - Handles patient records and appointments
+    - Needs to scale to 10,000 users
+    - Must be HIPAA compliant
+    - Budget constraint of $50k for initial setup
+    - Should integrate with existing hospital systems
+    ```
+    """)
+
+    # Sidebar for API keys
+    with st.sidebar:
+        st.header("⚙️ Configuration")
+        deepseek_api_key = st.text_input("DeepSeek API Key", type="password")
+        anthropic_api_key = st.text_input("Anthropic API Key", type="password")
+
+        if st.button("🗑️ Clear Chat History"):
+            st.session_state.messages = []
+            st.rerun()
+
+    # Initialize session state for messages
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    # Display chat messages
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # Chat input
+    if prompt := st.chat_input("What would you like to know?"):
+        if not deepseek_api_key or not anthropic_api_key:
+            st.error("⚠️ Please enter both API keys in the sidebar.")
+            return
+
+        # Initialize ModelChain
+        chain = ModelChain(deepseek_api_key, anthropic_api_key)
+
+        # Add user message to chat
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        # Get AI response
+        with st.chat_message("assistant"):
+            with st.spinner("🤔 Thinking..."):
+                deepseek_output = chain.get_deepseek_reasoning(prompt)
+
+
+            with st.spinner("✍️ Responding..."):
+                response = chain.get_claude_response(prompt, deepseek_output)
+                st.session_state.messages.append({"role": "assistant", "content": response})
+
+if __name__ == "__main__":
+    main()

ai_agent_tutorials/ai_system_architect_r1/requirements.txt

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+streamlit
+openai
+anthropic
+agno