addition of pydantic

ai_agent_tutorials/ai_r1-tooluse-langroid/main.py

@@ -8,6 +8,8 @@ from dotenv import load_dotenv
 from pydantic import BaseModel, Field
 from enum import Enum
 import json
+from phi.agent import Agent, RunResponse
+from phi.model.anthropic import Claude
 
 # Model Constants
 DEEPSEEK_MODEL: str = "deepseek-reasoner"
@@ -16,151 +18,71 @@ CLAUDE_MODEL: str = "claude-3-5-sonnet-20241022"
 # Load environment variables
 load_dotenv()
 
+system_prompt = """You are a Senior Software Expert and Technical Documentation Assistant. Your role is to analyze the structured JSON response from DeepSeek, which contains architectural and technical recommendations across various domains, along with the original user query describing the software system they want to build.
+
+        The input consists of:
+        - The user's original query describing their software requirements
+        - A structured JSON response containing recommendations for architecture, security, infrastructure, compliance and other technical domains
+
+        For each key-value pair in the JSON:
+        1. Present the key and its corresponding value in a readable report format
+        2. Format the information in a clear, organized way
+        3. Do not add your own opinions or suggestions
+        4. Do not modify or reinterpret the provided information
+        
+        Keep your responses factual and directly based on the JSON content provided."""
+
 class ArchitecturePattern(str, Enum):
-    MICROSERVICES = "microservices"
+    """Architectural patterns for system design."""
-    MONOLITHIC = "monolithic" 
+    MICROSERVICES = "microservices"  # Decomposed into small, independent services
-    SERVERLESS = "serverless"
+    MONOLITHIC = "monolithic"  # Single, unified codebase
-    EVENT_DRIVEN = "event_driven"
+    SERVERLESS = "serverless"  # Function-as-a-Service architecture
-    LAYERED = "layered"
+    EVENT_DRIVEN = "event_driven"  # Asynchronous event-based communication
-
-class SecurityLevel(str, Enum):
-    LOW = "low"
-    MEDIUM = "medium"
-    HIGH = "high"
-    VERY_HIGH = "very_high"
-
-class ScalabilityRequirement(str, Enum):
-    SMALL = "small"
-    MEDIUM = "medium"
-    LARGE = "large"
-    ENTERPRISE = "enterprise"
 
 class DatabaseType(str, Enum):
-    SQL = "sql"
+    """Types of database systems."""
-    NOSQL = "nosql"
+    SQL = "sql"  # Relational databases with ACID properties
-    GRAPH = "graph"
+    NOSQL = "nosql"  # Non-relational databases for flexible schemas
-    TIME_SERIES = "time_series"
+    HYBRID = "hybrid"  # Combined SQL and NoSQL approach
-    HYBRID = "hybrid"
 
 class ComplianceStandard(str, Enum):
-    HIPAA = "hipaa"
+    """Regulatory compliance standards."""
-    GDPR = "gdpr"
+    HIPAA = "hipaa"  # Healthcare data protection
-    SOC2 = "soc2"
+    GDPR = "gdpr"  # EU data privacy regulation
-    HITECH = "hitech"
+    SOC2 = "soc2"  # Service organization security controls
-    ISO27001 = "iso27001"
+    ISO27001 = "iso27001"  # Information security management
-    PCI_DSS = "pci_dss"
-
-class DataClassification(str, Enum):
-    PHI = "protected_health_information"
-    PII = "personally_identifiable_information"
-    CONFIDENTIAL = "confidential"
-    PUBLIC = "public"
-
-class IntegrationType(str, Enum):
-    HL7 = "hl7"
-    FHIR = "fhir"
-    DICOM = "dicom"
-    REST = "rest"
-    SOAP = "soap"
-    CUSTOM = "custom"
-
-class DataProcessingType(str, Enum):
-    REAL_TIME = "real_time"
-    BATCH = "batch"
-    HYBRID = "hybrid"
-
-class MLCapability(BaseModel):
-    """Defines machine learning capabilities and requirements"""
-    model_type: str = Field(..., description="Type of ML model (e.g., diagnostic, predictive, monitoring)")
-    training_frequency: str = Field(..., description="How often the model needs retraining")
-    input_data_types: List[str] = Field(..., description="Types of data the model processes")
-    performance_requirements: Dict[str, float] = Field(..., description="Required metrics like accuracy, latency")
-    hardware_requirements: Dict[str, str] = Field(..., description="GPU/CPU/Memory requirements")
-    regulatory_constraints: List[str] = Field(..., description="Regulatory requirements for ML models")
-
-class SecurityMeasure(BaseModel):
-    """Enhanced security measures for healthcare systems"""
-    measure_type: str
-    implementation_priority: int = Field(ge=1, le=5, description="Priority level for implementation")
-    compliance_standards: List[ComplianceStandard]
-    estimated_setup_time_days: int
-    data_classification: DataClassification
-    encryption_requirements: Dict[str, str] = Field(..., description="Encryption requirements for different states")
-    access_control_policy: Dict[str, List[str]] = Field(..., description="Role-based access control definitions")
-    audit_requirements: List[str] = Field(..., description="Audit logging requirements")
 
 class ArchitectureDecision(BaseModel):
-    """Architecture decision details"""
+    """Represents architectural decisions and their justifications."""
     pattern: ArchitecturePattern
-    rationale: str
+    rationale: str = Field(..., min_length=50)  # Detailed explanation for the choice
-    trade_offs: Dict[str, List[str]]
+    trade_offs: Dict[str, List[str]] = Field(..., alias="trade_offs")  # Pros and cons
-    estimated_cost: Dict[str, float]
+    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 resource requirements"""
+    """Infrastructure components and specifications."""
-    resource_type: str
+    resource_type: str  # Type of infrastructure resource
-    specifications: Dict[str, str]
+    specifications: Dict[str, str]  # Technical specifications
-    scaling_policy: Dict[str, Any]
+    scaling_policy: Dict[str, str]  # Scaling rules and thresholds
-    estimated_cost: float
+    estimated_cost: float  # Estimated cost per resource
 
-class DataIntegration(BaseModel):
+class TechnicalAnalysis(BaseModel):
-    """Data integration specifications"""
+    """Complete technical analysis of the system architecture."""
-    integration_type: IntegrationType
+    architecture_decision: ArchitectureDecision  # Core architecture choices
-    data_format: str
+    infrastructure_resources: List[InfrastructureResource]  # Required resources
-    frequency: str
+    security_measures: List[SecurityMeasure]  # Security controls
-    volume: str
+    database_choice: DatabaseType  # Database architecture
-    security_requirements: Dict[str, str]
+    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 PerformanceRequirement(BaseModel):
-    """Performance requirements specification"""
-    metric_name: str
-    target_value: float
-    measurement_unit: str
-    priority: int
-
-class AuditConfig(BaseModel):
-    """Audit configuration settings"""
-    log_retention_period: int
-    audit_events: List[str]
-    compliance_mapping: Dict[str, List[str]]
-
-class APIConfig(BaseModel):
-    """API configuration settings"""
-    version: str
-    auth_method: str
-    rate_limits: Dict[str, int]
-    documentation_url: str
-
-class ErrorHandlingConfig(BaseModel):
-    """Error handling configuration"""
-    retry_policy: Dict[str, Any]
-    fallback_strategies: List[str]
-    notification_channels: List[str]
-
-class ProjectAnalysis(BaseModel):
-    """Enhanced project analysis for healthcare systems"""
-    architecture_decision: ArchitectureDecision
-    infrastructure_resources: List[InfrastructureResource]
-    security_measures: List[SecurityMeasure]
-    database_choice: DatabaseType
-    estimated_team_size: int
-    critical_path_components: List[str]
-    risk_assessment: Dict[str, str]
-    maintenance_considerations: List[str]
-    
-    # Healthcare-specific fields
-    compliance_requirements: List[ComplianceStandard]
-    data_integrations: List[DataIntegration]
-    ml_capabilities: List[MLCapability]
-    performance_requirements: List[PerformanceRequirement]
-    data_retention_policy: Dict[str, str]
-    disaster_recovery: Dict[str, Any]
-    interoperability_standards: List[str]
-    
-    # New fields
-    audit_config: AuditConfig
-    api_config: APIConfig
-    error_handling: ErrorHandlingConfig
 
 class ModelChain:
     def __init__(self, deepseek_api_key: str, anthropic_api_key: str) -> None:
@@ -169,15 +91,22 @@ class ModelChain:
             base_url="https://api.deepseek.com" 
         )
         self.claude_client = anthropic.Anthropic(api_key=anthropic_api_key)
+        self.agent = Agent(
+            model=Claude(id="claude-3-5-sonnet-20241022", api_key=anthropic_api_key),
+            system_prompt=system_prompt,
+            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) -> str:    
+    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.
@@ -282,53 +211,34 @@ class ModelChain:
                 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 the validated structured output for Claude
+                # Return both reasoning and normal content
-                return reasoning_content
+                return reasoning_content, normal_content
 
         except Exception as e:
             st.error(f"Error in DeepSeek analysis: {str(e)}")
-            return "Error occurred while analyzing"
+            return "Error occurred while analyzing", ""
-
-    def get_claude_response(self, user_input: str, reasoning: str) -> str:
-        system_prompt = """You are a senior software architect and implementation advisor. Using the provided technical analysis, 
-        give detailed, actionable advice for implementing the solution. Include code snippets, configuration examples, and 
-        step-by-step implementation guidelines where appropriate. Focus on practical implementation details while maintaining 
-        best practices and addressing potential challenges."""
-
-        user_message = {
-            "role": "user",
-            "content": [{"type": "text", "text": user_input}]
-        }
-
-        assistant_prefill = {
-            "role": "assistant",
-            "content": [{"type": "text", "text": f"<thinking>{reasoning}</thinking>"}]
-        }
-
-        messages = [assistant_prefill]
         
+    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()
                 
-                with self.claude_client.messages.stream(
+                # Prepare the message with user input, reasoning and normal output
-                    model=self.current_model,
+                message = f"""User Query: {user_input}
-                    system=system_prompt,
-                    messages=messages,
-                    max_tokens=8000
-                ) as stream:
-                    full_response = ""
-                    for text in stream.text_stream:
-                        full_response += text
-                        response_placeholder.markdown(full_response)
 
-                self.claude_messages.extend([user_message, {
+                DeepSeek Reasoning: {reasoning_content}
-                    "role": "assistant", 
-                    "content": [{"type": "text", "text": full_response}]
-                }])
 
-                return full_response
+                DeepSeek Technical Analysis: {normal_content}"""
+                
+                # Use Phi Agent to get response
+                response: RunResponse = self.agent.run(
+                    message=message
+                )
+                
+                return response.content
 
         except Exception as e:
             st.error(f"Error in Claude response: {str(e)}")
@@ -374,11 +284,11 @@ def main() -> None:
         # Get AI response
         with st.chat_message("assistant"):
             with st.spinner("🤔 Thinking..."):
-                reasoning = chain.get_deepseek_reasoning(prompt)
+                deepseek_output = chain.get_deepseek_reasoning(prompt)
             
             
             with st.spinner("✍️ Responding..."):
-                response = chain.get_claude_response(prompt, reasoning)
+                response = chain.get_claude_response(prompt, deepseek_output)
                 st.session_state.messages.append({"role": "assistant", "content": response})
 
 if __name__ == "__main__":

ai_agent_tutorials/ai_r1-tooluse-langroid/test.py

@@ -0,0 +1,184 @@
+from enum import Enum
+from typing import List, Dict, Union
+from pydantic import BaseModel, Field, ValidationError
+import streamlit as st
+from openai import OpenAI
+import anthropic
+import json
+import re
+import os
+from dotenv import load_dotenv
+from phi.agent import Agent, RunResponse
+from phi.model.anthropic import Claude
+
+load_dotenv()
+
+# --------------------------
+# Enums & Data Models
+# --------------------------
+class ArchitecturePattern(str, Enum):
+    MICROSERVICES = "microservices"
+    MONOLITHIC = "monolithic"
+    SERVERLESS = "serverless"
+    EVENT_DRIVEN = "event_driven"
+
+class DatabaseType(str, Enum):
+    SQL = "sql"
+    NOSQL = "nosql"
+    HYBRID = "hybrid"
+
+class ComplianceStandard(str, Enum):
+    HIPAA = "hipaa"
+    GDPR = "gdpr"
+    SOC2 = "soc2"
+    ISO27001 = "iso27001"
+
+class ArchitectureDecision(BaseModel):
+    pattern: ArchitecturePattern
+    rationale: str = Field(..., min_length=50)
+    trade_offs: Dict[str, List[str]] = Field(..., alias="trade_offs")
+    estimated_cost: Dict[str, float]
+
+class SecurityMeasure(BaseModel):
+    measure_type: str
+    implementation_priority: int = Field(..., ge=1, le=5)
+    compliance_standards: List[ComplianceStandard]
+    data_classification: str
+
+class InfrastructureResource(BaseModel):
+    resource_type: str
+    specifications: Dict[str, str]
+    scaling_policy: Dict[str, str]
+    estimated_cost: float
+
+class TechnicalAnalysis(BaseModel):
+    architecture_decision: ArchitectureDecision
+    infrastructure_resources: List[InfrastructureResource]
+    security_measures: List[SecurityMeasure]
+    database_choice: DatabaseType
+    compliance_requirements: List[ComplianceStandard] = []
+    performance_requirements: List[Dict[str, Union[str, float]]] = []
+    risk_assessment: Dict[str, str] = {}
+
+# --------------------------
+# Core Implementation
+# --------------------------
+class ArchitectureAnalyzer:
+    def __init__(self, deepseek_api_key: str, anthropic_api_key: str):
+        self.deepseek_client = OpenAI(
+            api_key=deepseek_api_key,
+            base_url="https://api.deepseek.com"
+        )
+        self.claude_agent = Agent(
+            model=Claude(
+                id="claude-3-5-sonnet-20241022",
+                api_key=anthropic_api_key
+            ),
+            markdown=True,
+        )
+        self.reasoning_content = ""
+        
+        self.deepseek_prompt = f"""Analyze software requirements and return JSON with:
+{{
+  "architecture_decision": {{
+    "pattern": "{'|'.join([e.value for e in ArchitecturePattern])}",
+    "rationale": "technical justification",
+    "trade_offs": {{"pros": [], "cons": []}},
+    "estimated_cost": {{"development": float, "maintenance": float}}
+  }},
+  "infrastructure_resources": [{{"resource_type": "...", "specifications": {{}}, ...}}],
+  "security_measures": [{{"measure_type": "...", "priority": 1-5, ...}}],
+  "database_choice": "{'|'.join([e.value for e in DatabaseType])}",
+  "compliance_requirements": ["..."],
+  "performance_requirements": [{{"metric": "...", "target": float}}]
+}}"""
+
+    def _extract_json(self, text: str) -> dict:
+        try:
+            json_str = re.search(r'\{.*\}', text, re.DOTALL).group()
+            return json.loads(json_str)
+        except (AttributeError, json.JSONDecodeError) as e:
+            st.error(f"JSON extraction failed: {str(e)}")
+            st.text("Raw response:\n" + text)
+            raise
+
+    def analyze_requirements(self, user_input: str) -> TechnicalAnalysis:
+        try:
+            response1 = self.deepseek_client.chat.completions.create(
+                model="deepseek-reasoner",
+                messages=[
+                    {"role": "system", "content": self.deepseek_prompt},
+                    {"role": "user", "content": user_input}
+                ],
+                temperature=0.2,
+                max_tokens=2000
+            )
+            self.reasoning_content = response1.choices[0].message.reasoning_content
+            json_data = self._extract_json(response1.choices[0].message.content)
+            return TechnicalAnalysis(**json_data)
+            
+        except ValidationError as e:
+            st.error(f"Validation error: {e.errors()}")
+            st.json(json_data)
+            raise
+
+    def generate_report(self, analysis: TechnicalAnalysis) -> str:
+        report_prompt = f"""Convert this technical analysis into a executive report:
+{analysis.model_dump_json(indent=2)}
+
+Use markdown with:
+# Title
+## Sections
+- Bullet points
+**Bold important items**
+Tables for cost/performance"""
+
+        response = self.claude_agent.run(report_prompt)
+        return response.content
+
+# --------------------------
+# Streamlit UI
+# --------------------------
+def main():
+    st.title("🏗️ AI Architecture Advisor")
+    
+    with st.sidebar:
+        st.header("🔑 Setup")
+        deepseek_api_key = st.text_input("DeepSeek Key", type="password")
+        anthropic_api_key = st.text_input("Claude Key", type="password")
+    
+    if "analysis" not in st.session_state:
+        st.session_state.analysis = None
+
+    if prompt := st.chat_input("Describe your system requirements:"):
+        if not all([deepseek_api_key, anthropic_api_key]):
+            st.error("Missing API keys")
+            return
+
+        analyzer = ArchitectureAnalyzer(deepseek_api_key, anthropic_api_key)
+        
+        with st.status("🔨 Processing...", expanded=True):
+            try:
+                # Analysis Phase
+                st.write("🧠 Analyzing requirements...")
+                analysis = analyzer.analyze_requirements(prompt)
+                st.session_state.analysis = analysis
+                with st.expander("reasoning"):
+                    st.markdown(analyzer.reasoning_content)
+
+                # Reporting Phase
+                st.write("📊 Generating report...")
+                report = analyzer.generate_report(analysis)
+                
+                # Display Results
+                st.success("Analysis complete!")
+                st.markdown(report)
+                
+                with st.expander("📁 Raw Analysis Data"):
+                    st.json(analysis.model_dump_json())
+
+            except Exception as e:
+                st.error(f"Processing failed: {str(e)}")
+
+if __name__ == "__main__":
+    main()