complete schemas
This commit is contained in:
Madhu
parent
125b29ca1d
commit
b6d2a8ab05
2 changed files with 148 additions and 84 deletions
|
|
@ -18,7 +18,7 @@ load_dotenv()
|
|||
|
||||
class ArchitecturePattern(str, Enum):
|
||||
MICROSERVICES = "microservices"
|
||||
MONOLITHIC = "monolithic"
|
||||
MONOLITHIC = "monolithic"
|
||||
SERVERLESS = "serverless"
|
||||
EVENT_DRIVEN = "event_driven"
|
||||
LAYERED = "layered"
|
||||
|
|
@ -42,22 +42,6 @@ class DatabaseType(str, Enum):
|
|||
TIME_SERIES = "time_series"
|
||||
HYBRID = "hybrid"
|
||||
|
||||
class DevTool(BaseModel):
|
||||
name: str
|
||||
purpose: str
|
||||
complexity: int = Field(ge=1, le=10)
|
||||
setup_time_minutes: int
|
||||
learning_curve: int = Field(ge=1, le=10)
|
||||
alternatives: List[str]
|
||||
|
||||
class InfrastructureComponent(BaseModel):
|
||||
service_name: str
|
||||
provider: str
|
||||
estimated_cost: float
|
||||
scaling_capability: ScalabilityRequirement
|
||||
region: Optional[str]
|
||||
backup_strategy: Optional[str]
|
||||
|
||||
class ComplianceStandard(str, Enum):
|
||||
HIPAA = "hipaa"
|
||||
GDPR = "gdpr"
|
||||
|
|
@ -94,16 +78,6 @@ class MLCapability(BaseModel):
|
|||
hardware_requirements: Dict[str, str] = Field(..., description="GPU/CPU/Memory requirements")
|
||||
regulatory_constraints: List[str] = Field(..., description="Regulatory requirements for ML models")
|
||||
|
||||
class DataIntegration(BaseModel):
|
||||
"""Defines integration points with external systems"""
|
||||
system_name: str
|
||||
integration_type: IntegrationType
|
||||
data_frequency: str = Field(..., description="Frequency of data exchange")
|
||||
data_volume: str = Field(..., description="Expected data volume per time unit")
|
||||
transformation_rules: List[str] = Field(..., description="Data transformation requirements")
|
||||
error_handling: Dict[str, str] = Field(..., description="Error handling strategies")
|
||||
fallback_mechanism: Optional[str] = Field(None, description="Fallback approach when integration fails")
|
||||
|
||||
class SecurityMeasure(BaseModel):
|
||||
"""Enhanced security measures for healthcare systems"""
|
||||
measure_type: str
|
||||
|
|
@ -115,62 +89,78 @@ class SecurityMeasure(BaseModel):
|
|||
access_control_policy: Dict[str, List[str]] = Field(..., description="Role-based access control definitions")
|
||||
audit_requirements: List[str] = Field(..., description="Audit logging requirements")
|
||||
|
||||
class PerformanceRequirement(BaseModel):
|
||||
"""System performance requirements"""
|
||||
metric_name: str = Field(..., description="Name of the performance metric")
|
||||
threshold: float = Field(..., description="Required threshold value")
|
||||
measurement_unit: str = Field(..., description="Unit of measurement")
|
||||
criticality: int = Field(ge=1, le=5, description="How critical is this metric")
|
||||
monitoring_frequency: str = Field(..., description="How often to monitor this metric")
|
||||
|
||||
class ArchitectureDecision(BaseModel):
|
||||
"""Architecture decision details"""
|
||||
pattern: ArchitecturePattern
|
||||
reasoning: str
|
||||
rationale: str
|
||||
trade_offs: Dict[str, List[str]]
|
||||
estimated_implementation_time_months: float
|
||||
estimated_cost: Dict[str, float]
|
||||
|
||||
class TechnicalDebtItem(BaseModel):
|
||||
description: str
|
||||
severity: int = Field(ge=1, le=5)
|
||||
estimated_fix_time_days: int
|
||||
affected_components: List[str]
|
||||
potential_risks: List[str]
|
||||
class InfrastructureResource(BaseModel):
|
||||
"""Infrastructure resource requirements"""
|
||||
resource_type: str
|
||||
specifications: Dict[str, str]
|
||||
scaling_policy: Dict[str, Any]
|
||||
estimated_cost: float
|
||||
|
||||
class DataIntegration(BaseModel):
|
||||
"""Data integration specifications"""
|
||||
integration_type: IntegrationType
|
||||
data_format: str
|
||||
frequency: str
|
||||
volume: str
|
||||
security_requirements: Dict[str, str]
|
||||
|
||||
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
|
||||
recommended_tools: List[DevTool]
|
||||
infrastructure: List[InfrastructureComponent]
|
||||
infrastructure_resources: List[InfrastructureResource]
|
||||
security_measures: List[SecurityMeasure]
|
||||
database_choice: DatabaseType
|
||||
technical_debt_assessment: List[TechnicalDebtItem]
|
||||
estimated_team_size: int
|
||||
critical_path_components: List[str]
|
||||
risk_assessment: Dict[str, str]
|
||||
maintenance_considerations: List[str]
|
||||
|
||||
# New healthcare-specific fields
|
||||
compliance_requirements: List[ComplianceStandard] = Field(
|
||||
..., description="Required compliance standards"
|
||||
)
|
||||
data_integrations: List[DataIntegration] = Field(
|
||||
..., description="External system integrations"
|
||||
)
|
||||
ml_capabilities: List[MLCapability] = Field(
|
||||
..., description="ML model requirements and capabilities"
|
||||
)
|
||||
performance_requirements: List[PerformanceRequirement] = Field(
|
||||
..., description="System performance requirements"
|
||||
)
|
||||
data_retention_policy: Dict[str, str] = Field(
|
||||
..., description="Data retention requirements by type"
|
||||
)
|
||||
disaster_recovery: Dict[str, Any] = Field(
|
||||
..., description="Disaster recovery and business continuity plans"
|
||||
)
|
||||
interoperability_standards: List[str] = Field(
|
||||
..., description="Required healthcare interoperability standards"
|
||||
)
|
||||
# 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:
|
||||
|
|
@ -183,13 +173,88 @@ class ModelChain:
|
|||
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:
|
||||
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. Your output must be a valid
|
||||
JSON that matches the ProjectAnalysis schema. Consider scalability, security, maintenance, and technical debt in your analysis.
|
||||
and provide structured reasoning about architecture, tools, and implementation strategies.
|
||||
|
||||
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:
|
||||
|
|
@ -204,24 +269,22 @@ class ModelChain:
|
|||
)
|
||||
|
||||
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)
|
||||
|
||||
# Validate the reasoning content as ProjectAnalysis
|
||||
try:
|
||||
project_analysis = ProjectAnalysis.parse_raw(reasoning_content)
|
||||
formatted_reasoning = json.dumps(json.loads(reasoning_content), indent=2)
|
||||
|
||||
with st.expander("💭 Technical Analysis", expanded=True):
|
||||
st.json(formatted_reasoning)
|
||||
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}")
|
||||
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 the validated structured output for Claude
|
||||
return reasoning_content
|
||||
|
||||
except Exception as validation_error:
|
||||
st.error(f"Invalid analysis format: {str(validation_error)}")
|
||||
return "Error in analysis format"
|
||||
|
||||
except Exception as e:
|
||||
st.error(f"Error in DeepSeek analysis: {str(e)}")
|
||||
return "Error occurred while analyzing"
|
||||
|
|
@ -251,6 +314,7 @@ class ModelChain:
|
|||
|
||||
with self.claude_client.messages.stream(
|
||||
model=self.current_model,
|
||||
system=system_prompt,
|
||||
messages=messages,
|
||||
max_tokens=8000
|
||||
) as stream:
|
||||
|
|
|
|||
Loading…
Reference in a new issue