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Pydantic models for the RAG API
"""
from pydantic import BaseModel, Field, ConfigDict
from typing import List, Dict, Any, Optional
from enum import Enum
class QueryType(str, Enum):
SEARCH = "search"
CVE_LOOKUP = "cve_lookup"
VENDOR_ANALYSIS = "vendor_analysis"
SIMILARITY_SEARCH = "similarity_search"
TEMPORAL_QUERY = "temporal_query"
GENERAL = "general"
class QueryRequest(BaseModel):
"""Request model for RAG queries"""
model_config = ConfigDict(protected_namespaces=())
query: str = Field(..., description="The search query")
top_k: int = Field(default=10, ge=1, le=50, description="Number of results to return")
max_context_docs: int = Field(default=5, ge=1, le=20, description="Maximum context documents for LLM")
use_large_model: bool = Field(default=False, description="Use 70B model instead of 8B")
severity_filter: Optional[str] = Field(None, description="Filter by severity (CRITICAL, HIGH, MEDIUM, LOW)")
vendor_filter: Optional[str] = Field(None, description="Filter by vendor name")
class SearchResult(BaseModel):
"""Search result model"""
model_config = ConfigDict(protected_namespaces=())
id: str
text: str
metadata: Dict[str, Any]
score: float
distance: float
class QueryResponse(BaseModel):
"""Response model for RAG queries"""
model_config = ConfigDict(protected_namespaces=())
query: str
response: str
search_results: List[SearchResult]
query_type: QueryType
processing_time: float
model_used: str
class StreamResponse(BaseModel):
"""Streaming response model"""
model_config = ConfigDict(protected_namespaces=())
chunk: str
done: bool = False
class SummaryRequest(BaseModel):
"""Request model for vulnerability summaries"""
model_config = ConfigDict(protected_namespaces=())
query: str = Field(..., description="The summary query")
max_results: int = Field(default=50, ge=10, le=100, description="Maximum results to analyze")
class SummaryResponse(BaseModel):
"""Response model for vulnerability summaries"""
model_config = ConfigDict(protected_namespaces=())
query: str
total_results: int
severity_distribution: Dict[str, int]
top_vendors: List[str]
top_products: List[str]
common_weaknesses: List[str]
sample_results: List[SearchResult]
class HealthResponse(BaseModel):
"""Health check response model"""
model_config = ConfigDict(protected_namespaces=())
status: str
gpu_available: bool
gpu_name: Optional[str] = None
vector_db_documents: int
model_loaded: bool
class YearSearchRequest(BaseModel):
query: str
years: List[str] = ['2021', '2022', '2023', '2024','2025'] # Default to recent years
n_results: int = 10
severity_filter: Optional[str] = None
vendor_filter: Optional[str] = None
class AgentQueryRequest(BaseModel):
"""Request for agentic RAG with full adaptive pipeline."""
model_config = ConfigDict(protected_namespaces=())
query: str = Field(..., description="The user query")
session_id: str = Field(default="default_session", description="Session/user ID for memory")
top_k: int = Field(default=10, ge=1, le=30)
max_context_docs: int = Field(default=6, ge=1, le=20)
class AgentResponse(BaseModel):
"""Response from the agentic RAG pipeline."""
model_config = ConfigDict(protected_namespaces=())
query: str
answer: str
route_decision: str
search_collections: List[str]
rewritten_query: Optional[str] = None
docs_retrieved: int = 0
docs_relevant: int = 0
kg_enriched: bool = False
hallucination_check: Optional[bool] = None
completeness_check: Optional[bool] = None
retries_used: int = 0
memory_context_used: bool = False
processing_time: float
model_used: str
search_results: List[SearchResult] = []
class AgentEvent(BaseModel):
"""Single SSE event emitted during agent streaming."""
model_config = ConfigDict(protected_namespaces=())
phase: str
status: Optional[str] = None
data: Optional[Dict[str, Any]] = None
token: Optional[str] = None |