| """ |
| 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'] |
| 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 |
