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Running on Zero
| from __future__ import annotations | |
| from typing import Any, Dict, List, Literal, Optional | |
| from pydantic import BaseModel, Field | |
| QueryType = Literal[ | |
| "fact", | |
| "detailed", | |
| "general", | |
| "technical_exact", | |
| "comparison", | |
| "metadata_constrained", | |
| "unanswerable", | |
| ] | |
| QueryComplexity = Literal["low", "medium", "high"] | |
| ExecutionMode = Literal["fast", "balanced", "deep"] | |
| Answerability = Literal["supported", "unsupported", "unanswerable"] | |
| class QueryPlan(BaseModel): | |
| """ | |
| Structured execution plan produced by the query planner. | |
| Acts as the central brain dictating execution mode, candidate scoping, | |
| retriever routing, candidate budgets, and reranking requirements. | |
| """ | |
| query: str | |
| normalized_query: str | |
| query_type: QueryType = "general" | |
| complexity: QueryComplexity = "low" | |
| execution_mode: ExecutionMode = "balanced" | |
| answerability: Answerability = "supported" | |
| sub_queries: List[str] = Field(default_factory=list) | |
| candidate_document_ids: List[str] = Field(default_factory=list) | |
| explicit_document_ids: List[str] = Field(default_factory=list) | |
| inferred_document_ids: List[str] = Field(default_factory=list) | |
| is_explicit_filter: bool = False | |
| metadata_filters: Dict[str, Any] = Field(default_factory=dict) | |
| retrieval_filter: Optional[Any] = None | |
| retrieval_signals: Dict[str, Any] = Field(default_factory=dict) | |
| requires_dense: bool = True | |
| requires_keyword: bool = True | |
| requires_dense_search: bool = True | |
| requires_keyword_search: bool = True | |
| requires_hybrid: bool = True | |
| requires_parent_child: bool = False | |
| requires_multi_query: bool = False | |
| requires_decomposition: bool = False | |
| requires_metadata_filter: bool = False | |
| requires_reranking: bool = True | |
| top_k: int = 10 | |
| top_k_dense: int = 10 | |
| top_k_keyword: int = 10 | |
| rerank_top_k: int = 5 | |
| max_parents: int = 3 | |
| max_parent_tokens: int = 1200 | |
| context_budget: int = 2048 | |
| confidence: float = 0.85 | |
| reasoning: str = "" | |