Agent Zero
3d9b76cea4
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- SPIKE-E: FTS+Vector+Permission benchmark on 10k records (all <30ms) - E-PROV: supports_fts/vector/rag/graph capability flags on all providers - E-FTS/VEC: All 11 providers refactored to BaseSearchProvider with permission filtering - E-PERM: Over-fetch strategy for vector+permission (15x faster than ANY() filter) - E-FUSE: rrf_fusion_multi() for N-way RRF over FTS+Vector+RAG+Graph - E-LLM: Query understanding cleaned up to use central llm_complete() - E-CHUNK: Document chunking module + document_chunks table with HNSW index - E-EMB: Chunk embedding ARQ jobs (index_file_chunks, reindex_chunks) - E-RAG: RAG retrieval via FileSearchProvider.search_rag() - E-GRAPH: GraphRAG BFS traversal via GraphRAGSearchProvider.search_graph() - E-IX-EVT: Auto-indexing via outbox events + delete/cleanup handlers - E-IX-RE: Batch reindex with progress tracking + reindex_all job - E-DATA-LIFE: Lifecycle module (remove/rebuild/restore/correct) + API endpoints - E-K-MEM: AgentMemorySearchProvider - E-P-AI: AIChatSearchProvider - E-P-WF: WorkflowSearchProvider - E-P-COMM: ConversationSearchProvider verified (already on BaseSearchProvider) - E-API: Filter params (date_from/to, tags, sort) + /facets endpoint - E-TOOL: unified_search AI tool registered in ToolRegistry - E-MCP: Search tool in MCP server with normal RBAC/tenant checks - E-UI-CMD: CommandPalette (Cmd+K) with debounced search + recent searches - E-UI-FAC: SearchFacets, SearchResultCard, SavedSearches components - E-TEST: 40 new tests in test_unified_search_phase_e.py (105 total green) - E-DOC: api-documentation.md, plugin-development-guide.md, test-strategy.md updated 105 tests passing, TypeScript clean.
98 lines
2.4 KiB
Python
98 lines
2.4 KiB
Python
"""Pydantic schemas for the Unified Search plugin."""
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from __future__ import annotations
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from typing import Any
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from pydantic import BaseModel, Field
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# ─── Search ───
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class SearchRequest(BaseModel):
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query: str = Field(..., min_length=1, max_length=500)
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entity_types: list[str] | None = None
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limit: int = Field(default=20, ge=1, le=100)
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offset: int = Field(default=0, ge=0)
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date_from: str | None = Field(
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default=None, description="ISO date (YYYY-MM-DD) — filter by created_at/updated_at >= date_from"
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)
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date_to: str | None = Field(
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default=None, description="ISO date (YYYY-MM-DD) — filter by created_at/updated_at <= date_to"
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)
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tags: list[str] | None = Field(
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default=None, description="Filter results by tags (comma-separated on entities)"
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)
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sort: str = Field(
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default="relevance",
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description="Sort order: relevance, date, name",
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)
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class SearchResult(BaseModel):
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entity_type: str
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entity_id: str
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title: str
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snippet: str
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score: float
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data: dict[str, Any] = Field(default_factory=dict)
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class SearchResponse(BaseModel):
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query: str
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normalized_query: str
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results: list[SearchResult]
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facets: dict[str, Any]
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summary: str
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suggestions: list[str]
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class FacetsResponse(BaseModel):
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"""Available facets for search filtering."""
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entity_types: list[str]
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tags: list[str]
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date_ranges: dict[str, Any]
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# ─── Similar ───
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class SimilarRequest(BaseModel):
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entity_type: str
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entity_id: str
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limit: int = Field(default=5, ge=1, le=50)
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class SimilarResponse(BaseModel):
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similar: dict[str, list[SearchResult]]
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# ─── Suggest ───
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class SuggestRequest(BaseModel):
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q: str = Field(..., min_length=1, max_length=200)
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limit: int = Field(default=10, ge=1, le=50)
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class SuggestResponse(BaseModel):
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suggestions: list[str]
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# ─── Reindex ───
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class ReindexRequest(BaseModel):
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entity_types: list[str] = Field(default_factory=list)
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include_chunks: bool = Field(default=True, description="Re-index document chunks for files")
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# ─── Provider ───
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class ProviderResponse(BaseModel):
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entity_type: str
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plugin_name: str
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is_active: bool
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supports_fts: bool = True
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supports_vector: bool = True
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supports_rag: bool = False
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supports_graph: bool = False
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