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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.
138 lines
4.9 KiB
Python
138 lines
4.9 KiB
Python
"""AI chat search provider — FTS search on ai_chat_messages table."""
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from __future__ import annotations
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import logging
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import uuid
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from typing import Any
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.plugins.builtins.unified_search.base_provider import BaseSearchProvider
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logger = logging.getLogger(__name__)
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class AIChatSearchProvider(BaseSearchProvider):
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"""Search provider for AI chat messages."""
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entity_type = "ai_chat"
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supports_fts = True
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supports_vector = False
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async def _search_fts_filtered(
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self,
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db: AsyncSession,
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tsquery: str,
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tenant_id: uuid.UUID,
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limit: int,
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visible_ids: set[uuid.UUID] | None,
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) -> list[dict[str, Any]]:
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"""Full-text search on ai_chat_messages.content, joined with sessions for title."""
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if visible_ids is not None:
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sql = text(
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"""
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SELECT m.id, m.tenant_id, m.session_id, m.role, m.content,
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m.model_used, m.tokens,
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s.title AS session_title,
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ts_rank(to_tsvector('pg_catalog.german', m.content),
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to_tsquery('pg_catalog.german', :q)) AS rank
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FROM ai_chat_messages m
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JOIN ai_chat_sessions s ON s.id = m.session_id
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WHERE m.tenant_id = :tid
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AND to_tsvector('pg_catalog.german', m.content) @@ to_tsquery('pg_catalog.german', :q)
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AND m.id = ANY(:visible_ids)
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ORDER BY rank DESC
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LIMIT :lim
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"""
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)
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result = await db.execute(
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sql,
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{
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"q": tsquery,
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"tid": tenant_id,
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"lim": limit,
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"visible_ids": list(visible_ids),
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},
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)
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else:
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sql = text(
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"""
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SELECT m.id, m.tenant_id, m.session_id, m.role, m.content,
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m.model_used, m.tokens,
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s.title AS session_title,
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ts_rank(to_tsvector('pg_catalog.german', m.content),
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to_tsquery('pg_catalog.german', :q)) AS rank
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FROM ai_chat_messages m
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JOIN ai_chat_sessions s ON s.id = m.session_id
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WHERE m.tenant_id = :tid
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AND to_tsvector('pg_catalog.german', m.content) @@ to_tsquery('pg_catalog.german', :q)
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ORDER BY rank DESC
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LIMIT :lim
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"""
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)
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result = await db.execute(
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sql,
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{"q": tsquery, "tid": tenant_id, "lim": limit},
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)
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rows = result.mappings().all()
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return [dict(r) for r in rows]
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async def _search_vector_filtered(
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self,
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db: AsyncSession,
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embedding: list[float],
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tenant_id: uuid.UUID,
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limit: int,
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visible_ids: set[uuid.UUID] | None,
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) -> list[dict[str, Any]]:
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"""No vector support for chat messages — return empty list."""
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return []
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async def get_embedding_text(
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self, db: AsyncSession, entity_id: uuid.UUID, tenant_id: uuid.UUID
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) -> str:
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"""Get text for embedding generation — returns message content."""
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sql = text(
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"""
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SELECT content
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FROM ai_chat_messages
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WHERE id = :eid AND tenant_id = :tid
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"""
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)
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result = await db.execute(sql, {"eid": entity_id, "tid": tenant_id})
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row = result.mappings().first()
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if not row:
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return ""
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return str(row.get("content", ""))
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def to_search_result(self, entity: object) -> dict[str, Any]:
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"""Convert chat message to search result dict."""
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if isinstance(entity, dict):
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entity_id = str(entity.get("id", ""))
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content = entity.get("content", "")
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role = entity.get("role", "")
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session_title = entity.get("session_title", "")
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session_id = str(entity.get("session_id", ""))
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score = entity.get("rank", 0.0)
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else:
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entity_id = str(getattr(entity, "id", ""))
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content = getattr(entity, "content", "")
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role = getattr(entity, "role", "")
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session_title = getattr(entity, "session_title", "")
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session_id = str(getattr(entity, "session_id", ""))
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score = getattr(entity, "rank", 0.0)
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return {
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"entity_type": self.entity_type,
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"entity_id": entity_id,
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"title": session_title or content[:80] if content else "",
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"snippet": content,
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"score": float(score) if score else 0.0,
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"data": {
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"role": role,
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"session_id": session_id,
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"session_title": session_title,
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},
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}
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