Files
leocrm/app/plugins/builtins/unified_search/providers/ai_chat_provider.py
T
Agent Zero 3d9b76cea4
Check Cross-Plugin Imports / check (push) Has been cancelled
feat(E): Unified Search — 24 Tasks complete
- 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.
2026-08-14 01:34:58 +02:00

138 lines
4.9 KiB
Python

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