Agent Zero
4a43745b50
- query_understanding.py: get API key/base_url/provider_type from ai_providers DB - embedding.py: get API key from DB, pass db+tenant_id through call chain - routes.py: pass db+tenant_id to llm_analyze_query and llm_aggregate_results - search_engine.py: pass db+tenant_id to generate_embedding - unified_search/jobs.py: pass db+tenant_id to generate_embedding - Fix all default model names: ollama/deepseek-v4 -> ollama/deepseek-v4-flash - Ollama Cloud has no embedding endpoint; embedding calls fail gracefully
104 lines
2.6 KiB
Python
104 lines
2.6 KiB
Python
"""Pydantic v2 schemas for the AI Proactive plugin API."""
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from __future__ import annotations
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from datetime import datetime
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from typing import Any
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from pydantic import BaseModel, Field
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class ContextReport(BaseModel):
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"""Frontend reports the current page/entity context."""
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page: str = Field(..., description="Current page path")
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entity_type: str | None = Field(None, description="Entity type being viewed")
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entity_id: str | None = Field(None, description="Entity ID being viewed")
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entity_data: dict[str, Any] | None = Field(
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None, description="Additional entity metadata"
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)
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class SuggestionAction(BaseModel):
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"""A suggested CRM action."""
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method: str = Field(..., description="HTTP method")
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path: str = Field(..., description="API path")
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body: dict[str, Any] | None = Field(None, description="Request body")
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description: str = Field(..., description="Human-readable description")
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class SuggestionResponse(BaseModel):
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"""API response for a single suggestion."""
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id: str
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entity_type: str
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entity_id: str | None
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suggestion_type: str
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title: str
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content: str
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confidence: float
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actions: list[dict[str, Any]]
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created_at: datetime
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is_dismissed: bool
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is_acted_upon: bool = False
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class SuggestionListResponse(BaseModel):
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"""Paginated list of suggestions."""
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items: list[SuggestionResponse]
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total: int
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class ActRequest(BaseModel):
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"""Execute a suggested action by index."""
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action_index: int = Field(..., ge=0, description="Index into actions array")
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class ActResponse(BaseModel):
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"""Result of executing a suggested action."""
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success: bool
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data: dict[str, Any] | None = None
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error: str | None = None
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class SettingsResponse(BaseModel):
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"""Proactive AI settings for the current user."""
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enabled: bool
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suggestion_categories: list[str]
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confidence_threshold: float
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rate_limit_seconds: int
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model: str
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available_models: list[str] = Field(default_factory=lambda: [
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'ollama/deepseek-v4-flash',
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'ollama/deepseek-v4-pro',
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'ollama/llama3.2',
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'ollama/gpt-4o-mini',
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'gpt-4o-mini',
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])
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class SettingsUpdate(BaseModel):
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"""Partial update for proactive AI settings."""
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enabled: bool | None = None
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suggestion_categories: list[str] | None = None
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confidence_threshold: float | None = None
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rate_limit_seconds: int | None = None
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model: str | None = None
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class StatsResponse(BaseModel):
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"""Proactive AI usage statistics."""
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total_suggestions: int = 0
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dismissed: int = 0
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acted_upon: int = 0
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active: int = 0
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dismiss_rate: float = 0.0
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act_rate: float = 0.0
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