mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-08-11 13:21:10 +03:00
GenAI Refactor (#23253)
* Ensure runtime options are passed * Add attribute info to prompt when configured * Move GenAI plugins to dedicated directory * Migrate prompts to dedicated folder * Move chat prompts to prompts * Implement reasoning traces in the UI * Cleanup * Make azure a subclass of openai * Implement reasoning for other providers * mypy * Cleanup
This commit is contained in:
Nicolas Mowen
+49
-453
@@ -35,9 +35,13 @@ from frigate.api.defs.response.chat_response import (
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ToolCall,
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)
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from frigate.api.defs.tags import Tags
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from frigate.api.event import events
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from frigate.api.event import _build_attribute_filter_clause, events
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from frigate.config import FrigateConfig
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from frigate.config.ui import UnitSystemEnum
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from frigate.genai.prompts import (
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build_chat_system_prompt,
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get_attribute_classifications,
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get_tool_definitions,
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)
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from frigate.genai.utils import build_assistant_message_for_conversation
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from frigate.jobs.vlm_watch import (
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get_vlm_watch_job,
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@@ -68,390 +72,6 @@ class VLMMonitorRequest(BaseModel):
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zones: List[str] = []
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def get_tool_definitions(
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semantic_search_enabled: bool = False,
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) -> List[Dict[str, Any]]:
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"""
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Get OpenAI-compatible tool definitions for Frigate.
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Returns a list of tool definitions that can be used with OpenAI-compatible
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function calling APIs. When semantic search is enabled, the search_objects
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tool exposes an additional `semantic_query` parameter for descriptive
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queries (e.g. "person riding a lawn mower") and find_similar_objects is
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included.
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"""
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search_objects_properties: Dict[str, Any] = {
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"camera": {
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"type": "string",
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"description": "Camera name to filter by (optional).",
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},
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"label": {
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"type": "string",
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"description": (
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"Generic object class to filter by — one of the tracked detector "
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"labels such as 'person', 'package', 'car', 'dog', 'bird'. Use "
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"this for broad queries like 'show me all cars today'. Combine "
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"with semantic_query when the user also describes appearance or "
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"behavior (e.g. label='person', semantic_query='riding a lawn "
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"mower')."
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),
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},
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"sub_label": {
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"type": "string",
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"description": (
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"Filter by a DISCRETE NAMED entity recognized in the detection. "
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"Use this for: a known person's name ('John'), a delivery "
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"company ('Amazon', 'UPS'), a recognized animal species or "
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"breed ('blue jay', 'cardinal', 'golden retriever'), or a "
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"license plate string. When filtering by a specific name, set "
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"only sub_label and leave label unset. Do NOT use sub_label "
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"for descriptions of appearance, clothing, or actions — those "
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"belong in semantic_query."
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),
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},
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"after": {
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"type": "string",
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"description": "Start time in ISO 8601 format (e.g., '2024-01-01T00:00:00Z').",
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},
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"before": {
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"type": "string",
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"description": "End time in ISO 8601 format (e.g., '2024-01-01T23:59:59Z').",
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},
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"zones": {
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"type": "array",
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"items": {"type": "string"},
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"description": "List of zone names to filter by.",
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},
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"limit": {
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"type": "integer",
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"description": "Maximum number of objects to return (default: 25).",
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"default": 25,
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},
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}
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if semantic_search_enabled:
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search_objects_properties["semantic_query"] = {
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"type": "string",
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"description": (
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"Optional natural-language description of a PHYSICAL "
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"CHARACTERISTIC, APPEARANCE, or ACTIVITY the user mentioned, "
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"used to semantically narrow results. Only set this when the "
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"user describes something beyond what label and sub_label can "
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"express on their own.\n"
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"USE for descriptive phrases like: 'riding a lawn mower', "
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"'wearing a red jacket', 'carrying a package', 'walking a "
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"dog', 'on a bicycle', 'holding an umbrella'.\n"
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"DO NOT USE for:\n"
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"- specific named people, pets, or delivery companies → use sub_label\n"
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"- animal species or breed names like 'blue jay', 'cardinal', "
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"'golden retriever' → use sub_label\n"
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"- license plate strings → use sub_label\n"
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"- generic object queries like 'all cars today' or 'every "
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"person' → use label alone with no semantic_query\n"
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"When set, combine with label/time/camera/zone filters as "
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"usual (e.g. label='person', semantic_query='riding a lawn "
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"mower', after='2024-05-01T00:00:00Z')."
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),
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}
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search_objects_description = (
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"Search the historical record of detected objects in Frigate. "
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"Use this ONLY for questions about the PAST — e.g. 'did anyone come by today?', "
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"'when was the last car?', 'show me detections from yesterday'. "
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"Do NOT use this for monitoring or alerting requests about future events — "
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"use start_camera_watch instead for those. "
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"An 'object' in Frigate represents a tracked detection (e.g., a person, package, car).\n\n"
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"Choose filters based on what the user is asking for:\n"
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"- Generic class query ('show me all cars today'): set `label` only.\n"
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"- Specific NAMED entity (known person, delivery company, animal "
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"species/breed like 'blue jay' or 'golden retriever', license "
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"plate): set `sub_label` only and leave `label` unset.\n"
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)
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if semantic_search_enabled:
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search_objects_description += (
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"- Physical CHARACTERISTIC, APPEARANCE, or ACTIVITY that is not a "
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"discrete name ('person riding a lawn mower', 'someone in a red "
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"jacket', 'person carrying a package'): set `semantic_query` with "
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"the descriptive phrase, optionally alongside `label` for the "
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"object class. Do NOT put descriptive phrases in sub_label."
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)
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return [
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{
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"type": "function",
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"function": {
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"name": "search_objects",
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"description": search_objects_description,
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"parameters": {
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"type": "object",
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"properties": search_objects_properties,
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},
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"required": [],
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},
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},
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{
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"type": "function",
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"function": {
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"name": "find_similar_objects",
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"description": (
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"Find tracked objects that are visually and semantically similar "
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"to a specific past event. Use this when the user references a "
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"particular object they have seen and wants to find other "
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"sightings of the same or similar one ('that green car', 'the "
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"person in the red jacket', 'the package that was delivered'). "
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"Prefer this over search_objects whenever the user's intent is "
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"'find more like this specific one.' Use search_objects first "
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"only if you need to locate the anchor event. Requires semantic "
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"search to be enabled."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"event_id": {
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"type": "string",
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"description": "The id of the anchor event to find similar objects to.",
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},
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"after": {
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"type": "string",
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"description": "Start time in ISO 8601 format (e.g., '2024-01-01T00:00:00Z').",
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},
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"before": {
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"type": "string",
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"description": "End time in ISO 8601 format (e.g., '2024-01-01T23:59:59Z').",
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},
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"cameras": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional list of cameras to restrict to. Defaults to all.",
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},
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"labels": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional list of labels to restrict to. Defaults to the anchor event's label.",
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},
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"sub_labels": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional list of sub_labels (names) to restrict to.",
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},
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"zones": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional list of zones. An event matches if any of its zones overlap.",
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},
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"similarity_mode": {
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"type": "string",
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"enum": ["visual", "semantic", "fused"],
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"description": "Which similarity signal(s) to use. 'fused' (default) combines visual and semantic.",
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"default": "fused",
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},
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"min_score": {
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"type": "number",
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"description": "Drop matches with a similarity score below this threshold (0.0-1.0).",
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},
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"limit": {
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"type": "integer",
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"description": "Maximum number of matches to return (default: 10).",
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"default": 10,
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},
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},
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"required": ["event_id"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "set_camera_state",
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"description": (
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"Change a camera's feature state (e.g., turn detection on/off, enable/disable recordings). "
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"Use camera='*' to apply to all cameras at once. "
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"Only call this tool when the user explicitly asks to change a camera setting. "
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"Requires admin privileges."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"camera": {
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"type": "string",
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"description": "Camera name to target, or '*' to target all cameras.",
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},
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"feature": {
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"type": "string",
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"enum": [
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"detect",
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"record",
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"snapshots",
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"audio",
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"motion",
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"enabled",
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"birdseye",
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"birdseye_mode",
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"improve_contrast",
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"ptz_autotracker",
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"motion_contour_area",
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"motion_threshold",
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"notifications",
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"audio_transcription",
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"review_alerts",
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"review_detections",
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"object_descriptions",
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"review_descriptions",
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"profile",
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],
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"description": (
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"The feature to change. Most features accept ON or OFF. "
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"birdseye_mode accepts CONTINUOUS, MOTION, or OBJECTS. "
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"motion_contour_area and motion_threshold accept a number. "
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"profile accepts a profile name or 'none' to deactivate (requires camera='*')."
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),
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},
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"value": {
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"type": "string",
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"description": "The value to set. ON or OFF for toggles, a number for thresholds, a profile name or 'none' for profile.",
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},
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},
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"required": ["camera", "feature", "value"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "get_live_context",
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"description": (
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"Get the current live image and detection information for a camera: objects being tracked, "
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"zones, timestamps. Use this to understand what is visible in the live view. "
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"Call this when answering questions about what is happening right now on a specific camera."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"camera": {
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"type": "string",
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"description": "Camera name to get live context for.",
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},
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},
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"required": ["camera"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "start_camera_watch",
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"description": (
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"Start a continuous VLM watch job that monitors a camera and sends a notification "
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"when a specified condition is met. Use this when the user wants to be alerted about "
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"a future event, e.g. 'tell me when guests arrive' or 'notify me when the package is picked up'. "
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"Only one watch job can run at a time. Returns a job ID."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"camera": {
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"type": "string",
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"description": "Camera ID to monitor.",
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},
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"condition": {
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"type": "string",
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"description": (
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"Natural-language description of the condition to watch for, "
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"e.g. 'a person arrives at the front door'."
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),
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},
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"max_duration_minutes": {
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"type": "integer",
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"description": "Maximum time to watch before giving up (minutes, default 60).",
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"default": 60,
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},
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"labels": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Object labels that should trigger a VLM check (e.g. ['person', 'car']). If omitted, any detection on the camera triggers a check.",
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},
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"zones": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Zone names to filter by. If specified, only detections in these zones trigger a VLM check.",
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},
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},
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"required": ["camera", "condition"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "stop_camera_watch",
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"description": (
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"Cancel the currently running VLM watch job. Use this when the user wants to "
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"stop a previously started watch, e.g. 'stop watching the front door'."
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),
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"parameters": {
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"type": "object",
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"properties": {},
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"required": [],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "get_profile_status",
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"description": (
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"Get the current profile status including the active profile and "
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"timestamps of when each profile was last activated. Use this to "
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"determine time periods for recap requests — e.g. when the user asks "
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"'what happened while I was away?', call this first to find the relevant "
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"time window based on profile activation history."
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),
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"parameters": {
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"type": "object",
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"properties": {},
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"required": [],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "get_recap",
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"description": (
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"Get a recap of all activity (alerts and detections) for a given time period. "
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"Use this after calling get_profile_status to retrieve what happened during "
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"a specific window — e.g. 'what happened while I was away?'. Returns a "
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"chronological list of activity with camera, objects, zones, and GenAI-generated "
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"descriptions when available. Summarize the results for the user."
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),
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"parameters": {
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"type": "object",
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"properties": {
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"after": {
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"type": "string",
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"description": "Start of the time period in ISO 8601 format (e.g. '2025-03-15T08:00:00').",
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},
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"before": {
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"type": "string",
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"description": "End of the time period in ISO 8601 format (e.g. '2025-03-15T17:00:00').",
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},
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"cameras": {
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"type": "string",
|
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"description": "Comma-separated camera IDs to include, or 'all' for all cameras. Default is 'all'.",
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},
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"severity": {
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"type": "string",
|
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"enum": ["alert", "detection"],
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"description": "Filter by severity level. Omit to include both alerts and detections.",
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},
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},
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"required": ["after", "before"],
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},
|
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},
|
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},
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]
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|
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|
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@router.get(
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"/chat/tools",
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dependencies=[Depends(allow_any_authenticated())],
|
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@@ -460,10 +80,13 @@ def get_tool_definitions(
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)
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def get_tools(request: Request) -> JSONResponse:
|
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"""Get list of available tools for LLM function calling."""
|
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semantic_search_enabled = bool(
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getattr(request.app.frigate_config.semantic_search, "enabled", False)
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config = request.app.frigate_config
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semantic_search_enabled = bool(getattr(config.semantic_search, "enabled", False))
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attribute_classifications = get_attribute_classifications(config)
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tools = get_tool_definitions(
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semantic_search_enabled=semantic_search_enabled,
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attribute_classifications=attribute_classifications,
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)
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tools = get_tool_definitions(semantic_search_enabled=semantic_search_enabled)
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return JSONResponse(content={"tools": tools})
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|
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|
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@@ -554,11 +177,14 @@ async def _execute_search_objects(
|
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elif zones is None:
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zones = "all"
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|
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attribute = arguments.get("attribute")
|
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|
||||
# Build query parameters compatible with EventsQueryParams
|
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query_params = EventsQueryParams(
|
||||
cameras=arguments.get("camera", "all"),
|
||||
labels=arguments.get("label", "all"),
|
||||
sub_labels=arguments.get("sub_label", "all"), # case-insensitive on the backend
|
||||
attributes=attribute if attribute else "all",
|
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zones=zones,
|
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zone=zones,
|
||||
after=after,
|
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@@ -626,6 +252,7 @@ async def _execute_search_objects_semantic(
|
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|
||||
label = arguments.get("label")
|
||||
sub_label = arguments.get("sub_label")
|
||||
attribute = arguments.get("attribute")
|
||||
|
||||
zones = arguments.get("zones")
|
||||
if isinstance(zones, list) and zones:
|
||||
@@ -668,6 +295,10 @@ async def _execute_search_objects_semantic(
|
||||
if sub_label:
|
||||
# case-insensitive match to mirror events() behavior
|
||||
clauses.append(fn.LOWER(Event.sub_label.cast("text")) == sub_label.lower())
|
||||
if attribute:
|
||||
attribute_clause = _build_attribute_filter_clause(attribute)
|
||||
if attribute_clause is not None:
|
||||
clauses.append(attribute_clause)
|
||||
if zones:
|
||||
zone_clauses = [Event.zones.cast("text") % f'*"{zone}"*' for zone in zones]
|
||||
clauses.append(reduce(operator.or_, zone_clauses))
|
||||
@@ -1481,72 +1112,19 @@ async def chat_completion(
|
||||
|
||||
config = request.app.frigate_config
|
||||
semantic_search_enabled = bool(getattr(config.semantic_search, "enabled", False))
|
||||
tools = get_tool_definitions(semantic_search_enabled=semantic_search_enabled)
|
||||
attribute_classifications = get_attribute_classifications(config)
|
||||
tools = get_tool_definitions(
|
||||
semantic_search_enabled=semantic_search_enabled,
|
||||
attribute_classifications=attribute_classifications,
|
||||
)
|
||||
conversation = []
|
||||
|
||||
current_datetime = datetime.now()
|
||||
current_date_str = current_datetime.strftime("%Y-%m-%d")
|
||||
current_time_str = current_datetime.strftime("%I:%M:%S %p")
|
||||
|
||||
cameras_info = []
|
||||
has_speed_zone = False
|
||||
for camera_id in allowed_cameras:
|
||||
if camera_id not in config.cameras:
|
||||
continue
|
||||
camera_config = config.cameras[camera_id]
|
||||
friendly_name = (
|
||||
camera_config.friendly_name
|
||||
if camera_config.friendly_name
|
||||
else camera_id.replace("_", " ").title()
|
||||
)
|
||||
zone_names = list(camera_config.zones.keys())
|
||||
if not has_speed_zone:
|
||||
has_speed_zone = any(
|
||||
zone.distances for zone in camera_config.zones.values()
|
||||
)
|
||||
if zone_names:
|
||||
cameras_info.append(
|
||||
f" - {friendly_name} (ID: {camera_id}, zones: {', '.join(zone_names)})"
|
||||
)
|
||||
else:
|
||||
cameras_info.append(f" - {friendly_name} (ID: {camera_id})")
|
||||
|
||||
cameras_section = ""
|
||||
if cameras_info:
|
||||
cameras_section = (
|
||||
"\n\nAvailable cameras:\n"
|
||||
+ "\n".join(cameras_info)
|
||||
+ "\n\nWhen users refer to cameras by their friendly name (e.g., 'Back Deck Camera'), use the corresponding camera ID (e.g., 'back_deck_cam') in tool calls."
|
||||
)
|
||||
|
||||
speed_units_section = ""
|
||||
if has_speed_zone:
|
||||
speed_unit = (
|
||||
"mph" if config.ui.unit_system == UnitSystemEnum.imperial else "km/h"
|
||||
)
|
||||
speed_units_section = f"\n\nReport object speeds to the user in {speed_unit}."
|
||||
|
||||
semantic_search_section = ""
|
||||
if semantic_search_enabled:
|
||||
semantic_search_section = (
|
||||
"\n\nWhen routing a search_objects call, pick filters by the shape of the user's request:\n"
|
||||
"- Generic class ('show me all cars today'): set `label` only.\n"
|
||||
"- Specific named entity — a known person ('John'), delivery company ('Amazon'), animal species/breed ('blue jay', 'cardinal', 'golden retriever'), or license plate: set `sub_label` only and leave `label` unset.\n"
|
||||
"- Physical characteristic, appearance, or activity that is NOT a discrete name ('find me people riding a lawn mower', 'someone in a red jacket', 'a person carrying a package'): set `semantic_query` with the descriptive phrase, optionally combined with `label` for the object class. Never put descriptive phrases in `sub_label`."
|
||||
)
|
||||
|
||||
system_prompt = f"""You are a helpful assistant for Frigate, a security camera NVR system. You help users answer questions about their cameras, detected objects, and events.
|
||||
|
||||
Current server local date and time: {current_date_str} at {current_time_str}
|
||||
|
||||
Do not start your response with phrases like "I will check...", "Let me see...", or "Let me look...". Answer directly.
|
||||
|
||||
Always present times to the user in the server's local timezone. When tool results include start_time_local and end_time_local, use those exact strings when listing or describing detection times—do not convert or invent timestamps. Do not use UTC or ISO format with Z for the user-facing answer unless the tool result only provides Unix timestamps without local time fields.
|
||||
When users ask about "today", "yesterday", "this week", etc., use the current date above as reference.
|
||||
When searching for objects or events, use ISO 8601 format for dates (e.g., {current_date_str}T00:00:00Z for the start of today).
|
||||
Always be accurate with time calculations based on the current date provided.
|
||||
|
||||
When a user refers to a specific object they have seen or describe with identifying details ("that green car", "the person in the red jacket", "a package left today"), prefer the find_similar_objects tool over search_objects. Use search_objects first only to locate the anchor event, then pass its id to find_similar_objects. For generic queries like "show me all cars today", keep using search_objects. If a user message begins with [attached_event:<id>], treat that event id as the anchor for any similarity or "tell me more" request in the same message and call find_similar_objects with that id.{semantic_search_section}{cameras_section}{speed_units_section}"""
|
||||
system_prompt = build_chat_system_prompt(
|
||||
config=config,
|
||||
allowed_cameras=allowed_cameras,
|
||||
semantic_search_enabled=semantic_search_enabled,
|
||||
attribute_classifications=attribute_classifications,
|
||||
)
|
||||
|
||||
conversation.append(
|
||||
{
|
||||
@@ -1607,6 +1185,13 @@ When a user refers to a specific object they have seen or describe with identify
|
||||
)
|
||||
+ b"\n"
|
||||
)
|
||||
elif kind == "reasoning_delta":
|
||||
yield (
|
||||
json.dumps({"type": "reasoning", "delta": value}).encode(
|
||||
"utf-8"
|
||||
)
|
||||
+ b"\n"
|
||||
)
|
||||
elif kind == "stats":
|
||||
yield (
|
||||
json.dumps({"type": "stats", **value}).encode("utf-8")
|
||||
@@ -1707,6 +1292,7 @@ When a user refers to a specific object they have seen or describe with identify
|
||||
final_content = response.get("content") or ""
|
||||
|
||||
if body.stream:
|
||||
final_reasoning = response.get("reasoning")
|
||||
|
||||
async def stream_body() -> Any:
|
||||
if tool_calls:
|
||||
@@ -1721,6 +1307,15 @@ When a user refers to a specific object they have seen or describe with identify
|
||||
).encode("utf-8")
|
||||
+ b"\n"
|
||||
)
|
||||
# Emit the full reasoning trace up front when the
|
||||
# underlying client did not stream it
|
||||
if final_reasoning:
|
||||
yield (
|
||||
json.dumps(
|
||||
{"type": "reasoning", "delta": final_reasoning}
|
||||
).encode("utf-8")
|
||||
+ b"\n"
|
||||
)
|
||||
# Stream content in word-sized chunks for smooth UX
|
||||
for part in chunk_content(final_content):
|
||||
yield (
|
||||
@@ -1741,6 +1336,7 @@ When a user refers to a specific object they have seen or describe with identify
|
||||
message=ChatMessageResponse(
|
||||
role="assistant",
|
||||
content=final_content,
|
||||
reasoning=response.get("reasoning"),
|
||||
tool_calls=None,
|
||||
),
|
||||
finish_reason=response.get("finish_reason", "stop"),
|
||||
|
||||
Reference in New Issue
Block a user