blakeblackshear.frigate/frigate/api/chat.py

"""Chat and LLM tool calling APIs."""

import base64
import json
import logging
from datetime import datetime, timezone
from typing import Any, Dict, List, Optional

import cv2
from fastapi import APIRouter, Body, Depends, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel

from frigate.api.auth import (
    allow_any_authenticated,
    get_allowed_cameras_for_filter,
)
from frigate.api.defs.query.events_query_parameters import EventsQueryParams
from frigate.api.defs.request.chat_body import ChatCompletionRequest
from frigate.api.defs.response.chat_response import (
    ChatCompletionResponse,
    ChatMessageResponse,
)
from frigate.api.defs.tags import Tags
from frigate.api.event import events
from frigate.genai import get_genai_client

logger = logging.getLogger(__name__)

router = APIRouter(tags=[Tags.chat])


class ToolExecuteRequest(BaseModel):
    """Request model for tool execution."""

    tool_name: str
    arguments: Dict[str, Any]


def get_tool_definitions() -> List[Dict[str, Any]]:
    """
    Get OpenAI-compatible tool definitions for Frigate.

    Returns a list of tool definitions that can be used with OpenAI-compatible
    function calling APIs.
    """
    return [
        {
            "type": "function",
            "function": {
                "name": "search_objects",
                "description": (
                    "Search for detected objects in Frigate by camera, object label, time range, "
                    "zones, and other filters. Use this to answer questions about when "
                    "objects were detected, what objects appeared, or to find specific object detections. "
                    "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car)."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "camera": {
                            "type": "string",
                            "description": "Camera name to filter by (optional). Use 'all' for all cameras.",
                        },
                        "label": {
                            "type": "string",
                            "description": "Object label to filter by (e.g., 'person', 'package', 'car').",
                        },
                        "after": {
                            "type": "string",
                            "description": "Start time in ISO 8601 format (e.g., '2024-01-01T00:00:00Z').",
                        },
                        "before": {
                            "type": "string",
                            "description": "End time in ISO 8601 format (e.g., '2024-01-01T23:59:59Z').",
                        },
                        "zones": {
                            "type": "array",
                            "items": {"type": "string"},
                            "description": "List of zone names to filter by.",
                        },
                        "limit": {
                            "type": "integer",
                            "description": "Maximum number of objects to return (default: 10).",
                            "default": 10,
                        },
                    },
                },
                "required": [],
            },
        },
        {
            "type": "function",
            "function": {
                "name": "get_live_context",
                "description": (
                    "Get the current detection information for a camera: objects being tracked, "
                    "zones, timestamps. Use this to understand what is visible in the live view. "
                    "Call this when the user has included a live image (via include_live_image) or "
                    "when answering questions about what is happening right now on a specific camera."
                ),
                "parameters": {
                    "type": "object",
                    "properties": {
                        "camera": {
                            "type": "string",
                            "description": "Camera name to get live context for.",
                        },
                    },
                    "required": ["camera"],
                },
            },
        },
    ]


@router.get(
    "/chat/tools",
    dependencies=[Depends(allow_any_authenticated())],
    summary="Get available tools",
    description="Returns OpenAI-compatible tool definitions for function calling.",
)
def get_tools(request: Request) -> JSONResponse:
    """Get list of available tools for LLM function calling."""
    tools = get_tool_definitions()
    return JSONResponse(content={"tools": tools})


async def _execute_search_objects(
    request: Request,
    arguments: Dict[str, Any],
    allowed_cameras: List[str],
) -> JSONResponse:
    """
    Execute the search_objects tool.

    This searches for detected objects (events) in Frigate using the same
    logic as the events API endpoint.
    """
    # Parse ISO 8601 timestamps to Unix timestamps if provided
    after = arguments.get("after")
    before = arguments.get("before")

    if after:
        try:
            after_dt = datetime.fromisoformat(after.replace("Z", "+00:00"))
            after = after_dt.timestamp()
        except (ValueError, AttributeError):
            logger.warning(f"Invalid 'after' timestamp format: {after}")
            after = None

    if before:
        try:
            before_dt = datetime.fromisoformat(before.replace("Z", "+00:00"))
            before = before_dt.timestamp()
        except (ValueError, AttributeError):
            logger.warning(f"Invalid 'before' timestamp format: {before}")
            before = None

    # Convert zones array to comma-separated string if provided
    zones = arguments.get("zones")
    if isinstance(zones, list):
        zones = ",".join(zones)
    elif zones is None:
        zones = "all"

    # Build query parameters compatible with EventsQueryParams
    query_params = EventsQueryParams(
        camera=arguments.get("camera", "all"),
        cameras=arguments.get("camera", "all"),
        label=arguments.get("label", "all"),
        labels=arguments.get("label", "all"),
        zones=zones,
        zone=zones,
        after=after,
        before=before,
        limit=arguments.get("limit", 10),
    )

    try:
        # Call the events endpoint function directly
        # The events function is synchronous and takes params and allowed_cameras
        response = events(query_params, allowed_cameras)

        # The response is already a JSONResponse with event data
        # Return it as-is for the LLM
        return response
    except Exception as e:
        logger.error(f"Error executing search_objects: {e}", exc_info=True)
        return JSONResponse(
            content={
                "success": False,
                "message": f"Error searching objects: {str(e)}",
            },
            status_code=500,
        )


@router.post(
    "/chat/execute",
    dependencies=[Depends(allow_any_authenticated())],
    summary="Execute a tool",
    description="Execute a tool function call from an LLM.",
)
async def execute_tool(
    request: Request,
    body: ToolExecuteRequest = Body(...),
    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
) -> JSONResponse:
    """
    Execute a tool function call.

    This endpoint receives tool calls from LLMs and executes the corresponding
    Frigate operations, returning results in a format the LLM can understand.
    """
    tool_name = body.tool_name
    arguments = body.arguments

    logger.debug(f"Executing tool: {tool_name} with arguments: {arguments}")

    if tool_name == "search_objects":
        return await _execute_search_objects(request, arguments, allowed_cameras)

    return JSONResponse(
        content={
            "success": False,
            "message": f"Unknown tool: {tool_name}",
            "tool": tool_name,
        },
        status_code=400,
    )


async def _execute_get_live_context(
    request: Request,
    camera: str,
    allowed_cameras: List[str],
) -> Dict[str, Any]:
    if camera not in allowed_cameras:
        return {
            "error": f"Camera '{camera}' not found or access denied",
        }

    if camera not in request.app.frigate_config.cameras:
        return {
            "error": f"Camera '{camera}' not found",
        }

    try:
        frame_processor = request.app.detected_frames_processor
        camera_state = frame_processor.camera_states.get(camera)

        if camera_state is None:
            return {
                "error": f"Camera '{camera}' state not available",
            }

        tracked_objects_dict = {}
        with camera_state.current_frame_lock:
            tracked_objects = camera_state.tracked_objects.copy()
            frame_time = camera_state.current_frame_time

        for obj_id, tracked_obj in tracked_objects.items():
            obj_dict = tracked_obj.to_dict()
            if obj_dict.get("frame_time") == frame_time:
                tracked_objects_dict[obj_id] = {
                    "label": obj_dict.get("label"),
                    "zones": obj_dict.get("current_zones", []),
                    "sub_label": obj_dict.get("sub_label"),
                    "stationary": obj_dict.get("stationary", False),
                }

        return {
            "camera": camera,
            "timestamp": frame_time,
            "detections": list(tracked_objects_dict.values()),
        }

    except Exception as e:
        logger.error(f"Error executing get_live_context: {e}", exc_info=True)
        return {
            "error": f"Error getting live context: {str(e)}",
        }


async def _get_live_frame_image_url(
    request: Request,
    camera: str,
    allowed_cameras: List[str],
) -> Optional[str]:
    """
    Fetch the current live frame for a camera as a base64 data URL.

    Returns None if the frame cannot be retrieved. Used when include_live_image
    is set to attach the image to the first user message.
    """
    if (
        camera not in allowed_cameras
        or camera not in request.app.frigate_config.cameras
    ):
        return None
    try:
        frame_processor = request.app.detected_frames_processor
        if camera not in frame_processor.camera_states:
            return None
        frame = frame_processor.get_current_frame(camera, {})
        if frame is None:
            return None
        height, width = frame.shape[:2]
        max_dimension = 1024
        if height > max_dimension or width > max_dimension:
            scale = max_dimension / max(height, width)
            frame = cv2.resize(
                frame,
                (int(width * scale), int(height * scale)),
                interpolation=cv2.INTER_AREA,
            )
        _, img_encoded = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
        b64 = base64.b64encode(img_encoded.tobytes()).decode("utf-8")
        return f"data:image/jpeg;base64,{b64}"
    except Exception as e:
        logger.debug("Failed to get live frame for %s: %s", camera, e)
        return None


async def _execute_tool_internal(
    tool_name: str,
    arguments: Dict[str, Any],
    request: Request,
    allowed_cameras: List[str],
) -> Dict[str, Any]:
    """
    Internal helper to execute a tool and return the result as a dict.

    This is used by the chat completion endpoint to execute tools.
    """
    if tool_name == "search_objects":
        response = await _execute_search_objects(request, arguments, allowed_cameras)
        try:
            if hasattr(response, "body"):
                body_str = response.body.decode("utf-8")
                return json.loads(body_str)
            elif hasattr(response, "content"):
                return response.content
            else:
                return {}
        except (json.JSONDecodeError, AttributeError) as e:
            logger.warning(f"Failed to extract tool result: {e}")
            return {"error": "Failed to parse tool result"}
    elif tool_name == "get_live_context":
        camera = arguments.get("camera")
        if not camera:
            return {"error": "Camera parameter is required"}
        return await _execute_get_live_context(request, camera, allowed_cameras)
    else:
        return {"error": f"Unknown tool: {tool_name}"}


@router.post(
    "/chat/completion",
    response_model=ChatCompletionResponse,
    dependencies=[Depends(allow_any_authenticated())],
    summary="Chat completion with tool calling",
    description=(
        "Send a chat message to the configured GenAI provider with tool calling support. "
        "The LLM can call Frigate tools to answer questions about your cameras and events."
    ),
)
async def chat_completion(
    request: Request,
    body: ChatCompletionRequest = Body(...),
    allowed_cameras: List[str] = Depends(get_allowed_cameras_for_filter),
) -> JSONResponse:
    """
    Chat completion endpoint with tool calling support.

    This endpoint:
    1. Gets the configured GenAI client
    2. Gets tool definitions
    3. Sends messages + tools to LLM
    4. Handles tool_calls if present
    5. Executes tools and sends results back to LLM
    6. Repeats until final answer
    7. Returns response to user
    """
    genai_client = get_genai_client(request.app.frigate_config)
    if not genai_client:
        return JSONResponse(
            content={
                "error": "GenAI is not configured. Please configure a GenAI provider in your Frigate config.",
            },
            status_code=400,
        )

    tools = get_tool_definitions()
    conversation = []

    current_datetime = datetime.now(timezone.utc)
    current_date_str = current_datetime.strftime("%Y-%m-%d")
    current_time_str = current_datetime.strftime("%H:%M:%S %Z")

    cameras_info = []
    config = request.app.frigate_config
    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()
        )
        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."
        )

    live_image_note = ""
    if body.include_live_image:
        live_image_note = (
            f"\n\nThe first user message includes a live image from camera "
            f"'{body.include_live_image}'. Use get_live_context for that camera to get "
            "current detection details (objects, zones) to aid in understanding the image."
        )

    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 date and time: {current_date_str} at {current_time_str} (UTC)

When users ask questions 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.{cameras_section}{live_image_note}"""

    conversation.append(
        {
            "role": "system",
            "content": system_prompt,
        }
    )

    first_user_message_seen = False
    for msg in body.messages:
        msg_dict = {
            "role": msg.role,
            "content": msg.content,
        }
        if msg.tool_call_id:
            msg_dict["tool_call_id"] = msg.tool_call_id
        if msg.name:
            msg_dict["name"] = msg.name

        if (
            msg.role == "user"
            and not first_user_message_seen
            and body.include_live_image
        ):
            first_user_message_seen = True
            image_url = await _get_live_frame_image_url(
                request, body.include_live_image, allowed_cameras
            )
            if image_url:
                msg_dict["content"] = [
                    {"type": "text", "text": msg.content},
                    {"type": "image_url", "image_url": {"url": image_url}},
                ]

        conversation.append(msg_dict)

    tool_iterations = 0
    max_iterations = body.max_tool_iterations

    logger.debug(
        f"Starting chat completion with {len(conversation)} message(s), "
        f"{len(tools)} tool(s) available, max_iterations={max_iterations}"
    )

    try:
        while tool_iterations < max_iterations:
            logger.debug(
                f"Calling LLM (iteration {tool_iterations + 1}/{max_iterations}) "
                f"with {len(conversation)} message(s) in conversation"
            )
            response = genai_client.chat_with_tools(
                messages=conversation,
                tools=tools if tools else None,
                tool_choice="auto",
            )

            if response.get("finish_reason") == "error":
                logger.error("GenAI client returned an error")
                return JSONResponse(
                    content={
                        "error": "An error occurred while processing your request.",
                    },
                    status_code=500,
                )

            assistant_message = {
                "role": "assistant",
                "content": response.get("content"),
            }
            if response.get("tool_calls"):
                assistant_message["tool_calls"] = [
                    {
                        "id": tc["id"],
                        "type": "function",
                        "function": {
                            "name": tc["name"],
                            "arguments": json.dumps(tc["arguments"]),
                        },
                    }
                    for tc in response["tool_calls"]
                ]
            conversation.append(assistant_message)

            tool_calls = response.get("tool_calls")
            if not tool_calls:
                logger.debug(
                    f"Chat completion finished with final answer (iterations: {tool_iterations})"
                )
                return JSONResponse(
                    content=ChatCompletionResponse(
                        message=ChatMessageResponse(
                            role="assistant",
                            content=response.get("content"),
                            tool_calls=None,
                        ),
                        finish_reason=response.get("finish_reason", "stop"),
                        tool_iterations=tool_iterations,
                    ).model_dump(),
                )

            # Execute tools
            tool_iterations += 1
            logger.debug(
                f"Tool calls detected (iteration {tool_iterations}/{max_iterations}): "
                f"{len(tool_calls)} tool(s) to execute"
            )
            tool_results = []

            for tool_call in tool_calls:
                tool_name = tool_call["name"]
                tool_args = tool_call["arguments"]
                tool_call_id = tool_call["id"]

                logger.debug(
                    f"Executing tool: {tool_name} (id: {tool_call_id}) with arguments: {json.dumps(tool_args, indent=2)}"
                )

                try:
                    tool_result = await _execute_tool_internal(
                        tool_name, tool_args, request, allowed_cameras
                    )

                    if isinstance(tool_result, dict):
                        result_content = json.dumps(tool_result)
                        result_summary = tool_result
                        if isinstance(tool_result, dict) and isinstance(
                            tool_result.get("content"), list
                        ):
                            result_count = len(tool_result.get("content", []))
                            result_summary = {
                                "count": result_count,
                                "sample": tool_result.get("content", [])[:2]
                                if result_count > 0
                                else [],
                            }
                        logger.debug(
                            f"Tool {tool_name} (id: {tool_call_id}) completed successfully. "
                            f"Result: {json.dumps(result_summary, indent=2)}"
                        )
                    elif isinstance(tool_result, str):
                        result_content = tool_result
                        logger.debug(
                            f"Tool {tool_name} (id: {tool_call_id}) completed successfully. "
                            f"Result length: {len(result_content)} characters"
                        )
                    else:
                        result_content = str(tool_result)
                        logger.debug(
                            f"Tool {tool_name} (id: {tool_call_id}) completed successfully. "
                            f"Result type: {type(tool_result).__name__}"
                        )

                    tool_results.append(
                        {
                            "role": "tool",
                            "tool_call_id": tool_call_id,
                            "content": result_content,
                        }
                    )
                except Exception as e:
                    logger.error(
                        f"Error executing tool {tool_name} (id: {tool_call_id}): {e}",
                        exc_info=True,
                    )
                    error_content = json.dumps(
                        {"error": f"Tool execution failed: {str(e)}"}
                    )
                    tool_results.append(
                        {
                            "role": "tool",
                            "tool_call_id": tool_call_id,
                            "content": error_content,
                        }
                    )
                    logger.debug(
                        f"Tool {tool_name} (id: {tool_call_id}) failed. Error result added to conversation."
                    )

            conversation.extend(tool_results)
            logger.debug(
                f"Added {len(tool_results)} tool result(s) to conversation. "
                f"Continuing with next LLM call..."
            )

        logger.warning(
            f"Max tool iterations ({max_iterations}) reached. Returning partial response."
        )
        return JSONResponse(
            content=ChatCompletionResponse(
                message=ChatMessageResponse(
                    role="assistant",
                    content="I reached the maximum number of tool call iterations. Please try rephrasing your question.",
                    tool_calls=None,
                ),
                finish_reason="length",
                tool_iterations=tool_iterations,
            ).model_dump(),
        )

    except Exception as e:
        logger.error(f"Error in chat completion: {e}", exc_info=True)
        return JSONResponse(
            content={
                "error": "An error occurred while processing your request.",
            },
            status_code=500,
        )