Add support for markdown tables

docs/docs/configuration/semantic_search.md

@@ -76,40 +76,6 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
 
 :::
 
-### GenAI Provider (llama.cpp)
-
-Frigate can use a GenAI provider for semantic search embeddings when that provider has the `embeddings` role. Currently, only **llama.cpp** supports multimodal embeddings (both text and images).
-
-To use llama.cpp for semantic search:
-
-1. Configure a GenAI provider in your config with `embeddings` in its `roles`.
-2. Set `semantic_search.model` to the GenAI config key (e.g. `default`).
-3. Start the llama.cpp server with `--embeddings` and `--mmproj` for image support:
-
-```yaml
-genai:
-  default:
-    provider: llamacpp
-    base_url: http://localhost:8080
-    model: your-model-name
-    roles:
-      - embeddings
-      - vision
-      - tools
-
-semantic_search:
-  enabled: True
-  model: default
-```
-
-The llama.cpp server must be started with `--embeddings` for the embeddings API, and `--mmproj <mmproj.gguf>` when using image embeddings. See the [llama.cpp server documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for details.
-
-:::note
-
-Switching between Jina models and a GenAI provider requires reindexing. Embeddings from different backends are incompatible.
-
-:::
-
 ### GPU Acceleration
 
 The CLIP models are downloaded in ONNX format, and the `large` model can be accelerated using GPU hardware, when available. This depends on the Docker build that is used. You can also target a specific device in a multi-GPU installation.

frigate/api/chat.py

@@ -3,12 +3,13 @@
 import base64
 import json
 import logging
-from datetime import datetime, timezone
-from typing import Any, Dict, List, Optional
+import time
+from datetime import datetime
+from typing import Any, Dict, Generator, List, Optional
 
 import cv2
 from fastapi import APIRouter, Body, Depends, Request
-from fastapi.responses import JSONResponse
+from fastapi.responses import JSONResponse, StreamingResponse
 from pydantic import BaseModel
 
 from frigate.api.auth import (
@@ -20,15 +21,60 @@ from frigate.api.defs.request.chat_body import ChatCompletionRequest
 from frigate.api.defs.response.chat_response import (
     ChatCompletionResponse,
     ChatMessageResponse,
+    ToolCall,
 )
 from frigate.api.defs.tags import Tags
 from frigate.api.event import events
+from frigate.genai.utils import build_assistant_message_for_conversation
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter(tags=[Tags.chat])
 
 
+def _chunk_content(content: str, chunk_size: int = 80) -> Generator[str, None, None]:
+    """Yield content in word-aware chunks for streaming."""
+    if not content:
+        return
+    words = content.split(" ")
+    current: List[str] = []
+    current_len = 0
+    for w in words:
+        current.append(w)
+        current_len += len(w) + 1
+        if current_len >= chunk_size:
+            yield " ".join(current) + " "
+            current = []
+            current_len = 0
+    if current:
+        yield " ".join(current)
+
+
+def _format_events_with_local_time(
+    events_list: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    """Add human-readable local start/end times to each event for the LLM."""
+    result = []
+    for evt in events_list:
+        if not isinstance(evt, dict):
+            result.append(evt)
+            continue
+        copy_evt = dict(evt)
+        try:
+            start_ts = evt.get("start_time")
+            end_ts = evt.get("end_time")
+            if start_ts is not None:
+                dt_start = datetime.fromtimestamp(start_ts)
+                copy_evt["start_time_local"] = dt_start.strftime("%Y-%m-%d %I:%M:%S %p")
+            if end_ts is not None:
+                dt_end = datetime.fromtimestamp(end_ts)
+                copy_evt["end_time_local"] = dt_end.strftime("%Y-%m-%d %I:%M:%S %p")
+        except (TypeError, ValueError, OSError):
+            pass
+        result.append(copy_evt)
+    return result
+
+
 class ToolExecuteRequest(BaseModel):
     """Request model for tool execution."""
 
@@ -52,19 +98,25 @@ def get_tool_definitions() -> List[Dict[str, Any]]:
                     "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)."
+                    "An 'object' in Frigate represents a tracked detection (e.g., a person, package, car). "
+                    "When the user asks about a specific name (person, delivery company, animal, etc.), "
+                    "filter by sub_label only and do not set label."
                 ),
                 "parameters": {
                     "type": "object",
                     "properties": {
                         "camera": {
                             "type": "string",
-                            "description": "Camera name to filter by (optional). Use 'all' for all cameras.",
+                            "description": "Camera name to filter by (optional).",
                         },
                         "label": {
                             "type": "string",
                             "description": "Object label to filter by (e.g., 'person', 'package', 'car').",
                         },
+                        "sub_label": {
+                            "type": "string",
+                            "description": "Name of a person, delivery company, animal, etc. When filtering by a specific name, use only sub_label; do not set label.",
+                        },
                         "after": {
                             "type": "string",
                             "description": "Start time in ISO 8601 format (e.g., '2024-01-01T00:00:00Z').",
@@ -80,8 +132,8 @@ def get_tool_definitions() -> List[Dict[str, Any]]:
                         },
                         "limit": {
                             "type": "integer",
-                            "description": "Maximum number of objects to return (default: 10).",
-                            "default": 10,
+                            "description": "Maximum number of objects to return (default: 25).",
+                            "default": 25,
                         },
                     },
                 },
@@ -119,14 +171,13 @@ def get_tool_definitions() -> List[Dict[str, Any]]:
     summary="Get available tools",
     description="Returns OpenAI-compatible tool definitions for function calling.",
 )
-def get_tools(request: Request) -> JSONResponse:
+def get_tools() -> 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:
@@ -136,23 +187,26 @@ async def _execute_search_objects(
     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
+    # Parse after/before as server local time; convert to Unix timestamp
     after = arguments.get("after")
     before = arguments.get("before")
 
+    def _parse_as_local_timestamp(s: str):
+        s = s.replace("Z", "").strip()[:19]
+        dt = datetime.strptime(s, "%Y-%m-%dT%H:%M:%S")
+        return time.mktime(dt.timetuple())
+
     if after:
         try:
-            after_dt = datetime.fromisoformat(after.replace("Z", "+00:00"))
-            after = after_dt.timestamp()
-        except (ValueError, AttributeError):
+            after = _parse_as_local_timestamp(after)
+        except (ValueError, AttributeError, TypeError):
             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):
+            before = _parse_as_local_timestamp(before)
+        except (ValueError, AttributeError, TypeError):
             logger.warning(f"Invalid 'before' timestamp format: {before}")
             before = None
 
@@ -165,15 +219,14 @@ async def _execute_search_objects(
 
     # 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"),
+        sub_labels=arguments.get("sub_label", "all").lower(),
         zones=zones,
         zone=zones,
         after=after,
         before=before,
-        limit=arguments.get("limit", 10),
+        limit=arguments.get("limit", 25),
     )
 
     try:
@@ -202,7 +255,6 @@ async def _execute_search_objects(
     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:
@@ -218,7 +270,7 @@ async def execute_tool(
     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 await _execute_search_objects(arguments, allowed_cameras)
 
     return JSONResponse(
         content={
@@ -334,7 +386,7 @@ async def _execute_tool_internal(
     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)
+        response = await _execute_search_objects(arguments, allowed_cameras)
         try:
             if hasattr(response, "body"):
                 body_str = response.body.decode("utf-8")
@@ -349,15 +401,109 @@ async def _execute_tool_internal(
     elif tool_name == "get_live_context":
         camera = arguments.get("camera")
         if not camera:
+            logger.error(
+                "Tool get_live_context failed: camera parameter is required. "
+                "Arguments: %s",
+                json.dumps(arguments),
+            )
             return {"error": "Camera parameter is required"}
         return await _execute_get_live_context(request, camera, allowed_cameras)
     else:
+        logger.error(
+            "Tool call failed: unknown tool %r. Expected one of: search_objects, get_live_context. "
+            "Arguments received: %s",
+            tool_name,
+            json.dumps(arguments),
+        )
         return {"error": f"Unknown tool: {tool_name}"}
 
 
+async def _execute_pending_tools(
+    pending_tool_calls: List[Dict[str, Any]],
+    request: Request,
+    allowed_cameras: List[str],
+) -> tuple[List[ToolCall], List[Dict[str, Any]]]:
+    """
+    Execute a list of tool calls; return (ToolCall list for API response, tool result dicts for conversation).
+    """
+    tool_calls_out: List[ToolCall] = []
+    tool_results: List[Dict[str, Any]] = []
+    for tool_call in pending_tool_calls:
+        tool_name = tool_call["name"]
+        tool_args = tool_call.get("arguments") or {}
+        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) and tool_result.get("error"):
+                logger.error(
+                    "Tool call %s (id: %s) returned error: %s. Arguments: %s",
+                    tool_name,
+                    tool_call_id,
+                    tool_result.get("error"),
+                    json.dumps(tool_args),
+                )
+            if tool_name == "search_objects" and isinstance(tool_result, list):
+                tool_result = _format_events_with_local_time(tool_result)
+                _keys = {
+                    "id",
+                    "camera",
+                    "label",
+                    "zones",
+                    "start_time_local",
+                    "end_time_local",
+                    "sub_label",
+                    "event_count",
+                }
+                tool_result = [
+                    {k: evt[k] for k in _keys if k in evt}
+                    for evt in tool_result
+                    if isinstance(evt, dict)
+                ]
+            result_content = (
+                json.dumps(tool_result)
+                if isinstance(tool_result, (dict, list))
+                else (tool_result if isinstance(tool_result, str) else str(tool_result))
+            )
+            tool_calls_out.append(
+                ToolCall(name=tool_name, arguments=tool_args, response=result_content)
+            )
+            tool_results.append(
+                {
+                    "role": "tool",
+                    "tool_call_id": tool_call_id,
+                    "content": result_content,
+                }
+            )
+        except Exception as e:
+            logger.error(
+                "Error executing tool %s (id: %s): %s. Arguments: %s",
+                tool_name,
+                tool_call_id,
+                e,
+                json.dumps(tool_args),
+                exc_info=True,
+            )
+            error_content = json.dumps({"error": f"Tool execution failed: {str(e)}"})
+            tool_calls_out.append(
+                ToolCall(name=tool_name, arguments=tool_args, response=error_content)
+            )
+            tool_results.append(
+                {
+                    "role": "tool",
+                    "tool_call_id": tool_call_id,
+                    "content": error_content,
+                }
+            )
+    return (tool_calls_out, tool_results)
+
+
 @router.post(
     "/chat/completion",
-    response_model=ChatCompletionResponse,
     dependencies=[Depends(allow_any_authenticated())],
     summary="Chat completion with tool calling",
     description=(
@@ -369,7 +515,7 @@ 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.
 
@@ -394,9 +540,9 @@ async def chat_completion(
     tools = get_tool_definitions()
     conversation = []
 
-    current_datetime = datetime.now(timezone.utc)
+    current_datetime = datetime.now()
     current_date_str = current_datetime.strftime("%Y-%m-%d")
-    current_time_str = current_datetime.strftime("%H:%M:%S %Z")
+    current_time_str = current_datetime.strftime("%I:%M:%S %p")
 
     cameras_info = []
     config = request.app.frigate_config
@@ -429,9 +575,12 @@ async def chat_completion(
 
     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)
+Current server local date and time: {current_date_str} at {current_time_str}
 
-When users ask questions about "today", "yesterday", "this week", etc., use the current date above as reference.
+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.{cameras_section}{live_image_note}"""
 
@@ -471,6 +620,7 @@ Always be accurate with time calculations based on the current date provided.{ca
         conversation.append(msg_dict)
 
     tool_iterations = 0
+    tool_calls: List[ToolCall] = []
     max_iterations = body.max_tool_iterations
 
     logger.debug(
@@ -478,6 +628,81 @@ Always be accurate with time calculations based on the current date provided.{ca
         f"{len(tools)} tool(s) available, max_iterations={max_iterations}"
     )
 
+    # True LLM streaming when client supports it and stream requested
+    if body.stream and hasattr(genai_client, "chat_with_tools_stream"):
+        stream_tool_calls: List[ToolCall] = []
+        stream_iterations = 0
+
+        async def stream_body_llm():
+            nonlocal conversation, stream_tool_calls, stream_iterations
+            while stream_iterations < max_iterations:
+                logger.debug(
+                    f"Streaming LLM (iteration {stream_iterations + 1}/{max_iterations}) "
+                    f"with {len(conversation)} message(s)"
+                )
+                async for event in genai_client.chat_with_tools_stream(
+                    messages=conversation,
+                    tools=tools if tools else None,
+                    tool_choice="auto",
+                ):
+                    kind, value = event
+                    if kind == "content_delta":
+                        yield (
+                            json.dumps({"type": "content", "delta": value}).encode(
+                                "utf-8"
+                            )
+                            + b"\n"
+                        )
+                    elif kind == "message":
+                        msg = value
+                        if msg.get("finish_reason") == "error":
+                            yield (
+                                json.dumps(
+                                    {
+                                        "type": "error",
+                                        "error": "An error occurred while processing your request.",
+                                    }
+                                ).encode("utf-8")
+                                + b"\n"
+                            )
+                            return
+                        pending = msg.get("tool_calls")
+                        if pending:
+                            stream_iterations += 1
+                            conversation.append(
+                                build_assistant_message_for_conversation(
+                                    msg.get("content"), pending
+                                )
+                            )
+                            executed_calls, tool_results = await _execute_pending_tools(
+                                pending, request, allowed_cameras
+                            )
+                            stream_tool_calls.extend(executed_calls)
+                            conversation.extend(tool_results)
+                            yield (
+                                json.dumps(
+                                    {
+                                        "type": "tool_calls",
+                                        "tool_calls": [
+                                            tc.model_dump() for tc in stream_tool_calls
+                                        ],
+                                    }
+                                ).encode("utf-8")
+                                + b"\n"
+                            )
+                            break
+                        else:
+                            yield (json.dumps({"type": "done"}).encode("utf-8") + b"\n")
+                            return
+            else:
+                yield json.dumps({"type": "done"}).encode("utf-8") + b"\n"
+
+        return StreamingResponse(
+            stream_body_llm(),
+            media_type="application/x-ndjson",
+            headers={"X-Accel-Buffering": "no"},
+        )
+
     try:
         while tool_iterations < max_iterations:
             logger.debug(
@@ -499,119 +724,71 @@ Always be accurate with time calculations based on the current date provided.{ca
                     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)
+            conversation.append(
+                build_assistant_message_for_conversation(
+                    response.get("content"), response.get("tool_calls")
+                )
+            )
 
-            tool_calls = response.get("tool_calls")
-            if not tool_calls:
+            pending_tool_calls = response.get("tool_calls")
+            if not pending_tool_calls:
                 logger.debug(
                     f"Chat completion finished with final answer (iterations: {tool_iterations})"
                 )
+                final_content = response.get("content") or ""
+
+                if body.stream:
+
+                    async def stream_body() -> Any:
+                        if tool_calls:
+                            yield (
+                                json.dumps(
+                                    {
+                                        "type": "tool_calls",
+                                        "tool_calls": [
+                                            tc.model_dump() for tc in tool_calls
+                                        ],
+                                    }
+                                ).encode("utf-8")
+                                + b"\n"
+                            )
+                        # Stream content in word-sized chunks for smooth UX
+                        for part in _chunk_content(final_content):
+                            yield (
+                                json.dumps({"type": "content", "delta": part}).encode(
+                                    "utf-8"
+                                )
+                                + b"\n"
+                            )
+                        yield json.dumps({"type": "done"}).encode("utf-8") + b"\n"
+
+                    return StreamingResponse(
+                        stream_body(),
+                        media_type="application/x-ndjson",
+                    )
+
                 return JSONResponse(
                     content=ChatCompletionResponse(
                         message=ChatMessageResponse(
                             role="assistant",
-                            content=response.get("content"),
+                            content=final_content,
                             tool_calls=None,
                         ),
                         finish_reason=response.get("finish_reason", "stop"),
                         tool_iterations=tool_iterations,
+                        tool_calls=tool_calls,
                     ).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"
+                f"{len(pending_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."
-                    )
-
+            executed_calls, tool_results = await _execute_pending_tools(
+                pending_tool_calls, request, allowed_cameras
+            )
+            tool_calls.extend(executed_calls)
             conversation.extend(tool_results)
             logger.debug(
                 f"Added {len(tool_results)} tool result(s) to conversation. "
@@ -630,6 +807,7 @@ Always be accurate with time calculations based on the current date provided.{ca
                 ),
                 finish_reason="length",
                 tool_iterations=tool_iterations,
+                tool_calls=tool_calls,
             ).model_dump(),
         )
 

frigate/api/defs/request/chat_body.py

@@ -39,3 +39,7 @@ class ChatCompletionRequest(BaseModel):
             "user message as multimodal content. Use with get_live_context for detection info."
         ),
     )
+    stream: bool = Field(
+        default=False,
+        description="If true, stream the final assistant response in the body as newline-delimited JSON.",
+    )

frigate/api/defs/response/chat_response.py

@@ -5,8 +5,8 @@ from typing import Any, Optional
 from pydantic import BaseModel, Field
 
 
-class ToolCall(BaseModel):
-    """A tool call from the LLM."""
+class ToolCallInvocation(BaseModel):
+    """A tool call requested by the LLM (before execution)."""
 
     id: str = Field(description="Unique identifier for this tool call")
     name: str = Field(description="Tool name to call")
@@ -20,11 +20,24 @@ class ChatMessageResponse(BaseModel):
     content: Optional[str] = Field(
         default=None, description="Message content (None if tool calls present)"
     )
-    tool_calls: Optional[list[ToolCall]] = Field(
+    tool_calls: Optional[list[ToolCallInvocation]] = Field(
         default=None, description="Tool calls if LLM wants to call tools"
     )
 
 
+class ToolCall(BaseModel):
+    """A tool that was executed during the completion, with its response."""
+
+    name: str = Field(description="Tool name that was called")
+    arguments: dict[str, Any] = Field(
+        default_factory=dict, description="Arguments passed to the tool"
+    )
+    response: str = Field(
+        default="",
+        description="The response or result returned from the tool execution",
+    )
+
+
 class ChatCompletionResponse(BaseModel):
     """Response from chat completion."""
 
@@ -35,3 +48,7 @@ class ChatCompletionResponse(BaseModel):
     tool_iterations: int = Field(
         default=0, description="Number of tool call iterations performed"
     )
+    tool_calls: list[ToolCall] = Field(
+        default_factory=list,
+        description="List of tool calls that were executed during this completion",
+    )

frigate/config/classification.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Dict, List, Optional, Union
+from typing import Dict, List, Optional
 
 from pydantic import ConfigDict, Field
 
@@ -128,10 +128,9 @@ class SemanticSearchConfig(FrigateBaseModel):
     reindex: Optional[bool] = Field(
         default=False, title="Reindex all tracked objects on startup."
     )
-    model: Optional[Union[SemanticSearchModelEnum, str]] = Field(
+    model: Optional[SemanticSearchModelEnum] = Field(
         default=SemanticSearchModelEnum.jinav1,
-        title="The CLIP model or GenAI provider name for semantic search.",
-        description="Use 'jinav1', 'jinav2' for ONNX models, or a GenAI config key (e.g. 'default') when that provider has the embeddings role.",
+        title="The CLIP model to use for semantic search.",
     )
     model_size: str = Field(
         default="small", title="The size of the embeddings model used."

frigate/config/config.py

@@ -443,22 +443,6 @@ class FrigateConfig(FrigateBaseModel):
                     )
                 role_to_name[role] = name
 
-        # validate semantic_search.model when it is a GenAI provider name
-        if self.semantic_search.enabled and isinstance(
-            self.semantic_search.model, str
-        ):
-            if self.semantic_search.model not in self.genai:
-                raise ValueError(
-                    f"semantic_search.model '{self.semantic_search.model}' is not a "
-                    "valid GenAI config key. Must match a key in genai config."
-                )
-            genai_cfg = self.genai[self.semantic_search.model]
-            if GenAIRoleEnum.embeddings not in genai_cfg.roles:
-                raise ValueError(
-                    f"GenAI provider '{self.semantic_search.model}' must have "
-                    "'embeddings' in its roles for semantic search."
-                )
-
         # set default min_score for object attributes
         for attribute in self.model.all_attributes:
             if not self.objects.filters.get(attribute):

frigate/embeddings/embeddings.py

@@ -28,7 +28,6 @@ from frigate.types import ModelStatusTypesEnum
 from frigate.util.builtin import EventsPerSecond, InferenceSpeed, serialize
 from frigate.util.file import get_event_thumbnail_bytes
 
-from .genai_embedding import GenAIEmbedding
 from .onnx.jina_v1_embedding import JinaV1ImageEmbedding, JinaV1TextEmbedding
 from .onnx.jina_v2_embedding import JinaV2Embedding
 
@@ -74,13 +73,11 @@ class Embeddings:
         config: FrigateConfig,
         db: SqliteVecQueueDatabase,
         metrics: DataProcessorMetrics,
-        genai_manager=None,
     ) -> None:
         self.config = config
         self.db = db
         self.metrics = metrics
         self.requestor = InterProcessRequestor()
-        self.genai_manager = genai_manager
 
         self.image_inference_speed = InferenceSpeed(self.metrics.image_embeddings_speed)
         self.image_eps = EventsPerSecond()
@@ -107,27 +104,7 @@ class Embeddings:
                 },
             )
 
-        model_cfg = self.config.semantic_search.model
-        is_genai_model = isinstance(model_cfg, str)
-
-        if is_genai_model:
-            embeddings_client = (
-                genai_manager.embeddings_client if genai_manager else None
-            )
-            if not embeddings_client:
-                raise ValueError(
-                    f"semantic_search.model is '{model_cfg}' (GenAI provider) but "
-                    "no embeddings client is configured. Ensure the GenAI provider "
-                    "has 'embeddings' in its roles."
-                )
-            self.embedding = GenAIEmbedding(embeddings_client)
-            self.text_embedding = lambda input_data: self.embedding(
-                input_data, embedding_type="text"
-            )
-            self.vision_embedding = lambda input_data: self.embedding(
-                input_data, embedding_type="vision"
-            )
-        elif model_cfg == SemanticSearchModelEnum.jinav2:
+        if self.config.semantic_search.model == SemanticSearchModelEnum.jinav2:
             # Single JinaV2Embedding instance for both text and vision
             self.embedding = JinaV2Embedding(
                 model_size=self.config.semantic_search.model_size,
@@ -141,8 +118,7 @@ class Embeddings:
             self.vision_embedding = lambda input_data: self.embedding(
                 input_data, embedding_type="vision"
             )
-        else:
-            # Default to jinav1
+        else:  # Default to jinav1
             self.text_embedding = JinaV1TextEmbedding(
                 model_size=config.semantic_search.model_size,
                 requestor=self.requestor,
@@ -160,11 +136,8 @@ class Embeddings:
         self.metrics.text_embeddings_eps.value = self.text_eps.eps()
 
     def get_model_definitions(self):
-        model_cfg = self.config.semantic_search.model
-        if isinstance(model_cfg, str):
-            # GenAI provider: no ONNX models to download
-            models = []
-        elif model_cfg == SemanticSearchModelEnum.jinav2:
+        # Version-specific models
+        if self.config.semantic_search.model == SemanticSearchModelEnum.jinav2:
             models = [
                 "jinaai/jina-clip-v2-tokenizer",
                 "jinaai/jina-clip-v2-model_fp16.onnx"
@@ -251,14 +224,6 @@ class Embeddings:
 
         embeddings = self.vision_embedding(valid_thumbs)
 
-        if len(embeddings) != len(valid_ids):
-            logger.warning(
-                "Batch embed returned %d embeddings for %d thumbnails; skipping batch",
-                len(embeddings),
-                len(valid_ids),
-            )
-            return []
-
         if upsert:
             items = []
             for i in range(len(valid_ids)):
@@ -281,15 +246,9 @@ class Embeddings:
 
     def embed_description(
         self, event_id: str, description: str, upsert: bool = True
-    ) -> np.ndarray | None:
+    ) -> np.ndarray:
         start = datetime.datetime.now().timestamp()
-        embeddings = self.text_embedding([description])
-        if not embeddings:
-            logger.warning(
-                "Failed to generate description embedding for event %s", event_id
-            )
-            return None
-        embedding = embeddings[0]
+        embedding = self.text_embedding([description])[0]
 
         if upsert:
             self.db.execute_sql(
@@ -312,32 +271,8 @@ class Embeddings:
         # upsert embeddings one by one to avoid token limit
         embeddings = []
 
-        for eid, desc in event_descriptions.items():
-            result = self.text_embedding([desc])
-            if not result:
-                logger.warning(
-                    "Failed to generate description embedding for event %s", eid
-                )
-                continue
-            embeddings.append(result[0])
-
-        if not embeddings:
-            logger.warning("No description embeddings generated in batch")
-            return np.array([])
-
-        # Build ids list for only successful embeddings - we need to track which succeeded
-        ids = list(event_descriptions.keys())
-        if len(embeddings) != len(ids):
-            # Rebuild ids/embeddings for only successful ones (match by order)
-            ids = []
-            embeddings_filtered = []
-            for eid, desc in event_descriptions.items():
-                result = self.text_embedding([desc])
-                if result:
-                    ids.append(eid)
-                    embeddings_filtered.append(result[0])
-            ids = ids
-            embeddings = embeddings_filtered
+        for desc in event_descriptions.values():
+            embeddings.append(self.text_embedding([desc])[0])
 
         if upsert:
             ids = list(event_descriptions.keys())
@@ -379,10 +314,7 @@ class Embeddings:
 
         batch_size = (
             4
-            if (
-                isinstance(self.config.semantic_search.model, str)
-                or self.config.semantic_search.model == SemanticSearchModelEnum.jinav2
-            )
+            if self.config.semantic_search.model == SemanticSearchModelEnum.jinav2
             else 32
         )
         current_page = 1
@@ -669,8 +601,6 @@ class Embeddings:
         if trigger.type == "description":
             logger.debug(f"Generating embedding for trigger description {trigger_name}")
             embedding = self.embed_description(None, trigger.data, upsert=False)
-            if embedding is None:
-                return b""
             return embedding.astype(np.float32).tobytes()
 
         elif trigger.type == "thumbnail":
@@ -706,8 +636,6 @@ class Embeddings:
                 embedding = self.embed_thumbnail(
                     str(trigger.data), thumbnail, upsert=False
                 )
-                if embedding is None:
-                    return b""
                 return embedding.astype(np.float32).tobytes()
 
         else:

frigate/embeddings/genai_embedding.py

@@ -1,85 +0,0 @@
-"""GenAI-backed embeddings for semantic search."""
-
-import io
-import logging
-from typing import TYPE_CHECKING
-
-import numpy as np
-from PIL import Image
-
-if TYPE_CHECKING:
-    from frigate.genai import GenAIClient
-
-logger = logging.getLogger(__name__)
-
-EMBEDDING_DIM = 768
-
-
-class GenAIEmbedding:
-    """Embedding adapter that delegates to a GenAI provider's embed API.
-
-    Provides the same interface as JinaV2Embedding for semantic search:
-    __call__(inputs, embedding_type) -> list[np.ndarray]. Output embeddings are
-    normalized to 768 dimensions for Frigate's sqlite-vec schema.
-    """
-
-    def __init__(self, client: "GenAIClient") -> None:
-        self.client = client
-
-    def __call__(
-        self,
-        inputs: list[str] | list[bytes] | list[Image.Image],
-        embedding_type: str = "text",
-    ) -> list[np.ndarray]:
-        """Generate embeddings for text or images.
-
-        Args:
-            inputs: List of strings (text) or bytes/PIL images (vision).
-            embedding_type: "text" or "vision".
-
-        Returns:
-            List of 768-dim numpy float32 arrays.
-        """
-        if not inputs:
-            return []
-
-        if embedding_type == "text":
-            texts = [str(x) for x in inputs]
-            embeddings = self.client.embed(texts=texts)
-        elif embedding_type == "vision":
-            images: list[bytes] = []
-            for inp in inputs:
-                if isinstance(inp, bytes):
-                    images.append(inp)
-                elif isinstance(inp, Image.Image):
-                    buf = io.BytesIO()
-                    inp.convert("RGB").save(buf, format="JPEG")
-                    images.append(buf.getvalue())
-                else:
-                    logger.warning(
-                        "GenAIEmbedding: skipping unsupported vision input type %s",
-                        type(inp).__name__,
-                    )
-            if not images:
-                return []
-            embeddings = self.client.embed(images=images)
-        else:
-            raise ValueError(
-                f"Invalid embedding_type '{embedding_type}'. Must be 'text' or 'vision'."
-            )
-
-        result = []
-        for emb in embeddings:
-            arr = np.asarray(emb, dtype=np.float32).flatten()
-            if arr.size != EMBEDDING_DIM:
-                if arr.size > EMBEDDING_DIM:
-                    arr = arr[:EMBEDDING_DIM]
-                else:
-                    arr = np.pad(
-                        arr,
-                        (0, EMBEDDING_DIM - arr.size),
-                        mode="constant",
-                        constant_values=0,
-                    )
-            result.append(arr)
-        return result

frigate/embeddings/maintainer.py

@@ -116,10 +116,8 @@ class EmbeddingMaintainer(threading.Thread):
         models = [Event, Recordings, ReviewSegment, Trigger]
         db.bind(models)
 
-        self.genai_manager = GenAIClientManager(config)
-
         if config.semantic_search.enabled:
-            self.embeddings = Embeddings(config, db, metrics, self.genai_manager)
+            self.embeddings = Embeddings(config, db, metrics)
 
             # Check if we need to re-index events
             if config.semantic_search.reindex:
@@ -146,6 +144,7 @@ class EmbeddingMaintainer(threading.Thread):
         self.frame_manager = SharedMemoryFrameManager()
 
         self.detected_license_plates: dict[str, dict[str, Any]] = {}
+        self.genai_manager = GenAIClientManager(config)
 
         # model runners to share between realtime and post processors
         if self.config.lpr.enabled:

frigate/genai/__init__.py

@@ -7,7 +7,6 @@ import os
 import re
 from typing import Any, Optional
 
-import numpy as np
 from playhouse.shortcuts import model_to_dict
 
 from frigate.config import CameraConfig, FrigateConfig, GenAIConfig, GenAIProviderEnum
@@ -305,25 +304,6 @@ Guidelines:
         """Get the context window size for this provider in tokens."""
         return 4096
 
-    def embed(
-        self,
-        texts: list[str] | None = None,
-        images: list[bytes] | None = None,
-    ) -> list[np.ndarray]:
-        """Generate embeddings for text and/or images.
-
-        Returns list of numpy arrays (one per input). Expected dimension is 768
-        for Frigate semantic search compatibility.
-
-        Providers that support embeddings should override this method.
-        """
-        logger.warning(
-            "%s does not support embeddings. "
-            "This method should be overridden by the provider implementation.",
-            self.__class__.__name__,
-        )
-        return []
-
     def chat_with_tools(
         self,
         messages: list[dict[str, Any]],

frigate/genai/llama_cpp.py

@@ -1,35 +1,20 @@
 """llama.cpp Provider for Frigate AI."""
 
 import base64
-import io
 import json
 import logging
 from typing import Any, Optional
 
-import numpy as np
+import httpx
 import requests
-from PIL import Image
 
 from frigate.config import GenAIProviderEnum
 from frigate.genai import GenAIClient, register_genai_provider
+from frigate.genai.utils import parse_tool_calls_from_message
 
 logger = logging.getLogger(__name__)
 
 
-def _to_jpeg(img_bytes: bytes) -> bytes | None:
-    """Convert image bytes to JPEG. llama.cpp/STB does not support WebP."""
-    try:
-        img = Image.open(io.BytesIO(img_bytes))
-        if img.mode != "RGB":
-            img = img.convert("RGB")
-        buf = io.BytesIO()
-        img.save(buf, format="JPEG", quality=85)
-        return buf.getvalue()
-    except Exception as e:
-        logger.warning("Failed to convert image to JPEG: %s", e)
-        return None
-
-
 @register_genai_provider(GenAIProviderEnum.llamacpp)
 class LlamaCppClient(GenAIClient):
     """Generative AI client for Frigate using llama.cpp server."""
@@ -116,105 +101,76 @@ class LlamaCppClient(GenAIClient):
 
     def get_context_size(self) -> int:
         """Get the context window size for llama.cpp."""
-        return self.genai_config.provider_options.get("context_size", 4096)
+        return self.provider_options.get("context_size", 4096)
 
-    def embed(
+    def _build_payload(
         self,
-        texts: list[str] | None = None,
-        images: list[bytes] | None = None,
-    ) -> list[np.ndarray]:
-        """Generate embeddings via llama.cpp /embeddings endpoint.
+        messages: list[dict[str, Any]],
+        tools: Optional[list[dict[str, Any]]],
+        tool_choice: Optional[str],
+        stream: bool = False,
+    ) -> dict[str, Any]:
+        """Build request payload for chat completions (sync or stream)."""
+        openai_tool_choice = None
+        if tool_choice:
+            if tool_choice == "none":
+                openai_tool_choice = "none"
+            elif tool_choice == "auto":
+                openai_tool_choice = "auto"
+            elif tool_choice == "required":
+                openai_tool_choice = "required"
 
-        Supports batch requests. Uses content format with prompt_string and
-        multimodal_data for images (PR #15108). Server must be started with
-        --embeddings and --mmproj for multimodal support.
-        """
-        if self.provider is None:
-            logger.warning(
-                "llama.cpp provider has not been initialized. Check your llama.cpp configuration."
+        payload: dict[str, Any] = {"messages": messages}
+        if stream:
+            payload["stream"] = True
+        if tools:
+            payload["tools"] = tools
+            if openai_tool_choice is not None:
+                payload["tool_choice"] = openai_tool_choice
+        provider_opts = {
+            k: v for k, v in self.provider_options.items() if k != "context_size"
+        }
+        payload.update(provider_opts)
+        return payload
+
+    def _message_from_choice(self, choice: dict[str, Any]) -> dict[str, Any]:
+        """Parse OpenAI-style choice into {content, tool_calls, finish_reason}."""
+        message = choice.get("message", {})
+        content = message.get("content")
+        content = content.strip() if content else None
+        tool_calls = parse_tool_calls_from_message(message)
+        finish_reason = choice.get("finish_reason") or (
+            "tool_calls" if tool_calls else "stop" if content else "error"
+        )
+        return {
+            "content": content,
+            "tool_calls": tool_calls,
+            "finish_reason": finish_reason,
+        }
+
+    @staticmethod
+    def _streamed_tool_calls_to_list(
+        tool_calls_by_index: dict[int, dict[str, Any]],
+    ) -> Optional[list[dict[str, Any]]]:
+        """Convert streamed tool_calls index map to list of {id, name, arguments}."""
+        if not tool_calls_by_index:
+            return None
+        result = []
+        for idx in sorted(tool_calls_by_index.keys()):
+            t = tool_calls_by_index[idx]
+            args_str = t.get("arguments") or "{}"
+            try:
+                arguments = json.loads(args_str)
+            except json.JSONDecodeError:
+                arguments = {}
+            result.append(
+                {
+                    "id": t.get("id", ""),
+                    "name": t.get("name", ""),
+                    "arguments": arguments,
+                }
             )
-            return []
-
-        texts = texts or []
-        images = images or []
-        if not texts and not images:
-            return []
-
-        EMBEDDING_DIM = 768
-
-        content = []
-        for text in texts:
-            content.append({"prompt_string": text})
-        for img in images:
-            # llama.cpp uses STB which does not support WebP; convert to JPEG
-            jpeg_bytes = _to_jpeg(img)
-            to_encode = jpeg_bytes if jpeg_bytes is not None else img
-            encoded = base64.b64encode(to_encode).decode("utf-8")
-            # prompt_string must contain <__media__> placeholder for image tokenization
-            content.append({
-                "prompt_string": "<__media__>\n",
-                "multimodal_data": [encoded],
-            })
-
-        try:
-            response = requests.post(
-                f"{self.provider}/embeddings",
-                json={"content": content},
-                timeout=self.timeout,
-            )
-            response.raise_for_status()
-            result = response.json()
-
-            items = result.get("data", result) if isinstance(result, dict) else result
-            if not isinstance(items, list):
-                logger.warning("llama.cpp embeddings returned unexpected format")
-                return []
-
-            embeddings = []
-            for item in items:
-                emb = item.get("embedding") if isinstance(item, dict) else None
-                if emb is None:
-                    logger.warning("llama.cpp embeddings item missing embedding field")
-                    continue
-                arr = np.array(emb, dtype=np.float32)
-                orig_dim = arr.size
-                if orig_dim != EMBEDDING_DIM:
-                    if orig_dim > EMBEDDING_DIM:
-                        arr = arr[:EMBEDDING_DIM]
-                        logger.debug(
-                            "Truncated llama.cpp embedding from %d to %d dimensions",
-                            orig_dim,
-                            EMBEDDING_DIM,
-                        )
-                    else:
-                        arr = np.pad(
-                            arr,
-                            (0, EMBEDDING_DIM - orig_dim),
-                            mode="constant",
-                            constant_values=0,
-                        )
-                        logger.debug(
-                            "Padded llama.cpp embedding from %d to %d dimensions",
-                            orig_dim,
-                            EMBEDDING_DIM,
-                        )
-                embeddings.append(arr)
-            return embeddings
-        except requests.exceptions.Timeout:
-            logger.warning("llama.cpp embeddings request timed out")
-            return []
-        except requests.exceptions.RequestException as e:
-            error_detail = str(e)
-            if hasattr(e, "response") and e.response is not None:
-                try:
-                    error_detail = f"{str(e)} - Response: {e.response.text[:500]}"
-                except Exception:
-                    pass
-            logger.warning("llama.cpp embeddings error: %s", error_detail)
-            return []
-        except Exception as e:
-            logger.warning("Unexpected error in llama.cpp embeddings: %s", str(e))
-            return []
+        return result if result else None
 
     def chat_with_tools(
         self,
@@ -237,31 +193,8 @@ class LlamaCppClient(GenAIClient):
                 "tool_calls": None,
                 "finish_reason": "error",
             }
-
         try:
-            openai_tool_choice = None
-            if tool_choice:
-                if tool_choice == "none":
-                    openai_tool_choice = "none"
-                elif tool_choice == "auto":
-                    openai_tool_choice = "auto"
-                elif tool_choice == "required":
-                    openai_tool_choice = "required"
-
-            payload = {
-                "messages": messages,
-            }
-
-            if tools:
-                payload["tools"] = tools
-                if openai_tool_choice is not None:
-                    payload["tool_choice"] = openai_tool_choice
-
-            provider_opts = {
-                k: v for k, v in self.provider_options.items() if k != "context_size"
-            }
-            payload.update(provider_opts)
-
+            payload = self._build_payload(messages, tools, tool_choice, stream=False)
             response = requests.post(
                 f"{self.provider}/v1/chat/completions",
                 json=payload,
@@ -269,60 +202,13 @@ class LlamaCppClient(GenAIClient):
             )
             response.raise_for_status()
             result = response.json()
-
             if result is None or "choices" not in result or len(result["choices"]) == 0:
                 return {
                     "content": None,
                     "tool_calls": None,
                     "finish_reason": "error",
                 }
-
-            choice = result["choices"][0]
-            message = choice.get("message", {})
-
-            content = message.get("content")
-            if content:
-                content = content.strip()
-            else:
-                content = None
-
-            tool_calls = None
-            if "tool_calls" in message and message["tool_calls"]:
-                tool_calls = []
-                for tool_call in message["tool_calls"]:
-                    try:
-                        function_data = tool_call.get("function", {})
-                        arguments_str = function_data.get("arguments", "{}")
-                        arguments = json.loads(arguments_str)
-                    except (json.JSONDecodeError, KeyError, TypeError) as e:
-                        logger.warning(
-                            f"Failed to parse tool call arguments: {e}, "
-                            f"tool: {function_data.get('name', 'unknown')}"
-                        )
-                        arguments = {}
-
-                    tool_calls.append(
-                        {
-                            "id": tool_call.get("id", ""),
-                            "name": function_data.get("name", ""),
-                            "arguments": arguments,
-                        }
-                    )
-
-            finish_reason = "error"
-            if "finish_reason" in choice and choice["finish_reason"]:
-                finish_reason = choice["finish_reason"]
-            elif tool_calls:
-                finish_reason = "tool_calls"
-            elif content:
-                finish_reason = "stop"
-
-            return {
-                "content": content,
-                "tool_calls": tool_calls,
-                "finish_reason": finish_reason,
-            }
-
+            return self._message_from_choice(result["choices"][0])
         except requests.exceptions.Timeout as e:
             logger.warning("llama.cpp request timed out: %s", str(e))
             return {
@@ -334,8 +220,7 @@ class LlamaCppClient(GenAIClient):
             error_detail = str(e)
             if hasattr(e, "response") and e.response is not None:
                 try:
-                    error_body = e.response.text
-                    error_detail = f"{str(e)} - Response: {error_body[:500]}"
+                    error_detail = f"{str(e)} - Response: {e.response.text[:500]}"
                 except Exception:
                     pass
             logger.warning("llama.cpp returned an error: %s", error_detail)
@@ -351,3 +236,111 @@ class LlamaCppClient(GenAIClient):
                 "tool_calls": None,
                 "finish_reason": "error",
             }
+
+    async def chat_with_tools_stream(
+        self,
+        messages: list[dict[str, Any]],
+        tools: Optional[list[dict[str, Any]]] = None,
+        tool_choice: Optional[str] = "auto",
+    ):
+        """Stream chat with tools via OpenAI-compatible streaming API."""
+        if self.provider is None:
+            logger.warning(
+                "llama.cpp provider has not been initialized. Check your llama.cpp configuration."
+            )
+            yield (
+                "message",
+                {
+                    "content": None,
+                    "tool_calls": None,
+                    "finish_reason": "error",
+                },
+            )
+            return
+        try:
+            payload = self._build_payload(messages, tools, tool_choice, stream=True)
+            content_parts: list[str] = []
+            tool_calls_by_index: dict[int, dict[str, Any]] = {}
+            finish_reason = "stop"
+
+            async with httpx.AsyncClient(timeout=float(self.timeout)) as client:
+                async with client.stream(
+                    "POST",
+                    f"{self.provider}/v1/chat/completions",
+                    json=payload,
+                ) as response:
+                    response.raise_for_status()
+                    async for line in response.aiter_lines():
+                        if not line.startswith("data: "):
+                            continue
+                        data_str = line[6:].strip()
+                        if data_str == "[DONE]":
+                            break
+                        try:
+                            data = json.loads(data_str)
+                        except json.JSONDecodeError:
+                            continue
+                        choices = data.get("choices") or []
+                        if not choices:
+                            continue
+                        delta = choices[0].get("delta", {})
+                        if choices[0].get("finish_reason"):
+                            finish_reason = choices[0]["finish_reason"]
+                        if delta.get("content"):
+                            content_parts.append(delta["content"])
+                            yield ("content_delta", delta["content"])
+                        for tc in delta.get("tool_calls") or []:
+                            idx = tc.get("index", 0)
+                            fn = tc.get("function") or {}
+                            if idx not in tool_calls_by_index:
+                                tool_calls_by_index[idx] = {
+                                    "id": tc.get("id", ""),
+                                    "name": tc.get("name") or fn.get("name", ""),
+                                    "arguments": "",
+                                }
+                            t = tool_calls_by_index[idx]
+                            if tc.get("id"):
+                                t["id"] = tc["id"]
+                            name = tc.get("name") or fn.get("name")
+                            if name:
+                                t["name"] = name
+                            arg = tc.get("arguments") or fn.get("arguments")
+                            if arg is not None:
+                                t["arguments"] += (
+                                    arg if isinstance(arg, str) else json.dumps(arg)
+                                )
+
+            full_content = "".join(content_parts).strip() or None
+            tool_calls_list = self._streamed_tool_calls_to_list(tool_calls_by_index)
+            if tool_calls_list:
+                finish_reason = "tool_calls"
+            yield (
+                "message",
+                {
+                    "content": full_content,
+                    "tool_calls": tool_calls_list,
+                    "finish_reason": finish_reason,
+                },
+            )
+        except httpx.HTTPStatusError as e:
+            logger.warning("llama.cpp streaming HTTP error: %s", e)
+            yield (
+                "message",
+                {
+                    "content": None,
+                    "tool_calls": None,
+                    "finish_reason": "error",
+                },
+            )
+        except Exception as e:
+            logger.warning(
+                "Unexpected error in llama.cpp chat_with_tools_stream: %s", str(e)
+            )
+            yield (
+                "message",
+                {
+                    "content": None,
+                    "tool_calls": None,
+                    "finish_reason": "error",
+                },
+            )

frigate/genai/ollama.py

@@ -1,15 +1,16 @@
 """Ollama Provider for Frigate AI."""
 
-import json
 import logging
 from typing import Any, Optional
 
 from httpx import RemoteProtocolError, TimeoutException
+from ollama import AsyncClient as OllamaAsyncClient
 from ollama import Client as ApiClient
 from ollama import ResponseError
 
 from frigate.config import GenAIProviderEnum
 from frigate.genai import GenAIClient, register_genai_provider
+from frigate.genai.utils import parse_tool_calls_from_message
 
 logger = logging.getLogger(__name__)
 
@@ -88,6 +89,73 @@ class OllamaClient(GenAIClient):
             "num_ctx", 4096
         )
 
+    def _build_request_params(
+        self,
+        messages: list[dict[str, Any]],
+        tools: Optional[list[dict[str, Any]]],
+        tool_choice: Optional[str],
+        stream: bool = False,
+    ) -> dict[str, Any]:
+        """Build request_messages and params for chat (sync or stream)."""
+        request_messages = []
+        for msg in messages:
+            msg_dict = {
+                "role": msg.get("role"),
+                "content": msg.get("content", ""),
+            }
+            if msg.get("tool_call_id"):
+                msg_dict["tool_call_id"] = msg["tool_call_id"]
+            if msg.get("name"):
+                msg_dict["name"] = msg["name"]
+            if msg.get("tool_calls"):
+                msg_dict["tool_calls"] = msg["tool_calls"]
+            request_messages.append(msg_dict)
+
+        request_params: dict[str, Any] = {
+            "model": self.genai_config.model,
+            "messages": request_messages,
+            **self.provider_options,
+        }
+        if stream:
+            request_params["stream"] = True
+        if tools:
+            request_params["tools"] = tools
+            if tool_choice:
+                request_params["tool_choice"] = (
+                    "none"
+                    if tool_choice == "none"
+                    else "required"
+                    if tool_choice == "required"
+                    else "auto"
+                )
+        return request_params
+
+    def _message_from_response(self, response: dict[str, Any]) -> dict[str, Any]:
+        """Parse Ollama chat response into {content, tool_calls, finish_reason}."""
+        if not response or "message" not in response:
+            return {
+                "content": None,
+                "tool_calls": None,
+                "finish_reason": "error",
+            }
+        message = response["message"]
+        content = message.get("content", "").strip() if message.get("content") else None
+        tool_calls = parse_tool_calls_from_message(message)
+        finish_reason = "error"
+        if response.get("done"):
+            finish_reason = (
+                "tool_calls" if tool_calls else "stop" if content else "error"
+            )
+        elif tool_calls:
+            finish_reason = "tool_calls"
+        elif content:
+            finish_reason = "stop"
+        return {
+            "content": content,
+            "tool_calls": tool_calls,
+            "finish_reason": finish_reason,
+        }
+
     def chat_with_tools(
         self,
         messages: list[dict[str, Any]],
@@ -103,93 +171,12 @@ class OllamaClient(GenAIClient):
                 "tool_calls": None,
                 "finish_reason": "error",
             }
-
         try:
-            request_messages = []
-            for msg in messages:
-                msg_dict = {
-                    "role": msg.get("role"),
-                    "content": msg.get("content", ""),
-                }
-                if msg.get("tool_call_id"):
-                    msg_dict["tool_call_id"] = msg["tool_call_id"]
-                if msg.get("name"):
-                    msg_dict["name"] = msg["name"]
-                if msg.get("tool_calls"):
-                    msg_dict["tool_calls"] = msg["tool_calls"]
-                request_messages.append(msg_dict)
-
-            request_params = {
-                "model": self.genai_config.model,
-                "messages": request_messages,
-            }
-
-            if tools:
-                request_params["tools"] = tools
-                if tool_choice:
-                    if tool_choice == "none":
-                        request_params["tool_choice"] = "none"
-                    elif tool_choice == "required":
-                        request_params["tool_choice"] = "required"
-                    elif tool_choice == "auto":
-                        request_params["tool_choice"] = "auto"
-
-            request_params.update(self.provider_options)
-
-            response = self.provider.chat(**request_params)
-
-            if not response or "message" not in response:
-                return {
-                    "content": None,
-                    "tool_calls": None,
-                    "finish_reason": "error",
-                }
-
-            message = response["message"]
-            content = (
-                message.get("content", "").strip() if message.get("content") else None
+            request_params = self._build_request_params(
+                messages, tools, tool_choice, stream=False
             )
-
-            tool_calls = None
-            if "tool_calls" in message and message["tool_calls"]:
-                tool_calls = []
-                for tool_call in message["tool_calls"]:
-                    try:
-                        function_data = tool_call.get("function", {})
-                        arguments_str = function_data.get("arguments", "{}")
-                        arguments = json.loads(arguments_str)
-                    except (json.JSONDecodeError, KeyError, TypeError) as e:
-                        logger.warning(
-                            f"Failed to parse tool call arguments: {e}, "
-                            f"tool: {function_data.get('name', 'unknown')}"
-                        )
-                        arguments = {}
-
-                    tool_calls.append(
-                        {
-                            "id": tool_call.get("id", ""),
-                            "name": function_data.get("name", ""),
-                            "arguments": arguments,
-                        }
-                    )
-
-            finish_reason = "error"
-            if "done" in response and response["done"]:
-                if tool_calls:
-                    finish_reason = "tool_calls"
-                elif content:
-                    finish_reason = "stop"
-            elif tool_calls:
-                finish_reason = "tool_calls"
-            elif content:
-                finish_reason = "stop"
-
-            return {
-                "content": content,
-                "tool_calls": tool_calls,
-                "finish_reason": finish_reason,
-            }
-
+            response = self.provider.chat(**request_params)
+            return self._message_from_response(response)
         except (TimeoutException, ResponseError, ConnectionError) as e:
             logger.warning("Ollama returned an error: %s", str(e))
             return {
@@ -204,3 +191,89 @@ class OllamaClient(GenAIClient):
                 "tool_calls": None,
                 "finish_reason": "error",
             }
+
+    async def chat_with_tools_stream(
+        self,
+        messages: list[dict[str, Any]],
+        tools: Optional[list[dict[str, Any]]] = None,
+        tool_choice: Optional[str] = "auto",
+    ):
+        """Stream chat with tools; yields content deltas then final message."""
+        if self.provider is None:
+            logger.warning(
+                "Ollama provider has not been initialized. Check your Ollama configuration."
+            )
+            yield (
+                "message",
+                {
+                    "content": None,
+                    "tool_calls": None,
+                    "finish_reason": "error",
+                },
+            )
+            return
+        try:
+            request_params = self._build_request_params(
+                messages, tools, tool_choice, stream=True
+            )
+            async_client = OllamaAsyncClient(
+                host=self.genai_config.base_url,
+                timeout=self.timeout,
+            )
+            content_parts: list[str] = []
+            final_message: dict[str, Any] | None = None
+            try:
+                stream = await async_client.chat(**request_params)
+                async for chunk in stream:
+                    if not chunk or "message" not in chunk:
+                        continue
+                    msg = chunk.get("message", {})
+                    delta = msg.get("content") or ""
+                    if delta:
+                        content_parts.append(delta)
+                        yield ("content_delta", delta)
+                    if chunk.get("done"):
+                        full_content = "".join(content_parts).strip() or None
+                        tool_calls = parse_tool_calls_from_message(msg)
+                        final_message = {
+                            "content": full_content,
+                            "tool_calls": tool_calls,
+                            "finish_reason": "tool_calls" if tool_calls else "stop",
+                        }
+                        break
+            finally:
+                await async_client.close()
+
+            if final_message is not None:
+                yield ("message", final_message)
+            else:
+                yield (
+                    "message",
+                    {
+                        "content": "".join(content_parts).strip() or None,
+                        "tool_calls": None,
+                        "finish_reason": "stop",
+                    },
+                )
+        except (TimeoutException, ResponseError, ConnectionError) as e:
+            logger.warning("Ollama streaming error: %s", str(e))
+            yield (
+                "message",
+                {
+                    "content": None,
+                    "tool_calls": None,
+                    "finish_reason": "error",
+                },
+            )
+        except Exception as e:
+            logger.warning(
+                "Unexpected error in Ollama chat_with_tools_stream: %s", str(e)
+            )
+            yield (
+                "message",
+                {
+                    "content": None,
+                    "tool_calls": None,
+                    "finish_reason": "error",
+                },
+            )

frigate/genai/utils.py

@@ -0,0 +1,70 @@
+"""Shared helpers for GenAI providers and chat (OpenAI-style messages, tool call parsing)."""
+
+import json
+import logging
+from typing import Any, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+def parse_tool_calls_from_message(
+    message: dict[str, Any],
+) -> Optional[list[dict[str, Any]]]:
+    """
+    Parse tool_calls from an OpenAI-style message dict.
+
+    Message may have "tool_calls" as a list of:
+      {"id": str, "function": {"name": str, "arguments": str}, ...}
+
+    Returns a list of {"id", "name", "arguments"} with arguments parsed as dict,
+    or None if no tool_calls. Used by Ollama and LlamaCpp (non-stream) responses.
+    """
+    raw = message.get("tool_calls")
+    if not raw or not isinstance(raw, list):
+        return None
+    result = []
+    for tool_call in raw:
+        function_data = tool_call.get("function") or {}
+        try:
+            arguments_str = function_data.get("arguments") or "{}"
+            arguments = json.loads(arguments_str)
+        except (json.JSONDecodeError, KeyError, TypeError) as e:
+            logger.warning(
+                "Failed to parse tool call arguments: %s, tool: %s",
+                e,
+                function_data.get("name", "unknown"),
+            )
+            arguments = {}
+        result.append(
+            {
+                "id": tool_call.get("id", ""),
+                "name": function_data.get("name", ""),
+                "arguments": arguments,
+            }
+        )
+    return result if result else None
+
+
+def build_assistant_message_for_conversation(
+    content: Any,
+    tool_calls_raw: Optional[List[dict[str, Any]]],
+) -> dict[str, Any]:
+    """
+    Build the assistant message dict in OpenAI format for appending to a conversation.
+
+    tool_calls_raw: list of {"id", "name", "arguments"} (arguments as dict), or None.
+    """
+    msg: dict[str, Any] = {"role": "assistant", "content": content}
+    if tool_calls_raw:
+        msg["tool_calls"] = [
+            {
+                "id": tc["id"],
+                "type": "function",
+                "function": {
+                    "name": tc["name"],
+                    "arguments": json.dumps(tc.get("arguments") or {}),
+                },
+            }
+            for tc in tool_calls_raw
+        ]
+    return msg

web/package.json

@@ -71,6 +71,8 @@
     "react-icons": "^5.5.0",
     "react-konva": "^18.2.10",
     "react-router-dom": "^6.30.3",
+    "react-markdown": "^9.0.1",
+    "remark-gfm": "^4.0.0",
     "react-swipeable": "^7.0.2",
     "react-tracked": "^2.0.1",
     "react-transition-group": "^4.4.5",

web/public/locales/en/common.json

@@ -127,6 +127,7 @@
     "cancel": "Cancel",
     "close": "Close",
     "copy": "Copy",
+    "copiedToClipboard": "Copied to clipboard",
     "back": "Back",
     "history": "History",
     "fullscreen": "Fullscreen",
@@ -245,6 +246,7 @@
     "uiPlayground": "UI Playground",
     "faceLibrary": "Face Library",
     "classification": "Classification",
+    "chat": "Chat",
     "user": {
       "title": "User",
       "account": "Account",

web/public/locales/en/views/chat.json

@@ -0,0 +1,13 @@
+{
+  "placeholder": "Ask anything...",
+  "error": "Something went wrong. Please try again.",
+  "processing": "Processing...",
+  "toolsUsed": "Used: {{tools}}",
+  "showTools": "Show tools ({{count}})",
+  "hideTools": "Hide tools",
+  "call": "Call",
+  "result": "Result",
+  "arguments": "Arguments:",
+  "response": "Response:",
+  "send": "Send"
+}

web/src/App.tsx

@@ -27,6 +27,7 @@ const Settings = lazy(() => import("@/pages/Settings"));
 const UIPlayground = lazy(() => import("@/pages/UIPlayground"));
 const FaceLibrary = lazy(() => import("@/pages/FaceLibrary"));
 const Classification = lazy(() => import("@/pages/ClassificationModel"));
+const Chat = lazy(() => import("@/pages/Chat"));
 const Logs = lazy(() => import("@/pages/Logs"));
 const AccessDenied = lazy(() => import("@/pages/AccessDenied"));
 
@@ -106,6 +107,7 @@ function DefaultAppView() {
               <Route path="/logs" element={<Logs />} />
               <Route path="/faces" element={<FaceLibrary />} />
               <Route path="/classification" element={<Classification />} />
+              <Route path="/chat" element={<Chat />} />
               <Route path="/playground" element={<UIPlayground />} />
             </Route>
             <Route path="/unauthorized" element={<AccessDenied />} />

web/src/components/chat/ChatMessage.tsx

@@ -0,0 +1,208 @@
+import { useState, useEffect, useRef } from "react";
+import ReactMarkdown from "react-markdown";
+import remarkGfm from "remark-gfm";
+import { useTranslation } from "react-i18next";
+import copy from "copy-to-clipboard";
+import { toast } from "sonner";
+import { FaCopy, FaPencilAlt } from "react-icons/fa";
+import { FaArrowUpLong } from "react-icons/fa6";
+import { Button } from "@/components/ui/button";
+import { Textarea } from "@/components/ui/textarea";
+import {
+  Tooltip,
+  TooltipContent,
+  TooltipTrigger,
+} from "@/components/ui/tooltip";
+import { cn } from "@/lib/utils";
+
+type MessageBubbleProps = {
+  role: "user" | "assistant";
+  content: string;
+  messageIndex?: number;
+  onEditSubmit?: (messageIndex: number, newContent: string) => void;
+  isComplete?: boolean;
+};
+
+export function MessageBubble({
+  role,
+  content,
+  messageIndex = 0,
+  onEditSubmit,
+  isComplete = true,
+}: MessageBubbleProps) {
+  const { t } = useTranslation(["views/chat", "common"]);
+  const isUser = role === "user";
+  const [isEditing, setIsEditing] = useState(false);
+  const [draftContent, setDraftContent] = useState(content);
+  const editInputRef = useRef<HTMLTextAreaElement>(null);
+
+  useEffect(() => {
+    setDraftContent(content);
+  }, [content]);
+
+  useEffect(() => {
+    if (isEditing) {
+      editInputRef.current?.focus();
+      editInputRef.current?.setSelectionRange(
+        editInputRef.current.value.length,
+        editInputRef.current.value.length,
+      );
+    }
+  }, [isEditing]);
+
+  const handleCopy = () => {
+    const text = content?.trim() || "";
+    if (!text) return;
+    if (copy(text)) {
+      toast.success(t("button.copiedToClipboard", { ns: "common" }));
+    }
+  };
+
+  const handleEditClick = () => {
+    setDraftContent(content);
+    setIsEditing(true);
+  };
+
+  const handleEditSubmit = () => {
+    const trimmed = draftContent.trim();
+    if (!trimmed || onEditSubmit == null) return;
+    onEditSubmit(messageIndex, trimmed);
+    setIsEditing(false);
+  };
+
+  const handleEditCancel = () => {
+    setDraftContent(content);
+    setIsEditing(false);
+  };
+
+  const handleEditKeyDown = (e: React.KeyboardEvent<HTMLTextAreaElement>) => {
+    if (e.key === "Enter" && !e.shiftKey) {
+      e.preventDefault();
+      handleEditSubmit();
+    }
+    if (e.key === "Escape") {
+      handleEditCancel();
+    }
+  };
+
+  if (isUser && isEditing) {
+    return (
+      <div className="flex w-full max-w-full flex-col gap-2 self-end">
+        <Textarea
+          ref={editInputRef}
+          value={draftContent}
+          onChange={(e) => setDraftContent(e.target.value)}
+          onKeyDown={handleEditKeyDown}
+          className="min-h-[80px] w-full resize-y rounded-lg bg-primary px-3 py-2 text-primary-foreground placeholder:text-primary-foreground/60"
+          placeholder={t("placeholder")}
+          rows={3}
+        />
+        <div className="flex items-center gap-2 self-end">
+          <Button
+            variant="ghost"
+            size="sm"
+            className="text-muted-foreground hover:text-foreground"
+            onClick={handleEditCancel}
+          >
+            {t("button.cancel", { ns: "common" })}
+          </Button>
+          <Button
+            variant="select"
+            size="icon"
+            className="size-9 rounded-full"
+            disabled={!draftContent.trim()}
+            onClick={handleEditSubmit}
+            aria-label={t("send")}
+          >
+            <FaArrowUpLong size="16" />
+          </Button>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div
+      className={cn(
+        "flex flex-col gap-1",
+        isUser ? "items-end self-end" : "items-start self-start",
+      )}
+    >
+      <div
+        className={cn(
+          "rounded-lg px-3 py-2",
+          isUser ? "bg-primary text-primary-foreground" : "bg-muted",
+        )}
+      >
+        {isUser ? (
+          content
+        ) : (
+          <ReactMarkdown
+            remarkPlugins={[remarkGfm]}
+            components={{
+              table: ({ node: _n, ...props }) => (
+                <table
+                  className="my-2 w-full border-collapse border border-border"
+                  {...props}
+                />
+              ),
+              th: ({ node: _n, ...props }) => (
+                <th
+                  className="border border-border bg-muted/50 px-2 py-1 text-left text-sm font-medium"
+                  {...props}
+                />
+              ),
+              td: ({ node: _n, ...props }) => (
+                <td
+                  className="border border-border px-2 py-1 text-sm"
+                  {...props}
+                />
+              ),
+            }}
+          >
+            {content}
+          </ReactMarkdown>
+        )}
+      </div>
+      <div className="flex items-center gap-0.5">
+        {isUser && onEditSubmit != null && (
+          <Tooltip>
+            <TooltipTrigger asChild>
+              <Button
+                variant="ghost"
+                size="icon"
+                className="size-7 text-muted-foreground hover:text-foreground"
+                onClick={handleEditClick}
+                aria-label={t("button.edit", { ns: "common" })}
+              >
+                <FaPencilAlt className="size-3" />
+              </Button>
+            </TooltipTrigger>
+            <TooltipContent>
+              {t("button.edit", { ns: "common" })}
+            </TooltipContent>
+          </Tooltip>
+        )}
+        {isComplete && (
+          <Tooltip>
+            <TooltipTrigger asChild>
+              <Button
+                variant="ghost"
+                size="icon"
+                className="size-7 text-muted-foreground hover:text-foreground"
+                onClick={handleCopy}
+                disabled={!content?.trim()}
+                aria-label={t("button.copy", { ns: "common" })}
+              >
+                <FaCopy className="size-3" />
+              </Button>
+            </TooltipTrigger>
+            <TooltipContent>
+              {t("button.copy", { ns: "common" })}
+            </TooltipContent>
+          </Tooltip>
+        )}
+      </div>
+    </div>
+  );
+}

web/src/components/chat/ToolCallBubble.tsx

@@ -0,0 +1,88 @@
+import { useState } from "react";
+import { useTranslation } from "react-i18next";
+import {
+  Collapsible,
+  CollapsibleContent,
+  CollapsibleTrigger,
+} from "@/components/ui/collapsible";
+import { Button } from "@/components/ui/button";
+import { cn } from "@/lib/utils";
+import { ChevronDown, ChevronRight } from "lucide-react";
+
+type ToolCallBubbleProps = {
+  name: string;
+  arguments?: Record<string, unknown>;
+  response?: string;
+  side: "left" | "right";
+};
+
+export function ToolCallBubble({
+  name,
+  arguments: args,
+  response,
+  side,
+}: ToolCallBubbleProps) {
+  const { t } = useTranslation(["views/chat"]);
+  const [open, setOpen] = useState(false);
+  const isLeft = side === "left";
+  const normalizedName = name
+    .replace(/_/g, " ")
+    .split(" ")
+    .map((word) => word.charAt(0).toUpperCase() + word.slice(1).toLowerCase())
+    .join(" ");
+
+  return (
+    <div
+      className={cn(
+        "rounded-lg px-3 py-2",
+        isLeft
+          ? "self-start bg-muted"
+          : "self-end bg-primary text-primary-foreground",
+      )}
+    >
+      <Collapsible open={open} onOpenChange={setOpen}>
+        <CollapsibleTrigger asChild>
+          <Button
+            variant="ghost"
+            size="sm"
+            className={cn(
+              "h-auto w-full min-w-0 justify-start gap-2 whitespace-normal p-0 text-left text-xs hover:bg-transparent",
+              !isLeft && "hover:text-primary-foreground",
+            )}
+          >
+            {open ? (
+              <ChevronDown size={12} className="shrink-0" />
+            ) : (
+              <ChevronRight size={12} className="shrink-0" />
+            )}
+            <span className="break-words font-medium">
+              {isLeft ? t("call") : t("result")} {normalizedName}
+            </span>
+          </Button>
+        </CollapsibleTrigger>
+        <CollapsibleContent>
+          <div className="mt-2 space-y-2">
+            {isLeft && args && Object.keys(args).length > 0 && (
+              <div className="text-xs">
+                <div className="font-medium text-muted-foreground">
+                  {t("arguments")}
+                </div>
+                <pre className="scrollbar-container mt-1 max-h-32 overflow-auto whitespace-pre-wrap break-words rounded bg-muted/50 p-2 text-[10px]">
+                  {JSON.stringify(args, null, 2)}
+                </pre>
+              </div>
+            )}
+            {!isLeft && response && response !== "" && (
+              <div className="text-xs">
+                <div className="font-medium opacity-80">{t("response")}</div>
+                <pre className="scrollbar-container mt-1 max-h-32 overflow-auto whitespace-pre-wrap break-words rounded bg-primary/20 p-2 text-[10px]">
+                  {response}
+                </pre>
+              </div>
+            )}
+          </div>
+        </CollapsibleContent>
+      </Collapsible>
+    </div>
+  );
+}

web/src/hooks/use-navigation.ts

@@ -6,7 +6,7 @@ import { isDesktop } from "react-device-detect";
 import { FaCompactDisc, FaVideo } from "react-icons/fa";
 import { IoSearch } from "react-icons/io5";
 import { LuConstruction } from "react-icons/lu";
-import { MdCategory, MdVideoLibrary } from "react-icons/md";
+import { MdCategory, MdChat, MdVideoLibrary } from "react-icons/md";
 import { TbFaceId } from "react-icons/tb";
 import useSWR from "swr";
 import { useIsAdmin } from "./use-is-admin";
@@ -18,6 +18,7 @@ export const ID_EXPORT = 4;
 export const ID_PLAYGROUND = 5;
 export const ID_FACE_LIBRARY = 6;
 export const ID_CLASSIFICATION = 7;
+export const ID_CHAT = 8;
 
 export default function useNavigation(
   variant: "primary" | "secondary" = "primary",
@@ -82,7 +83,15 @@ export default function useNavigation(
           url: "/classification",
           enabled: isDesktop && isAdmin,
         },
+        {
+          id: ID_CHAT,
+          variant,
+          icon: MdChat,
+          title: "menu.chat",
+          url: "/chat",
+          enabled: isDesktop && isAdmin && config?.genai?.model !== "none",
+        },
       ] as NavData[],
-    [config?.face_recognition?.enabled, variant, isAdmin],
+    [config?.face_recognition?.enabled, config?.genai?.model, variant, isAdmin],
   );
 }

web/src/lib/const.ts

@@ -1,6 +1,3 @@
-/** ONNX embedding models that require local model downloads. GenAI providers are not in this list. */
-export const JINA_EMBEDDING_MODELS = ["jinav1", "jinav2"] as const;
-
 export const supportedLanguageKeys = [
   "en",
   "es",

web/src/pages/Chat.tsx

@@ -0,0 +1,199 @@
+import { Button } from "@/components/ui/button";
+import { Input } from "@/components/ui/input";
+import { FaArrowUpLong } from "react-icons/fa6";
+import { useTranslation } from "react-i18next";
+import { useState, useCallback } from "react";
+import axios from "axios";
+import { MessageBubble } from "@/components/chat/ChatMessage";
+import { ToolCallBubble } from "@/components/chat/ToolCallBubble";
+import type { ChatMessage } from "@/types/chat";
+import { streamChatCompletion } from "@/utils/chatUtil";
+
+export default function ChatPage() {
+  const { t } = useTranslation(["views/chat"]);
+  const [input, setInput] = useState("");
+  const [messages, setMessages] = useState<ChatMessage[]>([]);
+  const [isLoading, setIsLoading] = useState(false);
+  const [error, setError] = useState<string | null>(null);
+
+  const submitConversation = useCallback(
+    async (messagesToSend: ChatMessage[]) => {
+      if (isLoading) return;
+      const last = messagesToSend[messagesToSend.length - 1];
+      if (!last || last.role !== "user" || !last.content.trim()) return;
+
+      setError(null);
+      const assistantPlaceholder: ChatMessage = {
+        role: "assistant",
+        content: "",
+        toolCalls: undefined,
+      };
+      setMessages([...messagesToSend, assistantPlaceholder]);
+      setIsLoading(true);
+
+      const apiMessages = messagesToSend.map((m) => ({
+        role: m.role,
+        content: m.content,
+      }));
+
+      const baseURL = axios.defaults.baseURL ?? "";
+      const url = `${baseURL}chat/completion`;
+      const headers: Record<string, string> = {
+        "Content-Type": "application/json",
+        ...(axios.defaults.headers.common as Record<string, string>),
+      };
+
+      await streamChatCompletion(url, headers, apiMessages, {
+        updateMessages: (updater) => setMessages(updater),
+        onError: (message) => setError(message),
+        onDone: () => setIsLoading(false),
+        defaultErrorMessage: t("error"),
+      });
+    },
+    [isLoading, t],
+  );
+
+  const sendMessage = useCallback(() => {
+    const text = input.trim();
+    if (!text || isLoading) return;
+    setInput("");
+    submitConversation([...messages, { role: "user", content: text }]);
+  }, [input, isLoading, messages, submitConversation]);
+
+  const handleEditSubmit = useCallback(
+    (messageIndex: number, newContent: string) => {
+      const newList: ChatMessage[] = [
+        ...messages.slice(0, messageIndex),
+        { role: "user", content: newContent },
+      ];
+      submitConversation(newList);
+    },
+    [messages, submitConversation],
+  );
+
+  return (
+    <div className="flex size-full justify-center p-2">
+      <div className="flex size-full flex-col xl:w-[50%] 3xl:w-[35%]">
+        <div className="scrollbar-container flex min-h-0 w-full flex-1 flex-col gap-2 overflow-y-auto">
+          {messages.map((msg, i) => {
+            const isStreamingPlaceholder =
+              i === messages.length - 1 &&
+              msg.role === "assistant" &&
+              isLoading &&
+              !msg.content?.trim() &&
+              !(msg.toolCalls && msg.toolCalls.length > 0);
+            if (isStreamingPlaceholder) {
+              return <div key={i} />;
+            }
+            return (
+              <div key={i} className="flex flex-col gap-2">
+                {msg.role === "assistant" && msg.toolCalls && (
+                  <>
+                    {msg.toolCalls.map((tc, tcIdx) => (
+                      <div key={tcIdx} className="flex flex-col gap-2">
+                        <ToolCallBubble
+                          name={tc.name}
+                          arguments={tc.arguments}
+                          side="left"
+                        />
+                        {tc.response && (
+                          <ToolCallBubble
+                            name={tc.name}
+                            response={tc.response}
+                            side="right"
+                          />
+                        )}
+                      </div>
+                    ))}
+                  </>
+                )}
+                <MessageBubble
+                  role={msg.role}
+                  content={msg.content}
+                  messageIndex={i}
+                  onEditSubmit={
+                    msg.role === "user" ? handleEditSubmit : undefined
+                  }
+                  isComplete={
+                    msg.role === "user" || !isLoading || i < messages.length - 1
+                  }
+                />
+              </div>
+            );
+          })}
+          {(() => {
+            const lastMsg = messages[messages.length - 1];
+            const showProcessing =
+              isLoading &&
+              lastMsg?.role === "assistant" &&
+              !lastMsg.content?.trim() &&
+              !(lastMsg.toolCalls && lastMsg.toolCalls.length > 0);
+            return showProcessing ? (
+              <div className="self-start rounded-lg bg-muted px-3 py-2 text-muted-foreground">
+                {t("processing")}
+              </div>
+            ) : null;
+          })()}
+          {error && (
+            <p className="self-start text-sm text-destructive" role="alert">
+              {error}
+            </p>
+          )}
+        </div>
+        <ChatEntry
+          input={input}
+          setInput={setInput}
+          sendMessage={sendMessage}
+          isLoading={isLoading}
+          placeholder={t("placeholder")}
+        />
+      </div>
+    </div>
+  );
+}
+
+type ChatEntryProps = {
+  input: string;
+  setInput: (value: string) => void;
+  sendMessage: () => void;
+  isLoading: boolean;
+  placeholder: string;
+};
+
+function ChatEntry({
+  input,
+  setInput,
+  sendMessage,
+  isLoading,
+  placeholder,
+}: ChatEntryProps) {
+  const handleKeyDown = (e: React.KeyboardEvent<HTMLInputElement>) => {
+    if (e.key === "Enter" && !e.shiftKey) {
+      e.preventDefault();
+      sendMessage();
+    }
+  };
+
+  return (
+    <div className="flex w-full flex-col items-center justify-center rounded-xl bg-secondary p-2">
+      <div className="flex w-full flex-row items-center gap-2">
+        <Input
+          className="w-full flex-1 border-transparent bg-transparent shadow-none focus-visible:ring-0 dark:bg-transparent"
+          placeholder={placeholder}
+          value={input}
+          onChange={(e) => setInput(e.target.value)}
+          onKeyDown={handleKeyDown}
+          aria-busy={isLoading}
+        />
+        <Button
+          variant="select"
+          className="size-10 shrink-0 rounded-full"
+          disabled={!input.trim() || isLoading}
+          onClick={sendMessage}
+        >
+          <FaArrowUpLong size="16" />
+        </Button>
+      </div>
+    </div>
+  );
+}

web/src/pages/Explore.tsx

@@ -23,7 +23,6 @@ import { toast } from "sonner";
 import useSWR from "swr";
 import useSWRInfinite from "swr/infinite";
 import { useDocDomain } from "@/hooks/use-doc-domain";
-import { JINA_EMBEDDING_MODELS } from "@/lib/const";
 
 const API_LIMIT = 25;
 
@@ -294,12 +293,7 @@ export default function Explore() {
   const modelVersion = config?.semantic_search.model || "jinav1";
   const modelSize = config?.semantic_search.model_size || "small";
 
-  // GenAI providers have no local models to download
-  const isGenaiEmbeddings =
-    typeof modelVersion === "string" &&
-    !(JINA_EMBEDDING_MODELS as readonly string[]).includes(modelVersion);
-
-  // Text model state (skipped for GenAI - no local models)
+  // Text model state
   const { payload: textModelState } = useModelState(
     modelVersion === "jinav1"
       ? "jinaai/jina-clip-v1-text_model_fp16.onnx"
@@ -334,10 +328,6 @@ export default function Explore() {
   );
 
   const allModelsLoaded = useMemo(() => {
-    if (isGenaiEmbeddings) {
-      return true;
-    }
-
     return (
       textModelState === "downloaded" &&
       textTokenizerState === "downloaded" &&
@@ -345,7 +335,6 @@ export default function Explore() {
       visionFeatureExtractorState === "downloaded"
     );
   }, [
-    isGenaiEmbeddings,
     textModelState,
     textTokenizerState,
     visionModelState,
@@ -369,11 +358,10 @@ export default function Explore() {
     !defaultViewLoaded ||
     (config?.semantic_search.enabled &&
       (!reindexState ||
-        (!isGenaiEmbeddings &&
-          (!textModelState ||
-            !textTokenizerState ||
-            !visionModelState ||
-            !visionFeatureExtractorState))))
+        !textModelState ||
+        !textTokenizerState ||
+        !visionModelState ||
+        !visionFeatureExtractorState))
   ) {
     return (
       <ActivityIndicator className="absolute left-1/2 top-1/2 -translate-x-1/2 -translate-y-1/2" />

web/src/types/chat.ts

@@ -0,0 +1,11 @@
+export type ToolCall = {
+  name: string;
+  arguments?: Record<string, unknown>;
+  response?: string;
+};
+
+export type ChatMessage = {
+  role: "user" | "assistant";
+  content: string;
+  toolCalls?: ToolCall[];
+};

web/src/utils/chatUtil.ts

@@ -0,0 +1,163 @@
+import type { ChatMessage, ToolCall } from "@/types/chat";
+
+export type StreamChatCallbacks = {
+  /** Update the messages array (e.g. pass to setState). */
+  updateMessages: (updater: (prev: ChatMessage[]) => ChatMessage[]) => void;
+  /** Called when the stream sends an error or fetch fails. */
+  onError: (message: string) => void;
+  /** Called when the stream finishes (success or error). */
+  onDone: () => void;
+  /** Message used when fetch throws and no server error is available. */
+  defaultErrorMessage?: string;
+};
+
+type StreamChunk =
+  | { type: "error"; error: string }
+  | { type: "tool_calls"; tool_calls: ToolCall[] }
+  | { type: "content"; delta: string };
+
+/**
+ * POST to chat/completion with stream: true, parse NDJSON stream, and invoke
+ * callbacks so the caller can update UI (e.g. React state).
+ */
+export async function streamChatCompletion(
+  url: string,
+  headers: Record<string, string>,
+  apiMessages: { role: string; content: string }[],
+  callbacks: StreamChatCallbacks,
+): Promise<void> {
+  const {
+    updateMessages,
+    onError,
+    onDone,
+    defaultErrorMessage = "Something went wrong. Please try again.",
+  } = callbacks;
+
+  try {
+    const res = await fetch(url, {
+      method: "POST",
+      headers,
+      body: JSON.stringify({ messages: apiMessages, stream: true }),
+    });
+
+    if (!res.ok) {
+      const errBody = await res.json().catch(() => ({}));
+      const message = (errBody as { error?: string }).error ?? res.statusText;
+      onError(message);
+      onDone();
+      return;
+    }
+
+    const reader = res.body?.getReader();
+    const decoder = new TextDecoder();
+    if (!reader) {
+      onError("No response body");
+      onDone();
+      return;
+    }
+
+    let buffer = "";
+    let hadStreamError = false;
+
+    const applyChunk = (data: StreamChunk) => {
+      if (data.type === "error") {
+        onError(data.error);
+        updateMessages((prev) =>
+          prev.filter((m) => !(m.role === "assistant" && m.content === "")),
+        );
+        return "break";
+      }
+      if (data.type === "tool_calls" && data.tool_calls?.length) {
+        updateMessages((prev) => {
+          const next = [...prev];
+          const lastMsg = next[next.length - 1];
+          if (lastMsg?.role === "assistant")
+            next[next.length - 1] = {
+              ...lastMsg,
+              toolCalls: data.tool_calls,
+            };
+          return next;
+        });
+        return "continue";
+      }
+      if (data.type === "content" && data.delta !== undefined) {
+        updateMessages((prev) => {
+          const next = [...prev];
+          const lastMsg = next[next.length - 1];
+          if (lastMsg?.role === "assistant")
+            next[next.length - 1] = {
+              ...lastMsg,
+              content: lastMsg.content + data.delta,
+            };
+          return next;
+        });
+        return "continue";
+      }
+      return "continue";
+    };
+
+    for (;;) {
+      const { done, value } = await reader.read();
+      if (done) break;
+      buffer += decoder.decode(value, { stream: true });
+      const lines = buffer.split("\n");
+      buffer = lines.pop() ?? "";
+      for (const line of lines) {
+        const trimmed = line.trim();
+        if (!trimmed) continue;
+        try {
+          const data = JSON.parse(trimmed) as StreamChunk & { type: string };
+          const result = applyChunk(data as StreamChunk);
+          if (result === "break") {
+            hadStreamError = true;
+            break;
+          }
+        } catch {
+          // skip malformed JSON lines
+        }
+      }
+      if (hadStreamError) break;
+    }
+
+    // Flush remaining buffer
+    if (!hadStreamError && buffer.trim()) {
+      try {
+        const data = JSON.parse(buffer.trim()) as StreamChunk & {
+          type: string;
+          delta?: string;
+        };
+        if (data.type === "content" && data.delta !== undefined) {
+          updateMessages((prev) => {
+            const next = [...prev];
+            const lastMsg = next[next.length - 1];
+            if (lastMsg?.role === "assistant")
+              next[next.length - 1] = {
+                ...lastMsg,
+                content: lastMsg.content + data.delta!,
+              };
+            return next;
+          });
+        }
+      } catch {
+        // ignore final malformed chunk
+      }
+    }
+
+    if (!hadStreamError) {
+      updateMessages((prev) => {
+        const next = [...prev];
+        const lastMsg = next[next.length - 1];
+        if (lastMsg?.role === "assistant" && lastMsg.content === "")
+          next[next.length - 1] = { ...lastMsg, content: " " };
+        return next;
+      });
+    }
+  } catch {
+    onError(defaultErrorMessage);
+    updateMessages((prev) =>
+      prev.filter((m) => !(m.role === "assistant" && m.content === "")),
+    );
+  } finally {
+    onDone();
+  }
+}

web/src/utils/i18n.ts

@@ -46,6 +46,7 @@ i18n
       "components/icons",
       "components/player",
       "views/events",
+      "views/chat",
       "views/explore",
       "views/live",
       "views/settings",