QtWS2025 release

CMakeLists.txt

-
 cmake_minimum_required(VERSION 3.21.1)
-
-add_subdirectory(aimodel)
-
 project(QtAiInferenceApi LANGUAGES CXX)
 
-list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}" "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
-
-set(CMAKE_AUTOMOC ON)
-set(CMAKE_INCLUDE_CURRENT_DIR ON)
-set(QT_QML_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/qml)
-set(QML_IMPORT_PATH ${QT_QML_OUTPUT_DIRECTORY}
-    CACHE STRING "Import paths for Qt Creator's code model"
-    FORCE
-)
-
-find_package(Qt6 6.8 REQUIRED COMPONENTS Core Gui Qml Quick Multimedia)
-qt_standard_project_setup(REQUIRES 6.8)
 
-qt_add_executable(${CMAKE_PROJECT_NAME}
-    main.cpp
-)
-
-qt_add_qml_module(${CMAKE_PROJECT_NAME}
-    URI qtaiinferenceapi
-    VERSION 1.0
-    RESOURCES
-        qtquickcontrols2.conf
-    QML_FILES
-        App.qml
-        Screen01.ui.qml
-    )
-
-target_link_libraries(${CMAKE_PROJECT_NAME}
-    PRIVATE
-        Qt6::Quick
-        Qt6::Multimedia
-        QtAiModelApi
-)
-
-
-include(GNUInstallDirs)
-install(TARGETS ${CMAKE_PROJECT_NAME}
-  BUNDLE DESTINATION .
-  LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
-  RUNTIME DESTINATION ${CMAKE_INSTALL_BINDIR}
-)
+add_subdirectory(aimodel)
+add_subdirectory(tests)

README.md

@@ -2,17 +2,22 @@
 
 This projects contains the proof-of-concept for a new Qt AI Inference API. The purpose of the API is to let you easily use different types of AI models for inference from your Qt code, either from C++ or directly from QML! The API abstracts the details of the underlying model and framework implementations, allowing you to just tell what type of input and output you would like to use, and Qt will set things up for you! You can also chain different models together for pipelines.
 
+**Disclaimer**
+This API is in proof-of-concept stage and under active development, and not yet a part of the Qt framework. Hence, Qt's compatibility promise does not apply; the API can still change in breaking ways. But, it is also a great time to impact the direction it will take! For suggestions feel free to create a ticket in the Qt project's [JIRA](https://bugreports.qt.io), please use the label "QtAiApi" so we can easily find them and collect them together.
+
 ## How it works
 
 When you declare a model in your code, Qt will infer from the given input and output type what backend it will set up for the model. The backends are implemented as QPlugins. Currently, the backends are:
 
-| Input type | Output type | Qt backend    | Description                                                                   |
-|------------|-------------|---------------|-------------------------------------------------------------------------------|
-| Text\|Image| Text        | QtOllamaModel | Uses ollama to load LLM models and communicate to them with ollama's REST API |
-| Speech     | Text        | QtAsrModel    | Uses Whisper for Automatic Speech Recognition (ASR), or speech-to-text        |
-| Image      | Json        | QtTritonModel | Uses Triton to load a model for object detection from images                  |
-| Image      | Json        | QtYoloModel   | Uses a YOLO model for object detection from images                            |
-| Text       | Speech      | QtPiperModel  | Uses Piper TTS model to convert text into speech                              |
+| Input type | Output type | Qt backend      | Description                                                                   |
+|------------|-------------|-----------------|-------------------------------------------------------------------------------|
+| Text\|Image| Text        | QtOllamaModel   | Uses ollama to load LLM models and communicate to them with ollama's REST API |
+| Speech     | Text        | QtAsrModel      | Uses Whisper for Automatic Speech Recognition (ASR), or speech-to-text        |
+| Image      | Json        | QtTritonModel   | Uses Triton to load a model for object detection from images                  |
+| Image      | Json        | QtYoloModel     | Uses a YOLO model for object detection from images                            |
+| Text       | Speech      | QtTtsModel      | Uses QtTextToSpeech (QtSpeech) to convert text into speech                    |
+| Text       | Speech      | QtPiperModel    | Uses Piper TTS model to convert text into speech                              |
+| Text       | Image       | QtDiffuserModel | Uses Diffusers to convert text into images                                    |
 
 Note, the Qt backends expect the underlying backend implementation (ollama, Whisper...) to be running, and will not take care of starting them up for you. You need to start them yourself, e.g. in the case of QtOllamaModel, loading the intended model to ollama's memory by running:
 ```
@@ -51,10 +56,10 @@ A combination of AiModelType flags to tell what type of model to instantiate. Po
 | InputImage  | 0x00008 | The model takes image as input                                           |
 | InputJson   | 0x00010 | The model takes JSON as input                                            |
 | OutputText  | 0x00100 | The model outputs text                                                   |
-| OutputAudio | 0x00200 |The model outputs speech                                                  |
-| OutputVideo | 0x00400 |The model outputs video                                                   |
-| OutputImage | 0x00800 |The model outputs image                                                   |
-| OutputJson  | 0x01000 |The model outputs JSON                                                    |
+| OutputAudio | 0x00200 | The model outputs speech                                                 |
+| OutputVideo | 0x00400 | The model outputs video                                                  |
+| OutputImage | 0x00800 | The model outputs image                                                  |
+| OutputJson  | 0x01000 | The model outputs JSON                                                   |
 
 For supported input-output combinations, see the table under "How it works" section.
 
@@ -121,7 +126,7 @@ MultiModal {
 | Read method:     | QString model()                                     |
 | Notifier signal: | void modelChanged()                                 |
 
-**QVariantList rag**
+**QVariantList documents**
 
 Retrieval-Augmented Generation data to use for the model, if it supports it. RAG supports currently only chromadb, which should be running on background.
 
@@ -134,7 +139,7 @@ import qtaimodel
         type: (MultiModal.InputText | MultiModal.OutputText)
         model: "llama3.2"
         prompt: "Which item has best armor bonus?"
-        rag: ["Cloth of Authority | Armour Class +1",
+        documents: ["Cloth of Authority | Armour Class +1",
               "Drunken Cloth |  Constitution +2 (up to 20)",
               "Icebite Robe | Resistance to Damage Types: Cold damage.",
               "Obsidian Laced Robe | Grants Resistance to Damage Types: Fire damage.",
@@ -145,11 +150,34 @@ import qtaimodel
 
 |                  |                                                     |
 |------------------|-----------------------------------------------------|
-| Write method:    | void setRag(QByteArray)                             |
-| Read method:     | QByteArray rag()                                    |
-| Notifier signal: | void ragChanged()                                   |
+| Write method:    | void setDocuments(QVariantList)                     |
+| Read method:     | QVariantList documents()                            |
+| Notifier signal: | void documentsChanged()                             |
+
+**int seed**
+
+Seed to use with model prompts. Seed reduces randomness in model answers.
+
+Example:
+```
+import qtaimodel
+
+    MultiModal {
+        id: llamaModel
+        type: (MultiModal.InputText | MultiModal.OutputText)
+        model: "gemma3"
+        prompt: "Say hello?"
+        seed: 3453654
+    }
+```
+
+|                  |                                                     |
+|------------------|-----------------------------------------------------|
+| Write method:    | void setDocuments(QByteArray)                       |
+| Read method:     | QByteArray documents()                              |
+| Notifier signal: | void documentsChanged()                             |
 
-**QVector<QAiModel*> inputs**
+**QVector<QAiModel\*> inputs**
 
 A list of models this model will use as its inputs. This allows for chaining models together to create pipelines. You can use the Optional flag with the model's type to tell whether it's an optional or mandatory input. For mandatory inputs, this model will not process any other inputs before the mandatory one has something to offer. For optional ones, other inputs will be processed regardless if that input has data available or not.
 

Screen01.ui.qml

-// Copyright (C) 2025 The Qt Company Ltd.
-// SPDX-License-Identifier: LicenseRef-Qt-Commercial OR LGPL-3.0-only
-
-// TODO: check if all is really needed here
-import QtQuick 2.15
-import QtQuick.Layouts
-import QtQuick.Controls
-import Qt.labs.platform // TODO: Is this used here?
-import QtMultimedia
-
-import qtaimodel
-
-Rectangle {
-    id: rectangle
-    anchors.fill: parent
-    color: "#ffffff"  // TODO: Use some better color?
-
-    property string llamaPrompt: "You are an assistant.\n"
-    property string imageFile: ""
-
-    ColumnLayout {
-        RowLayout {
-            id: buttonRow
-            Button {
-                text: "Record audio"
-                onClicked: {
-                    recorder.record()
-                }
-            }
-            Button {
-                text: "Stop audio recording"
-                onClicked: {
-                    recorder.stop()
-                    if (recorder.actualLocation != "") {
-                        speechToText.pushData(recorder.actualLocation)
-                    }
-                    if (imageFile != "") {
-                        imageToText.pushData(imageFile)
-                    }
-                }
-            }
-        }
-
-        RowLayout {
-            Button {
-                text: qsTr("Open image")
-                onClicked: fileDialog.open()
-            }
-            Text {
-                id: result
-                text: rectangle.imageFile
-            }
-        }
-
-        TextField {
-            text: llamaPrompt
-            implicitWidth: 300
-            onEditingFinished: llamaModel.prompt = text
-        }
-
-        TextArea {
-            placeholderText: "Enter context"
-            background: Rectangle {
-                color: "lightgreen"
-            }
-
-            implicitWidth: 300
-            implicitHeight: 200
-            onEditingFinished: llamaModel.rag = [text]
-        }
-
-        Image {
-            source: imageFile
-        }
-    }
-
-    FileDialog {
-        id: fileDialog
-        folder: StandardPaths.standardLocations(StandardPaths.PicturesLocation)[0]
-        nameFilters: ["*.*"]
-        onAccepted: {
-            imageFile = fileDialog.file
-        }
-        onRejected: {}
-    }
-
-    CaptureSession {
-        audioInput: AudioInput {}
-        recorder: MediaRecorder {
-            id: recorder
-            mediaFormat {
-                fileFormat: MediaFormat.Wave
-            }
-        }
-    }
-
-    MultiModal {
-        id: imageToText
-        type: (MultiModal.InputImage | MultiModal.OutputText)
-        model: "llava-phi3"  // TODO: replace with Janus model from DeepSeek
-        prompt: "What is in the picture?"
-        optional: true
-        buffered: true
-    }
-
-    MultiModal {
-        id: speechToText
-        type: (MultiModal.InputAudio | MultiModal.OutputText)
-        model: "turbo"
-    }
-
-    MultiModal {
-        id: llamaModel
-        type: (MultiModal.InputText | MultiModal.OutputText)
-        model: "gemma3:4b"
-        prompt: llamaPrompt
-        inputs: [ imageToText, speechToText ]
-    }
-
-    MultiModal {
-        id: text2speech
-        type: (MultiModal.InputText | MultiModal.OutputAudio)
-        inputs: [ llamaModel ]
-    }
-}

aimodel/CMakeLists.txt

@@ -19,25 +19,26 @@ add_subdirectory(plugins)
 find_package(Qt6 6.8 REQUIRED COMPONENTS Core Qml Quick Network)
 qt_standard_project_setup(REQUIRES 6.8)
 
-qt_add_library(QtAiModelPluginInterface
-    qaimodelinterface_p.h
+qt_add_library(QtAiModelPluginInterface SHARED
+    qaimodelinterface_p.h qtaiapiexports_p.h
     chromadb.h chromadb.cpp
 )
+
+target_compile_definitions(QtAiModelPluginInterface PRIVATE QTAIAPI_LIBRARY)
+
 target_link_libraries(QtAiModelPluginInterface
     PRIVATE
         Qt6::Core
         Qt6::Network
 )
 
-qt_add_qml_module(QtAiModelApi
+qt_add_qml_module(QtAiModelApi STATIC
     URI qtaimodel
     VERSION 1.0
-    SHARED
     SOURCES
         qaimodel.h qaimodel.cpp
     )
 
-qt_import_qml_plugins(QtAiModelApi)
 
 target_link_libraries(QtAiModelApi
     PRIVATE

aimodel/chromadb.cpp

@@ -25,7 +25,7 @@ void ChromaDb::sendRequest(
         if (reply.isHttpStatusSuccess()) {
             lambda(json ? json.value() : QJsonDocument(), reply.httpStatus());
         } else {
-            qDebug() << "JSON decode error:" << request.url() << "HTTP status:" << reply.httpStatus();
+            qDebug() << request.url() << "responded with error:" << reply.errorString();
             setError(true);
         }
     });
@@ -74,7 +74,7 @@ void ChromaDb::reset()
                 connect(true);
             }
         } else {
-            qDebug() << url << "deleted";
+            qDebug() << url << "responded with error:" << reply.errorString();
         }
 
     });

aimodel/chromadb.h

@@ -6,8 +6,9 @@
 
 #include <QObject>
 #include <QRestAccessManager>
+#include "qtaiapiexports_p.h"
 
-class ChromaDb : public QObject
+class QTAIAPI_EXPORT ChromaDb : public QObject
 {
     Q_OBJECT
     Q_PROPERTY(bool connected READ connected WRITE connect NOTIFY connectedChanged FINAL)

aimodel/diffuser_server/diffuser_server.py

+#!/usr/bin/python3
+
+# Copyright (C) 2025 The Qt Company Ltd.
+# SPDX-License-Identifier: LicenseRef-Qt-Commercial OR LGPL-3.0-only
+
+from http.server import BaseHTTPRequestHandler,HTTPServer
+from os import curdir, sep
+import torch
+from diffusers import AutoPipelineForText2Image, LCMScheduler
+import simplejson
+import base64
+from io import BytesIO
+from PIL import Image
+
+
+
+PORT_NUMBER = 8005
+
+#This class will handles any incoming request from
+#the browser 
+class myHandler(BaseHTTPRequestHandler):
+    model = ''
+
+
+    #Handler for the POST requests
+    def do_POST(self):
+        print("do_POST");
+        if self.path=="/send":
+            self.data_string = self.rfile.read(int(self.headers['Content-Length']))
+            print("data_string: " + self.data_string.decode())
+            json_data = simplejson.loads(self.data_string)
+            #print("json_data: " + simplejson.dumps(json_data))
+            print("MODEL: " + json_data["model"])
+            if self.model != json_data["model"]:
+                self.pipeline = AutoPipelineForText2Image.from_pretrained(
+                    json_data["model"],
+                    #'black-forest-labs/FLUX.1-dev',
+                    #'IDKiro/sdxs-512-dreamshaper',
+                    #"stabilityai/stable-diffusion-2-1-base",
+                    #'black-forest-labs/FLUX.1-schnell',
+                    torch_dtype=torch.float32,
+                    #variant="fp16",
+                    use_safetensor=True).to('cpu')
+                self.model = json_data["model"]
+
+            image = self.pipeline(json_data["prompt"],
+                height=512,
+                width=512,
+                guidance_scale=0.0,
+                target_size=(1024, 1024),
+                original_size=(4096, 4096),
+                num_inference_steps=1
+                #max_sequence_length=256
+                ).images[0]
+            
+            buffered = BytesIO()
+            image.save(buffered, format="PNG")
+            b64image = base64.b64encode(buffered.getvalue())
+            print("Sending response")
+            self.send_response(200)
+            self.end_headers()
+            json_response = {}
+            json_response["image"] = b64image
+            self.wfile.write(simplejson.dumps(json_response).encode("utf-8"))
+            return
+
+
+try:
+    #Create a web server and define the handler to manage the
+    #incoming request
+    server = HTTPServer(('', PORT_NUMBER), myHandler)
+    print('Started httpserver on port ' , PORT_NUMBER)
+
+
+    #Wait forever for incoming htto requests
+    server.serve_forever()
+
+except KeyboardInterrupt:
+    print('^C received, shutting down the web server')
+    server.socket.close()
+
+

aimodel/plugins/CMakeLists.txt

@@ -4,3 +4,4 @@ add_subdirectory(ollama)
 add_subdirectory(triton)
 add_subdirectory(yolo)
 add_subdirectory(piper-tts)
+add_subdirectory(diffuser)

aimodel/plugins/asr/CMakeLists.txt

@@ -5,7 +5,7 @@ qt_add_plugin(QtAsrModel
     qasraimodel_p.h qasraimodel_p.cpp
     )
 set_target_properties(QtAsrModel PROPERTIES
-    LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/plugins"
+    LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/plugins/aimodel"
 )
 target_link_libraries(QtAsrModel
     PRIVATE

aimodel/plugins/asr/qasraimodel_p.cpp

@@ -3,6 +3,7 @@
 
 #include "qaimodel.h"
 #include "qasraimodel_p.h"
+#include <QDir>
 #include <QJsonDocument>
 #include <QJsonObject>
 #include <QNetworkReply>
@@ -15,12 +16,12 @@ QAsrAiModel::QAsrAiModel()
 {
 }
 
-void QAsrAiModel::pushData(QVariantList data)
+void QAsrAiModel::pushData(QVariantList data, int seed)
 {
     qDebug() << "QAsrAiModel::pushData(): data:" << data;
 
     if (data.isEmpty() || data.first().toUrl().isEmpty()) {
-        emit dataReceived(data.first().toUrl());
+        emit dataReceived(data.first().toUrl().toLocalFile());
         return;
     }
 
@@ -29,7 +30,7 @@ void QAsrAiModel::pushData(QVariantList data)
     QJsonDocument doc;
     QJsonObject obj = doc.object();
     obj["model"] = m_owner->model();
-    obj["file"] = data.first().toUrl().path();
+    obj["file"] = data.first().toUrl().toLocalFile();
     //obj["stream"] = false;
     doc.setObject(obj);
     m_restApi.post(request, doc.toJson(), this, [this](QRestReply &reply) {

aimodel/plugins/asr/qasraimodel_p.h

@@ -13,7 +13,7 @@ class QAsrAiModel : public AiModelPrivateInterface
     Q_OBJECT
 public:
     QAsrAiModel();
-    void pushData(QVariantList data) override;
+    void pushData(QVariantList data, int seed) override;
 
 private:
     QNetworkAccessManager m_manager;

aimodel/plugins/diffuser/CMakeLists.txt

+find_package(Qt6 REQUIRED COMPONENTS Core Network Quick)
+
+qt_add_plugin(QtDiffuserModel
+    CLASS_NAME QAiModelPluginFactory
+    qdiffuseraimodel_p.h qdiffuseraimodel_p.cpp
+    )
+set_target_properties(QtDiffuserModel PROPERTIES
+    LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/plugins/aimodel"
+)
+target_link_libraries(QtDiffuserModel
+    PRIVATE
+        Qt6::Core
+        Qt6::Network
+        Qt6::Quick
+        QtAiModelPluginInterface)
+include_directories(../..)

aimodel/plugins/diffuser/plugin.json

+{ "name": "diffuserplugin",
+  "supportedTypes": ["InputText", "OutputImage"]
+}

aimodel/plugins/diffuser/qdiffuseraimodel_p.cpp

+// Copyright (C) 2025 The Qt Company Ltd.
+// SPDX-License-Identifier: LicenseRef-Qt-Commercial OR LGPL-3.0-only
+
+#include "qaimodel.h"
+#include "qdiffuseraimodel_p.h"
+#include <QJsonDocument>
+#include <QJsonObject>
+#include <QNetworkReply>
+#include <QRestReply>
+#include <QImage>
+
+QDiffuserAiModel::QDiffuserAiModel()
+    : AiModelPrivateInterface(),
+      m_manager(this)
+    , m_restApi(&m_manager)
+{
+}
+
+void QDiffuserAiModel::pushData(QVariantList data, int seed)
+{
+    qDebug() << "QDiffuserAiModel::pushData(): data:" << data;
+
+    if (data.isEmpty() || data.first().toString().isEmpty()) {
+        emit dataReceived(data.first().toString());
+        return;
+    }
+
+    QNetworkRequest request(QUrl("http://localhost:8005/send"));
+    request.setRawHeader("Content-Type", "application/json");
+    QJsonDocument doc;
+    QJsonObject obj = doc.object();
+    obj["model"] = m_owner->model();
+    obj["prompt"] = data.first().toString();
+    doc.setObject(obj);
+    qDebug() << doc.toJson();
+
+    m_restApi.post(request, doc.toJson(), this, [this](QRestReply &reply) {
+        if (auto json = reply.readJson()) {
+            emit dataReceived(QUrl(
+                QString("data:image/png;base64,") + json->object()["image"].toString().toUtf8()));
+        }
+    });
+}

aimodel/plugins/diffuser/qdiffuseraimodel_p.h

+// Copyright (C) 2025 The Qt Company Ltd.
+// SPDX-License-Identifier: LicenseRef-Qt-Commercial OR LGPL-3.0-only
+
+#ifndef QDIFFUSERAIMODEL_P_H
+#define QDIFFUSERAIMODEL_P_H
+
+#include <QObject>
+#include <QRestAccessManager>
+#include "qaimodelinterface_p.h"
+
+class QDiffuserAiModel : public AiModelPrivateInterface
+{
+    Q_OBJECT
+public:
+    QDiffuserAiModel();
+    void pushData(QVariantList data, int seed) override;
+
+private:
+    QNetworkAccessManager m_manager;
+    QRestAccessManager m_restApi;
+};
+
+class QDiffuserAiModelPlugin : public QAiModelPluginFactory
+{
+    Q_OBJECT
+    Q_PLUGIN_METADATA(IID "org.qt-project.Qt.QAiModelPluginFactory/1.0" FILE "plugin.json")
+    Q_INTERFACES(QAiModelPluginFactory)
+public:
+    QDiffuserAiModelPlugin() {}
+    AiModelPrivateInterface* createInterface() { return new QDiffuserAiModel(); }
+};
+
+#endif // QDIFFUSERAIMODEL_P_H

aimodel/plugins/ollama/CMakeLists.txt

@@ -6,7 +6,7 @@ qt_add_plugin(QtOllamaModel
     )
 
 set_target_properties(QtOllamaModel PROPERTIES
-    LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/plugins"
+    LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/plugins/aimodel"
 )
 target_link_libraries(QtOllamaModel
     PRIVATE

aimodel/plugins/ollama/qllmaimodel_p.cpp

@@ -6,6 +6,7 @@
 #include <QJsonArray>
 #include <QJsonDocument>
 #include <QJsonObject>
+#include <QJsonValue>
 #include <QNetworkReply>
 #include <QNetworkRequest>
 #include <QRestReply>
@@ -35,7 +36,9 @@ static inline void sendRequest(
     QNetworkRequest request(url);
     request.setRawHeader("Content-Type", "application/json");
     restApi->post(request, QJsonDocument(object).toJson(), owner, [=](QRestReply &reply) {
-        if (std::optional<QJsonDocument> json = reply.readJson()) {
+        if (!reply.isHttpStatusSuccess()) {
+            qDebug() << request.url() << "responded with error" << reply.errorString() << " and status:" << reply.httpStatus();
+        } else if (std::optional<QJsonDocument> json = reply.readJson()) {
             lambda(json.value());
         } else {
             qDebug() << "Error. No data received from" << request.url() << reply;
@@ -43,7 +46,7 @@ static inline void sendRequest(
     });
 }
 
-void QLlmAiModel::pushData(QVariantList data)
+void QLlmAiModel::pushData(QVariantList data, int seed)
 {
     QString query = m_owner->prompt();
     QJsonArray images;
@@ -52,7 +55,7 @@ void QLlmAiModel::pushData(QVariantList data)
         query.append(QString::fromLatin1(i.toByteArray()));
 
         if (i.canConvert<QUrl>()) {
-            QFile file(QUrl(i.toUrl()).path());
+            QFile file(QUrl(i.toUrl()).toLocalFile());
             if (file.open(QIODevice::ReadOnly) != 0) {
                 QByteArray ba = file.readAll();
                 QByteArray ba2 = ba.toBase64();
@@ -63,13 +66,16 @@ void QLlmAiModel::pushData(QVariantList data)
     }
     qDebug() << this << "[\"prompt\"]: " << query << "[images]" << images.count();
 
-
-
-
     auto promptResponseReceived = [=](auto json) {
         emit dataReceived(json.object()["response"].toString().toUtf8());
     };
 
+    QPair<QString, QJsonValue> options;
+    if (seed != 0) {
+        options = {"options", QJsonObject({{"seed", {seed}}})};
+    }
+
+
     if (m_chromadb.connected()) {
         connect(&m_chromadb, &ChromaDb::embeddingsFound, this, [=](auto embeddings) {
             QString documents;
@@ -83,14 +89,19 @@ void QLlmAiModel::pushData(QVariantList data)
             qDebug() << q;
             sendRequest(&m_restApi,
                         m_ollama_url_base + "generate",
-                        QJsonObject({{"model", m_owner->model()}, {"prompt", q}, {"stream", false}}),
+                        QJsonObject({{"model", m_owner->model()},
+                                     {"prompt", q},
+                                     {"stream", false},
+                                     options}),
                         this,
                         promptResponseReceived);
         }, Qt::SingleShotConnection);
 
         sendRequest(&m_restApi,
                     m_ollama_url_base + "embed",
-                    QJsonObject({{"model", m_owner->model()}, {"input", query}}),
+                    QJsonObject({{"model", m_owner->model()},
+                                 {"input", query},
+                                options}),
                     this,
                     [this](auto json) {
                         m_chromadb.fetchEmbeddings(json.object()["embeddings"].toArray().toVariantList());
@@ -101,7 +112,8 @@ void QLlmAiModel::pushData(QVariantList data)
                     QJsonObject({{"model", m_owner->model()},
                                  {"prompt", query},
                                  {"stream", false},
-                                 {"images", images}}),
+                                 {"images", images},
+                                 options}),
                     this,
                     promptResponseReceived);
     }

aimodel/plugins/ollama/qllmaimodel_p.h

@@ -14,7 +14,7 @@ class QLlmAiModel : public AiModelPrivateInterface
     Q_OBJECT
 public:
     QLlmAiModel();
-    void pushData(QVariantList data) override;
+    void pushData(QVariantList data, int seed) override;
     void setRag(QVariantList data) override;
 
 private:

aimodel/plugins/piper-tts/CMakeLists.txt

@@ -6,7 +6,7 @@ qt_add_plugin(QtPiperModel
     )
 
 set_target_properties(QtPiperModel PROPERTIES
-    LIBRARY_OUTPUT_DIRECTORY "${PROJECT_BINARY_DIR}/plugins"
+    LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/plugins/aimodel"
 )
 target_link_libraries(QtPiperModel
     PRIVATE

aimodel/plugins/piper-tts/plugin.json

-{ "name": "ttsplugin",
+{ "name": "ttspiperplugin",
   "supportedTypes": ["InputText", "OutputAudio"]
 }