gpt4all/gpt4all-chat/embllm.cpp

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#include "embllm.h"
#include "modellist.h"
#include "mysettings.h"
#include "../gpt4all-backend/llmodel.h"
#include <QCoreApplication>
#include <QDebug>
#include <QFile>
#include <QFileInfo>
#include <QGuiApplication>
#include <QIODevice>
#include <QJsonArray>
#include <QJsonDocument>
#include <QJsonObject>
#include <QList>
#include <QMutexLocker>
#include <QNetworkAccessManager>
#include <QNetworkReply>
#include <QNetworkRequest>
#include <QUrl>
#include <Qt>
#include <QtGlobal>
#include <QtLogging>
#include <exception>
#include <utility>
#include <vector>
using namespace Qt::Literals::StringLiterals;
static const QString EMBEDDING_MODEL_NAME = u"nomic-embed-text-v1.5"_s;
static const QString LOCAL_EMBEDDING_MODEL = u"nomic-embed-text-v1.5.f16.gguf"_s;
EmbeddingLLMWorker::EmbeddingLLMWorker()
: QObject(nullptr)
, m_networkManager(new QNetworkAccessManager(this))
, m_stopGenerating(false)
{
moveToThread(&m_workerThread);
connect(this, &EmbeddingLLMWorker::requestAtlasQueryEmbedding, this, &EmbeddingLLMWorker::atlasQueryEmbeddingRequested);
connect(this, &EmbeddingLLMWorker::finished, &m_workerThread, &QThread::quit, Qt::DirectConnection);
m_workerThread.setObjectName("embedding");
m_workerThread.start();
}
EmbeddingLLMWorker::~EmbeddingLLMWorker()
{
m_stopGenerating = true;
m_workerThread.quit();
m_workerThread.wait();
if (m_model) {
delete m_model;
m_model = nullptr;
}
}
void EmbeddingLLMWorker::wait()
{
m_workerThread.wait();
}
bool EmbeddingLLMWorker::loadModel()
{
constexpr int n_ctx = 2048;
m_nomicAPIKey.clear();
m_model = nullptr;
// TODO(jared): react to setting changes without restarting
if (MySettings::globalInstance()->localDocsUseRemoteEmbed()) {
m_nomicAPIKey = MySettings::globalInstance()->localDocsNomicAPIKey();
return true;
}
#ifdef Q_OS_DARWIN
static const QString embPathFmt = u"%1/../Resources/%2"_s;
#else
static const QString embPathFmt = u"%1/../resources/%2"_s;
#endif
QString filePath = embPathFmt.arg(QCoreApplication::applicationDirPath(), LOCAL_EMBEDDING_MODEL);
if (!QFileInfo::exists(filePath)) {
qWarning() << "embllm WARNING: Local embedding model not found";
return false;
}
QString requestedDevice = MySettings::globalInstance()->localDocsEmbedDevice();
std::string backend = "auto";
#ifdef Q_OS_MAC
if (requestedDevice == "Auto" || requestedDevice == "CPU")
backend = "cpu";
#else
if (requestedDevice.startsWith("CUDA: "))
backend = "cuda";
#endif
try {
m_model = LLModel::Implementation::construct(filePath.toStdString(), backend, n_ctx);
} catch (const std::exception &e) {
qWarning() << "embllm WARNING: Could not load embedding model:" << e.what();
return false;
}
bool actualDeviceIsCPU = true;
#if defined(Q_OS_MAC) && defined(__aarch64__)
if (m_model->implementation().buildVariant() == "metal")
actualDeviceIsCPU = false;
#else
if (requestedDevice != "CPU") {
const LLModel::GPUDevice *device = nullptr;
std::vector<LLModel::GPUDevice> availableDevices = m_model->availableGPUDevices(0);
if (requestedDevice != "Auto") {
// Use the selected device
for (const LLModel::GPUDevice &d : availableDevices) {
if (QString::fromStdString(d.selectionName()) == requestedDevice) {
device = &d;
break;
}
}
}
std::string unavail_reason;
if (!device) {
// GPU not available
} else if (!m_model->initializeGPUDevice(device->index, &unavail_reason)) {
qWarning().noquote() << "embllm WARNING: Did not use GPU:" << QString::fromStdString(unavail_reason);
} else {
actualDeviceIsCPU = false;
}
}
#endif
bool success = m_model->loadModel(filePath.toStdString(), n_ctx, 100);
// CPU fallback
if (!actualDeviceIsCPU && !success) {
// llama_init_from_file returned nullptr
qWarning() << "embllm WARNING: Did not use GPU: GPU loading failed (out of VRAM?)";
if (backend == "cuda") {
// For CUDA, make sure we don't use the GPU at all - ngl=0 still offloads matmuls
try {
m_model = LLModel::Implementation::construct(filePath.toStdString(), "auto", n_ctx);
} catch (const std::exception &e) {
qWarning() << "embllm WARNING: Could not load embedding model:" << e.what();
return false;
}
}
success = m_model->loadModel(filePath.toStdString(), n_ctx, 0);
}
if (!success) {
qWarning() << "embllm WARNING: Could not load embedding model";
delete m_model;
m_model = nullptr;
return false;
}
if (!m_model->supportsEmbedding()) {
qWarning() << "embllm WARNING: Model type does not support embeddings";
delete m_model;
m_model = nullptr;
return false;
}
// FIXME(jared): the user may want this to take effect without having to restart
int n_threads = MySettings::globalInstance()->threadCount();
m_model->setThreadCount(n_threads);
return true;
}
std::vector<float> EmbeddingLLMWorker::generateQueryEmbedding(const QString &text)
{
{
QMutexLocker locker(&m_mutex);
if (!hasModel() && !loadModel()) {
qWarning() << "WARNING: Could not load model for embeddings";
return {};
}
if (!isNomic()) {
std::vector<float> embedding(m_model->embeddingSize());
try {
m_model->embed({text.toStdString()}, embedding.data(), /*isRetrieval*/ true);
} catch (const std::exception &e) {
qWarning() << "WARNING: LLModel::embed failed:" << e.what();
return {};
}
return embedding;
}
}
EmbeddingLLMWorker worker;
emit worker.requestAtlasQueryEmbedding(text);
worker.wait();
return worker.lastResponse();
}
void EmbeddingLLMWorker::sendAtlasRequest(const QStringList &texts, const QString &taskType, const QVariant &userData)
{
QJsonObject root;
root.insert("model", "nomic-embed-text-v1");
root.insert("texts", QJsonArray::fromStringList(texts));
root.insert("task_type", taskType);
QJsonDocument doc(root);
QUrl nomicUrl("https://api-atlas.nomic.ai/v1/embedding/text");
const QString authorization = u"Bearer %1"_s.arg(m_nomicAPIKey).trimmed();
QNetworkRequest request(nomicUrl);
request.setHeader(QNetworkRequest::ContentTypeHeader, "application/json");
request.setRawHeader("Authorization", authorization.toUtf8());
request.setAttribute(QNetworkRequest::User, userData);
QNetworkReply *reply = m_networkManager->post(request, doc.toJson(QJsonDocument::Compact));
connect(qGuiApp, &QCoreApplication::aboutToQuit, reply, &QNetworkReply::abort);
connect(reply, &QNetworkReply::finished, this, &EmbeddingLLMWorker::handleFinished);
}
void EmbeddingLLMWorker::atlasQueryEmbeddingRequested(const QString &text)
{
{
QMutexLocker locker(&m_mutex);
if (!hasModel() && !loadModel()) {
qWarning() << "WARNING: Could not load model for embeddings";
return;
}
if (!isNomic()) {
qWarning() << "WARNING: Request to generate sync embeddings for local model invalid";
return;
}
Q_ASSERT(hasModel());
}
sendAtlasRequest({text}, "search_query");
}
void EmbeddingLLMWorker::docEmbeddingsRequested(const QVector<EmbeddingChunk> &chunks)
{
if (m_stopGenerating)
return;
bool isNomic;
{
QMutexLocker locker(&m_mutex);
if (!hasModel() && !loadModel()) {
qWarning() << "WARNING: Could not load model for embeddings";
return;
}
isNomic = this->isNomic();
}
if (!isNomic) {
QVector<EmbeddingResult> results;
results.reserve(chunks.size());
std::vector<std::string> texts;
texts.reserve(chunks.size());
for (const auto &c: chunks) {
EmbeddingResult result;
result.model = c.model;
result.folder_id = c.folder_id;
result.chunk_id = c.chunk_id;
result.embedding.resize(m_model->embeddingSize());
results << result;
texts.push_back(c.chunk.toStdString());
}
constexpr int BATCH_SIZE = 4;
std::vector<float> result;
result.resize(chunks.size() * m_model->embeddingSize());
for (int j = 0; j < chunks.size(); j += BATCH_SIZE) {
QMutexLocker locker(&m_mutex);
std::vector batchTexts(texts.begin() + j, texts.begin() + std::min(j + BATCH_SIZE, int(texts.size())));
try {
m_model->embed(batchTexts, result.data() + j * m_model->embeddingSize(), /*isRetrieval*/ false);
} catch (const std::exception &e) {
qWarning() << "WARNING: LLModel::embed failed:" << e.what();
return;
}
}
for (int i = 0; i < chunks.size(); i++)
memcpy(results[i].embedding.data(), &result[i * m_model->embeddingSize()], m_model->embeddingSize() * sizeof(float));
emit embeddingsGenerated(results);
return;
};
QStringList texts;
for (auto &c: chunks)
texts.append(c.chunk);
sendAtlasRequest(texts, "search_document", QVariant::fromValue(chunks));
}
std::vector<float> jsonArrayToVector(const QJsonArray &jsonArray)
{
std::vector<float> result;
for (const auto &innerValue: jsonArray) {
if (innerValue.isArray()) {
QJsonArray innerArray = innerValue.toArray();
result.reserve(result.size() + innerArray.size());
for (const auto &value: innerArray) {
result.push_back(static_cast<float>(value.toDouble()));
}
}
}
return result;
}
QVector<EmbeddingResult> jsonArrayToEmbeddingResults(const QVector<EmbeddingChunk>& chunks, const QJsonArray& embeddings)
{
QVector<EmbeddingResult> results;
if (chunks.size() != embeddings.size()) {
qWarning() << "WARNING: Size of json array result does not match input!";
return results;
}
for (int i = 0; i < chunks.size(); ++i) {
const EmbeddingChunk& chunk = chunks.at(i);
const QJsonArray embeddingArray = embeddings.at(i).toArray();
std::vector<float> embeddingVector;
for (const auto &value: embeddingArray)
embeddingVector.push_back(static_cast<float>(value.toDouble()));
EmbeddingResult result;
result.model = chunk.model;
result.folder_id = chunk.folder_id;
result.chunk_id = chunk.chunk_id;
result.embedding = std::move(embeddingVector);
results.push_back(std::move(result));
}
return results;
}
void EmbeddingLLMWorker::handleFinished()
{
QNetworkReply *reply = qobject_cast<QNetworkReply *>(sender());
if (!reply)
return;
QVariant retrievedData = reply->request().attribute(QNetworkRequest::User);
QVector<EmbeddingChunk> chunks;
if (retrievedData.isValid() && retrievedData.canConvert<QVector<EmbeddingChunk>>())
chunks = retrievedData.value<QVector<EmbeddingChunk>>();
QVariant response = reply->attribute(QNetworkRequest::HttpStatusCodeAttribute);
Q_ASSERT(response.isValid());
bool ok;
int code = response.toInt(&ok);
if (!ok || code != 200) {
QString errorDetails;
QString replyErrorString = reply->errorString().trimmed();
QByteArray replyContent = reply->readAll().trimmed();
errorDetails = u"ERROR: Nomic Atlas responded with error code \"%1\""_s.arg(code);
if (!replyErrorString.isEmpty())
errorDetails += u". Error Details: \"%1\""_s.arg(replyErrorString);
if (!replyContent.isEmpty())
errorDetails += u". Response Content: \"%1\""_s.arg(QString::fromUtf8(replyContent));
qWarning() << errorDetails;
emit errorGenerated(chunks, errorDetails);
return;
}
QByteArray jsonData = reply->readAll();
QJsonParseError err;
QJsonDocument document = QJsonDocument::fromJson(jsonData, &err);
if (err.error != QJsonParseError::NoError) {
qWarning() << "ERROR: Couldn't parse Nomic Atlas response:" << jsonData << err.errorString();
return;
}
const QJsonObject root = document.object();
const QJsonArray embeddings = root.value("embeddings").toArray();
if (!chunks.isEmpty()) {
emit embeddingsGenerated(jsonArrayToEmbeddingResults(chunks, embeddings));
} else {
m_lastResponse = jsonArrayToVector(embeddings);
emit finished();
}
reply->deleteLater();
}
EmbeddingLLM::EmbeddingLLM()
: QObject(nullptr)
, m_embeddingWorker(new EmbeddingLLMWorker)
{
connect(this, &EmbeddingLLM::requestDocEmbeddings, m_embeddingWorker,
&EmbeddingLLMWorker::docEmbeddingsRequested, Qt::QueuedConnection);
connect(m_embeddingWorker, &EmbeddingLLMWorker::embeddingsGenerated, this,
&EmbeddingLLM::embeddingsGenerated, Qt::QueuedConnection);
connect(m_embeddingWorker, &EmbeddingLLMWorker::errorGenerated, this,
&EmbeddingLLM::errorGenerated, Qt::QueuedConnection);
}
EmbeddingLLM::~EmbeddingLLM()
{
delete m_embeddingWorker;
m_embeddingWorker = nullptr;
}
QString EmbeddingLLM::model()
{
return EMBEDDING_MODEL_NAME;
}
// TODO(jared): embed using all necessary embedding models given collection
std::vector<float> EmbeddingLLM::generateQueryEmbedding(const QString &text)
{
return m_embeddingWorker->generateQueryEmbedding(text);
}
void EmbeddingLLM::generateDocEmbeddingsAsync(const QVector<EmbeddingChunk> &chunks)
{
emit requestDocEmbeddings(chunks);
}