llama.cpp/examples/server/server.cpp

#include "common.h"
#include "llama.h"
#include "grammar-parser.h"

#include "../llava/clip.h"

#include "stb_image.h"

#ifndef NDEBUG
// crash the server in debug mode, otherwise send an http 500 error
#define CPPHTTPLIB_NO_EXCEPTIONS 1
#endif
// increase max payload length to allow use of larger context size
#define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
#include "httplib.h"
#include "json.hpp"

// auto generated files (update with ./deps.sh)
#include "index.html.hpp"
#include "index.js.hpp"
#include "completion.js.hpp"
#include "json-schema-to-grammar.mjs.hpp"

#include <cstddef>
#include <thread>
#include <mutex>
#include <chrono>
#include <condition_variable>
#include <atomic>

#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
#endif

#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"

using json = nlohmann::json;

struct server_params
{
    std::string hostname = "127.0.0.1";
    std::vector<std::string> api_keys;
    std::string public_path = "examples/server/public";
    int32_t port = 8080;
    int32_t read_timeout = 600;
    int32_t write_timeout = 600;
};

static bool server_verbose = false;

#if SERVER_VERBOSE != 1
#define LOG_VERBOSE(MSG, ...)
#else
#define LOG_VERBOSE(MSG, ...)                                            \
    do                                                                   \
    {                                                                    \
        if (server_verbose)                                              \
        {                                                                \
            server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
        }                                                                \
    } while (0)
#endif

#define LOG_ERROR(  MSG, ...) server_log("ERROR",   __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO(   MSG, ...) server_log("INFO",    __func__, __LINE__, MSG, __VA_ARGS__)

json oaicompat_completion_params_parse(const json &body);
std::string format_chatml(std::vector<json> messages);


//
// base64 utils (TODO: move to common in the future)
//

static const std::string base64_chars =
             "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
             "abcdefghijklmnopqrstuvwxyz"
             "0123456789+/";

static inline bool is_base64(uint8_t c)
{
    return (isalnum(c) || (c == '+') || (c == '/'));
}

static std::vector<uint8_t> base64_decode(const std::string & encoded_string)
{
    int i = 0;
    int j = 0;
    int in_ = 0;

    int in_len = encoded_string.size();

    uint8_t char_array_4[4];
    uint8_t char_array_3[3];

    std::vector<uint8_t> ret;

    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
    {
        char_array_4[i++] = encoded_string[in_]; in_++;
        if (i == 4)
        {
            for (i = 0; i <4; i++)
            {
                char_array_4[i] = base64_chars.find(char_array_4[i]);
            }

            char_array_3[0] = ((char_array_4[0]      ) << 2) + ((char_array_4[1] & 0x30) >> 4);
            char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
            char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];

            for (i = 0; (i < 3); i++)
            {
                ret.push_back(char_array_3[i]);
            }
            i = 0;
        }
    }

    if (i)
    {
        for (j = i; j <4; j++)
        {
            char_array_4[j] = 0;
        }

        for (j = 0; j <4; j++)
        {
            char_array_4[j] = base64_chars.find(char_array_4[j]);
        }

        char_array_3[0] = ((char_array_4[0]      ) << 2) + ((char_array_4[1] & 0x30) >> 4);
        char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
        char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];

        for (j = 0; (j < i - 1); j++)
        {
            ret.push_back(char_array_3[j]);
        }
    }

    return ret;
}

//
// parallel
//

enum server_state {
    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
    SERVER_STATE_READY,          // Server is ready and model is loaded
    SERVER_STATE_ERROR           // An error occurred, load_model failed
};

enum task_type {
    TASK_TYPE_COMPLETION,
    TASK_TYPE_CANCEL,
};

struct task_server {
    int id;
    int target_id;
    task_type type;
    json data;
    bool infill_mode = false;
    bool embedding_mode = false;
    int multitask_id = -1;
};

struct task_result {
    int id;
    int multitask_id = -1;
    bool stop;
    bool error;
    json result_json;
};

struct task_multi {
    int id;
    std::set<int> subtasks_remaining{};
    std::vector<task_result> results{};
};

// TODO: can become bool if we can't find use of more states
enum slot_state
{
    IDLE,
    PROCESSING,
};

enum slot_command
{
    NONE,
    LOAD_PROMPT,
    RELEASE,
};

struct slot_params
{
    bool stream       = true;
    bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt

    uint32_t seed      = -1; // RNG seed
    int32_t  n_keep    =  0; // number of tokens to keep from initial prompt
    int32_t  n_predict = -1; // new tokens to predict

    std::vector<std::string> antiprompt;

    json input_prefix;
    json input_suffix;
};

struct slot_image
{
    int32_t id;

    bool request_encode_image = false;
    float * image_embedding = nullptr;
    int32_t image_tokens = 0;

    clip_image_u8 * img_data;

    std::string prefix_prompt; // before of this image
};

// completion token output with probabilities
struct completion_token_output
{
    struct token_prob
    {
        llama_token tok;
        float prob;
    };

    std::vector<token_prob> probs;
    llama_token tok;
    std::string text_to_send;
};

static size_t common_part(const std::vector<llama_token> &a, const std::vector<llama_token> &b)
{
    size_t i;
    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++)
    {
    }
    return i;
}

enum stop_type
{
    STOP_FULL,
    STOP_PARTIAL,
};

static bool ends_with(const std::string &str, const std::string &suffix)
{
    return str.size() >= suffix.size() &&
           0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
}

static size_t find_partial_stop_string(const std::string &stop,
                                       const std::string &text)
{
    if (!text.empty() && !stop.empty())
    {
        const char text_last_char = text.back();
        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--)
        {
            if (stop[char_index] == text_last_char)
            {
                const std::string current_partial = stop.substr(0, char_index + 1);
                if (ends_with(text, current_partial))
                {
                    return text.size() - char_index - 1;
                }
            }
        }
    }
    return std::string::npos;
}

// TODO: reuse llama_detokenize
template <class Iter>
static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
{
    std::string ret;
    for (; begin != end; ++begin)
    {
        ret += llama_token_to_piece(ctx, *begin);
    }
    return ret;
}

static void server_log(const char *level, const char *function, int line,
                       const char *message, const nlohmann::ordered_json &extra)
{
    nlohmann::ordered_json log
    {
        {"timestamp", time(nullptr)},
        {"level",     level},
        {"function",  function},
        {"line",      line},
        {"message",   message},
    };

    if (!extra.empty())
    {
        log.merge_patch(extra);
    }

    const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
    printf("%.*s\n", (int)str.size(), str.data());
    fflush(stdout);
}

// format incomplete utf-8 multibyte character for output
static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
{
    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
    // if the size is 1 and first bit is 1, meaning it's a partial character
    //   (size > 1 meaning it's already a known token)
    if (out.size() == 1 && (out[0] & 0x80) == 0x80)
    {
        std::stringstream ss;
        ss << std::hex << (out[0] & 0xff);
        std::string res(ss.str());
        out = "byte: \\x" + res;
    }
    return out;
}

// convert a vector of completion_token_output to json
static json probs_vector_to_json(const llama_context *ctx, const std::vector<completion_token_output> &probs)
{
    json out = json::array();
    for (const auto &prob : probs)
    {
        json probs_for_token = json::array();
        for (const auto &p : prob.probs)
        {
            std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
            probs_for_token.push_back(json
            {
                {"tok_str", tok_str},
                {"prob",    p.prob},
            });
        }
        std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
        out.push_back(json{
            {"content", tok_str},
            {"probs",   probs_for_token},
        });
    }
    return out;
}

template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value)
{
    // Fallback null to default value
    return body.contains(key) && !body.at(key).is_null()
        ? body.value(key, default_value)
        : default_value;
}

struct llama_client_slot
{
    int id;
    int task_id = -1;

    struct slot_params params;

    slot_state state = IDLE;
    slot_command command = NONE;

    // used to determine the slot that has been used the longest
    int64_t t_last_used = -1;

    // generation props
    int32_t n_ctx       = 0;  // context size per slot
    int32_t n_past      = 0;
    int32_t n_decoded   = 0;
    int32_t n_remaining = -1;
    int32_t i_batch     = -1;

    int32_t num_prompt_tokens           = 0;
    int32_t num_prompt_tokens_processed = 0;

    json prompt;
    std::string generated_text;
    llama_token sampled;
    std::vector<llama_token> cache_tokens;
    std::vector<completion_token_output> generated_token_probs;

    bool infill = false;
    bool embedding = false;
    bool has_next_token = true;
    bool truncated = false;
    bool stopped_eos = false;
    bool stopped_word = false;
    bool stopped_limit = false;

    bool oaicompat = false;
    std::string oaicompat_model;

    std::string stopping_word;

    // sampling
    struct llama_sampling_params sparams;
    llama_sampling_context *ctx_sampling = nullptr;

    // multimodal
    std::vector<slot_image> images;

    // stats
    size_t sent_count = 0;
    size_t sent_token_probs_index = 0;

    int64_t t_start_process_prompt;
    int64_t t_start_genereration;

    double t_prompt_processing; // ms
    double t_token_generation; // ms

    // multitasks
    int multitask_id = -1;

    void reset() {
        num_prompt_tokens      = 0;
        generated_text         = "";
        truncated              = false;
        stopped_eos            = false;
        stopped_word           = false;
        stopped_limit          = false;
        stopping_word          = "";
        n_past                 = 0;
        sent_count             = 0;
        sent_token_probs_index = 0;
        infill                 = false;

        generated_token_probs.clear();

        for (slot_image & img : images)
        {
            free(img.image_embedding);
            if (img.img_data) {
                clip_image_u8_free(img.img_data);
            }
            img.prefix_prompt = "";
        }

        images.clear();
    }

    bool has_budget(gpt_params &global_params) {
        if (params.n_predict == -1 && global_params.n_predict == -1)
        {
            return true; // limitless
        }

        n_remaining = -1;

        if (params.n_predict != -1)
        {
            n_remaining = params.n_predict - n_decoded;
        }
        else if (global_params.n_predict != -1)
        {
            n_remaining = global_params.n_predict - n_decoded;
        }

        return n_remaining > 0; // no budget
    }

    bool available() const {
        return state == IDLE && command == NONE;
    }

    bool is_processing() const {
        return (state == IDLE && command == LOAD_PROMPT) || state == PROCESSING;
    }

    void add_token_string(const completion_token_output &token) {
        if (command == RELEASE)
        {
            return;
        }
        cache_tokens.push_back(token.tok);
        generated_token_probs.push_back(token);
    }

    void release() {
        if (state == IDLE || state == PROCESSING)
        {
            t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3;
            command = RELEASE;
        }
    }

    json get_formated_timings() {
        return json
        {
            {"prompt_n",               num_prompt_tokens_processed},
            {"prompt_ms",              t_prompt_processing},
            {"prompt_per_token_ms",    t_prompt_processing / num_prompt_tokens_processed},
            {"prompt_per_second",      1e3 / t_prompt_processing * num_prompt_tokens_processed},

            {"predicted_n",            n_decoded},
            {"predicted_ms",           t_token_generation},
            {"predicted_per_token_ms", t_token_generation / n_decoded},
            {"predicted_per_second",   1e3 / t_token_generation * n_decoded},
        };
    }

    void print_timings() const {
        LOG_TEE("\n");
        LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
        LOG_TEE("%s:        eval time = %10.2f ms / %5d runs   (%8.2f ms per token, %8.2f tokens per second)\n",
            __func__, t_token_generation, n_decoded,t_token_generation / n_decoded, 1e3 / t_token_generation * n_decoded);
        LOG_TEE("%s:       total time = %10.2f ms\n", __func__, t_prompt_processing + t_token_generation);
    }
};

struct llama_server_context
{
    llama_model *model = nullptr;
    llama_context *ctx = nullptr;

    clip_ctx *clp_ctx = nullptr;

    gpt_params params;

    llama_batch batch;

    bool multimodal         = false;
    bool clean_kv_cache     = true;
    bool all_slots_are_idle = false;
    bool add_bos_token      = true;

    int32_t id_gen;
    int32_t n_ctx;  // total context for all clients / slots

    // system prompt
    bool system_need_update = false;

    std::string              system_prompt;
    std::vector<llama_token> system_tokens;

    std::string name_user;      // this should be the antiprompt
    std::string name_assistant;

    // slots / clients
    std::vector<llama_client_slot> slots;

    std::vector<task_server> queue_tasks;
    std::vector<task_result> queue_results;
    std::vector<task_multi>  queue_multitasks;
    std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks
    std::condition_variable condition_tasks;
    std::mutex mutex_results;
    std::condition_variable condition_results;

    ~llama_server_context()
    {
        if (ctx)
        {
            llama_free(ctx);
            ctx = nullptr;
        }
        if (model)
        {
            llama_free_model(model);
            model = nullptr;
        }
    }

    bool load_model(const gpt_params &params_)
    {
        params = params_;
        if (!params.mmproj.empty()) {
            multimodal = true;
            LOG_TEE("Multi Modal Mode Enabled");
            clp_ctx = clip_model_load(params.mmproj.c_str(), /*verbosity=*/ 1);
            if(clp_ctx == nullptr) {
                LOG_ERROR("unable to load clip model", {{"model", params.mmproj}});
                return false;
            }

            if (params.n_ctx < 2048) { // request larger context for the image embedding
                params.n_ctx = 2048;
            }
        }

        std::tie(model, ctx) = llama_init_from_gpt_params(params);
        if (model == nullptr)
        {
            LOG_ERROR("unable to load model", {{"model", params.model}});
            return false;
        }

        if (multimodal) {
            const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
            const int n_embd_llm  = llama_n_embd(model);
            if (n_embd_clip != n_embd_llm) {
                LOG_TEE("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
                llama_free(ctx);
                llama_free_model(model);
                return false;
            }
        }

        n_ctx = llama_n_ctx(ctx);

        add_bos_token = llama_should_add_bos_token(model);

        return true;
    }

    void initialize() {
        id_gen = 0;

        // create slots
        all_slots_are_idle = true;

        const int32_t n_ctx_slot = n_ctx / params.n_parallel;

        LOG_TEE("Available slots:\n");
        for (int i = 0; i < params.n_parallel; i++)
        {
            llama_client_slot slot;

            slot.id = i;
            slot.n_ctx = n_ctx_slot;
            slot.reset();

            LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot);
            slots.push_back(slot);
        }

        batch = llama_batch_init(n_ctx, 0, params.n_parallel);

        // empty system prompt
        system_prompt = "";
        system_tokens.clear();
    }

    std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
    {
        // TODO: currently, we tokenize using special tokens by default
        //       this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
        //       but it's better compared to completely ignoring ChatML and other chat templates
        const bool TMP_FORCE_SPECIAL = true;

        // If `add_bos` is true, we only add BOS, when json_prompt is a string,
        // or the first element of the json_prompt array is a string.
        std::vector<llama_token> prompt_tokens;

        if (json_prompt.is_array())
        {
            bool first = true;
            for (const auto& p : json_prompt)
            {
                if (p.is_string())
                {
                    auto s = p.template get<std::string>();
                    std::vector<llama_token> p;
                    if (first)
                    {
                        p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
                        first = false;
                    }
                    else
                    {
                        p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
                    }
                    prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
                }
                else
                {
                    if (first)
                    {
                        first = false;
                    }
                    prompt_tokens.push_back(p.template get<llama_token>());
                }
            }
        }
        else
        {
            auto s = json_prompt.template get<std::string>();
            prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
        }

        return prompt_tokens;
    }

    llama_client_slot* get_slot(int id) {
        int64_t t_last = ggml_time_us();
        llama_client_slot *last_used = nullptr;

        for (llama_client_slot & slot : slots)
        {
            if (slot.id == id && slot.available())
            {
                return &slot;
            }

            if (slot.available() && slot.t_last_used < t_last)
            {
                last_used = &slot;
                t_last = slot.t_last_used;
            }
        }

        return last_used;
    }

    bool launch_slot_with_data(llama_client_slot* &slot, json data) {
        slot_params default_params;
        llama_sampling_params default_sparams;

        if (data.count("__oaicompat") != 0) {
            slot->oaicompat = true;
            slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
        } else {
            slot->oaicompat = false;
            slot->oaicompat_model = "";
        }

        slot->params.stream           = json_value(data, "stream",            false);
        slot->params.cache_prompt     = json_value(data, "cache_prompt",      false);
        slot->params.n_predict        = json_value(data, "n_predict",         default_params.n_predict);
        slot->sparams.top_k           = json_value(data, "top_k",             default_sparams.top_k);
        slot->sparams.top_p           = json_value(data, "top_p",             default_sparams.top_p);
        slot->sparams.min_p           = json_value(data, "min_p",             default_sparams.min_p);
        slot->sparams.tfs_z           = json_value(data, "tfs_z",             default_sparams.tfs_z);
        slot->sparams.typical_p       = json_value(data, "typical_p",         default_sparams.typical_p);
        slot->sparams.temp            = json_value(data, "temperature",       default_sparams.temp);
        slot->sparams.penalty_last_n  = json_value(data, "repeat_last_n",     default_sparams.penalty_last_n);
        slot->sparams.penalty_repeat  = json_value(data, "repeat_penalty",    default_sparams.penalty_repeat);
        slot->sparams.penalty_freq    = json_value(data, "frequency_penalty", default_sparams.penalty_freq);
        slot->sparams.penalty_present = json_value(data, "presence_penalty",  default_sparams.penalty_present);
        slot->sparams.mirostat        = json_value(data, "mirostat",          default_sparams.mirostat);
        slot->sparams.mirostat_tau    = json_value(data, "mirostat_tau",      default_sparams.mirostat_tau);
        slot->sparams.mirostat_eta    = json_value(data, "mirostat_eta",      default_sparams.mirostat_eta);
        slot->sparams.penalize_nl     = json_value(data, "penalize_nl",       default_sparams.penalize_nl);
        slot->params.n_keep           = json_value(data, "n_keep",            slot->params.n_keep);
        slot->params.seed             = json_value(data, "seed",              default_params.seed);
        slot->sparams.grammar         = json_value(data, "grammar",           default_sparams.grammar);
        slot->sparams.n_probs         = json_value(data, "n_probs",           default_sparams.n_probs);

        // infill
        if (data.count("input_prefix") != 0)
        {
            slot->params.input_prefix = data["input_prefix"];
        }
        else
        {
            slot->params.input_prefix = "";
        }

        if (data.count("input_suffix") != 0)
        {
            slot->params.input_suffix = data["input_suffix"];
        }
        else
        {
            slot->params.input_suffix = "";
        }

        if (data.count("prompt") != 0)
        {
            slot->prompt = data["prompt"];
        }
        else
        {
            slot->prompt = "";
        }

        slot->sparams.penalty_prompt_tokens.clear();
        slot->sparams.use_penalty_prompt_tokens = false;
        const auto &penalty_prompt = data.find("penalty_prompt");
        if (penalty_prompt != data.end())
        {
            if (penalty_prompt->is_string())
            {
                const auto penalty_prompt_string = penalty_prompt->get<std::string>();
                auto penalty_tokens = llama_tokenize(model, penalty_prompt_string, false);
                slot->sparams.penalty_prompt_tokens.swap(penalty_tokens);
                if (slot->params.n_predict > 0)
                {
                    slot->sparams.penalty_prompt_tokens.reserve(slot->sparams.penalty_prompt_tokens.size() + slot->params.n_predict);
                }
                slot->sparams.use_penalty_prompt_tokens = true;
            }
            else if (penalty_prompt->is_array())
            {
                const auto n_tokens = penalty_prompt->size();
                slot->sparams.penalty_prompt_tokens.reserve(n_tokens + std::max(0, slot->params.n_predict));
                const int n_vocab = llama_n_vocab(model);
                for (const auto &penalty_token : *penalty_prompt)
                {
                    if (penalty_token.is_number_integer())
                    {
                        const auto tok = penalty_token.get<llama_token>();
                        if (tok >= 0 && tok < n_vocab)
                        {
                            slot->sparams.penalty_prompt_tokens.push_back(tok);
                        }
                    }
                }
                slot->sparams.use_penalty_prompt_tokens = true;
            }
        }

        slot->sparams.logit_bias.clear();

        if (json_value(data, "ignore_eos", false))
        {
            slot->sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
        }

        const auto &logit_bias = data.find("logit_bias");
        if (logit_bias != data.end() && logit_bias->is_array())
        {
            const int n_vocab = llama_n_vocab(model);
            for (const auto &el : *logit_bias)
            {
                if (el.is_array() && el.size() == 2 && el[0].is_number_integer())
                {
                    llama_token tok = el[0].get<llama_token>();
                    if (tok >= 0 && tok < n_vocab)
                    {
                        if (el[1].is_number())
                        {
                            slot->sparams.logit_bias[tok] = el[1].get<float>();
                        }
                        else if (el[1].is_boolean() && !el[1].get<bool>())
                        {
                            slot->sparams.logit_bias[tok] = -INFINITY;
                        }
                    }
                }
            }
        }

        slot->params.antiprompt.clear();

        const auto &stop = data.find("stop");
        if (stop != data.end() && stop->is_array())
        {
            for (const auto &word : *stop)
            {
                if (!word.empty())
                {
                    slot->params.antiprompt.push_back(word);
                }
            }
        }

        if (multimodal)
        {
            const auto &images_data = data.find("image_data");
            if (images_data != data.end() && images_data->is_array())
            {
                for (const auto &img : *images_data)
                {
                    const std::vector<uint8_t> image_buffer = base64_decode(img["data"].get<std::string>());

                    slot_image img_sl;
                    img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
                    img_sl.img_data = clip_image_u8_init();
                    if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
                    {
                        LOG_TEE("slot %i - failed to load image [id: %i]\n", slot->id, img_sl.id);
                        return false;
                    }
                    LOG_TEE("slot %i - loaded image\n", slot->id);
                    img_sl.request_encode_image = true;
                    slot->images.push_back(img_sl);
                }
                // process prompt
                // example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
                if (slot->images.size() > 0 && !slot->prompt.is_array())
                {
                    std::string prompt = slot->prompt.get<std::string>();
                    size_t pos = 0, begin_prefix = 0;
                    std::string pattern = "[img-";
                    while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
                        size_t end_prefix = pos;
                        pos += pattern.length();
                        size_t end_pos = prompt.find("]", pos);
                        if (end_pos != std::string::npos)
                        {
                            std::string image_id = prompt.substr(pos, end_pos - pos);
                            try
                            {
                                int img_id = std::stoi(image_id);
                                bool found = false;
                                for (slot_image &img : slot->images)
                                {
                                    if (img.id == img_id) {
                                        found = true;
                                        img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
                                        begin_prefix = end_pos + 1;
                                        break;
                                    }
                                }
                                if (!found) {
                                    LOG_TEE("ERROR: Image with id: %i, not found.\n", img_id);
                                    slot->images.clear();
                                    return false;
                                }
                            } catch (const std::invalid_argument& e) {
                                LOG_TEE("Invalid image number id in prompt\n");
                                slot->images.clear();
                                return false;
                            }
                        }
                    }
                    slot->prompt = "";
                    slot->params.input_suffix = prompt.substr(begin_prefix);
                    slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
                }
            }
        }

        if (slot->ctx_sampling != nullptr)
        {
            llama_sampling_free(slot->ctx_sampling);
        }
        slot->ctx_sampling = llama_sampling_init(slot->sparams);
        llama_set_rng_seed(ctx, slot->params.seed);
        slot->command = LOAD_PROMPT;

        all_slots_are_idle = false;

        LOG_TEE("slot %i is processing [task id: %i]\n", slot->id, slot->task_id);

        return true;
    }

    void kv_cache_clear() {
        // clear the entire KV cache
        llama_kv_cache_clear(ctx);
        clean_kv_cache = false;
    }

    void update_system_prompt() {
        system_tokens = ::llama_tokenize(ctx, system_prompt, add_bos_token);

        llama_batch_clear(batch);

        kv_cache_clear();

        for (int i = 0; i < (int) system_tokens.size(); ++i)
        {
            llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
        }

        if (llama_decode(ctx, batch) != 0)
        {
            LOG_TEE("%s: llama_decode() failed\n", __func__);
            return;
        }

        // assign the system KV cache to all parallel sequences
        for (int32_t i = 1; i < params.n_parallel; ++i)
        {
            llama_kv_cache_seq_cp(ctx, 0, i, 0, system_tokens.size());
        }

        LOG_TEE("system prompt updated\n");
        system_need_update = false;
    }

    void notify_system_prompt_changed() {
        // release all slots
        for (llama_client_slot &slot : slots)
        {
            slot.release();
        }

        system_need_update = true;
    }

    void process_system_prompt_data(const json &sys_props) {
        system_prompt  = sys_props.value("prompt", "");
        name_user      = sys_props.value("anti_prompt", "");
        name_assistant = sys_props.value("assistant_name", "");

        if (slots.size() > 0)
        {
            notify_system_prompt_changed();
        }
    }

    static size_t find_stopping_strings(const std::string &text, const size_t last_token_size,
                                        const stop_type type, llama_client_slot &slot)
    {
        size_t stop_pos = std::string::npos;

        for (const std::string &word : slot.params.antiprompt)
        {
            size_t pos;
            if (type == STOP_FULL)
            {
                const size_t tmp = word.size() + last_token_size;
                const size_t from_pos = text.size() > tmp ? text.size() - tmp : 0;
                pos = text.find(word, from_pos);
            }
            else
            {
                pos = find_partial_stop_string(word, text);
            }
            if (pos != std::string::npos &&
                (stop_pos == std::string::npos || pos < stop_pos))
            {
                if (type == STOP_FULL)
                {
                    slot.stopped_word = true;
                    slot.stopping_word = word;
                    slot.has_next_token = false;
                }
                stop_pos = pos;
            }
        }

        return stop_pos;
    }

    bool process_token(completion_token_output &result, llama_client_slot &slot) {
        // remember which tokens were sampled - used for repetition penalties during sampling
        const std::string token_str = llama_token_to_piece(ctx, result.tok);
        slot.sampled = result.tok;

        // search stop word and delete it
        slot.generated_text += token_str;
        slot.has_next_token = true;

        if (slot.ctx_sampling->params.use_penalty_prompt_tokens && result.tok != -1)
        {
            // we can change penalty_prompt_tokens because it is always created from scratch each request
            slot.ctx_sampling->params.penalty_prompt_tokens.push_back(result.tok);
        }

        // check if there is incomplete UTF-8 character at the end
        bool incomplete = false;
        for (unsigned i = 1; i < 5 && i <= slot.generated_text.size(); ++i)
        {
            unsigned char c = slot.generated_text[slot.generated_text.size() - i];
            if ((c & 0xC0) == 0x80)
            {
                // continuation byte: 10xxxxxx
                continue;
            }
            if ((c & 0xE0) == 0xC0)
            {
                // 2-byte character: 110xxxxx ...
                incomplete = i < 2;
            }
            else if ((c & 0xF0) == 0xE0)
            {
                // 3-byte character: 1110xxxx ...
                incomplete = i < 3;
            }
            else if ((c & 0xF8) == 0xF0)
            {
                // 4-byte character: 11110xxx ...
                incomplete = i < 4;
            }
            // else 1-byte character or invalid byte
            break;
        }

        if (!incomplete)
        {
            size_t pos = std::min(slot.sent_count, slot.generated_text.size());
            const std::string str_test = slot.generated_text.substr(pos);
            bool is_stop_full = false;
            size_t stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_FULL, slot);
            if (stop_pos != std::string::npos)
            {
                is_stop_full = true;
                slot.generated_text.erase(
                    slot.generated_text.begin() + pos + stop_pos,
                    slot.generated_text.end());
                pos = std::min(slot.sent_count, slot.generated_text.size());
            }
            else
            {
                is_stop_full = false;
                stop_pos = find_stopping_strings(str_test, token_str.size(), STOP_PARTIAL, slot);
            }

            // check if there is any token to predict
            if (stop_pos == std::string::npos || (!slot.has_next_token && !is_stop_full && stop_pos > 0))
            {
                // no send the stop word in the response
                result.text_to_send = slot.generated_text.substr(pos, std::string::npos);
                slot.sent_count += result.text_to_send.size();
                // add the token to slot queue and cache
            }
            slot.add_token_string(result);
            if (slot.params.stream)
            {
                send_partial_response(slot, result);
            }
        }

        if (incomplete)
        {
            slot.has_next_token = true;
        }

        // check the limits
        if (slot.n_decoded > 0 && slot.has_next_token && !slot.has_budget(params))
        {
            slot.stopped_limit = true;
            slot.has_next_token = false;
        }

        if (!slot.cache_tokens.empty() && result.tok == llama_token_eos(model))
        {
            slot.stopped_eos = true;
            slot.has_next_token = false;
            LOG_VERBOSE("eos token found", {});
        }

        LOG_VERBOSE("next token", {
                                      {"token", result.tok},
                                      {"token_text", tokens_to_output_formatted_string(ctx, result.tok)},
                                      {"has_next_token", slot.has_next_token},
                                      {"n_remain", slot.n_remaining},
                                      {"num_tokens_predicted", slot.n_decoded},
                                      {"stopped_eos", slot.stopped_eos},
                                      {"stopped_word", slot.stopped_word},
                                      {"stopped_limit", slot.stopped_limit},
                                      {"stopping_word", slot.stopping_word},
                                  });

        return slot.has_next_token; // continue
    }

    bool process_images(llama_client_slot &slot) const
    {
        for (slot_image &img : slot.images)
        {
            if (!img.request_encode_image)
            {
                continue;
            }
            clip_image_f32 * img_res = clip_image_f32_init();
            if (!clip_image_preprocess(clp_ctx, img.img_data, img_res, /*pad2square =*/ true))
            {
                LOG_TEE("Error processing the given image");
                clip_free(clp_ctx);
                return false;
            }
            img.image_tokens = clip_n_patches(clp_ctx);
            img.image_embedding = (float *)malloc(clip_embd_nbytes(clp_ctx));
            if (!img.image_embedding)
            {
                LOG_TEE("Unable to allocate memory for image embeddings\n");
                clip_free(clp_ctx);
                return false;
            }
            LOG_TEE("slot %i - encoding image [id: %i]\n", slot.id, img.id);
            if (!clip_image_encode(clp_ctx, params.n_threads, img_res, img.image_embedding))
            {
                LOG_TEE("Unable to encode image\n");
                return false;
            }
            clip_image_f32_free(img_res);
            img.request_encode_image = false;
        }

        return slot.images.size() > 0;
    }

    void send_error(task_server& task, std::string error)
    {
        std::unique_lock<std::mutex> lock(mutex_results);
        task_result res;
        res.id = task.id;
        res.multitask_id = task.multitask_id;
        res.stop = false;
        res.error = true;
        res.result_json = { { "content", error } };
        queue_results.push_back(res);
        condition_results.notify_all();
    }

    void add_multi_task(int id, std::vector<int>& sub_ids)
    {
        std::lock_guard<std::mutex> lock(mutex_tasks);
        task_multi multi;
        multi.id = id;
        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
        queue_multitasks.push_back(multi);
        condition_tasks.notify_one();
    }

    void update_multi_task(int multitask_id, int subtask_id, task_result& result)
    {
        std::lock_guard<std::mutex> lock(mutex_tasks);
        for (auto& multitask : queue_multitasks)
        {
            if (multitask.id == multitask_id)
            {
                multitask.subtasks_remaining.erase(subtask_id);
                multitask.results.push_back(result);
                condition_tasks.notify_one();
            }
        }
    }

    json get_model_props()
    {
        return get_formated_generation(slots[0]);
    }

    json get_formated_generation(llama_client_slot &slot)
    {
        const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model));
        const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() &&
                                eos_bias->second < 0.0f && std::isinf(eos_bias->second);
        return json {
            {"n_ctx",             slot.n_ctx},
            {"model",             params.model_alias},
            {"seed",              slot.params.seed},
            {"temperature",       slot.sparams.temp},
            {"top_k",             slot.sparams.top_k},
            {"top_p",             slot.sparams.top_p},
            {"min_p",             slot.sparams.min_p},
            {"tfs_z",             slot.sparams.tfs_z},
            {"typical_p",         slot.sparams.typical_p},
            {"repeat_last_n",     slot.sparams.penalty_last_n},
            {"repeat_penalty",    slot.sparams.penalty_repeat},
            {"presence_penalty",  slot.sparams.penalty_present},
            {"frequency_penalty", slot.sparams.penalty_freq},
            {"penalty_prompt_tokens", slot.sparams.penalty_prompt_tokens},
            {"use_penalty_prompt_tokens", slot.sparams.use_penalty_prompt_tokens},
            {"mirostat",          slot.sparams.mirostat},
            {"mirostat_tau",      slot.sparams.mirostat_tau},
            {"mirostat_eta",      slot.sparams.mirostat_eta},
            {"penalize_nl",       slot.sparams.penalize_nl},
            {"stop",              slot.params.antiprompt},
            {"n_predict",         slot.params.n_predict},
            {"n_keep",            params.n_keep},
            {"ignore_eos",        ignore_eos},
            {"stream",            slot.params.stream},
            {"logit_bias",        slot.sparams.logit_bias},
            {"n_probs",           slot.sparams.n_probs},
            {"grammar",           slot.sparams.grammar},
        };
    }

    void send_partial_response(llama_client_slot &slot, completion_token_output tkn)
    {
        std::unique_lock<std::mutex> lock(mutex_results);
        task_result res;
        res.id = slot.task_id;
        res.multitask_id = slot.multitask_id;
        res.error = false;
        res.stop = false;

        res.result_json = json
        {
            {"content",    tkn.text_to_send},
            {"stop",       false},
            {"slot_id",    slot.id},
            {"multimodal", multimodal}
        };

        if (slot.sparams.n_probs > 0)
        {
            std::vector<completion_token_output> probs_output = {};
            const std::vector<llama_token> to_send_toks = llama_tokenize(ctx, tkn.text_to_send, false);
            size_t probs_pos      = std::min(slot.sent_token_probs_index,                       slot.generated_token_probs.size());
            size_t probs_stop_pos = std::min(slot.sent_token_probs_index + to_send_toks.size(), slot.generated_token_probs.size());
            if (probs_pos < probs_stop_pos)
            {
                probs_output = std::vector<completion_token_output>(slot.generated_token_probs.begin() + probs_pos, slot.generated_token_probs.begin() + probs_stop_pos);
            }
            slot.sent_token_probs_index = probs_stop_pos;
            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
        }

        if (slot.oaicompat)
        {
            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
            res.result_json["model"] = slot.oaicompat_model;
        }

        queue_results.push_back(res);
        condition_results.notify_all();
    }

    void send_final_response(llama_client_slot &slot)
    {
        std::unique_lock<std::mutex> lock(mutex_results);
        task_result res;
        res.id = slot.task_id;
        res.multitask_id = slot.multitask_id;
        res.error = false;
        res.stop = true;

        res.result_json = json
        {
            {"content",             !slot.params.stream ? slot.generated_text : ""},
            {"slot_id",             slot.id},
            {"stop",                true},
            {"model",               params.model_alias},
            {"tokens_predicted",    slot.n_decoded},
            {"tokens_evaluated",    slot.num_prompt_tokens},
            {"generation_settings", get_formated_generation(slot)},
            {"prompt",              slot.prompt},
            {"truncated",           slot.truncated},
            {"stopped_eos",         slot.stopped_eos},
            {"stopped_word",        slot.stopped_word},
            {"stopped_limit",       slot.stopped_limit},
            {"stopping_word",       slot.stopping_word},
            {"tokens_cached",       slot.n_past},
            {"timings",             slot.get_formated_timings()}
        };

        if (slot.sparams.n_probs > 0)
        {
            std::vector<completion_token_output> probs = {};
            if (!slot.params.stream && slot.stopped_word)
            {
                const std::vector<llama_token> stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false);
                probs = std::vector<completion_token_output>(slot.generated_token_probs.begin(), slot.generated_token_probs.end() - stop_word_toks.size());
            }
            else
            {
                probs = std::vector<completion_token_output>(
                                    slot.generated_token_probs.begin(),
                                    slot.generated_token_probs.end());
            }
            res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
        }

        if (slot.oaicompat)
        {
            res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
            res.result_json["model"] = slot.oaicompat_model;
        }

        queue_results.push_back(res);
        condition_results.notify_all();

        // done with results, unlock
        lock.unlock();

        // parent multitask, if any, needs to be updated
        if (slot.multitask_id != -1)
        {
            update_multi_task(slot.multitask_id, slot.task_id, res);
        }
    }

    void send_embedding(llama_client_slot &slot)
    {
        std::unique_lock<std::mutex> lock(mutex_results);
        task_result res;
        res.id = slot.task_id;
        res.multitask_id = slot.multitask_id;
        res.error = false;
        res.stop = true;

        const int n_embd = llama_n_embd(model);
        if (!params.embedding)
        {
            LOG_WARNING("embedding disabled", {
                                                  {"params.embedding", params.embedding},
                                              });
            res.result_json = json
            {
                {"embedding", std::vector<float>(n_embd, 0.0f)},
            };
        }
        else
        {
            const float *data = llama_get_embeddings(ctx);
            std::vector<float> embedding(data, data + n_embd);
            res.result_json = json
            {
                {"embedding", embedding },
            };
        }
        queue_results.push_back(res);
        condition_results.notify_all();
    }

    int request_completion(json data, bool infill, bool embedding, int multitask_id)
    {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        task_server task;
        task.id = id_gen++;
        task.target_id = 0;
        task.data = std::move(data);
        task.infill_mode = infill;
        task.embedding_mode = embedding;
        task.type = TASK_TYPE_COMPLETION;
        task.multitask_id = multitask_id;

        // when a completion task's prompt array is not a singleton, we split it into multiple requests
        if (task.data.count("prompt") && task.data.at("prompt").size() > 1)
        {
            lock.unlock(); // entering new func scope
            return split_multiprompt_task(task);
        }

        // otherwise, it's a single-prompt task, we actually queue it
        queue_tasks.push_back(task);
        condition_tasks.notify_one();
        return task.id;
    }

    task_result next_result(int task_id)
    {
        while (true)
        {
            std::unique_lock<std::mutex> lock(mutex_results);
            condition_results.wait(lock, [&]{
                return !queue_results.empty();
            });

            for (int i = 0; i < (int) queue_results.size(); i++)
            {
                // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
                if (queue_results[i].multitask_id == task_id)
                {
                    update_multi_task(task_id, queue_results[i].id, queue_results[i]);
                    queue_results.erase(queue_results.begin() + i);
                    continue;
                }

                if (queue_results[i].id == task_id)
                {
                    assert(queue_results[i].multitask_id == -1);
                    task_result res = queue_results[i];
                    queue_results.erase(queue_results.begin() + i);
                    return res;
                }
            }
        }

        // never reached
        //return task_result{-1, false, false, {}};
    }

    // for multiple images processing
    bool ingest_images(llama_client_slot &slot, int n_batch)
    {
        int image_idx = 0;

        while (image_idx < (int) slot.images.size())
        {
            slot_image &img = slot.images[image_idx];

            // process prefix prompt
            for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
            {
                const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
                llama_batch batch_view = {
                    n_tokens,
                    batch.token    + i,
                    nullptr,
                    batch.pos      + i,
                    batch.n_seq_id + i,
                    batch.seq_id   + i,
                    batch.logits   + i,
                    0, 0, 0, // unused
                };
                if (llama_decode(ctx, batch_view))
                {
                    LOG_TEE("%s : failed to eval\n", __func__);
                    return false;
                }
            }

            // process image with llm
            for (int i = 0; i < img.image_tokens; i += n_batch)
            {
                int n_eval = img.image_tokens - i;
                if (n_eval > n_batch)
                {
                    n_eval = n_batch;
                }

                const int n_embd = llama_n_embd(model);
                llama_batch batch_img = { n_eval, nullptr, (img.image_embedding + i * n_embd), nullptr, nullptr, nullptr, nullptr, slot.n_past, 1, 0, };
                if (llama_decode(ctx, batch_img))
                {
                    LOG_TEE("%s : failed to eval image\n", __func__);
                    return false;
                }
                slot.n_past += n_eval;
            }
            image_idx++;

            llama_batch_clear(batch);

            // append prefix of next image
            const auto json_prompt = (image_idx >= (int) slot.images.size()) ?
                slot.params.input_suffix : // no more images, then process suffix prompt
                (json)(slot.images[image_idx].prefix_prompt);

            std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
            for (int i = 0; i < (int) append_tokens.size(); ++i)
            {
                llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true);
                slot.n_past += 1;
            }
        }

        return true;
    }

    void request_cancel(int task_id)
    {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        task_server task;
        task.id = id_gen++;
        task.type = TASK_TYPE_CANCEL;
        task.target_id = task_id;
        queue_tasks.push_back(task);
        condition_tasks.notify_one();
    }

    int split_multiprompt_task(task_server& multiprompt_task)
    {
        int prompt_count = multiprompt_task.data.at("prompt").size();
        assert(prompt_count > 1);

        int multitask_id = id_gen++;
        std::vector<int> subtask_ids(prompt_count);
        for (int i = 0; i < prompt_count; i++)
        {
            json subtask_data = multiprompt_task.data;
            subtask_data["prompt"] = subtask_data["prompt"][i];

            // subtasks inherit everything else (infill mode, embedding mode, etc.)
            subtask_ids[i] = request_completion(subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
        }

        // queue up the multitask so we can track its subtask progression
        add_multi_task(multitask_id, subtask_ids);
        return multitask_id;
    }

    void process_tasks()
    {
        std::unique_lock<std::mutex> lock(mutex_tasks);
        while (!queue_tasks.empty())
        {
            task_server task = queue_tasks.front();
            queue_tasks.erase(queue_tasks.begin());
            switch (task.type)
            {
                case TASK_TYPE_COMPLETION: {
                    llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1));
                    if (slot == nullptr)
                    {
                        LOG_TEE("slot unavailable\n");
                        // send error result
                        send_error(task, "slot unavailable");
                        return;
                    }

                    if (task.data.contains("system_prompt"))
                    {
                        process_system_prompt_data(task.data["system_prompt"]);
                    }

                    slot->reset();

                    slot->infill       = task.infill_mode;
                    slot->embedding    = task.embedding_mode;
                    slot->task_id      = task.id;
                    slot->multitask_id = task.multitask_id;

                    if (!launch_slot_with_data(slot, task.data))
                    {
                        // send error result
                        send_error(task, "internal_error");
                        break;
                    }
                } break;
                case TASK_TYPE_CANCEL: { // release slot linked with the task id
                    for (auto & slot : slots)
                    {
                        if (slot.task_id == task.target_id)
                        {
                            slot.release();
                            break;
                        }
                    }
                } break;
            }
        }

        // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue
        std::vector<task_result> agg_results;
        auto queue_iterator = queue_multitasks.begin();
        while (queue_iterator != queue_multitasks.end())
        {
            if (queue_iterator->subtasks_remaining.empty())
            {
                // all subtasks done == multitask is done
                task_result aggregate_result;
                aggregate_result.id = queue_iterator->id;
                aggregate_result.stop = true;
                aggregate_result.error = false;

                // collect json results into one json result
                std::vector<json> result_jsons;
                for (auto& subres : queue_iterator->results)
                {
                    result_jsons.push_back(subres.result_json);
                    aggregate_result.error = aggregate_result.error && subres.error;
                }
                aggregate_result.result_json = json{ "results", result_jsons };


                agg_results.push_back(aggregate_result);

                condition_results.notify_all();

                queue_iterator = queue_multitasks.erase(queue_iterator);
            }
            else
            {
                ++queue_iterator;
            }
        }

        // done with tasks, unlock
        lock.unlock();

        // copy aggregate results of complete multi-tasks to the results queue
        std::lock_guard<std::mutex> lock_results(mutex_results);
        queue_results.insert(queue_results.end(), agg_results.begin(), agg_results.end());
    }

    bool update_slots() {
        // attend tasks
        process_tasks();

        // update the system prompt wait until all slots are idle state
        if (system_need_update && all_slots_are_idle)
        {
            LOG_TEE("updating system prompt\n");
            update_system_prompt();
        }

        llama_batch_clear(batch);

        if (all_slots_are_idle)
        {
            if (system_prompt.empty() && clean_kv_cache)
            {
                LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n");
                kv_cache_clear();
            }
            std::unique_lock<std::mutex> lock(mutex_tasks);
            condition_tasks.wait(lock, [&]{
                return !queue_tasks.empty();
            });
        }

        for (llama_client_slot &slot : slots)
        {
            if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx)
            {
                // Shift context
                const int n_left    = slot.n_past - slot.params.n_keep - 1;
                const int n_discard = n_left / 2;

                LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard);
                llama_kv_cache_seq_rm   (ctx, slot.id, slot.params.n_keep + 1            , slot.params.n_keep + n_discard + 1);
                llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard);

                for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++)
                {
                    slot.cache_tokens[i - n_discard] = slot.cache_tokens[i];
                }

                slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard);

                slot.n_past -= n_discard;

                slot.truncated = true;

                LOG_VERBOSE("context shift", {
                                                {"n_ctx",  n_ctx},
                                                {"n_keep", params.n_keep},
                                                {"n_left", n_left},
                                            });
            }
        }

        // decode any currently ongoing sequences
        for (auto & slot : slots)
        {
            // release the slot
            if (slot.command == RELEASE)
            {
                slot.state = IDLE;
                slot.command = NONE;
                slot.t_last_used = ggml_time_us();

                LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size());

                continue;
            }

            if (slot.state == IDLE)
            {
                continue;
            }

            slot.i_batch = batch.n_tokens;

            llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true);

            slot.n_past += 1;
        }

        // process in chunks of params.n_batch
        int32_t n_batch = params.n_batch;

        // assign workload to the slots
        if (params.cont_batching || batch.n_tokens == 0)
        {
            for (auto & slot : slots)
            {
                const bool has_prompt = slot.prompt.is_array() || (slot.prompt.is_string() && !slot.prompt.get<std::string>().empty()) || !slot.images.empty();

                // empty prompt passed -> release the slot and send empty response
                // note: infill mode allows empty prompt
                if (slot.state == IDLE && slot.command == LOAD_PROMPT && !has_prompt && !slot.infill)
                {
                    slot.release();
                    slot.print_timings();
                    send_final_response(slot);
                    continue;
                }

                // need process the prompt
                if (slot.state == IDLE && slot.command == LOAD_PROMPT)
                {
                    slot.state = PROCESSING;
                    slot.command = NONE;
                    std::vector<llama_token> prompt_tokens;
                    slot.t_start_process_prompt = ggml_time_us();
                    slot.t_start_genereration = 0;

                    if (slot.infill)
                    {
                        bool suff_rm_leading_spc = true;
                        if (params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1)
                        {
                            params.input_suffix.erase(0, 1);
                            suff_rm_leading_spc = false;
                        }
                        auto prefix_tokens = tokenize(slot.params.input_prefix, false);
                        auto suffix_tokens = tokenize(slot.params.input_suffix, false);

                        const int space_token = 29871; // TODO: this should not be hardcoded
                        if (suff_rm_leading_spc && !suffix_tokens.empty() && suffix_tokens[0] == space_token) {
                            suffix_tokens.erase(suffix_tokens.begin());
                        }

                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model));
                        prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS
                        prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model));
                        prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end());
                        prefix_tokens.push_back(llama_token_middle(model));
                        prompt_tokens = prefix_tokens;
                    }
                    else
                    {
                        prompt_tokens = tokenize(slot.prompt, system_prompt.empty() && add_bos_token);  // add BOS if there isn't system prompt
                    }

                    slot.num_prompt_tokens = prompt_tokens.size();

                    if (slot.params.n_keep < 0)
                    {
                        slot.params.n_keep = slot.num_prompt_tokens;
                    }
                    slot.params.n_keep = std::min(slot.n_ctx - 4, slot.params.n_keep);

                    // if input prompt is too big, truncate it
                    if (slot.num_prompt_tokens >= slot.n_ctx)
                    {
                        const int n_left = slot.n_ctx - slot.params.n_keep;
                        const int n_block_size = n_left / 2;
                        const int erased_blocks = (slot.num_prompt_tokens - slot.params.n_keep - n_block_size) / n_block_size;

                        std::vector<llama_token> new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + slot.params.n_keep);
                        new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + slot.params.n_keep + erased_blocks * n_block_size, prompt_tokens.end());

                        LOG_VERBOSE("input truncated", {
                            {"n_ctx",  slot.n_ctx},
                            {"n_keep", slot.params.n_keep},
                            {"n_left", n_left},
                            {"new_tokens", tokens_to_str(ctx, new_tokens.cbegin(), new_tokens.cend())},
                        });
                        slot.truncated = true;
                        prompt_tokens = new_tokens;

                        slot.num_prompt_tokens = prompt_tokens.size();
                        GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
                    }

                    if (!slot.params.cache_prompt)
                    {
                        llama_sampling_reset(slot.ctx_sampling);

                        slot.n_past = 0;
                        slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
                    }
                    else
                    {
                        // push the prompt into the sampling context (do not apply grammar)
                        for (auto &token : prompt_tokens)
                        {
                            llama_sampling_accept(slot.ctx_sampling, ctx, token, false);
                        }

                        slot.n_past = common_part(slot.cache_tokens, prompt_tokens);
                        slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past;

                        LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed);
                    }

                    LOG_TEE("slot %d : kv cache rm - [%d, end)\n", slot.id, (int) system_tokens.size() + slot.n_past);

                    llama_kv_cache_seq_rm(ctx, slot.id, system_tokens.size() + slot.n_past, -1);

                    slot.cache_tokens = prompt_tokens;

                    if (slot.n_past == slot.num_prompt_tokens && slot.n_past > 0)
                    {
                        // we have to evaluate at least 1 token to generate logits.
                        LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id);
                        slot.n_past--;
                    }

                    LOG_VERBOSE("prompt ingested", {
                                                    {"n_past", slot.n_past},
                                                    {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)},
                                                    {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())},
                                                });

                    const bool has_images = process_images(slot);

                    // process the prefix of first image
                    std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
                    for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past)
                    {
                       llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false);
                    }

                    if (has_images && !ingest_images(slot, n_batch))
                    {
                        LOG_TEE("failed processing images\n");
                        return false;
                    }

                    // extract the logits only for the last token
                    if (batch.n_tokens > 0)
                    {
                        batch.logits[batch.n_tokens - 1] = true;
                    }

                    slot.n_decoded = 0;
                    slot.i_batch   = batch.n_tokens - 1;
                }
            }
        }

        if (batch.n_tokens == 0)
        {
            all_slots_are_idle = true;
            return true;
        }

        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
        {
            const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
            llama_batch batch_view =
            {
                n_tokens,
                batch.token    + i,
                nullptr,
                batch.pos      + i,
                batch.n_seq_id + i,
                batch.seq_id   + i,
                batch.logits   + i,
                0, 0, 0, // unused
            };

            const int ret = llama_decode(ctx, batch_view);
            if (ret != 0)
            {
                if (n_batch == 1 || ret < 0)
                {
                    // if you get here, it means the KV cache is full - try increasing it via the context size
                    LOG_TEE("%s : failed to decode the batch, n_batch = %d, ret = %d\n", __func__, n_batch, ret);
                    return false;
                }

                LOG_TEE("%s : failed to find free space in the KV cache, retrying with smaller n_batch = %d\n", __func__, n_batch / 2);

                // retry with half the batch size to try to find a free slot in the KV cache
                n_batch /= 2;
                i -= n_batch;
                continue;
            }

            for (auto & slot : slots)
            {
                if (slot.i_batch < (int) i || slot.i_batch >= (int) (i + n_tokens))
                {
                    continue;
                }

                // prompt evaluated for embedding
                if (slot.embedding)
                {
                    send_embedding(slot);
                    slot.release();
                    slot.i_batch = -1;
                    return true;
                }

                completion_token_output result;
                const llama_token id = llama_sampling_sample(slot.ctx_sampling, ctx, NULL, slot.i_batch - i);

                llama_sampling_accept(slot.ctx_sampling, ctx, id, true);

                slot.n_decoded += 1;
                if (slot.n_decoded == 1)
                {
                    slot.t_start_genereration = ggml_time_us();
                    slot.t_prompt_processing = (slot.t_start_genereration - slot.t_start_process_prompt) / 1e3;
                }

                llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false };
                result.tok = id;

                const int32_t n_probs = slot.sparams.n_probs;
                if (slot.sparams.temp <= 0 && n_probs > 0)
                {
                    // for llama_sample_token_greedy we need to sort candidates
                    llama_sample_softmax(ctx, &cur_p);
                }

                for (size_t i = 0; i < std::min(cur_p.size, (size_t)n_probs); ++i)
                {
                    result.probs.push_back({cur_p.data[i].id, cur_p.data[i].p});
                }

                if (!process_token(result, slot))
                {
                    slot.release();
                    slot.print_timings();
                    send_final_response(slot);
                }

                slot.i_batch = -1;
            }
        }
        return true;
    }
};

static void server_print_usage(const char *argv0, const gpt_params &params,
                               const server_params &sparams)
{
    printf("usage: %s [options]\n", argv0);
    printf("\n");
    printf("options:\n");
    printf("  -h, --help                show this help message and exit\n");
    printf("  -v, --verbose             verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
    printf("  -t N, --threads N         number of threads to use during computation (default: %d)\n", params.n_threads);
    printf("  -tb N, --threads-batch N  number of threads to use during batch and prompt processing (default: same as --threads)\n");
    printf("  -c N, --ctx-size N        size of the prompt context (default: %d)\n", params.n_ctx);
    printf("  --rope-scaling {none,linear,yarn}\n");
    printf("                            RoPE frequency scaling method, defaults to linear unless specified by the model\n");
    printf("  --rope-freq-base N        RoPE base frequency (default: loaded from model)\n");
    printf("  --rope-freq-scale N       RoPE frequency scaling factor, expands context by a factor of 1/N\n");
    printf("  --yarn-ext-factor N       YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n");
    printf("  --yarn-attn-factor N      YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n");
    printf("  --yarn-beta-slow N        YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow);
    printf("  --yarn-beta-fast N        YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
    printf("  -b N, --batch-size N      batch size for prompt processing (default: %d)\n", params.n_batch);
    printf("  --memory-f32              use f32 instead of f16 for memory key+value (default: disabled)\n");
    printf("                            not recommended: doubles context memory required and no measurable increase in quality\n");
    if (llama_mlock_supported())
    {
        printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
    }
    if (llama_mmap_supported())
    {
        printf("  --no-mmap             do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
    }
    printf("  --numa                attempt optimizations that help on some NUMA systems\n");
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
    printf("  -ngl N, --n-gpu-layers N\n");
    printf("                        number of layers to store in VRAM\n");
    printf("  -sm SPLIT_MODE, --split-mode SPLIT_MODE\n");
    printf("                        how to split the model across multiple GPUs, one of:\n");
    printf("                          - none: use one GPU only\n");
    printf("                          - layer (default): split layers and KV across GPUs\n");
    printf("                          - row: split rows across GPUs\n");
    printf("  -ts SPLIT --tensor-split SPLIT\n");
    printf("                        fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n");
    printf("  -mg i, --main-gpu i   the GPU to use for the model (with split-mode = none),\n");
    printf("                        or for intermediate results and KV (with split-mode = row)\n");
#endif
    printf("  -m FNAME, --model FNAME\n");
    printf("                        model path (default: %s)\n", params.model.c_str());
    printf("  -a ALIAS, --alias ALIAS\n");
    printf("                        set an alias for the model, will be added as `model` field in completion response\n");
    printf("  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
    printf("  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
    printf("  --host                ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
    printf("  --port PORT           port to listen (default  (default: %d)\n", sparams.port);
    printf("  --path PUBLIC_PATH    path from which to serve static files (default %s)\n", sparams.public_path.c_str());
    printf("  --api-key API_KEY     optional api key to enhance server security. If set, requests must include this key for access.\n");
    printf("  --api-key-file FNAME  path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n");
    printf("  -to N, --timeout N    server read/write timeout in seconds (default: %d)\n", sparams.read_timeout);
    printf("  --embedding           enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled");
    printf("  -np N, --parallel N   number of slots for process requests (default: %d)\n", params.n_parallel);
    printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
    printf("    -spf FNAME, --system-prompt-file FNAME\n");
    printf("                        Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
    printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA.\n");
    printf("  --log-disable         disables logging to a file.\n");
    printf("\n");
    printf("  --override-kv KEY=TYPE:VALUE\n");
    printf("                        advanced option to override model metadata by key. may be specified multiple times.\n");
    printf("                        types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
    printf("\n");
}

static void server_params_parse(int argc, char **argv, server_params &sparams,
                                gpt_params &params, llama_server_context& llama)
{
    gpt_params default_params;
    server_params default_sparams;
    std::string arg;
    bool invalid_param = false;

    for (int i = 1; i < argc; i++)
    {
        arg = argv[i];
        if (arg == "--port")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            sparams.port = std::stoi(argv[i]);
        }
        else if (arg == "--host")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            sparams.hostname = argv[i];
        }
        else if (arg == "--path")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            sparams.public_path = argv[i];
        }
        else if (arg == "--api-key")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            sparams.api_keys.push_back(argv[i]);
        }
        else if (arg == "--api-key-file")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            std::ifstream key_file(argv[i]);
            if (!key_file) {
                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
                invalid_param = true;
                break;
            }
            std::string key;
            while (std::getline(key_file, key)) {
               if (key.size() > 0) {
                   sparams.api_keys.push_back(key);
               }
            }
            key_file.close();
        }
        else if (arg == "--timeout" || arg == "-to")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            sparams.read_timeout = std::stoi(argv[i]);
            sparams.write_timeout = std::stoi(argv[i]);
        }
        else if (arg == "-m" || arg == "--model")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.model = argv[i];
        }
        else if (arg == "-a" || arg == "--alias")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.model_alias = argv[i];
        }
        else if (arg == "-h" || arg == "--help")
        {
            server_print_usage(argv[0], default_params, default_sparams);
            exit(0);
        }
        else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.n_ctx = std::stoi(argv[i]);
        }
        else if (arg == "--rope-scaling")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            std::string value(argv[i]);
            /**/ if (value == "none")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_NONE; }
            else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_LINEAR; }
            else if (value == "yarn")   { params.rope_scaling_type = LLAMA_ROPE_SCALING_YARN; }
            else { invalid_param = true; break; }
        }
        else if (arg == "--rope-freq-base")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.rope_freq_base = std::stof(argv[i]);
        }
        else if (arg == "--rope-freq-scale")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.rope_freq_scale = std::stof(argv[i]);
        }
        else if (arg == "--yarn-ext-factor")
        {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_ext_factor = std::stof(argv[i]);
        }
        else if (arg == "--yarn-attn-factor")
        {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_attn_factor = std::stof(argv[i]);
        }
        else if (arg == "--yarn-beta-fast")
        {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_beta_fast = std::stof(argv[i]);
        }
        else if (arg == "--yarn-beta-slow")
        {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            params.yarn_beta_slow = std::stof(argv[i]);
        }
        else if (arg == "--threads" || arg == "-t")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.n_threads = std::stoi(argv[i]);
        }
        else if (arg == "--threads-batch" || arg == "-tb")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.n_threads_batch = std::stoi(argv[i]);
        }
        else if (arg == "-b" || arg == "--batch-size")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.n_batch = std::stoi(argv[i]);
            params.n_batch = std::min(512, params.n_batch);
        }
        else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
            params.n_gpu_layers = std::stoi(argv[i]);
#else
            LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. "
                        "See main README.md for information on enabling GPU BLAS support",
                        {{"n_gpu_layers", params.n_gpu_layers}});
#endif
        }
        else if (arg == "--split-mode" || arg == "-sm")
        {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            std::string arg_next = argv[i];
            if (arg_next == "none")
            {
                params.split_mode = LLAMA_SPLIT_NONE;
            }
            else if (arg_next == "layer")
            {
                params.split_mode = LLAMA_SPLIT_LAYER;
            }
            else if (arg_next == "row")
            {
                params.split_mode = LLAMA_SPLIT_ROW;
            }
            else {
                invalid_param = true;
                break;
            }
#ifndef GGML_USE_CUBLAS
            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUBLAS
        }
        else if (arg == "--tensor-split" || arg == "-ts")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
#ifdef GGML_USE_CUBLAS
            std::string arg_next = argv[i];

            // split string by , and /
            const std::regex regex{R"([,/]+)"};
            std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1};
            std::vector<std::string> split_arg{it, {}};
            GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES);

            for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device)
            {
                if (i_device < split_arg.size())
                {
                    params.tensor_split[i_device] = std::stof(split_arg[i_device]);
                }
                else
                {
                    params.tensor_split[i_device] = 0.0f;
                }
            }
#else
            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUBLAS
        }
        else if (arg == "--no-mul-mat-q" || arg == "-nommq")
        {
#ifdef GGML_USE_CUBLAS
            params.mul_mat_q = false;
#else
            LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
#endif // GGML_USE_CUBLAS
        }
        else if (arg == "--main-gpu" || arg == "-mg")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
#ifdef GGML_USE_CUBLAS
            params.main_gpu = std::stoi(argv[i]);
#else
            LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
#endif
        }
        else if (arg == "--lora")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f));
            params.use_mmap = false;
        }
        else if (arg == "--lora-scaled")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            const char * lora_adapter = argv[i];
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i])));
            params.use_mmap = false;
        }
        else if (arg == "--lora-base")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.lora_base = argv[i];
        }
        else if (arg == "-v" || arg == "--verbose")
        {
#if SERVER_VERBOSE != 1
            LOG_WARNING("server.cpp is not built with verbose logging.", {});
#else
            server_verbose = true;
#endif
        }
        else if (arg == "--mlock")
        {
            params.use_mlock = true;
        }
        else if (arg == "--no-mmap")
        {
            params.use_mmap = false;
        }
        else if (arg == "--numa")
        {
            params.numa = true;
        }
        else if (arg == "--embedding")
        {
            params.embedding = true;
        }
        else if (arg == "-cb" || arg == "--cont-batching")
        {
            params.cont_batching = true;
        }
        else if (arg == "-np" || arg == "--parallel")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.n_parallel = std::stoi(argv[i]);
        } else if (arg == "-n" || arg == "--n-predict")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.n_predict = std::stoi(argv[i]);
        } else if (arg == "-spf" || arg == "--system-prompt-file")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            std::ifstream file(argv[i]);
            if (!file) {
                fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
                invalid_param = true;
                break;
            }
            std::string systm_content;
            std::copy(
                std::istreambuf_iterator<char>(file),
                std::istreambuf_iterator<char>(),
                std::back_inserter(systm_content)
            );
            llama.process_system_prompt_data(json::parse(systm_content));
        }
        else if(arg == "--mmproj")
        {
            if (++i >= argc)
            {
                invalid_param = true;
                break;
            }
            params.mmproj = argv[i];
        }
        else if (arg == "--log-disable")
        {
            log_set_target(stdout);
            LOG_INFO("logging to file is disabled.", {});
        }
        else if (arg == "--override-kv")
        {
            if (++i >= argc) {
                invalid_param = true;
                break;
            }
            char * sep = strchr(argv[i], '=');
            if (sep == nullptr || sep - argv[i] >= 128) {
                fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
                invalid_param = true;
                break;
            }
            struct llama_model_kv_override kvo;
            std::strncpy(kvo.key, argv[i], sep - argv[i]);
            kvo.key[sep - argv[i]] = 0;
            sep++;
            if (strncmp(sep, "int:", 4) == 0) {
                sep += 4;
                kvo.tag = LLAMA_KV_OVERRIDE_INT;
                kvo.int_value = std::atol(sep);
            } else if (strncmp(sep, "float:", 6) == 0) {
                sep += 6;
                kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
                kvo.float_value = std::atof(sep);
            } else if (strncmp(sep, "bool:", 5) == 0) {
                sep += 5;
                kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
                if (std::strcmp(sep, "true") == 0) {
                    kvo.bool_value = true;
                } else if (std::strcmp(sep, "false") == 0) {
                    kvo.bool_value = false;
                } else {
                    fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
                    invalid_param = true;
                    break;
                }
            } else {
                fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
                invalid_param = true;
                break;
            }
            params.kv_overrides.push_back(kvo);
        }
        else
        {
            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
            server_print_usage(argv[0], default_params, default_sparams);
            exit(1);
        }
    }
    if (!params.kv_overrides.empty()) {
        params.kv_overrides.emplace_back(llama_model_kv_override());
        params.kv_overrides.back().key[0] = 0;
    }

    if (invalid_param)
    {
        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
        server_print_usage(argv[0], default_params, default_sparams);
        exit(1);
    }
}

static std::string random_string()
{
    static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");

    std::random_device rd;
    std::mt19937 generator(rd());

    std::string result(32, ' ');

    for (int i = 0; i < 32; ++i) {
        result[i] = str[generator() % str.size()];
    }

    return result;
}

static std::string gen_chatcmplid()
{
    std::stringstream chatcmplid;
    chatcmplid << "chatcmpl-" << random_string();
    return chatcmplid.str();
}

std::string format_chatml(std::vector<json> messages)
{
    std::ostringstream chatml_msgs;

    for (auto it = messages.begin(); it != messages.end(); ++it) {
        chatml_msgs << "<|im_start|>"
                    << json_value(*it, "role",    std::string("user")) << '\n';
        chatml_msgs << json_value(*it, "content", std::string(""))
                    << "<|im_end|>\n";
    }

    chatml_msgs << "<|im_start|>assistant" << '\n';

    return chatml_msgs.str();
}

/* llama.cpp completion api semantics */
json oaicompat_completion_params_parse(
    const json &body /* openai api json semantics */)
{
    json llama_params;

    llama_params["__oaicompat"] = true;

    // Map OpenAI parameters to llama.cpp parameters
    //
    // For parameters that are defined by the OpenAI documentation (e.g.
    // temperature), we explicitly specify OpenAI's intended default; we
    // need to do that because sometimes OpenAI disagrees with llama.cpp
    //
    // https://platform.openai.com/docs/api-reference/chat/create
    llama_sampling_params default_sparams;
    llama_params["model"]             = json_value(body, "model", std::string("unknown"));
    llama_params["prompt"]            = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
    llama_params["cache_prompt"]      = json_value(body, "cache_prompt", false);
    llama_params["temperature"]       = json_value(body, "temperature", 0.0);
    llama_params["top_k"]             = json_value(body, "top_k", default_sparams.top_k);
    llama_params["top_p"]             = json_value(body, "top_p", 1.0);
    llama_params["n_predict"]         = json_value(body, "max_tokens", -1);
    llama_params["logit_bias"]        = json_value(body, "logit_bias",json::object());
    llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
    llama_params["presence_penalty"]  = json_value(body, "presence_penalty", 0.0);
    llama_params["seed"]              = json_value(body, "seed", LLAMA_DEFAULT_SEED);
    llama_params["stream"]            = json_value(body, "stream", false);
    llama_params["mirostat"]          = json_value(body, "mirostat", default_sparams.mirostat);
    llama_params["mirostat_tau"]      = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
    llama_params["mirostat_eta"]      = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
    llama_params["penalize_nl"]       = json_value(body, "penalize_nl", default_sparams.penalize_nl);
    llama_params["typical_p"]         = json_value(body, "typical_p", default_sparams.typical_p);
    llama_params["repeat_last_n"]     = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
    llama_params["ignore_eos"]        = json_value(body, "ignore_eos", false);
    llama_params["tfs_z"]             = json_value(body, "tfs_z", default_sparams.tfs_z);

    if (body.count("grammar") != 0) {
        llama_params["grammar"] = json_value(body, "grammar", json::object());
    }

    // Handle 'stop' field
    if (body.contains("stop") && body["stop"].is_string()) {
        llama_params["stop"] = json::array({body["stop"].get<std::string>()});
    } else {
        llama_params["stop"] = json_value(body, "stop", json::array());
    }

    // Ensure there is ChatML-specific end sequence among stop words
    llama_params["stop"].push_back("<|im_end|>");

    return llama_params;
}

static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
{
    json result = response.result_json;

    bool stopped_word        = result.count("stopped_word") != 0;
    bool stopped_eos         = json_value(result, "stopped_eos", false);
    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
    std::string content      = json_value(result, "content", std::string(""));

    std::string finish_reason = "length";
    if (stopped_word || stopped_eos) {
        finish_reason = "stop";
    }

    json choices =
        streaming ? json::array({json{{"finish_reason", finish_reason},
                                        {"index", 0},
                                        {"delta", json::object()}}})
                  : json::array({json{{"finish_reason", finish_reason},
                                        {"index", 0},
                                        {"message", json{{"content", content},
                                                         {"role", "assistant"}}}}});

    std::time_t t = std::time(0);

    json res =
        json{{"choices", choices},
            {"created", t},
            {"model",
                json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
            {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
            {"usage",
                json{{"completion_tokens", num_tokens_predicted},
                     {"prompt_tokens",     num_prompt_tokens},
                     {"total_tokens",      num_tokens_predicted + num_prompt_tokens}}},
            {"id", gen_chatcmplid()}};

    if (server_verbose) {
        res["__verbose"] = result;
    }

    if (result.contains("completion_probabilities")) {
        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
    }

    return res;
}

// return value is vector as there is one case where we might need to generate two responses
static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
    json result = response.result_json;

    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
        return std::vector<json>({response.result_json});
    }

    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));

    bool stopped_word   = json_value(result, "stopped_word", false);
    bool stopped_eos    = json_value(result, "stopped_eos", false);
    bool stopped_limit  = json_value(result, "stopped_limit", false);
    std::string content = json_value(result, "content", std::string(""));

    std::string finish_reason;
    if (stopped_word || stopped_eos) {
        finish_reason = "stop";
    }
    if (stopped_limit) {
        finish_reason = "length";
    }

    std::time_t t = std::time(0);

    json choices;

    if (!finish_reason.empty()) {
        choices = json::array({json{{"finish_reason", finish_reason},
                                    {"index", 0},
                                    {"delta", json::object()}}});
    } else {
        if (first) {
            if (content.empty()) {
                choices = json::array({json{{"finish_reason", nullptr},
                                            {"index", 0},
                                            {"delta", json{{"role", "assistant"}}}}});
            } else {
                // We have to send this as two updates to conform to openai behavior
                json initial_ret = json{{"choices", json::array({json{
                                        {"finish_reason", nullptr},
                                        {"index", 0},
                                        {"delta", json{
                                            {"role", "assistant"}
                                        }}}})},
                            {"created", t},
                            {"id", gen_chatcmplid()},
                            {"model", modelname},
                            {"object", "chat.completion.chunk"}};

                json second_ret = json{
                            {"choices", json::array({json{{"finish_reason", nullptr},
                                                            {"index", 0},
                                                            {"delta", json{
                                                            {"content", content}}}
                                                            }})},
                            {"created", t},
                            {"id", gen_chatcmplid()},
                            {"model", modelname},
                            {"object", "chat.completion.chunk"}};

                return std::vector<json>({initial_ret, second_ret});
            }
        } else {
            // Some idiosyncrasy in task processing logic makes several trailing calls
            // with empty content, we ignore these at the calee site.
            if (content.empty()) {
                return std::vector<json>({json::object()});
            }

            choices = json::array({json{
                {"finish_reason", nullptr},
                {"index", 0},
                {"delta",
                json{
                    {"content", content},
                }},
            }});
        }
    }

    json ret = json{{"choices", choices},
                    {"created", t},
                    {"id", gen_chatcmplid()},
                    {"model", modelname},
                    {"object", "chat.completion.chunk"}};

    return std::vector<json>({ret});
}

static json format_partial_response(
    llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
) {
    json res = json
    {
        {"content",    content },
        {"stop",       false},
        {"slot_id",    slot->id },
        {"multimodal", llama.multimodal }
    };

    if (slot->sparams.n_probs > 0)
    {
        res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs);
    }

    return res;
}

static json format_tokenizer_response(const std::vector<llama_token> &tokens)
{
    return json{
        {"tokens", tokens}};
}

static json format_detokenized_response(std::string content)
{
    return json{
        {"content", content}};
}


static void log_server_request(const httplib::Request &req, const httplib::Response &res)
{
    LOG_INFO("request", {
                            {"remote_addr", req.remote_addr},
                            {"remote_port", req.remote_port},
                            {"status", res.status},
                            {"method", req.method},
                            {"path", req.path},
                            {"params", req.params},
                        });

    LOG_VERBOSE("request", {
                               {"request", req.body},
                               {"response", res.body},
                           });
}

struct token_translator
{
    llama_context * ctx;
    std::string operator()(llama_token tok)                    const { return llama_token_to_piece(ctx, tok); }
    std::string operator()(const completion_token_output &cto) const { return (*this)(cto.tok); }
};

static void append_to_generated_text_from_generated_token_probs(llama_server_context &llama, llama_client_slot *slot)
{
    auto & gtps = slot->generated_token_probs;
    auto translator = token_translator{llama.ctx};
    auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
    const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
    if (slot->generated_text.capacity() < slot->generated_text.size() + len)
    {
        slot->generated_text.reserve(slot->generated_text.size() + len);
    }
    for (const completion_token_output & cto : gtps)
    {
        slot->generated_text += translator(cto);
    }
}

int main(int argc, char **argv)
{
#if SERVER_VERBOSE != 1
    log_disable();
#endif
    // own arguments required by this example
    gpt_params params;
    server_params sparams;

    // struct that contains llama context and inference
    llama_server_context llama;

    server_params_parse(argc, argv, sparams, params, llama);

    if (params.model_alias == "unknown")
    {
        params.model_alias = params.model;
    }

    llama_backend_init(params.numa);

    LOG_INFO("build info", {{"build", LLAMA_BUILD_NUMBER},
                            {"commit", LLAMA_COMMIT}});

    LOG_INFO("system info", {
                                {"n_threads", params.n_threads},
                                {"n_threads_batch", params.n_threads_batch},
                                {"total_threads", std::thread::hardware_concurrency()},
                                {"system_info", llama_print_system_info()},
                            });

    httplib::Server svr;

    std::atomic<server_state> state{SERVER_STATE_LOADING_MODEL};

    svr.set_default_headers({{"Server", "llama.cpp"}});

    // CORS preflight
    svr.Options(R"(.*)", [](const httplib::Request &req, httplib::Response &res) {
        res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
        res.set_header("Access-Control-Allow-Credentials", "true");
        res.set_header("Access-Control-Allow-Methods", "POST");
        res.set_header("Access-Control-Allow-Headers", "*");
    });

    svr.Get("/health", [&](const httplib::Request&, httplib::Response& res) {
        server_state current_state = state.load();
        switch(current_state) {
            case SERVER_STATE_READY:
                res.set_content(R"({"status": "ok"})", "application/json");
                res.status = 200; // HTTP OK
                break;
            case SERVER_STATE_LOADING_MODEL:
                res.set_content(R"({"status": "loading model"})", "application/json");
                res.status = 503; // HTTP Service Unavailable
                break;
            case SERVER_STATE_ERROR:
                res.set_content(R"({"status": "error", "error": "Model failed to load"})", "application/json");
                res.status = 500; // HTTP Internal Server Error
                break;
        }
    });

    svr.set_logger(log_server_request);

    svr.set_exception_handler([](const httplib::Request &, httplib::Response &res, std::exception_ptr ep)
            {
                const char fmt[] = "500 Internal Server Error\n%s";
                char buf[BUFSIZ];
                try
                {
                    std::rethrow_exception(std::move(ep));
                }
                catch (std::exception &e)
                {
                    snprintf(buf, sizeof(buf), fmt, e.what());
                }
                catch (...)
                {
                    snprintf(buf, sizeof(buf), fmt, "Unknown Exception");
                }
                res.set_content(buf, "text/plain; charset=utf-8");
                res.status = 500;
            });

    svr.set_error_handler([](const httplib::Request &, httplib::Response &res)
            {
                if (res.status == 401)
                {
                    res.set_content("Unauthorized", "text/plain; charset=utf-8");
                }
                if (res.status == 400)
                {
                    res.set_content("Invalid request", "text/plain; charset=utf-8");
                }
                else if (res.status == 404)
                {
                    res.set_content("File Not Found", "text/plain; charset=utf-8");
                    res.status = 404;
                }
            });

    // set timeouts and change hostname and port
    svr.set_read_timeout (sparams.read_timeout);
    svr.set_write_timeout(sparams.write_timeout);

    if (!svr.bind_to_port(sparams.hostname, sparams.port))
    {
        fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port);
        return 1;
    }

    // Set the base directory for serving static files
    svr.set_base_dir(sparams.public_path);

    // to make it ctrl+clickable:
    LOG_TEE("\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port);

    std::unordered_map<std::string, std::string> log_data;
    log_data["hostname"] = sparams.hostname;
    log_data["port"] = std::to_string(sparams.port);

    if (sparams.api_keys.size() == 1) {
        log_data["api_key"] = "api_key: ****" + sparams.api_keys[0].substr(sparams.api_keys[0].length() - 4);
    } else if (sparams.api_keys.size() > 1) {
        log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded";
    }

    LOG_INFO("HTTP server listening", log_data);
    // run the HTTP server in a thread - see comment below
    std::thread t([&]()
            {
                if (!svr.listen_after_bind())
                {
                    state.store(SERVER_STATE_ERROR);
                    return 1;
                }

                return 0;
            });

    // load the model
    if (!llama.load_model(params))
    {
        state.store(SERVER_STATE_ERROR);
        return 1;
    } else {
        llama.initialize();
        state.store(SERVER_STATE_READY);
        LOG_INFO("model loaded", {});
    }

    // Middleware for API key validation
    auto validate_api_key = [&sparams](const httplib::Request &req, httplib::Response &res) -> bool {
        // If API key is not set, skip validation
        if (sparams.api_keys.empty()) {
            return true;
        }

        // Check for API key in the header
        auto auth_header = req.get_header_value("Authorization");
        std::string prefix = "Bearer ";
        if (auth_header.substr(0, prefix.size()) == prefix) {
            std::string received_api_key = auth_header.substr(prefix.size());
            if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) {
                return true; // API key is valid
            }
        }

        // API key is invalid or not provided
        res.set_content("Unauthorized: Invalid API Key", "text/plain; charset=utf-8");
        res.status = 401; // Unauthorized

        LOG_WARNING("Unauthorized: Invalid API Key", {});

        return false;
    };

    // this is only called if no index.html is found in the public --path
    svr.Get("/", [](const httplib::Request &, httplib::Response &res)
            {
                res.set_content(reinterpret_cast<const char*>(&index_html), index_html_len, "text/html; charset=utf-8");
                return false;
            });

    // this is only called if no index.js is found in the public --path
    svr.Get("/index.js", [](const httplib::Request &, httplib::Response &res)
            {
                res.set_content(reinterpret_cast<const char *>(&index_js), index_js_len, "text/javascript; charset=utf-8");
                return false;
            });

    // this is only called if no index.html is found in the public --path
    svr.Get("/completion.js", [](const httplib::Request &, httplib::Response &res)
            {
                res.set_content(reinterpret_cast<const char*>(&completion_js), completion_js_len, "application/javascript; charset=utf-8");
                return false;
            });

    // this is only called if no index.html is found in the public --path
    svr.Get("/json-schema-to-grammar.mjs", [](const httplib::Request &, httplib::Response &res)
            {
                res.set_content(reinterpret_cast<const char*>(&json_schema_to_grammar_mjs), json_schema_to_grammar_mjs_len, "application/javascript; charset=utf-8");
                return false;
            });

    svr.Get("/props", [&llama](const httplib::Request & req, httplib::Response &res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                json data = {
                    { "user_name",      llama.name_user.c_str() },
                    { "assistant_name", llama.name_assistant.c_str() }
                };
                res.set_content(data.dump(), "application/json; charset=utf-8");
            });

    svr.Post("/completion", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                if (!validate_api_key(req, res)) {
                    return;
                }
                json data = json::parse(req.body);
                const int task_id = llama.request_completion(data, false, false, -1);
                if (!json_value(data, "stream", false)) {
                    std::string completion_text;
                    task_result result = llama.next_result(task_id);
                    if (!result.error && result.stop) {
                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
                    }
                    else
                    {
                        res.status = 404;
                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
                        return;
                    }
                } else {
                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink)
                    {
                        while (true)
                        {
                            task_result result = llama.next_result(task_id);
                            if (!result.error) {
                                const std::string str =
                                    "data: " +
                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
                                    "\n\n";
                                LOG_VERBOSE("data stream", {
                                    { "to_send", str }
                                });
                                if (!sink.write(str.c_str(), str.size()))
                                {
                                    return false;
                                }
                                if (result.stop) {
                                    break;
                                }
                            } else {
                                const std::string str =
                                    "error: " +
                                    result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
                                    "\n\n";
                                LOG_VERBOSE("data stream", {
                                    { "to_send", str }
                                });
                                if (!sink.write(str.c_str(), str.size()))
                                {
                                    return false;
                                }
                                break;
                            }
                        }
                        sink.done();
                        return true;
                    };

                    auto on_complete = [task_id, &llama] (bool)
                    {
                        // cancel
                        llama.request_cancel(task_id);
                    };

                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
                }
            });

    svr.Get("/v1/models", [&params](const httplib::Request& req, httplib::Response& res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                std::time_t t = std::time(0);

                json models = {
                    {"object", "list"},
                    {"data", {
                        {
                            {"id", params.model_alias},
                            {"object", "model"},
                            {"created", t},
                            {"owned_by", "llamacpp"}
                        },
                    }}
                };

                res.set_content(models.dump(), "application/json; charset=utf-8");
            });


    // TODO: add mount point without "/v1" prefix -- how?
    svr.Post("/v1/chat/completions", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                if (!validate_api_key(req, res)) {
                    return;
                }
                json data = oaicompat_completion_params_parse(json::parse(req.body));

                const int task_id = llama.request_completion(data, false, false, -1);

                if (!json_value(data, "stream", false)) {
                    std::string completion_text;
                    task_result result = llama.next_result(task_id);

                    if (!result.error && result.stop) {
                        json oaicompat_result = format_final_response_oaicompat(data, result);

                        res.set_content(oaicompat_result.dump(-1, ' ', false,
                                            json::error_handler_t::replace),
                                            "application/json; charset=utf-8");
                    } else {
                        res.status = 500;
                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
                        return;
                    }
                } else {
                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
                        while (true) {
                            task_result llama_result = llama.next_result(task_id);
                            if (!llama_result.error) {
                                std::vector<json> result_array = format_partial_response_oaicompat( llama_result);

                                for (auto it = result_array.begin(); it != result_array.end(); ++it)
                                {
                                    if (!it->empty()) {
                                        const std::string str =
                                            "data: " +
                                            it->dump(-1, ' ', false, json::error_handler_t::replace) +
                                            "\n\n";
                                        LOG_VERBOSE("data stream", {{"to_send", str}});
                                        if (!sink.write(str.c_str(), str.size())) {
                                            return false;
                                        }
                                    }
                                }
                                if (llama_result.stop) {
                                    break;
                                }
                            } else {
                                const std::string str =
                                    "error: " +
                                    llama_result.result_json.dump(-1, ' ', false,
                                            json::error_handler_t::replace) +
                                    "\n\n";
                                LOG_VERBOSE("data stream", {{"to_send", str}});
                                if (!sink.write(str.c_str(), str.size())) {
                                    return false;
                                }
                                break;
                            }
                        }
                        sink.done();
                        return true;
                    };

                    auto on_complete = [task_id, &llama](bool) {
                        // cancel request
                        llama.request_cancel(task_id);
                    };

                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
                }
            });

    svr.Post("/infill", [&llama, &validate_api_key](const httplib::Request &req, httplib::Response &res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                if (!validate_api_key(req, res)) {
                    return;
                }
                json data = json::parse(req.body);
                const int task_id = llama.request_completion(data, true, false, -1);
                if (!json_value(data, "stream", false)) {
                    std::string completion_text;
                    task_result result = llama.next_result(task_id);
                    if (!result.error && result.stop)
                    {
                        res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8");
                    }
                    else
                    {
                        res.status = 404;
                        res.set_content(result.result_json["content"], "text/plain; charset=utf-8");
                        return;
                    }
                } else {
                    const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) {
                        while (true)
                        {
                            task_result result = llama.next_result(task_id);
                            if (!result.error) {
                                const std::string str =
                                "data: " +
                                result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
                                "\n\n";
                                LOG_VERBOSE("data stream", {
                                    { "to_send", str }
                                });
                                if (!sink.write(str.c_str(), str.size()))
                                {
                                    return false;
                                }
                                if (result.stop)
                                {
                                    break;
                                }
                            }
                            else
                            {
                                break;
                            }
                        }

                        sink.done();

                        return true;
                    };

                    auto on_complete = [task_id, &llama] (bool)
                    {
                        // cancel
                        llama.request_cancel(task_id);
                    };

                    res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
                }
            });

    svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res)
            {
                const json data = llama.get_model_props();
                return res.set_content(data.dump(), "application/json; charset=utf-8");
            });

    svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res)
                { return res.set_content("", "application/json; charset=utf-8"); });

    svr.Post("/tokenize", [&llama](const httplib::Request &req, httplib::Response &res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                const json body = json::parse(req.body);
                std::vector<llama_token> tokens;
                if (body.count("content") != 0)
                {
                    tokens = llama.tokenize(body["content"], false);
                }
                const json data = format_tokenizer_response(tokens);
                return res.set_content(data.dump(), "application/json; charset=utf-8");
            });

    svr.Post("/detokenize", [&llama](const httplib::Request &req, httplib::Response &res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                const json body = json::parse(req.body);
                std::string content;
                if (body.count("tokens") != 0)
                {
                    const std::vector<llama_token> tokens = body["tokens"];
                    content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
                }

                const json data = format_detokenized_response(content);
                return res.set_content(data.dump(), "application/json; charset=utf-8");
            });

    svr.Post("/embedding", [&llama](const httplib::Request &req, httplib::Response &res)
            {
                res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin"));
                const json body = json::parse(req.body);
                json prompt;
                if (body.count("content") != 0)
                {
                    prompt = body["content"];
                }
                else
                {
                    prompt = "";
                }

                json image_data;
                if (body.count("image_data") != 0) {
                    image_data = body["image_data"];
                }
                else
                {
                    image_data = "";
                }

                const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1);
                task_result result = llama.next_result(task_id);
                return res.set_content(result.result_json.dump(), "application/json; charset=utf-8");
            });

    // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!?
    //     "Bus error: 10" - this is on macOS, it does not crash on Linux
    //std::thread t2([&]()
    {
        bool running = true;
        while (running)
        {
            running = llama.update_slots();
        }
    }
    //);

    t.join();

    llama_backend_free();
    return 0;
}