chat.h

// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers.

#pragma once

#include "common.h"
#include "peg-parser.h"
#include "jinja/parser.h"
#include "jinja/runtime.h"
#include "jinja/caps.h"

#include "nlohmann/json_fwd.hpp"

#include <chrono>
#include <functional>
#include <map>
#include <string>
#include <vector>

using chat_template_caps = jinja::caps;
using json = nlohmann::ordered_json;

struct common_chat_templates;

namespace autoparser {
struct generation_params;
}  // namespace autoparser

struct common_chat_tool_call {
    std::string name;
    std::string arguments;
    std::string id;

    bool operator==(const common_chat_tool_call & other) const {
        return name == other.name && arguments == other.arguments && id == other.id;
    }
};

struct common_chat_msg_content_part {
    std::string type;
    std::string text;

    // TODO @ngxson : no known chat templates support reasoning_content in content parts yet
    //                this can be useful for models with interleaved thinking (like Kimi-K2)
    //                if you see any templates explicitly support this, please ping me
    // std::string reasoning_content;

    bool operator==(const common_chat_msg_content_part & other) const {
        return type == other.type && text == other.text;
    }
};

struct common_chat_template {
    jinja::program prog;
    std::string bos_tok;
    std::string eos_tok;
    std::string src;
    chat_template_caps caps;

    common_chat_template(const std::string & src, const std::string & bos_token, const std::string & eos_token) {
        jinja::lexer lexer;
        auto lexer_res = lexer.tokenize(src);
        this->prog = jinja::parse_from_tokens(lexer_res);

        this->src = lexer_res.source;
        this->bos_tok = bos_token;
        this->eos_tok = eos_token;

        this->caps = jinja::caps_get(prog);
        // LOG_INF("%s: caps:\n%s\n", __func__, this->caps.to_string().c_str());
    }

    const std::string & source() const { return src; }
    const std::string & bos_token() const { return bos_tok; }
    const std::string & eos_token() const { return eos_tok; }

    chat_template_caps original_caps() const {
        return caps;
    }
};

struct common_chat_msg {
    std::string                               role;
    std::string                               content;
    std::vector<common_chat_msg_content_part> content_parts;
    std::vector<common_chat_tool_call>        tool_calls;
    std::string                               reasoning_content;
    std::string                               tool_name;
    std::string                               tool_call_id;

    nlohmann::ordered_json to_json_oaicompat(bool concat_typed_text = false) const;

    std::string render_content(const std::string & delimiter = "\n\n") const;

    bool empty() const {
        return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() &&
               tool_name.empty() && tool_call_id.empty();
    }

    bool contains_media() const {
        for (const auto & part : content_parts) {
            if (part.type == "media_marker") {
                return true;
            }
        }
        return false;
    }

    void set_tool_call_ids(std::vector<std::string> &           ids_cache,
                           const std::function<std::string()> & gen_tool_call_id) {
        for (auto i = 0u; i < tool_calls.size(); i++) {
            if (ids_cache.size() <= i) {
                auto id = tool_calls[i].id;
                if (id.empty()) {
                    id = gen_tool_call_id();
                }
                ids_cache.push_back(id);
            }
            tool_calls[i].id = ids_cache[i];
        }
    }

    bool operator==(const common_chat_msg & other) const {
        return role == other.role && content == other.content && content_parts == other.content_parts &&
               tool_calls == other.tool_calls && reasoning_content == other.reasoning_content &&
               tool_name == other.tool_name && tool_call_id == other.tool_call_id;
    }

    bool operator!=(const common_chat_msg & other) const { return !(*this == other); }
};

struct common_chat_msg_diff {
    std::string           reasoning_content_delta;
    std::string           content_delta;
    size_t                tool_call_index = std::string::npos;
    common_chat_tool_call tool_call_delta;

    static std::vector<common_chat_msg_diff> compute_diffs(const common_chat_msg & msg_prv,
                                                           const common_chat_msg & msg_new);

    bool operator==(const common_chat_msg_diff & other) const {
        return content_delta == other.content_delta && tool_call_index == other.tool_call_index &&
               tool_call_delta == other.tool_call_delta;
    }
};

enum common_chat_role {
    COMMON_CHAT_ROLE_UNKNOWN,
    COMMON_CHAT_ROLE_SYSTEM,
    COMMON_CHAT_ROLE_ASSISTANT,
    COMMON_CHAT_ROLE_USER,
    COMMON_CHAT_ROLE_TOOL
};

common_chat_role common_chat_role_from_string(const std::string & role);
const char *     common_chat_role_to_string(common_chat_role role);

struct common_chat_msg_span {
    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
    std::size_t pos = 0;
    std::size_t len = 0;

    bool valid() const {
        return role != COMMON_CHAT_ROLE_UNKNOWN;
    }
};

struct common_chat_msg_spans {
    std::vector<common_chat_msg_span> spans;

    void add(common_chat_role role, size_t pos, size_t len) {
        spans.push_back({ role, pos, len });
    }

    bool is_user_start(int32_t pos) const {
        for (auto it = spans.begin(); it != spans.end(); ++it) {
            if (it->role == COMMON_CHAT_ROLE_USER && pos == (int32_t) it->pos) {
                return true;
            }
        }
        return false;
    }

    int32_t last_user_message_pos() const {
        for (auto it = spans.rbegin(); it != spans.rend(); ++it) {
            if (it->role == COMMON_CHAT_ROLE_USER) {
                return (int32_t) it->pos;
            }
        }
        return -1;
    }
};

struct common_chat_msg_delimiter {
    common_chat_role role = COMMON_CHAT_ROLE_UNKNOWN;
    std::string      delimiter;
    llama_tokens     tokens = {};
};

struct common_chat_msg_delimiters {
    std::vector<common_chat_msg_delimiter> delimiters;

    common_chat_msg_delimiters() = default;
    common_chat_msg_delimiters(std::initializer_list<common_chat_msg_delimiter> delims) : delimiters(delims) {}

    void add(common_chat_role role, const std::string & delimiter) {
        delimiters.push_back({ role, delimiter });
    }

    void tokenize(const llama_vocab * vocab);

    // split tokens into message spans. skips maps a start index to a length of a region to jump over without matching
    common_chat_msg_spans split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips = {}) const;

    nlohmann::ordered_json to_json() const;
};

struct common_chat_tool {
    std::string name;
    std::string description;
    std::string parameters;
};

enum common_chat_tool_choice {
    COMMON_CHAT_TOOL_CHOICE_AUTO,
    COMMON_CHAT_TOOL_CHOICE_REQUIRED,
    COMMON_CHAT_TOOL_CHOICE_NONE,
};

enum common_chat_format {
    COMMON_CHAT_FORMAT_CONTENT_ONLY,

    // These are intended to be parsed by the PEG parser
    COMMON_CHAT_FORMAT_PEG_SIMPLE,
    COMMON_CHAT_FORMAT_PEG_NATIVE,
    COMMON_CHAT_FORMAT_PEG_GEMMA4,

    COMMON_CHAT_FORMAT_COUNT,  // Not a format, just the # formats
};


// Continuation method provided via `continue_final_message`
enum common_chat_continuation {
    COMMON_CHAT_CONTINUATION_NONE,
    COMMON_CHAT_CONTINUATION_AUTO,
    COMMON_CHAT_CONTINUATION_REASONING,
    COMMON_CHAT_CONTINUATION_CONTENT,
};

struct common_chat_templates_inputs {
    std::vector<common_chat_msg>          messages;
    std::string                           grammar;
    std::string                           json_schema;
    bool                                  add_generation_prompt  = true;
    common_chat_continuation              continue_final_message = COMMON_CHAT_CONTINUATION_NONE;
    bool                                  use_jinja              = true;
    // Parameters below only supported when use_jinja is true
    std::vector<common_chat_tool>         tools;
    common_chat_tool_choice               tool_choice         = COMMON_CHAT_TOOL_CHOICE_AUTO;
    bool                                  parallel_tool_calls = false;
    common_reasoning_format               reasoning_format    = COMMON_REASONING_FORMAT_NONE; // TODO: refactor this to "bool enable_thinking"
    bool                                  enable_thinking     = true;
    std::chrono::system_clock::time_point now                 = std::chrono::system_clock::now();
    std::map<std::string, std::string>    chat_template_kwargs;
    bool                                  add_bos = false;
    bool                                  add_eos = false;
    bool                                  force_pure_content = false;
};

struct common_chat_params {
    common_chat_format                  format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
    std::string                         prompt;
    std::string                         grammar;
    bool                                grammar_lazy         = false;
    std::string                         generation_prompt;
    bool                                supports_thinking    = false;
    std::string                         thinking_start_tag;  // e.g., "<think>"
    std::string                         thinking_end_tag;    // e.g., "</think>"
    std::vector<common_grammar_trigger> grammar_triggers;
    std::vector<std::string>            preserved_tokens;
    std::vector<std::string>            additional_stops;
    std::string                         parser;
    common_chat_msg_delimiters          message_delimiters;
};

// per-message parsing syntax
// should be derived from common_chat_params
struct common_chat_parser_params {
    common_chat_format      format               = COMMON_CHAT_FORMAT_CONTENT_ONLY;
    common_reasoning_format reasoning_format     = COMMON_REASONING_FORMAT_NONE; // TODO: refactor this to "bool parse_reasoning"
    // Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode)
    bool                    reasoning_in_content = false;
    std::string             generation_prompt;
    bool                    parse_tool_calls     = true;
    bool                    is_continuation      = false;
    bool                    echo                 = false;  // Include assistant prefilled msg in output
    bool                    debug                = false;  // Enable debug output for PEG parser
    common_peg_arena        parser               = {};
    common_chat_parser_params() = default;
    common_chat_parser_params(const common_chat_params & chat_params) {
        format  = chat_params.format;
        generation_prompt = chat_params.generation_prompt;
    }
};

// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);

void common_chat_templates_free(struct common_chat_templates * tmpls);

struct common_chat_templates_deleter {
    void operator()(common_chat_templates * tmpls) { common_chat_templates_free(tmpls); }
};

typedef std::unique_ptr<struct common_chat_templates, common_chat_templates_deleter> common_chat_templates_ptr;

common_chat_templates_ptr common_chat_templates_init(const struct llama_model * model,
                                                     const std::string &        chat_template_override,
                                                     const std::string &        bos_token_override = "",
                                                     const std::string &        eos_token_override = "");

bool        common_chat_templates_was_explicit(const struct common_chat_templates * tmpls);
std::string common_chat_templates_source(const struct common_chat_templates * tmpls, const std::string & variant = "");

struct common_chat_params common_chat_templates_apply(const struct common_chat_templates *        tmpls,
                                                      const struct common_chat_templates_inputs & inputs);

// Format single message, while taking into account the position of that message in chat history
std::string common_chat_format_single(const struct common_chat_templates * tmpls,
                                      const std::vector<common_chat_msg> & past_msg,
                                      const common_chat_msg &              new_msg,
                                      bool                                 add_ass,
                                      bool                                 use_jinja);

// Returns an example of formatted chat
std::string common_chat_format_example(const struct common_chat_templates *       tmpls,
                                       bool                                       use_jinja,
                                       const std::map<std::string, std::string> & chat_template_kwargs);

const char *    common_chat_format_name(common_chat_format format);
common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_parser_params & params);
common_chat_msg common_chat_peg_parse(const common_peg_arena & src_parser, const std::string & input, bool is_partial, const common_chat_parser_params & params);

// used by arg and server
const char *            common_reasoning_format_name(common_reasoning_format format);
common_reasoning_format common_reasoning_format_from_name(const std::string & format);

common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);

bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates);

// Parses a JSON array of messages in OpenAI's chat completion API format.
std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const nlohmann::ordered_json & messages);

std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const nlohmann::ordered_json & tools);

common_chat_continuation common_chat_continuation_parse(const nlohmann::ordered_json & value);

// DEPRECATED: only used in tests
nlohmann::ordered_json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);

nlohmann::ordered_json common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools);

// get template caps, useful for reporting to server /props endpoint
std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_templates * chat_templates);

std::string common_chat_template_direct_apply(
    const common_chat_template & tmpl,
    const autoparser::generation_params & inputs);

std::string common_chat_template_generation_prompt(
    const common_chat_template &          tmpl,
    const autoparser::generation_params & inputs);

std::optional<common_chat_params> common_chat_try_specialized_template(
        const common_chat_template &          tmpl,
        const std::string &                   src,
        autoparser::generation_params & params);


// specialized per-task preset
struct common_chat_prompt_preset {
    std::string system;
    std::string user;
};

common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates);

common_chat_msg_delimiters common_chat_msg_delimiters_parse(const nlohmann::ordered_json & delimiters);