llama.cpp/common/parsers/ling3.cpp

#include "parsers.h"

// Ling 3.0 / Bailing V3 - <role>X</role> sections with tagged tool calls:
//   assistant := [<think> ... </think>] [content] {<tool_call>name
//                  <arg_key>k</arg_key>\n<arg_value>v</arg_value> ...</tool_call>}
// The generation prompt ends with "<role>ASSISTANT</role>\n<think>", so the model
// never emits the opening think tag, and a tool call can arrive before any
// </think>. Reasoning therefore terminates at the think close tag or at a tool
// call start, like the Qwen3-Coder and Kimi K3 parsers. With thinking off the
// template pre-closes the think block instead, and the model emits bare content.
common_chat_params common_chat_params_init_ling3(const common_chat_template &          tmpl,
                                                        const autoparser::generation_params & inputs) {
    common_chat_params data;

    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
    data.supports_thinking = true;

    const std::string ROLE        = "<role>ASSISTANT</role>";
    const std::string THINK_START = "<think>";
    const std::string THINK_END   = "</think>";
    const std::string CALL_START  = "<tool_call>";
    const std::string CALL_END    = "</tool_call>";
    const std::string ARG_KEY     = "<arg_key>";
    const std::string ARG_KEY_END = "</arg_key>";
    const std::string ARG_VAL     = "<arg_value>";
    const std::string ROLE_END    = "<|role_end|>";
    const std::string ARG_VAL_END = "</arg_value>";

    data.preserved_tokens = {
        THINK_START, THINK_END, CALL_START, CALL_END,
        ARG_KEY, ARG_KEY_END, ARG_VAL, ARG_VAL_END, ROLE_END,
    };

    data.thinking_start_tag = THINK_START;
    // Support both </think> and <tool_call> as reasoning end sequences: a call
    // can be emitted before the think block is closed.
    data.thinking_end_tags  = { THINK_END, CALL_START };

    data.message_delimiters = {
        { COMMON_CHAT_ROLE_ASSISTANT, "<role>ASSISTANT</role>" },
        { COMMON_CHAT_ROLE_USER,      "<role>HUMAN</role>"     },
        { COMMON_CHAT_ROLE_TOOL,      "<role>OBSERVATION</role>" },
        { COMMON_CHAT_ROLE_SYSTEM,    "<role>SYSTEM</role>"    },
    };

    // the model may spell the end-of-turn control token out as text tokens,
    // which does not stop generation; a literal stop string catches it either
    // way (as the Laguna patch does for its </assistant> token)
    data.additional_stops = { ROLE_END };

    if (inputs.has_continuation()) {
        const auto & msg = inputs.continue_msg;

        data.generation_prompt = ROLE + "\n" + THINK_START + msg.reasoning_content;
        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
            data.generation_prompt += THINK_END + msg.render_content();
        }

        data.prompt += data.generation_prompt;
    }

    // The generation prompt pre-opens the think block when thinking is on, so
    // the opening tag is optional here and reasoning runs until </think> or a
    // tool call start; with thinking off the template pre-closes the block and
    // everything the model emits is content.
    bool think_open = false;
    if (inputs.has_continuation()) {
        think_open = inputs.continue_final_message != COMMON_CHAT_CONTINUATION_CONTENT;
    } else {
        auto last_open  = data.generation_prompt.rfind(THINK_START);
        auto last_close = data.generation_prompt.rfind(THINK_END);
        think_open      = last_open != std::string::npos &&
                     (last_close == std::string::npos || last_open > last_close);
    }

    auto has_tools         = inputs.tools.is_array() && !inputs.tools.empty();
    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
    auto include_grammar   = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;

    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
        auto end = p.end();

        // the effective parse input is generation_prompt + model output, so the
        // assistant opener is optionally consumed here
        auto opener = p.optional(p.literal(ROLE) + p.optional(p.space()));

        // the generation prompt pre-opens the think block, so the opening tag
        // is optional; a missing close tag does not swallow a tool call
        auto body_end   = think_open ? p.until_one_of({ THINK_END, CALL_START }) : p.until_one_of({ THINK_END });
        auto think_body = extract_reasoning ? p.reasoning(body_end) : p.content(body_end);

        auto reasoning = p.optional(p.optional(p.literal(THINK_START)) + think_body +
                                    p.optional(p.literal(THINK_END)));

        // content between the think block and the first tool call, plus any
        // trailing text after the last tool call, are plain content
        auto content = p.optional(p.content(p.until_one_of({ CALL_START })));

        // a trailing end-of-turn token is consumed instead of leaking into content
        auto tail = p.optional(p.content(p.until(ROLE_END))) + p.optional(p.literal(ROLE_END));

        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
            return opener + reasoning + tail + end;
        }

        auto tool_choices = p.choice();
        auto arg_close    = p.tool_arg_close(p.literal(ARG_VAL_END));
        auto arg_string   = p.rule("ling3-arg-string",
                                   p.tool_arg_string_value(p.until(ARG_VAL_END)) + arg_close);

        foreach_function(inputs.tools, [&](const json & tool) {
            const auto & function = tool.at("function");
            std::string  name     = function.at("name");

            std::vector<common_peg_parser> required_args;
            std::vector<common_peg_parser> optional_args;

            // each argument may be preceded by whitespace: the model emits
            // newlines between arguments, the template history does not
            foreach_parameter(function, [&](const common_chat_schema_property & param, const common_chat_schema_document_ptr & doc) {
                auto rule_name = "ling3-arg-" + name + "-" + param.name;

                auto types = param.schema->value_types();

                // string arguments are raw text up to the closing tag, other
                // types parse as JSON per their schema; each alternative
                // consumes the closing tag itself so a JSON prefix can not
                // commit the choice before the tag matches
                auto arg_value = p.eps();
                if (!types.has(common_chat_schema::TYPE_STRING)) {
                    arg_value = p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", doc, *param.schema)) + arg_close;
                } else if (types.is_only(common_chat_schema::TYPE_STRING)) {
                    arg_value = arg_string;
                } else {
                    // the parser tries the JSON alternative first to type the value
                    arg_value = p.gbnf(p.atomic(p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", doc, *param.schema)) + arg_close) | arg_string,
                                       "ling3-arg-string");
                }

                auto arg = p.rule(rule_name,
                    p.optional(p.space()) +
                    p.tool_arg(p.tool_arg_open(p.literal(ARG_KEY) + p.tool_arg_name(p.literal(param.name)) +
                                               p.literal(ARG_KEY_END)) +
                               p.optional(p.space()) + p.literal(ARG_VAL) +
                               arg_value));

                (param.required ? required_args : optional_args).push_back(arg);
            });

            // required arguments in any order (as Qwen3-Coder does), then
            // optional ones in any order and number
            auto args = p.permute("ling3-" + name + "-args", required_args);
            if (!optional_args.empty()) {
                args = args + p.zero_or_more(p.choice(optional_args));
            }

            auto call = p.tool(p.tool_open(p.literal(CALL_START) + p.tool_name(p.literal(name)) +
                                           p.optional(p.space())) +
                               p.tool_args(args) +
                               p.tool_close(p.optional(p.space()) + p.literal(CALL_END)));

            tool_choices |= p.rule("ling3-tool-" + name, call);
        });

        auto calls = inputs.parallel_tool_calls ?
                     tool_choices + p.zero_or_more(p.space() + tool_choices) :
                     tool_choices;

        auto tools_section = p.trigger_rule("ling3-tool-call", calls + p.space() +
                                        p.optional(p.content(p.until(ROLE_END))) + p.optional(p.literal(ROLE_END)));

        auto tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? tools_section :
                                                                             p.optional(tools_section);

        return opener + reasoning + content + tools + tail + end;
    });

    data.parser = parser.save();

    if (include_grammar) {
        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
            parser.build_grammar(builder, data.grammar_lazy);
        });

        data.grammar_triggers = {
            { COMMON_GRAMMAR_TRIGGER_TYPE_WORD, CALL_START },
        };
    }

    return data;
}