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koboldcpp/common/chat.cpp
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Piotr Wilkin (ilintar) 895c045fd1 chat : split specialized parsers into common/parsers (#27764) 
* chat : split specialized parsers into common/parsers

Move the 14 dedicated template parsers out of chat.cpp into one file each under
common/parsers, mirroring the src/models split. chat.cpp keeps the template
detection in common_chat_try_specialized_template() and drops from 3915 to 1513
lines.

common/parsers/parsers.h holds the shared helpers and one declaration per
parser. foreach_function/foreach_parameter become inline there since nothing in
chat.cpp uses them any more; common_chat_template_direct_apply_impl and
common_chat_template_generation_prompt_impl lose static and carry their default
arguments in the header. Parser-specific helpers move with their parser:
is_lfm2_template, deepseek_v4_sort_tool_results and the gemma4 turn builder.

No functional change.

Assisted-by: Claude Opus 5

* chat : enumerate parser sources instead of globbing

file(GLOB) does not re-run CMake when a source file is added or removed, so an
incremental build silently keeps building the old set. List the parsers in
common/parsers/sources.cmake and include it from common/CMakeLists.txt.

Assisted-by: Claude Opus 5

* split helpers, add newlines
2026-09-08 09:29:37 +03:00

1514 lines
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#include "chat.h"
#include "chat-auto-parser-helpers.h"
#include "chat-auto-parser.h"
#include "chat-peg-parser.h"
#include "common.h"
#include "ggml.h"
#include "json-schema-to-grammar.h"
#include "json.h"
#include "log.h"
#include "parsers/parsers.h"
#include "jinja/value.h"
#include "jinja/runtime.h"
#include "jinja/caps.h"
#include "peg-parser.h"
#include <algorithm>
#include <cstdio>
#include <cstdlib>
#include <ctime>
#include <exception>
#include <functional>
#include <iomanip>
#include <map>
#include <optional>
#include <sstream>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>
using json = common_json;
static std::string format_time(const std::chrono::system_clock::time_point & now, const std::string & format) {
auto time = std::chrono::system_clock::to_time_t(now);
auto local_time = *std::localtime(&time);
std::ostringstream ss;
ss << std::put_time(&local_time, format.c_str());
auto res = ss.str();
return res;
}
static json safe_args_parse(const std::string & to_parse) {
std::string stripped = to_parse;
if (to_parse.at(0) == '"' && to_parse.at(to_parse.length() - 1) == '"') {
stripped = to_parse.substr(1, to_parse.length() - 1);
}
try {
return json::parse(stripped);
} catch (const common_json_error & e) {
return stripped;
}
}
static std::string string_diff(const std::string & last, const std::string & current) {
if (last.empty()) {
return current;
}
if (!string_starts_with(current, last)) {
if (string_starts_with(last, current)) {
// This happens if the last generation ended on a partial stop word (not erased),
// and the current ended on a stop word (erased).
return "";
}
throw std::runtime_error("Invalid diff: '" + last + "' not found at start of '" + current + "'");
}
return current.substr(last.size());
}
static bool has_content_or_tool_calls(const common_chat_msg & msg) {
return !msg.content.empty() || !msg.tool_calls.empty();
}
std::string common_chat_msg::render_content(const std::string & delimiter) const {
if (!content.empty() && !content_parts.empty()) {
throw std::runtime_error("Cannot specify both content and content_parts");
}
if (!content.empty()) {
return content;
}
std::string text;
for (const auto & part : content_parts) {
if (part.type == "text") {
if (!text.empty()) {
text += delimiter;
}
text += part.text;
}
}
return text;
}
common_chat_role common_chat_role_from_string(const std::string & role) {
if (role == "system") { return COMMON_CHAT_ROLE_SYSTEM; }
if (role == "assistant") { return COMMON_CHAT_ROLE_ASSISTANT; }
if (role == "user") { return COMMON_CHAT_ROLE_USER; }
if (role == "tool") { return COMMON_CHAT_ROLE_TOOL; }
return COMMON_CHAT_ROLE_UNKNOWN;
}
const char * common_chat_role_to_string(common_chat_role role) {
switch (role) {
case COMMON_CHAT_ROLE_SYSTEM: return "system";
case COMMON_CHAT_ROLE_ASSISTANT: return "assistant";
case COMMON_CHAT_ROLE_USER: return "user";
case COMMON_CHAT_ROLE_TOOL: return "tool";
case COMMON_CHAT_ROLE_UNKNOWN: return "";
}
return "";
}
json common_chat_msg_delimiters::to_json() const {
json result = json::array();
for (const auto & d : delimiters) {
result.push_back({
{ "role", common_chat_role_to_string(d.role) },
{ "delimiter", d.delimiter },
});
}
return result;
}
common_chat_msg_delimiters common_chat_msg_delimiters_parse(const json & delimiters) {
common_chat_msg_delimiters result;
if (!delimiters.is_array()) {
return result;
}
result.delimiters.reserve(delimiters.size());
for (const auto & d : delimiters) {
if (!d.is_object()) {
continue;
}
result.delimiters.push_back({
common_chat_role_from_string(d.value("role", std::string())),
d.value("delimiter", std::string()),
});
}
return result;
}
void common_chat_msg_delimiters::tokenize(const llama_vocab * vocab) {
for (auto & d : delimiters) {
d.tokens = common_tokenize(vocab, d.delimiter, false, true);
}
}
common_chat_msg_spans common_chat_msg_delimiters::split(const llama_tokens & tokens, const std::map<size_t, size_t> & skips) const {
std::vector<std::pair<common_chat_role, size_t>> matches;
auto skip = skips.begin();
for (size_t i = 0; i < tokens.size();) {
if (skip != skips.end() && i == skip->first) {
i += skip->second;
++skip;
continue;
}
for (const auto & d : delimiters) {
if (i + d.tokens.size() > tokens.size()) {
continue;
}
if (std::equal(d.tokens.begin(), d.tokens.end(), tokens.begin() + i)) {
matches.emplace_back(d.role, i);
break;
}
}
i++;
}
matches.emplace_back(COMMON_CHAT_ROLE_UNKNOWN, tokens.size());
common_chat_msg_spans spans;
for (size_t i = 0; i + 1 < matches.size(); i++) {
const auto & curr = matches[i];
const auto & next = matches[i + 1];
spans.add(curr.first, curr.second, next.second - curr.second);
}
return spans;
}
json common_chat_msg::to_json_oaicompat(bool concat_typed_text) const {
if (!content.empty() && !content_parts.empty()) {
throw std::runtime_error("Cannot specify both content and content_parts");
}
json jmsg {
{"role", role},
};
if (!content.empty()) {
jmsg["content"] = content;
} else if (!content_parts.empty()) {
if (concat_typed_text || contains_media()) {
std::string text;
bool last_was_media_marker = false;
// join parts with newline, do not add newline before or after media markers
for (const auto & part : content_parts) {
bool add_new_line = true;
if (part.type == "text") {
add_new_line = !last_was_media_marker && !text.empty();
last_was_media_marker = false;
} else if (part.type == "media_marker") {
add_new_line = false;
last_was_media_marker = true;
} else {
LOG_WRN("Ignoring content part type: %s\n", part.type.c_str());
continue;
}
if (add_new_line) {
text += '\n';
}
text += part.text;
}
jmsg["content"] = text;
} else {
auto & parts = jmsg["content"] = json::array();
for (const auto & part : content_parts) {
parts.push_back({
{"type", part.type},
{"text", part.text},
});
}
}
} else {
jmsg["content"] = "";
}
if (!reasoning_content.empty()) {
jmsg["reasoning_content"] = reasoning_content;
}
if (!tool_name.empty()) {
jmsg["name"] = tool_name;
}
if (!tool_call_id.empty()) {
jmsg["tool_call_id"] = tool_call_id;
}
if (!tool_calls.empty()) {
jmsg["tool_calls"] = json::array();
auto & jtool_calls = jmsg["tool_calls"];
for (const auto & tool_call : tool_calls) {
json tc {
{"type", "function"},
{"function", {
{"name", tool_call.name},
{"arguments", json(tool_call.arguments)},
}},
};
if (!tool_call.id.empty()) {
tc["id"] = tool_call.id;
}
// Some templates generate and require an id (sometimes in a very specific format, e.g. Mistral Nemo).
// We only generate a random id for the ones that don't generate one by themselves
// (they also won't get to see it as their template likely doesn't use it, so it's all for the client)
// {"id", tc.id.empty() ? gen_tool_call_id() : tc.id},
jtool_calls.push_back(tc);
}
}
return jmsg;
}
std::vector<common_chat_msg_diff> common_chat_msg_diff::compute_diffs(const common_chat_msg & msg_prv,
const common_chat_msg & msg_new) {
std::vector<common_chat_msg_diff> diffs;
if (msg_new.tool_calls.size() > msg_prv.tool_calls.size()) {
diffs.reserve(msg_new.tool_calls.size() - msg_prv.tool_calls.size() + 3);
} else {
diffs.reserve(3);
}
// TODO: these can become expensive for long messages - how to optimize?
if (msg_prv.reasoning_content != msg_new.reasoning_content) {
auto & diff = diffs.emplace_back();
diff.reasoning_content_delta = string_diff(msg_prv.reasoning_content, msg_new.reasoning_content);
}
if (msg_prv.content != msg_new.content) {
auto & diff = diffs.emplace_back();
diff.content_delta = string_diff(msg_prv.content, msg_new.content);
}
if (msg_new.tool_calls.size() < msg_prv.tool_calls.size()) {
std::string err = "Invalid diff: now finding less tool calls!\n";
err += " Previous (" + std::to_string(msg_prv.tool_calls.size()) + "):\n";
for (const auto & tc : msg_prv.tool_calls) {
err += " - name: '" + tc.name + "', args: '" + tc.arguments + "'\n";
}
err += " Current (" + std::to_string(msg_new.tool_calls.size()) + "):\n";
for (const auto & tc : msg_new.tool_calls) {
err += " - name: '" + tc.name + "', args: '" + tc.arguments + "'\n";
}
err += " Current msg text content:\n" + msg_new.content + "\n";
throw std::runtime_error(err);
}
if (!msg_prv.tool_calls.empty()) {
const auto idx = msg_prv.tool_calls.size() - 1;
const auto & pref = msg_prv.tool_calls[idx];
const auto & newf = msg_new.tool_calls[idx];
// Allow tool name to change during incremental parsing:
// - empty -> non-empty (initial discovery)
// - prefix -> longer string (name grows as more input is parsed)
if (pref.name != newf.name && !pref.name.empty() && !newf.name.empty()) {
// Check if one is a prefix of the other (for incremental parsing where names grow or shrink)
bool is_prefix = (newf.name.rfind(pref.name, 0) == 0);
if (!is_prefix) {
LOG_ERR("Tool call mismatch: prev='%s' new='%s'\n", pref.name.c_str(), newf.name.c_str());
throw std::runtime_error("Invalid diff: tool call mismatch!");
}
}
const auto args_diff = string_diff(pref.arguments, newf.arguments);
if (!args_diff.empty() || pref.id != newf.id || pref.name != newf.name) {
auto & diff = diffs.emplace_back();
diff.tool_call_index = idx;
if (pref.id != newf.id || pref.name != newf.name) {
diff.tool_call_delta.id = newf.id;
diff.tool_call_delta.name = newf.name;
}
diff.tool_call_delta.arguments = args_diff;
}
}
for (size_t idx = msg_prv.tool_calls.size(); idx < msg_new.tool_calls.size(); ++idx) {
auto & diff = diffs.emplace_back();
diff.tool_call_index = idx;
diff.tool_call_delta = msg_new.tool_calls[idx];
}
return diffs;
}
using chat_template_caps = jinja::caps;
struct common_chat_templates {
bool add_bos;
bool add_eos;
bool has_explicit_template; // Model had builtin template or template overridden was specified.
std::unique_ptr<common_chat_template> template_default; // always set (defaults to chatml)
std::unique_ptr<common_chat_template> template_tool_use;
};
common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice) {
if (tool_choice == "auto") {
return COMMON_CHAT_TOOL_CHOICE_AUTO;
}
if (tool_choice == "none") {
return COMMON_CHAT_TOOL_CHOICE_NONE;
}
if (tool_choice == "required") {
return COMMON_CHAT_TOOL_CHOICE_REQUIRED;
}
throw std::invalid_argument("Invalid tool_choice: " + tool_choice);
}
bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates) {
common_chat_templates_inputs inputs;
inputs.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
common_chat_msg msg;
msg.role = "user";
msg.content = "test";
inputs.messages = { msg };
inputs.enable_thinking = true;
inputs.add_generation_prompt = true;
inputs.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
auto params = common_chat_templates_apply(chat_templates, inputs);
return params.supports_thinking;
}
std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messages) {
std::vector<common_chat_msg> msgs;
try {
if (!messages.is_array()) {
throw std::invalid_argument("Expected 'messages' to be an array, got " + messages.dump());
}
for (const auto & message : messages) {
if (!message.is_object()) {
throw std::invalid_argument("Expected 'message' to be an object, got " + message.dump());
}
common_chat_msg msg;
if (!message.contains("role")) {
throw std::invalid_argument("Missing 'role' in message: " + message.dump());
}
msg.role = message.at("role");
auto has_content = message.contains("content");
auto has_tool_calls = message.contains("tool_calls");
if (has_content) {
const auto & content = message.at("content");
if (content.is_string()) {
msg.content = content;
} else if (content.is_array()) {
for (const auto & part : content) {
if (!part.contains("type")) {
throw std::invalid_argument("Missing content part type: " + part.dump());
}
const auto & type = part.at("type");
if (type != "text" && type != "media_marker") {
throw std::invalid_argument("Unsupported content part type: " + type.dump());
}
common_chat_msg_content_part msg_part;
msg_part.type = type;
msg_part.text = part.at("text");
msg.content_parts.push_back(msg_part);
}
} else if (!content.is_null()) {
throw std::invalid_argument("Invalid 'content' type: expected string or array, got " +
content.dump() +
" (ref: https://github.com/ggml-org/llama.cpp/issues/8367)");
}
}
if (has_tool_calls) {
for (const auto & tool_call : message.at("tool_calls")) {
common_chat_tool_call tc;
if (!tool_call.contains("type")) {
throw std::invalid_argument("Missing tool call type: " + tool_call.dump());
}
const auto & type = tool_call.at("type");
if (type != "function") {
throw std::invalid_argument("Unsupported tool call type: " + tool_call.dump());
}
if (!tool_call.contains("function")) {
throw std::invalid_argument("Missing tool call function: " + tool_call.dump());
}
const auto & fc = tool_call.at("function");
if (!fc.contains("name")) {
throw std::invalid_argument("Missing tool call name: " + tool_call.dump());
}
tc.name = fc.at("name");
const auto & args = fc.at("arguments");
if (args.is_string()) {
tc.arguments = args;
} else {
tc.arguments = args.dump();
}
if (tool_call.contains("id")) {
tc.id = tool_call.at("id");
}
msg.tool_calls.push_back(tc);
}
}
if (!has_content && !has_tool_calls) {
throw std::invalid_argument(
"Expected 'content' or 'tool_calls' (ref: https://github.com/ggml-org/llama.cpp/issues/8367 & "
"https://github.com/ggml-org/llama.cpp/issues/12279)");
}
if (message.contains("reasoning_content")) {
msg.reasoning_content = message.at("reasoning_content");
}
if (message.contains("name")) {
msg.tool_name = message.at("name");
}
if (message.contains("tool_call_id")) {
msg.tool_call_id = message.at("tool_call_id");
}
msgs.push_back(msg);
}
} catch (const std::exception & e) {
// @ngxson : disable otherwise it's bloating the API response
// printf("%s\n", std::string("; messages = ") + messages.dump(2));
throw std::runtime_error("Failed to parse messages: " + std::string(e.what()));
}
return msgs;
}
struct messages_inp_normalizer {
const jinja::caps & caps;
messages_inp_normalizer(const jinja::caps & c) : caps(c) {}
// handle supports_string_content / supports_typed_content
// if string=true and array=false, convert array to string
// if string=false and array=true, convert string to array
// if both are true, do nothing
json normalize(const json & messages) {
bool only_string = caps.supports_string_content && !caps.supports_typed_content;
bool only_typed = !caps.supports_string_content && caps.supports_typed_content;
if ((!only_string && !only_typed) || !messages.is_array()) {
return messages;
}
json normalized = json::array();
for (const auto & msg : messages) {
json copy = msg;
if (copy.contains("content")) {
json & it = copy.at("content");
if (only_typed && it.is_string()) {
it = json::array({
json{
{"type", "text"},
{"text", it.get<std::string>()},
}
});
} else if (only_string && it.is_array()) {
it = concat_content_parts(it);
}
}
normalized.push_back(std::move(copy));
}
return normalized;
}
// join parts with newline, do not add newline before or after media markers
static std::string concat_content_parts(const json & parts) {
std::string text;
bool last_was_media_marker = false;
for (const auto & part : parts) {
std::string type = part.value("type", "");
bool add_new_line = true;
if (type == "text") {
add_new_line = !last_was_media_marker && !text.empty();
last_was_media_marker = false;
} else if (type == "media_marker") {
add_new_line = false;
last_was_media_marker = true;
} else {
LOG_WRN("Ignoring content part type: %s\n", type.c_str());
continue;
}
if (add_new_line) {
text += '\n';
}
text += part.value("text", "");
}
return text;
}
};
static json render_message_to_json(const std::vector<common_chat_msg> & msgs, const jinja::caps & c) {
if (!c.supports_string_content && !c.supports_typed_content) {
LOG_WRN("%s: Neither string content nor typed content is supported by the template. This is unexpected and may lead to issues.\n", __func__);
}
json messages = json::array();
for (const auto & msg : msgs) {
messages.push_back(msg.to_json_oaicompat(/* concat_typed_text= */ false));
}
return messages_inp_normalizer(c).normalize(messages);
}
// DEPRECATED: only used in tests
json common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text) {
jinja::caps c;
c.supports_string_content = true;
c.supports_typed_content = !concat_typed_text;
return render_message_to_json(msgs, c);
}
json common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools) {
if (tools.empty()) {
return json();
}
auto result = json::array();
for (const auto & tool : tools) {
result.push_back({
{ "type", "function" },
{ "function", {
{ "name", tool.name },
{ "description", tool.description },
{ "parameters", json::parse(tool.parameters) },
}},
});
}
return result;
}
std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & tools) {
std::vector<common_chat_tool> result;
try {
if (!tools.is_null()) {
if (!tools.is_array()) {
throw std::invalid_argument("Expected 'tools' to be an array, got " + tools.dump());
}
for (const auto & tool : tools) {
if (!tool.contains("type")) {
throw std::invalid_argument("Missing tool type: " + tool.dump());
}
const auto & type = tool.at("type");
if (!type.is_string() || type != "function") {
throw std::invalid_argument("Unsupported tool type: " + tool.dump());
}
if (!tool.contains("function")) {
throw std::invalid_argument("Missing tool function: " + tool.dump());
}
const auto & function = tool.at("function");
result.push_back({
/* .name = */ function.at("name"),
/* .description = */ function.value("description", ""),
/* .parameters = */ function.value("parameters", json::object()).dump(),
});
}
}
} catch (const std::exception & e) {
throw std::runtime_error("Failed to parse tools: " + std::string(e.what()) + "; tools = " + tools.dump(2));
}
return result;
}
common_chat_continuation common_chat_continuation_parse(const common_json & value) {
if (value.is_boolean() && value.get<bool>()) {
return COMMON_CHAT_CONTINUATION_AUTO;
}
if (value.is_string()) {
auto value_str = value.get<std::string>();
if (value_str == "reasoning_content") {
return COMMON_CHAT_CONTINUATION_REASONING;
}
if (value_str == "content") {
return COMMON_CHAT_CONTINUATION_CONTENT;
}
}
return COMMON_CHAT_CONTINUATION_NONE;
}
bool common_chat_verify_template(const std::string & tmpl, bool use_jinja) {
if (use_jinja) {
try {
common_chat_msg msg;
msg.role = "user";
msg.content = "test";
auto tmpls = common_chat_templates_init(/* model= */ nullptr, tmpl);
common_chat_templates_inputs inputs;
inputs.messages = { msg };
common_chat_templates_apply(tmpls.get(), inputs);
return true;
} catch (const std::exception & e) {
LOG_ERR("%s: failed to apply template: %s\n", __func__, e.what());
return false;
}
}
llama_chat_message chat[] = {
{ "user", "test" }
};
const int res = llama_chat_apply_template(tmpl.c_str(), chat, 1, true, nullptr, 0);
return res >= 0;
}
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) {
common_chat_templates_inputs inputs;
inputs.use_jinja = use_jinja;
inputs.add_bos = tmpls->add_bos;
inputs.add_eos = tmpls->add_eos;
std::string fmt_past_msg;
if (!past_msg.empty()) {
inputs.messages = past_msg;
inputs.add_generation_prompt = false;
fmt_past_msg = common_chat_templates_apply(tmpls, inputs).prompt;
}
std::ostringstream ss;
// if the past_msg ends with a newline, we must preserve it in the formatted version
if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
ss << "\n";
};
// format chat with new_msg
inputs.messages.push_back(new_msg);
inputs.add_generation_prompt = add_ass;
auto fmt_new_msg = common_chat_templates_apply(tmpls, inputs).prompt;
// get the diff part
ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
return ss.str();
}
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) {
common_chat_templates_inputs inputs;
inputs.use_jinja = use_jinja;
inputs.add_bos = tmpls->add_bos;
inputs.add_eos = tmpls->add_eos;
inputs.chat_template_kwargs = chat_template_kwargs;
auto add_simple_msg = [&](auto role, auto content) {
common_chat_msg msg;
msg.role = role;
msg.content = content;
inputs.messages.push_back(msg);
};
add_simple_msg("system", "You are a helpful assistant");
add_simple_msg("user", "Hello");
add_simple_msg("assistant", "Hi there");
add_simple_msg("user", "How are you?");
return common_chat_templates_apply(tmpls, inputs).prompt;
}
#define CHATML_TEMPLATE_SRC \
"{%- for message in messages -%}\n" \
" {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' -}}\n" \
"{%- endfor -%}\n" \
"{%- if add_generation_prompt -%}\n" \
" {{- '<|im_start|>assistant\n' -}}\n" \
"{%- endif -%}"
void common_chat_templates_free(struct common_chat_templates * tmpls) {
delete tmpls;
}
bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls) {
return tmpls->has_explicit_template;
}
common_chat_prompt_preset common_chat_get_asr_prompt(const common_chat_templates * chat_templates) {
common_chat_prompt_preset asr_preset;
asr_preset.system = "";
asr_preset.user = "Transcribe audio to text";
if (chat_templates && chat_templates->template_default && is_lfm2_template(chat_templates->template_default->source())) {
asr_preset.system = "Perform ASR.";
asr_preset.user = "";
}
return asr_preset;
}
std::string common_chat_templates_source(const struct common_chat_templates * tmpls, const std::string & variant) {
if (!variant.empty()) {
if (variant == "tool_use") {
if (tmpls->template_tool_use) {
return tmpls->template_tool_use->source();
}
return "";
}
LOG_DBG("%s: unknown template variant: %s\n", __func__, variant.c_str());
}
return tmpls->template_default->source();
}
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) {
std::string default_template_src;
std::string template_tool_use_src;
bool has_explicit_template = !chat_template_override.empty();
if (chat_template_override.empty()) {
GGML_ASSERT(model != nullptr);
const auto * str = llama_model_chat_template(model, /* name */ nullptr);
if (str) {
default_template_src = str;
has_explicit_template = true;
}
str = llama_model_chat_template(model, /* name */ "tool_use");
if (str) {
template_tool_use_src = str;
has_explicit_template = true;
}
} else {
default_template_src = chat_template_override;
}
if (default_template_src.empty() || default_template_src == "chatml") {
if (!template_tool_use_src.empty()) {
default_template_src = template_tool_use_src;
} else {
default_template_src = CHATML_TEMPLATE_SRC;
}
}
// TODO @ngxson : this is a temporary hack to prevent chat template from throwing an error
// Ref: https://github.com/ggml-org/llama.cpp/pull/15230#issuecomment-3173959633
if (default_template_src.find("<|channel|>") != std::string::npos
// search for the error message and patch it
&& default_template_src.find("in message.content or") != std::string::npos) {
string_replace_all(default_template_src,
"{%- if \"<|channel|>analysis<|message|>\" in message.content or "
"\"<|channel|>final<|message|>\" in message.content %}",
"{%- if false %}");
}
// TODO @aldehir : this is a temporary fix, pending Minja changes
// Ref: https://github.com/ggml-org/llama.cpp/pull/17713#issuecomment-3631342664
if (default_template_src.find("[TOOL_CALLS]") != std::string::npos
// search for the error message and patch it
&& default_template_src.find("if (message['content'] is none or") != std::string::npos) {
string_replace_all(default_template_src,
"{%- if (message['content'] is none or message['content'] == '' or "
"message['content']|length == 0) and (message['tool_calls'] is not defined or "
"message['tool_calls'] is none or message['tool_calls']|length == 0) %}",
"{%- if false %}");
}
std::string token_bos = bos_token_override;
std::string token_eos = eos_token_override;
bool add_bos = false;
bool add_eos = false;
if (model) {
const auto * vocab = llama_model_get_vocab(model);
const auto get_token = [&](llama_token token, const char * name, const char * jinja_variable_name) {
if (token == LLAMA_TOKEN_NULL) {
if (default_template_src.find(jinja_variable_name) != std::string::npos ||
template_tool_use_src.find(jinja_variable_name) != std::string::npos) {
LOG_WRN(
"common_chat_templates_init: warning: vocab does not have a %s token, jinja template won't "
"work as intended.\n",
name);
}
return std::string();
}
return common_token_to_piece(vocab, token, true);
};
token_bos = get_token(llama_vocab_bos(vocab), "BOS", "bos_token");
token_eos = get_token(llama_vocab_eos(vocab), "EOS", "eos_token");
add_bos = llama_vocab_get_add_bos(vocab);
add_eos = llama_vocab_get_add_eos(vocab);
}
common_chat_templates_ptr tmpls(new common_chat_templates());
tmpls->has_explicit_template = has_explicit_template;
tmpls->add_bos = add_bos;
tmpls->add_eos = add_eos;
try {
tmpls->template_default = std::make_unique<common_chat_template>(default_template_src, token_bos, token_eos);
} catch (const std::exception & e) {
LOG_ERR("%s: error: %s\n", __func__, e.what());
LOG_ERR("%s: failed to initialize chat template\n", __func__);
LOG_ERR("%s: please consider disabling jinja via --no-jinja, or using another chat template\n", __func__);
throw e;
}
if (!template_tool_use_src.empty()) {
try {
tmpls->template_tool_use = std::make_unique<common_chat_template>(template_tool_use_src, token_bos, token_eos);
} catch (const std::exception & e) {
LOG_ERR("%s: failed to parse tool use chat template (ignoring it): %s\n", __func__, e.what());
}
}
return tmpls;
}
const char * common_chat_format_name(common_chat_format format) {
switch (format) {
case COMMON_CHAT_FORMAT_CONTENT_ONLY:
return "Content-only";
case COMMON_CHAT_FORMAT_PEG_SIMPLE:
return "peg-simple";
case COMMON_CHAT_FORMAT_PEG_NATIVE:
return "peg-native";
case COMMON_CHAT_FORMAT_PEG_GEMMA4:
return "peg-gemma4";
case COMMON_CHAT_FORMAT_PEG_MINIMAX_M3:
return "peg-minimax-m3";
default:
throw std::runtime_error("Unknown chat format");
}
}
const char * common_reasoning_format_name(common_reasoning_format format) {
switch (format) {
case COMMON_REASONING_FORMAT_NONE:
return "none";
case COMMON_REASONING_FORMAT_AUTO:
return "auto";
case COMMON_REASONING_FORMAT_DEEPSEEK:
return "deepseek";
case COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY:
return "deepseek-legacy";
default:
throw std::runtime_error("Unknown reasoning format");
}
}
common_reasoning_format common_reasoning_format_from_name(const std::string & format) {
if (format == "none") {
return COMMON_REASONING_FORMAT_NONE;
}
if (format == "auto") {
return COMMON_REASONING_FORMAT_AUTO;
}
if (format == "deepseek") {
return COMMON_REASONING_FORMAT_DEEPSEEK;
}
if (format == "deepseek-legacy") {
return COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY;
}
throw std::runtime_error("Unknown reasoning format: " + format);
}
std::string common_chat_template_direct_apply_impl(
const common_chat_template & tmpl,
const autoparser::generation_params & inputs,
const std::optional<json> & messages_override,
const std::optional<json> & tools_override,
const std::optional<json> & additional_context) {
jinja::context ctx(tmpl.source());
// messages_override is already built for this template, do not touch its content parts
json inp = json{
{"messages", messages_override.has_value()
? *messages_override
: messages_inp_normalizer(tmpl.original_caps()).normalize(inputs.messages)},
{"bos_token", tmpl.bos_token()},
{"eos_token", tmpl.eos_token()},
{"enable_thinking", inputs.enable_thinking},
};
if (tools_override.has_value() || !inputs.tools.empty()) {
inp["tools"] = tools_override.has_value() ? *tools_override : inputs.tools;
}
if (inputs.extra_context.is_object()) {
// TODO: do we need to merge, or replacing is fine?
for (const auto & [k, v] : inputs.extra_context.items()) {
inp[k] = v;
}
}
if (additional_context.has_value()) {
// TODO: merge properly instead of overwriting (matching old behavior)
for (const auto & [k, v] : additional_context->items()) {
inp[k] = v;
}
}
if (inputs.add_generation_prompt) {
inp["add_generation_prompt"] = true;
}
if (inp.contains("preserve_reasoning") && inp["preserve_reasoning"].is_boolean()) {
bool enabled = inp["preserve_reasoning"].get<bool>();
jinja::caps_apply_preserve_reasoning(ctx, enabled);
}
if (inp.contains("reasoning_effort") && inp["reasoning_effort"].is_string() && !inp["reasoning_effort"].empty()) {
std::string reasoning_effort = inp["reasoning_effort"].get<std::string>();
jinja::caps_apply_reasoning_effort(ctx, reasoning_effort);
}
jinja::global_from_json(ctx, inp, inputs.mark_input);
// render
jinja::runtime runtime(ctx);
const jinja::value results = runtime.execute(tmpl.prog);
auto parts = jinja::runtime::gather_string_parts(results);
std::string result = parts->as_string().str();
// TODO: improve this later
if (inputs.add_bos && string_starts_with(result, tmpl.bos_token())) {
result = result.substr(tmpl.bos_token().size());
}
if (inputs.add_eos && string_ends_with(result, tmpl.eos_token())) {
result = result.substr(0, result.size() - tmpl.eos_token().size());
}
return result;
}
std::string common_chat_template_direct_apply(
const common_chat_template & tmpl,
const autoparser::generation_params & inputs) {
return common_chat_template_direct_apply_impl(tmpl, inputs, std::nullopt, std::nullopt, std::nullopt);
}
std::string common_chat_template_generation_prompt_impl(
const common_chat_template & tmpl,
const autoparser::generation_params & inputs,
const std::optional<json> & messages_override,
const std::optional<json> & tools_override,
const std::optional<json> & additional_context) {
autoparser::generation_params params = inputs;
params.add_generation_prompt = false;
params.continue_final_message = COMMON_CHAT_CONTINUATION_NONE;
std::string no_gen_prompt = common_chat_template_direct_apply_impl(tmpl, params, messages_override, tools_override, additional_context);
params.add_generation_prompt = true;
std::string gen_prompt = common_chat_template_direct_apply_impl(tmpl, params, messages_override, tools_override, additional_context);
size_t prefix_len = 0;
size_t min_size = std::min(no_gen_prompt.size(), gen_prompt.size());
while (prefix_len < min_size && no_gen_prompt[prefix_len] == gen_prompt[prefix_len]) {
prefix_len++;
}
return gen_prompt.substr(prefix_len);
}
std::string common_chat_template_generation_prompt(
const common_chat_template & tmpl,
const autoparser::generation_params & inputs) {
return common_chat_template_generation_prompt_impl(tmpl, inputs, std::nullopt, std::nullopt, std::nullopt);
}
namespace workaround {
static void map_developer_role_to_system(json & messages) {
for (auto & message : messages) {
if (message.contains("role")) {
if (message["role"] == "developer") {
message["role"] = "system";
}
}
}
}
// if first message is system and template does not support it, merge it with next message
static void system_message_not_supported(json & messages) {
if (!messages.empty() && messages.front().at("role") == "system") {
if (messages.size() > 1) {
LOG_DBG("Merging system prompt into next message\n");
auto & first_msg = messages.front();
auto & second_msg = messages[1];
second_msg["content"] = first_msg.at("content").get<std::string>()
+ "\n" + second_msg.at("content").get<std::string>();
messages.erase(0);
} else {
LOG_WRN("Removing system prompt due to template not supporting system role\n");
messages.erase(0);
}
}
}
static void requires_non_null_content(json & messages) {
GGML_ASSERT(messages.is_array());
for (auto & message : messages) {
if (message.contains("tool_calls") && !message.contains("content")) {
message["content"] = "";
}
}
}
static void func_args_not_string(json & messages) {
GGML_ASSERT(messages.is_array());
for (auto & message : messages) {
if (message.contains("tool_calls")) {
for (auto & tool_call : message["tool_calls"]) {
if (tool_call.contains("function") && tool_call["function"].contains("arguments")) {
auto & args = tool_call["function"]["arguments"];
if (args.is_string()) {
try {
args = json::parse(args.get<std::string>());
} catch (const std::exception & e) {
throw std::runtime_error("Failed to parse tool call arguments as JSON: " + std::string(e.what()));
}
}
}
}
}
}
}
// Trim leading/trailing whitespace from message contents before rendering. This
// has to run on the messages (not on the rendered JSON) because templates with
// string-only content caps concatenate typed content parts into a single string
// during rendering, after which the per-part whitespace can no longer be reached.
// Both the plain string content and the text of typed content parts are trimmed.
static void trim_all_content(std::vector<common_chat_msg> & messages) {
for (auto & message : messages) {
message.content = trim_whitespace(message.content);
message.reasoning_content = trim_whitespace(message.reasoning_content);
for (auto & part : message.content_parts) {
if (part.type == "text") {
part.text = trim_whitespace(part.text);
}
}
}
}
}
static json common_chat_extra_context() {
json ctx = json::object();
std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
std::string datetime_str = format_time(now, "%b %d %Y");
std::string date_str = format_time(now, "%d %b %Y");
ctx["datetime"] = datetime_str;
ctx["date_string"] = date_str;
return ctx;
}
std::optional<common_chat_params> common_chat_try_specialized_template(
const common_chat_template & tmpl,
const std::string & src,
autoparser::generation_params & params) {
// Ministral/Mistral Large 3 - uses special reasoning structure fixes, can't use autoparser
// Note: Mistral Small 3.2 uses [CALL_ID] which Ministral doesn't have, so we can distinguish them
if (src.find("[SYSTEM_PROMPT]") != std::string::npos && src.find("[TOOL_CALLS]") != std::string::npos &&
src.find("[ARGS]") != std::string::npos && src.find("[CALL_ID]") == std::string::npos) {
LOG_DBG("Using specialized template: Ministral/Magistral Large 3\n");
return common_chat_params_init_ministral_3(tmpl, params);
}
// GPT-OSS - has unique channel-based structure that needs dedicated handler
if (src.find("<|channel|>") != std::string::npos) {
LOG_DBG("Using specialized template: GPT-OSS\n");
return common_chat_params_init_gpt_oss(tmpl, params);
}
// Muse Glimmer format using " to=<recipient>" recipients and <|eom|>/<|eot|> message terminators.
if (src.find("<atem:function_calls>") != std::string::npos && src.find("<|eom|>") != std::string::npos) {
LOG_DBG("Using specialized template: Muse Glimmer\n");
return common_chat_params_init_muse_glimmer(tmpl, params);
}
// Functionary v3.2 - uses recipient-based format with >>>recipient\n{content}
// Detection: template has ">>>all" for content and ">>>" prefix for tool calls
if (src.find(">>>all") != std::string::npos && src.find(">>>${recipient}") != std::string::npos) {
LOG_DBG("Using specialized template: Functionary v3.2\n");
return common_chat_params_init_functionary_v3_2(tmpl, params);
}
// Kimi K2 Thinking - uses unique tool call ID format: functions.<name>:<index>
// Detection: template has "<|tool_calls_section_begin|>" and "functions." prefix in tool call IDs
if (src.find("<|tool_calls_section_begin|>") != std::string::npos &&
src.find("<|tool_call_begin|>") != std::string::npos) {
LOG_DBG("Using specialized template: Kimi K2 Thinking\n");
return common_chat_params_init_kimi_k2(tmpl, params);
}
// Kimi K3 - the <|open|>/<|close|>/<|end_of_msg|> markers are unique to it
if (src.find("<|open|>") != std::string::npos && src.find("<|close|>") != std::string::npos &&
src.find("<|end_of_msg|>") != std::string::npos) {
LOG_DBG("Using specialized template: Kimi K3\n");
return common_chat_params_init_kimi_k3(tmpl, params);
}
// Cohere2 MoE / North Code - marker-wrapped format with <|START_TEXT|> content and
// <|START_ACTION|> JSON tool calls. <|START_TEXT|> is unique to this template (the older
// Command-R templates use <|START_RESPONSE|>).
if (src.find("<|START_TEXT|>") != std::string::npos &&
src.find("<|START_ACTION|>") != std::string::npos) {
LOG_DBG("Using specialized template: Cohere2 MoE\n");
return common_chat_params_init_cohere2moe(tmpl, params);
}
if (is_lfm2_template(src)) {
LOG_DBG("Using specialized template: LFM2\n");
return common_chat_params_init_lfm2(tmpl, params, /* tool_list_tokens = */ true);
}
// LFM2.5 format detection: template uses plain "List of tools: [...]" with no special tokens
if (src.find("List of tools: [") != std::string::npos &&
src.find("<|tool_list_start|>") == std::string::npos) {
LOG_DBG("Using specialized template: LFM2.5\n");
return common_chat_params_init_lfm2(tmpl, params, /* tool_list_tokens = */ false);
}
// GigaChatV3 format detection
if (src.find("<|role_sep|>") != std::string::npos &&
src.find("<|message_sep|>") != std::string::npos &&
src.find("<|function_call|>") == std::string::npos) {
LOG_DBG("Using specialized template: GigaChatV3\n");
return common_chat_params_init_gigachat_v3(tmpl, params);
}
// MiniMax-M3: the namespace token "]<]minimax[>[" collides with the autoparser's
// markup delimiters, so detect the template and use a dedicated parser.
if (src.find("]<]minimax[>[") != std::string::npos &&
src.find("<tool_call>") != std::string::npos &&
src.find("<invoke name=") != std::string::npos) {
LOG_DBG("Using specialized template: MiniMax-M3\n");
return common_chat_params_init_minimax_m3(tmpl, params);
}
// DeepSeek V3.2/V4 format detection: template defines dsml_token and uses it for tool calls.
// The template source contains the token as a variable assignment, not as a literal in markup.
// V3.2 names the tool call block "function_calls", V4 names it "tool_calls".
if (src.find("dsml_token") != std::string::npos &&
src.find("DSML") != std::string::npos &&
(src.find("function_calls") != std::string::npos ||
src.find("tool_calls") != std::string::npos)) {
LOG_DBG("Using specialized template: DeepSeek V3.2/V4\n");
return common_chat_params_init_deepseek_v3_2(tmpl, params);
}
// Gemma4 format detection
if (src.find("'<|tool_call>call:'") != std::string::npos) {
if (src.find("{#- OpenAI Chat Completions:") == std::string::npos) {
// apply workarounds if using the older gemma4 templates
LOG_WRN("%s: detected an outdated gemma4 chat template, applying compatibility workarounds. "
"Consider updating to the official template.\n", __func__);
workaround::convert_tool_responses_gemma4(params.messages);
}
return common_chat_params_init_gemma4(tmpl, params);
}
// MiniCPM5 - XML tool calls with <function name="..."><param name="...">...</param></function>
if (src.find("Tool usage guidelines:") != std::string::npos &&
src.find("<function name=\"") != std::string::npos &&
src.find("<param name=\"") != std::string::npos) {
LOG_DBG("Using specialized template: MiniCPM5\n");
return common_chat_params_init_minicpm5(tmpl, params);
}
// Qwen3-Coder XML tool calls, also used by Nemotron Nano 3, Qwen3.5 and StepFun-3.5-Flash
if (src.find("<tool_call>") != std::string::npos &&
src.find("<function=") != std::string::npos &&
src.find("<parameter=") != std::string::npos) {
LOG_DBG("Using specialized template: Qwen3-Coder\n");
return common_chat_params_init_qwen3_coder(tmpl, params);
}
return std::nullopt;
}
static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates * tmpls,
const struct common_chat_templates_inputs & inputs) {
autoparser::generation_params params;
params.tools = common_chat_tools_to_json_oaicompat(inputs.tools);
const auto & tmpl =
params.tools.is_array() && tmpls->template_tool_use ? *tmpls->template_tool_use : *tmpls->template_default;
const auto & src = tmpl.source();
const auto & caps = tmpl.original_caps();
std::vector<common_chat_msg> trimmed_messages;
const std::vector<common_chat_msg> * messages_to_render = &inputs.messages;
if (src.find("You have access to the following functions in JSONSchema format") != std::string::npos) {
// StepFun: trim message contents (including typed content parts) before rendering,
// otherwise leftover whitespace drives the model into reasoning loops (issue #24181)
trimmed_messages = inputs.messages;
workaround::trim_all_content(trimmed_messages);
messages_to_render = &trimmed_messages;
}
params.messages = render_message_to_json(*messages_to_render, tmpl.original_caps());
params.tool_choice = inputs.tool_choice;
params.reasoning_format = inputs.reasoning_format;
params.enable_thinking = inputs.enable_thinking;
params.grammar = inputs.grammar;
params.now = inputs.now;
params.add_generation_prompt = inputs.add_generation_prompt;
params.add_bos = tmpls->add_bos;
params.add_eos = tmpls->add_eos;
params.continue_final_message = inputs.continue_final_message;
if (params.continue_final_message != COMMON_CHAT_CONTINUATION_NONE) {
params.add_generation_prompt = false;
if (!inputs.messages.empty()) {
// Render messages[:-1] and store continuation message separately
params.continue_msg = inputs.messages.back();
params.messages.erase(params.messages.size() - 1);
}
if (params.continue_final_message == COMMON_CHAT_CONTINUATION_AUTO && !inputs.messages.empty()) {
// Resolve based on message content
params.continue_final_message = COMMON_CHAT_CONTINUATION_CONTENT;
if (!params.continue_msg.reasoning_content.empty() &&
params.continue_msg.content.empty() &&
params.continue_msg.content_parts.empty()) {
params.continue_final_message = COMMON_CHAT_CONTINUATION_REASONING;
}
}
}
if (src.find("<|channel|>") == std::string::npos) {
// map developer to system for all models except for GPT-OSS
workaround::map_developer_role_to_system(params.messages);
}
if (!tmpl.original_caps().supports_system_role) {
workaround::system_message_not_supported(params.messages);
}
if (tmpl.original_caps().supports_tool_calls) {
// some templates will require the content field in tool call messages
// to still be non-null, this puts an empty string everywhere where the
// content field is null
workaround::requires_non_null_content(params.messages);
}
if (tmpl.original_caps().supports_object_arguments) {
workaround::func_args_not_string(params.messages);
}
params.extra_context = common_chat_extra_context();
for (auto el : inputs.chat_template_kwargs) {
params.extra_context[el.first] = json::parse(el.second);
}
if (!inputs.json_schema.empty()) {
params.json_schema = json::parse(inputs.json_schema);
}
params.parallel_tool_calls = inputs.parallel_tool_calls;
if (params.tools.is_array()) {
if (params.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && !params.grammar.empty()) {
throw std::runtime_error("Cannot specify grammar with tools");
}
if (caps.supports_tool_calls && !caps.supports_tools) {
LOG_WRN(
"Template supports tool calls but does not natively describe tools. The fallback behaviour used may "
"produce bad results, inspect prompt w/ --verbose & consider overriding the template.\n");
}
}
if (inputs.force_pure_content) {
LOG_WRN("Forcing pure content template, will not render reasoning or tools separately.");
// Create the result structure
common_chat_params data;
auto params_copy = params;
params_copy.reasoning_format = COMMON_REASONING_FORMAT_NONE;
data.prompt = common_chat_template_direct_apply_impl(tmpl, params_copy);
data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, params);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
auto parser = build_chat_peg_parser([&data](common_chat_peg_builder &p) {
return p.literal(data.generation_prompt) << p.content(p.rest());
});
data.parser = parser.save();
return data;
}
if (auto result = common_chat_try_specialized_template(tmpl, src, params)) {
return *result;
}
try {
LOG_DBG("%s: using differential autoparser\n", __func__);
struct autoparser::autoparser autoparser;
autoparser.analyze_template(tmpl);
auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
common_chat_msg_delimiters delimiters;
if (!autoparser.assistant_start.empty()) {
delimiters.add(COMMON_CHAT_ROLE_ASSISTANT, autoparser.assistant_start);
}
if (!autoparser.user_start.empty()) {
delimiters.add(COMMON_CHAT_ROLE_USER, autoparser.user_start);
}
auto_params.message_delimiters = std::move(delimiters);
auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
if (auto_params.supports_thinking) {
auto_params.thinking_start_tag = trim_whitespace(autoparser.reasoning.start);
auto end_tag = trim_whitespace(autoparser.reasoning.end);
if (!end_tag.empty()) {
auto_params.thinking_end_tags = {std::move(end_tag)};
}
}
common_peg_arena arena;
arena.load(auto_params.parser);
LOG_DBG("%s: generated parser:\n%s\n\nparser generation prompt: %s\n", __func__, arena.dump(arena.root()).c_str(), auto_params.generation_prompt.c_str());
return auto_params;
} catch (const std::exception & e) {
throw std::invalid_argument(std::string("Unable to generate parser for this template. Automatic parser generation failed: ") + e.what());
}
}
// Legacy template route (adhoc C++ implementation of known templates), forward to llama_chat_apply_template.
static common_chat_params common_chat_templates_apply_legacy(const struct common_chat_templates * tmpls,
const struct common_chat_templates_inputs & inputs) {
size_t alloc_size = 0;
std::vector<llama_chat_message> chat;
std::vector<std::string> contents;
for (const auto & msg : inputs.messages) {
auto content = msg.content;
for (const auto & part : msg.content_parts) {
if (part.type != "text" && part.type != "media_marker") {
LOG_WRN("Ignoring non-text content part: %s\n", part.type.c_str());
continue;
}
if (!content.empty()) {
content += "\n";
;
}
content += part.text;
}
contents.emplace_back(std::move(content));
}
for (size_t i = 0; i < contents.size(); ++i) {
const auto & msg = inputs.messages[i];
const auto & content = contents[i];
chat.push_back({ msg.role.c_str(), content.c_str() });
size_t msg_size = msg.role.size() + content.size();
alloc_size += msg_size + (msg_size / 4); // == msg_size * 1.25 but avoiding float ops
}
std::vector<char> buf(alloc_size);
// run the first time to get the total output length
const auto & src = tmpls->template_default->source();
int32_t res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt,
buf.data(), buf.size());
// error: chat template is not supported
if (res < 0) {
// if the custom "tmpl" is not supported, we throw an error
// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
throw std::runtime_error("this custom template is not supported, try using --jinja");
}
// if it turns out that our buffer is too small, we resize it
if ((size_t) res > buf.size()) {
buf.resize(res);
res = llama_chat_apply_template(src.c_str(), chat.data(), chat.size(), inputs.add_generation_prompt, buf.data(),
buf.size());
}
// for safety, we check the result again
if (res < 0 || (size_t) res > buf.size()) {
throw std::runtime_error("failed to apply chat template, try using --jinja");
}
common_chat_params params;
params.prompt = std::string(buf.data(), res);
if (!inputs.json_schema.empty()) {
params.grammar = json_schema_to_grammar(json::parse(inputs.json_schema));
} else {
params.grammar = inputs.grammar;
}
return params;
}
common_chat_params common_chat_templates_apply(const struct common_chat_templates * tmpls,
const struct common_chat_templates_inputs & inputs) {
GGML_ASSERT(tmpls != nullptr);
return inputs.use_jinja ? common_chat_templates_apply_jinja(tmpls, inputs) :
common_chat_templates_apply_legacy(tmpls, inputs);
}
common_chat_msg common_chat_parse(const std::string & input,
bool is_partial,
const common_chat_parser_params & params) {
return common_chat_peg_parse(params.parser, input, is_partial, 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) {
const common_peg_arena & parser = src_parser.empty() ?
build_chat_peg_parser([](common_chat_peg_builder & p) { return p.content(p.rest()) + p.end(); }) :
src_parser;
if (src_parser.empty()) {
LOG_DBG("No parser definition detected, assuming pure content parser.");
}
const std::string effective_input = params.generation_prompt.empty()
? input
: params.generation_prompt + input;
//LOG_DBG("Parsing PEG input with format %s: %s\n", common_chat_format_name(params.format), effective_input.c_str());
common_peg_parse_flags flags = COMMON_PEG_PARSE_FLAG_LENIENT;
if (params.debug) {
flags |= COMMON_PEG_PARSE_FLAG_DEBUG;
}
common_peg_parse_context ctx(effective_input, flags);
auto result = parser.parse(ctx);
if (result.fail()) {
// During partial parsing, return partial results if any AST nodes were captured
// This allows streaming to work correctly for formats like FUNC_MARKDOWN_CODE_BLOCK
if (is_partial && result.end > 0) {
// Try to extract any partial results from what was successfully parsed
common_chat_msg msg;
msg.role = "assistant";
std::unique_ptr<common_chat_peg_mapper> mapper;
if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) {
mapper = std::make_unique<common_chat_peg_gemma4_mapper>(msg);
} else if (params.format == COMMON_CHAT_FORMAT_PEG_MINIMAX_M3) {
mapper = std::make_unique<common_chat_peg_minimax_m3_mapper>(msg);
} else {
mapper = std::make_unique<common_chat_peg_mapper>(msg);
}
mapper->from_ast(ctx.ast, result);
if (ctx.is_debug()) {
fprintf(stderr, "\nAST for partial parse (fail):\n%s\n", ctx.ast.dump().c_str());
fflush(stderr);
}
return msg;
}
LOG_WRN("%s: unparsed %s output: %s\n", __func__, common_chat_format_name(params.format), effective_input.substr(result.end).c_str());
LOG_DBG("%s: full %s output triggering error:\n=== BEGIN ===\n%s\n=== END ===\n", __func__, common_chat_format_name(params.format), effective_input.c_str());
throw std::runtime_error(std::string("The model produced output that does not match the expected ") + common_chat_format_name(params.format) + " format");
}
common_chat_msg msg;
msg.role = "assistant";
std::unique_ptr<common_chat_peg_mapper> mapper;
if (params.format == COMMON_CHAT_FORMAT_PEG_GEMMA4) {
mapper = std::make_unique<common_chat_peg_gemma4_mapper>(msg);
} else if (params.format == COMMON_CHAT_FORMAT_PEG_MINIMAX_M3) {
mapper = std::make_unique<common_chat_peg_minimax_m3_mapper>(msg);
} else {
mapper = std::make_unique<common_chat_peg_mapper>(msg);
}
mapper->from_ast(ctx.ast, result);
if (ctx.is_debug()) {
fprintf(stderr, "\nAST for %s parse:\n%s\n", is_partial ? "partial" : "full", ctx.ast.dump().c_str());
fflush(stderr);
}
if (!is_partial) {
LOG_DBG("Parsed message: %s\n", common_chat_msgs_to_json_oaicompat({ msg }).at(0).dump().c_str());
}
return msg;
}
std::map<std::string, bool> common_chat_templates_get_caps(const common_chat_templates * chat_templates) {
GGML_ASSERT(chat_templates != nullptr);
GGML_ASSERT(chat_templates->template_default != nullptr);
if (chat_templates->template_tool_use != nullptr) {
// take the more expressive template when available
return chat_templates->template_tool_use->caps.to_map();
}
return chat_templates->template_default->caps.to_map();
}
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