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1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 | #include "chat.h"
#include "chat-auto-parser.h"
#include "chat-peg-parser.h"
#include "common.h"
#include "ggml.h"
#include "json-schema-to-grammar.h"
#include "log.h"
#include "jinja/value.h"
#include "jinja/runtime.h"
#include "jinja/caps.h"
#include "peg-parser.h"
#include <cstdio>
#include <cstdlib>
#include <ctime>
#include <exception>
#include <functional>
#include <optional>
#include <sstream>
#include <stdexcept>
#include <string>
#include <vector>
using json = nlohmann::ordered_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 (json::exception & 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();
}
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) {
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 overridde 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;
}
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__);
}
bool only_string_accepted = c.supports_string_content && !c.supports_typed_content;
bool only_typed_accepted = !c.supports_string_content && c.supports_typed_content;
json messages = json::array();
for (const auto & msg : msgs) {
if (only_string_accepted) {
json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ true);
messages.push_back(jmsg);
} else if (only_typed_accepted) {
json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
if (jmsg.at("content").is_string()) {
jmsg["content"] = json::array({
json{
{"type", "text"},
{"text", jmsg.at("content").get<std::string>()},
}
});
}
messages.push_back(jmsg);
} else {
json jmsg = msg.to_json_oaicompat(/* concat_typed_text= */ false);
messages.push_back(jmsg);
}
}
return 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);
}
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;
}
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;
}
json common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff) {
json delta = json::object();
if (!diff.reasoning_content_delta.empty()) {
delta["reasoning_content"] = diff.reasoning_content_delta;
}
if (!diff.content_delta.empty()) {
delta["content"] = diff.content_delta;
}
if (diff.tool_call_index != std::string::npos) {
json tool_call;
tool_call["index"] = diff.tool_call_index;
if (!diff.tool_call_delta.id.empty()) {
tool_call["id"] = diff.tool_call_delta.id;
tool_call["type"] = "function";
}
if (!diff.tool_call_delta.name.empty() || !diff.tool_call_delta.arguments.empty()) {
json function = json::object();
if (!diff.tool_call_delta.name.empty()) {
function["name"] = diff.tool_call_delta.name;
}
if (!diff.tool_call_delta.arguments.empty()) {
function["arguments"] = diff.tool_call_delta.arguments;
}
tool_call["function"] = function;
}
delta["tool_calls"] = json::array({ tool_call });
}
return delta;
}
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;
}
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";
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);
}
static void foreach_function(const json & tools, const std::function<void(const json &)> & fn) {
for (const auto & tool : tools) {
if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) {
LOG_INF("Skipping tool without function: %s", tool.dump(2).c_str());
continue;
}
fn(tool);
}
}
static void foreach_parameter(const json & function,
const std::function<void(const std::string &, const json &, bool)> & fn) {
if (!function.contains("parameters") || !function.at("parameters").is_object()) {
return;
}
const auto & params = function.at("parameters");
if (!params.contains("properties") || !params.at("properties").is_object()) {
return;
}
const auto & props = params.at("properties");
std::set<std::string> required;
if (params.contains("required") && params.at("required").is_array()) {
params.at("required").get_to(required);
}
for (const auto & [name, prop] : props.items()) {
bool is_required = (required.find(name) != required.end());
fn(name, prop, is_required);
}
}
std::string common_chat_template_direct_apply(
const common_chat_template & tmpl,
const autoparser::templates_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());
nlohmann::ordered_json inp = nlohmann::ordered_json{
{"messages", messages_override.has_value() ? *messages_override : 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;
}
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;
}
static common_chat_params common_chat_params_init_ministral_3(const common_chat_template & tmpl,
const autoparser::templates_params & inputs) {
common_chat_params data;
// Build up messages to follow the format: https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512/blob/main/chat_template.jinja
auto adjusted_messages = json::array();
for (const auto & msg : inputs.messages) {
auto role = msg.value("role", "");
if (role != "system" && role != "assistant") {
// Only adjust system and assistant messages. Interestingly, the system message may contain thinking.
adjusted_messages.push_back(msg);
continue;
}
auto content = json::array();
// If message contains `reasoning_content`, add it as a block of type `thinking`
if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
content.push_back({
{ "type", "thinking" },
{ "thinking", msg.at("reasoning_content").get<std::string>() },
});
}
// If message contains `content`, add it as a block of type `text`
if (msg.contains("content")) {
if (msg.at("content").is_string()) {
content.push_back({
{ "type", "text" },
{ "text", msg.at("content").get<std::string>() },
});
} else if (msg.at("content").is_array()) {
auto blocks = msg.at("content");
content.insert(content.end(), blocks.begin(), blocks.end());
}
}
auto adjusted = msg;
adjusted["content"] = content;
adjusted.erase("reasoning_content");
adjusted_messages.push_back(adjusted);
}
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
auto include_grammar = true;
data.supports_thinking = true;
data.thinking_start_tag = "[THINK]";
data.thinking_end_tag = "[/THINK]";
data.prompt = common_chat_template_direct_apply(tmpl, inputs, /* messages_override = */ adjusted_messages);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
data.preserved_tokens = {
"[THINK]",
"[/THINK]",
"[TOOL_CALLS]",
"[ARGS]",
};
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
auto reasoning =
extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps();
// Response format parser
if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
// Ministral wants to emit json surrounded by code fences
return reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema))
<< "```";
}
// Tool call parser
if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
auto tool_choice = p.choice();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
std::string name = function.at("name");
const auto & schema = function.at("parameters");
tool_choice |=
p.rule("tool-" + name, p.tool_open(p.tool_name(p.literal(name)) + "[ARGS]") +
p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
});
auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
auto tool_calls = p.trigger_rule("tool-call", p.repeat("[TOOL_CALLS]" + tool_choice, min_calls, max_calls));
return reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls;
}
// Content only parser
include_grammar = false;
return reasoning << p.content(p.rest());
});
data.parser = parser.save();
if (include_grammar) {
data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
auto schema = function.at("parameters");
builder.resolve_refs(schema);
});
parser.build_grammar(builder, data.grammar_lazy);
});
data.grammar_triggers = {
{ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]" }
};
}
return data;
}
static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl,
const autoparser::templates_params & inputs) {
common_chat_params data;
// Copy reasoning to the "thinking" field as expected by the gpt-oss template
auto adjusted_messages = json::array();
for (auto msg : inputs.messages) {
if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
msg["thinking"] = msg.at("reasoning_content");
msg.erase("content");
}
adjusted_messages.push_back(msg);
}
auto prompt = common_chat_template_direct_apply(tmpl, inputs, /* messages_override= */ adjusted_messages);
// Check if we need to replace the return token with end token during
// inference and without generation prompt. For more details see:
// https://github.com/ggml-org/llama.cpp/issues/15417
if (inputs.is_inference && !inputs.add_generation_prompt) {
static constexpr std::string_view return_token = "<|return|>";
static constexpr std::string_view end_token = "<|end|>";
if (size_t pos = prompt.rfind(return_token); pos != std::string::npos) {
prompt.replace(pos, return_token.length(), end_token);
}
}
data.prompt = prompt;
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
data.supports_thinking = true;
// These special tokens are required to parse properly, so we include them
// even if parse_tool_calls is false.
data.preserved_tokens = {
"<|channel|>", "<|constrain|>", "<|message|>", "<|start|>", "<|end|>",
};
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
auto include_grammar = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
auto start = p.rule("start", p.literal("<|start|>assistant"));
auto end = p.rule("end", p.literal("<|end|>"));
auto content = p.rule("message-content", p.until("<|end|>"));
auto channel = p.literal("<|channel|>") + (p.literal("commentary") | p.literal("analysis"));
auto constrain_type = p.chars("[A-Za-z0-9_-]", 1, -1);
auto analysis = p.rule("analysis", p.literal("<|channel|>analysis<|message|>") + p.reasoning(content) + end);
auto preamble = p.rule("preamble", p.literal("<|channel|>commentary<|message|>") + p.content(content) + end);
auto final_msg = p.rule("final", p.literal("<|channel|>final<|message|>") + p.content(content));
auto any = p.rule("any", preamble | analysis);
if (has_response_format) {
auto constraint = p.optional(p.space() + p.literal("<|constrain|>") + constrain_type);
auto response_format = p.rule("response-format",
p.literal("<|channel|>final") + constraint + p.literal("<|message|>") +
p.content(p.schema(p.json(), "response-format-schema", inputs.json_schema)));
return response_format | (analysis + p.zero_or_more(start + analysis) + start + response_format);
}
if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
auto tool_choice = p.choice();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
std::string name = function.at("name");
const auto & params = function.at("parameters");
auto func_name = p.literal(" to=functions.") + p.tool_name(p.literal(name));
auto constraint = p.optional(p.space() + p.literal("<|constrain|>") + constrain_type);
auto args = p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", params));
// recipient in role header
// <|start|>assistant to=functions.NAME<|channel|>(commentary|analysis)[constraint]<|message|>ARGS
auto tool_in_role = p.tool(p.tool_open(func_name + channel + constraint + p.literal("<|message|>")) + args);
// recipient in channel header
// <|channel|>(commentary|analysis) to=functions.NAME[constraint]<|message|>ARGS
auto tool_in_channel = p.tool(p.tool_open(channel + func_name + constraint + p.literal("<|message|>")) + args);
tool_choice |= p.rule("tool-" + name, tool_in_role | tool_in_channel);
});
auto tool_call = p.trigger_rule("tool-call", tool_choice);
if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
return tool_call | ( any + p.zero_or_more(start + any) + start + tool_call);
}
return tool_call | final_msg | (any + p.zero_or_more(start + any) + start + (tool_call | final_msg));
}
return final_msg | (any + p.zero_or_more(start + any) + start + final_msg);
});
data.parser = parser.save();
if (include_grammar) {
data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
auto schema = function.at("parameters");
builder.resolve_refs(schema);
});
parser.build_grammar(builder, data.grammar_lazy);
});
data.grammar_triggers = {
{ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "^\\s+to$" },
{ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(\\s+to)" },
{ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, "<\\|start\\|>assistant(<\\|channel\\|>(?:commentary|analysis)\\s+to)" }
};
}
return data;
}
// Functionary v3.2 - uses recipient-based format: >>>recipient\n{content}
static common_chat_params common_chat_params_init_functionary_v3_2(const common_chat_template & tmpl,
const autoparser::templates_params & inputs) {
common_chat_params data;
data.prompt = common_chat_template_direct_apply(tmpl, inputs);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
data.preserved_tokens = {
">>>all",
};
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
// Functionary v3.2 format:
// - Normal content: >>>all\n{content}
// - Tool calls: >>>function_name\n{json_args}
// Generation prompt ends with ">>>" so model outputs recipient immediately
// Build content parser for >>>all\n{content}
// When tools are present, content stops before the next ">>>" (tool call)
// When no tools, content goes until end
auto content_until_tool = p.literal(">>>all\n") + p.content(p.until(">>>"));
auto content_until_end = p.literal(">>>all\n") + p.content(p.rest());
// If no tools or tool_choice is NONE, just parse content
if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
// When no tools, just match the prefix and capture everything after
return content_until_end + p.end();
}
// Build tool call parsers for each available function
auto tool_choice = p.choice();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
std::string name = function.at("name");
const auto & schema = function.at("parameters");
// Tool format: >>>function_name\n{json_args}
auto tool_parser = p.tool(
p.tool_open(p.literal(">>>") + p.tool_name(p.literal(name)) + p.literal("\n")) +
p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))
);
tool_choice |= p.rule("tool-" + name, tool_parser);
});
auto content_only = content_until_end;
auto tools_only = p.trigger_rule("tools", p.one_or_more(tool_choice));
auto content_and_tools = content_until_tool + tools_only;
if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
if (inputs.parallel_tool_calls) {
return p.choice({ content_and_tools, tools_only }) + p.end();
}
return p.choice({ content_until_tool + tool_choice, tools_only }) + p.end();
}
if (inputs.parallel_tool_calls) {
return p.choice({ content_and_tools, content_only, tools_only }) + p.end();
}
auto content_and_tool = content_until_tool + tool_choice;
return p.choice({ content_and_tool, content_only, tool_choice }) + p.end();
});
data.parser = parser.save();
if (include_grammar) {
data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
auto schema = function.at("parameters");
builder.resolve_refs(schema);
});
parser.build_grammar(builder, data.grammar_lazy);
});
// Grammar trigger for when the model starts outputting a tool call
// (after the initial ">>>" in the generation prompt but recipient other than "all")
data.grammar_triggers = {
{ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN, ">>>(?!all)" }
};
}
return data;
}
// Kimi K2 Thinking - uses unique tool call ID format: functions.<name>:<index>
// The ID contains both the function name and an incrementing counter
static common_chat_params common_chat_params_init_kimi_k2(const common_chat_template & tmpl,
const autoparser::templates_params & inputs) {
common_chat_params data;
data.prompt = common_chat_template_direct_apply(tmpl, inputs);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
data.supports_thinking = true;
data.thinking_start_tag = "<think>";
data.thinking_end_tag = "</think>";
data.preserved_tokens = {
"<|tool_calls_section_begin|>",
"<|tool_calls_section_end|>",
"<|tool_call_begin|>",
"<|tool_call_argument_begin|>",
"<|tool_call_end|>",
"<think>",
"</think>",
};
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) {
// Kimi K2 Thinking format:
// - Reasoning: <think>{reasoning}</think>
// - Content: text after reasoning
// - Tool calls section:
// <|tool_calls_section_begin|>
// <|tool_call_begin|>functions.<name>:<index><|tool_call_argument_begin|>{json_args}<|tool_call_end|>
// ...
// <|tool_calls_section_end|>
// The ID format is: functions.<function_name>:<counter> where counter is 0, 1, 2, ...
// Tool call markers
const std::string SECTION_BEGIN = "<|tool_calls_section_begin|>";
const std::string SECTION_END = "<|tool_calls_section_end|>";
const std::string CALL_BEGIN = "<|tool_call_begin|>";
const std::string ARGS_BEGIN = "<|tool_call_argument_begin|>";
const std::string CALL_END = "<|tool_call_end|>";
const std::string THINK_START = "<think>";
const std::string THINK_END = "</think>";
auto end = p.end();
// Note: this model is CRAZY. It can diverge from its supposed tool calling pattern in so many ways it's not funny.
// For example, it can call tools at the end of reasoning without closing reasoning...
auto reasoning = extract_reasoning ? p.optional(THINK_START + p.reasoning(
p.until_one_of({ THINK_END, "<|tool_calls_section_begin|>", "<|tool_call_begin|>" })) +
p.optional(p.literal(THINK_END))) : p.eps();
// Content only parser (no tools)
if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
return reasoning + p.content(p.rest()) + end;
}
// Build tool call parsers for each available function
// The ID format is: functions.<name>:<index>
// We need to match: functions.<name>:<digits>
auto tool_choice = p.choice();
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
std::string name = function.at("name");
const auto & schema = function.at("parameters");
// Match: functions.<name>:<digits>
// Capture the full call id (functions.<name>:<digits>) using tool_id tag
auto tool_id = p.tool_id(p.literal("functions.") + p.tool_name(p.literal(name)) + p.literal(":") + p.chars("[0-9]", 1, -1));
auto tool_parser = p.tool(
p.tool_open(tool_id + p.literal(ARGS_BEGIN)) +
p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)) +
p.tool_close(p.optional((p.literal(CALL_END))))
);
tool_choice |= p.rule("tool-" + name, tool_parser);
});
// Tool calls section: <|tool_calls_section_begin|> tool_calls <|tool_calls_section_end|>
auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
// Use trigger_rule so grammar generator knows where to start generating rules
auto tool_calls = p.rule("tool-calls",
p.optional(p.literal(SECTION_BEGIN)) +
p.trigger_rule("tool-call", p.repeat(CALL_BEGIN + tool_choice, min_calls, max_calls) +
p.optional(p.literal(SECTION_END)))
);
auto content_before_tools = p.content(p.until_one_of({ SECTION_BEGIN, CALL_BEGIN }));
return reasoning + content_before_tools + tool_calls + end;
});
data.parser = parser.save();
if (include_grammar) {
data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
auto schema = function.at("parameters");
builder.resolve_refs(schema);
});
parser.build_grammar(builder, data.grammar_lazy);
});
data.grammar_triggers = {
{ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<|tool_call_begin|>" }
};
}
return data;
}
// LFM2 format:
// - Reasoning: <think>{reasoning}</think> (optional, only if enable_thinking is true)
// - Content: text after reasoning (optional)
// - Tool calls: <|tool_call_start|>[function_name(arg1="value1", arg2="value2")]<|tool_call_end|>
// Tool calls can appear multiple times (parallel tool calls)
static common_chat_params common_chat_params_init_lfm2(const common_chat_template & tmpl,
const autoparser::templates_params & inputs) {
common_chat_params data;
data.prompt = common_chat_template_direct_apply(tmpl, inputs);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
data.supports_thinking = true;
data.preserved_tokens = {
"<|tool_list_start|>",
"<|tool_list_end|>",
"<|tool_call_start|>",
"<|tool_call_end|>",
"<think>",
"</think>",
};
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;
const std::string TOOL_CALL_START = "<|tool_call_start|>";
const std::string TOOL_CALL_END = "<|tool_call_end|>";
const std::string THINK_START = "<think>";
const std::string THINK_END = "</think>";
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
auto end = p.end();
auto reasoning = p.eps();
if (extract_reasoning && inputs.enable_thinking) {
reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
}
if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
return reasoning + p.content(p.rest()) + end;
}
auto tool_calls = p.rule("tool-calls",
p.trigger_rule("tool-call", p.literal(TOOL_CALL_START) +
p.python_style_tool_calls(inputs.tools, inputs.parallel_tool_calls) +
p.literal(TOOL_CALL_END)
)
);
auto content = p.content(p.until(TOOL_CALL_START));
return reasoning + content + tool_calls + end;
});
data.parser = parser.save();
if (include_grammar) {
data.grammar_lazy = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
auto schema = function.at("parameters");
builder.resolve_refs(schema);
});
parser.build_grammar(builder, data.grammar_lazy);
});
data.grammar_triggers = {
{ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, TOOL_CALL_START }
};
}
return data;
}
static common_chat_params common_chat_params_init_gigachat_v3(
const common_chat_template & tmpl,
const autoparser::templates_params & inputs) {
common_chat_params data;
data.prompt = common_chat_template_direct_apply(tmpl, inputs);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
data.supports_thinking = false;
data.preserved_tokens = {
"<|message_sep|>\n\n",
"<|role_sep|>\n",
};
auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
auto tool_call_start_prefix = "<|message_sep|>\n\nfunction call<|role_sep|>\n";
auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
// Build a choice of all available tools
auto tool_choice = p.choice();
for (const auto & tool : inputs.tools) {
const auto & function = tool.at("function");
std::string name = function.at("name");
const auto & schema = function.at("parameters");
auto tool_name = p.json_member("name", "\"" + p.tool_name(p.literal(name)) + "\"");
auto tool_args = p.json_member("arguments", p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
auto tool_open = p.tool_open(p.literal("{") << tool_name);
tool_choice |= p.rule("tool-" + name, tool_open << "," << tool_args << "}");
}
// Define the tool call structure
auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
auto max_calls = 1; // parallel toolcalls are not supported
auto tool_call = p.rule("tool-call", p.literal(tool_call_start_prefix) + tool_choice);
auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls));
return p.content(p.until("<|message_sep|>\n\n")) << tool_calls;
}
// Content only parser
include_grammar = false;
return p.content(p.rest());
});
data.parser = parser.save();
if (include_grammar) {
data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
foreach_function(inputs.tools, [&](const json & tool) {
const auto & function = tool.at("function");
auto schema = function.at("parameters");
builder.resolve_refs(schema);
});
parser.build_grammar(builder, data.grammar_lazy);
});
data.grammar_triggers = {
{COMMON_GRAMMAR_TRIGGER_TYPE_WORD, tool_call_start_prefix}
};
}
return data;
}
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(messages.begin());
} else {
LOG_WRN("Removing system prompt due to template not supporting system role\n");
messages.erase(messages.begin());
}
}
}
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()));
}
}
}
}
}
}
}
}
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;
}
static common_chat_params common_chat_templates_apply_jinja(const struct common_chat_templates * tmpls,
const struct common_chat_templates_inputs & inputs) {
autoparser::templates_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();
params.messages = render_message_to_json(inputs.messages, tmpl.original_caps());
params.add_generation_prompt = inputs.add_generation_prompt;
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_bos = tmpls->add_bos;
params.add_eos = tmpls->add_eos;
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);
}
// if (inputs.parallel_tool_calls && !tmpl.original_caps().supports_parallel_tool_calls) {
// LOG_DBG("Disabling parallel_tool_calls because the template does not support it\n");
// params.parallel_tool_calls = false;
// } else {
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(tmpl, params_copy);
data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
auto parser = build_chat_peg_parser([](common_chat_peg_builder &p) {
return p.content(p.rest());
});
data.parser = parser.save();
return data;
}
// 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);
}
// 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);
}
// LFM2 - uses <|tool_list_start|>/<|tool_list_end|> markers and <|tool_call_start|>[name(args)]<|tool_call_end|> format
// Detection: template has "<|tool_list_start|>" and "<|tool_list_end|>" markers
if (src.find("<|tool_list_start|>") != std::string::npos &&
src.find("<|tool_list_end|>") != std::string::npos) {
LOG_DBG("Using specialized template: LFM2\n");
return common_chat_params_init_lfm2(tmpl, params);
}
// 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);
}
try {
LOG_DBG("Using differential autoparser\n");
struct autoparser::autoparser autoparser;
autoparser.analyze_template(tmpl);
auto auto_params = autoparser::peg_generator::generate_parser(tmpl, params, autoparser);
auto_params.supports_thinking = autoparser.reasoning.mode != autoparser::reasoning_mode::NONE;
if (auto_params.supports_thinking) {
auto_params.thinking_start_tag = autoparser.reasoning.start;
auto_params.thinking_end_tag = autoparser.reasoning.end;
// FORCED_OPEN and FORCED_CLOSED both put <think> in the generation prompt
// (FORCED_CLOSED forces empty <think></think> when thinking is disabled,
// but forces <think> open when thinking is enabled)
auto_params.thinking_forced_open =
autoparser.reasoning.mode == autoparser::reasoning_mode::FORCED_OPEN ||
autoparser.reasoning.mode == autoparser::reasoning_mode::FORCED_CLOSED;
}
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.");
}
LOG_DBG("Parsing PEG input with format %s: %s\n", common_chat_format_name(params.format), 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(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";
auto mapper = 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;
}
throw std::runtime_error(std::string("Failed to parse input at pos ") + std::to_string(result.end) + ": " +
input.substr(result.end));
}
common_chat_msg msg;
msg.role = "assistant";
auto mapper = 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);
return chat_templates->template_default->caps.to_map();
}
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