tool-call: refactor common chat / tool-call api (+ tests / fixes) (#11900)

* tool-call refactoring: moved common_chat_* to chat.h, common_chat_templates_init return a unique_ptr to opaque type

* addressed clang-tidy lints in [test-]chat.*

* rm minja deps from util & common & move it to common/minja/

* add name & tool_call_id to common_chat_msg

* add common_chat_tool

* added json <-> tools, msgs conversions to chat.h

* fix double bos/eos jinja avoidance hack (was preventing inner bos/eos tokens)

* fix deepseek r1 slow test (no longer <think> opening w/ new template)

* allow empty tools w/ auto + grammar

* fix & test server grammar & json_schema params w/ & w/o --jinja

examples/main/main.cpp

@@ -4,7 +4,7 @@
 #include "log.h"
 #include "sampling.h"
 #include "llama.h"
-#include "chat-template.hpp"
+#include "chat.h"
 
 #include <cstdio>
 #include <cstring>
@@ -158,7 +158,7 @@ int main(int argc, char ** argv) {
     }
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
-    auto chat_templates = common_chat_templates_from_model(model, params.chat_template);
+    auto chat_templates = common_chat_templates_init(model, params.chat_template);
 
     LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads);
 
@@ -201,7 +201,7 @@ int main(int argc, char ** argv) {
     }
 
     // auto enable conversation mode if chat template is available
-    const bool has_chat_template = chat_templates.has_explicit_template && chat_templates.template_default;
+    const bool has_chat_template = common_chat_templates_was_explicit(chat_templates.get());
     if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) {
         if (has_chat_template) {
             LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__);
@@ -219,7 +219,7 @@ int main(int argc, char ** argv) {
     // print chat template example in conversation mode
     if (params.conversation_mode) {
         if (params.enable_chat_template) {
-            LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(*chat_templates.template_default, params.use_jinja).c_str());
+            LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(chat_templates.get(), params.use_jinja).c_str());
         } else {
             LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
         }
@@ -264,9 +264,11 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd_inp;
 
     auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) {
-        common_chat_msg new_msg{role, content, {}};
-        auto formatted = common_chat_format_single(*chat_templates.template_default, chat_msgs, new_msg, role == "user", g_params->use_jinja);
-        chat_msgs.push_back({role, content, {}});
+        common_chat_msg new_msg;
+        new_msg.role = role;
+        new_msg.content = content;
+        auto formatted = common_chat_format_single(chat_templates.get(), chat_msgs, new_msg, role == "user", g_params->use_jinja);
+        chat_msgs.push_back(new_msg);
         LOG_DBG("formatted: '%s'\n", formatted.c_str());
         return formatted;
     };
@@ -755,11 +757,14 @@ int main(int argc, char ** argv) {
 
                 // check for reverse prompt using special tokens
                 llama_token last_token = common_sampler_last(smpl);
-                if (std::find(antiprompt_token.begin(), antiprompt_token.end(), last_token) != antiprompt_token.end()) {
-                    if (params.interactive) {
-                        is_interacting = true;
+                for (auto token : antiprompt_token) {
+                    if (token == last_token) {
+                        if (params.interactive) {
+                            is_interacting = true;
+                        }
+                        is_antiprompt = true;
+                        break;
                     }
-                    is_antiprompt = true;
                 }
 
                 if (is_antiprompt) {

examples/run/run.cpp

@@ -24,7 +24,7 @@
 #include <string>
 #include <vector>
 
-#include "chat-template.hpp"
+#include "chat.h"
 #include "common.h"
 #include "json.hpp"
 #include "linenoise.cpp/linenoise.h"
@@ -557,7 +557,7 @@ class LlamaData {
     llama_model_ptr                 model;
     llama_sampler_ptr               sampler;
     llama_context_ptr               context;
-    std::vector<llama_chat_message> messages;
+    std::vector<llama_chat_message> messages; // TODO: switch to common_chat_msg
     std::list<std::string>          msg_strs;
     std::vector<char>               fmtted;
 
@@ -834,44 +834,23 @@ static void add_message(const char * role, const std::string & text, LlamaData &
 }
 
 // Function to apply the chat template and resize `formatted` if needed
-static int apply_chat_template(const common_chat_template & tmpl, LlamaData & llama_data, const bool append, bool use_jinja) {
-    if (use_jinja) {
-        json messages = json::array();
-        for (const auto & msg : llama_data.messages) {
-            messages.push_back({
-                {"role", msg.role},
-                {"content", msg.content},
-            });
-        }
-        try {
-            minja::chat_template_inputs tmpl_inputs;
-            tmpl_inputs.messages = messages;
-            tmpl_inputs.add_generation_prompt = append;
-
-            minja::chat_template_options tmpl_opts;
-            tmpl_opts.use_bos_token = false;
-            tmpl_opts.use_eos_token = false;
-
-            auto result = tmpl.apply(tmpl_inputs, tmpl_opts);
-            llama_data.fmtted.resize(result.size() + 1);
-            memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1);
-            return result.size();
-        } catch (const std::exception & e) {
-            printe("failed to render the chat template: %s\n", e.what());
-            return -1;
-        }
-    }
-    int result = llama_chat_apply_template(
-        tmpl.source().c_str(), llama_data.messages.data(), llama_data.messages.size(), append,
-        append ? llama_data.fmtted.data() : nullptr, append ? llama_data.fmtted.size() : 0);
-    if (append && result > static_cast<int>(llama_data.fmtted.size())) {
-        llama_data.fmtted.resize(result);
-        result = llama_chat_apply_template(tmpl.source().c_str(), llama_data.messages.data(),
-                                           llama_data.messages.size(), append, llama_data.fmtted.data(),
-                                           llama_data.fmtted.size());
+static int apply_chat_template(const struct common_chat_templates * tmpls, LlamaData & llama_data, const bool append, bool use_jinja) {
+    common_chat_templates_inputs inputs;
+    for (const auto & msg : llama_data.messages) {
+        common_chat_msg cmsg;
+        cmsg.role    = msg.role;
+        cmsg.content = msg.content;
+        inputs.messages.push_back(cmsg);
     }
+    inputs.add_generation_prompt = append;
+    inputs.use_jinja = use_jinja;
 
-    return result;
+    auto chat_params = common_chat_templates_apply(tmpls, inputs);
+    // TODO: use other params for tool calls.
+    auto result = chat_params.prompt;
+    llama_data.fmtted.resize(result.size() + 1);
+    memcpy(llama_data.fmtted.data(), result.c_str(), result.size() + 1);
+    return result.size();
 }
 
 // Function to tokenize the prompt
@@ -1015,8 +994,8 @@ static int generate_response(LlamaData & llama_data, const std::string & prompt,
 }
 
 // Helper function to apply the chat template and handle errors
-static int apply_chat_template_with_error_handling(const common_chat_template & tmpl, LlamaData & llama_data, const bool append, int & output_length, bool use_jinja) {
-    const int new_len = apply_chat_template(tmpl, llama_data, append, use_jinja);
+static int apply_chat_template_with_error_handling(const common_chat_templates * tmpls, LlamaData & llama_data, const bool append, int & output_length, bool use_jinja) {
+    const int new_len = apply_chat_template(tmpls, llama_data, append, use_jinja);
     if (new_len < 0) {
         printe("failed to apply the chat template\n");
         return -1;
@@ -1078,8 +1057,7 @@ static int get_user_input(std::string & user_input, const std::string & user) {
 static int chat_loop(LlamaData & llama_data, const std::string & user, bool use_jinja) {
     int prev_len = 0;
     llama_data.fmtted.resize(llama_n_ctx(llama_data.context.get()));
-    auto chat_templates = common_chat_templates_from_model(llama_data.model.get(), "");
-    GGML_ASSERT(chat_templates.template_default);
+    auto chat_templates = common_chat_templates_init(llama_data.model.get(), "");
     static const bool stdout_a_terminal = is_stdout_a_terminal();
     while (true) {
         // Get user input
@@ -1090,7 +1068,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user, bool use_
 
         add_message("user", user.empty() ? user_input : user, llama_data);
         int new_len;
-        if (apply_chat_template_with_error_handling(*chat_templates.template_default, llama_data, true, new_len, use_jinja) < 0) {
+        if (apply_chat_template_with_error_handling(chat_templates.get(), llama_data, true, new_len, use_jinja) < 0) {
             return 1;
         }
 
@@ -1105,7 +1083,7 @@ static int chat_loop(LlamaData & llama_data, const std::string & user, bool use_
         }
 
         add_message("assistant", response, llama_data);
-        if (apply_chat_template_with_error_handling(*chat_templates.template_default, llama_data, false, prev_len, use_jinja) < 0) {
+        if (apply_chat_template_with_error_handling(chat_templates.get(), llama_data, false, prev_len, use_jinja) < 0) {
             return 1;
         }
     }

examples/server/server.cpp

@@ -329,9 +329,6 @@ struct server_task {
         }
 
         // process "json_schema" and "grammar"
-        if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) {
-            throw std::runtime_error("Either \"json_schema\" or \"grammar\" can be specified, but not both");
-        }
         if (data.contains("json_schema") && !data.contains("grammar")) {
             try {
                 auto schema                  = json_value(data, "json_schema", json::object());
@@ -1807,7 +1804,7 @@ struct server_context {
     // Necessary similarity of prompt for slot selection
     float slot_prompt_similarity = 0.0f;
 
-    common_chat_templates chat_templates;
+    common_chat_templates_ptr chat_templates;
 
     ~server_context() {
         // Clear any sampling context
@@ -1891,45 +1888,17 @@ struct server_context {
             llama_init_dft.context.reset();
         }
 
-        if (params_base.chat_template.empty() && !validate_builtin_chat_template(params.use_jinja)) {
+        chat_templates = common_chat_templates_init(model, params_base.chat_template);
+        try {
+            common_chat_format_example(chat_templates.get(), params.use_jinja);
+        } catch (const std::exception & e) {
             SRV_WRN("%s: The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses\n", __func__);
-            chat_templates = common_chat_templates_from_model(model, "chatml");
-        } else {
-            chat_templates = common_chat_templates_from_model(model, params_base.chat_template);
+            chat_templates = common_chat_templates_init(model, "chatml");
         }
-        GGML_ASSERT(chat_templates.template_default.get() != nullptr);
 
         return true;
     }
 
-    bool validate_builtin_chat_template(bool use_jinja) const {
-        llama_chat_message chat[] = {{"user", "test"}};
-
-        if (use_jinja) {
-            auto templates = common_chat_templates_from_model(model, "");
-            common_chat_inputs inputs;
-            inputs.messages = json::array({{
-                {"role", "user"},
-                {"content", "test"},
-            }});
-            GGML_ASSERT(templates.template_default);
-            try {
-                common_chat_params_init(*templates.template_default, inputs);
-                if (templates.template_tool_use) {
-                    common_chat_params_init(*templates.template_tool_use, inputs);
-                }
-                return true;
-            } catch (const std::exception & e) {
-                SRV_ERR("failed to apply template: %s\n", e.what());
-                return false;
-            }
-        } else {
-            const char * tmpl = llama_model_chat_template(model, /* name */ nullptr);
-            const int32_t chat_res = llama_chat_apply_template(tmpl, chat, 1, true, nullptr, 0);
-            return chat_res > 0;
-        }
-    }
-
     void init() {
         const int32_t n_ctx_slot = n_ctx / params_base.n_parallel;
 
@@ -3822,13 +3791,15 @@ int main(int argc, char ** argv) {
             { "default_generation_settings", ctx_server.default_generation_settings_for_props },
             { "total_slots",                 ctx_server.params_base.n_parallel },
             { "model_path",                  ctx_server.params_base.model },
-            { "chat_template",               ctx_server.chat_templates.template_default->source() },
-            { "bos_token",                   ctx_server.chat_templates.template_default->bos_token() },
-            { "eos_token",                   ctx_server.chat_templates.template_default->eos_token() },
+            { "chat_template",               common_chat_templates_source(ctx_server.chat_templates.get()) },
+            { "bos_token",                   common_token_to_piece(ctx_server.ctx, llama_vocab_bos(ctx_server.vocab), /* special= */ true)},
+            { "eos_token",                   common_token_to_piece(ctx_server.ctx, llama_vocab_eos(ctx_server.vocab), /* special= */ true)},
             { "build_info",                  build_info },
         };
-        if (ctx_server.params_base.use_jinja && ctx_server.chat_templates.template_tool_use) {
-            data["chat_template_tool_use"] = ctx_server.chat_templates.template_tool_use->source();
+        if (ctx_server.params_base.use_jinja) {
+            if (auto tool_use_src = common_chat_templates_source(ctx_server.chat_templates.get(), "tool_use")) {
+                data["chat_template_tool_use"] = tool_use_src;
+            }
         }
 
         res_ok(res, data);
@@ -4063,7 +4034,7 @@ int main(int argc, char ** argv) {
         }
 
         auto body = json::parse(req.body);
-        json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates);
+        json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates.get());
 
         return handle_completions_impl(
             SERVER_TASK_TYPE_COMPLETION,
@@ -4076,7 +4047,7 @@ int main(int argc, char ** argv) {
     // same with handle_chat_completions, but without inference part
     const auto handle_apply_template = [&ctx_server, &params, &res_ok](const httplib::Request & req, httplib::Response & res) {
         auto body = json::parse(req.body);
-        json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates);
+        json data = oaicompat_completion_params_parse(body, params.use_jinja, params.reasoning_format, ctx_server.chat_templates.get());
         res_ok(res, {{ "prompt", std::move(data.at("prompt")) }});
     };
 
@@ -4493,8 +4464,8 @@ int main(int argc, char ** argv) {
 
     // print sample chat example to make it clear which template is used
     LOG_INF("%s: chat template, chat_template: %s, example_format: '%s'\n", __func__,
-        ctx_server.chat_templates.template_default->source().c_str(),
-        common_chat_format_example(*ctx_server.chat_templates.template_default, ctx_server.params_base.use_jinja).c_str());
+        common_chat_templates_source(ctx_server.chat_templates.get()),
+        common_chat_format_example(ctx_server.chat_templates.get(), ctx_server.params_base.use_jinja).c_str());
 
     ctx_server.queue_tasks.on_new_task([&ctx_server](const server_task & task) {
         ctx_server.process_single_task(task);

examples/server/tests/unit/test_chat_completion.py

@@ -21,6 +21,8 @@ def create_server():
         (None, "Book", "What is the best book", 8, "^ blue",                    23, 8, "length", True, "This is not a chat template, it is"),
         ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", False, None),
         ("codellama70b", "You are a coding assistant.", "Write the fibonacci function in c++.", 128, "(Aside|she|felter|alonger)+", 104, 64, "length", True, None),
+        (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", False, None),
+        (None, "Book", [{"type": "text", "text": "What is"}, {"type": "text", "text": "the best book"}], 8, "Whillicter", 79, 8, "length", True, None),
     ]
 )
 def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_content, n_prompt, n_predicted, finish_reason, jinja, chat_template):
@@ -44,7 +46,7 @@ def test_chat_completion(model, system_prompt, user_prompt, max_tokens, re_conte
     assert res.body["usage"]["completion_tokens"] == n_predicted
     choice = res.body["choices"][0]
     assert "assistant" == choice["message"]["role"]
-    assert match_regex(re_content, choice["message"]["content"])
+    assert match_regex(re_content, choice["message"]["content"]), f'Expected {re_content}, got {choice["message"]["content"]}'
     assert choice["finish_reason"] == finish_reason
 
 
@@ -169,6 +171,47 @@ def test_completion_with_response_format(response_format: dict, n_predicted: int
         assert "error" in res.body
 
 
+@pytest.mark.parametrize("jinja,json_schema,n_predicted,re_content", [
+    (False, {"const": "42"}, 6, "\"42\""),
+    (True, {"const": "42"}, 6, "\"42\""),
+])
+def test_completion_with_json_schema(jinja: bool, json_schema: dict, n_predicted: int, re_content: str):
+    global server
+    server.jinja = jinja
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predicted,
+        "messages": [
+            {"role": "system", "content": "You are a coding assistant."},
+            {"role": "user", "content": "Write an example"},
+        ],
+        "json_schema": json_schema,
+    })
+    assert res.status_code == 200, f'Expected 200, got {res.status_code}'
+    choice = res.body["choices"][0]
+    assert match_regex(re_content, choice["message"]["content"]), f'Expected {re_content}, got {choice["message"]["content"]}'
+
+
+@pytest.mark.parametrize("jinja,grammar,n_predicted,re_content", [
+    (False, 'root ::= "a"{5,5}', 6, "a{5,5}"),
+    (True, 'root ::= "a"{5,5}', 6, "a{5,5}"),
+])
+def test_completion_with_grammar(jinja: bool, grammar: str, n_predicted: int, re_content: str):
+    global server
+    server.jinja = jinja
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": n_predicted,
+        "messages": [
+            {"role": "user", "content": "Does not matter what I say, does it?"},
+        ],
+        "grammar": grammar,
+    })
+    assert res.status_code == 200, res.body
+    choice = res.body["choices"][0]
+    assert match_regex(re_content, choice["message"]["content"]), choice["message"]["content"]
+
+
 @pytest.mark.parametrize("messages", [
     None,
     "string",

examples/server/tests/unit/test_tool_call.py

@@ -356,12 +356,12 @@ def test_weather(hf_repo: str, template_override: str | Tuple[str, str | None] |
     (None,                                           128,  "bartowski/functionary-small-v3.2-GGUF:Q8_0",        ("meetkai/functionary-medium-v3.2", None)),
     (None,                                           128,  "bartowski/Meta-Llama-3.1-8B-Instruct-GGUF:Q4_K_M",  None),
     (None,                                           128,  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M",  None),
-    ("^> 0.56$",                                     128,  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M",  "chatml"),
+    (None,                                           128,  "bartowski/Mistral-Nemo-Instruct-2407-GGUF:Q4_K_M",  "chatml"),
     (None,                                           128,  "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",       None),
 
     # TODO: fix these (wrong results, either didn't respect decimal instruction or got wrong value)
-    ("^The y-coordinate [\\s\\S]*?\\*\\*0.5\\*\\*",  8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
-    ("[\\s\\S]*?\\*\\*0\\.5\\*\\*",                  8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", ("llama-cpp-deepseek-r1", None)),
+    ("[\\s\\S]*?\\*\\*\\s*0.5($|\\*\\*)",            8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+    # ("[\\s\\S]*?\\*\\*\\s*0.5($|\\*\\*)",            8192, "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", ("llama-cpp-deepseek-r1", None)),
 ])
 def test_calc_result(result_override: str | None, n_predict: int, hf_repo: str, template_override: str | Tuple[str, str | None] | None):
     global server
@@ -401,7 +401,7 @@ def test_calc_result(result_override: str | None, n_predict: int, hf_repo: str,
             {
                 "role": "tool",
                 "name": "calculate",
-                "content": 0.55644242476,
+                "content": "0.55644242476",
                 "tool_call_id": "call_6789"
             }
         ],
@@ -444,7 +444,7 @@ def test_calc_result(result_override: str | None, n_predict: int, hf_repo: str,
     (128,  None,        "^The sum of 102 and 7 is 109.*",                       None,                                          "bartowski/Phi-3.5-mini-instruct-GGUF:Q4_K_M",       None),
 
     (1024, 'deepseek',  "To find the sum of.*",                                 "I need to calculate the sum of 102 and 7.*",  "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
-    (1024, 'none',      "<think>\n?I need[\\s\\S]*?</think>\n?To find.*",       None,                                          "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
+    (1024, 'none',      "^I need[\\s\\S]*?</think>\n?To find.*",                None,                                          "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", None),
 
     (1024, 'deepseek',  "To find the sum of.*",                                 "First, I [\\s\\S]*",                          "bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M", ("llama-cpp-deepseek-r1", None)),
 ])

examples/server/utils.hpp

@@ -12,9 +12,7 @@
 // Change JSON_ASSERT from assert() to GGML_ASSERT:
 #define JSON_ASSERT GGML_ASSERT
 #include "json.hpp"
-#include "minja.hpp"
-#include "chat.hpp"
-#include "chat-template.hpp"
+#include "chat.h"
 
 #include <random>
 #include <sstream>
@@ -347,41 +345,6 @@ static llama_tokens format_infill(
     return embd_inp;
 }
 
-// Format given chat. If tmpl is empty, we take the template from model metadata
-inline std::string format_chat(const common_chat_template & tmpl, const std::vector<json> & messages) {
-    std::vector<common_chat_msg> chat;
-
-    for (size_t i = 0; i < messages.size(); ++i) {
-        const auto & curr_msg = messages[i];
-
-        std::string role = json_value(curr_msg, "role", std::string(""));
-
-        std::string content;
-        if (curr_msg.contains("content")) {
-            if (curr_msg["content"].is_string()) {
-                content = curr_msg["content"].get<std::string>();
-            } else if (curr_msg["content"].is_array()) {
-                for (const auto & part : curr_msg["content"]) {
-                    if (part.contains("text")) {
-                        content += "\n" + part["text"].get<std::string>();
-                    }
-                }
-            } else {
-                throw std::runtime_error("Invalid 'content' type (ref: https://github.com/ggml-org/llama.cpp/issues/8367)");
-            }
-        } else {
-            throw std::runtime_error("Missing 'content' (ref: https://github.com/ggml-org/llama.cpp/issues/8367)");
-        }
-
-        chat.push_back({role, content, /* tool_calls= */ {}});
-    }
-
-    const auto formatted_chat = common_chat_apply_template(tmpl, chat, true, /* use_jinja= */ false);
-    LOG_DBG("formatted_chat: '%s'\n", formatted_chat.c_str());
-
-    return formatted_chat;
-}
-
 //
 // base64 utils (TODO: move to common in the future)
 //
@@ -579,12 +542,9 @@ static json oaicompat_completion_params_parse(
     const json & body, /* openai api json semantics */
     bool use_jinja,
     common_reasoning_format reasoning_format,
-    const common_chat_templates & chat_templates)
+    const struct common_chat_templates * tmpls)
 {
     json llama_params;
-    const auto & tmpl = body.contains("tools") && chat_templates.template_tool_use
-        ? *chat_templates.template_tool_use
-        : *chat_templates.template_default;
 
     auto tools = json_value(body, "tools", json());
     auto stream = json_value(body, "stream", false);
@@ -610,62 +570,58 @@ static json oaicompat_completion_params_parse(
         llama_params["stop"] = json_value(body, "stop", json::array());
     }
 
+    auto json_schema = json_value(body, "json_schema", json());
+    auto grammar = json_value(body, "grammar", std::string());
+    if (!json_schema.is_null() && !grammar.empty()) {
+        throw std::runtime_error("Cannot use both json_schema and grammar");
+    }
+
     // Handle "response_format" field
     if (body.contains("response_format")) {
         json response_format      = json_value(body, "response_format", json::object());
         std::string response_type = json_value(response_format, "type", std::string());
         if (response_type == "json_object") {
-            llama_params["json_schema"] = json_value(response_format, "schema", json::object());
+            json_schema = json_value(response_format, "schema", json::object());
         } else if (response_type == "json_schema") {
             json json_schema = json_value(response_format, "json_schema", json::object());
-            llama_params["json_schema"] = json_value(json_schema, "schema", json::object());
+            json_schema = json_value(json_schema, "schema", json::object());
         } else if (!response_type.empty() && response_type != "text") {
             throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type);
         }
     }
 
-    // Apply chat template to the list of messages
-    if (use_jinja) {
-        auto tool_choice = json_value(body, "tool_choice", std::string("auto"));
-        if (tool_choice != "none" && tool_choice != "auto" && tool_choice != "required") {
-            throw std::runtime_error("Invalid tool_choice: " + tool_choice);
-        }
-        if (tool_choice != "none" && llama_params.contains("grammar")) {
-            throw std::runtime_error("Cannot use custom grammar constraints with tools.");
-        }
-        common_chat_inputs inputs;
-        inputs.extract_reasoning   = reasoning_format != COMMON_REASONING_FORMAT_NONE;
-        inputs.messages            = body.at("messages");
-        inputs.tools               = tools;
-        inputs.tool_choice         = tool_choice;
-        inputs.parallel_tool_calls = json_value(body, "parallel_tool_calls", false);
-        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");
-            inputs.parallel_tool_calls = false;
-        }
-        inputs.stream = stream;
-        // TODO: support mixing schema w/ tools beyond generic format.
-        inputs.json_schema = json_value(llama_params, "json_schema", json());
-        auto chat_params = common_chat_params_init(tmpl, inputs);
+    common_chat_templates_inputs inputs;
+    inputs.messages              = common_chat_msgs_parse_oaicompat(body.at("messages"));
+    inputs.tools                 = common_chat_tools_parse_oaicompat(tools);
+    inputs.tool_choice           = common_chat_tool_choice_parse_oaicompat(json_value(body, "tool_choice", std::string("auto")));
+    inputs.json_schema           = json_schema.is_null() ? "" : json_schema.dump();
+    inputs.grammar               = grammar;
+    inputs.add_generation_prompt = true;
+    inputs.use_jinja             = use_jinja;
+    inputs.parallel_tool_calls   = json_value(body, "parallel_tool_calls", false);
+    inputs.extract_reasoning     = reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    if (!inputs.tools.empty() && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE && body.contains("grammar")) {
+        throw std::runtime_error("Cannot use custom grammar constraints with tools.");
+    }
 
-        llama_params["chat_format"] = static_cast<int>(chat_params.format);
-        llama_params["prompt"] = chat_params.prompt;
-        llama_params["grammar"] = chat_params.grammar;
-        llama_params["grammar_lazy"] = chat_params.grammar_lazy;
-        auto grammar_triggers = json::array();
-        for (const auto & trigger : chat_params.grammar_triggers) {
-            grammar_triggers.push_back({
-                {"word", trigger.word},
-                {"at_start", trigger.at_start},
-            });
-        }
-        llama_params["grammar_triggers"] = grammar_triggers;
-        llama_params["preserved_tokens"] = chat_params.preserved_tokens;
-        for (const auto & stop : chat_params.additional_stops) {
-            llama_params["stop"].push_back(stop);
-        }
-    } else {
-        llama_params["prompt"] = format_chat(tmpl, body.at("messages"));
+    // Apply chat template to the list of messages
+    auto chat_params = common_chat_templates_apply(tmpls, inputs);
+
+    llama_params["chat_format"]      = static_cast<int>(chat_params.format);
+    llama_params["prompt"]           = chat_params.prompt;
+    llama_params["grammar"]          = chat_params.grammar;
+    llama_params["grammar_lazy"]     = chat_params.grammar_lazy;
+    auto grammar_triggers = json::array();
+    for (const auto & trigger : chat_params.grammar_triggers) {
+        grammar_triggers.push_back({
+            {"word", trigger.word},
+            {"at_start", trigger.at_start},
+        });
+    }
+    llama_params["grammar_triggers"] = grammar_triggers;
+    llama_params["preserved_tokens"] = chat_params.preserved_tokens;
+    for (const auto & stop : chat_params.additional_stops) {
+        llama_params["stop"].push_back(stop);
     }
 
     // Handle "n" field