parser: fix structured output bug (#22302)

* fix very stupid structured output bug

* Things just cannot be too easy.

diff --git a/scripts/server-test-structured.py b/scripts/server-test-structured.py
index 98ff473b9fee1c930f39a57f84e7922f3689e222..da217fc46c86e82e62430fdd3a4ff6e2d1a0e1fa 100755 (executable)
--- a/scripts/server-test-structured.py
+++ b/scripts/server-test-structured.py
@@ -3,8 +3,12 @@
 Test structured output capability via chat completions endpoint.
 
 Each test case contains:
-  - response_format: OpenAI-compatible response_format specification
-                     (json_schema only — llama.cpp does not support json_object)
+  - response_format: OpenAI-compatible response_format specification.
+                     Both "json_schema" and "json_object" are accepted; with
+                     "json_object" a schema can be supplied via extra_body.
+  - extra_body (optional): dict of extra top-level request fields merged into
+                     the request payload (mirrors the OpenAI SDK's extra_body
+                     feature; llama.cpp reads a top-level "json_schema" here).
   - messages: initial conversation messages
   - tools (optional): tool definitions (for mixed tool + structured tests)
   - mock_tool_responses (optional): dict mapping tool_name -> callable(arguments) -> str (JSON)
@@ -81,11 +85,14 @@ def print_info(msg):
     _print(f"{DIM}{msg}{RESET}")
 
 
-def print_schema_note(label, rf):
+def print_schema_note(label, rf, extra_body=None):
     kind = rf.get("type", "?")
     name = ""
     if kind == "json_schema":
         name = rf.get("json_schema", {}).get("name", "")
+    elif kind == "json_object" and extra_body and "json_schema" in extra_body:
+        extra_schema = extra_body["json_schema"] or {}
+        name = extra_schema.get("title") or "extra_body.json_schema"
     _print(f"{DIM}{MAGENTA}  ⟐ response_format [{label}]: {kind}"
            f"{(' / ' + name) if name else ''}{RESET}")
 
@@ -95,17 +102,20 @@ def print_schema_note(label, rf):
 # ---------------------------------------------------------------------------
 
 
-def chat_completion(url, messages, tools=None, response_format=None, stream=False):
+def chat_completion(url, messages, tools=None, response_format=None, stream=False,
+                    extra_body=None):
     payload = {
         "messages": messages,
         "stream": stream,
-        "max_tokens": 4096,
+        "max_tokens": 8192,
     }
     if tools:
         payload["tools"] = tools
         payload["tool_choice"] = "auto"
     if response_format is not None:
         payload["response_format"] = response_format
+    if extra_body:
+        payload.update(extra_body)
 
     try:
         response = requests.post(url, json=payload, stream=stream)
@@ -180,7 +190,7 @@ def chat_completion(url, messages, tools=None, response_format=None, stream=Fals
 
 def run_tool_loop(
     url, messages, tools, mock_tool_responses, stream, response_format=None,
-    max_turns=6,
+    extra_body=None, max_turns=6,
 ):
     """
     Drive the tool-call loop. If response_format is provided it is applied to
@@ -191,7 +201,8 @@ def run_tool_loop(
 
     for _ in range(max_turns):
         result = chat_completion(
-            url, msgs, tools=tools, response_format=response_format, stream=stream
+            url, msgs, tools=tools, response_format=response_format, stream=stream,
+            extra_body=extra_body,
         )
         if result is None:
             return all_tool_calls, msgs, None
@@ -274,7 +285,8 @@ def run_test(url, test_case, stream):
     print_header(f"{name}  [{mode}] ({apply_stage})")
 
     response_format = test_case["response_format"]
-    print_schema_note(apply_stage, response_format)
+    extra_body = test_case.get("extra_body")
+    print_schema_note(apply_stage, response_format, extra_body)
 
     tools = test_case.get("tools")
     mocks = test_case.get("mock_tool_responses") or {}
@@ -290,6 +302,7 @@ def run_test(url, test_case, stream):
             mock_tool_responses=mocks,
             stream=stream,
             response_format=response_format,
+            extra_body=extra_body,
         )
     elif apply_stage == "after_tools":
         # Phase 1: plain tool loop, no response_format applied yet.
@@ -314,7 +327,8 @@ def run_test(url, test_case, stream):
         # model focuses on producing the schema-constrained answer.
         _print(f"\n{DIM}{MAGENTA}  ⟐ follow-up turn with response_format applied{RESET}")
         result = chat_completion(
-            url, msgs, tools=None, response_format=response_format, stream=stream
+            url, msgs, tools=None, response_format=response_format, stream=stream,
+            extra_body=extra_body,
         )
         final_content = result["content"] if result else None
     else:
@@ -481,6 +495,51 @@ def _validate_sentiment(parsed):
     return True, f"sentiment={parsed['sentiment']} conf={conf} kws={kws}"
 
 
+# ---- Test: json_object + extra_body.json_schema (always) ----
+#
+# Exercises the llama.cpp-specific path where the OpenAI SDK would send
+# response_format={"type": "json_object"} and tunnel the schema through
+# extra_body.json_schema (which becomes a top-level "json_schema" field on
+# the request body).
+
+_PRODUCT_JSON_OBJECT_SCHEMA = {
+    "$schema": "https://json-schema.org/draft/2020-12/schema",
+    "$id": "https://example.com/product.schema.json",
+    "title": "Product",
+    "description": "A product in the catalog",
+    "type": "object",
+}
+
+PRODUCT_JSON_OBJECT_TEST_CASE = {
+    "name": "json_object response_format with extra_body json_schema",
+    "response_format": {"type": "json_object"},
+    "extra_body": {"json_schema": _PRODUCT_JSON_OBJECT_SCHEMA},
+    "apply_stage": "always",
+    "messages": [
+        {
+            "role": "system",
+            "content": (
+                "Extract structured data from the provided text according to the "
+                "JSON schema. Return only valid JSON matching the schema exactly."
+            ),
+        },
+        {
+            "role": "user",
+            "content": "Product: Wireless Headphones, ID: 101, In Stock: Yes",
+        },
+    ],
+    "validate": lambda parsed, tcs, raw: _validate_product_json_object(parsed),
+}
+
+
+def _validate_product_json_object(parsed):
+    if not isinstance(parsed, dict):
+        return False, f"expected JSON object, got {type(parsed).__name__}: {parsed!r}"
+    if not parsed:
+        return False, f"expected non-empty object, got {parsed!r}"
+    return True, f"product object with {len(parsed)} field(s): {sorted(parsed.keys())}"
+
+
 # ---- Test 3: Nested recipe schema (always) ----
 
 _RECIPE_SCHEMA = {
@@ -915,6 +974,7 @@ def _validate_country_report(parsed, tcs):
 ALL_TEST_CASES = [
     BOOK_TEST_CASE,
     SENTIMENT_TEST_CASE,
+    PRODUCT_JSON_OBJECT_TEST_CASE,
     RECIPE_TEST_CASE,
     SHOP_COMPARISON_TEST_CASE,
     COUNTRY_REPORT_TEST_CASE,

diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp
index ad8834e317a0cd16ce6aeaf85426ca185d95fe39..21c843c0d69902fd3c6ad65f1a07f9af92c525bc 100644 (file)
--- a/tools/server/server-common.cpp
+++ b/tools/server/server-common.cpp
@@ -947,7 +947,9 @@ json oaicompat_chat_params_parse(
         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") {
-            json_schema = json_value(response_format, "schema", json::object());
+            if (response_format.contains("schema") || json_schema.empty()) {
+                json_schema = json_value(response_format, "schema", json::object());
+            }
         } else if (response_type == "json_schema") {
             auto schema_wrapper = json_value(response_format, "json_schema", json::object());
             json_schema = json_value(schema_wrapper, "schema", json::object());