Merge origin/main into fix/structured-retry-classification-main

Made-with: Cursor

nanobot/providers/openai_compat_provider.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 import asyncio
 import email.utils
 import hashlib
+import importlib.util
 import os
 import secrets
 import string
@@ -14,7 +15,17 @@ from collections.abc import Awaitable, Callable
 from typing import TYPE_CHECKING, Any
 
 import json_repair
-from openai import AsyncOpenAI
+
+if os.environ.get("LANGFUSE_SECRET_KEY") and importlib.util.find_spec("langfuse"):
+    from langfuse.openai import AsyncOpenAI
+else:
+    if os.environ.get("LANGFUSE_SECRET_KEY"):
+        import logging
+        logging.getLogger(__name__).warning(
+            "LANGFUSE_SECRET_KEY is set but langfuse is not installed; "
+            "install with `pip install langfuse` to enable tracing"
+        )
+    from openai import AsyncOpenAI
 
 from nanobot.providers.base import LLMProvider, LLMResponse, ToolCallRequest
 
@@ -23,6 +34,7 @@ if TYPE_CHECKING:
 
 _ALLOWED_MSG_KEYS = frozenset({
     "role", "content", "tool_calls", "tool_call_id", "name",
+    "reasoning_content", "extra_content",
 })
 _ALNUM = string.ascii_letters + string.digits
 
@@ -154,8 +166,9 @@ class OpenAICompatProvider(LLMProvider):
             resolved = env_val.replace("{api_key}", api_key).replace("{api_base}", effective_base)
             os.environ.setdefault(env_name, resolved)
 
-    @staticmethod
+    @classmethod
     def _apply_cache_control(
+        cls,
         messages: list[dict[str, Any]],
         tools: list[dict[str, Any]] | None,
     ) -> tuple[list[dict[str, Any]], list[dict[str, Any]] | None]:
@@ -183,7 +196,8 @@ class OpenAICompatProvider(LLMProvider):
         new_tools = tools
         if tools:
             new_tools = list(tools)
-            new_tools[-1] = {**new_tools[-1], "cache_control": cache_marker}
+            for idx in cls._tool_cache_marker_indices(new_tools):
+                new_tools[idx] = {**new_tools[idx], "cache_control": cache_marker}
         return new_messages, new_tools
 
     @staticmethod
@@ -224,6 +238,21 @@ class OpenAICompatProvider(LLMProvider):
     # Build kwargs
     # ------------------------------------------------------------------
 
+    @staticmethod
+    def _supports_temperature(
+        model_name: str,
+        reasoning_effort: str | None = None,
+    ) -> bool:
+        """Return True when the model accepts a temperature parameter.
+
+        GPT-5 family and reasoning models (o1/o3/o4) reject temperature
+        when reasoning_effort is set to anything other than ``"none"``.
+        """
+        if reasoning_effort and reasoning_effort.lower() != "none":
+            return False
+        name = model_name.lower()
+        return not any(token in name for token in ("gpt-5", "o1", "o3", "o4"))
+
     def _build_kwargs(
         self,
         messages: list[dict[str, Any]],
@@ -248,9 +277,13 @@ class OpenAICompatProvider(LLMProvider):
         kwargs: dict[str, Any] = {
             "model": model_name,
             "messages": self._sanitize_messages(self._sanitize_empty_content(messages)),
-            "temperature": temperature,
         }
 
+        # GPT-5 and reasoning models (o1/o3/o4) reject temperature when
+        # reasoning_effort is active.  Only include it when safe.
+        if self._supports_temperature(model_name, reasoning_effort):
+            kwargs["temperature"] = temperature
+
         if spec and getattr(spec, "supports_max_completion_tokens", False):
             kwargs["max_completion_tokens"] = max(1, max_tokens)
         else:
@@ -266,6 +299,24 @@ class OpenAICompatProvider(LLMProvider):
         if reasoning_effort:
             kwargs["reasoning_effort"] = reasoning_effort
 
+        # Provider-specific thinking parameters.
+        # Only sent when reasoning_effort is explicitly configured so that
+        # the provider default is preserved otherwise.
+        if spec and reasoning_effort is not None:
+            thinking_enabled = reasoning_effort.lower() != "minimal"
+            extra: dict[str, Any] | None = None
+            if spec.name == "dashscope":
+                extra = {"enable_thinking": thinking_enabled}
+            elif spec.name in (
+                "volcengine", "volcengine_coding_plan",
+                "byteplus", "byteplus_coding_plan",
+            ):
+                extra = {
+                    "thinking": {"type": "enabled" if thinking_enabled else "disabled"}
+                }
+            if extra:
+                kwargs.setdefault("extra_body", {}).update(extra)
+
         if tools:
             kwargs["tools"] = tools
             kwargs["tool_choice"] = tool_choice or "auto"
@@ -740,9 +791,6 @@ class OpenAICompatProvider(LLMProvider):
                     break
                 chunks.append(chunk)
                 if on_content_delta and chunk.choices:
-                    text = getattr(chunk.choices[0].delta, "reasoning_content", None)
-                    if text:
-                        await on_content_delta(text)
                     text = getattr(chunk.choices[0].delta, "content", None)
                     if text:
                         await on_content_delta(text)