refactor(provider): share assistant payload replay matching (#3307)

* Share assistant payload replay matching

* fix(provider): recover assistant field when ordinal AI index is taken

The mismatch-length fallback in `_match_ai_message` only tried the exact
`fallback_ordinal` AI index. When serialization drops or reorders an
assistant message, a unique signature match can consume a non-ordinal
index, leaving a later ambiguous payload's ordinal already used — so its
provider field (e.g. `reasoning_content`) was silently dropped.

Scan forward from the ordinal for the next unused `AIMessage` (wrapping to
earlier indices) to preserve the positional bias while still recovering
the field. Forward scanning avoids a naive min-unused pick that could
restore the wrong field after a leading message is dropped.

Add a regression test for the dropped-leading-message case.

* fix(provider): avoid earlier assistant fallback replay

backend/packages/harness/deerflow/models/patched_deepseek.py

@@ -10,9 +10,10 @@ on all assistant messages when thinking mode is enabled.
 from typing import Any
 
 from langchain_core.language_models import LanguageModelInput
-from langchain_core.messages import AIMessage
 from langchain_deepseek import ChatDeepSeek
 
+from deerflow.models.assistant_payload_replay import restore_assistant_payloads, restore_reasoning_content
+
 
 class PatchedChatDeepSeek(ChatDeepSeek):
     """ChatDeepSeek with proper reasoning_content preservation.
@@ -49,25 +50,10 @@ class PatchedChatDeepSeek(ChatDeepSeek):
         # Call parent to get the base payload
         payload = super()._get_request_payload(input_, stop=stop, **kwargs)
 
-        # Match payload messages with original messages to restore reasoning_content
-        payload_messages = payload.get("messages", [])
-
-        # The payload messages and original messages should be in the same order
-        # Iterate through both and match by position
-        if len(payload_messages) == len(original_messages):
-            for payload_msg, orig_msg in zip(payload_messages, original_messages):
-                if payload_msg.get("role") == "assistant" and isinstance(orig_msg, AIMessage):
-                    reasoning_content = orig_msg.additional_kwargs.get("reasoning_content")
-                    if reasoning_content is not None:
-                        payload_msg["reasoning_content"] = reasoning_content
-        else:
-            # Fallback: match by counting assistant messages
-            ai_messages = [m for m in original_messages if isinstance(m, AIMessage)]
-            assistant_payloads = [(i, m) for i, m in enumerate(payload_messages) if m.get("role") == "assistant"]
-
-            for (idx, payload_msg), ai_msg in zip(assistant_payloads, ai_messages):
-                reasoning_content = ai_msg.additional_kwargs.get("reasoning_content")
-                if reasoning_content is not None:
-                    payload_messages[idx]["reasoning_content"] = reasoning_content
+        restore_assistant_payloads(
+            payload.get("messages", []),
+            original_messages,
+            restore_reasoning_content,
+        )
 
         return payload