From c7c2eb4518f452bb9ec2feff1bc4cb7a276012e6 Mon Sep 17 00:00:00 2001
From: fdc310 <82008029+fdc310@users.noreply.github.com>
Date: Wed, 20 Aug 2025 22:39:16 +0800
Subject: [PATCH] fix:in the gmini tool_calls no id The resulting call failure
(#1622)
* fix:in the dify agent llm return message can not joint
* fix:in the gmini tool_calls no id The resulting call failure
---
.../modelmgr/requesters/geminichatcmpl.py | 131 ++++++++++++++++++
1 file changed, 131 insertions(+)
diff --git a/pkg/provider/modelmgr/requesters/geminichatcmpl.py b/pkg/provider/modelmgr/requesters/geminichatcmpl.py
index 85395f91..df2db312 100644
--- a/pkg/provider/modelmgr/requesters/geminichatcmpl.py
+++ b/pkg/provider/modelmgr/requesters/geminichatcmpl.py
@@ -4,6 +4,13 @@ import typing
from . import chatcmpl
+import uuid
+
+from .. import errors, requester
+from ....core import entities as core_entities
+from ... import entities as llm_entities
+from ...tools import entities as tools_entities
+
class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
"""Google Gemini API 请求器"""
@@ -12,3 +19,127 @@ class GeminiChatCompletions(chatcmpl.OpenAIChatCompletions):
'base_url': 'https://generativelanguage.googleapis.com/v1beta/openai',
'timeout': 120,
}
+
+
+ async def _closure_stream(
+ self,
+ query: core_entities.Query,
+ req_messages: list[dict],
+ use_model: requester.RuntimeLLMModel,
+ use_funcs: list[tools_entities.LLMFunction] = None,
+ extra_args: dict[str, typing.Any] = {},
+ remove_think: bool = False,
+ ) -> llm_entities.MessageChunk:
+ self.client.api_key = use_model.token_mgr.get_token()
+
+ args = {}
+ args['model'] = use_model.model_entity.name
+
+ if use_funcs:
+ tools = await self.ap.tool_mgr.generate_tools_for_openai(use_funcs)
+ if tools:
+ args['tools'] = tools
+
+ # 设置此次请求中的messages
+ messages = req_messages.copy()
+
+ # 检查vision
+ for msg in messages:
+ if 'content' in msg and isinstance(msg['content'], list):
+ for me in msg['content']:
+ if me['type'] == 'image_base64':
+ me['image_url'] = {'url': me['image_base64']}
+ me['type'] = 'image_url'
+ del me['image_base64']
+
+ args['messages'] = messages
+ args['stream'] = True
+
+ # 流式处理状态
+ tool_calls_map: dict[str, llm_entities.ToolCall] = {}
+ chunk_idx = 0
+ thinking_started = False
+ thinking_ended = False
+ role = 'assistant' # 默认角色
+ tool_id = ""
+ tool_name = ''
+ # accumulated_reasoning = '' # 仅用于判断何时结束思维链
+
+ async for chunk in self._req_stream(args, extra_body=extra_args):
+ # 解析 chunk 数据
+
+ if hasattr(chunk, 'choices') and chunk.choices:
+ choice = chunk.choices[0]
+ delta = choice.delta.model_dump() if hasattr(choice, 'delta') else {}
+
+ finish_reason = getattr(choice, 'finish_reason', None)
+ else:
+ delta = {}
+ finish_reason = None
+ # 从第一个 chunk 获取 role，后续使用这个 role
+ if 'role' in delta and delta['role']:
+ role = delta['role']
+
+ # 获取增量内容
+ delta_content = delta.get('content', '')
+ reasoning_content = delta.get('reasoning_content', '')
+
+ # 处理 reasoning_content
+ if reasoning_content:
+ # accumulated_reasoning += reasoning_content
+ # 如果设置了 remove_think，跳过 reasoning_content
+ if remove_think:
+ chunk_idx += 1
+ continue
+
+ # 第一次出现 reasoning_content，添加 开始标签
+ if not thinking_started:
+ thinking_started = True
+ delta_content = '\n' + reasoning_content
+ else:
+ # 继续输出 reasoning_content
+ delta_content = reasoning_content
+ elif thinking_started and not thinking_ended and delta_content:
+ # reasoning_content 结束，normal content 开始，添加 结束标签
+ thinking_ended = True
+ delta_content = '\n\n' + delta_content
+
+ # 处理 content 中已有的 标签（如果需要移除）
+ # if delta_content and remove_think and '' in delta_content:
+ # import re
+ #
+ # # 移除 标签及其内容
+ # delta_content = re.sub(r'.*?', '', delta_content, flags=re.DOTALL)
+
+ # 处理工具调用增量
+ # delta_tool_calls = None
+ if delta.get('tool_calls'):
+ for tool_call in delta['tool_calls']:
+ if tool_call['id'] == '' and tool_id == '':
+ tool_id = str(uuid.uuid4())
+ if tool_call['function']['name']:
+ tool_name = tool_call['function']['name']
+ tool_call['id'] = tool_id
+ tool_call['function']['name'] = tool_name
+ if tool_call['type'] is None:
+ tool_call['type'] = 'function'
+
+
+
+ # 跳过空的第一个 chunk（只有 role 没有内容）
+ if chunk_idx == 0 and not delta_content and not reasoning_content and not delta.get('tool_calls'):
+ chunk_idx += 1
+ continue
+ # 构建 MessageChunk - 只包含增量内容
+ chunk_data = {
+ 'role': role,
+ 'content': delta_content if delta_content else None,
+ 'tool_calls': delta.get('tool_calls'),
+ 'is_final': bool(finish_reason),
+ }
+
+ # 移除 None 值
+ chunk_data = {k: v for k, v in chunk_data.items() if v is not None}
+
+ yield llm_entities.MessageChunk(**chunk_data)
+ chunk_idx += 1
\ No newline at end of file