feat: 实现流式消息处理支持

pkg/provider/runners/localagent.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import json
 import copy
+from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE
 import typing
 from .. import runner
 from ...core import entities as core_entities
@@ -27,7 +28,13 @@ Respond in the same language as the user's input.
 class LocalAgentRunner(runner.RequestRunner):
     """本地Agent请求运行器"""
 
-    async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message, None]:
+    class ToolCallTracker:
+        """工具调用追踪器"""
+        def __init__(self):
+            self.active_calls: dict[str,dict] = {}
+            self.completed_calls: list[llm_entities.ToolCall] = []
+
+    async def run(self, query: core_entities.Query) -> typing.AsyncGenerator[llm_entities.Message | llm_entities.MessageChunk, None]:
         """运行请求"""
         pending_tool_calls = []
 
@@ -80,20 +87,57 @@ class LocalAgentRunner(runner.RequestRunner):
 
         req_messages = query.prompt.messages.copy() + query.messages.copy() + [user_message]
 
-        # 首次请求
-        msg = await query.use_llm_model.requester.invoke_llm(
-            query,
-            query.use_llm_model,
-            req_messages,
-            query.use_funcs,
-            extra_args=query.use_llm_model.model_entity.extra_args,
-        )
+        is_stream = query.adapter.is_stream_output_supported()
+        # while True:
+        #     pass
+        if not is_stream:
+            # 非流式输出，直接请求
+            msg = await query.use_llm_model.requester.invoke_llm(
+                query,
+                query.use_llm_model,
+                req_messages,
+                query.use_funcs,
+                extra_args=query.use_llm_model.model_entity.extra_args,
+            )
+            yield msg
+            final_msg = msg
+        else:
+            # 流式输出，需要处理工具调用
+            tool_calls_map: dict[str, llm_entities.ToolCall] = {}
+            async for msg in await query.use_llm_model.requester.invoke_llm(
+                query,
+                query.use_llm_model,
+                req_messages,
+                query.use_funcs,
+                stream=is_stream,
+                extra_args=query.use_llm_model.model_entity.extra_args,
+            ):  
+                assert isinstance(msg, llm_entities.MessageChunk)
+                yield msg
+                if msg.tool_calls:
+                    for tool_call in msg.tool_calls:
+                        if tool_call.id not in tool_calls_map:
+                            tool_calls_map[tool_call.id] = llm_entities.ToolCall(
+                                id=tool_call.id,
+                                type=tool_call.type,
+                                function=llm_entities.FunctionCall(
+                                    name=tool_call.function.name if tool_call.function else '',
+                                    arguments=''
+                                ),
+                            )
+                        if tool_call.function and tool_call.function.arguments:
+                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
+                            tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
+            final_msg = llm_entities.Message(
+                role=msg.role,
+                content=msg.all_content,
+                tool_calls=list(tool_calls_map.values()),
+            )
 
-        yield msg
+        
+        pending_tool_calls = final_msg.tool_calls
 
-        pending_tool_calls = msg.tool_calls
-
-        req_messages.append(msg)
+        req_messages.append(final_msg)
 
         # 持续请求，只要还有待处理的工具调用就继续处理调用
         while pending_tool_calls:
@@ -122,17 +166,50 @@ class LocalAgentRunner(runner.RequestRunner):
 
                     req_messages.append(err_msg)
 
-            # 处理完所有调用，再次请求
-            msg = await query.use_llm_model.requester.invoke_llm(
-                query,
-                query.use_llm_model,
-                req_messages,
-                query.use_funcs,
-                extra_args=query.use_llm_model.model_entity.extra_args,
-            )
+            if is_stream:
+                tool_calls_map = {}
+                async for msg in await query.use_llm_model.requester.invoke_llm(
+                    query,
+                    query.use_llm_model,
+                    req_messages,
+                    query.use_funcs,
+                    stream=is_stream,
+                    extra_args=query.use_llm_model.model_entity.extra_args, 
+                ):  
+                    assert isinstance(msg, llm_entities.MessageChunk)
+                    yield msg
+                if msg.tool_calls:
+                    for tool_call in msg.tool_calls:
+                        if tool_call.id not in tool_calls_map:
+                            tool_calls_map[tool_call.id] = llm_entities.ToolCall(
+                                id=tool_call.id,
+                                type=tool_call.type,
+                                function=llm_entities.FunctionCall(
+                                    name=tool_call.function.name if tool_call.function else '',
+                                    arguments=''
+                                ),
+                            )
+                        if tool_call.function and tool_call.function.arguments:
+                            # 流式处理中，工具调用参数可能分多个chunk返回，需要追加而不是覆盖
+                            tool_calls_map[tool_call.id].function.arguments += tool_call.function.arguments
+                final_msg = llm_entities.Message(
+                    role=msg.role,
+                    content=msg.all_content,
+                    tool_calls=list(tool_calls_map.values()),
+                )
+            else:
+                # 处理完所有调用，再次请求
+                msg = await query.use_llm_model.requester.invoke_llm(
+                    query,
+                    query.use_llm_model,
+                    req_messages,
+                    query.use_funcs,
+                    extra_args=query.use_llm_model.model_entity.extra_args,
+                )
 
-            yield msg
+                yield msg
+                final_msg = msg
 
-            pending_tool_calls = msg.tool_calls
+            pending_tool_calls = final_msg.tool_calls
 
-            req_messages.append(msg)
+            req_messages.append(final_msg)