[New Feature] 支持 stream agent 流式返回step和message (#345)

* stream agent 流式返回step和message

* 使用make format-check进行代码优化

* 使用make format-check检查代码、使用make format优化代码

* 使用`python -m mypy src`检查和优化代码

* 使用`make lint`检查优化代码

* 根据review意见进行修改

* 使用`python -m black --check`检查格式

* 判断typing.TYPE_CHECKING来处理编译器检查

erniebot-agent/src/erniebot_agent/agents/agent.py

@@ -2,7 +2,9 @@
 import json
 import logging
 from typing import (
+    TYPE_CHECKING,
     Any,
+    AsyncIterator,
     Dict,
     Final,
     Iterable,
@@ -20,7 +22,13 @@
 from erniebot_agent.agents.callback.default import get_default_callbacks
 from erniebot_agent.agents.callback.handlers.base import CallbackHandler
 from erniebot_agent.agents.mixins import GradioMixin
-from erniebot_agent.agents.schema import AgentResponse, LLMResponse, ToolResponse
+from erniebot_agent.agents.schema import (
+    DEFAULT_FINISH_STEP,
+    AgentResponse,
+    AgentStep,
+    LLMResponse,
+    ToolResponse,
+)
 from erniebot_agent.chat_models.erniebot import BaseERNIEBot
 from erniebot_agent.file import (
     File,
@@ -131,13 +139,46 @@ async def run(self, prompt: str, files: Optional[Sequence[File]] = None) -> Agen
             await self._callback_manager.on_run_end(agent=self, response=agent_resp)
         return agent_resp
 
+    @final
+    async def run_stream(
+        self, prompt: str, files: Optional[Sequence[File]] = None
+    ) -> AsyncIterator[Tuple[AgentStep, List[Message]]]:
+        """Run the agent asynchronously, returning an async iterator of responses.
+
+        Args:
+            prompt: A natural language text describing the task that the agent
+                should perform.
+            files: A list of files that the agent can use to perform the task.
+        Returns:
+            Iterator of responses from the agent.
+        """
+        if files:
+            await self._ensure_managed_files(files)
+        await self._callback_manager.on_run_start(agent=self, prompt=prompt)
+        try:
+            async for step, msg in self._run_stream(prompt, files):
+                yield (step, msg)
+        except BaseException as e:
+            await self._callback_manager.on_run_error(agent=self, error=e)
+            raise e
+        else:
+            await self._callback_manager.on_run_end(
+                agent=self,
+                response=AgentResponse(
+                    text="Agent run stopped.",
+                    chat_history=self.memory.get_messages(),
+                    steps=[step],
+                    status="STOPPED",
+                ),
+            )
+
     @final
     async def run_llm(
         self,
         messages: List[Message],
         **llm_opts: Any,
     ) -> LLMResponse:
-        """Run the LLM asynchronously.
+        """Run the LLM asynchronously, returning final response.
 
         Args:
             messages: The input messages.
@@ -156,6 +197,34 @@ async def run_llm(
             await self._callback_manager.on_llm_end(agent=self, llm=self.llm, response=llm_resp)
         return llm_resp
 
+    @final
+    async def run_llm_stream(
+        self,
+        messages: List[Message],
+        **llm_opts: Any,
+    ) -> AsyncIterator[LLMResponse]:
+        """Run the LLM asynchronously, returning an async iterator of responses
+
+        Args:
+            messages: The input messages.
+            llm_opts: Options to pass to the LLM.
+
+        Returns:
+            Iterator of responses from the LLM.
+        """
+        llm_resp = None
+        await self._callback_manager.on_llm_start(agent=self, llm=self.llm, messages=messages)
+        try:
+            # The LLM will return an async iterator.
+            async for llm_resp in self._run_llm_stream(messages, **(llm_opts or {})):
+                yield llm_resp
+        except (Exception, KeyboardInterrupt) as e:
+            await self._callback_manager.on_llm_error(agent=self, llm=self.llm, error=e)
+            raise e
+        else:
+            await self._callback_manager.on_llm_end(agent=self, llm=self.llm, response=llm_resp)
+        return
+
     @final
     async def run_tool(self, tool_name: str, tool_args: str) -> ToolResponse:
         """Run the specified tool asynchronously.
@@ -221,7 +290,32 @@ def get_file_manager(self) -> FileManager:
     async def _run(self, prompt: str, files: Optional[Sequence[File]] = None) -> AgentResponse:
         raise NotImplementedError
 
+    @abc.abstractmethod
+    async def _run_stream(
+        self, prompt: str, files: Optional[Sequence[File]] = None
+    ) -> AsyncIterator[Tuple[AgentStep, List[Message]]]:
+        """
+        Abstract asynchronous generator method that should be implemented by subclasses.
+        This method should yield a sequence of (AgentStep, List[Message]) tuples based on the given
+        prompt and optionally accompanying files.
+        """
+        if TYPE_CHECKING:
+            # HACK
+            # This conditional block is strictly for static type-checking purposes (e.g., mypy)
+            # and will not be executed.
+            only_for_mypy_type_check: Tuple[AgentStep, List[Message]] = (DEFAULT_FINISH_STEP, [])
+            yield only_for_mypy_type_check
+
     async def _run_llm(self, messages: List[Message], **opts: Any) -> LLMResponse:
+        """Run the LLM with the given messages and options.
+
+        Args:
+            messages: The input messages.
+            opts: Options to pass to the LLM.
+
+        Returns:
+            Response from the LLM.
+        """
         for reserved_opt in ("stream", "system", "plugins"):
             if reserved_opt in opts:
                 raise TypeError(f"`{reserved_opt}` should not be set.")
@@ -241,6 +335,36 @@ async def _run_llm(self, messages: List[Message], **opts: Any) -> LLMResponse:
         llm_ret = await self.llm.chat(messages, stream=False, functions=functions, **opts)
         return LLMResponse(message=llm_ret)
 
+    async def _run_llm_stream(self, messages: List[Message], **opts: Any) -> AsyncIterator[LLMResponse]:
+        """Run the LLM, yielding an async iterator of responses.
+
+        Args:
+            messages: The input messages.
+            opts: Options to pass to the LLM.
+
+        Returns:
+            Async iterator of responses from the LLM.
+        """
+        for reserved_opt in ("stream", "system", "plugins"):
+            if reserved_opt in opts:
+                raise TypeError(f"`{reserved_opt}` should not be set.")
+
+        if "functions" not in opts:
+            functions = self._tool_manager.get_tool_schemas()
+        else:
+            functions = opts.pop("functions")
+
+        if hasattr(self.llm, "system"):
+            _logger.warning(
+                "The `system` message has already been set in the agent;"
+                "the `system` message configured in ERNIEBot will become ineffective."
+            )
+        opts["system"] = self.system.content if self.system is not None else None
+        opts["plugins"] = self._plugins
+        llm_ret = await self.llm.chat(messages, stream=True, functions=functions, **opts)
+        async for msg in llm_ret:
+            yield LLMResponse(message=msg)
+
     async def _run_tool(self, tool: BaseTool, tool_args: str) -> ToolResponse:
         parsed_tool_args = self._parse_tool_args(tool_args)
         file_manager = self.get_file_manager()

erniebot-agent/src/erniebot_agent/agents/function_agent.py

@@ -13,7 +13,16 @@
 # limitations under the License.
 
 import logging
-from typing import Final, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import (
+    AsyncIterator,
+    Final,
+    Iterable,
+    List,
+    Optional,
+    Sequence,
+    Tuple,
+    Union,
+)
 
 from erniebot_agent.agents.agent import Agent
 from erniebot_agent.agents.callback.callback_manager import CallbackManager
@@ -31,7 +40,12 @@
 from erniebot_agent.chat_models.erniebot import BaseERNIEBot
 from erniebot_agent.file import File, FileManager
 from erniebot_agent.memory import Memory
-from erniebot_agent.memory.messages import FunctionMessage, HumanMessage, Message
+from erniebot_agent.memory.messages import (
+    AIMessage,
+    FunctionMessage,
+    HumanMessage,
+    Message,
+)
 from erniebot_agent.tools.base import BaseTool
 from erniebot_agent.tools.tool_manager import ToolManager
 
@@ -136,7 +150,7 @@ async def _run(self, prompt: str, files: Optional[Sequence[File]] = None) -> Age
         chat_history.append(run_input)
 
         for tool in self._first_tools:
-            curr_step, new_messages = await self._step(chat_history, selected_tool=tool)
+            curr_step, new_messages = await self._call_first_tools(chat_history, selected_tool=tool)
             if not isinstance(curr_step, EndStep):
                 chat_history.extend(new_messages)
                 num_steps_taken += 1
@@ -167,23 +181,122 @@ async def _run(self, prompt: str, files: Optional[Sequence[File]] = None) -> Age
         response = self._create_stopped_response(chat_history, steps_taken)
         return response
 
-    async def _step(
+    async def _call_first_tools(
         self, chat_history: List[Message], selected_tool: Optional[BaseTool] = None
     ) -> Tuple[AgentStep, List[Message]]:
-        new_messages: List[Message] = []
         input_messages = self.memory.get_messages() + chat_history
-        if selected_tool is not None:
-            tool_choice = {"type": "function", "function": {"name": selected_tool.tool_name}}
-            llm_resp = await self.run_llm(
-                messages=input_messages,
-                functions=[selected_tool.function_call_schema()],  # only regist one tool
-                tool_choice=tool_choice,
-            )
-        else:
+        if selected_tool is None:
             llm_resp = await self.run_llm(messages=input_messages)
+            return await self._process_step(llm_resp, chat_history)
+
+        tool_choice = {"type": "function", "function": {"name": selected_tool.tool_name}}
+        llm_resp = await self.run_llm(
+            messages=input_messages,
+            functions=[selected_tool.function_call_schema()],  # only regist one tool
+            tool_choice=tool_choice,
+        )
+        return await self._process_step(llm_resp, chat_history)
+
+    async def _step(self, chat_history: List[Message]) -> Tuple[AgentStep, List[Message]]:
+        """Run a step of the agent.
+        Args:
+            chat_history: The chat history to provide to the agent.
+        Returns:
+            A tuple of an agent step and a list of new messages.
+        """
+        input_messages = self.memory.get_messages() + chat_history
+        llm_resp = await self.run_llm(messages=input_messages)
+        return await self._process_step(llm_resp, chat_history)
 
+    async def _step_stream(
+        self, chat_history: List[Message]
+    ) -> AsyncIterator[Tuple[AgentStep, List[Message]]]:
+        """Run a step of the agent in streaming mode.
+        Args:
+            chat_history: The chat history to provide to the agent.
+        Returns:
+            An async iterator that yields a tuple of an agent step and a list ofnew messages.
+        """
+        input_messages = self.memory.get_messages() + chat_history
+        async for llm_resp in self.run_llm_stream(messages=input_messages):
+            yield await self._process_step(llm_resp, chat_history)
+
+    async def _run_stream(
+        self, prompt: str, files: Optional[Sequence[File]] = None
+    ) -> AsyncIterator[Tuple[AgentStep, List[Message]]]:
+        """Run the agent with the given prompt and files in streaming mode.
+        Args:
+            prompt: The prompt for the agent to run.
+            files: A list of files for the agent to use. If `None`, use an empty
+                list.
+        Returns:
+            If `stream` is `False`, an agent response object. If `stream` is
+            `True`, an async iterator that yields agent steps one by one.
+        """
+        chat_history: List[Message] = []
+        steps_taken: List[AgentStep] = []
+
+        run_input = await HumanMessage.create_with_files(
+            prompt, files or [], include_file_urls=self.file_needs_url
+        )
+
+        num_steps_taken = 0
+        chat_history.append(run_input)
+
+        for tool in self._first_tools:
+            curr_step, new_messages = await self._call_first_tools(chat_history, selected_tool=tool)
+            if not isinstance(curr_step, EndStep):
+                chat_history.extend(new_messages)
+                num_steps_taken += 1
+                steps_taken.append(curr_step)
+            else:
+                # If tool choice not work, skip this round
+                _logger.warning(f"Selected tool [{tool.tool_name}] not work")
+
+        is_finished = False
+        new_messages = []
+        end_step_msgs = []
+        while is_finished is False:
+            # IMPORTANT~! We use following code to get the response from LLM
+            # When finish_reason is fuction_call, run_llm_stream return all info in one step, but
+            # When finish_reason is normal chat, run_llm_stream return info in multiple steps.
+            async for curr_step, new_messages in self._step_stream(chat_history):
+                if isinstance(curr_step, ToolStep):
+                    steps_taken.append(curr_step)
+                    yield curr_step, new_messages
+
+                elif isinstance(curr_step, PluginStep):
+                    steps_taken.append(curr_step)
+                    # 预留 调用了Plugin之后不结束的接口
+
+                    # 此处为调用了Plugin之后直接结束的Plugin
+                    curr_step = DEFAULT_FINISH_STEP
+                    yield curr_step, new_messages
+
+                elif isinstance(curr_step, EndStep):
+                    is_finished = True
+                    end_step_msgs.extend(new_messages)
+                    yield curr_step, new_messages
+                else:
+                    raise RuntimeError("Invalid step type")
+            chat_history.extend(new_messages)
+
+        self.memory.add_message(run_input)
+        end_step_msg = AIMessage(content="".join([item.content for item in end_step_msgs]))
+        self.memory.add_message(end_step_msg)
+
+    async def _process_step(self, llm_resp, chat_history) -> Tuple[AgentStep, List[Message]]:
+        """Process and execute a step of the agent from LLM response.
+        Args:
+            llm_resp: The LLM response to convert.
+            chat_history: The chat history to provide to the agent.
+        Returns:
+            A tuple of an agent step and a list of new messages.
+        """
+        new_messages: List[Message] = []
         output_message = llm_resp.message  # AIMessage
         new_messages.append(output_message)
+        # handle function call
         if output_message.function_call is not None:
             tool_name = output_message.function_call["name"]
             tool_args = output_message.function_call["arguments"]
@@ -198,6 +311,7 @@ async def _step(
                 ),
                 new_messages,
             )
+        # handle plugin info with input/output files
         elif output_message.plugin_info is not None:
             file_manager = self.get_file_manager()
             return (