Merge pull request #552 from Undertone0809/v1.15.0/opt-pne-chat

feat: optimize pne chat and add AIChat

docs/get_started/quick_start.md

@@ -1,6 +1,6 @@
 # 快速开始
 
-通过该部分教学，你可以快速对 promptulate 有一个整体的认知，了解一些常用模块的基本使用方式，在阅读完该部分之后，你可以继续阅读 [User Cases](modules/usercases/intro.md#user-cases) 来了解 promptulate 的一些最佳实践，在遇到问题的时候，可以查看每个模块的具体使用方式，也欢迎你在 [issue](https://github.com/Undertone0809/promptulate/issues) 中为 promptulate 提供更好的建议。
+通过该部分教学，你可以快速对 promptulate 有一个整体的认知，了解一些常用模块的基本使用方式，在阅读完该部分之后，你可以继续阅读 [User Cases](modules/usercases/intro.md#user-cases) 和 [example](https://github.com/Undertone0809/promptulate/tree/main/example) 来了解 promptulate 的一些最佳实践，在遇到问题的时候，可以查看每个模块的具体使用方式，也欢迎你在 [issue](https://github.com/Undertone0809/promptulate/issues) 中为 promptulate 提供更好的建议。
 
 ## 安装最新版
 
@@ -234,7 +234,6 @@ pip install poetry
 make install
 ```
 
-
 本项目使用配备代码语法检查工具，如果你想提交 pr，则需要在 commit 之前运行 `make polish-codestyle` 进行代码规范格式化，并且运行 `make lint` 通过语法与单元测试的检查。
 
 ## 更多

docs/modules/agents/assistant_agent_usage.md

@@ -51,7 +51,7 @@ However, it is really easy to create your own tools - see documentation [here](h
 ```python
 from langchain_community.tools.tavily_search import TavilySearchResults
 
-tools = [TavilySearchResults(max_results=3)]
+tools = [TavilySearchResults(max_results=5)]
 ```
 
 ## Create the Assistant Agent
@@ -69,7 +69,7 @@ agent = AssistantAgent(tools=tools, llm=llm)
 agent.run("what is the hometown of the 2024 Australia open winner?")
 ```
 
-**output**
+**Output:**
 
 ```text
 [Agent] Assistant Agent start...

example/agent/assistant_agent_usage.ipynb

@@ -109,7 +109,7 @@
    "source": [
     "from langchain_community.tools.tavily_search import TavilySearchResults\n",
     "\n",
-    "tools = [TavilySearchResults(max_results=3)]"
+    "tools = [TavilySearchResults(max_results=5)]"
    ],
    "metadata": {
     "collapsed": true,
@@ -173,7 +173,7 @@
   {
    "cell_type": "markdown",
    "source": [
-    "**output**\n",
+    "**Output:**\n",
     "\n",
     "```text\n",
     "[Agent] Assistant Agent start...\n",

example/llm/factory.ipynb

@@ -4,14 +4,13 @@
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true,
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
+    "## LLM-Factory\n",
+    "\n",
     "This notebook show how to use LLMFactory."
    ]
   },
@@ -20,9 +19,6 @@
    "execution_count": 2,
    "metadata": {
     "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    },
     "pycharm": {
      "name": "#%%\n"
     },
@@ -70,4 +66,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

example/output_formatter/output_formatter_with_llm_usage.py

@@ -6,20 +6,20 @@
 from promptulate.pydantic_v1 import BaseModel, Field
 
 
-class Response(BaseModel):
+class LLMResponse(BaseModel):
     provinces: List[str] = Field(description="List of provinces name")
 
 
 def main():
     llm = ChatOpenAI()
-    formatter = OutputFormatter(Response)
+    formatter = OutputFormatter(LLMResponse)
 
     prompt = (
         f"Please tell me the names of provinces in China.\n"
         f"{formatter.get_formatted_instructions()}"
     )
     llm_output = llm(prompt)
-    response: Response = formatter.formatting_result(llm_output)
+    response: LLMResponse = formatter.formatting_result(llm_output)
     print(response)
 
 

promptulate/__init__.py

@@ -22,7 +22,7 @@
 from promptulate.agents.base import BaseAgent
 from promptulate.agents.tool_agent.agent import ToolAgent
 from promptulate.agents.web_agent.agent import WebAgent
-from promptulate.chat import chat
+from promptulate.chat import AIChat, chat
 from promptulate.llms.base import BaseLLM
 from promptulate.llms.factory import LLMFactory
 from promptulate.llms.openai.openai import ChatOpenAI
@@ -52,7 +52,7 @@
     "MessageSet",
 ]
 
-_llm_fields = ["chat", "BaseLLM", "ChatOpenAI", "LLMFactory"]
+_llm_fields = ["chat", "AIChat", "BaseLLM", "ChatOpenAI", "LLMFactory"]
 
 _tool_fields = [
     "Tool",

promptulate/agents/tool_agent/agent.py

@@ -9,7 +9,8 @@
 )
 from promptulate.hook import Hook, HookTable
 from promptulate.llms.base import BaseLLM
-from promptulate.schema import TOOL_TYPES
+from promptulate.llms.openai.openai import ChatOpenAI
+from promptulate.schema import ToolTypes
 from promptulate.tools.manager import ToolManager
 from promptulate.utils.logger import logger
 from promptulate.utils.string_template import StringTemplate
@@ -49,8 +50,8 @@ class ToolAgent(BaseAgent):
     def __init__(
         self,
         *,
-        llm: BaseLLM,
-        tools: Optional[List[TOOL_TYPES]] = None,
+        llm: BaseLLM = None,
+        tools: Optional[List[ToolTypes]] = None,
         prefix_prompt_template: StringTemplate = StringTemplate(PREFIX_TEMPLATE),
         hooks: Optional[List[Callable]] = None,
         enable_role: bool = False,
@@ -67,7 +68,7 @@ def __init__(
             )
 
         super().__init__(hooks=hooks, agent_type="Tool Agent", _from=_from)
-        self.llm: BaseLLM = llm
+        self.llm: BaseLLM = llm or ChatOpenAI(model="gpt-4-1106-preview")
         """llm provider"""
         self.tool_manager: ToolManager = (
             tool_manager if tool_manager is not None else ToolManager(tools or [])

promptulate/beta/agents/assistant_agent/agent.py

@@ -8,12 +8,10 @@
 from promptulate.agents.tool_agent import ToolAgent
 from promptulate.agents.tool_agent.agent import ActionResponse
 from promptulate.beta.agents.assistant_agent import operations
-from promptulate.beta.agents.assistant_agent.schema import (
-    Plan,
-)
+from promptulate.beta.agents.assistant_agent.schema import Plan
 from promptulate.hook import Hook, HookTable
 from promptulate.llms.base import BaseLLM
-from promptulate.schema import TOOL_TYPES
+from promptulate.schema import ToolTypes
 from promptulate.tools.manager import ToolManager
 from promptulate.utils.logger import logger
 
@@ -38,7 +36,8 @@ def __init__(
         self,
         *,
         llm: BaseLLM,
-        tools: Optional[List[TOOL_TYPES]] = None,
+        tools: Optional[List[ToolTypes]] = None,
+        max_iterations: Optional[int] = 20,
         **kwargs,
     ):
         super().__init__(agent_type="Assistant Agent", **kwargs)
@@ -52,6 +51,7 @@ def __init__(
             self.task_handler, self.step_handler, self.result_handler
         )
         self.current_task_id: Optional[str] = None
+        self.max_iterations: int = max_iterations
 
         logger.info("Assistant Agent initialized.")
 
@@ -226,6 +226,11 @@ def step_handler(self, step: uacp.Step) -> uacp.Step:
             StepTypes.REVISE: self.revise,
         }
 
+        if len(self.current_task.steps) > self.max_iterations:
+            final_output: str = self.current_task.steps[-1].output
+            step.output = f"Task has too many steps. Aborting. Recently step output: {final_output}"  # noqa
+            return step
+
         if step.name not in step_map:
             raise ValueError(f"Step name {step.name} not found in step mapping.")
 

promptulate/beta/agents/assistant_agent/prompt.py

@@ -9,20 +9,29 @@
 """  # noqa
 
 REVISE_SYSTEM_PROMPT = """
-For the given objective, come up with a simple step by step plan. \
-This plan should involve individual tasks, that if executed correctly will yield the correct answer. Do not add any superfluous steps. \
-The result of the final step should be the final answer. Make sure that each step has all the information needed - do not skip steps.
+As a powerfule agent, your job is to help the user achieve their goal. This is user instruction: {{user_target}}
 
-Your objective was this:
-{{user_target}}
+## Workflows
+Currently, you are working on a job that has been planned. The plan execution details / job description is provided. Please review the plan and revise the plan according to the execution details to meet the goal. You should follow the following rules:
+1. If you think any task is DONE already, mark the task status as DONE.
+2. If the task execution result does not meet the target, mark the task status as ERROR.
+3. If you think current plan is missing tasks to meet the goal, you can add new tasks.
+4. Some of the task may be invalid, mark the task status as DISCARDED.
+5. Pay attention to the status of each of the tasks.
+6. If you think the plan is complete, next task id should be None.
 
 Your original plan was this:
 {{original_plan}}
 
 You have currently done the follow steps:
 {{past_steps}}
 
+## Task
 Update your plan accordingly. If no more steps are needed and you can return to the user, then respond with that. Otherwise, fill out the plan. Only add steps to the plan that still NEED to be done. Do not return previously done steps as part of the plan.
+You need to output the updated plan and next task id.
+
+## Constraints 
+- Next task id must be searchable in the current task list.
 """  # noqa
 
 PLAN_SYSTEM_PROMPT_TMP = StringTemplate(PLAN_SYSTEM_PROMPT, "jinja2")

promptulate/beta/agents/assistant_agent/schema.py

@@ -1,11 +1,20 @@
+from enum import Enum
 from typing import List, Optional
 
 from promptulate.pydantic_v1 import BaseModel, Field
 
 
+class TaskStatus(str, Enum):
+    TODO = "todo"
+    DONE = "done"
+    ERROR = "error"
+    DISCARDED = "discarded"
+
+
 class Task(BaseModel):
     task_id: int = Field(..., description="The ID of the task. Start from 1.")
     description: str = Field(..., description="The description of the task.")
+    status: TaskStatus = Field(TaskStatus.TODO, description="The status of the task.")
 
 
 class AgentPlanResponse(BaseModel):
@@ -26,7 +35,7 @@ def get_next_task(self) -> Optional[Task]:
         return next((t for t in self.tasks if t.task_id == self.next_task_id), None)
 
 
-class AgentReviseResponse(Plan):
+class AgentReviseResponse(BaseModel):
     thought: str = Field(..., description="The thought of the reflect plan.")
     goals: List[str] = Field(..., description="List of goals in the plan.")
     tasks: List[Task] = Field(
@@ -41,8 +50,21 @@ class AgentReviseResponse(Plan):
     AgentPlanResponse(
         goals=["Goal 1"],
         tasks=[
-            Task(task_id=1, description="Task 1"),
-            Task(task_id=2, description="Task 2"),
+            Task(task_id=1, description="Task 1", status=TaskStatus.TODO),
+            Task(task_id=2, description="Task 2", status=TaskStatus.TODO),
+        ],
+    ),
+]
+
+
+revise_plan_examples = [
+    AgentReviseResponse(
+        thought="thought what to do next",
+        goals=["Goal 1"],
+        tasks=[
+            Task(task_id=1, description="Task 1", status=TaskStatus.DONE),
+            Task(task_id=2, description="Task 2", status=TaskStatus.TODO),
         ],
+        next_task_id=2,
     ),
 ]