feat
This commit is contained in:
liailing1026
zhaoweijie
24
backend/.claude/settings.local.json
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24
backend/.claude/settings.local.json
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@@ -0,0 +1,24 @@
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{
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"permissions": {
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"allow": [
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"Read(//Users/zhaoweijie/Desktop/agent/AgentCoord/**)",
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"Bash(python3:*)",
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"Bash(source:*)",
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"Bash(pip install:*)",
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"Bash(python:*)",
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"Bash(tree:*)",
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"Bash(export FAST_DESIGN_MODE=True:*)",
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"Bash(echo:*)",
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"Bash(chmod:*)",
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"Bash(lsof:*)",
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"Bash(curl:*)",
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"Bash(xargs kill:*)",
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"Bash(pip:*)",
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"WebSearch",
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"WebFetch(domain:pypi.org)",
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"Bash(cp:*)"
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],
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"deny": [],
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"ask": []
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}
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}
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@@ -42,7 +42,7 @@ def generate_AbilityRequirement(General_Goal, Current_Task):
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},
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]
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print(messages[1]["content"])
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return read_LLM_Completion(messages)["AbilityRequirement"]
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return read_LLM_Completion(messages)["AbilityRequirement"]
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PROMPT_AGENT_ABILITY_SCORING = """
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@@ -83,7 +83,6 @@ class JSON_Agent(BaseModel):
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class JSON_AGENT_ABILITY_SCORING(RootModel):
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root: Dict[str, JSON_Agent]
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def agentAbilityScoring(Agent_Board, Ability_Requirement_List):
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scoreTable = {}
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for Ability_Requirement in Ability_Requirement_List:
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@@ -105,7 +104,7 @@ def agentAbilityScoring(Agent_Board, Ability_Requirement_List):
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return scoreTable
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def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
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async def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
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Current_Task = {
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"TaskName": stepTask["StepName"],
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"InputObject_List": stepTask["InputObject_List"],
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@@ -125,10 +124,10 @@ def AgentSelectModify_init(stepTask, General_Goal, Agent_Board):
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),
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},
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]
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Ability_Requirement_List = read_LLM_Completion(messages)[
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Ability_Requirement_List = await read_LLM_Completion(messages)[
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"AbilityRequirement"
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]
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scoreTable = agentAbilityScoring(Agent_Board, Ability_Requirement_List)
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scoreTable = await agentAbilityScoring(Agent_Board, Ability_Requirement_List)
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return scoreTable
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@@ -76,8 +76,7 @@ def generate_AbilityRequirement(General_Goal, Current_Task):
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},
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]
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print(messages[1]["content"])
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return read_LLM_Completion(messages)["AbilityRequirement"]
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return read_LLM_Completion(messages)["AbilityRequirement"]
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def generate_AgentSelection(General_Goal, Current_Task, Agent_Board):
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# Check if Agent_Board is None or empty
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@@ -103,7 +102,7 @@ def generate_AgentSelection(General_Goal, Current_Task, Agent_Board):
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agentboard_set = {agent["Name"] for agent in Agent_Board}
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while True:
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candidate = read_LLM_Completion(messages)["AgentSelectionPlan"]
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candidate = read_LLM_Completion(messages)["AgentSelectionPlan"]
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if len(candidate) > MAX_TEAM_SIZE:
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teamSize = random.randint(2, MAX_TEAM_SIZE)
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candidate = candidate[0:teamSize]
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@@ -9,6 +9,14 @@ import AgentCoord.util as util
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def generate_basePlan(
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General_Goal, Agent_Board, AgentProfile_Dict, InitialObject_List
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):
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Agent_Board = [
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{"Name": (a.get("Name") or "").strip(),"Profile": (a.get("Profile") or "").strip()}
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for a in Agent_Board
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if a and a.get("Name") is not None
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]
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if not Agent_Board: # 洗完后还是空 → 直接返回空计划
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return {"Plan_Outline": []}
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basePlan = {
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"Initial Input Object": InitialObject_List,
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"Collaboration Process": [],
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@@ -95,7 +95,7 @@ def branch_PlanOutline(
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},
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{"role": "system", "content": prompt},
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]
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Remaining_Steps = read_LLM_Completion(messages, useGroq=False)[
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Remaining_Steps = read_LLM_Completion(messages)[
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"Remaining Steps"
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]
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branch_List.append(Remaining_Steps)
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@@ -145,7 +145,7 @@ def branch_TaskProcess(
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},
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{"role": "system", "content": prompt},
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]
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Remaining_Steps = read_LLM_Completion(messages, useGroq=False)[
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Remaining_Steps = read_LLM_Completion(messages)[
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"Remaining Steps"
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]
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@@ -106,6 +106,9 @@ class TaskProcessPlan(BaseModel):
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def generate_TaskProcess(General_Goal, Current_Task_Description):
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# 新增参数验证
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if not General_Goal or str(General_Goal).strip() == "":
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raise ValueError("General_Goal cannot be empty")
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messages = [
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{
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"role": "system",
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@@ -90,7 +90,7 @@ class BaseAction():
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prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(agentName = agentName, agentProfile = agentProfile, General_Goal = General_Goal, Current_Task_Description = TaskDescription, Input_Objects = inputObject_Record, History_Action = action_Record, Action_Description = self.info["Description"], Action_Custom_Note = self.Action_Custom_Note)
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print_colored(text = prompt, text_color="red")
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messages = [{"role":"system", "content": prompt}]
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ActionResult = LLM_Completion(messages,True,False)
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ActionResult = LLM_Completion(messages,stream=False)
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ActionInfo_with_Result = copy.deepcopy(self.info)
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ActionInfo_with_Result["Action_Result"] = ActionResult
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@@ -1,2 +1,23 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# -*- coding: utf-8 -*-
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# 导出常用模块和函数，根据实际需求调整
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from .PlanEngine.planOutline_Generator import generate_PlanOutline
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from .PlanEngine.basePlan_Generator import generate_basePlan
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from .PlanEngine.AgentSelection_Generator import generate_AgentSelection
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from .LLMAPI.LLMAPI import LLM_Completion
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from .util.converter import read_LLM_Completion
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# 定义包元数据
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__version__ = "0.1.0"
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__all__ = [
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"generate_PlanOutline",
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"generate_basePlan",
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"generate_AgentSelection",
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"LLM_Completion",
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"read_LLM_Completion"
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]
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@@ -1,12 +1,12 @@
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## config for default LLM
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OPENAI_API_BASE: ""
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OPENAI_API_KEY: ""
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OPENAI_API_MODEL: "gpt-4-turbo-preview"
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OPENAI_API_BASE: "https://ai.gitee.com/v1"
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OPENAI_API_KEY: "HYCNGM39GGFNSB1F8MBBMI9QYJR3P1CRSYS2PV1A"
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OPENAI_API_MODEL: "DeepSeek-V3"
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## config for fast mode
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FAST_DESIGN_MODE: True
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GROQ_API_KEY: ""
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MISTRAL_API_KEY: ""
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## options under experimentation, leave them as Fasle unless you know what it is for
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USE_CACHE: False
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## options under experimentation, leave them as False unless you know what it is for
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USE_CACHE: False
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@@ -3,5 +3,5 @@ openai==2.8.1
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PyYAML==6.0.1
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termcolor==2.4.0
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groq==0.4.2
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mistralai==0.1.6
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mistralai==1.5.2
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socksio==1.0.0
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13
backend/restart.ps1
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13
backend/restart.ps1
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# restart.ps1
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$port=8000
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$env:PYTHONUNBUFFERED="1"
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python server.py --port 8000
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Write-Host "Killing PID on port $port..." -ForegroundColor Red
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Get-NetTCPConnection -LocalPort $port -ErrorAction SilentlyContinue | ForEach-Object {
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Stop-Process -Id $_.OwningProcess -Force
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}
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Write-Host "Starting Flask..." -ForegroundColor Green
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python server.py --port $port
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159
backend/代码逻辑架构分析.md
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159
backend/代码逻辑架构分析.md
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# AgentCoord Backend 代码逻辑架构分析
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## 🏗️ Backend 代码逻辑架构
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### 📁 核心目录结构
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```
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backend/
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├── server.py # Flask主服务器入口
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├── config/config.yaml # LLM API配置
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├── AgentRepo/agentBoard_v1.json # 智能体定义库
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├── DataProcess/ # 数据处理层
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├── RequestCache/ # 缓存机制
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└── AgentCoord/ # 核心业务逻辑
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├── LLMAPI/ # LLM接口封装
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├── PlanEngine/ # 计划生成引擎
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├── RehearsalEngine_V2/ # 计划执行引擎
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└── util/ # 工具模块
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```
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### 🔄 主要工作流程
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#### 1️⃣ **计划生成流程** (PlanEngine)
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```
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用户目标 → 生成计划大纲 → 选择智能体 → 生成任务流程 → 输出完整计划
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```
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**核心模块：**
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- `basePlan_Generator.py` - 整合所有计划生成组件
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- `planOutline_Generator.py` - 生成高级计划大纲
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- `taskProcess_Generator.py` - 生成详细任务执行流程
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- `AgentSelection_Generator.py` - 选择最适合的智能体
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#### 2️⃣ **计划执行流程** (RehearsalEngine_V2)
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```
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协作计划 → 初始化执行环境 → 按步骤执行 → 智能体协作 → 记录执行日志
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```
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**动作类型：**
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- **Propose** - 提出建议和方案
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- **Critique** - 提供反馈和批评
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- **Improve** - 基于反馈改进结果
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- **Finalize** - 最终确定输出
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#### 3️⃣ **LLM接口层** (LLMAPI)
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支持的模型：
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- OpenAI GPT-4/GPT-3.5
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- Groq Mixtral-8x7b (快速模式)
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- Mistral Open-Mixtral-8x7b
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### 🌐 API端点架构
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#### **计划生成APIs**
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- `POST /generate_basePlan` - 生成基础协作计划
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- `POST /fill_stepTask` - 填充步骤任务详情
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- `POST /branch_PlanOutline` - 处理计划分支
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#### **计划执行APIs**
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- `POST /executePlan` - 执行协作计划
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- `POST /agentSelectModify_init` - 初始化智能体选择
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#### **系统管理APIs**
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- `POST /setAgents` - 设置智能体板
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- `POST /_saveRequestCache` - 保存请求缓存
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### 💾 数据流设计
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#### **输入层**
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- HTTP请求验证
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- 参数提取和格式化
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- 缓存检查
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#### **业务逻辑层**
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- LLM API调用
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- 多智能体协调
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- 任务状态管理
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#### **输出层**
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- 结果格式化
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- 前端渲染模板生成
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- JSON响应
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### ⚙️ 配置与优化
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#### **配置文件 (config.yaml)**
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```yaml
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OPENAI_API_BASE: "https://api.openai.com"
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OPENAI_API_KEY: "your-key"
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OPENAI_API_MODEL: "gpt-4-turbo-preview"
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FAST_DESIGN_MODE: True # 启用快速模式
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USE_CACHE: False # 缓存开关
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```
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#### **性能优化**
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- 请求缓存机制
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- 快速模式支持（Groq）
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- 异步LLM调用
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- 重试和错误处理
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### 🔧 关键技术特点
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1. **模块化架构** - 清晰的职责分离
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2. **多LLM支持** - 灵活的模型切换
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3. **智能体协作** - 复杂的多智能体工作流
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4. **前端适配** - 自动生成渲染模板
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5. **可扩展性** - 支持自定义智能体和动作
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## 📋 详细模块说明
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### 1. 服务器入口 (server.py)
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- **功能**: Flask应用主入口，提供RESTful API
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- **特点**: 支持请求缓存、全局状态管理、参数化端口配置
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### 2. 计划引擎 (PlanEngine)
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**核心功能**: 生成多智能体协作计划
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- **basePlan_Generator.py**: 整合所有生成器，生成完整协作计划
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- **planOutline_Generator.py**: 基于目标生成计划大纲
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- **taskProcess_Generator.py**: 为每个任务步骤生成执行流程
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- **AgentSelection_Generator.py**: 选择合适的智能体执行任务
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### 3. 排练引擎 (RehearsalEngine_V2)
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**核心功能**: 执行生成的协作计划
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- **ExecutePlan.py**: 计划执行控制器
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- **Action模块**: 实现各种协作动作（Propose, Critique, Improve, Finalize）
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### 4. LLM API接口 (LLMAPI)
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**核心功能**: 封装多种大语言模型API
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- 支持流式响应
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- 异步处理
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- 快速模式切换
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### 5. 数据处理 (DataProcess)
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**核心功能**: 格式转换和前端适配
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- 颜色映射：不同元素类型的视觉区分
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- 模板生成：为前端生成渲染模板
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- 格式化：处理驼峰命名和自然语言转换
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## 🚀 启动和调试
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### 开发环境启动
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```bash
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cd backend
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source venv/bin/activate
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python server.py --port 8017
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```
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### 调试模式
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Flask已内置debug=True，支持：
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- 交互式调试器
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- 自动重载
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- 详细错误页面
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### Docker部署
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```bash
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docker-compose up
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```
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这个backend实现了一个完整的多智能体协作平台，通过精心设计的模块化架构，支持复杂任务的规划和执行。
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Block a user