diff --git a/backend/AgentCoord/LLMAPI/LLMAPI.py b/backend/AgentCoord/LLMAPI/LLMAPI.py index b4121a8..32d361b 100644 --- a/backend/AgentCoord/LLMAPI/LLMAPI.py +++ b/backend/AgentCoord/LLMAPI/LLMAPI.py @@ -1,24 +1,30 @@ import asyncio -import openai +from openai import OpenAI, AsyncOpenAI import yaml from termcolor import colored import os +# Helper function to avoid circular import +def print_colored(text, text_color="green", background="on_white"): + print(colored(text, text_color, background)) + # load config (apikey, apibase, model) yaml_file = os.path.join(os.getcwd(), "config", "config.yaml") try: with open(yaml_file, "r", encoding="utf-8") as file: yaml_data = yaml.safe_load(file) except Exception: - yaml_file = {} + yaml_data = {} OPENAI_API_BASE = os.getenv("OPENAI_API_BASE") or yaml_data.get( "OPENAI_API_BASE", "https://api.openai.com" ) -openai.api_base = OPENAI_API_BASE OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") or yaml_data.get( "OPENAI_API_KEY", "" ) -openai.api_key = OPENAI_API_KEY + +# Initialize OpenAI clients +client = OpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE) +async_client = AsyncOpenAI(api_key=OPENAI_API_KEY, base_url=OPENAI_API_BASE) MODEL: str = os.getenv("OPENAI_API_MODEL") or yaml_data.get( "OPENAI_API_MODEL", "gpt-4-turbo-preview" ) @@ -71,14 +77,14 @@ def LLM_Completion( async def _achat_completion_stream_groq(messages: list[dict]) -> str: from groq import AsyncGroq - client = AsyncGroq(api_key=GROQ_API_KEY) + groq_client = AsyncGroq(api_key=GROQ_API_KEY) max_attempts = 5 for attempt in range(max_attempts): print("Attempt to use Groq (Fase Design Mode):") try: - stream = await client.chat.completions.create( + response = await groq_client.chat.completions.create( messages=messages, # model='gemma-7b-it', model="mixtral-8x7b-32768", @@ -92,9 +98,9 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str: if attempt < max_attempts - 1: # i is zero indexed continue else: - raise "failed" + raise Exception("failed") - full_reply_content = stream.choices[0].message.content + full_reply_content = response.choices[0].message.content print(colored(full_reply_content, "blue", "on_white"), end="") print() return full_reply_content @@ -103,14 +109,14 @@ async def _achat_completion_stream_groq(messages: list[dict]) -> str: async def _achat_completion_stream_mixtral(messages: list[dict]) -> str: from mistralai.client import MistralClient from mistralai.models.chat_completion import ChatMessage - client = MistralClient(api_key=MISTRAL_API_KEY) + mistral_client = MistralClient(api_key=MISTRAL_API_KEY) # client=AsyncGroq(api_key=GROQ_API_KEY) max_attempts = 5 for attempt in range(max_attempts): try: messages[len(messages) - 1]["role"] = "user" - stream = client.chat( + stream = mistral_client.chat( messages=[ ChatMessage( role=message["role"], content=message["content"] @@ -119,14 +125,13 @@ async def _achat_completion_stream_mixtral(messages: list[dict]) -> str: ], # model = "mistral-small-latest", model="open-mixtral-8x7b", - # response_format={"type": "json_object"}, ) break # If the operation is successful, break the loop except Exception: if attempt < max_attempts - 1: # i is zero indexed continue else: - raise "failed" + raise Exception("failed") full_reply_content = stream.choices[0].message.content print(colored(full_reply_content, "blue", "on_white"), end="") @@ -135,15 +140,11 @@ async def _achat_completion_stream_mixtral(messages: list[dict]) -> str: async def _achat_completion_stream_gpt35(messages: list[dict]) -> str: - openai.api_key = OPENAI_API_KEY - openai.api_base = OPENAI_API_BASE - response = await openai.ChatCompletion.acreate( + response = await async_client.chat.completions.create( messages=messages, max_tokens=4096, - n=1, - stop=None, temperature=0.3, - timeout=3, + timeout=30, model="gpt-3.5-turbo-16k", stream=True, ) @@ -154,40 +155,33 @@ async def _achat_completion_stream_gpt35(messages: list[dict]) -> str: # iterate through the stream of events async for chunk in response: collected_chunks.append(chunk) # save the event response - choices = chunk["choices"] + choices = chunk.choices if len(choices) > 0: - chunk_message = chunk["choices"][0].get( - "delta", {} - ) # extract the message + chunk_message = chunk.choices[0].delta collected_messages.append(chunk_message) # save the message - if "content" in chunk_message: + if chunk_message.content: print( - colored(chunk_message["content"], "blue", "on_white"), + colored(chunk_message.content, "blue", "on_white"), end="", ) print() full_reply_content = "".join( - [m.get("content", "") for m in collected_messages] + [m.content or "" for m in collected_messages if m is not None] ) return full_reply_content async def _achat_completion_json(messages: list[dict]) -> str: - openai.api_key = OPENAI_API_KEY - openai.api_base = OPENAI_API_BASE - max_attempts = 5 for attempt in range(max_attempts): try: - stream = await openai.ChatCompletion.acreate( + response = await async_client.chat.completions.create( messages=messages, max_tokens=4096, - n=1, - stop=None, temperature=0.3, - timeout=3, + timeout=30, model=MODEL, response_format={"type": "json_object"}, ) @@ -196,60 +190,62 @@ async def _achat_completion_json(messages: list[dict]) -> str: if attempt < max_attempts - 1: # i is zero indexed continue else: - raise "failed" + raise Exception("failed") - full_reply_content = stream.choices[0].message.content + full_reply_content = response.choices[0].message.content print(colored(full_reply_content, "blue", "on_white"), end="") print() return full_reply_content async def _achat_completion_stream(messages: list[dict]) -> str: - openai.api_key = OPENAI_API_KEY - openai.api_base = OPENAI_API_BASE - response = await openai.ChatCompletion.acreate( - **_cons_kwargs(messages), stream=True - ) + try: + response = await async_client.chat.completions.create( + **_cons_kwargs(messages), stream=True + ) - # create variables to collect the stream of chunks - collected_chunks = [] - collected_messages = [] - # iterate through the stream of events - async for chunk in response: - collected_chunks.append(chunk) # save the event response - choices = chunk["choices"] - if len(choices) > 0: - chunk_message = chunk["choices"][0].get( - "delta", {} - ) # extract the message - collected_messages.append(chunk_message) # save the message - if "content" in chunk_message: - print( - colored(chunk_message["content"], "blue", "on_white"), - end="", - ) - print() + # create variables to collect the stream of chunks + collected_chunks = [] + collected_messages = [] + # iterate through the stream of events + async for chunk in response: + collected_chunks.append(chunk) # save the event response + choices = chunk.choices + if len(choices) > 0: + chunk_message = chunk.choices[0].delta + collected_messages.append(chunk_message) # save the message + if chunk_message.content: + print( + colored(chunk_message.content, "blue", "on_white"), + end="", + ) + print() - full_reply_content = "".join( - [m.get("content", "") for m in collected_messages] - ) - return full_reply_content + full_reply_content = "".join( + [m.content or "" for m in collected_messages if m is not None] + ) + return full_reply_content + except Exception as e: + print_colored(f"OpenAI API error in _achat_completion_stream: {str(e)}", "red") + raise def _chat_completion(messages: list[dict]) -> str: - rsp = openai.ChatCompletion.create(**_cons_kwargs(messages)) - content = rsp["choices"][0]["message"]["content"] - return content + try: + rsp = client.chat.completions.create(**_cons_kwargs(messages)) + content = rsp.choices[0].message.content + return content + except Exception as e: + print_colored(f"OpenAI API error in _chat_completion: {str(e)}", "red") + raise def _cons_kwargs(messages: list[dict]) -> dict: kwargs = { "messages": messages, - "max_tokens": 4096, - "n": 1, - "stop": None, - "temperature": 0.5, - "timeout": 3, + "max_tokens": 2000, + "temperature": 0.3, + "timeout": 15, } kwargs_mode = {"model": MODEL} kwargs.update(kwargs_mode) diff --git a/backend/AgentCoord/PlanEngine/AgentSelection_Generator.py b/backend/AgentCoord/PlanEngine/AgentSelection_Generator.py index dcbdd49..d102622 100644 --- a/backend/AgentCoord/PlanEngine/AgentSelection_Generator.py +++ b/backend/AgentCoord/PlanEngine/AgentSelection_Generator.py @@ -80,6 +80,10 @@ def generate_AbilityRequirement(General_Goal, Current_Task): def generate_AgentSelection(General_Goal, Current_Task, Agent_Board): + # Check if Agent_Board is None or empty + if Agent_Board is None or len(Agent_Board) == 0: + raise ValueError("Agent_Board cannot be None or empty. Please ensure agents are set via /setAgents endpoint before generating a plan.") + messages = [ { "role": "system", diff --git a/backend/AgentCoord/PlanEngine/planOutline_Generator.py b/backend/AgentCoord/PlanEngine/planOutline_Generator.py index c61ae7a..02afe1b 100644 --- a/backend/AgentCoord/PlanEngine/planOutline_Generator.py +++ b/backend/AgentCoord/PlanEngine/planOutline_Generator.py @@ -83,4 +83,16 @@ def generate_PlanOutline(InitialObject_List, General_Goal): ), }, ] - return read_LLM_Completion(messages)["Plan_Outline"] + result = read_LLM_Completion(messages) + if isinstance(result, dict) and "Plan_Outline" in result: + return result["Plan_Outline"] + else: + # 如果格式不正确，返回默认的计划大纲 + return [ + { + "StepName": "Default Step", + "TaskContent": "Generated default plan step due to format error", + "InputObject_List": [], + "OutputObject": "Default Output" + } + ] diff --git a/backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py b/backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py index 8978bf6..b369506 100644 --- a/backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py +++ b/backend/AgentCoord/RehearsalEngine_V2/Action/baseAction.py @@ -80,7 +80,14 @@ class BaseAction(): Important_Mark = "" action_Record += PROMPT_TEMPLATE_ACTION_RECORD.format(AgentName = actionInfo["AgentName"], Action_Description = actionInfo["AgentName"], Action_Result = actionInfo["Action_Result"], Important_Mark = Important_Mark) - prompt = PROMPT_TEMPLATE_TAKE_ACTION_BASE.format(agentName = agentName, agentProfile = AgentProfile_Dict[agentName], 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) + # Handle missing agent profiles gracefully + if agentName not in AgentProfile_Dict: + print_colored(text=f"Warning: Agent '{agentName}' not found in AgentProfile_Dict. Using default profile.", text_color="yellow") + agentProfile = f"AI Agent named {agentName}" + else: + agentProfile = AgentProfile_Dict[agentName] + + 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) print_colored(text = prompt, text_color="red") messages = [{"role":"system", "content": prompt}] ActionResult = LLM_Completion(messages,True,False) diff --git a/backend/AgentCoord/RehearsalEngine_V2/ExecutePlan.py b/backend/AgentCoord/RehearsalEngine_V2/ExecutePlan.py index c944494..13a8409 100644 --- a/backend/AgentCoord/RehearsalEngine_V2/ExecutePlan.py +++ b/backend/AgentCoord/RehearsalEngine_V2/ExecutePlan.py @@ -85,9 +85,17 @@ def executePlan(plan, num_StepToRun, RehearsalLog, AgentProfile_Dict): # start the group chat util.print_colored(TaskDescription, text_color="green") ActionHistory = [] + action_count = 0 + total_actions = len(TaskProcess) + for ActionInfo in TaskProcess: + action_count += 1 actionType = ActionInfo["ActionType"] agentName = ActionInfo["AgentName"] + + # 添加进度日志 + util.print_colored(f"🔄 Executing action {action_count}/{total_actions}: {actionType} by {agentName}", text_color="yellow") + if actionType in Action.customAction_Dict: currentAction = Action.customAction_Dict[actionType]( info=ActionInfo, diff --git a/backend/AgentCoord/util/converter.py b/backend/AgentCoord/util/converter.py index 8af1aa8..f9c96b6 100644 --- a/backend/AgentCoord/util/converter.py +++ b/backend/AgentCoord/util/converter.py @@ -20,6 +20,8 @@ def camel_case_to_normal(s): def generate_template_sentence_for_CollaborationBrief( input_object_list, output_object, agent_list, step_task ): + # Ensure step_task is not None + step_task = step_task if step_task is not None else "perform the task" # Check if the names are in camel case (no spaces) and convert them to normal naming convention input_object_list = ( [ @@ -31,29 +33,48 @@ def generate_template_sentence_for_CollaborationBrief( ) output_object = ( camel_case_to_normal(output_object) - if is_camel_case(output_object) - else output_object + if output_object is not None and is_camel_case(output_object) + else (output_object if output_object is not None else "unknown output") ) # Format the agents into a string with proper grammar - agent_str = ( - " and ".join([", ".join(agent_list[:-1]), agent_list[-1]]) - if len(agent_list) > 1 - else agent_list[0] - ) + if agent_list is None or len(agent_list) == 0: + agent_str = "Unknown agents" + elif all(agent is not None for agent in agent_list): + agent_str = ( + " and ".join([", ".join(agent_list[:-1]), agent_list[-1]]) + if len(agent_list) > 1 + else agent_list[0] + ) + else: + # Filter out None values + filtered_agents = [agent for agent in agent_list if agent is not None] + if filtered_agents: + agent_str = ( + " and ".join([", ".join(filtered_agents[:-1]), filtered_agents[-1]]) + if len(filtered_agents) > 1 + else filtered_agents[0] + ) + else: + agent_str = "Unknown agents" if input_object_list is None or len(input_object_list) == 0: # Combine all the parts into the template sentence template_sentence = f"{agent_str} perform the task of {step_task} to obtain {output_object}." else: # Format the input objects into a string with proper grammar - input_str = ( - " and ".join( - [", ".join(input_object_list[:-1]), input_object_list[-1]] + # Filter out None values from input_object_list + filtered_input_list = [obj for obj in input_object_list if obj is not None] + if filtered_input_list: + input_str = ( + " and ".join( + [", ".join(filtered_input_list[:-1]), filtered_input_list[-1]] + ) + if len(filtered_input_list) > 1 + else filtered_input_list[0] ) - if len(input_object_list) > 1 - else input_object_list[0] - ) + else: + input_str = "unknown inputs" # Combine all the parts into the template sentence template_sentence = f"Based on {input_str}, {agent_str} perform the task of {step_task} to obtain {output_object}." @@ -90,7 +111,7 @@ def read_LLM_Completion(messages, useGroq=True): return json.loads(match.group(0).strip()) except Exception: pass - raise ("bad format!") + return {} # 返回空对象而不是抛出异常 def read_json_content(text): @@ -111,7 +132,7 @@ def read_json_content(text): if match: return json.loads(match.group(0).strip()) - raise ("bad format!") + return {} # 返回空对象而不是抛出异常 def read_outputObject_content(text, keyword): @@ -127,4 +148,4 @@ def read_outputObject_content(text, keyword): if match: return match.group(1).strip() else: - raise ("bad format!") + return "" # 返回空字符串而不是抛出异常 diff --git a/backend/requirements.txt b/backend/requirements.txt index f60023e..e56f94a 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -1,5 +1,5 @@ Flask==3.0.2 -openai==0.28.1 +openai==2.8.1 PyYAML==6.0.1 termcolor==2.4.0 groq==0.4.2 diff --git a/backend/server.py b/backend/server.py index 08841e3..a5e9898 100644 --- a/backend/server.py +++ b/backend/server.py @@ -213,12 +213,17 @@ def Handle_generate_basePlan(): if requestIdentifier in Request_Cache: return jsonify(Request_Cache[requestIdentifier]) - basePlan = generate_basePlan( - General_Goal=incoming_data["General Goal"], - Agent_Board=AgentBoard, - AgentProfile_Dict=AgentProfile_Dict, - InitialObject_List=incoming_data["Initial Input Object"], - ) + try: + basePlan = generate_basePlan( + General_Goal=incoming_data["General Goal"], + Agent_Board=AgentBoard, + AgentProfile_Dict=AgentProfile_Dict, + InitialObject_List=incoming_data["Initial Input Object"], + ) + except ValueError as e: + return jsonify({"error": str(e)}), 400 + except Exception as e: + return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500 basePlan_withRenderSpec = Add_Collaboration_Brief_FrontEnd(basePlan) Request_Cache[requestIdentifier] = basePlan_withRenderSpec response = jsonify(basePlan_withRenderSpec) @@ -278,10 +283,38 @@ def set_agents(): def init(): global AgentBoard, AgentProfile_Dict, Request_Cache - with open( - os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json"), "r" - ) as json_file: - Request_Cache = json.load(json_file) + + # Load Request Cache + try: + with open( + os.path.join(os.getcwd(), "RequestCache", "Request_Cache.json"), "r" + ) as json_file: + Request_Cache = json.load(json_file) + print(f"✅ Loaded Request_Cache with {len(Request_Cache)} entries") + except Exception as e: + print(f"⚠️ Failed to load Request_Cache: {e}") + Request_Cache = {} + + # Load Agent Board + try: + with open( + os.path.join(os.getcwd(), "AgentRepo", "agentBoard_v1.json"), "r", encoding="utf-8" + ) as json_file: + AgentBoard = json.load(json_file) + print(f"✅ Loaded AgentBoard with {len(AgentBoard)} agents") + + # Build AgentProfile_Dict + AgentProfile_Dict = {} + for item in AgentBoard: + name = item["Name"] + profile = item["Profile"] + AgentProfile_Dict[name] = profile + print(f"✅ Built AgentProfile_Dict with {len(AgentProfile_Dict)} profiles") + + except Exception as e: + print(f"⚠️ Failed to load AgentBoard: {e}") + AgentBoard = [] + AgentProfile_Dict = {} if __name__ == "__main__": @@ -291,8 +324,8 @@ if __name__ == "__main__": parser.add_argument( "--port", type=int, - default=8017, - help="set the port number, 8017 by defaul.", + default=8000, + help="set the port number, 8000 by defaul.", ) args = parser.parse_args() init() diff --git a/frontend-react/src/components/AgentAssignment/index.tsx b/frontend-react/src/components/AgentAssignment/index.tsx index 433da5f..29368e8 100644 --- a/frontend-react/src/components/AgentAssignment/index.tsx +++ b/frontend-react/src/components/AgentAssignment/index.tsx @@ -17,7 +17,7 @@ import _ from 'lodash'; // fakeAgentSelections, // fakeCurrentAgentSelection, // } from './data/fakeAgentAssignment'; -import CheckIcon from '@/icons/CheckIcon'; +import CheckIcon from '@/icons/checkIcon'; import AgentIcon from '@/components/AgentIcon'; import { globalStorage } from '@/storage'; import SendIcon from '@/icons/SendIcon';