Fix BadRequestError 400 – Dr. Trippers Stuff

help solve this: Traceback (most recent call last):
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenstudiowebapp.py”, line 68, in add_message
response_message: Message = chatmanager.chat(
^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenstudiochatmanager.py”, line 29, in chat
flow.run(message=f”{message_text}”, clear_history=False)
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenstudioworkflowmanager.py”, line 191, in run
self.sender.initiate_chat(
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenagentchatconversable_agent.py”, line 672, in initiate_chat
self.send(self.generate_init_message(**context), recipient, silent=silent)
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenagentchatconversable_agent.py”, line 420, in send
recipient.receive(message, self, request_reply, silent)
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenagentchatconversable_agent.py”, line 580, in receive
self.send(reply, sender, silent=silent)
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenagentchatconversable_agent.py”, line 420, in send
recipient.receive(message, self, request_reply, silent)
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenagentchatconversable_agent.py”, line 578, in receive
reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenagentchatconversable_agent.py”, line 1241, in generate_reply
final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple[“config”])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenagentchatconversable_agent.py”, line 761, in generate_oai_reply
response = client.create(
^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenoaiclient.py”, line 266, in create
response = self._completions_create(client, params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesautogenoaiclient.py”, line 531, in _completions_create
response = completions.create(**params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesopenai_utils_utils.py”,
line 299, in wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesopenairesourceschatcompletions.py”, line 598, in create
return self._post(
^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesopenai_base_client.py”, line 1055, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File “c:ProgramDataminiconda3envsautogensLibsite-packagesopenai_base_client.py”, line 834, in request
return self._request(
^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsautogensLibsite-packagesopenai_base_client.py”, line 877, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 – {‘error’: “‘messages’ array must only contain objects with a ‘content’ field that is not empty.”}

The error traceback indicates that there is a `BadRequestError` with the code 400, and the specific error message is “‘messages’ array must only contain objects with a ‘content’ field that is not empty.” This error is coming from the OpenAI API in the `completions.py` file.

To resolve this issue, you need to ensure that the ‘messages’ array in your code only contains objects with a ‘content’ field that is not empty. It seems that there might be an empty or missing ‘content’ field in one of the messages.

Here’s a general guide on how to troubleshoot and fix this issue:

1. Examine the ‘messages’ array in your code: Check the messages that you are passing to the OpenAI API. Ensure that each message object in the array has a ‘content’ field, and that this field is not empty.

2. Validate message content: Make sure that the content of each message is not empty or null. If any message has an empty content, it can lead to the BadRequestError.

3. Check for accidental deletions: Verify that no code modifications or accidental deletions are causing the ‘content’ field to be missing or empty.

4. Debug the workflow: You may want to add some logging or print statements to debug and inspect the content of the ‘messages’ array before it is sent to the OpenAI API.

Here’s an example of how the ‘messages’ array should look:

“`python
‘messages’: [
{‘role’: ‘system’, ‘content’: ‘You are a chatbot.’},
{‘role’: ‘user’, ‘content’: ‘Hello, how can you help me?’},
{‘role’: ‘assistant’, ‘content’: ‘I can assist you with various tasks.’}
]
“`

Make sure that each message object in the array follows a similar structure with a non-empty ‘content’ field.

By carefully reviewing and fixing the ‘messages’ array, you should be able to resolve the BadRequestError.
here is the code:import json
import os
import traceback
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from fastapi.staticfiles import StaticFiles
from fastapi import HTTPException

from ..datamodel import (
ChatWebRequestModel,
DBWebRequestModel,
DeleteMessageWebRequestModel,
Message,
Session,
)
from ..utils import md5_hash, init_webserver_folders, DBManager, dbutils

from ..chatmanager import AutoGenChatManager

app = FastAPI()

# allow cross origin requests for testing on localhost:800* ports only
app.add_middleware(
CORSMiddleware,
allow_origins=[
“http://localhost:8000”,
“http://127.0.0.1:8000”,
“http://localhost:8001”,
“http://localhost:8081”,
],
allow_credentials=True,
allow_methods=[“*”],
allow_headers=[“*”],
)

root_file_path = os.path.dirname(os.path.abspath(__file__))
# init folders skills, workdir, static, files etc
folders = init_webserver_folders(root_file_path)

api = FastAPI(root_path=”/api”)
# mount an api route such that the main route serves the ui and the /api
app.mount(“/api”, api)

app.mount(“/”, StaticFiles(directory=folders[“static_folder_root”], html=True), name=”ui”)
api.mount(“/files”, StaticFiles(directory=folders[“files_static_root”], html=True), name=”files”)

db_path = os.path.join(root_file_path, “database.sqlite”)
dbmanager = DBManager(path=db_path) # manage database operations
chatmanager = AutoGenChatManager() # manage calls to autogen

@api.post(“/messages”)
async def add_message(req: ChatWebRequestModel):
message = Message(**req.message.dict())
user_history = dbutils.get_messages(user_id=message.user_id, session_id=req.message.session_id, dbmanager=dbmanager)

# save incoming message to db
dbutils.create_message(message=message, dbmanager=dbmanager)
user_dir = os.path.join(folders[“files_static_root”], “user”, md5_hash(message.user_id))
os.makedirs(user_dir, exist_ok=True)

try:
response_message: Message = chatmanager.chat(
message=message,
history=user_history,
work_dir=user_dir,
flow_config=req.flow_config,
)

# save assistant response to db
dbutils.create_message(message=response_message, dbmanager=dbmanager)
response = {
“status”: True,
“message”: response_message.content,
“metadata”: json.loads(response_message.metadata),
}
return response
except Exception as ex_error:
print(traceback.format_exc())
return {
“status”: False,
“message”: “Error occurred while processing message: ” + str(ex_error),
}

@api.get(“/messages”)
async def get_messages(user_id: str = None, session_id: str = None):
if user_id is None:
raise HTTPException(status_code=400, detail=”user_id is required”)
try:
user_history = dbutils.get_messages(user_id=user_id, session_id=session_id, dbmanager=dbmanager)

return {
“status”: True,
“data”: user_history,
“message”: “Messages retrieved successfully”,
}
except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while retrieving messages: ” + str(ex_error),
}

@api.get(“/gallery”)
async def get_gallery_items(gallery_id: str = None):
try:
gallery = dbutils.get_gallery(gallery_id=gallery_id, dbmanager=dbmanager)
return {
“status”: True,
“data”: gallery,
“message”: “Gallery items retrieved successfully”,
}
except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while retrieving messages: ” + str(ex_error),
}

@api.get(“/sessions”)
async def get_user_sessions(user_id: str = None):
“””Return a list of all sessions for a user”””
if user_id is None:
raise HTTPException(status_code=400, detail=”user_id is required”)

try:
user_sessions = dbutils.get_sessions(user_id=user_id, dbmanager=dbmanager)

return {
“status”: True,
“data”: user_sessions,
“message”: “Sessions retrieved successfully”,
}
except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while retrieving sessions: ” + str(ex_error),
}

@api.post(“/sessions”)
async def create_user_session(req: DBWebRequestModel):
“””Create a new session for a user”””
# print(req.session, “**********” )

try:
session = Session(user_id=req.session.user_id, flow_config=req.session.flow_config)
user_sessions = dbutils.create_session(user_id=req.user_id, session=session, dbmanager=dbmanager)
return {
“status”: True,
“message”: “Session created successfully”,
“data”: user_sessions,
}
except Exception as ex_error:
print(traceback.format_exc())
return {
“status”: False,
“message”: “Error occurred while creating session: ” + str(ex_error),
}

@api.post(“/sessions/publish”)
async def publish_user_session_to_gallery(req: DBWebRequestModel):
“””Create a new session for a user”””

try:
gallery_item = dbutils.create_gallery(req.session, tags=req.tags, dbmanager=dbmanager)
return {
“status”: True,
“message”: “Session successfully published”,
“data”: gallery_item,
}
except Exception as ex_error:
print(traceback.format_exc())
return {
“status”: False,
“message”: “Error occurred while publishing session: ” + str(ex_error),
}

@api.delete(“/sessions/delete”)
async def delete_user_session(req: DBWebRequestModel):
“””Delete a session for a user”””

try:
sessions = dbutils.delete_session(session=req.session, dbmanager=dbmanager)
return {
“status”: True,
“message”: “Session deleted successfully”,
“data”: sessions,
}
except Exception as ex_error:
print(traceback.format_exc())
return {
“status”: False,
“message”: “Error occurred while deleting session: ” + str(ex_error),
}

@api.post(“/messages/delete”)
async def remove_message(req: DeleteMessageWebRequestModel):
“””Delete a message from the database”””

try:
messages = dbutils.delete_message(
user_id=req.user_id, msg_id=req.msg_id, session_id=req.session_id, dbmanager=dbmanager
)
return {
“status”: True,
“message”: “Message deleted successfully”,
“data”: messages,
}
except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while deleting message: ” + str(ex_error),
}

@api.get(“/skills”)
async def get_user_skills(user_id: str):
try:
skills = dbutils.get_skills(user_id, dbmanager=dbmanager)

return {
“status”: True,
“message”: “Skills retrieved successfully”,
“data”: skills,
}
except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while retrieving skills: ” + str(ex_error),
}

@api.post(“/skills”)
async def create_user_skills(req: DBWebRequestModel):
try:
skills = dbutils.upsert_skill(skill=req.skill, dbmanager=dbmanager)

return {
“status”: True,
“message”: “Skills retrieved successfully”,
“data”: skills,
}

except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while creating skills: ” + str(ex_error),
}

@api.delete(“/skills/delete”)
async def delete_user_skills(req: DBWebRequestModel):
“””Delete a skill for a user”””

try:
skills = dbutils.delete_skill(req.skill, dbmanager=dbmanager)

return {
“status”: True,
“message”: “Skill deleted successfully”,
“data”: skills,
}

except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while deleting skill: ” + str(ex_error),
}

@api.get(“/agents”)
async def get_user_agents(user_id: str):
try:
agents = dbutils.get_agents(user_id, dbmanager=dbmanager)

return {
“status”: True,
“message”: “Agents retrieved successfully”,
“data”: agents,
}
except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while retrieving agents: ” + str(ex_error),
}

@api.post(“/agents”)
async def create_user_agents(req: DBWebRequestModel):
“””Create a new agent for a user”””

try:
agents = dbutils.upsert_agent(agent_flow_spec=req.agent, dbmanager=dbmanager)

return {
“status”: True,
“message”: “Agent created successfully”,
“data”: agents,
}

except Exception as ex_error:
print(traceback.format_exc())
return {
“status”: False,
“message”: “Error occurred while creating agent: ” + str(ex_error),
}

@api.delete(“/agents/delete”)
async def delete_user_agent(req: DBWebRequestModel):
“””Delete an agent for a user”””

try:
agents = dbutils.delete_agent(agent=req.agent, dbmanager=dbmanager)

return {
“status”: True,
“message”: “Agent deleted successfully”,
“data”: agents,
}

except Exception as ex_error:
print(traceback.format_exc())
return {
“status”: False,
“message”: “Error occurred while deleting agent: ” + str(ex_error),
}

@api.get(“/workflows”)
async def get_user_workflows(user_id: str):
try:
workflows = dbutils.get_workflows(user_id, dbmanager=dbmanager)

return {
“status”: True,
“message”: “Workflows retrieved successfully”,
“data”: workflows,
}
except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while retrieving workflows: ” + str(ex_error),
}

@api.post(“/workflows”)
async def create_user_workflow(req: DBWebRequestModel):
“””Create a new workflow for a user”””

try:
workflow = dbutils.upsert_workflow(workflow=req.workflow, dbmanager=dbmanager)
return {
“status”: True,
“message”: “Workflow created successfully”,
“data”: workflow,
}

except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while creating workflow: ” + str(ex_error),
}

@api.delete(“/workflows/delete”)
async def delete_user_workflow(req: DBWebRequestModel):
“””Delete a workflow for a user”””

try:
workflow = dbutils.delete_workflow(workflow=req.workflow, dbmanager=dbmanager)
return {
“status”: True,
“message”: “Workflow deleted successfully”,
“data”: workflow,
}

except Exception as ex_error:
print(ex_error)
return {
“status”: False,
“message”: “Error occurred while deleting workflow: ” + str(ex_error),
}

Your code appears to be a FastAPI application with various endpoints for managing messages, sessions, gallery items, user skills, agents, workflows, etc. The error you provided earlier seems to be related to the interaction with the OpenAI API in the `chatmanager` module.

To address the OpenAI API error “‘messages’ array must only contain objects with a ‘content’ field that is not empty,” you should review how the `chatmanager` is handling messages and ensure that the ‘messages’ array passed to the OpenAI API complies with the expected structure.

Here are a few suggestions to troubleshoot and fix the issue:

1. **Check the ‘chatmanager’ Module:**
Examine the implementation of the `chatmanager` module, especially the part where it constructs the ‘messages’ array. Ensure that each message object within the ‘messages’ array has a ‘content’ field that is not empty.

2. **Debug the ‘chat’ Method:**
Add print statements or logging within the `chat` method of the `AutoGenChatManager` class to inspect the content of the ‘messages’ array before it is sent to the OpenAI API. Verify that each message has a valid ‘content’ field.

3. **Validate Message Content:**
Before calling the OpenAI API, make sure that the content of each message is not empty or null. You can add a check before constructing the ‘messages’ array to ensure that the ‘content’ field is populated.

4. **Verify ‘messages’ Structure:**
Ensure that the ‘messages’ array is structured correctly according to the OpenAI API requirements. It should contain objects with at least a ‘role’ and ‘content’ field.

Example:
“`python
‘messages’: [
{‘role’: ‘system’, ‘content’: ‘You are a chatbot.’},
{‘role’: ‘user’, ‘content’: ‘Hello, how can you help me?’},
{‘role’: ‘assistant’, ‘content’: ‘I can assist you with various tasks.’}
]
“`

By carefully reviewing and addressing these points, you should be able to resolve the BadRequestError from the OpenAI API. Additionally, consider checking the OpenAI API documentation for any specific requirements related to the ‘messages’ array structure.
chatmanager = AutoGenChatManager()
To troubleshoot the issue with the OpenAI API error, you should inspect the implementation of the `AutoGenChatManager` class, especially the method responsible for constructing the ‘messages’ array. Look for the place where it interacts with the OpenAI API and ensure that it follows the correct format for message objects.

Here is a generic example of how the ‘messages’ array should be structured when making a request to the OpenAI API:

Make sure that each message object in the ‘messages’ array has a ‘role’ and ‘content’ field, and the ‘content’ field is not empty.

Additionally, check if the `chat` method in the `AutoGenChatManager` class is handling messages correctly. You may need to modify this method to ensure that it constructs the ‘messages’ array properly.

Here is an example of what the relevant part of the `chat` method might look like:

“`python
class AutoGenChatManager:
# … other methods …

def chat(self, message, history, work_dir, flow_config):
# Construct the ‘messages’ array
messages = [
{‘role’: ‘system’, ‘content’: ‘You are a chatbot.’},
{‘role’: ‘user’, ‘content’: message.content},
# … other messages from history …
]

try:
# Call the OpenAI API with the constructed ‘messages’ array
response_message = self.openai_api_call(messages, flow_config)
return response_message
except Exception as ex:
# Handle the exception, log, or print traceback
print(traceback.format_exc())
raise ex
“`

Ensure that your `chat` method is constructing the ‘messages’ array properly based on the user’s input and the chat history. If there are specific requirements or constraints for the ‘messages’ array in the OpenAI API documentation, make sure to follow them.

By reviewing and updating the `chat` method in the `AutoGenChatManager` class, you should be able to resolve the issue with the OpenAI API error.

class AutoGenChatManager:
def __init__(self) -> None:
pass

def chat(self, message: Message, history: List, flow_config: AgentWorkFlowConfig = None, **kwargs) -> None:
work_dir = kwargs.get(“work_dir”, None)
scratch_dir = os.path.join(work_dir, “scratch”)

# if no flow config is provided, use the default
if flow_config is None:
flow_config = get_default_agent_config(scratch_dir)

flow = AutoGenWorkFlowManager(config=flow_config, history=history, work_dir=scratch_dir)
message_text = message.content.strip()

output = “”
start_time = time.time()

metadata = {}
flow.run(message=f”{message_text}”, clear_history=False)

metadata[“messages”] = flow.agent_history

output = “”

if flow_config.summary_method == “last”:
successful_code_blocks = extract_successful_code_blocks(flow.agent_history)
last_message = flow.agent_history[-1][“message”][“content”]
successful_code_blocks = “nn”.join(successful_code_blocks)
output = (last_message + “n” + successful_code_blocks) if successful_code_blocks else last_message
elif flow_config.summary_method == “llm”:
output = “”
elif flow_config.summary_method == “none”:
output = “”

metadata[“code”] = “”
end_time = time.time()
metadata[“time”] = end_time – start_time
modified_files = get_modified_files(start_time, end_time, scratch_dir, dest_dir=work_dir)
metadata[“files”] = modified_files

print(“Modified files: “, len(modified_files))

output_message = Message(
user_id=message.user_id,
root_msg_id=message.root_msg_id,
role=”assistant”,
content=output,
metadata=json.dumps(metadata),
session_id=message.session_id,
)

return output_message
Thank you for providing the `AutoGenChatManager` class implementation. Based on the provided code, it appears that the `chat` method is responsible for processing a user’s message using the AutoGen framework. Here are some recommendations to address the issue:

1. **Ensure Proper Handling of Messages:**
Make sure that the ‘messages’ array, which is part of the `metadata` dictionary, is constructed properly. Verify that each message in the `flow.agent_history` has a ‘content’ field that is not empty.

2. **Inspect ‘output’ and ‘output_message’ Handling:**
Check the logic for constructing the ‘output’ and ‘output_message’. Ensure that the ‘content’ field of the ‘output_message’ is populated correctly based on the processed user message.

3. **Handle Empty ‘content’ in ‘output_message’:**
If the ‘content’ of the ‘output_message’ is empty, consider handling it appropriately to avoid any issues when interacting with the OpenAI API.

4. **Review Summary Methods:**
If the summary methods like ‘last’, ‘llm’, or ‘none’ are relevant to the OpenAI API request, ensure that they are implemented correctly. Check if there are specific requirements for the ‘messages’ array based on the chosen summary method.

5. **Logging or Debugging:**
Consider adding logging or print statements at key points in the `chat` method to help debug and understand the flow of execution. This can be useful for inspecting variables and identifying any issues.

Here’s an updated version of your `chat` method with some comments for clarification:

“`python
import json
import os
import time
from typing import List

class AutoGenChatManager:
def __init__(self) -> None:
pass

def chat(self, message: Message, history: List, flow_config: AgentWorkFlowConfig = None, **kwargs) -> Message:
work_dir = kwargs.get(“work_dir”, None)
scratch_dir = os.path.join(work_dir, “scratch”)

if flow_config is None:
flow_config = get_default_agent_config(scratch_dir)

flow = AutoGenWorkFlowManager(config=flow_config, history=history, work_dir=scratch_dir)
message_text = message.content.strip()

output = “”
start_time = time.time()

metadata = {}
flow.run(message=f”{message_text}”, clear_history=False)

# Ensure ‘messages’ array is constructed properly
metadata[“messages”] = flow.agent_history

output = “”

# Handle different summary methods
if flow_config.summary_method == “last”:
successful_code_blocks = extract_successful_code_blocks(flow.agent_history)
last_message = flow.agent_history[-1][“message”][“content”]
successful_code_blocks = “nn”.join(successful_code_blocks)
output = (last_message + “n” + successful_code_blocks) if successful_code_blocks else last_message
elif flow_config.summary_method == “llm”:
output = “”
elif flow_config.summary_method == “none”:
output = “”

print(“Modified files: “, len(modified_files))

# Construct the output message
output_message = Message(
user_id=message.user_id,
root_msg_id=message.root_msg_id,
role=”assistant”,
content=output,
metadata=json.dumps(metadata),
session_id=message.session_id,
)

return output_message
“`

Please adapt this to your specific requirements and use case. The key is to ensure that the ‘messages’ array is constructed correctly and that the ‘content’ of the messages is not empty.
Not able to solve this issue as the codebase is too lengthy for me to understand. I will have to ask the authors for help.
Certainly, reaching out to the authors or maintainers of the codebase for assistance is a reasonable and recommended approach. They are likely to have a deeper understanding of the code and can provide more targeted guidance or solutions to the specific issue you’re facing.

When reaching out for help, consider providing them with the specific error message, the relevant code snippets, and any details about the steps you’ve taken to troubleshoot the issue. This will help them better understand the context and provide more effective assistance.

If you have any further questions or if there’s anything else I can assist you with, feel free to let me know. Good luck with resolving the issue!