Connection Error Troubleshooting Guide

How is this possible when one flow works and another doesn’t autogen Error occurred while processing message: Connection error.
The error message “Error occurred while processing message: Connection error” indicates that there is an issue with the connection in the flow that is encountering the error. Here are some steps you can take to troubleshoot and resolve the issue:

1. **Check Network Connection:**
Ensure that the device running the flow has a stable and active network connection. A loss of network connectivity can lead to connection errors.

2. **Verify Endpoint URLs:**
If your flow involves connecting to external services or APIs, double-check that the endpoint URLs are correct. Any mistake in the URL can result in a connection error.

3. **Firewall and Security Settings:**
Check if there are any firewall or security settings that might be blocking the connection. Ensure that the necessary ports are open and that security configurations allow the flow to communicate with external services.

4. **Authentication Issues:**
If the flow involves authentication with external services, ensure that the credentials are valid and have the necessary permissions. Incorrect or expired credentials can lead to connection errors.

5. **Retry Mechanism:**
Implement a retry mechanism in your flow to handle transient connection issues. Sometimes, connection errors are temporary, and retrying the operation after a short delay can resolve the issue.

6. **Logging and Error Handling:**
Implement detailed logging and error handling in your flow. This will help you identify the specific point where the connection error is occurring and provide more insights into the root cause.

7. **Testing with a Different Environment:**
If possible, test the flow in a different environment to see if the issue persists. This can help determine if the problem is specific to the current environment or if it’s a more general issue.

8. **Update Dependencies:**
Ensure that all relevant libraries, frameworks, or dependencies in your flow are up-to-date. Sometimes, compatibility issues or bug fixes in newer versions can resolve connection problems.

9. **Consult Documentation:**
Review the documentation for the services or tools involved in your flow. Look for any specific recommendations or troubleshooting steps related to connection errors.

10. **Seek Support:**
If the issue persists, consider reaching out to the support channels of the services or tools you are using. They may provide specific guidance based on the nature of the connection error.

By systematically going through these steps, you should be able to identify and resolve the connection error in your flow.
On flow works with the smae connectoin. I just tried to create another flow and it won;’t connet
If one flow is working with the same connection while another is not, it suggests that the issue may be specific to the configuration or implementation of the non-working flow. Here are some steps you can take to troubleshoot and resolve the problem:

1. **Review Flow Configuration:**
Double-check the configuration settings of the non-working flow. Ensure that all the connection details, including endpoints, credentials, and any other relevant parameters, are correctly set.

2. **Compare Working and Non-Working Flows:**
Compare the configurations of the working flow and the non-working flow. Look for any differences in the way connections are established, and make sure that the non-working flow aligns with the successful configuration.

3. **Error Messages and Logs:**
Examine error messages and logs generated by the non-working flow. The error messages might provide specific details about what went wrong. Look for any clues in the logs that could lead you to the root cause of the connection issue.

4. **Test Connection Manually:**
If possible, test the connection manually using the same parameters as in the non-working flow. This can help you isolate whether the issue is related to the flow itself or the underlying connection.

5. **Dependency Versions:**
Check if there are any differences in the versions of libraries or dependencies used by the working and non-working flows. Incompatibilities between versions could cause issues.

6. **Isolation of Components:**
Temporarily isolate components of the non-working flow to identify which part is causing the connection error. This could involve disabling certain steps or segments of the flow and testing each one independently.

7. **Environment Variables:**
Ensure that any environment variables or configuration settings used by the non-working flow are correctly set. Incorrect or missing environment variables can lead to connection problems.

8. **Firewall and Security Policies:**
Verify that there are no specific firewall rules or security policies affecting the non-working flow. Ensure that the flow has the necessary permissions to establish connections.

9. **Flow Execution Environment:**
Check if there are differences in the execution environment between the working and non-working flows. This includes factors such as server settings, permissions, and resource availability.

10. **Consult Documentation and Community:**
Review the documentation for the tools, services, or platforms involved in the non-working flow. Additionally, check community forums or support channels for insights from others who may have encountered similar issues.

By systematically examining these aspects, you should be able to pinpoint the cause of the connection issue in the non-working flow and take appropriate corrective actions.
GroupChat select_speaker failed to resolve the next speaker’s name. This is because the speaker selection OAI call returned:
User_Proxy
The error message “GroupChat select_speaker failed to resolve the next speaker’s name. This is because the speaker selection OAI call returned: User_Proxy” indicates that there was an issue resolving the next speaker’s name during a call to the “select_speaker” function in a GroupChat environment. The response from the speaker selection OpenAI API call returned “User_Proxy,” and this might be an unexpected or erroneous result.

Here are steps you can take to troubleshoot and address this issue:

1. **Check API Documentation:**
Review the documentation for the GroupChat API, specifically the “select_speaker” function. Ensure that you are using the correct parameters and that your request aligns with the expected format and behavior.

2. **Verify Input Parameters:**
Double-check the input parameters you are providing when calling the “select_speaker” function. Ensure that all required parameters are included and have the correct values.

3. **Handle Unexpected Responses:**
Implement error handling in your code to deal with unexpected or erroneous responses. If the API returns “User_Proxy” unexpectedly, your code should be able to log the error, provide context, and potentially retry the request.

4. **Logging and Debugging:**
Add detailed logging statements to your code to capture the input parameters, the API response, and any intermediate steps. This can help you trace the issue and identify where the unexpected “User_Proxy” response is originating.

5. **Review Speaker Selection Logic:**
Examine the logic you are using to select the next speaker. Ensure that it aligns with the intended behavior and that it handles edge cases appropriately. If the speaker selection logic relies on external data, make sure that data is accurate and up-to-date.

6. **API Rate Limits:**
Check if you are hitting any rate limits with the GroupChat API. Exceeding rate limits can result in unexpected behavior. Make sure that your application adheres to the API’s rate-limiting policies.

7. **Contact OpenAI Support:**
If the issue persists and you cannot identify the problem through your troubleshooting efforts, consider reaching out to OpenAI support for assistance. They may be able to provide insights or guidance specific to your situation.

8. **Update SDK or Library:**
Ensure that you are using the latest version of the OpenAI SDK or library. There might be bug fixes or improvements in newer versions that address issues similar to the one you are encountering.

By following these steps, you should be able to identify and resolve the issue with the “select_speaker” function in your GroupChat implementation.
I have two agents and I want themeach using their own ai: config_list =[
{
“model”: “llama-7B”,
“base_url”: “http://127.0.0.1:5151/v1/”,
“api_type”: “open_ai”,
}
]
config_list_Funct=[{
“model” : “Gorilla”,
“base_url”: “Http://127.0.0.1:6000/v1”,
“api_type”: “open_ai”
}]
It looks like you have two different configuration lists (`config_list` and `config_list_Funct`) for two agents, each using a different model and base URL. If you want to create separate agents with their own configurations, you can use the following approach:

“`python
import openai

# Configure the first agent
config_agent1 = {
“model”: “llama-7B”,
“base_url”: “http://127.0.0.1:5151/v1/”,
“api_type”: “open_ai”,
}

# Configure the second agent
config_agent2 = {
“model”: “Gorilla”,
“base_url”: “http://127.0.0.1:6000/v1”,
“api_type”: “open_ai”,
}

# Create instances of agents using their respective configurations
agent1 = openai.Agent(**config_agent1)
agent2 = openai.Agent(**config_agent2)

# Example usage of the agents
text_input_agent1 = “This is input for agent 1.”
response_agent1 = agent1.completion.create(engine=”text-davinci-002″, prompt=text_input_agent1)

text_input_agent2 = “This is input for agent 2.”
response_agent2 = agent2.completion.create(engine=”text-davinci-003″, prompt=text_input_agent2)

# Access the generated responses
output_agent1 = response_agent1[“choices”][0][“text”]
output_agent2 = response_agent2[“choices”][0][“text”]

# Print the outputs
print(“Agent 1 Output:”, output_agent1)
print(“Agent 2 Output:”, output_agent2)
“`

In this example:

– `config_agent1` and `config_agent2` are dictionaries containing the configuration details for each agent.
– `openai.Agent(**config_agent1)` and `openai.Agent(**config_agent2)` create instances of the OpenAI Agent class with the specified configurations.
– You can then use each agent independently, passing its specific configuration when making API calls.

Make sure to adapt the engine and prompt parameters according to your specific use case and API requirements.

Note: Ensure that you have the OpenAI Python library installed (`pip install openai`) and that your endpoints (`http://127.0.0.1:5151/v1/` and `http://127.0.0.1:6000/v1`) are correctly configured and accessible.
Now I need to be able to enter a port number: @echo off
setlocal

set /p model=”Enter the model file path: ”

:: Set default values for other parameters
set port=5151
set host=localhost

:: Prompt the user to choose a context_size from the list
echo Choose a context_size:
echo [1] 256
echo [2] 512
echo [3] 1024
echo [4] 2048
echo [5] 3072
echo [6] 4096
echo [7] 6144
echo [8] 8192
echo [9] 12288
echo [10] 16384
echo [11] 24576
echo [12] 32768
echo [13] 65536

set /p context_choice=”Enter the number corresponding to your choice: ”

:: Map the user’s choice to the actual context_size value
if “%context_choice%”==”1” set context_size=256
if “%context_choice%”==”2” set context_size=512
if “%context_choice%”==”3” set context_size=1024
if “%context_choice%”==”4” set context_size=2048
if “%context_choice%”==”5” set context_size=3072
if “%context_choice%”==”6” set context_size=4096
if “%context_choice%”==”7” set context_size=6144
if “%context_choice%”==”8” set context_size=8192
if “%context_choice%”==”9” set context_size=12288
if “%context_choice%”==”10” set context_size=16384
if “%context_choice%”==”11” set context_size=24576
if “%context_choice%”==”12” set context_size=32768
if “%context_choice%”==”13” set context_size=65536

:: Construct the koboldcpp command
set koboldcpp_cmd=kb –model “%model%” –port %port% –host %host% –contextsize %context_size% –noblas –usemlock

echo Running the following command:
echo %koboldcpp_cmd%

%koboldcpp_cmd%

endlocal

It looks like you have a batch script that prompts the user for various parameters and then constructs and executes a command for `koboldcpp`. To allow the user to enter a custom port number, you can modify your script as follows:

“`batch
@echo off
setlocal

set /p model=”Enter the model file path: ”

:: Prompt the user to enter a custom port number
set /p port=”Enter the port number (default is 5151): ”
if not defined port set port=5151

:: Set default values for other parameters
set host=localhost

set /p context_choice=”Enter the number corresponding to your choice: ”

:: Construct the koboldcpp command
set koboldcpp_cmd=kb –model “%model%” –port %port% –host %host% –contextsize %context_size% –noblas –usemlock

echo Running the following command:
echo %koboldcpp_cmd%

%koboldcpp_cmd%

endlocal
“`

This modification introduces a new line to prompt the user to enter a custom port number, and the `set /p port=”Enter the port number (default is 5151): “` line captures the user input. If the user doesn’t provide any input, it defaults to `5151`. The `%port%` variable is then used in the `koboldcpp_cmd` construction.
GREAT! Now I have two llms doing something. Not sure what yet.
That’s great to hear that you have two language models (LLMs) up and running! If you’re not sure what they are doing, you might want to explore their capabilities by interacting with them through prompts or queries. Here are some steps you can take to better understand what the models are capable of:

1. **Prompt-Based Interaction:**
Use prompts to interact with the language models. Provide a prompt or input text, and observe the generated responses. This can help you understand how well the models perform for different types of queries.

2. **Experiment with Input Variations:**
Try different input variations, such as asking questions, completing sentences, or providing context. This can give you insights into the models’ abilities to understand and generate coherent and contextually relevant text.

3. **Adjust Parameters:**
If applicable, experiment with different parameters, such as context size or other model-specific settings. Adjusting these parameters can sometimes influence the output generated by the models.

4. **Evaluate Response Quality:**
Assess the quality and relevance of the responses generated by the models. Look for coherent and contextually appropriate answers. If necessary, you can refine your prompts or queries to get more accurate results.

5. **Explore Model Capabilities:**
Explore the models’ capabilities by asking about a wide range of topics. Language models are trained on diverse datasets, so they can provide information and context on a variety of subjects.

6. **Check Model Documentation:**
Review the documentation for the specific language models you are using. This can provide information on their strengths, limitations, and recommended use cases.

7. **Test with Real-World Use Cases:**
If you have specific use cases in mind, test the models with real-world examples or scenarios relevant to your application. This can help you assess how well the models align with your intended use.

8. **Monitor Outputs and Adjustments:**
Keep an eye on the outputs generated by the models and make adjustments as needed. Iteratively refine your prompts or queries based on the feedback you receive from the models.

Remember that language models like LLMs are versatile and can be used for a wide range of natural language processing tasks. Understanding their behavior and capabilities through interaction and experimentation will help you leverage their strengths effectively. If you encounter any specific challenges or have questions about their output, feel free to ask for further assistance!
The taska gent isn’t following instructions
If the task agent is not following instructions as expected, there are several steps you can take to address the issue and improve the model’s output:

1. **Clarify and Specify Instructions:**
Make sure that your instructions to the model are clear and specific. Clearly define the desired format and structure of the output. If necessary, provide explicit examples or guidelines for the task.

2. **Break Down Complex Instructions:**
If your instructions are complex, consider breaking them down into smaller, more manageable steps. This can help the model better understand and follow each component of the instructions.

3. **Use Explicit Examples:**
Include explicit examples in your prompt or instruction to show the model the desired output. Providing concrete examples can help guide the model in generating responses that align with your expectations.

4. **Adjust Prompt Engineering:**
Experiment with different prompt engineering techniques. Adjust the wording or structure of your prompt to elicit the desired response. Fine-tuning the way you present the task can influence the model’s behavior.

5. **Increase Model “Temperature”:**
If you’re using a model with a temperature parameter (e.g., GPT-3), adjusting it can impact the randomness of the output. Higher temperature values make the output more diverse, while lower values make it more focused. Experiment with different temperature settings to see how they affect the results.

6. **Provide More Context:**
If the task requires context, consider providing more background information or context in your prompt. This can help the model better understand the context of the task and generate more relevant responses.

7. **Experiment with System and User Messages:**
In conversational tasks, if you’re using system and user messages, experiment with the placement and content of these messages. System messages can provide high-level instructions to guide the model, while user messages can provide specific task details.

8. **Use Additional Instructions in Tokens:**
Utilize the `instructions` parameter to provide additional guidance to the model. This parameter allows you to specify instructions that the model should follow during generation.

9. **Review Model Documentation:**
Check the model’s documentation for any specific instructions or tips provided by the model’s developers. It might contain insights into how to structure your requests for optimal results.

10. **Experiment with Alternative Approaches:**
If the task agent continues to produce unsatisfactory results, consider trying alternative approaches or models. You may experiment with different models, architectures, or even consider fine-tuning the model on a custom dataset to better align with your specific task.

Remember that the quality of the model’s output can be influenced by various factors, and iterative experimentation is often necessary to achieve the desired results. If the issue persists, you can provide specific examples or details about the task you’re working on, and I can offer more targeted advice.
Well for now I would like to understand this more: groupchat = GroupChat(agents=[Taskms, coder, critic, user_proxy], messages=[], max_round=8)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
user_proxy.initiate_chat(manager, message=”Write a Web Gui app in streamlit that utilizes the KoboldCPP backend and has a chat gui frontend. Clearly comment your code and save file to the work directory”, clear_history=False, config_list=config_list)
It appears that you are working with a GroupChat system involving different agents (Taskms, coder, critic, user_proxy) and a GroupChatManager in a conversational setting. The goal is to initiate a chat and provide a task to the agents. Let’s break down the code snippet:

“`python
# Import the necessary modules/classes
from groupchat import GroupChat, GroupChatManager
from agents import Taskms, coder, critic, user_proxy

# Assuming llm_config, config_list, and other necessary variables are defined elsewhere

# Create a GroupChat instance with specified agents, an empty message history, and a maximum round limit
groupchat = GroupChat(agents=[Taskms, coder, critic, user_proxy], messages=[], max_round=8)

# Create a GroupChatManager instance, passing the GroupChat instance and llm_config
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)

# Initialize a chat using the user_proxy agent
user_proxy.initiate_chat(
manager,
message=”Write a Web Gui app in streamlit that utilizes the KoboldCPP backend and has a chat gui frontend. Clearly comment your code and save file to the work directory”,
clear_history=False,
config_list=config_list
)
“`

Explanation:

1. **GroupChat Creation:**
– `GroupChat` is a class representing a group chat environment. It takes parameters such as a list of agents (`[Taskms, coder, critic, user_proxy]`), an empty list of messages, and a maximum round limit (`max_round=8`).

2. **GroupChatManager Creation:**
– `GroupChatManager` is a class responsible for managing the group chat. It takes a `GroupChat` instance (`groupchat`) and llm_config as parameters.

3. **Agent Initialization:**
– `user_proxy.initiate_chat` is used to initialize a chat using the `user_proxy` agent. It takes parameters such as the `manager` instance, a message to kick off the conversation, a flag indicating whether to clear chat history (`clear_history=False`), and a configuration list (`config_list`).

4. **Provided Task:**
– The message provided to the `user_proxy.initiate_chat` method specifies a task for the agents. It instructs them to write a web GUI app in Streamlit that uses the KoboldCPP backend, has a chat GUI frontend, and requires clear code comments. Additionally, it mentions saving the file to the work directory.

It seems like you are orchestrating a collaborative chat-based task among different agents. If you have specific questions or if there’s anything specific you’d like to achieve or understand further, feel free to provide more details, and I’ll be happy to assist.
GroupChat select_speaker failed to resolve the next speaker’s name. This is because the speaker selection OAI call returned:
It looks like the message you provided is incomplete, and there’s no information about the actual response or error message from the speaker selection OpenAI API call. To address the issue, you’ll need to investigate the error further. Here are some steps you can take:

1. **Check API Response:**
Look into the response returned by the speaker selection OpenAI API call. The complete response may contain more details about why the speaker selection failed. Inspect the response object or log the entire response for analysis.

2. **Error Handling:**
Ensure that your code includes proper error handling to capture and log detailed information about errors, including the specific error message returned by the API. This will help in identifying the root cause.

3. **API Documentation:**
Consult the documentation for the speaker selection API to understand the possible error messages and their meanings. The documentation might provide guidance on how to troubleshoot common issues.

4. **Retry Mechanism:**
Implement a retry mechanism in your code for the speaker selection process. Sometimes, transient issues may cause failures, and retrying the operation after a short delay can resolve the problem.

5. **Parameter Validation:**
Double-check the parameters passed to the speaker selection API. Ensure that they are correctly formatted and that the necessary information is provided for the API to make a speaker selection.

6. **Rate Limiting:**
Verify if there are any rate-limiting restrictions on the speaker selection API. Exceeding rate limits can result in connection errors. Ensure that your application adheres to the API’s rate-limiting policies.

7. **Debugging Output:**
Add detailed debugging output to your code to log intermediate steps, parameters, and responses. This can help you trace the execution flow and identify the point of failure.

8. **Consult Support or Community:**
If the issue persists and you are unable to identify the problem, consider reaching out to OpenAI support or checking community forums. Others may have encountered similar issues and can provide insights or solutions.

Without the specific error message or response details, it’s challenging to pinpoint the exact cause of the problem. If you can provide more information or share relevant parts of your code, I can offer more targeted assistance.
GroupChat select_speaker failed to resolve the next speaker’s name. This is because the speaker selection OAI call returned:
roles = [‘Coder’, ‘taskms’, ‘critic’, ‘user_proxy’]
next_role = roles[0]
print(f’Next role is: {next_role}’)

Traceback (most recent call last):
File “D:SarahOneautogenaapp.py”, line 68, in
user_proxy.initiate_chat(manager, message=”Write a Web Gui app in streamlit that utilizes the KoboldCPP backend and has a chat gui frontend.
Clearly comment your code and save file to the work directory”, clear_history=False, config_list=config_list)
File “D:SarahOneautogenautogenagentchatconversable_agent.py”, line 698, in initiate_chat
self.send(self.generate_init_message(**context), recipient, silent=silent)
File “D:SarahOneautogenautogenagentchatconversable_agent.py”, line 441, in send
recipient.receive(message, self, request_reply, silent)
File “D:SarahOneautogenautogenagentchatconversable_agent.py”, line 599, in receive
reply = self.generate_reply(messages=self.chat_messages[sender], sender=sender)
File “D:SarahOneautogenautogenagentchatconversable_agent.py”, line 1298, in generate_reply
final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple[“config”])
File “D:SarahOneautogenautogenagentchatgroupchat.py”, line 383, in run_chat
reply = speaker.generate_reply(sender=self)
File “D:SarahOneautogenautogenagentchatconversable_agent.py”, line 1298, in generate_reply
final, reply = reply_func(self, messages=messages, sender=sender, config=reply_func_tuple[“config”])
File “D:SarahOneautogenautogenagentchatconversable_agent.py”, line 856, in generate_code_execution_reply
last_n_messages = code_execution_config.pop(“last_n_messages”, “auto”)
AttributeError: ‘bool’ object has no attribute ‘pop’
The error you are encountering is an `AttributeError` at the line:

“`python
last_n_messages = code_execution_config.pop(“last_n_messages”, “auto”)
“`

The error message indicates that the variable `code_execution_config` is a boolean (`True` or `False`), and booleans don’t have a `pop` method. The `pop` method is typically used with dictionaries, not with boolean values.

To resolve this issue, you need to ensure that `code_execution_config` is a dictionary before trying to use the `pop` method on it. Here’s an example of how you might modify the code:

“`python
# Check if code_execution_config is a dictionary, otherwise provide a default empty dictionary
if isinstance(code_execution_config, dict):
last_n_messages = code_execution_config.pop(“last_n_messages”, “auto”)
else:
# Provide a default value when code_execution_config is not a dictionary
last_n_messages = “auto”
“`

This modification checks if `code_execution_config` is a dictionary using `isinstance`. If it is a dictionary, it uses the `pop` method; otherwise, it sets `last_n_messages` to the default value “auto.”

Apply this change to your code, and it should resolve the `AttributeError`. If you have additional questions or if there’s more context you’d like to provide, feel free to do so.
GroupChat select_speaker failed to resolve the next speaker’s name. This is because the speaker selection OAI call returned:
“`python :: Ita pears that the ‘message’ or ‘return’ from teh agent is being used as the agents name rather than the actual name of the agent
It seems like you are encountering an issue where the message or return value from the agent is being incorrectly used as the agent’s name during the speaker selection process in the GroupChat. To resolve this, you may need to ensure that the agent’s name is explicitly defined and separate from the content of the message or the return value.

Here are some steps you can take to address the issue:

1. **Define Agent Names:**
Make sure that the agents in your GroupChat are assigned distinct and well-defined names. This should be done when creating the agents, and the names should be used consistently throughout the code.

For example:
“`python
user_proxy = Agent(name=”UserProxy”)
“`

2. **Ensure Proper Usage in Messages:**
When agents send or return messages, ensure that the agent’s name is used correctly. Avoid using the content of the message or return value as the agent’s name.

For example, if your agent sends a message:
“`python
user_proxy.send_message(“Hello, this is UserProxy.”)
“`

3. **Check Speaker Selection Logic:**
Inspect the logic responsible for selecting the next speaker in your GroupChat. Ensure that it correctly references the agent’s name instead of using the message content as the name.

For example, if your code includes a speaker selection logic:
“`python
next_speaker = select_next_speaker(groupchat.agents)
“`

4. **Examine the Speaker Selection API Call:**
If you are using an OpenAI API call for speaker selection, double-check the parameters and the way agent names are provided to the API. Ensure that the agent names are explicitly specified and not inferred from the message content.

5. **Debugging Output:**
Add debugging statements to your code to print relevant information during the speaker selection process. This can help you identify where the incorrect agent name is being introduced.

Without the specific code snippet where the speaker selection occurs, it’s challenging to provide more precise guidance. If you can share relevant parts of your code related to speaker selection or provide additional context, I can offer more targeted assistance.
def select_speaker_msg(self, agents: Optional[List[Agent]] = None) -> str:
“””Return the system message for selecting the next speaker. This is always the *first* message in the context.”””
if agents is None:
agents = self.agents
def select_speaker_msg(self, agents: Optional[List[Agent]] = None) -> str:
“””Return the system message for selecting the next speaker. This is always the *first* message in the context.”””
if agents is None:
agents = self.agents
The `select
Thank you for sharing the `select_speaker_msg` method. It seems like this method is designed to generate a system message for selecting the next speaker in a conversational context. However, I don’t see the part of the code where the actual selection of the next speaker takes place.

Here’s an example of how you might modify the method to explicitly reference the agent’s name:

“`python
from typing import Optional, List

class YourGroupChatClass:
def __init__(self, agents: Optional[List[Agent]] = None):
self.agents = agents if agents is not None else []

def select_speaker_msg(self, agents: Optional[List[Agent]] = None) -> str:
“””Return the system message for selecting the next speaker. This is always the *first* message in the context.”””
if agents is None:
agents = self.agents

# Ensure there are agents available
if not agents:
return “No agents available.”

# Assuming you have a method to get the next speaker based on some logic
next_speaker = self.get_next_speaker(agents)

# Use the agent’s name explicitly in the system message
return f”System: The next speaker is {next_speaker.name}.”

def get_next_speaker(self, agents: List[Agent]) -> Agent:
# Implement your logic to determine the next speaker
# For example, you might choose the next speaker based on a round-robin approach
# Here, we’ll simply use the first agent in the list as an example
return agents[0]
“`

In this example, the `get_next_speaker` method is introduced to encapsulate the logic for selecting the next speaker. Ensure that this logic explicitly uses the agent’s name rather than relying on the message content. The `select_speaker_msg` method then calls this logic and constructs the system message using the agent’s name.

If you can share more details about the logic for selecting the next speaker or if there are specific requirements for the speaker selection process, I can provide more tailored assistance.
def select_speaker_prompt(self, agents: Optional[List[Agent]] = None) -> str:
“””Return the floating system prompt selecting the next speaker. This is always the *last* message in the context.”””
if agents is None:
agents = self.agents
return f”Read the above conversation. Then select the next role from {[agent.name for agent in agents]} to play. Only return the role.”

The `select_speaker_prompt` method you’ve provided generates a system prompt for selecting the next speaker in the conversation. It instructs the reader to read the above conversation and then choose the next role from a list of roles. The reader is asked to return only the selected role.

Here’s the modified method that explicitly references the agent’s name in the prompt:

“`python
from typing import Optional, List

class YourGroupChatClass:
def __init__(self, agents: Optional[List[Agent]] = None):
self.agents = agents if agents is not None else []

def select_speaker_prompt(self, agents: Optional[List[Agent]] = None) -> str:
“””Return the floating system prompt selecting the next speaker. This is always the *last* message in the context.”””
if agents is None:
agents = self.agents

# Ensure there are agents available
if not agents:
return “No agents available.”

# Explicitly reference the agent’s name in the prompt
role_names = [agent.name for agent in agents]
role_list_str = “, “.join(role_names[:-1]) + f” or {role_names[-1]}” if len(role_names) > 1 else role_names[0]

return f”System: Read the above conversation. Then select the next role from {role_list_str} to play. Only return the role.”
“`

In this modification, the agent names are explicitly referenced when constructing the prompt. The `role_list_str` variable is created by joining the agent names with commas and using “or” for the last agent in the list, ensuring that the prompt is clear and instructive.

Feel free to adjust the code further based on your specific requirements or if you have additional details about the agent names or the conversation context.
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