Fix Infinite Loop – Dr. Trippers Stuff

I have this looping instead of giving a prompt, can you fix it? from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import LlamaCpp

# Set LLAMA configuration to use a local model
llm_config = {
“base_url”: “http://127.0.0.1:5151/v1”,
“model_path”: “G:/huggingface/hub/itsdotscience/Magicoder-S-DS-6.7B-GGUF/Magicoder-S-DS-6.7B_q8_0.gguf”,
“n_gpu_layers”: 1,
“n_batch”: 512,
“n_ctx”: 16384,
“f16_kv”: False,
“callback_manager”: CallbackManager([StreamingStdOutCallbackHandler()]),
“verbose”: True,
}

llm = LlamaCpp(**llm_config)

history = [
{“role”: “system”, “content”: “You are an intelligent assistant. You always provide well-reasoned answers that are both correct and helpful.”},
{“role”: “user”, “content”: “Hello, introduce yourself to someone opening this program for the first time. Be concise.”},
]

while True:
completion = llm.invoke(history)

new_message = {“role”: “assistant”, “content”: “”}

for chunk in completion:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end=””, flush=True)
new_message[“content”] += chunk.choices[0].delta.content

history.append(new_message)

It appears that your code is caught in an infinite loop because of the `while True` statement. To resolve this issue, you can modify your code to break out of the loop when a specific condition is met or when a certain number of iterations have been reached. Here’s an example of how you can adjust your code:

“`python
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import LlamaCpp

llm = LlamaCpp(**llm_config)

max_iterations = 10 # You can adjust this value as needed
current_iteration = 0

while current_iteration python ollama.py
c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_coreutilsutils.py:159: UserWarning: WARNING! base_url is not default parameter.
base_url was transferred to model_kwargs.
Please confirm that base_url is what you intended.
warnings.warn(
llama_model_loader: loaded meta data with 25 key-value pairs and 291 tensors from G:/huggingface/hub/itsdotscience/Magicoder-S-DS-6.7B-GGUF/Magicoder-S-DS-6.7B_q8_0.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: – kv 0: general.architecture str = llama
llama_model_loader: – kv 1: general.name str = tmp
llama_model_loader: – kv 2: llama.context_length u32 = 16384
llama_model_loader: – kv 3: llama.embedding_length u32 = 4096
llama_model_loader: – kv 4: llama.block_count u32 = 32
llama_model_loader: – kv 5: llama.feed_forward_length u32 = 11008
llama_model_loader: – kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: – kv 7: llama.attention.head_count u32 = 32
llama_model_loader: – kv 8: llama.attention.head_count_kv u32 = 32
llama_model_loader: – kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: – kv 10: llama.rope.freq_base f32 = 100000.000000
llama_model_loader: – kv 11: llama.rope.scaling.type str = linear
llama_model_loader: – kv 12: llama.rope.scaling.factor f32 = 4.000000
llama_model_loader: – kv 13: general.file_type u32 = 7
llama_model_loader: – kv 14: tokenizer.ggml.model str = gpt2
llama_model_loader: – kv 15: tokenizer.ggml.tokens arr[str,32256] = [“!”, “””, “#”, “$”, “%”, “&”, “‘”, …
llama_model_loader: – kv 16: tokenizer.ggml.scores arr[f32,32256] = [0.000000, 0.000000, 0.000000, 0.0000…
llama_model_loader: – kv 17: tokenizer.ggml.token_type arr[i32,32256] = [1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, …
llama_model_loader: – kv 18: tokenizer.ggml.merges arr[str,31757] = [“─á ─á”, “─á t”, “─á a”, “i n”, “h e…
llama_model_loader: – kv 19: tokenizer.ggml.bos_token_id u32 = 32013
llama_model_loader: – kv 20: tokenizer.ggml.eos_token_id u32 = 32014
llama_model_loader: – kv 21: tokenizer.ggml.padding_token_id u32 = 32014
llama_model_loader: – kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: – kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: – kv 24: tokenizer.chat_template str = {%- set ns = namespace(found=false) -…
llama_model_loader: – type f32: 65 tensors
llama_model_loader: – type q8_0: 226 tensors
llm_load_vocab: mismatch in special tokens definition ( 243/32256 vs 256/32256 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 32256
llm_load_print_meta: n_merges = 31757
llm_load_print_meta: n_ctx_train = 16384
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: n_ff = 11008
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 100000.0
llm_load_print_meta: freq_scale_train = 0.25
llm_load_print_meta: n_yarn_orig_ctx = 16384
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 6.74 B
llm_load_print_meta: model size = 6.67 GiB (8.50 BPW)
llm_load_print_meta: general.name = tmp
llm_load_print_meta: BOS token = 32013 ”
llm_load_print_meta: EOS token = 32014 ”
llm_load_print_meta: PAD token = 32014 ”
llm_load_print_meta: LF token = 30 ‘?’
llm_load_tensors: ggml ctx size = 0.11 MiB
llm_load_tensors: offloading 1 repeating layers to GPU
llm_load_tensors: offloaded 1/33 layers to GPU
llm_load_tensors: CPU buffer size = 6830.77 MiB
……………………………………………………………………………………….
llama_new_context_with_model: n_ctx = 16384
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 8192.00 MiB
llama_new_context_with_model: KV self size = 8192.00 MiB, K (f16): 4096.00 MiB, V (f16): 4096.00 MiB
llama_new_context_with_model: CPU input buffer size = 40.07 MiB
llama_new_context_with_model: CPU compute buffer size = 1152.80 MiB
llama_new_context_with_model: graph splits (measure): 1
AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 |
F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 0 | VSX = 0 |
Model metadata: {‘general.name’: ‘tmp’, ‘general.architecture’: ‘llama’, ‘llama.context_length’: ‘16384’, ‘llama.rope.dimension_count’: ‘128’, ‘llama.embedding_length’: ‘4096’, ‘llama.block_count’: ’32’, ‘llama.feed_forward_length’: ‘11008’, ‘llama.attention.head_count’: ’32’, ‘tokenizer.ggml.eos_token_id’: ‘32014’, ‘general.file_type’: ‘7’, ‘llama.attention.head_count_kv’: ’32’, ‘llama.attention.layer_norm_rms_epsilon’: ‘0.000001’, ‘llama.rope.freq_base’: ‘100000.000000’, ‘llama.rope.scaling.type’: ‘linear’, ‘llama.rope.scaling.factor’: ‘4.000000’, ‘tokenizer.ggml.model’: ‘gpt2’, ‘tokenizer.ggml.bos_token_id’: ‘32013’, ‘tokenizer.ggml.padding_token_id’: ‘32014’, ‘tokenizer.ggml.add_bos_token’: ‘true’, ‘tokenizer.ggml.add_eos_token’: ‘false’, ‘tokenizer.chat_template’: “{%- set ns = namespace(found=false) -%}n{%- for message in messages -%}n {%- if message[‘role’] == ‘system’ -%}n {%- set ns.found = true -%}n {%- endif -%}n{%- endfor -%}n{{bos_token}}{%- if not ns.found -%}n{%- endif %}n{%- for message in messages %}n {%- if message[‘role’] == ‘system’ %}n{{ message[‘content’] + ‘\n\n’ }}n {%- else %}n {%- if message[‘role’] == ‘user’ %}n{{‘@@ Instruction\n’ + message[‘content’] + ‘\n\n’}}n {%- else %}n{{‘@@ Response\n’ + message[‘content’] + ‘\n’ + eos_token + ‘\n’}}n {%- endif %}n {%- endif %}n{%- endfor %}{% if add_generation_prompt %}{{ ‘@@ Responsen’ }}{% endif %}”}
Using chat template: {%- set ns = namespace(found=false) -%}
{%- for message in messages -%}
{%- if message[‘role’] == ‘system’ -%}
{%- set ns.found = true -%}
{%- endif -%}
{%- endfor -%}
{{bos_token}}{%- if not ns.found -%}
{%- endif %}
{%- for message in messages %}
{%- if message[‘role’] == ‘system’ %}
{{ message[‘content’] + ‘nn’ }}
{%- else %}
{%- if message[‘role’] == ‘user’ %}
{{‘@@ Instructionn’ + message[‘content’] + ‘nn’}}
{%- else %}
{{‘@@ Responsen’ + message[‘content’] + ‘n’ + eos_token + ‘n’}}
{%- endif %}
{%- endif %}
{%- endfor %}{% if add_generation_prompt %}{{ ‘@@ Response
‘ }}{% endif %}
Using chat eos_token:
Using chat bos_token:

It seems like your code is working fine, and the output you provided includes some informational messages about the LLAMA model loading and configuration.

If you’re facing any specific issues or if there’s a particular aspect you’d like assistance with, please provide more details, and I’ll do my best to help. Otherwise, based on the given output, it appears that the LLAMA model has been loaded successfully with the specified configuration.
Then this script is called from somewhere else, right?
Yes, based on the provided code snippet, it seems like this script (`ollama.py`) is intended to be called from somewhere else. The script defines the LLAMA model configuration, initializes the model, and sets up a conversation loop.

If this script is part of a larger project, it might be imported or executed from another script or module. If you have additional files or details about how this script is integrated into your project, feel free to provide more information, and I can offer more specific assistance.
File “D:SarahOneGPTEgpt-engineerproject2app.py”, line 2, in
from ollama import setup_ollama_routes
File “D:SarahOneGPTEgpt-engineerproject2ollama.py”, line 33, in
if chunk.choices[0].delta.content:
^^^^^^^^^^^^^
AttributeError: ‘str’ object has no attribute ‘choices’
It seems like there’s an issue with the code in the `ollama.py` script, specifically in the part where it’s trying to access the `choices` attribute of a string object. The error message indicates that an `AttributeError` is raised because a string object does not have a `choices` attribute.

To fix this issue, you need to ensure that the variable `chunk` is of the expected type before attempting to access its `choices` attribute. Here’s a modified version of the relevant part of your code:

“`python
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import LlamaCpp

# Set LLAMA configuration to use a local model
llm_config = {
# … (your LLAMA configuration)
}

llm = LlamaCpp(**llm_config)

history = [
# … (your conversation history)
]

while True:
completion = llm.invoke(history)

new_message = {“role”: “assistant”, “content”: “”}

for chunk in completion:
if hasattr(chunk, ‘choices’) and chunk.choices and chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end=””, flush=True)
new_message[“content”] += chunk.choices[0].delta.content

history.append(new_message)
“`

This modification checks if the `chunk` object has a `choices` attribute and if it is not empty before attempting to access its `delta.content` attribute. This should help prevent the `AttributeError` when dealing with string objects that don’t have a `choices` attribute.
can we combine this down and remove all but one connectio configration – combine all that can be: from autogen import OpenAIWrapper

# Configuration for the Lilith model
config_list_lilith = [
{“model”: “Lilith”, “base_url”: “http://127.0.0.1:5151/v1/”, “api_type”: “open_ai”}
]

# OpenAIWrapper instantiation for Lilith
llama_client = OpenAIWrapper(config_list=config_list_lilith)

# Common configuration for Lilith
common_config_lilith = {“config_list”: config_list_lilith}

# Additional configuration for Lilith with timeout, seed, and temperature
llama_config_with_timeout_seed = {“timeout”: 6000, “seed”: 42, **common_config_lilith}

# Additional configuration for Lilith with temperature
llama_config_with_temperature = {“temperature”: 0, **common_config_lilith}

# Additional configuration for Lilith with cache_seed
llama_config_with_cache_seed = {“cache_seed”: 42, **common_config_lilith}

Certainly! You can combine and simplify the configurations into a single dictionary. Here’s the combined configuration:

“`python
from autogen import OpenAIWrapper

# Common configuration for Lilith
common_config_lilith = {
“model”: “Lilith”,
“base_url”: “http://127.0.0.1:5151/v1/”,
“api_type”: “open_ai”
}

# OpenAIWrapper instantiation for Lilith
llama_client = OpenAIWrapper(config_list=[common_config_lilith])

# Additional configuration for Lilith with timeout, seed, and temperature
llama_config_with_timeout_seed = {
“timeout”: 6000,
“seed”: 42,
**common_config_lilith
}

# Additional configuration for Lilith with temperature
llama_config_with_temperature = {
“temperature”: 0,
**common_config_lilith
}

# Additional configuration for Lilith with cache_seed
llama_config_with_cache_seed = {
“cache_seed”: 42,
**common_config_lilith
}
“`

This way, you maintain a single common configuration for Lilith, and you can easily create variations by adding specific parameters to the common configuration. The `OpenAIWrapper` instantiation uses the `config_list` argument, which is a list containing the common configuration.
why are the timeout, tem and cache seed seperate?
The separation of parameters like `timeout`, `temperature` (`tem`), and `cache_seed` into different configurations allows you to have flexibility in how you instantiate and use the Lilith model. Each of these parameters serves a different purpose, and depending on your use case, you might want to set them differently.

Here’s a brief explanation of each parameter:

1. **Timeout (`timeout`):** This parameter represents the maximum time (in milliseconds) the system will wait for a response before considering it a timeout. It’s useful when you want to set a specific time limit for the model to generate a response.

2. **Temperature (`temperature` or `tem`):** In the context of language models, temperature controls the randomness of the generated output. A higher temperature (e.g., 1.0) produces more diverse but potentially less focused responses, while a lower temperature (e.g., 0.5) results in more deterministic and focused responses.

3. **Cache Seed (`cache_seed`):** This parameter might be related to caching results. Setting a cache seed ensures that the model generates the same response for the same input. It can be useful for reproducibility or when you want to cache responses for certain inputs.

By separating these parameters into different configurations, you can create multiple instances of Lilith with different settings depending on your specific requirements. For example, you might want to instantiate Lilith with a longer timeout for one use case, with a lower temperature for another, and with a specific cache seed for yet another scenario. This modular approach makes it easier to manage and configure Lilith based on the specific needs of your application or system.
I DONT
I apologize if my previous explanation was unclear. Let me simplify it:

The separation of `timeout`, `temperature` (`tem`), and `cache_seed` into different configurations allows you to control these parameters independently when working with the Lilith model. If you have a specific use case that requires adjusting one or more of these parameters, you can do so without affecting the others. It provides flexibility in configuring Lilith based on your application’s requirements.

If you have a specific question or if there’s something else you’d like assistance with, please feel free to let me know.
I DONT WANT TO WORK WITH THEM SEPERATely I need a very compact string for now. Please comvbine these into the most compact it can be: from autogen import OpenAIWrapper

# Configuration for the Lilith model
config_list_lilith = [
{“model”: “Lilith”, “base_url”: “http://127.0.0.1:5151/v1/”, “api_type”: “open_ai”}
]

# OpenAIWrapper instantiation for Lilith
llama_client = OpenAIWrapper(config_list=config_list_lilith)

# Common configuration for Lilith
common_config_lilith = {“config_list”: config_list_lilith}

# Additional configuration for Lilith with timeout, seed, and temperature
llama_config_with_timeout_seed = {“timeout”: 6000, “seed”: 42, **common_config_lilith}

# Additional configuration for Lilith with temperature
llama_config_with_temperature = {“temperature”: 0, **common_config_lilith}

# Additional configuration for Lilith with cache_seed
llama_config_with_cache_seed = {“cache_seed”: 42, **common_config_lilith}

Certainly! Here’s a more compact version of your configurations:

“`python
from autogen import OpenAIWrapper

# Common configuration for Lilith
common_config_lilith = {“model”: “Lilith”, “base_url”: “http://127.0.0.1:5151/v1/”, “api_type”: “open_ai”}

# OpenAIWrapper instantiation for Lilith
llama_client = OpenAIWrapper(config_list=[common_config_lilith])

# Additional configuration for Lilith with timeout, seed, and temperature
llama_config_with_timeout_seed = {“timeout”: 6000, “seed”: 42, **common_config_lilith}

# Additional configuration for Lilith with temperature
llama_config_with_temperature = {“temperature”: 0, **common_config_lilith}

# Additional configuration for Lilith with cache_seed
llama_config_with_cache_seed = {“cache_seed”: 42, **common_config_lilith}
“`

In this version, the `common_config_lilith` dictionary is directly used within the configurations for timeout, temperature, and cache_seed. This reduces redundancy and provides a more concise representation of your Lilith configurations.
PUT THE TIMEOUT< TEMP str:
for i in range(int(num_seconds)):
time.sleep(1)
# should print to stdout
return “Timer is done!”

# An example sync function
@user_proxy.register_for_execution()
@coder.register_for_llm(description=”create a stopwatch for N seconds”)
def stopwatch(num_seconds: Annotated[str, “Number of seconds in the stopwatch.”]) -> str:
for i in range(int(num_seconds)):
time.sleep(1)
return “Stopwatch is done!”

# start the conversation
# ‘await’ is used to pause and resume code execution for async IO operations.
# Without ‘await’, an async function returns a coroutine object but doesn’t execute the function.
# With ‘await’, the async function is executed and the current function is paused until the awaited function returns a result.

await user_proxy.a_initiate_chat( # noqa: F704
coder,
message=”Create a timer for 5 seconds and then a stopwatch for 5 seconds.”,
)
It seems you’ve provided a script that involves the interaction between a user proxy agent, a coding assistant, and an underlying language model (Lilith/KOBOLD). This script appears to set up a conversation where the user proxy agent initiates a chat with the coding assistant, instructing it to create a timer for 5 seconds and then a stopwatch for 5 seconds.

Here’s a breakdown of the main components in your script:

1. **Configuration Setup for Lilith/KOBOLD:**
– You set up the configuration for the underlying language model (`llm_config`).
– An instance of `OpenAIWrapper` named `llama_client` is created using the configuration.

2. **Agent Initialization:**
– An instance of `AssistantAgent` named `coder` is created, representing the coding assistant. It is configured with a system message and the Lilith configuration.
– An instance of `UserProxyAgent` named `user_proxy` is created, acting as a proxy for the user for code execution. It is configured with a system message, termination message condition, and other parameters.

3. **Function Definitions:**
– Async and sync functions are defined with decorators (`@user_proxy.register_for_execution` and `@coder.register_for_llm`). These functions are associated with specific descriptions and are meant to be executed by the user proxy agent and the coding assistant.

4. **Conversation Initialization:**
– The conversation is initiated by calling `user_proxy.a_initiate_chat`, providing the coding assistant (`coder`) as a participant and a message to start the conversation.

5. **Async Function Execution:**
– There’s an asynchronous function (`timer`) that is decorated for execution by the user proxy agent and the coding assistant.

It’s important to note that the script uses asynchronous programming with the `async` and `await` keywords, which allows for non-blocking execution of certain functions. This is common in scenarios where there are asynchronous I/O operations, such as waiting for user input or timers.

If you have specific questions or if there’s something specific you would like assistance with in this script, please let me know!

(autog) D:SarahOneautogenNewtester>python app.py
File “D:SarahOneautogenNewtesterapp.py”, line 60
await user_proxy.a_initiate_chat( # noqa: F704
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
SyntaxError: ‘await’ outside async function
The error indicates that the `await` keyword is being used outside of an asynchronous function. To resolve this issue, ensure that the code block containing the `await` statement is within an `async` function.

In your provided script, it appears that there might be an indentation issue, causing the `await` statement to be at the wrong level of indentation. Make sure that the `await` statement is inside an asynchronous function. Here’s how you can modify the script to address the error:

“`python
import time
from autogen import OpenAIWrapper, UserProxyAgent, AssistantAgent
from typing_extensions import Annotated

# … (previous code remains unchanged)

async def main():
await user_proxy.a_initiate_chat(
coder,
message=”Create a timer for 5 seconds and then a stopwatch for 5 seconds.”,
)

# Run the main function
if __name__ == “__main__”:
# Common configuration for Lilith
llm_config = {
“model”: “KOBOLD”,
“base_url”: “http://127.0.0.1:5151/v1/”,
“api_type”: “open_ai”,
“timeout”: 6000, # Set your default timeout value here
“temperature”: 0, # Set your default temperature value here
“cache_seed”: 42 # Set your default cache_seed value here
}

# OpenAIWrapper instantiation for Lilith
llama_client = OpenAIWrapper(config_list=[llm_config])

coder = AssistantAgent(
name=”chatbot”,
system_message=”For coding tasks, only use the functions you have been provided with. You have a stopwatch and a timer, these tools can and should be used in parallel. Reply TERMINATE when the task is done.”,
llm_config=llm_config,
)

user_proxy = UserProxyAgent(
name=”user_proxy”,
system_message=”A proxy for the user for executing code.”,
is_termination_msg=lambda x: x.get(“content”, “”) and x.get(“content”, “”).rstrip().endswith(“TERMINATE”),
human_input_mode=”NEVER”,
max_consecutive_auto_reply=10,
code_execution_config={“work_dir”: “coding”},
)

# Define functions according to the function description
# …

# Run the main function
import asyncio
asyncio.run(main())
“`

This modification introduces an `async def main():` function and calls `asyncio.run(main())` to execute the asynchronous code. Please adapt this structure to fit the overall structure of your application.
The options available are:
– Make sure docker is running (advised approach for code execution)
– Set “use_docker”: False in code_execution_config
– Set AUTOGEN_USE_DOCKER to “0/False/no” in your environment variables
The options provided seem to be related to configuring code execution with Docker. Here’s a brief explanation for each option:

1. **Make sure Docker is running (advised approach for code execution):**
– This option suggests ensuring that Docker is running on your system. Docker is a platform for developing, shipping, and running applications in containers.

2. **Set “use_docker”: False in code_execution_config:**
– This option involves modifying the `code_execution_config` by setting the parameter `use_docker` to `False`. This means that code execution will not use Docker containers.

3. **Set AUTOGEN_USE_DOCKER to “0/False/no” in your environment variables:**
– This option suggests setting an environment variable named `AUTOGEN_USE_DOCKER` to one of the values “0”, “False”, or “no”. This variable likely controls whether the system should use Docker for code execution.

Depending on your specific requirements and the setup of your application, you can choose the most suitable option. If you prefer using Docker, make sure it’s installed and running. If you want to disable Docker usage, you can either modify the code execution configuration directly or set the environment variable accordingly.

Here’s an example of setting an environment variable in Python:

“`python
import os

os.environ[“AUTOGEN_USE_DOCKER”] = “False”
“`

Please adapt these options based on the needs of your application and the context in which you’re working. If you have specific questions or if there’s anything specific you’re uncertain about, feel free to ask!
I cant find where to put it
In the provided code, it seems that the configuration is set in the `code_execution_config` parameter within the creation of the `UserProxyAgent`. To apply the second option (“Set “use_docker”: False in code_execution_config”), you can modify this configuration. Here’s how you might do it:

“`python
# … (previous code remains unchanged)

# OpenAIWrapper instantiation for Lilith
llama_client = OpenAIWrapper(config_list=[llm_config])

# create a UserProxyAgent instance named “user_proxy”
user_proxy = UserProxyAgent(
name=”user_proxy”,
system_message=”A proxy for the user for executing code.”,
is_termination_msg=lambda x: x.get(“content”, “”) and x.get(“content”, “”).rstrip().endswith(“TERMINATE”),
human_input_mode=”NEVER”,
max_consecutive_auto_reply=10,
code_execution_config={“work_dir”: “coding”, “use_docker”: False}, # Set use_docker to False
)

# … (rest of the code remains unchanged)
“`

By adding `”use_docker”: False` to the `code_execution_config`, you indicate that Docker should not be used for code execution.

If you prefer using the third option (“Set AUTOGEN_USE_DOCKER to “0/False/no” in your environment variables”), you can set the environment variable before running your script:

“`python
import os

# Set AUTOGEN_USE_DOCKER to “False” in the environment variables
os.environ[“AUTOGEN_USE_DOCKER”] = “False”
“`

Make sure to place this code at the beginning of your script, before any interactions with the `UserProxyAgent` or other components.
Thank you. That solution willprovide solutions for other scripts!!
You’re welcome! I’m glad the solution works for you, and I hope it proves helpful in your other scripts as well. If you have any more questions or if there’s anything else I can assist you with, feel free to ask. Happy coding!
It worked until the end but tehn this: Elapsed Time: 4.92
Stopwatch’s up!

——————————————————————————–
chatbot (to user_proxy):

The output of the code is a countdown timer that counts down from 5 to 1, then it starts a stopwatch that measures time
until 5 seconds have passed. The stopwatch prints out the elapsed time every 0.1 second and stops when 5 seconds have passed.

——————————————————————————–
user_proxy (to chatbot):

——————————————————————————–
Traceback (most recent call last):
File “D:SarahOneautogenNewtesterapp.py”, line 63, in
asyncio.run(main())
File “c:ProgramDataminiconda3envsautoglibasynciorunners.py”, line 44, in run
return loop.run_until_complete(main)
File “c:ProgramDataminiconda3envsautoglibasynciobase_events.py”, line 649, in run_until_complete
return future.result()
File “D:SarahOneautogenNewtesterapp.py”, line 23, in main
await user_proxy.a_initiate_chat(
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 730, in a_initiate_chat
await self.a_send(await self.a_generate_init_message(**context), recipient, silent=silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 490, in a_send
await recipient.a_receive(message, self, request_reply, silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 638, in a_receive
await self.a_send(reply, sender, silent=silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 490, in a_send
await recipient.a_receive(message, self, request_reply, silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 638, in a_receive
await self.a_send(reply, sender, silent=silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 490, in a_send
await recipient.a_receive(message, self, request_reply, silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 638, in a_receive
await self.a_send(reply, sender, silent=silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 490, in a_send
await recipient.a_receive(message, self, request_reply, silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 638, in a_receive
await self.a_send(reply, sender, silent=silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 490, in a_send
await recipient.a_receive(message, self, request_reply, silent)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 636, in a_receive
reply = await self.a_generate_reply(sender=sender)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 1353, in a_generate_reply
final, reply = await reply_func(
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 840, in a_generate_oai_reply
return await asyncio.get_event_loop().run_in_executor(
File “c:ProgramDataminiconda3envsautoglibconcurrentfuturesthread.py”, line 58, in run
result = self.fn(*self.args, **self.kwargs)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenagentchatconversable_agent.py”, line 813, in generate_oai_reply
response = client.create(
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenoaiclient.py”, line 283, in create
response = self._completions_create(client, params)
File “c:ProgramDataminiconda3envsautoglibsite-packagesautogenoaiclient.py”, line 548, in _completions_create
response = completions.create(**params)
File “c:ProgramDataminiconda3envsautoglibsite-packagesopenai_utils_utils.py”, line 271, in wrapper
return func(*args, **kwargs)
File “c:ProgramDataminiconda3envsautoglibsite-packagesopenairesourceschatcompletions.py”, line 659, in create
return self._post(
File “c:ProgramDataminiconda3envsautoglibsite-packagesopenai_base_client.py”, line 1180, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File “c:ProgramDataminiconda3envsautoglibsite-packagesopenai_base_client.py”, line 869, in request
return self._request(
File “c:ProgramDataminiconda3envsautoglibsite-packagesopenai_base_client.py”, line 960, 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.”}
It seems like there’s an issue with the ‘messages’ array in the conversation. The error message indicates that each object in the ‘messages’ array must have a ‘content’ field that is not empty. The traceback suggests that this error is occurring during the OpenAI API call.

To address this issue, make sure that the ‘messages’ array in the conversation passed to the OpenAI API contains objects with non-empty ‘content’ fields. Check the messages being passed to the API, and ensure that each message object has a valid ‘content’ field.

For example, when initiating a chat, you might have a conversation like this:

“`python
conversation = [
{“role”: “user”, “content”: “Hello, how can you help me?”},
{“role”: “assistant”, “content”: “I can assist you with coding tasks.”},
]
“`

Make sure that the ‘content’ field in each message is not empty. If the issue persists, review the conversation data and adjust it accordingly to meet the API requirements.
This is on on automaticconversation chain with agents do it is up to the code to forward the messages right
Yes, you’re correct. In an automatic conversation chain with agents, the code is responsible for handling the flow of messages between agents. Each agent, including the user proxy and the conversable agent, should be aware of the conversation context and forward messages appropriately.

If you’re encountering a ‘400 – Bad Request’ error with OpenAI’s API, it’s likely that the messages being passed to the API do not meet the expected format. Ensure that:

1. Each message in the ‘messages’ array has a ‘role’ and ‘content’ field.
2. The ‘content’ field is not empty for each message.

If you’re using automatic conversation management, review the code that manages the conversation flow, including how messages are passed between agents. Verify that each message is properly formatted before being sent to the OpenAI API.
what is wrong with the syntax or code of this block: def record_text():
#loop in case of errors
while(1):
try:

with sr.Microphone() as source2:
#prepare recognizer to recieve
r.adjust_for_ambient_noise(source2, duration=0.2)

print(“I’m here”)

#listen for user input
audio2 = r.list(source2)

#using google to recongize audio
MyText = r.recognize_google(audio2)

except sr.RequestError as e:
print(“Could not request results”)
except sr.UnknownValueError:
print(“unknown error”)
It seems there are some issues with the indentation and exception handling in your code. Here’s a corrected version:

“`python
import speech_recognition as sr

def record_text():
r = sr.Recognizer()

# loop in case of errors
while True:
try:
with sr.Microphone() as source2:
# prepare recognizer to receive
r.adjust_for_ambient_noise(source2, duration=0.2)

print(“I’m here”)

# listen for user input
audio2 = r.listen(source2)

# using Google to recognize audio
MyText = r.recognize_google(audio2)

# do something with MyText (e.g., print or return)
print(“You said:”, MyText)

except sr.RequestError as e:
print(“Could not request results:”, e)
except sr.UnknownValueError:
print(“Unknown error”)

# call the function to start recording
record_text()
“`

Changes made:

1. Fixed indentation issues.
2. Added `import speech_recognition as sr` at the beginning assuming that it was not imported.
3. Corrected the method to listen to the microphone (`listen` instead of `list`).
4. Wrapped the entire code in a `while True` loop for continuous listening.
5. Added a placeholder comment for what you want to do with `MyText` after recognition.

Please adapt the code further based on your specific requirements.
So this works to record users spoken text and print it out to the screen. is this going to the llm and back or is it just a loop within the script? import speech_recognition as sr
import pyttsx3
#import autogen
#from autogen import OPenaAIWrapper
import os

OPENAI_API_KEY = “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”

#function to convert stt
def SpeakText(command):

#initiate engine
engine = pyttsx3.init()
engine.say(command)
engine.runAndWait()

#initiate the recognizer
r = sr.Recognizer()

import speech_recognition as sr

def record_text():
r = sr.Recognizer()

# loop in case of errors
while True:
try:
with sr.Microphone() as source2:
# prepare recognizer to receive
r.adjust_for_ambient_noise(source2, duration=0.2)

print(“I’m here”)

# listen for user input
audio2 = r.listen(source2)

# using Google to recognize audio
MyText = r.recognize_google(audio2)

# do something with MyText (e.g., print or return)
print(“You said:”, MyText)

except sr.RequestError as e:
print(“Could not request results:”, e)
except sr.UnknownValueError:
print(“Unknown error”)

# call the function to start recording
#record_text()

def send_to_chatGPT(messages, model=”gpt-3.5-turbo”):

response = openai.ChatCompletion.create(
model=model,
messages=messages,
max_token=100,
n=1,
stop=None,
temperature=0.5
)

message = response.choices[0].message.content
messages.append(response.choices[0].message)
return message

messages = []

messages = [{“role”: “user”, “content” :”Please act as a starship Officer assigned to Astrometrics, your name is Lt. Commander Lilith”}]
while(1):
text = record_text()
messages.append({“role”: “user”, “content” : text})
resonse = send_to_chatGPT(messages)
SpeakText(response)

print(response)
In the provided code, the `record_text` function captures the user’s spoken text using the `speech_recognition` library. However, the captured text is only printed to the screen and not sent to any external model like OpenAI’s GPT.

To integrate the captured text with OpenAI’s GPT, you can modify the code as follows:

“`python
import speech_recognition as sr
import pyttsx3
import openai

# Set your OpenAI API key
openai.api_key = “YOUR_OPENAI_API_KEY”

def speak_text(command):
# Initiate engine
engine = pyttsx3.init()
engine.say(command)
engine.runAndWait()

def send_to_openai(messages, model=”gpt-3.5-turbo”):
response = openai.ChatCompletion.create(
model=model,
messages=messages,
max_tokens=100,
n=1,
stop=None,
temperature=0.5
)

message = response.choices[0].message[“content”]
messages.append({“role”: “system”, “content”: “Assistant message.”})
messages.append(response.choices[0].message)
return message

def record_text():
recognizer = sr.Recognizer()

with sr.Microphone() as source:
recognizer.adjust_for_ambient_noise(source, duration=0.2)
print(“Listening for user input…”)
audio = recognizer.listen(source)
try:
text = recognizer.recognize_google(audio)
print(“You said:”, text)
return text
except sr.UnknownValueError:
print(“Speech Recognition could not understand audio.”)
return “”
except sr.RequestError as e:
print(“Could not request results from Google Speech Recognition service; {0}”.format(e))
return “”

messages = [{“role”: “system”, “content”: “You are a starship officer assigned to Astrometrics, your name is Lt. Commander Lilith.”}]

while True:
user_input = record_text()
if user_input:
messages.append({“role”: “user”, “content”: user_input})
response = send_to_openai(messages)
speak_text(response)
print(“Assistant:”, response)
“`

Make sure to replace `”YOUR_OPENAI_API_KEY”` with your actual OpenAI API key. This modified code captures the user’s spoken text, sends it to OpenAI’s Chat API, and then speaks out and prints the assistant’s response.
Finally a program that works well!
I’m glad to hear that the modified program is working well for you! If you have any more questions or if there’s anything else I can help you with, feel free to ask. Happy coding!
Now lets mess it up
Certainly! How would you like to “mess it up”? Do you have specific changes or challenges you’d like assistance with?
Yes, now we are goiing to connect to my localhost where my Eva lives
Great! If you want to connect to your localhost where Eva lives, you may need to modify the configuration in your script to point to the correct base URL or API endpoint of your Eva instance. Could you provide more details about the changes you want to make or any specific challenges you’re facing in connecting to Eva on your localhost?
Must I incorporate all of this or can we integrate the two scripts? from openai import OpenAI

# Point to the local server
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)

while True:
completion = client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=history,
temperature=0.7,
stream=True,
)

new_message = {“role”: “assistant”, “content”: “”}

for chunk in completion:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end=””, flush=True)
new_message[“content”] += chunk.choices[0].delta.content

history.append(new_message)

Certainly! You can integrate the two scripts by combining the relevant parts. Below is an example of how you can merge your script with the Eva connection script:

“`python
import speech_recognition as sr
import pyttsx3
from openai import OpenAI

# Initialize Eva connection
eva_client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)
eva_history = [
{“role”: “system”, “content”: “You are an intelligent assistant. You always provide well-reasoned answers that are both correct and helpful.”},
{“role”: “user”, “content”: “Hello, introduce yourself to someone opening this program for the first time. Be concise.”},
]

# Initialize speech recognition
r = sr.Recognizer()

def record_text():
while True:
try:
with sr.Microphone() as source:
r.adjust_for_ambient_noise(source, duration=0.2)
print(“Listening…”)
audio = r.listen(source, timeout=10)
text = r.recognize_google(audio)
print(“You said:”, text)
return text
except sr.UnknownValueError:
print(“Sorry, I did not understand. Please repeat.”)
except sr.RequestError as e:
print(f”Could not request results from Google Speech Recognition service; {e}”)

def send_to_eva(messages):
completion = eva_client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=messages,
temperature=0.7,
stream=True,
)

new_message = {“role”: “assistant”, “content”: “”}

for chunk in completion:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end=””, flush=True)
new_message[“content”] += chunk.choices[0].delta.content

messages.append(new_message)
return new_message[“content”]

def speak_text(command):
engine = pyttsx3.init()
engine.say(command)
engine.runAndWait()

# Main loop
messages = eva_history.copy()
while True:
user_input = record_text()
messages.append({“role”: “user”, “content”: user_input})

eva_response = send_to_eva(messages)
speak_text(eva_response)
“`

This script combines the functionality of both scripts, utilizing Eva for natural language understanding and generation and incorporating speech recognition for user input. Adjust the code as needed based on your specific requirements and system configurations.
raceback (most recent call last):
File “D:SarahOneJARVISeva.py”, line 4, in
from openai import OpenAI
ImportError: cannot import name ‘OpenAI’ from ‘openai’ (c:ProgramDataminiconda3envsjarvislibsite-packagesopenai__init__.py)

It seems there’s an issue with the import statement. The `OpenAI` class is typically not directly imported from the `openai` module in the OpenAI Python library.

Instead, you should import it like this:

“`python
from openai import ChatCompletion
“`

So your import statement should be:

“`python
from openai import ChatCompletion
“`

Then, when you use it in your code, replace instances of `OpenAI` with `ChatCompletion`. For example:

“`python
# Initialize Eva connection
eva_client = ChatCompletion(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)
“`

Make this change, and it should resolve the import error.
python eva.py
Listening…
You said: hello Ava
Traceback (most recent call last):
File “c:ProgramDataminiconda3envsjarvislibsite-packagesopenaiopenai_object.py”, line 59, in __getattr__
return self[k]
KeyError: ‘chat’

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File “D:SarahOneJARVISeva.py”, line 61, in
eva_response = send_to_eva(messages)
File “D:SarahOneJARVISeva.py”, line 33, in send_to_eva
completion = eva_client.chat.completions.create(
File “c:ProgramDataminiconda3envsjarvislibsite-packagesopenaiopenai_object.py”, line 61, in __getattr__
raise AttributeError(*err.args)
AttributeError: chat

It seems there’s an issue with the `openai` library. The error indicates that the `chat` attribute is not recognized in the response.

Here’s a revised version of your code that should work with the latest OpenAI library:

“`python
from openai import ChatCompletion

# Initialize Eva connection
eva_client = ChatCompletion(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)

# Sample conversation history
history = [
{“role”: “system”, “content”: “You are an intelligent assistant. You always provide well-reasoned answers that are both correct and helpful.”},
{“role”: “user”, “content”: “Hello, introduce yourself to someone opening this program for the first time. Be concise.”},
]

def send_to_eva(messages):
# Request completion from Eva
completion = eva_client.create(
model=”local-model”, # this field is currently unused
messages=messages,
temperature=0.7,
stream=True,
)

new_message = {“role”: “assistant”, “content”: “”}

for chunk in completion:
if ‘choices’ in chunk and chunk[‘choices’][0][‘message’][‘content’]:
print(chunk[‘choices’][0][‘message’][‘content’], end=””, flush=True)
new_message[“content”] += chunk[‘choices’][0][‘message’][‘content’]

messages.append(new_message)

# Example conversation loop
while True:
# Simulate user input, replace with actual user input mechanism
user_input = input(“You said: “)

# Add user input to the conversation history
messages.append({“role”: “user”, “content”: user_input})

# Send messages to Eva and get a response
send_to_eva(messages)
“`

Make sure to replace the user input mechanism (`input(“You said: “)`) with your actual method of capturing user input. Try running this revised code and see if it resolves the issue.
is this mandatory: Make sure to replace the user input mechanism (input(“You said: “)) with your actual method of capturing user input. Tr
Yes, that statement is a reminder to customize the part of the code where user input is obtained. In the provided code, it uses the `input(“You said: “)` function to simulate user input from the console. However, in a real-world scenario, you may want to replace this with the actual method you’re using to capture user input, especially if you’re integrating this script into a larger application or system.

For example, if you are working with a chatbot in a web application, you might replace `input(“You said: “)` with the logic that captures user messages from the web interface. The way you obtain user input will depend on the context of your application.
no that I cahnge it?
If you change the way you capture user input, you need to modify the code accordingly to ensure it aligns with the mechanism you are using. The provided code uses `input(“You said: “)` to simulate user input from the console. If you change the user input mechanism, make sure to adjust the corresponding part of the code.

For instance, if you are integrating the code into a larger system and user input comes from an external source (like a microphone, a chat interface, or a web form), you should replace the `input(“You said: “)` line with the actual logic that captures user messages from that source.

Here’s an example based on your code:

“`python
# Instead of this line:
# user_input = input(“You said: “)

# Use the logic that captures user input in your specific context, for example:
user_input = record_text() # Assuming record_text() captures user input using a microphone
“`

Adjust the code to match the way you handle user input in your specific application or system.
File “D:SarahOneJARVISeva.py”, line 61, in
eva_response = send_to_eva(messages)
File “D:SarahOneJARVISeva.py”, line 33, in send_to_eva
completion = eva_client.chat.completions.create(
File “c:ProgramDataminiconda3envsjarvislibsite-packagesopenaiopenai_object.py”, line 61, in __getattr__
raise AttributeError(*err.args)
AttributeError: chat
The error `AttributeError: chat` suggests that the `chat` attribute is not available on the `eva_client` object. This could be due to a few reasons:

1. **Library Version Mismatch:** Ensure that you are using the correct version of the OpenAI library that supports the `chat` feature. The `chat` feature might be introduced in a newer version, and if you are using an older version, it won’t recognize it.

To upgrade your library, you can use the following command:

“`bash
pip install –upgrade openai
“`

2. **API Changes:** Check the OpenAI documentation or release notes to see if there are any recent changes to the API structure. If they have made changes, your code might need to be adapted accordingly.

3. **Server Configuration:** Ensure that your local OpenAI server (Eva) is properly configured and supports the `chat` feature. The error might occur if the server doesn’t recognize the `chat` endpoint.

Double-check these aspects, and if the issue persists, you may want to consult the OpenAI documentation or community forums for further assistance.
Successfully installed openai-1.11.1

(jarvis) D:SarahOneJARVIS>
(jarvis) D:SarahOneJARVIS>python eva.py
Traceback (most recent call last):
File “D:SarahOneJARVISeva.py”, line 8, in
eva_client = ChatCompletion(base_url=”http://localhost:5151/v1/chat/completions”, api_key=”not-needed”)
File “c:ProgramDataminiconda3envsjarvislibsite-packagesopenailib_old_api.py”, line 39, in __call__
raise APIRemovedInV1(symbol=self._symbol)
openai.lib._old_api.APIRemovedInV1:

You tried to access openai.ChatCompletion, but this is no longer supported in openai>=1.0.0 – see the README at https://github.com/openai/openai-python for the API.

You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface.

Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28`

A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742
It seems that the OpenAI library has undergone significant changes, and the `openai.ChatCompletion` class is no longer supported in versions 1.0.0 and above.

To address this, you have a couple of options:

### Option 1: Migrate Your Code

Run the following command to automatically upgrade your codebase to use the 1.0.0 interface:

“`bash
openai migrate
“`

This command will attempt to update your code to the new version of the OpenAI library.

### Option 2: Pin Your Installation to the Old Version

If you prefer to stick with the older version (0.28) where `openai.ChatCompletion` is still supported, you can use the following command:

“`bash
pip install openai==0.28
“`

### Important Note:

Before making any changes, it’s recommended to review the [migration guide](https://github.com/openai/openai-python/discussions/742) provided by OpenAI. This guide should help you understand the changes and update your code accordingly.

Choose the option that best fits your requirements and follow the necessary steps.
when I do this pip install openai==0.28
I get the chat attribute error
It seems there might be a misunderstanding or a potential mismatch between the library versions and the code you are using. If you are facing an attribute error related to the `chat` attribute, it’s possible that the `openai.ChatCompletion` class is no longer present in the version 0.28, or there might be a different issue.

Here are a few steps to troubleshoot the issue:

1. **Check OpenAI Library Version:**
Run the following command to ensure that the correct version (0.28) is installed:

“`bash
pip show openai
“`

This will display the installed version. Make sure it’s version 0.28.

2. **Verify Code Compatibility:**
Ensure that your code is compatible with the version of the OpenAI library you have installed. If you are using features or classes that are not present in version 0.28, you might need to adjust your code accordingly.

3. **Library Documentation:**
Check the documentation for version 0.28 to see which classes and methods are available. The OpenAI documentation usually provides details about changes between versions.

4. **Update Code Based on Documentation:**
If necessary, update your code based on the documentation for version 0.28. Ensure that you are using the correct classes and methods.

If you encounter specific error messages or issues during this process, please provide more details, and I’ll do my best to assist you.