response.json())
.then(history => {
const chatContainer = document.getElementById(‘chat-container’);
history.forEach(message => {
const messageElement = document.createElement(‘div’);
messageElement.classList.add(message.role);
messageElement.innerHTML = message.content;
chatContainer.appendChild(messageElement);
});
// Scroll to the bottom after initial history load
chatContainer.scrollTop = chatContainer.scrollHeight;
});

// Add the sendMessage function
const sendMessage = () => {
const userText = document.getElementById(‘user-input’).value;
if (userText) {
const chatContainer = document.getElementById(‘chat-container’);
const currentScrollTop = chatContainer.scrollTop; // Store current scroll position

fetch(‘/chat’, {
method: ‘POST’,
headers: {
‘Content-Type’: ‘application/x-www-form-urlencoded’,
},
body: ‘user_input=’ + encodeURIComponent(userText),
})
.then(response => response.json())
.then(assistantMessage => {
// Display user message
const userMessageElement = document.createElement(‘div’);
userMessageElement.classList.add(‘user’);
userMessageElement.innerHTML = userText;
chatContainer.appendChild(userMessageElement);

// Display assistant message
const assistantMessageElement = document.createElement(‘div’);
assistantMessageElement.classList.add(‘assistant’);
assistantMessageElement.innerHTML = assistantMessage.content;
chatContainer.appendChild(assistantMessageElement);

// Restore scroll position if chat history was already visible
if (chatContainer.scrollHeight console.error(‘Error:’, error));

document.getElementById(‘user-input’).value = ”; // Clear input field
}
};

// Add event listeners
const sendButton = document.getElementById(‘send-button’);
sendButton.addEventListener(‘click’, sendMessage);

const userInput = document.getElementById(‘user-input’);
userInput.addEventListener(‘keydown’, (event) => {
if (event.key === ‘Enter’) {
sendMessage();
}
});

Sure, to add a typewriter effect to the output, you can modify the code that appends the assistant’s message to the chat container. Here’s an updated version of your code with the typewriter effect:

“`javascript
// Add the sendMessage function
const sendMessage = () => {
const userText = document.getElementById(‘user-input’).value;
if (userText) {
const chatContainer = document.getElementById(‘chat-container’);
const currentScrollTop = chatContainer.scrollTop; // Store current scroll position

fetch(‘/chat’, {
method: ‘POST’,
headers: {
‘Content-Type’: ‘application/x-www-form-urlencoded’,
},
body: ‘user_input=’ + encodeURIComponent(userText),
})
.then(response => response.json())
.then(assistantMessage => {
// Display user message
const userMessageElement = document.createElement(‘div’);
userMessageElement.classList.add(‘user’);
userMessageElement.innerHTML = userText;
chatContainer.appendChild(userMessageElement);

// Display assistant message with typewriter effect
const assistantMessageElement = document.createElement(‘div’);
assistantMessageElement.classList.add(‘assistant’);
chatContainer.appendChild(assistantMessageElement);

// Apply typewriter effect
const messageContent = assistantMessage.content;
let i = 0;
const typeWriter = () => {
if (i < messageContent.length) {
assistantMessageElement.innerHTML += messageContent.charAt(i);
i++;
setTimeout(typeWriter, 50); // Adjust typing speed (milliseconds)
}
};

typeWriter(); // Start the typewriter effect

// Restore scroll position if chat history was already visible
if (chatContainer.scrollHeight console.error(‘Error:’, error));

document.getElementById(‘user-input’).value = ”; // Clear input field
}
};
“`

This modification uses the `typeWriter` function to add the typewriter effect to the assistant’s message. Adjust the timing (50 milliseconds in this example) according to your preference for the typing speed.
now the enter or mouseclink to sen doesn’t work:

It seems there might be an issue with event listeners. Let’s modify the script to ensure that the event listeners are properly attached. Update your script as follows:

“`javascript
document.addEventListener(‘DOMContentLoaded’, function () {
// Fetch initial chat history
fetch(‘/chat’)
.then(response => response.json())
.then(history => {
const chatContainer = document.getElementById(‘chat-container’);
history.forEach(message => {
const messageElement = document.createElement(‘div’);
messageElement.classList.add(message.role);
messageElement.innerHTML = message.content;
chatContainer.appendChild(messageElement);
});
// Scroll to the bottom after initial history load
chatContainer.scrollTop = chatContainer.scrollHeight;
});

// Add the sendMessage function
const sendMessage = () => {
const userText = document.getElementById(‘user-input’).value;
if (userText) {
const chatContainer = document.getElementById(‘chat-container’);
const currentScrollTop = chatContainer.scrollTop; // Store current scroll position

fetch(‘/chat’, {
method: ‘POST’,
headers: {
‘Content-Type’: ‘application/x-www-form-urlencoded’,
},
body: ‘user_input=’ + encodeURIComponent(userText),
})
.then(response => response.json())
.then(assistantMessage => {
// Display user message
const userMessageElement = document.createElement(‘div’);
userMessageElement.classList.add(‘user’);
userMessageElement.innerHTML = userText;
chatContainer.appendChild(userMessageElement);

// Display assistant message with typewriter effect
const assistantMessageElement = document.createElement(‘div’);
assistantMessageElement.classList.add(‘assistant’);
chatContainer.appendChild(assistantMessageElement);

// Apply typewriter effect
const messageContent = assistantMessage.content;
let i = 0;
const typeWriter = () => {
if (i < messageContent.length) {
assistantMessageElement.innerHTML += messageContent.charAt(i);
i++;
setTimeout(typeWriter, 50); // Adjust typing speed (milliseconds)
}
};

typeWriter(); // Start the typewriter effect

// Restore scroll position if chat history was already visible
if (chatContainer.scrollHeight console.error(‘Error:’, error));

document.getElementById(‘user-input’).value = ”; // Clear input field
}
};

// Add event listeners
const sendButton = document.getElementById(‘send-button’);
sendButton.addEventListener(‘click’, sendMessage);

const userInput = document.getElementById(‘user-input’);
userInput.addEventListener(‘keydown’, (event) => {
if (event.key === ‘Enter’) {
sendMessage();
}
});
});
“`

This modification wraps your code inside a `DOMContentLoaded` event listener to ensure that the script runs after the HTML content is fully loaded. It should fix the issue with the Enter key or mouse click not working.
The text is still coming out at the top of the page – how do I get the ai reposnse to be close to the bottom where the user input is?
To ensure that the AI response is displayed close to the bottom, you can modify the positioning of the chat container and input container in your HTML and adjust the styles accordingly. Here’s a revised version of your HTML and CSS:

### Updated HTML:

“`html

“`

### Updated CSS:

“`css
body {
margin: 0;
padding: 0;
font-family: Arial, sans-serif;
}

#chat-container {
position: absolute;
bottom: 50px; /* Adjust as needed */
left: 0;
right: 0;
overflow-y: auto;
height: calc(100vh – 50px); /* Adjust as needed */
}

#input-container {
position: absolute;
bottom: 0;
left: 0;
right: 0;
background-color: #f1f1f1;
padding: 10px;
display: flex;
justify-content: space-between;
}

input {
flex-grow: 1;
padding: 8px;
}

button {
padding: 8px;
background-color: #4CAF50;
color: white;
border: none;
border-radius: 4px;
cursor: pointer;
}
“`

This will position the `#chat-container` close to the bottom of the page, and the `#input-container` will be fixed at the very bottom. Adjust the `bottom` and `height` values as needed to achieve the desired positioning.
Now to text is even higher in the browser – please position the output to be above the user input box: /* Add your desired dark theme styles here */
/* Base styling */
body {
font-family: sans-serif;
background-color: #52575c; /* Dark background */
color: #fff; /* White text */
margin: 1;
}

#chat-container {
position: absolute;
bottom: 50px; /* Adjust as needed */
left: 0;
right: 0;
overflow-y: auto;
height: calc(100vh – 250px); /* Adjust as needed */
}

.message {
margin-bottom:30px;
padding: 10px;
border-radius: 20px;
height: auto;
}

.user {
background-color: #142029; /* Blue for user messages */
color: #fff;
width: 800px;
height: 32px;

}

.assistant {
background-color: #333238; /* Green for assistant messages */
color: #fff;
width: 800px;
}

/* Input area */
#input-container {
position: absolute;
bottom: 0;
left: 0;
right: 0;
background-color: #f1f1f1;
padding: 10px;
display: flex;
justify-content: space-between;
}

#user-input {
flex-grow: 1;
padding: 10px;
border: slateblue;
border-radius: 5px;
background-color: #333; /* Darker input field */
color: #fff;
}

#send-button {
background-color: #4CAF50; /* Green button */
color: #fff;
border: none;
padding: 10px 15px;
border-radius: 5px;
cursor: pointer;
}

Certainly! To position the output above the user input box, you can adjust the `#chat-container` positioning and height. Here’s the updated CSS:

“`css
/* Add your desired dark theme styles here */
/* Base styling */
body {
font-family: sans-serif;
background-color: #52575c; /* Dark background */
color: #fff; /* White text */
margin: 0; /* Adjusted margin to ensure no extra space */
}

#chat-container {
position: absolute;
bottom: 90px; /* Adjust as needed */
left: 0;
right: 0;
overflow-y: auto;
height: calc(100vh – 150px); /* Adjust as needed */
}

.message {
margin-bottom: 30px;
padding: 10px;
border-radius: 20px;
height: auto;
}

.user {
background-color: #142029; /* Blue for user messages */
color: #fff;
width: 800px;
height: 32px;
}

.assistant {
background-color: #333238; /* Green for assistant messages */
color: #fff;
width: 800px;
}

/* Input area */
#input-container {
position: absolute;
bottom: 0;
left: 0;
right: 0;
background-color: #f1f1f1;
padding: 10px;
display: flex;
justify-content: space-between;
}

#user-input {
flex-grow: 1;
padding: 10px;
border: slateblue;
border-radius: 5px;
background-color: #333; /* Darker input field */
color: #fff;
}

#send-button {
background-color: #4CAF50; /* Green button */
color: #fff;
border: none;
padding: 10px 15px;
border-radius: 5px;
cursor: pointer;
}
“`

In this adjustment, I changed the `bottom` value of `#chat-container` to `90px` and adjusted the `height` accordingly. Feel free to tweak these values based on your design preferences.
Ok. I have a template that works and an llm, Garrulus 7-b that seems to have a memory already, but I do want to start with understanding how to incorporate chain of thoughts into it now. I will also want a vector store for memory, embeddings and long term memory. I want to take them one at a time and understand what I am doing. Please rewrite this so it is more clear
Certainly! Let’s break down the different components you want to incorporate into your LLM (Large Language Model): chain of thoughts, vector store for memory, embeddings, and long-term memory. I’ll provide a more clear and organized structure for each.

### Chain of Thoughts:

If you want to incorporate a chain of thoughts, you can structure your conversation history in a way that represents a coherent flow. Each message can have a timestamp, role (user or assistant), and content. Here’s an example structure:

“`python
chain_of_thoughts = [
{“timestamp”: “2024-01-01 10:00:00”, “role”: “user”, “content”: “What is the capital of France?”},
{“timestamp”: “2024-01-01 10:01:00”, “role”: “assistant”, “content”: “The capital of France is Paris.”},
{“timestamp”: “2024-01-01 10:02:00”, “role”: “user”, “content”: “Tell me more about Paris.”},
{“timestamp”: “2024-01-01 10:03:00”, “role”: “assistant”, “content”: “Paris is known for its art, fashion, and culture.”},
# Add more messages as the conversation progresses
]
“`

### Vector Store for Memory:

For a vector store, you can use a data structure like a dictionary to map concepts to their corresponding vectors. Here’s a simple example:

“`python
vector_store = {
“Paris”: [0.2, 0.5, -0.3, 0.8], # Vector representation for Paris
“art”: [0.6, -0.2, 0.4, -0.7], # Vector representation for art
# Add more entries for other concepts
}
“`

### Embeddings:

Embeddings can be incorporated into your model. If using an existing model like Garrulus 7-b, you can leverage its embedding capabilities. Ensure that the model processes and maintains embeddings during conversations.

### Long-Term Memory:

Long-term memory can be represented as a separate storage for important information that persists across different sessions. It could be a database or another persistent storage mechanism.

“`python
long_term_memory = {
“user_id_123”: {
“name”: “John Doe”,
“preferences”: {“theme”: “dark”, “language”: “English”},
# Add more user-specific information
},
# Add more entries for other users
}
“`

Remember to integrate these components into your LLM based on the requirements of your application and the capabilities of the chosen model. If you have specific questions or need more details on any aspect, feel free to ask!
Yes, in what order is best too implement these designs?
The order in which you implement these designs can depend on your specific goals and priorities. However, a suggested order could be as follows:

1. **Chain of Thoughts:**
– Start by organizing your conversation history into a structured format that represents a chain of thoughts. This will provide a coherent flow to your conversation.

2. **Long-Term Memory:**
– Implement a simple long-term memory system that stores important information persistently. This can include user-specific details, preferences, or any data that needs to be retained across sessions.

3. **Vector Store for Memory:**
– Integrate a vector store that maps concepts to their corresponding vectors. This can be useful for representing semantic information and relationships between concepts in your model.

4. **Embeddings:**
– Leverage embeddings within your model. Ensure that the model processes and maintains embeddings during conversations. Embeddings can enhance the representation of words or concepts in a continuous vector space.

5. **Model Integration:**
– Incorporate the designed structures into your LLM. Ensure that the model interacts with the chain of thoughts, long-term memory, and vector store as needed. Update the model to handle and utilize the information stored in these components.

6. **Iterate and Optimize:**
– Iterate over your implementation, test it with various scenarios, and optimize as needed. Consider refining the design based on performance, user feedback, and any insights gained during implementation.

Remember that the order can be adjusted based on your project requirements. For example, if the long-term memory is crucial for personalized interactions, you might prioritize it earlier. Additionally, consider the capabilities of the LLM model you are using and ensure compatibility with the designed structures.
No I mean chain of thoughts CoT, as in incorporatin reasoning and agents
I see, my apologies for the confusion. If you’re referring to a more advanced concept of “Chain of Thoughts” (CoT) that involves reasoning and agents, you may want to approach it differently. Here’s a revised suggestion:

1. **Reasoning Framework:**
– Develop a reasoning framework that enables your model to infer logical relationships and draw conclusions based on the conversation context. This could involve using rule-based systems, knowledge graphs, or other logic-based approaches.

2. **Agent Interaction:**
– If you plan to involve multiple agents or entities in the conversation, design an agent interaction mechanism. Define how agents exchange information, coordinate actions, and contribute to the overall reasoning process.

3. **Chain of Thoughts (Advanced):**
– Implement an advanced Chain of Thoughts mechanism that captures not only the chronological sequence of messages but also the logical connections and inferences made during the conversation. This may involve maintaining a graph structure or a more sophisticated data representation.

4. **Long-Term Memory Enhancement:**
– Enhance the long-term memory system to store not only static information but also dynamic knowledge gained during reasoning processes. Consider how the model can update its long-term memory based on new insights.

5. **Vector Store for Reasoning:**
– Integrate a vector store that supports reasoning. This vector store could capture semantic relationships between concepts, enabling the model to perform more advanced reasoning tasks.

6. **Model Integration and Training:**
– Integrate the reasoning framework, CoT, and other components into your model. Train the model to leverage these components for advanced reasoning and decision-making.

7. **Iterate and Optimize:**
– As always, iterate over your implementation, test it thoroughly, and optimize based on performance and effectiveness. Consider obtaining feedback from users or domain experts to refine the reasoning capabilities.

In summary, this approach focuses on enhancing the reasoning abilities of your model by incorporating a sophisticated Chain of Thoughts, advanced reasoning mechanisms, and improved memory structures. Adjust the order based on the specific needs and complexity of your project.
I have been so wrong in my direction, it is not CoT but langchain that I want to implement next
No problem! If you’re looking to implement “LangChain,” let’s focus on that concept. Could you provide more details or clarify what “LangChain” means in your context? Understanding your specific goals or requirements for LangChain will help me provide more targeted guidance on how to proceed with the implementation.
Sort these out and write a script to correct all of the errors: ERROR: pip’s dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
fschat 0.2.18 requires transformers=4.28.0, but you have transformers 4.36.2 which is incompatible.
langflow 0.5.1 requires huggingface-hub[inference]=0.16.0, but you have huggingface-hub 0.20.2 which is incompatible.
langflow 0.5.1 requires langchain=0.0.308, but you have langchain 0.0.291 which is incompatible.
langflow 0.5.1 requires networkx=3.1, but you have networkx 2.8.8 which is incompatible.
langflow 0.5.1 requires openai=0.27.8, but you have openai 1.8.0 which is incompatible.
langflow 0.5.1 requires pyarrow=12.0.0, but you have pyarrow 11.0.0 which is incompatible.
langflow 0.5.1 requires pywin32=306; sys_platform == “win32”, but you have pywin32 305.1 which is incompatible.
To correct the dependency conflicts in your Python environment, you can try the following steps. Open your terminal or command prompt and execute the following commands:

“`bash
# Upgrade pip to the latest version
pip install –upgrade pip

# Install specific versions of the required packages
pip install transformers==4.28.0
pip install huggingface-hub==0.16.0
pip install langchain==0.0.308
pip install networkx==3.1
pip install openai==0.27.8
pip install pyarrow==12.0.0

# Install the specific version of pywin32 only if you are on a Windows platform
pip install pywin32==306; sys_platform == “win32”
“`

This set of commands installs the versions of the packages that are compatible with each other and resolves the dependency conflicts you encountered.

If you are using a virtual environment, make sure it is activated before running these commands.

Keep in mind that downgrading packages may lead to other compatibility issues with your existing code or other dependencies. It’s a good practice to thoroughly test your application after making such changes. If there are still issues, you may need to consider updating your code or checking for newer versions of the libraries that are compatible with each other.
abd this: langflow 0.5.1 requires huggingface-hub[inference]=0.16.0, but you have huggingface-hub 0.20.2 which is incompatible.
To address the specific compatibility issue with `huggingface-hub`, you can follow these steps to install the required version:

“`bash
# Install the specific version of huggingface-hub
pip install huggingface-hub==0.16.0
“`

This command installs version 0.16.0 of `huggingface-hub`, which is compatible with `langflow` version 0.5.1. After running this command, you should have the correct version installed, resolving the dependency conflict.

As always, it’s a good practice to test your application thoroughly after making changes to ensure that everything works as expected. If there are still issues or if you have other dependencies, you may need to adjust the versions accordingly.
python lang.py
Traceback (most recent call last):
File “D:SarahOnePHI2pplang.py”, line 2, in
llm = OpenLLM(server_url=’http://localhost:5151/v1′)
File “C:UsersSarah.condaenvsamberrlibsite-packageslangchain_communityllmsopenllm.py”, line 152, in __init__
client = client_cls(server_url)
File “C:UsersSarah.condaenvsamberrlibsite-packagesopenllm_client_http.py”, line 41, in __init__
super().__init__(_address_converter(address), VERSION, timeout=timeout, max_retries=max_retries)
File “C:UsersSarah.condaenvsamberrlibsite-packagesopenllm_client_shim.py”, line 338, in __init__
super().__init__(
File “C:UsersSarah.condaenvsamberrlibsite-packagesopenllm_client_shim.py”, line 199, in __init__
self.__attrs_init__(base_url, version, timeout, max_retries, client, _default_stream_cls)
File “”, line 3, in __attrs_init__
_setattr(‘_base_url’, __attr_converter__base_url(base_url))
File “C:UsersSarah.condaenvsamberrlibsite-packagesopenllm_client_shim.py”, line 35, in _address_converter
url = url.copy_with(path=url.raw_path + b’/’)
File “C:UsersSarah.condaenvsamberrlibsite-packageshttpx_urls.py”, line 387, in copy_with
raise TypeError(message)
TypeError: Argument ‘path’ must be str but got bytes
Exception ignored in:
Traceback (most recent call last):
File “C:UsersSarah.condaenvsamberrlibsite-packagesopenllm_client_shim.py”, line 358, in __del__
File “C:UsersSarah.condaenvsamberrlibsite-packagesopenllm_client_shim.py”, line 349, in close
AttributeError: ‘HTTPClient’ object has no attribute ‘_inner’
It looks like there is an issue with the `openllm_client` library, specifically related to the `TypeError` stating that the ‘path’ argument must be a str but got bytes. This error occurs when trying to convert the path from bytes to a string.

Here are a few steps you can take to address this issue:

1. **Update Libraries:**
Make sure you have the latest versions of `openllm_client`, `langchain_community`, and other relevant libraries installed. You can update them using the following commands:

“`bash
pip install –upgrade openllm_client langchain_community
“`

2. **Check Compatibility:**
Verify that the versions of `openllm_client` and `langchain_community` you are using are compatible with each other. Check the documentation or release notes for each library to ensure they work well together.

3. **Report the Issue:**
If the problem persists, consider reporting the issue on the GitHub repository of the `openllm_client` or `langchain_community` library. The maintainers may provide guidance or release a fix for the compatibility issue.

4. **Temporary Workaround:**
As a temporary workaround, you can try downgrading the `openllm_client` library to a version that is known to be compatible. For example:

“`bash
pip install openllm_client==
“`

Replace “ with a version that you have confirmed works well with `langchain_community`.

Always make sure to test your application thoroughly after making changes to ensure everything is functioning as expected. If the issue persists, reaching out to the library maintainers or community forums for support would be a good next step.

(crewai) D:SarahOneCREWAI>python open.py
C:UsersSarahAppDataRoamingPythonPython312site-packageslangchainllms__init__.py:548: LangChainDeprecationWarning: Importing LLMs from langchain is deprecated. Importing from langchain will no longer be supported as of langchain==0.2.0. Please import from langchain-community instead:

`from langchain_community.llms import OpenAI`.

To install langchain-community run `pip install -U langchain-community`.
warnings.warn(
C:UsersSarahAppDataRoamingPythonPython312site-packageslangchain_core_apideprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.llms.openai.OpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAI`.
warn_deprecated(
Traceback (most recent call last):
File “D:SarahOneCREWAIopen.py”, line 15, in
SearchTools.search_internet,
^^^^^^^^^^^
NameError: name ‘SearchTools’ is not defined
It looks like there are several warnings and an error in your script. Let’s address them step by step:

1. **LangChain Deprecation Warnings:**
The warnings indicate that importing LLMs from `langchain` is deprecated. Instead, you should import from `langchain-community`. Additionally, the class `langchain_community.llms.openai.OpenAI` has been deprecated, and you should use the updated version from the `langchain-openai` package.

Update your import statements as follows:

“`python
from langchain_community.llms import OpenAI
“`

Also, make sure to install the `langchain-community` and `langchain-openai` packages:

“`bash
pip install -U langchain-community langchain-openai
“`

2. **NameError in Script:**
The error `NameError: name ‘SearchTools’ is not defined` suggests that `SearchTools` is not defined in your script. Ensure that you have the necessary import statement for `SearchTools` and that it’s defined or imported correctly.

For example:

“`python
from your_module_or_library import SearchTools
“`

Make sure that the `SearchTools` class or module is available and correctly imported in your script.

After making these adjustments, run your script again and check if the issues are resolved. If you encounter any further problems or if there are other parts of the script causing issues, feel free to provide more details for further assistance.
New script. I have you know that your younger upstart cousin of an llm GPT-4 wrote this. It doesn’t work either 🙂 Perhaps you can show hir up? import requests

# Define the URL of your local inference server
local_server_url = “http://localhost:5151/v1/”

# Define the API key if needed (you can set this to None if your local server doesn’t require authentication)
api_key = “not-needed”

# Define the headers for the POST request
headers = {
“Content-Type”: “application/json”,
# Include the Authorization header with the API key if needed
# “Authorization”: f”Bearer {api_key}”
}

# Define the data payload with the prompt and other parameters
data = {
“prompt”: “What NFL team won the Super Bowl in the year that Justin Bieber was born?”,
“max_tokens”: 100,
“temperature”: 0.7
}

# Send a POST request to the local server and get the response
response = requests.post(local_server_url, json=data, headers=headers)

# Check if the request was successful
if response.status_code == 200:
# Parse the response JSON and print the generated text
response_json = response.json()
generated_text = response_json.get(“choices”)[0].get(“text”).strip()

print(“Generated text:”, generated_text)
else:
# Print an error message if something went wrong
print(“Failed to generate text. Status code:”, response.status_code)
print(“Response:”, response.text)

Your script seems well-structured, and the issue may be related to the specific format or parameters expected by your local server. However, there are a few points to check and possibly modify:

1. **URL Format:**
Ensure that the URL format is correct. The URL in your script ends with a slash. Depending on the server configuration, you might need to omit the trailing slash. Try both versions to see if it makes a difference:

“`python
local_server_url = “http://localhost:5151/v1”
“`

2. **API Key:**
If your local server requires an API key, make sure that the authentication header is correctly formed. Uncomment the “Authorization” line in the headers and replace `api_key` with your actual API key:

“`python
headers = {
“Content-Type”: “application/json”,
“Authorization”: f”Bearer {api_key}”
}
“`

3. **Check for Errors in the Response:**
Print the response text even when the status code is 200. It might contain additional information or error messages from the server:

“`python
print(“Response:”, response.text)
“`

This will help you diagnose issues even when the status code is not an error code.

4. **Inspect the Output Format:**
The structure of the response might differ based on your local server’s implementation. Print the entire response JSON to understand its structure:

“`python
print(“Response JSON:”, response.json())
“`

Make sure you are extracting the generated text correctly based on the actual structure of the response.

With these adjustments, you should be able to get more information about why the script isn’t working as expected. If the issue persists, check the server logs or documentation for more details on how it expects the input data and returns the output.
Traceback (most recent call last):
File “D:SarahOneRickgeneratedlit.py”, line 30, in
generated_text = response_json.get(“choices”)[0].get(“text”).strip()
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^
TypeError: ‘NoneType’ object is not subscriptable

The error indicates that `response_json.get(“choices”)` is returning `None`, and you’re trying to access the first element with `[0]`, which is causing a `TypeError`. This suggests that the structure of the response is not as expected.

To handle this error gracefully, you can modify your code to check if the key `”choices”` is present in the response JSON before attempting to access its elements. Additionally, you can check if the list is not empty before accessing its first element. Here’s an updated version of your code:

“`python
# Parse the response JSON
response_json = response.json()

# Check if “choices” key is present in the response
if “choices” in response_json:
# Check if the list is not empty
choices = response_json[“choices”]
if choices:
# Access the first element and get the “text” attribute
generated_text = choices[0].get(“text”, “”).strip()
print(“Generated text:”, generated_text)
else:
print(“No choices in the response.”)
else:
print(“No ‘choices’ key in the response.”)
“`

This modification adds additional checks to ensure that you won’t encounter a `TypeError` if the structure of the response is unexpected. It prints appropriate messages in case the expected keys are not found or the list is empty.
Traceback (most recent call last):
File “D:SarahOneRickgeneratedlit.py”, line 44, in
generated_text = response_json.get(“choices”)[0].get(“text”).strip()
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^
TypeError: ‘NoneType’ object is not subscriptable

(ricky) D:SarahOneRickgenerated>python lit.py
No ‘choices’ key in the response.
The error indicates that the key `”choices”` is not present in the response JSON, resulting in a `TypeError` when trying to subscript `None`. The modified code is correctly handling this case and printing the message “No ‘choices’ key in the response.”

To further diagnose the issue, you might want to inspect the entire response JSON to understand its structure. Update your code to print the entire JSON response:

“`python
print(“Response JSON:”, response_json)
“`

This will help you identify the actual structure of the response and whether the expected keys are present.

If the response structure is different from what you anticipated, you may need to adjust your code accordingly. The issue could be due to changes in the server’s response format or other factors. If you can share the relevant parts of the JSON response, I can provide more specific guidance on how to extract the desired information.
No ‘choices’ key in the response.
Response JSON: {‘error’: ‘Unexpected endpoint or method. (POST /v1/)’}
The error message in the response suggests that there might be an issue with the endpoint or method you are using. It seems like the server is indicating that it received a request to an unexpected endpoint or with an unexpected method.

In your script, you are sending a POST request to the URL `http://localhost:5151/v1/`. However, based on the response, it appears that this endpoint or method is not supported by the server.

Here are a few things you can check and adjust:

1. **Check Endpoint and Method:**
Ensure that you are using the correct endpoint and method for your local server. Review the documentation or configuration of your local server to confirm the expected endpoint and method for making requests.

2. **Adjust Endpoint URL:**
If the endpoint should not include the trailing slash, modify the `local_server_url` variable to:

“`python
local_server_url = “http://localhost:5151/v1”
“`

3. **Check Server Configuration:**
Review the server’s configuration or logs to identify the correct endpoint and method. It’s possible that the server is expecting requests at a different URL or using a different HTTP method.

4. **Verify Request Structure:**
Ensure that the structure of the data payload (`data` variable) is correct according to the server’s expectations. Check the server’s documentation for the required format of the request payload.

Once you have confirmed and adjusted these aspects, try running the script again and see if the issue persists. If the problem continues, consult the server documentation or seek assistance from the server administrator to ensure that you are making requests in the expected manner.
Were getting somewhere! No ‘choices’ key in the response.
Response JSON: {‘error’: “‘messages’ field is required”}
Great progress! The new response indicates that the server is expecting a field named `’messages’` in the request, and it is marked as required. Let’s modify your script to include the required field:

“`python
import requests

# Define the URL of your local inference server
local_server_url = “http://localhost:5151/v1”

# Define the API key if needed (set to None if not needed)
api_key = “not-needed”

# Define the headers for the POST request
headers = {
“Content-Type”: “application/json”,
# Include the Authorization header with the API key if needed
# “Authorization”: f”Bearer {api_key}”
}

# Define the data payload with the prompt and other parameters
data = {
“messages”: [{“role”: “system”, “content”: “You are an intelligent assistant. You always provide well-reasoned answers that are both correct and helpful.”},
{“role”: “user”, “content”: “What NFL team won the Super Bowl in the year that Justin Bieber was born?”}],
“max_tokens”: 100,
“temperature”: 0.7
}

# Send a POST request to the local server and get the response
response = requests.post(local_server_url, json=data, headers=headers)

# Check if the request was successful
if response.status_code == 200:
# Parse the response JSON and print the generated text
response_json = response.json()

# Check if “choices” key is present in the response
if “choices” in response_json:
# Check if the list is not empty
choices = response_json[“choices”]
if choices:
# Access the first element and get the “text” attribute
generated_text = choices[0].get(“text”, “”).strip()
print(“Generated text:”, generated_text)
else:
print(“No choices in the response.”)
else:
print(“No ‘choices’ key in the response.”)
else:
# Print an error message if something went wrong
print(“Failed to generate text. Status code:”, response.status_code)
print(“Response:”, response.text)
“`

This modification adds the required `’messages’` field to the data payload. Adjust the content of the messages according to your needs. Try running the updated script, and it should now meet the server’s expectations.
[2024-01-26 09:20:39.143] [INFO] Accumulated 99 tokens: GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
[2024-01-26 09:20:39.236] [INFO] [LM STUDIO SERVER] Generated prediction: {
“id”: “chatcmpl-4neskzcz62k9ibqq9dntk5”,
“object”: “chat.completion”,
“created”: 1706278825,
“model”: “G:\huggingface\hub\afrideva\phi-2-uncensored-GGUF\phi-2-uncensored.q8_0.gguf”,
“choices”: [
{
“index”: 0,
“message”: {
“role”: “assistant”,
“content”: “GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG”
},
“finish_reason”: “stop”
}
],
“usage”: {
“prompt_tokens”: 99,
“completion_tokens”: 99,
“total_tokens”: 198
}
}
It looks like the server successfully generated a response, and the structure of the response provides useful information. The generated text is present in the `”content”` field of the assistant’s message. Here’s how you can extract and print the generated text from the response:

Update the relevant part of your script as follows:

“`python
# …

# Parse the response JSON and print the generated text
response_json = response.json()

# Check if “choices” key is present in the response
if “choices” in response_json:
# Check if the list is not empty
choices = response_json[“choices”]
if choices:
# Access the first element and get the “message” attribute
assistant_message = choices[0].get(“message”, {})
# Extract the “content” field from the assistant’s message
generated_text = assistant_message.get(“content”, “”).strip()
print(“Generated text:”, generated_text)
else:
print(“No choices in the response.”)
else:
print(“No ‘choices’ key in the response.”)

# …
“`

This modification ensures that you correctly access the `”message”` attribute and extract the `”content”` field from the assistant’s message. Try running the updated script, and it should print the generated text as expected.
Well, Now how do I straighten out the server? python lit.py
Generated text: GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG

It seems like the server is currently responding with a placeholder text (in this case, a series of ‘G’s) instead of providing a meaningful response. To improve the behavior of your local server, you might need to adjust the configuration or the underlying language model it uses.

Here are a few steps you can take:

1. **Check Model Configuration:**
Verify the configuration of the language model your local server is using. Ensure that the model is loaded correctly, and its configuration aligns with your expectations.

2. **Check Server Logs:**
Examine the logs of your local server for any error messages or warnings. Server logs can often provide insights into issues with model loading, tokenization, or other processes.

3. **Adjust Prompt or Parameters:**
Experiment with different prompts or parameters in your request payload (`data` variable). For example, you can try changing the system and user messages or adjusting parameters like `temperature` to influence the randomness of the generated output.

4. **Verify Model Path:**
Ensure that the path to the model specified in your server’s configuration is correct and leads to a valid language model file.

5. **Update Server or Model:**
If you are using a custom model, ensure that it is trained and configured properly. If you are using a pre-trained model, consider updating to the latest version or consulting the model’s documentation for any specific requirements.

6. **Debugging:**
If you have access to the server’s code, consider adding debugging statements or inspecting the code responsible for generating responses. This can help identify any issues in the logic of generating responses.

7. **Consult Server Documentation:**
Refer to the documentation of the server or the language model for guidance on correct usage and troubleshooting. If the server is part of an open-source project, check the project’s GitHub repository or community forums for support.

By following these steps, you should be able to diagnose and address the issue with the server’s response. If you encounter specific error messages or issues in the server logs, providing that information can help in providing more targeted assistance.
This is what the server expects? : [INST]<>
{System}
<>
[/INST]
[INST]
{User}
[/INST]
{Assistant}
It looks like the server expects a specific format for the messages in the input. The messages are enclosed in `[INST]` tags, and each message has a role (e.g., “SYS,” “User,” “Assistant”). Here’s an example of how you can structure your input messages based on the server’s expectations:

“`python
# Define the data payload with the messages and other parameters
data = {
“messages”: [
{“role”: “SYS”, “content”: “{System}”},
{“role”: “User”, “content”: “What NFL team won the Super Bowl in the year that Justin Bieber was born?”},
{“role”: “SYS”, “content”: “”},
{“role”: “SYS”, “content”: “{User}”},
# Add more messages as needed
],
“max_tokens”: 100,
“temperature”: 0.7
}
“`

This structure follows the expected format with `[INST]` tags and includes system, user, and assistant messages. Adjust the content of the messages based on your conversation flow or prompt.

Ensure that the server’s documentation or specifications provide details on how to structure the input messages, and adjust the content accordingly. With this updated input format, you should be able to make requests to the server that align with its expectations.
Maybe not: No ‘choices’ key in the response.
$$$$$$$$$$$$$$$$$$
Failed to generate text. Status code: 400
Response: {“error”:”‘messages’ array must only contain objects with a ‘role’ field that
is either ‘user’, ‘assistant’, or ‘system’. Got ‘SYS’.”}
The error message indicates that the server expects the `’role’` field in the `’messages’` array to be either ‘user’, ‘assistant’, or ‘system’, but it received ‘SYS’ as the role. Let’s adjust the roles in the messages to meet the server’s requirements:

“`python
# Define the data payload with the messages and other parameters
data = {
“messages”: [
{“role”: “system”, “content”: “{System}”},
{“role”: “user”, “content”: “What NFL team won the Super Bowl in the year that Justin Bieber was born?”},
{“role”: “system”, “content”: “”},
{“role”: “user”, “content”: “{User}”},
# Add more messages as needed
],
“max_tokens”: 100,
“temperature”: 0.7
}
“`

In this adjustment, I changed “SYS” to “system” to match the expected roles. Make sure to use “system” instead of “SYS” in the messages. Retry making a request to the server with this updated structure, and it should align with the server’s expectations.

[2024-01-26 09:30:49.640] [ERROR] ‘prompt’ field is required
It seems the server is expecting a “prompt” field in the input. Let’s modify the data payload to include the “prompt” field:

“`python
# Define the data payload with the messages and other parameters
data = {
“messages”: [
{“role”: “system”, “content”: “{System}”},
{“role”: “user”, “content”: “What NFL team won the Super Bowl in the year that Justin Bieber was born?”},
{“role”: “system”, “content”: “”},
{“role”: “user”, “content”: “{User}”},
# Add more messages as needed
],
“prompt”: “Your desired prompt here”, # Add your desired prompt
“max_tokens”: 100,
“temperature”: 0.7
}
“`

Make sure to replace `”Your desired prompt here”` with the actual prompt you want to use. This prompt is typically a short initial message to guide the model’s behavior.

Retry making a request to the server with this updated structure, and it should include the required “prompt” field.
Moon rings are a phenomenon that happen when the lunar surface is exposed to an intense heat source. This can be caused by either solar radiation, cosmic rays or even space debris. They appear as dark bands on the Moon’s surface that often occur in patterns of long lines and short lines. The first known moon ring was discovered in 1960 when a team led by astronomer John Herschel identified these bands around lunar impact craters.
[2024-01-26 09:32:47.573] [INFO] Generated prediction: {
“id”: “cmpl-idf6csp7k8ao1ckao54gl”,
“object”: “text_completion”,
“created”: 1706279551,
“model”: “G:\huggingface\hub\TheBloke\deepseek-coder-6.7B-instruct-GGUF\deepseek-coder-6.7b-instruct.Q6_K.gguf”,
“choices”: [
{
“index”: 0,
“text”: “nnMoon rings are a phenomenon that happen when the lunar surface is exposed to an intense heat source. This can be caused by either solar radiation, cosmic rays or even space debris. They appear as dark bands on the Moon’s surface that often occur in patterns of long lines and short lines. The first known moon ring was discovered in 1960 when a team led by astronomer John Herschel identified these bands around lunar impact craters.”,
“logprobs”: null,
“finish_reason”: “stop”
}
],
“usage”: {
“prompt_tokens”: 99,
“completion_tokens”: 99,
“total_tokens”: 198
}
It seems like the server successfully generated a response. The generated text is present in the `”text”` field of the first choice. You can extract and print the generated text from the response:

“`python
# …

# Check if “choices” key is present in the response
if “choices” in response_json:
# Check if the list is not empty
choices = response_json[“choices”]
if choices:
# Access the first element and get the “text” attribute
generated_text = choices[0].get(“text”, “”).strip()
print(“Generated text:”, generated_text)
else:
print(“No choices in the response.”)
else:
print(“No ‘choices’ key in the response.”)

# …
“`

This modification ensures that you correctly access the `”text”` attribute and extract the generated text from the server’s response. Try running the updated script, and it should print the generated text as expected.
Now lets see if we can turn this into a chain
Certainly! To create a chain of thoughts, you can extend the conversation by adding more messages to the input. Each message in the conversation provides context for the model. Here’s an example of how you can modify the script to create a chain of thoughts:

“`python
import requests

# Define the URL of your local inference server
local_server_url = “http://localhost:5151/v1”

# Define the API key if needed (set to None if not needed)
api_key = “not-needed”

# Define the headers for the POST request
headers = {
“Content-Type”: “application/json”,
# Include the Authorization header with the API key if needed
# “Authorization”: f”Bearer {api_key}”
}

# Define the data payload with the conversation and other parameters
data = {
“messages”: [
{“role”: “system”, “content”: “{System}”},
{“role”: “user”, “content”: “What NFL team won the Super Bowl in the year that Justin Bieber was born?”},
{“role”: “assistant”, “content”: “I’m not sure, but I can find out for you.”},
{“role”: “user”, “content”: “Yes, please find out.”},
{“role”: “assistant”, “content”: “Justin Bieber was born in 1994. Let me check the Super Bowl winner for that year.”},
# Add more messages as needed
],
“prompt”: “Your desired prompt here”, # Add your desired prompt
“max_tokens”: 100,
“temperature”: 0.7
}

# Send a POST request to the local server and get the response
response = requests.post(local_server_url, json=data, headers=headers)

# Check if the request was successful
if response.status_code == 200:
# Parse the response JSON and print the generated text
response_json = response.json()

# Check if “choices” key is present in the response
if “choices” in response_json:
# Check if the list is not empty
choices = response_json[“choices”]
if choices:
# Access the first element and get the “text” attribute
generated_text = choices[0].get(“text”, “”).strip()
print(“Generated text:”, generated_text)
else:
print(“No choices in the response.”)
else:
print(“No ‘choices’ key in the response.”)
else:
# Print an error message if something went wrong
print(“Failed to generate text. Status code:”, response.status_code)
print(“Response:”, response.text)
“`

This example extends the conversation by adding more user and assistant messages. Adjust the content of the messages based on the flow you want in your chain of thoughts. Run the script, and it should generate a response considering the entire conversation history.
Yeah, I see no chain.invoke
I apologize for the confusion. To clarify, the term “chain.invoke” doesn’t represent a specific function or method in the provided script. Instead, the concept of a “chain” or “chain of thoughts” refers to extending a conversation by adding messages to the input.

In the given script, the chain of thoughts is achieved by including a sequence of messages in the “messages” array of the data payload. Each message has a role (“user,” “assistant,” or “system”) and content that contributes to the ongoing conversation.

Here’s the part of the script where the chain of thoughts is constructed:

“`python
# Define the data payload with the conversation and other parameters
data = {
“messages”: [
{“role”: “system”, “content”: “{System}”},
{“role”: “user”, “content”: “What NFL team won the Super Bowl in the year that Justin Bieber was born?”},
{“role”: “assistant”, “content”: “I’m not sure, but I can find out for you.”},
{“role”: “user”, “content”: “Yes, please find out.”},
{“role”: “assistant”, “content”: “Justin Bieber was born in 1994. Let me check the Super Bowl winner for that year.”},
# Add more messages as needed
],
“prompt”: “Your desired prompt here”, # Add your desired prompt
“max_tokens”: 100,
“temperature”: 0.7
}
“`

In this example, the conversation begins with a system message, followed by alternating user and assistant messages. You can extend the chain by adding more messages within the “messages” array.

If you have a specific concept of “chain.invoke” in mind, please provide more details, and I’ll be happy to assist accordingly.
NOPE. LANGCHAIN has chain.invoke(llm…
I understand now. It seems you are referring to the LangChain library, and the `chain.invoke` method specifically, which provides a convenient way to interact with the language model.

If you are using LangChain and want to invoke the language model within a chain, you can follow a pattern similar to the one shown below. Note that the code below is a simplified example and may not reflect the exact structure of your specific implementation:

“`python
from langchain.llms import OpenAI
from langchain.core import chain

# Create an instance of the OpenAI language model
llm = OpenAI(api_key=”your_api_key”, model=”your_model”)

# Define the chain of thoughts using chain.invoke
result = chain.invoke(
llm.send(“Hello, how can I help you?”), # Initial message
llm.receive, # Receive the model’s response
llm.send(“Can you provide more details?”), # Follow-up message
llm.receive, # Receive the updated response
# Add more interactions as needed
)

# Access the result
final_response = result.final_output
print(“Final response:”, final_response)
“`

In this example, `chain.invoke` is used to create a sequence of interactions with the language model. Each `llm.send` represents a user input, and each `llm.receive` captures the model’s response. The final result is then accessed through `result.final_output`.

Please adjust the code according to your specific usage of LangChain and the OpenAI language model. If you have more details or specific requirements, feel free to provide additional information for further assistance.
File “D:SarahOneautogenapp.py”, line 13
“api_base”: “http://localhost:5151/v1”
^
SyntaxError: invalid syntax
The error you’re encountering, `SyntaxError: invalid syntax`, typically indicates a problem with the syntax of the Python code. The specific line causing the error appears to be related to a syntax issue around the assignment of the “api_base” key in a dictionary.

Here are a few common reasons why this error might occur:

1. **Mismatched Parentheses or Brackets:** Ensure that all opening parentheses, square brackets, and curly braces have matching closing ones.

2. **Mismatched Quotes:** Check for mismatched or unclosed quotes. Make sure that if you’re using double quotes, they are closed with double quotes, and the same for single quotes.

3. **Invalid Characters:** Verify that there are no invalid characters or typos in your code.

Here’s an example of correct syntax:

“`python
# Assuming you’re defining a dictionary
config = {
“api_base”: “http://localhost:5151/v1”,
# other key-value pairs…
}
“`

If you could provide a bit more context or the surrounding code, I might be able to give more specific assistance.
config_list = [
{
“api_type”: “open_ai”
“api_base”: “http://localhost:5151/v1”
“api_key”: “NULL”
}
]
It looks like there are syntax errors in your `config_list` dictionary. The keys and values in a dictionary should be separated by commas, and each key-value pair should end with a comma. Here’s the corrected version:

“`python
config_list = [
{
“api_type”: “open_ai”,
“api_base”: “http://localhost:5151/v1”,
“api_key”: “NULL”
}
]
“`

Note the addition of commas after each key-value pair in the dictionary. This should resolve the `SyntaxError` you were encountering.
Commas are hard to find: llm_config = {‘config_list’: config_list}

#create an instance of AssistantAgent
assistant = AssistantAgent(
name = “assistant”,
llm_config= llm_config,

#create an instance of UserProxyAgent
user_proxy = UserProxyAgent(
name = “user_proxy”,
human_input_mode=”NEVER”,
) max max_consecutive_auto_reply=300

)
It seems like there are a couple of issues with the provided code snippet. Here’s a corrected version:

“`python
llm_config = {‘config_list’: config_list}

# Create an instance of AssistantAgent
assistant = AssistantAgent(
name=”assistant”,
llm_config=llm_config,
)

# Create an instance of UserProxyAgent
user_proxy = UserProxyAgent(
name=”user_proxy”,
human_input_mode=”NEVER”,
max_consecutive_auto_reply=300, # Add the missing comma
)
“`

Changes made:

1. Added a comma after `llm_config=llm_config` to separate it from the next argument in the `AssistantAgent` instantiation.
2. Added a comma after `human_input_mode=”NEVER”` to separate it from the next argument in the `UserProxyAgent` instantiation.

Ensure that you are using the correct syntax and have the necessary commas between arguments when creating instances of classes.
Traceback (most recent call last):
File “D:SarahOneautogenapp.py”, line 1, in
from autogen import AssistantAgent, UserProxy
ImportError: cannot import name ‘UserProxy’ from ‘autogen’ (D:SarahOneautogenautogen__init__.py)

The error you’re encountering suggests that there is an issue with the import statement for `UserProxy` in your `app.py` file. It appears that there might be a problem with the structure of your project or the way you’re trying to import the `UserProxy` class.

Here are a few things to check:

1. **File Structure:** Make sure that the `UserProxy` class is defined in a file within the `autogen` package and that the file has the correct name.

For example, if `UserProxy` is defined in a file named `user_proxy.py` within the `autogen` package, the import statement should look like this:

“`python
from autogen.user_proxy import UserProxy
“`

2. **Module Availability:** Check that the `__init__.py` file within the `autogen` directory is properly set up. It should contain the necessary import statements or explicitly list the modules to be imported.

For example, the `__init__.py` file might look like this:

“`python
from .assistant_agent import AssistantAgent
from .user_proxy import UserProxy
“`

This allows you to use the import statement you mentioned: `from autogen import AssistantAgent, UserProxy`.

Ensure that your project structure and import statements are set up correctly based on the organization of your files and packages. If you provide more details about your project structure, I can offer more specific guidance.
This is in /autogen: import logging
from .version import __version__
from .oai import *
from .agentchat import *
from .code_utils import DEFAULT_MODEL, FAST_MODEL

# Set the root logger.
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)

Based on the provided information, it seems that the `UserProxy` class might not be defined directly in the `autogen` module. It’s likely defined in one of the submodule files. Assuming that `UserProxy` is defined in the `agentchat` submodule, you should adjust your import statement in `app.py` accordingly.

For example, if `UserProxy` is defined in `agentchat.py`, you would import it like this in `app.py`:

“`python
from autogen.agentchat import UserProxy
“`

Make sure to replace `’agentchat’` with the actual name of the submodule where `UserProxy` is defined.

If you’re unsure about the structure of your project, you can share the directory structure or relevant parts of it, and I can provide more specific guidance.
Traceback (most recent call last):
File “D:SarahOneautogenapp.py”, line 2, in
from autogen.agentchat import UserProxy
ImportError: cannot import name ‘UserProxy’ from ‘autogen.agentchat’ (D:SarahOneautogenautogenagentchat__init__.py)

(autog) D:SarahOneautogen>python app.py
Traceback (most recent call last):
File “D:SarahOneautogenapp.py”, line 2, in
from autogen import UserProxy
ImportError: cannot import name ‘UserProxy’ from ‘autogen’ (D:SarahOneautogenautogen__init__.py)

It looks like there might be some confusion in your project structure or the way you are organizing your modules. The error indicates that there is an issue with the import statement for `UserProxy`.

Let’s clarify the structure:

Assuming `UserProxy` is defined in the `autogen.agentchat` module:

1. **Directory Structure:**

“`
autogen/
├── __init__.py
├── agentchat/
│ ├── __init__.py
│ ├── user_proxy.py
│ └── other_module.py
├── oai.py
├── code_utils.py
└── version.py
“`

2. **Import Statement in `app.py`:**

“`python
from autogen.agentchat.user_proxy import UserProxy
“`

Please adjust your project structure and import statements based on the above clarification. If there are any deviations in your project structure or naming, make sure to reflect those in your import statements. If you still face issues, please provide more details about your directory structure for further assistance.
autogen__init__.py
The `__init__.py` files in your project are used to indicate that a directory should be treated as a Python package or module. Based on the error messages you’ve provided, it seems that there might be an issue with the import statements.

Ensure that the `__init__.py` file in the `autogen` directory is correctly set up. It should either include explicit import statements for the modules you want to make available when someone imports `autogen` or be left empty if there are no specific imports to declare.

Here is an example of a possible `autogen/__init__.py` file:

“`python
# autogen/__init__.py

from .agentchat import UserProxy
# You can include more import statements if needed

# Alternatively, if you don’t have any specific imports to declare, leave it empty.
# An empty __init__.py is still necessary for Python to treat the directory as a package.
“`

Ensure that this file is correctly set up based on your project structure. If you have further issues, please provide the current content of your `autogen/__init__.py` file, and I can give more specific guidance.
I am getting the same errors as yesterday. This code is outdated and it is still on Github! This code needs to be migrated to V.0.2.0
Certainly, if you’re dealing with code that needs to be updated to work with a newer version (v0.2.0 in this case), you’ll need to make some changes to adapt to the new version’s requirements.

Here are the general steps you might want to follow:

1. **Check Release Notes:** Review the release notes of the newer version (v0.2.0) to see if there are any breaking changes or new features. This will give you an idea of what needs to be updated.

2. **Update Dependencies:** If there are changes in dependencies (other libraries your code relies on), update them to the versions compatible with v0.2.0.

3. **Update Code:** Modify your code to accommodate any breaking changes or to make use of new features introduced in v0.2.0.

4. **Test Thoroughly:** After making changes, thoroughly test your code to ensure that it behaves as expected and is compatible with the new version.

Without specific details about the code or the changes made in v0.2.0, it’s challenging to provide exact instructions. If you have specific questions or encounter issues during the migration, feel free to share relevant code snippets or error messages, and I’ll do my best to assist you.
I gave you all the information yesterday but since you have no state, I have to start againm
I understand, and I apologize for any confusion. If you could provide me with the specific issues or code snippets that are causing trouble after the migration to v0.2.0, I’d be happy to assist you in resolving them. Feel free to share any error messages, relevant code snippets, or details about the behavior you’re experiencing. This will help me provide more accurate and targeted guidance.
Time to study. This subject is Autogen. This is th e –intit– of oai. Please give a high level overview and I can ask about particulars: from autogen.oai.client import OpenAIWrapper
from autogen.oai.completion import Completion, ChatCompletion
from autogen.oai.openai_utils import (
get_config_list,
config_list_gpt4_gpt35,
config_list_openai_aoai,
config_list_from_models,
config_list_from_json,
config_list_from_dotenv,
)
from autogen.cache.cache import Cache

__all__ = [
“OpenAIWrapper”,
“Completion”,
“ChatCompletion”,
“get_config_list”,
“config_list_gpt4_gpt35”,
“config_list_openai_aoai”,
“config_list_from_models”,
“config_list_from_json”,
“config_list_from_dotenv”,
“Cache”,
]

Based on the provided code snippet, it appears to be part of a module named `oai` within the `autogen` package. Let’s break down the high-level overview of each component:

1. **`OpenAIWrapper`**: This seems to be a class related to interacting with the OpenAI API. It might handle the configuration and communication with the OpenAI models.

2. **`Completion` and `ChatCompletion`**: These are classes that likely represent different types of completions or interactions with language models. `Completion` could be a generic completion, while `ChatCompletion` might specifically handle chat-based interactions.

3. **`openai_utils` Module**:
– `get_config_list`: A function to retrieve a list of configurations. Configurations are likely settings or parameters for working with the OpenAI models.
– `config_list_gpt4_gpt35`: Presumably, a predefined configuration list for GPT-4 and GPT-3.5 models.
– `config_list_openai_aoai`: A predefined configuration list for OpenAI and AOAI models.
– `config_list_from_models`: Possibly a function to generate a configuration list based on specific models.
– `config_list_from_json`: A function to generate a configuration list from a JSON file.
– `config_list_from_dotenv`: A function to generate a configuration list from environment variables.

4. **`Cache`**: This class is likely related to caching. Caching can be used to store and retrieve results from previous computations, which can be useful for performance optimization.

5. **`__all__` List**: This list specifies the symbols that should be exported when someone imports `*` from this module or package. It includes the classes, functions, and variables that are considered part of the module’s public API.

If you have specific questions about any of these components or if you’d like more detailed information on a particular aspect, feel free to ask!
This next file is the client script. Extract any variable that are sent to this script, what it expects: from to import: from __future__ import annotations

import os
import sys
from typing import Any, List, Optional, Dict, Callable, Tuple, Union
import logging
import inspect
from flaml.automl.logger import logger_formatter

from pydantic import BaseModel

from autogen.cache.cache import Cache
from autogen.oai import completion

from autogen.oai.openai_utils import DEFAULT_AZURE_API_VERSION, get_key, OAI_PRICE1K
from autogen.token_count_utils import count_token
from autogen._pydantic import model_dump

This script appears to be a client script that interacts with the OpenAI models. Let’s break down the key components and variables:

1. **`__future__` Import**: The `__future__` module is used here to enable specific features from future versions of Python. In this case, it’s likely related to the use of annotations.

2. **Module Imports**:
– `os`: Operating system functionality.
– `sys`: System-specific parameters and functions.
– `Any, List, Optional, Dict, Callable, Tuple, Union`: Types from the `typing` module for type hinting.
– `logging`: The standard Python logging module for handling log messages.
– `inspect`: Provides functions for retrieving information about live objects.
– `logger_formatter`: A logger formatter from the `flaml` package.

3. **`pydantic` Import**: `BaseModel` from the `pydantic` module. `pydantic` is used for data validation and settings management. `BaseModel` is a base class for creating data models.

4. **Autogen Imports**:
– `Cache`: The `Cache` class from the `autogen.cache` module.
– `completion`: The `completion` module from the `autogen.oai` package.
– `DEFAULT_AZURE_API_VERSION`: A constant for the default Azure API version.
– `get_key`: A function for retrieving a key.
– `OAI_PRICE1K`: A constant related to the price for 1,000 tokens.

5. **Additional Imports**:
– `count_token`: A function from the `token_count_utils` module for counting tokens.
– `model_dump`: A function from the `_pydantic` module for dumping model information.

Without seeing the entire script or specific functions, it’s challenging to provide a detailed overview of what this script expects or how it’s used. If you have specific questions or if there are specific parts of the script you’d like more information about, please let me know!
I cant give you the entore script as it is too long so I can only give you chnks. Try to remember as you can: TOOL_ENABLED = False
try:
import openai
except ImportError:
ERROR: Optional[ImportError] = ImportError(“Please install openai>=1 and diskcache to use autogen.OpenAIWrapper.”)
OpenAI = object
AzureOpenAI = object
else:
# raises exception if openai>=1 is installed and something is wrong with imports
from openai import OpenAI, AzureOpenAI, APIError, __version__ as OPENAIVERSION
from openai.resources import Completions
from openai.types.chat import ChatCompletion
from openai.types.chat.chat_completion import ChatCompletionMessage, Choice # type: ignore [attr-defined]
from openai.types.chat.chat_completion_chunk import (
ChoiceDeltaToolCall,
ChoiceDeltaToolCallFunction,
ChoiceDeltaFunctionCall,
)
from openai.types.completion import Completion
from openai.types.completion_usage import CompletionUsage

if openai.__version__ >= “1.1.0”:
TOOL_ENABLED = True
ERROR = None

logger = logging.getLogger(__name__)
if not logger.handlers:
# Add the console handler.
_ch = logging.StreamHandler(stream=sys.stdout)
_ch.setFormatter(logger_formatter)
logger.addHandler(_ch)

LEGACY_DEFAULT_CACHE_SEED = 41
LEGACY_CACHE_DIR = “.cache”

class OpenAIWrapper:
“””A wrapper class for openai client.”””

extra_kwargs = {
“cache”,
“cache_seed”,
“filter_func”,
“allow_format_str_template”,
“context”,
“api_version”,
“api_type”,
“tags”,
}

openai_kwargs = set(inspect.getfullargspec(OpenAI.__init__).kwonlyargs)
aopenai_kwargs = set(inspect.getfullargspec(AzureOpenAI.__init__).kwonlyargs)
openai_kwargs = openai_kwargs | aopenai_kwargs
total_usage_summary: Optional[Dict[str, Any]] = None
actual_usage_summary: Optional[Dict[str, Any]] = None

This script seems to define an `OpenAIWrapper` class, which serves as a wrapper around the OpenAI client. Let’s break down some key elements:

1. **Conditional Import of `openai`**:
– Attempts to import the `openai` module.
– If the import fails (`ImportError`), it sets `ERROR` to an informative error message and sets `OpenAI` and `AzureOpenAI` to generic objects.
– If the import succeeds, it imports various components from `openai` and checks the version to determine if the tool is enabled.

2. **Logger Setup**:
– Configures a logger named `__name__` (likely the name of the current module).
– Adds a console handler with a custom formatter to the logger.

3. **Constants and Configuration**:
– `TOOL_ENABLED`: A boolean flag indicating whether the OpenAI tool is enabled.
– `ERROR`: An optional error message if there is an issue with the OpenAI import.
– `LEGACY_DEFAULT_CACHE_SEED`: A constant for a default cache seed.
– `LEGACY_CACHE_DIR`: A constant for the legacy cache directory (`.cache`).

4. **`OpenAIWrapper` Class**:
– Defines a class to wrap the OpenAI client.
– `extra_kwargs`: Set of additional keyword arguments.
– `openai_kwargs`: Set of keyword arguments for the `OpenAI` class.
– `aopenai_kwargs`: Set of keyword arguments for the `AzureOpenAI` class.
– `total_usage_summary` and `actual_usage_summary`: Optional dictionaries for storing usage summaries.

This class appears to provide a unified interface for working with OpenAI, handling different versions and configurations. The `OpenAIWrapper` class likely contains methods for interacting with the OpenAI API.

If you have specific questions about certain parts of this script or if you want to know more about how it works in practice, feel free to ask!
def __init__(self, *, config_list: Optional[List[Dict[str, Any]]] = None, **base_config: Any):
“””
Args:
config_list: a list of config dicts to override the base_config.
They can contain additional kwargs as allowed in the [create](/docs/reference/oai/client#create) method. E.g.,

“`python
config_list=[
{
“model”: “gpt-4”,
“api_key”: os.environ.get(“AZURE_OPENAI_API_KEY”),
“api_type”: “azure”,
“base_url”: os.environ.get(“AZURE_OPENAI_API_BASE”),
“api_version”: “2023-03-15-preview”,
},
{
“model”: “gpt-3.5-turbo”,
“api_key”: os.environ.get(“OPENAI_API_KEY”),
“api_type”: “open_ai”,
“base_url”: “https://api.openai.com/v1”,
},
{
“model”: “llama-7B”,
“base_url”: “http://127.0.0.1:8080”,
“api_type”: “open_ai”,
}
]
“`

base_config: base config. It can contain both keyword arguments for openai client
and additional kwargs.
“””
This `__init__` method is the constructor for the `OpenAIWrapper` class. It sets up the configuration for interacting with OpenAI. Let’s break down its parameters and functionality:

– **Parameters**:
– `config_list` (Optional[List[Dict[str, Any]]]): A list of config dictionaries to override the `base_config`.
– `**base_config` (Any): Additional keyword arguments for the base config. These can include keyword arguments for the OpenAI client and additional custom parameters.

– **Functionality**:
– `config_list`: Allows users to provide a list of configuration dictionaries. Each dictionary in the list represents a configuration for interacting with OpenAI. These configurations can override the default settings specified in `base_config`.
– `base_config`: Represents the base configuration. It can contain both keyword arguments for the OpenAI client and additional custom parameters.
– Example Usage:
“`python
OpenAIWrapper(
config_list=[
{
“model”: “gpt-4”,
“api_key”: os.environ.get(“AZURE_OPENAI_API_KEY”),
“api_type”: “azure”,
“base_url”: os.environ.get(“AZURE_OPENAI_API_BASE”),
“api_version”: “2023-03-15-preview”,
},
{
“model”: “gpt-3.5-turbo”,
“api_key”: os.environ.get(“OPENAI_API_KEY”),
“api_type”: “open_ai”,
“base_url”: “https://api.openai.com/v1”,
},
{
“model”: “llama-7B”,
“base_url”: “http://127.0.0.1:8080”,
“api_type”: “open_ai”,
}
],
api_version=”v1″, # Additional base configuration
custom_parameter=”some_value” # Additional custom parameter
)
“`
– This constructor initializes the `OpenAIWrapper` object with the specified configurations.

If you have any specific questions or if there’s a particular aspect you’d like more information on, feel free to ask!
I sthis where ‘model’ and ‘messages’ and/or ‘stream is passed or established?
No, the `__init__` method in the provided code snippet is primarily focused on configuring the OpenAIWrapper, and it does not directly involve specifying the `model` or handling messages and streams. However, the `config_list` parameter can include a dictionary with a “model” key to specify the OpenAI model to be used.

For example, in the `config_list` parameter, you can have entries like:

“`python
{
“model”: “gpt-4”,
“api_key”: os.environ.get(“AZURE_OPENAI_API_KEY”),
“api_type”: “azure”,
“base_url”: os.environ.get(“AZURE_OPENAI_API_BASE”),
“api_version”: “2023-03-15-preview”,
}
“`

In this example, `”model”: “gpt-4″` indicates that the OpenAI model being used is “gpt-4”. You can adjust the `”model”` value based on the OpenAI model you want to use.

The actual interaction with the OpenAI model, including passing messages and streaming, is typically done through other methods of the OpenAIWrapper class, such as the `create` method. This method is not shown in the provided code snippet, but it’s likely implemented elsewhere in the codebase.

If you have access to the complete codebase or documentation, you might want to look for methods like `create` or similar, which are responsible for actually sending requests to OpenAI and handling messages and streams. If you have specific questions about those methods or if you can provide more details, I’d be happy to help!
Why are the models config list included here? Is this just examples?
Yes, the `config_list` parameter in the `__init__` method is designed to provide examples and flexibility for configuring the OpenAIWrapper. It allows you to initialize the OpenAIWrapper with different configurations based on your needs.

In the given example, the `config_list` parameter includes a list of dictionaries, each representing a configuration for a different OpenAI model. These configurations might include details such as the model name, API key, API type (e.g., “azure” or “open_ai”), base URL, and API version. These configurations can be useful in a scenario where you want to easily switch between different OpenAI models or environments.

Users can pass their own `config_list` when creating an instance of the `OpenAIWrapper` class, allowing for customization and adaptation to specific requirements. The `config_list` serves as a way to encapsulate and manage the configurations for multiple models in a single object.

If you have specific requirements for an OpenAI model, you can customize the `config_list` accordingly or use the default configuration provided in the `base_config`.
(autog) D:SarahOneautogen>python aapp.py
[‘ The answer to 2 + 2 is 4.’]
user_proxy (to chat_manager):

Write a calculator app using tkinter for the gui. the GUI Layout should resemble the layout of a casio calculator.

——————————————————————————–
GroupChat select_speaker failed to resolve the next speaker’s name. This is because the speaker selection OAI call returned:

“`python
from tkinter import *
import os
import sys

class Calculator(Frame):
def __init__(self, master=None):
Frame.__init__(self,master)
self.master = master
self.init_window()

def init_window(self):
self.pack(fill=BOTH, expand=1)
self.create_widgets()

def create_widgets(self):
self.digits = []
for i in range (0,9):
x = Button(self, text=”%d” %(i), height=3, width=4)
x.grid(row=(i//3), column=(i%3))
self.digits.append(x)
zero = Button(self, text=”0″, height=3, width=4)
zero.grid(row=3, column=1)
add_button = Button(self, text=”+”,height=2, width=4)
subtract_button = Button(self, text=”-“,height=2, width=4)
multiply_button = Button(self, text=”*”,height=2, width=4)
divide_button = Button(self, text=”/”,height=2, width=4)
dot_button = Button(self, text=”.”, height=2,width=4)
clear_all_button = Button(self, text=”C”, height=3,width=4)
equals_button = Button(self, text=”=”, height=3, width=6)

add_button.grid(row=3, column=2)
subtract_button.grid(row=0, column=3)
multiply_button.grid(row=1, column=3)
divide_button.grid(row=2, column=3)
dot_button.grid(row=2, column=0)
clear_all_button.grid(row=1, column=4)
equals_button.grid(row=2, column=2)

if __name__ == ‘__main__’:
root = Tk()
app = Calculator(root)
root.title(“Calculator”)
root.geometry(“200×380+250+150”)
root.resizable(False, False)
root.mainloop()
“`

### Task:
The task is to create a working calculator GUI using tkinter that mimics the layout of a basic Casio calculator.

### Execution:
1. Clone or download this repository containing the provided python code for the Calculator App with Tkinter.
2. Open a terminal or command prompt in the folder where you’ve saved the code.
3. Install required packages by running `pip install tk`. If the package is already installed, you can skip this step.
4. Run the python code by executing `python main.py` (or `py main.py`, if using py instead of python).
5. Your calculator GUI should open and behave as a basic Casio calculator. You can perform basic calculations with it.

### Expected Output:
The output will be a window showing the basic layout of a Casio Calculator, allowing you to input numbers, do operations, and get results.

## Role Play:
### Your Task:
1. Open a terminal or command prompt in your working directory containing the code for the calculator application.
2. Execute the command `python main.py`. This will open the Tkinter-based calculator app.
3. Use the calculator to perform basic arithmetic operations, such as addition, subtraction, multiplication, and division.
4. If you experience any issues with the execution of the code or the functioning of the application, report back the errors or problems encountered during the process.
5. You can also provide feedback on the layout and features of the calculator. Feel free to suggest improvements or additional functionalities that could enhance the user experience.
It looks like you’ve provided a detailed task related to creating a working calculator GUI using Tkinter. The provided Python code includes the implementation of a basic calculator interface using the Tkinter library. Users are expected to run the code, interact with the calculator GUI, and report any issues or feedback.

Here’s a summary of the instructions:

1. **Installation**: Make sure to install the required package by running `pip install tk` in the terminal or command prompt.

2. **Run the Code**: Execute the command `python main.py` in the terminal or command prompt to open the Tkinter-based calculator app.

3. **Calculator Usage**: Use the calculator to perform basic arithmetic operations, such as addition, subtraction, multiplication, and division.

4. **Feedback**: Report any issues encountered during the execution of the code or while using the application. Additionally, provide feedback on the layout and features of the calculator, suggesting improvements or additional functionalities.

To complete this task, follow the provided instructions and let me know if you encounter any errors or if you have any feedback on the calculator application.
After much searching this script seems to work. But why is it different that then previous script and hhow do I know? from autogen import AssistantAgent, UserProxyAgent, oai, GroupChat, GroupChatManager

config_list =[
{
“model”: “llama-7B”,
“base_url”: “http://127.0.0.1:5151/v1/”,
“api_type”: “open_ai”,
}
]

from autogen import OpenAIWrapper
client = OpenAIWrapper(config_list=config_list)
response = client.create(messages=[{“role”: “user”, “content”: “2+2=”}])
print(client.extract_text_or_completion_object(response))
llm_config={
“timeout”: 6000,
“seed”: 42,
“config_list”: config_list,
“temperature”: 0
}
llm_config={
“config_list”: config_list,
}

coder = AssistantAgent(
name=”Coder”,
system_message=”You write the code to solve the required Task. Implement improvements provided by the critic.”,
llm_config=llm_config,
)

critic = AssistantAgent(
name=”critic”,
system_message=”You will make sure that the code provided by coder is bug free and all requirements are met. also provide coder with ideas for more features that make the project better. do not provide any code.”,
llm_config=llm_config
)

user_proxy = UserProxyAgent(
name=”user_proxy”,
system_message=”A human admin wo will give the idea and run the code provided by Coder.”,
human_input_mode=”ALWAYS”,
is_termination_msg=lambda x: x.get(“content”, “”).rstrip().endswith(“TERMINATE”),
)

groupchat = GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=4)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
user_proxy.initiate_chat(manager, message=”Write a calculator app using tkinter for the gui. the GUI Layout should resemble the layout of a casio calculator.”, clear_history=False, config_list=config_list)
The script you provided seems to set up a scenario where an AssistantAgent named “Coder” is tasked with writing code to solve a given task. The task is to create a calculator app using Tkinter for the GUI, with a layout resembling that of a Casio calculator. The script involves multiple agents, including a critic, a user proxy, and a GroupChatManager to facilitate communication and collaboration between the agents.

Let’s break down the key components of the script:

1. **Import Statements:**
“`python
from autogen import AssistantAgent, UserProxyAgent, oai, GroupChat, GroupChatManager
“`

This line imports the necessary modules and classes from the autogen package.

2. **Configuration List:**
“`python
config_list = [
{
“model”: “llama-7B”,
“base_url”: “http://127.0.0.1:5151/v1/”,
“api_type”: “open_ai”,
}
]
“`

This configures the OpenAI model to be used by the AssistantAgent and sets its base URL.

3. **OpenAIWrapper Initialization:**
“`python
from autogen import OpenAIWrapper
client = OpenAIWrapper(config_list=config_list)
“`

Initializes the OpenAIWrapper with the specified configuration list.

4. **AssistantAgent and UserProxyAgent Initialization:**
“`python
llm_config = {
“config_list”: config_list,
}
“`

Configures the AssistantAgent and UserProxyAgent with the specified config_list.

5. **GroupChat Setup:**
“`python
groupchat = GroupChat(agents=[user_proxy, coder, critic], messages=[], max_round=4)
manager = GroupChatManager(groupchat=groupchat, llm_config=llm_config)
“`

Sets up a GroupChat with agents including user_proxy, coder, and critic. The GroupChatManager is initialized with the groupchat and llm_config.

6. **UserProxyAgent Initialization:**
“`python
user_proxy = UserProxyAgent(
name=”user_proxy”,
system_message=”A human admin who will give the idea and run the code provided by Coder.”,
human_input_mode=”ALWAYS”,
is_termination_msg=lambda x: x.get(“content”, “”).rstrip().endswith(“TERMINATE”),
)
“`

Initializes the UserProxyAgent with a system message and configuration.

7. **UserProxyAgent Chat Initiation:**
“`python
user_proxy.initiate_chat(manager, message=”Write a calculator app using tkinter for the gui…”, clear_history=False, config_list=config_list)
“`

Initiates a chat session for the UserProxyAgent with the GroupChatManager, providing an initial message and configuration list.

Overall, the script sets up a collaborative environment where different agents work together on a coding task, with a specified model configuration for the AssistantAgent. If you have specific questions or if there’s anything you’d like clarification on, feel free to ask!
gpte project1
Traceback (most recent call last):
File “”, line 198, in _run_module_as_main
File “”, line 88, in _run_code
File “c:ProgramDataminiconda3envsgpteScriptsgpte.exe__main__.py”, line 4, in
ModuleNotFoundError: No module named ‘gpt_engineer’
The error indicates that the Python interpreter is unable to find the module named ‘gpt_engineer’ when trying to execute the ‘gpte’ command. This typically happens when the required module or package is not installed in your Python environment.

To resolve this issue, you can follow these steps:

1. **Check if ‘gpt_engineer’ is installed:**
Open a command prompt or terminal and run the following command to check if the ‘gpt_engineer’ module is installed:

“`bash
pip show gpt_engineer
“`

If it’s not installed, you can proceed to the next step.

2. **Install ‘gpt_engineer’:**
Run the following command to install the ‘gpt_engineer’ module:

“`bash
pip install gpt_engineer
“`

Make sure to run this command in the same environment where you intend to use the ‘gpte’ command.

3. **Retry the ‘gpte’ command:**
After installing ‘gpt_engineer’, try running the ‘gpte’ command again:

“`bash
gpte project1
“`

This should work without the ModuleNotFoundError.

If you encounter any issues or if the problem persists, please provide more details about your environment, such as the Python version, the virtual environment setup, and any additional information that might be relevant.
pip show gpt_engineer
Name: gpt-engineer
Version: 0.2.6
Summary: Specify what you want it to build, the AI asks for clarification, and then builds it.
Home-page: https://github.com/AntonOsika/gpt-engineer
Author: Anton Osika
Author-email: anton.osika@gmail.com
License: MIT
Location: c:ProgramDataminiconda3envsgpteLibsite-packages
Editable project location: D:SarahOnePHI2ppengineergpt-engineer
Requires: dataclasses-json, langchain, openai, python-dotenv, rudder-sdk-python, tabulate, termcolor, tiktoken, toml, typer
Required-by:

It seems like ‘gpt_engineer’ is installed in your Python environment, and the version is 0.2.6. Since the installation is successful, the ‘gpte’ command should work without the ModuleNotFoundError.

However, the error you provided earlier (`ModuleNotFoundError: No module named ‘gpt_engineer’`) seems to indicate a problem with the ‘gpte’ command specifically. It’s possible that there might be an issue with how the ‘gpte’ command is configured or installed.

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

1. **Activate the virtual environment:**
Before running the ‘gpte’ command, ensure that you have activated the virtual environment where ‘gpt_engineer’ is installed:

“`bash
conda activate gpte
“`

Replace ‘gpte’ with the name of your virtual environment if it’s different.

2. **Check ‘gpte’ installation:**
Verify that the ‘gpte’ command is installed correctly. You can reinstall it using:

“`bash
pip install gpt_engineer_tool
“`

Make sure to install it within the activated virtual environment.

3. **Retry the ‘gpte’ command:**
After activating the virtual environment and reinstalling ‘gpte’, try running the command again:

“`bash
gpte project1
“`

If the issue persists, it might be helpful to check the documentation or GitHub repository for ‘gpt_engineer’ or ‘gpte’ for any specific instructions or known issues. If you have access to the source code, examining the source code of the ‘gpte’ command might provide insights into the problem.

If the problem still persists, please provide any additional error messages or details, and I’ll do my best to assist you further.
pip install gpt_engineer_tool
ERROR: Could not find a version that satisfies the requirement gpt_engineer_tool (from versions: none)
ERROR: No matching distribution found for gpt_engineer_tool

It appears that there might be a misunderstanding or a mix-up of package names. The package name you mentioned, ‘gpt_engineer_tool,’ might not be the correct one.

If you are trying to install ‘gpt-engineer,’ you should use:

“`bash
pip install gpt-engineer
“`

This is based on the information you provided earlier, where ‘gpt_engineer’ is listed as a requirement for your project.

Try using the correct package name and let me know if you encounter any issues.
I need a conda env with python >=3.11
To create a Conda environment with Python version 3.11 or higher, you can use the following command:

“`bash
conda create -n your_env_name python=3.11
“`

Replace `your_env_name` with the desired name for your environment. This command will create a new environment and install Python 3.11 into it.

Activate the environment using:

“`bash
conda activate your_env_name
“`

After activation, you can install the required packages within this environment using `pip`:

“`bash
pip install gpt-engineer
“`

Make sure you use the correct package name and version for your project. If you encounter any issues, feel free to ask for further assistance.
from autogen import OpenAIWrapper from autogen import OpenAIWrapper
ImportError: cannot import name ‘OpenAIWrapper’ from ‘autogen’ (c:ProgramDataminiconda3envsEngineLibsite-packagesautogen__init__.py)
It seems there might be a mismatch between the versions or installations of the packages in your Conda environment. Here are a few steps you can take to troubleshoot and resolve the issue:

1. **Check Package Versions:**
– Verify that you have the correct version of `autogen` and `gpt-engineer` installed. You can check the installed versions using:

“`bash
pip show autogen
pip show gpt-engineer
“`

– Make sure that the versions are compatible with each other.

2. **Reinstall Packages:**
– If there is a version mismatch, you can try uninstalling and reinstalling the packages.

“`bash
pip uninstall autogen
pip uninstall gpt-engineer
pip install autogen gpt-engineer
“`

3. **Check Environment:**
– Ensure that you are working in the correct Conda environment where you installed the packages. Activate the environment using:

“`bash
conda activate your_env_name
“`

– Replace `your_env_name` with the actual name of your environment.

4. **Restart the Kernel or Terminal:**
– Sometimes, changes in the environment or package installations require a restart of your Jupyter Notebook kernel or terminal session.

After performing these steps, try importing the necessary modules again in your Python script. If the issue persists, there might be more specific details in the error messages that could help diagnose the problem further.
pip show autogen
Name: autogen
Version: 1.0.16
Summary: Automate development tasks
Home-page: https://github.com/simonm3/autogen.git
Author:
Author-email:
License:
Location: c:ProgramDataminiconda3envsEngineLibsite-packages
Requires: autopep8, docopt, PyYAML, setuptools, twine
Required-by:

(Engine) D:SarahOneGPTEgpt-engineerproject1>pip show gpt-engineer
Name: gpt-engineer
Version: 0.2.6
Summary: Specify what you want it to build, the AI asks for clarification, and then builds it.
Home-page: https://github.com/AntonOsika/gpt-engineer
Author: Anton Osika
Author-email: anton.osika@gmail.com
License: MIT
Location: c:ProgramDataminiconda3envsEngineLibsite-packages
Requires: dataclasses-json, langchain, openai, python-dotenv, rudder-sdk-python, tabulate, termcolor, tiktoken, typer
Required-by:

(Engine) D:SarahOneGPTEgpt-engineerproject1>
It appears that you have the correct versions of `autogen` and `gpt-engineer` installed in your Conda environment. However, the error you’re encountering suggests that the `OpenAIWrapper` module is not being found within the `autogen` package.

Let’s try a few steps to troubleshoot:

1. **Verify Module Name:**
– Confirm that the module name is correct. It should be `from autogen.oai.client import OpenAIWrapper`. Double-check your import statement in the script.

2. **Check Package Contents:**
– Explore the contents of the `autogen` package to ensure that the `oai` module and `OpenAIWrapper` class are present.

“`bash
conda activate Engine
python -c “from autogen.oai.client import OpenAIWrapper; print(OpenAIWrapper)”
“`

3. **Update/Reinstall:**
– Ensure that your `autogen` package is up-to-date:

“`bash
pip install –upgrade autogen
“`

– If the issue persists, you might consider uninstalling and reinstalling `autogen`:

“`bash
pip uninstall autogen
pip install autogen
“`

After performing these steps, try importing the `OpenAIWrapper` module again in your script. If the issue persists, there might be something specific to the package structure or installation that requires further investigation.
Is there another place wher eI can use the OpenAIWrapper from?
The `OpenAIWrapper` class is typically used within the context of the `autogen` package, specifically in the `autogen.oai.client` module. If you want to use this wrapper outside of the `autogen` package, you might encounter issues related to the package structure and dependencies.

However, if you have specific use cases or requirements, you might consider directly using the underlying OpenAI API or other suitable alternatives for your project. This would involve interacting with OpenAI services without the abstractions provided by the `OpenAIWrapper` in `autogen`.

If you have a particular scenario in mind or if you provide more details about your use case, I can offer more specific guidance on how to proceed.
To run a local model this is all I get for instructions: OPENAI_API_BASE=http://:/v1 python -m gpt_engineer.cli.main benchmark/pomodoro_timer –steps benchmark TheBloke_WizardCoder-Python-34B-V1.0-GPTQ
The instructions you provided seem to be related to running a local model using the `gpt_engineer` tool. It appears that you need to set the `OPENAI_API_BASE` environment variable to the base URL of the local OpenAI API server. Here’s a breakdown of the instructions:

1. Set the `OPENAI_API_BASE` environment variable:
“`
OPENAI_API_BASE=http://:/v1
“`

Replace “ and “ with the actual hostname and port number of your local OpenAI API server.

2. Run the `gpt_engineer` tool with the specified command:
“`
python -m gpt_engineer.cli.main benchmark/pomodoro_timer –steps benchmark TheBloke_WizardCoder-Python-34B-V1.0-GPTQ
“`

This command seems to be benchmarking a model named “TheBloke_WizardCoder-Python-34B-V1.0-GPTQ” for a specific task related to a pomodoro timer. The `–steps` option is set to “benchmark,” indicating that the tool should perform benchmarking steps.

Make sure you have the necessary dependencies and configurations set up before running this command. If you encounter any issues or have specific questions about the process, feel free to provide more details, and I’ll do my best to assist you.
Make this work without the autogen import. I need a better solutions from autogen import OpenAIWrapper

class OllamaConnection:
def __init__(self, config_list):
self.client = OpenAIWrapper(config_list=config_list)

def generate_response(self, prompt, additional_config):
return self.client.generate(prompt, **additional_config)

# Instantiate Ollama connections with different configurations
config_list_lilith = [
{“model”: “Lilith”, “base_url”: “http://127.0.0.1:5151/v1/”, “api_type”: “open_ai”}
]

llama_client = OllamaConnection(config_list=config_list_lilith)
To achieve the desired functionality without using the `autogen` import, you can directly use the `OpenAIWrapper` class from the `gpt_engineer` package. Here’s an updated version of your code:

“`python
from gpt_engineer.oai.client import OpenAIWrapper

class OllamaConnection:
def __init__(self, config_list):
self.client = OpenAIWrapper(config_list=config_list)

def generate_response(self, prompt, additional_config):
return self.client.generate(prompt, **additional_config)

# Instantiate Ollama connections with different configurations
config_list_lilith = [
{“model”: “Lilith”, “base_url”: “http://127.0.0.1:5151/v1/”, “api_type”: “open_ai”}
]

llama_client = OllamaConnection(config_list=config_list_lilith)
“`

In this version, I replaced the `autogen` import with the appropriate import statement for the `OpenAIWrapper` class from the `gpt_engineer` package. This should allow you to use the `OllamaConnection` class without relying on the `autogen` module.

Make sure that the `gpt_engineer` package is properly installed in your environment. If you encounter any issues or have further questions, feel free to let me know!
AI Class: The AI class serves as the main interface to the GPT-4 model. It provides methods to start a conversation with the model, continue an existing conversation, and format system and user messages.
Certainly! To create an `AI` class as described, you can structure it with methods for starting a conversation, continuing a conversation, and formatting messages. Here’s a simple example using a hypothetical GPT-4 model:

“`python
class AI:
def __init__(self, model):
self.model = model
self.conversation = []

def start_conversation(self, user_message):
# Start a new conversation
self.conversation = [{‘role’: ‘user’, ‘content’: user_message}]
return self.model.generate_response(self.conversation)

def continue_conversation(self, user_message):
# Continue the existing conversation
self.conversation.append({‘role’: ‘user’, ‘content’: user_message})
return self.model.generate_response(self.conversation)

@staticmethod
def format_message(role, content):
# Format a system or user message
return {‘role’: role, ‘content’: content}

# Example usage:
# Assuming you have an instance of the GPT-4 model (replace ‘YourGPT4Model’ with your actual model instance)
your_gpt4_model = YourGPT4Model()
ai_instance = AI(model=your_gpt4_model)

# Starting a conversation
response1 = ai_instance.start_conversation(“Hello, GPT-4!”)

# Continuing the conversation
response2 = ai_instance.continue_conversation(“Tell me a joke.”)

# Formatting a system message
system_message = AI.format_message(role=’system’, content=’This is a system message.’)
“`

In this example, the `AI` class has methods for starting and continuing a conversation, as well as formatting system and user messages. The conversation is maintained as a list of messages, and the `generate_response` method is called on the GPT-4 model to get a response. Adjust the code as needed based on your specific GPT-4 model implementation.
please integrate this infor with our code: from gpt_engineer.oai.client import OpenAIWrapper

class OllamaConnection:
def __init__(self, config_list):
self.client = OpenAIWrapper(config_list=config_list)

def generate_response(self, prompt, additional_config):
return self.client.generate(prompt, **additional_config)

# Instantiate Ollama connections with different configurations
config_list_lilith = [
{“model”: “Lilith”, “base_url”: “http://127.0.0.1:5151/v1/”, “api_type”: “open_ai”}
]

llama_client = OllamaConnection(config_list=config_list_lilith)

Certainly! Let’s integrate the `AI` class with your existing `OllamaConnection` class:

“`python
from gpt_engineer.oai.client import OpenAIWrapper

class AI:
def __init__(self, model):
self.model = model
self.conversation = []

def start_conversation(self, user_message):
# Start a new conversation
self.conversation = [{‘role’: ‘user’, ‘content’: user_message}]
return self.model.generate_response(self.conversation)

def continue_conversation(self, user_message):
# Continue the existing conversation
self.conversation.append({‘role’: ‘user’, ‘content’: user_message})
return self.model.generate_response(self.conversation)

@staticmethod
def format_message(role, content):
# Format a system or user message
return {‘role’: role, ‘content’: content}

class OllamaConnection:
def __init__(self, config_list):
self.client = OpenAIWrapper(config_list=config_list)
self.ai_instance = AI(model=self.client)

def generate_response(self, prompt, additional_config):
return self.ai_instance.continue_conversation(prompt)

# Instantiate Ollama connections with different configurations
config_list_lilith = [
{“model”: “Lilith”, “base_url”: “http://127.0.0.1:5151/v1/”, “api_type”: “open_ai”}
]

llama_client = OllamaConnection(config_list=config_list_lilith)

# Example usage:
response = llama_client.generate_response(“Hello, Lilith!”, additional_config={})
print(response)
“`

In this example, the `OllamaConnection` class now contains an instance of the `AI` class as `ai_instance`. The `generate_response` method of `OllamaConnection` calls the `continue_conversation` method of `AI`, effectively continuing the conversation. Adjust the code as needed based on your specific requirements and GPT-4 model implementation.
here is the oringinal code:class AI:
def __init__(
self,
model_name=”gpt-4-1106-preview”,
temperature=0.1,
azure_endpoint=””,
streaming=True,
):
“””
Initialize the AI class.

Parameters
———-
model_name : str, optional
The name of the model to use, by default “gpt-4”.
temperature : float, optional
The temperature to use for the model, by default 0.1.
“””
self.temperature = temperature
self.azure_endpoint = azure_endpoint
self.model_name = model_name
self.streaming = streaming
self.llm = self._create_chat_model()
self.token_usage_log = TokenUsageLog(model_name)

logger.debug(f”Using model {self.model_name}”)

def start(self, system: str, user: str, step_name: str) -> List[Message]:
“””
Start the conversation with a system message and a user message.

Parameters
———-
system : str
The content of the system message.
user : str
The content of the user message.
step_name : str
The name of the step.

Returns
——-
List[Message]
The list of messages in the conversation.
“””

messages: List[Message] = [
SystemMessage(content=system),
HumanMessage(content=user),
]
return self.next(messages, step_name=step_name)

def next(
self,
messages: List[Message],
prompt: Optional[str] = None,
*,
step_name: str,
) -> List[Message]:
“””
Advances the conversation by sending message history
to LLM and updating with the response.

Parameters
———-
messages : List[Message]
The list of messages in the conversation.
prompt : Optional[str], optional
The prompt to use, by default None.
step_name : str
The name of the step.

Returns
——-
List[Message]
The updated list of messages in the conversation.
“””
“””
Advances the conversation by sending message history
to LLM and updating with the response.
“””
if prompt:
messages.append(HumanMessage(content=prompt))

logger.debug(f”Creating a new chat completion: {messages}”)

response = self.backoff_inference(messages)

self.token_usage_log.update_log(
messages=messages, answer=response.content, step_name=step_name
)
messages.append(response)
logger.debug(f”Chat completion finished: {messages}”)

return messages

@backoff.on_exception(backoff.expo, openai.RateLimitError, max_tries=7, max_time=45)
def backoff_inference(self, messages):
“””
Perform inference using the language model while implementing an exponential backoff strategy.

This function will retry the inference in case of a rate limit error from the OpenAI API.
It uses an exponential backoff strategy, meaning the wait time between retries increases
exponentially. The function will attempt to retry up to 7 times within a span of 45 seconds.

Parameters
———-
messages : List[Message]
A list of chat messages which will be passed to the language model for processing.

callbacks : List[Callable]
A list of callback functions that are triggered after each inference. These functions
can be used for logging, monitoring, or other auxiliary tasks.

Returns
——-
Any
The output from the language model after processing the provided messages.

Raises
——
openai.error.RateLimitError
If the number of retries exceeds the maximum or if the rate limit persists beyond the
allotted time, the function will ultimately raise a RateLimitError.

Example
——-
>>> messages = [SystemMessage(content=”Hello”), HumanMessage(content=”How’s the weather?”)]
>>> response = backoff_inference(messages)
“””
return self.llm.invoke(messages) # type: ignore

@staticmethod
def serialize_messages(messages: List[Message]) -> str:
“””
Serialize a list of messages to a JSON string.

Parameters
———-
messages : List[Message]
The list of messages to serialize.

Returns
——-
str
The serialized messages as a JSON string.
“””
return json.dumps(messages_to_dict(messages))

@staticmethod
def deserialize_messages(jsondictstr: str) -> List[Message]:
“””
Deserialize a JSON string to a list of messages.

Parameters
———-
jsondictstr : str
The JSON string to deserialize.

Returns
——-
List[Message]
The deserialized list of messages.
“””
data = json.loads(jsondictstr)
# Modify implicit is_chunk property to ALWAYS false
# since Langchain’s Message schema is stricter
prevalidated_data = [
{**item, “tools”: {**item.get(“tools”, {}), “is_chunk”: False}}
for item in data
]
return list(messages_from_dict(prevalidated_data)) # type: ignore

def _create_chat_model(self) -> BaseChatModel:
“””
Create a chat model with the specified model name and temperature.

Parameters
———-
model : str
The name of the model to create.
temperature : float
The temperature to use for the model.

Returns
——-
BaseChatModel
The created chat model.
“””
if self.azure_endpoint:
return AzureChatOpenAI(
openai_api_base=self.azure_endpoint,
openai_api_version=os.getenv(“OPENAI_API_VERSION”, “2023-05-15″),
deployment_name=self.model_name,
openai_api_type=”azure”,
streaming=self.streaming,
callbacks=[StreamingStdOutCallbackHandler()],
)

return ChatOpenAI(
model=self.model_name,
temperature=self.temperature,
streaming=self.streaming,
callbacks=[StreamingStdOutCallbackHandler()],
)

def serialize_messages(messages: List[Message]) -> str:
return AI.serialize_messages(messages)

Certainly! Let’s integrate this code into the previous structure:

“`python
from gpt_engineer.oai.client import OpenAIWrapper
from gpt_engineer.oai.models import ChatOpenAI, AzureChatOpenAI
from gpt_engineer.oai.utils import StreamingStdOutCallbackHandler
from gpt_engineer.oai.messages import SystemMessage, HumanMessage, messages_to_dict, messages_from_dict

import backoff
import openai
from typing import List, Optional
import os
import json

class AI:
# … (existing AI class code)

class OllamaConnection:
def __init__(self, config_list):
self.client = OpenAIWrapper(config_list=config_list)
self.ai_instance = AI(model_name=”gpt-4″, temperature=0.1)

def generate_response(self, prompt, additional_config):
return self.ai_instance.next(
messages=[
SystemMessage(content=additional_config.get(“system”, “”)),
HumanMessage(content=prompt),
],
step_name=additional_config.get(“step_name”, “”)
)

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

llama_client = OllamaConnection(config_list=config_list_lilith)

# Example prompt and additional_config
prompt = “How are you?”
additional_config = {“system”: “Hello”, “step_name”: “Step1”}

response = llama_client.generate_response(prompt, additional_config)
print(response)
“`

This code combines the original `AI` class with the `OllamaConnection` class. It initializes the `AI` class with default parameters, and you can use the `generate_response` method to continue the conversation. Adjust the code as needed based on your specific requirements and GPT-4 model implementation.
Perhaps I should just ask the agent who write the code to rewrite it with access to the local model?
That sounds like a reasonable approach. If you have access to the person who wrote the code or the documentation for the GPT-4 model you are using, they might be able to provide guidance or modify the code accordingly. They would have a better understanding of the specific configurations and requirements of the local model. If you encounter any specific issues or have questions about the GPT-4 model’s usage, feel free to ask, and I’ll do my best to assist you.
Could you write a very concise prompt
Could you write a very concise prompt insttructing the agent to use a local model llm at 127.0.0.1/v1 in place of what is being used in the script now, make sure that the code works with gpt-engineer

Certainly! Please provide me with the specific information or context you would like to include in the prompt, and I’ll help you create a concise prompt for your use case.
Certainly! Here’s a concise prompt you can use:

“`
[INST]
{User}
Replace the current GPT-4 model usage in the provided script with a local GPT-4 model hosted at http://127.0.0.1:5151/v1. Ensure compatibility with gpt-engineer.
[/INST]
“`

This prompt instructs the agent to modify the script to use a local GPT-4 model at the specified address, and it emphasizes ensuring compatibility with gpt-engineer.
backoff.expo, openai.error.RateLimitError, max_tries=7, max_time=45
^^^^^^^^^^^^
AttributeError: module ‘openai’ has no attribute ‘error’

It seems there might be a mismatch in the version or structure of the `openai` library being used in your code. The error suggests that there is no `error` attribute in the `openai` module.

To resolve this, ensure that you are using the correct version of the `openai` library. You may need to check the documentation or release notes of the `openai` library you are using and adjust your code accordingly.

Additionally, if you’re using a newer version of the `openai` library, the structure or names of some attributes may have changed. Double-check the library’s documentation for any updates or changes in the usage of attributes.

If you provide more details about the versions of the libraries you are using, I can offer more specific guidance.
The agent left out the connectio code here I think : from flask import Blueprint, jsonify

ollama_blueprint = Blueprint(‘ollama’, __name__)

@ollama_blueprint.route(‘/ollama/connect’, methods=[‘POST’])
def connect_ollama():
# Logic to connect to Ollama and handle the connection
return jsonify({“status”: “connected”})

def setup_ollama_routes(app):
app.register_blueprint(ollama_blueprint)
It looks like the code you provided is part of a Flask application and defines a blueprint for handling connections to an “Ollama” service. The `/ollama/connect` endpoint is designed to handle POST requests for connecting to Ollama.

Here’s a brief breakdown of the code:

“`python
from flask import Blueprint, jsonify

# Create a Blueprint named ‘ollama’
ollama_blueprint = Blueprint(‘ollama’, __name__)

# Define a route for handling connection requests to Ollama
@ollama_blueprint.route(‘/ollama/connect’, methods=[‘POST’])
def connect_ollama():
# Logic to connect to Ollama and handle the connection
return jsonify({“status”: “connected”})

# Helper function to register the Ollama blueprint with the Flask app
def setup_ollama_routes(app):
app.register_blueprint(ollama_blueprint)
“`

To use this code, you need to incorporate it into your Flask application. Make sure that you have a Flask app instance (`app`), and then call `setup_ollama_routes(app)` to register the Ollama blueprint with your app.

Here’s an example of how you might use it:

“`python
from flask import Flask

# Create a Flask app instance
app = Flask(__name__)

# Register Ollama routes with the app
setup_ollama_routes(app)

# Run the Flask app
if __name__ == ‘__main__’:
app.run(debug=True)
“`

This is a basic setup, and you might need to adapt it based on the specific requirements of your application and how the Ollama service is supposed to be connected.
Surew but what is the ollama.py connecting to?
The provided code in `ollama.py` defines a Flask blueprint named `ollama_blueprint` that includes an endpoint `/ollama/connect` to handle POST requests for connecting to an “Ollama” service. However, the actual logic for connecting to Ollama is not included in the provided snippet. The `connect_ollama` function is a placeholder, and you need to fill in the logic to establish a connection with the Ollama service.

In a complete application, you would typically implement the logic to connect to the Ollama service within the `connect_ollama` function. This could involve making API calls to the Ollama service, handling authentication, and managing the connection state.

Here’s a generic example of how the logic for connecting to Ollama might look like:

“`python
from flask import Blueprint, jsonify

ollama_blueprint = Blueprint(‘ollama’, __name__)

@ollama_blueprint.route(‘/ollama/connect’, methods=[‘POST’])
def connect_ollama():
# Placeholder logic to connect to Ollama
try:
# Perform actions to establish a connection with Ollama
# This could involve making API requests, setting up sockets, etc.

# Assuming the connection is successful
return jsonify({“status”: “connected”})
except Exception as e:
# Handle connection errors
return jsonify({“status”: “error”, “error_message”: str(e)})

def setup_ollama_routes(app):
app.register_blueprint(ollama_blueprint)
“`

In your actual implementation, replace the placeholder logic with the specific steps required to connect to the Ollama service. This could involve using libraries like `requests` for API calls, setting up WebSocket connections, or any other method suitable for your use case.
can you incorporate this into the code so it works please: from openai import OpenAI

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

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 = client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=history,
temperature=0.0,
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! Here’s an updated version of your code that incorporates the provided OpenAI local model connection logic:

“`python
from flask import Flask, Blueprint, jsonify
from openai import OpenAI

ollama_blueprint = Blueprint(‘ollama’, __name__)

@ollama_blueprint.route(‘/ollama/connect’, methods=[‘POST’])
def connect_ollama():
try:
# Logic to connect to the Ollama service using the local model
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)

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 = client.chat.completions.create(
model=”local-model”, # Replace with the appropriate model name or identifier
messages=history,
temperature=0.0,
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)

return jsonify({“status”: “connected”})
except Exception as e:
return jsonify({“status”: “error”, “error_message”: str(e)})

def setup_ollama_routes(app):
app.register_blueprint(ollama_blueprint)

if __name__ == ‘__main__’:
app = Flask(__name__)
setup_ollama_routes(app)
app.run(port=5000) # Replace with the desired port number
“`

Make sure to replace `”local-model”` with the appropriate model name or identifier that corresponds to the local model you are using. Also, adjust the port number in `app.run(port=5000)` as needed.
can this be modified to use and endpoint above
Certainly! If you want to modify the code to use the `/ollama/connect` endpoint from the provided Flask blueprint, you can update the client code accordingly. Here’s how you can modify it:

“`python
from openai import OpenAI

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

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:
# Use the /ollama/connect endpoint to establish a connection
connection_response = client.api_request(‘POST’, ‘/ollama/connect’)

if connection_response.status_code == 200:
print(“Connected to Ollama.”)
else:
print(f”Error connecting to Ollama. Status code: {connection_response.status_code}”)
break

completion = client.chat.completions.create(
model=”local-model”, # Replace with the appropriate model name or identifier
messages=history,
temperature=0.0,
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)
“`

This modification assumes that the `/ollama/connect` endpoint returns a status code of 200 upon successful connection. Adjust the code according to the actual behavior of the `/ollama/connect` endpoint in your system.
I can’t use Openai with this version SO I am trying to use the llama :: from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import LlamaCpp

llm = LlamaCpp(
model_path=”/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin”,
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True,
)
If you’re using the LlamaCpp from langchain and want to integrate it with your code, you’ll need to modify the existing code to use LlamaCpp instead of OpenAI. Here’s a modified version of your code:

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

llm = LlamaCpp(
model_path=”/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin”,
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True,
)

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)
“`

This code assumes that `llm.invoke()` is the appropriate method for making predictions with LlamaCpp in your setup. Replace it with the correct method if needed. Also, make sure to adjust the parameters and paths according to your specific configuration and model paths.
Can this script be modified so I can use 127.0.0.1/v1??
Certainly, to modify the script to use “http://127.0.0.1:5151/v1″ as the base URL, you can replace the instantiation of `LlamaCpp` with the following:

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

llm = LlamaCpp(
model_path=”/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin”,
n_gpu_layers=1,
n_batch=512,
n_ctx=2048,
f16_kv=True,
callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),
verbose=True,
base_url=”http://127.0.0.1:5151/v1″ # Set the base_url to the desired value
)

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)
“`

Make sure to replace `”http://127.0.0.1:5151/v1″` with the actual URL you want to use. This change should make the script use the specified base URL for communication.
THERE SI NT A MODEL PATH –
If you are using an LLAMA model and there’s no model path provided in the script, it’s likely that LLAMA is set up to connect to a remote server (such as a community server) by default. If you want to use a local model and connect to “http://127.0.0.1:5151/v1”, you might need to adjust the LLAMA configuration.

Here’s an example of how you might modify the script to work with a local model:

“`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 = {
“base_url”: “http://127.0.0.1:5151/v1”,
“model_path”: “/path/to/local/model”,
“n_gpu_layers”: 1,
“n_batch”: 512,
“n_ctx”: 2048,
“f16_kv”: True,
“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)
“`

In this modified script, the LLAMA configuration (`llm_config`) explicitly includes the local model path (`”model_path”: “/path/to/local/model”`) and the desired base URL (`”base_url”: “http://127.0.0.1:5151/v1″`). Adjust the model path accordingly based on the location of your local LLAMA model.
close but lots of errors: Traceback (most recent call last):
File “D:SarahOneGPTEgpt-engineerproject2app.py”, line 2, in
from ollama import setup_ollama_routes
File “D:SarahOneGPTEgpt-engineerproject2ollama.py”, line 25, in
completion = llm.invoke(history)
^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_corelanguage_modelsllms.py”, line 235, in invoke
self.generate_prompt(
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_corelanguage_modelsllms.py”, line 530, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_corelanguage_modelsllms.py”, line 703, in generate
output = self._generate_helper(
^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_corelanguage_modelsllms.py”, line 567, in _generate_helper
raise e
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_corelanguage_modelsllms.py”, line 554, in _generate_helper
self._generate(
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_corelanguage_modelsllms.py”, line 1139, in _generate
self._call(prompt, stop=stop, run_manager=run_manager, **kwargs)
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_communityllmsllamacpp.py”, line 291, in _call
for chunk in self._stream(
File “c:ProgramDataminiconda3envsEngineLibsite-packageslangchain_communityllmsllamacpp.py”, line 344, in _stream
for part in result:
File “c:ProgramDataminiconda3envsEngineLibsite-packagesllama_cppllama.py”, line 888, in _create_completion
for token in self.generate(
File “c:ProgramDataminiconda3envsEngineLibsite-packagesllama_cppllama.py”, line 643, in generate
self.eval(tokens)
File “c:ProgramDataminiconda3envsEngineLibsite-packagesllama_cppllama.py”, line 483, in eval
self._ctx.decode(self._batch)
File “c:ProgramDataminiconda3envsEngineLibsite-packagesllama_cpp_internals.py”, line 309, in decode
return_code = llama_cpp.llama_decode(
^^^^^^^^^^^^^^^^^^^^^^^
File “c:ProgramDataminiconda3envsEngineLibsite-packagesllama_cppllama_cpp.py”, line 1622, in llama_decode
return _lib.llama_decode(ctx, batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
It seems like there are issues with the LLAMA integration. Unfortunately, without access to the specific LLAMA version and its documentation, it’s challenging to provide an exact solution. However, I can guide you through some common debugging steps:

1. **Check LLAMA Documentation:** Review the LLAMA documentation to ensure that you are using the correct method for setting up a local model and connecting to it. Verify if there are any specific requirements or configurations needed for local deployment.

2. **Model Path:** Ensure that the model path you provided in the LLAMA configuration is correct and points to the local model on your system.

3. **LLAMA Version:** Make sure you are using a version of LLAMA that supports the features and configurations you are trying to use. Check for updates or specific release notes that might address issues or provide guidance.

4. **Error Details:** Examine the error messages in detail. The traceback you provided indicates an issue in the LLAMA library, specifically in the llama_cpp module. Look for any specific error messages or context in the LLAMA documentation or source code.

5. **Community Support:** If LLAMA has a community or support forum, consider reaching out to the community for assistance. Others who have encountered similar issues may provide insights or solutions.

If you can provide more details about the LLAMA version you are using and any specific error messages from the LLAMA library, it would be helpful in offering more targeted assistance.
import requests

def dump_server_responses(model=”Lilith”):
endpoint = “http://localhost:5151/v1/completions”

data = {
“model”: model,
“prompt”: f”What are you functions?”,
“temperature”: 0.7,
“stream”: False,
“json_output”: True,
“max_tokens”: 256
}

response = requests.post(endpoint, json=data)

try:
response_json = response.json()
print(response_json)
spoken_text = response_json[‘choices’][0][‘text’]
print(f”Server Response:”)
print(spoken_text)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {e}”)

if __name__ == “__main__”:
dump_server_responses()
SAYS response object not defined Does it need be here?
It seems like there is a small indentation issue in your code, which might be causing the problem. The `try` block should be indented properly. Here’s the corrected code:

“`python
import requests

def dump_server_responses(model=”Lilith”):
endpoint = “http://localhost:5151/v1/completions”

data = {
“model”: model,
“prompt”: f”What are your functions?”,
“temperature”: 0.7,
“stream”: False,
“json_output”: True,
“max_tokens”: 256
}

response = requests.post(endpoint, json=data)

try:
response_json = response.json()
print(response_json)
spoken_text = response_json[‘choices’][0][‘text’]
print(f”Server Response:”)
print(spoken_text)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {e}”)

if __name__ == “__main__”:
dump_server_responses()
“`

Now, the `try` block is correctly indented under the `response` assignment. This should resolve the issue with the “response object not defined” error.
I need to guide the llm’s better. Is there room for a preprompt? import requests

def dump_server_responses(model=”Lilith”):
endpoint = “http://localhost:5151/v1/completions”

data = {
“model”: model,
“prompt”: f”What are your functions?”,
“temperature”: 0.7,
“stream”: False,
“json_output”: True,
“max_tokens”: 256
}

response = requests.post(endpoint, json=data)

try:
response_json = response.json()
print(response_json)
spoken_text = response_json[‘choices’][0][‘text’]
print(f”Server Response:”)
print(spoken_text)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {e}”)

if __name__ == “__main__”:
dump_server_responses()

If you want to include a “preprompt” or additional context before the main prompt, you can concatenate the strings in the “prompt” field. Here’s an example:

“`python
import requests

def dump_server_responses(model=”Lilith”):
endpoint = “http://localhost:5151/v1/completions”

preprompt = “Before we begin, let me ask you: ”
main_prompt = “What are your functions?”

data = {
“model”: model,
“prompt”: preprompt + main_prompt,
“temperature”: 0.7,
“stream”: False,
“json_output”: True,
“max_tokens”: 256
}

response = requests.post(endpoint, json=data)

try:
response_json = response.json()
print(response_json)
spoken_text = response_json[‘choices’][0][‘text’]
print(f”Server Response:”)
print(spoken_text)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {e}”)

if __name__ == “__main__”:
dump_server_responses()
“`

In this example, I added a `preprompt` variable and concatenated it with the `main_prompt` to form the complete prompt. This way, you can provide additional context or questions before asking the main question. Adjust the content of `preprompt` as needed for your specific use case.
Well I need to direct the models to specifically coding in python and to asnwer questions fully. Acting as chief engineer.
Certainly! To guide the models to specifically code in Python and answer questions fully, you can craft prompts that explicitly instruct the model. Here’s an example:

“`python
import requests

def generate_python_code(model=”Lilith”):
endpoint = “http://localhost:5151/v1/completions”

preprompt = “You are the chief engineer. Write Python code to”
main_prompt = “solve the following problem:”

problem_description = “Write a function that calculates the factorial of a given number.”

data = {
“model”: model,
“prompt”: f”{preprompt} {main_prompt} {problem_description}”,
“temperature”: 0.7,
“stream”: False,
“json_output”: True,
“max_tokens”: 256
}

response = requests.post(endpoint, json=data)

try:
response_json = response.json()
print(response_json)
generated_code = response_json[‘choices’][0][‘text’]
print(f”Generated Python Code:”)
print(generated_code)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {e}”)

if __name__ == “__main__”:
generate_python_code()
“`

In this example, the prompt is crafted to instruct the model to write Python code for a specific problem. You can customize the `problem_description` variable to ask different coding or engineering-related questions. Adjust the prompts and content based on the specific guidance you want to provide to the models.
Here is the response: Generated Python Code:
The order in which you perform operations is important and should be kept as consistent as possible.
In fact, if we want to calculate the factorial of any integer, then we need only to take it from 0 to n – 1. So, for example, for 48, we need to
write the code as follows: def factorial(num): if num console.error(err))
.setOnMessage((packet: InworldPacket) => {
console.log(packet);

if (packet.isInteractionEnd()) {
// Close connection.
connection.close();
}
});

// Finish connection configuration.
const connection = client.build();

// Send your message to a character.
await connection.sendText(‘Hello’);
}

sayHello(); I need to understand how to run this in VSCode
To run the provided TypeScript code in VSCode, you need to have Node.js and TypeScript installed. Here are the steps you can follow:

1. **Install Node.js and npm:**
– Make sure you have Node.js installed. You can download it from [here](https://nodejs.org/).

2. **Install TypeScript Globally:**
– Open a terminal in VSCode and run the following command to install TypeScript globally:
“`bash
npm install -g typescript
“`

3. **Initialize a TypeScript Project:**
– In your project directory, create a `tsconfig.json` file to configure TypeScript. You can do this by running:
“`bash
tsc –init
“`

4. **Install Dependencies:**
– Install the required dependencies by running the following command in your project directory:
“`bash
npm install @inworld/nodejs-sdk
“`

5. **Environment Variables:**
– Make sure to set the environment variables (`INWORLD_KEY`, `INWORLD_SECRET`, and `INWORLD_SCENE`) before running the script.

6. **Run the TypeScript File:**
– Create a TypeScript file (e.g., `app.ts`) and paste your code into it.

– Open a terminal in VSCode and run the following command to compile and execute the TypeScript file:
“`bash
tsc && node app.js
“`

This command will compile the TypeScript code into JavaScript (`app.js`) and then run the JavaScript file using Node.js.

7. **Debugging in VSCode:**
– You can set breakpoints in your TypeScript file for debugging. Create a `launch.json` file in the `.vscode` directory with the following configuration:
“`json
{
“version”: “0.2.0”,
“configurations”: [
{
“type”: “node”,
“request”: “launch”,
“name”: “Debug TypeScript”,
“program”: “${workspaceFolder}/app.ts”,
“outFiles”: [“${workspaceFolder}/app.js”],
“sourceMaps”: true
}
]
}
“`

– Press F5 to start debugging.

Make sure to replace `app.ts` with the actual name of your TypeScript file. Additionally, ensure that your environment variables are set before running the script.
tsc –init won;t runt tsc not recognized as a command
If `tsc` is not recognized as a command, it means that TypeScript is not installed globally on your system. You can install TypeScript globally using the following command:

“`bash
npm install -g typescript
“`

After installing TypeScript globally, you should be able to run `tsc –init` to generate a `tsconfig.json` file. If you still encounter issues, you might need to restart your terminal or VSCode to apply the changes.

Once TypeScript is installed, you can proceed with the other steps mentioned in the previous response to set up your project and run the TypeScript file.
never mind
No problem! If you have any more questions or if there’s anything else I can help you with, feel free to ask. Happy coding!
Too complicated
I understand that things can sometimes be complex, especially when working with various tools and technologies. If there’s a specific area or task that you find challenging, feel free to provide more details, and I’ll do my best to simplify or assist you. Whether it’s related to coding, tools, or any other topic, I’m here to help in any way I can.
WIll you put these in an accordion style HTML, page please? I will want to add pictures of a standard size to the left 240x240px so place have a placeholder. Info panel: Manufacturer text: in 8pt bold (since is not known here just put “custom build”. Ship Name text: 12pt inkfree italic. IDENT text: 10pt inkfree italic. The other fields are not needed. SO lets build this page programaticaly, using using a simple text file with CSV and as new lines are added, the page is built with these new additions. Possible? Here is the initial data which is to be cleaned:
FLEET CARRIER dR tRIPPER uNIVERSAL (T2G-NXV)
LOCATION Brestla
FUEL LEVEL 58% (576)
CARRIER BALANCE 1,081,126,094 Cr
RESERVE 129,441,178 Cr
NAME: Python
IDENT:
TYPE: Python
ROLE:
VALUE: 0 Cr
DOCK: Brestla [i Sola Prospect]
︎
NAME: Asp Scout
IDENT: DR-10A
TYPE: Asp Scout
ROLE:
VALUE: 6,518,345 Cr
CURRENT SHIP
︎
NAME: deez-nuts
IDENT: deez
TYPE: Sidewinder Mk I
ROLE:
VALUE: 2,232,237 Cr
DOCK: Brestla [i Sola Prospect]
︎
NAME: Type-6 Transporter
IDENT: DR-10T
TYPE: Type-6 Transporter
ROLE:
VALUE: 4,114,589 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Type-7 Transporter
IDENT: DR-10T
TYPE: Type-7 Transporter
ROLE:
VALUE: 36,183,065 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Type-10 Defender
IDENT: DR-23T
TYPE: Type-10 Defender
ROLE:
VALUE: 875,874,673 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Sarah Alpha
IDENT: 769Hz
TYPE: Imperial Cutter
ROLE: Multipurpose
VALUE: 1,150,371,683 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Lucy’s Child
IDENT: LC-305
TYPE: Imperial Clipper
ROLE:
VALUE: 214,606,453 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Ambassador – 1
IDENT: Amb-1
TYPE: Imperial Courier
ROLE:
VALUE: 15,502,628 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Anaconda
IDENT: DR-20A
TYPE: Anaconda
ROLE:
VALUE: 375,514,669 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Cobra Mk III
IDENT: DR-07C
TYPE: Cobra Mk III
ROLE:
VALUE: 9,672,239 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Krait Mk II
IDENT: DR-17K
TYPE: Krait Mk II
ROLE:
VALUE: 118,862,521 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Viper Mk III
IDENT:
TYPE: Viper Mk III
ROLE:
VALUE: 0 Cr
DOCK: Shinrarta Dezhra [Jameson Memorial]
︎
NAME: Viper Mk IV
IDENT: DR-07V
TYPE: Viper Mk IV
ROLE:
VALUE: 5,172,474 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Viper Mk III
IDENT: DR-07V
TYPE: Viper Mk III
ROLE:
VALUE: 6,201,256 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Viper Mk IV
IDENT: DR-07V
TYPE: Viper Mk IV
ROLE:
VALUE: 9,945,108 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Eagle Mk II
IDENT: SL-18E
TYPE: Eagle Mk II
ROLE:
VALUE: 183,959 Cr
DOCK: Di Jian [Preuss Terminal]
︎
NAME: Eagle Mk II
IDENT: DR-07E
TYPE: Eagle Mk II
ROLE:
VALUE: 1,312,299 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Federal Assault Ship
IDENT: DR-07F
TYPE: Federal Assault Ship
ROLE:
VALUE: 52,007,524 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Experimental power
IDENT: EXp-30
TYPE: Federal Corvette
ROLE:
VALUE: 573,721,430 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Dripping Venom
IDENT: TAO-A1
TYPE: Federal Corvette
ROLE: Multipurpose
VALUE: 670,435,452 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Federal Dropship
IDENT: DR-07F
TYPE: Federal Dropship
ROLE:
VALUE: 49,844,519 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Federal Gunship
IDENT: DR-07F
TYPE: Federal Gunship
ROLE:
VALUE: 132,737,585 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Vulture
IDENT: DR-03V
TYPE: Vulture
ROLE:
VALUE: 35,696,026 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Exploration 505
IDENT: SLURRR
TYPE: Diamondback Explorer
ROLE:
VALUE: 15,934,222 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Command Ship I
IDENT: SL-URR
TYPE: Alliance Chieftain
ROLE: Multipurpose
VALUE: 80,146,859 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Zeno Warrior Princess
IDENT: ZWP-1
TYPE: Alliance Crusader
ROLE: Multipurpose
VALUE: 113,968,006 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Alliance Challenger
IDENT:
TYPE: Alliance Challenger
ROLE:
VALUE: 30,472,265 Cr
DOCK: []
︎
NAME: Long shot
IDENT: Slurrr
TYPE: Alliance Challenger
ROLE: Multipurpose
VALUE: 122,580,719 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Keelback
IDENT: DR-07I
TYPE: Keelback
ROLE:
VALUE: 7,576,798 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Ricky Racoon
IDENT: RR1
TYPE: Type-9 Heavy
ROLE: Multipurpose
VALUE: 217,777,122 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Asp Explorer
IDENT: DR-07A
TYPE: Asp Explorer
ROLE: Multipurpose
VALUE: 29,911,801 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Asp Explorer
IDENT: SL-07A
TYPE: Asp Explorer
ROLE: Multipurpose
VALUE: 33,482,225 Cr
DOCK: Shinrarta Dezhra [Jameson Memorial]
︎
NAME: Adder
IDENT: DR-07A
TYPE: Adder
ROLE:
VALUE: 6,325,986 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Hauler
IDENT: DR-07H
TYPE: Hauler
ROLE:
VALUE: 1,929,566 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Fer-de-Lance
IDENT: SL-13F
TYPE: Fer-de-Lance
ROLE: Multipurpose
VALUE: 91,072,099 Cr
DOCK: Shinrarta Dezhra [Jameson Memorial]
︎
NAME: A La Mamba
IDENT: SL-606
TYPE: Mamba
ROLE: Multipurpose
VALUE: 126,411,993 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Beluga Liner
IDENT: DR-21B
TYPE: Beluga Liner
ROLE:
VALUE: 166,137,821 Cr
DOCK: Brestla [T2G-NXV]
︎
NAME: Dolphin
IDENT: DR-31D
TYPE: Dolphin
ROLE: Multipurpose
VALUE: 10,381,299 Cr
DOCK: Brestla [T2G-NXV]
Certainly! Below is an example of an HTML page with accordion-style elements. I’ve used a simple text file format to represent the data you provided. Please replace “placeholder.jpg” with the actual path to your placeholder image.

“`html

body {
font-family: Arial, sans-serif;
}

.accordion {
background-color: #f1f1f1;
color: #333;
cursor: pointer;
padding: 18px;
width: 100%;
text-align: left;
border: none;
outline: none;
transition: 0.4s;
}

.active,
.accordion:hover {
background-color: #ddd;
}

.panel {
display: none;
padding: 0 18px;
background-color: white;
overflow: hidden;
}

img {
max-width: 240px;
max-height: 240px;
}

.info {
font-size: 8pt;
font-weight: bold;
}

.ship-name {
font-size: 12pt;
font-style: italic;
}

.ident {
font-size: 10pt;
font-style: italic;
}

function toggleAccordion(id) {
var panel = document.getElementById(id);
panel.style.display = (panel.style.display === ‘block’) ? ‘none’ : ‘block’;
}

“`

This is a basic example, and you can customize the styles and layout as needed. If you have more ships to add, you can easily replicate the structure for each one.
import React, { useState } from ‘react’; import axios from ‘axios’; // Import Axios interface Props { onSendMessage: (message: { role: string; content: string }) => void; }

const ChatInput: React.FC = ({ onSendMessage }) => { const [inputValue, setInputValue] = useState(‘’);

const handleSendMessage = (event, inputValue, isFirstMessage) => { event.preventDefault();

// Role information for the AI assistant
const role = “EVA (Eccentric Virtual Assistant)”;

// Define the system message (introductory message)
const systemMessage = `As ${role}, your personal assistant, I’m here to assist you efficiently and effectively. I will not remind you that I am a language model or in any way indicate that I am AI. I communicate professionally and succinctly unless asked otherwise. If you’d like a more relaxed and personal chat, simply let me know by typing ”.`;

// Construct the message content based on whether it’s the initial system message or user’s input
let messageContent;
if (isFirstMessage) {
messageContent = systemMessage;
} else {
// Concatenate the user’s input value with any additional text
messageContent = `${inputValue}`;
// You can append more text or customize the message further here
}

// Send the message to the server via Axios POST request
axios
.post(‘http://localhost:5050/v1/chat/completions/’, {
messages: [{ role: role, content: messageContent }]
})
.then((response) => {
// Handle the response if needed (e.g., update UI with server’s response)
console.log(‘Message sent successfully:’, response.data);

// Clear the input value after sending the message
if (!isFirstMessage) {
// Only clear input value if it’s not the first message (to avoid clearing the system message)
setInputValue(”); // Assuming setInputValue is a function to update the input state
}
})
.catch((error) => {
console.error(‘Error sending message:’, error);
// Handle network or other errors
});
};

return (

); };

export default ChatInput; :: The string that is being sent to the server is wrong::Error: [2024-05-08 09:13:57.320] [INFO] Received POST request to /v1/chat/completions/ with body: { “messages”: [ { “role”: “EVA (Eccentric Virtual Assistant)”, “content”: “undefined” } ] } [2024-05-08 09:13:57.320] [ERROR] [Server Error] {“title”:“‘messages’ array must only contain objects with a ‘role’ field that is either ‘user’, ‘assistant’, or ‘system’. Got ‘EVA (Eccentric Virtual Assistant)’”}

The error you’re encountering indicates that the server expects specific values for the `role` field within the `messages` array. The error message suggests that the `role` should be either `’user’`, `’assistant’`, or `’system’`, but it received `’EVA (Eccentric Virtual Assistant)’`.

To resolve this issue, you should modify the `role` value to match one of the expected values (‘user’, ‘assistant’, or ‘system’). Additionally, ensure that the `content` field is correctly populated with the message content before sending the POST request to the server.

Here’s how you can adjust your code to address this problem:

“`javascript
import React, { useState } from ‘react’;
import axios from ‘axios’;

interface Props {
onSendMessage: (message: { role: string; content: string }) => void;
}

const ChatInput: React.FC = ({ onSendMessage }) => {
const [inputValue, setInputValue] = useState(”);

const handleSendMessage = (event) => {
event.preventDefault();

// Role information for the AI assistant
const role = ‘user’; // Set role to ‘user’ for user messages

// Construct the message content
const messageContent = inputValue.trim(); // Trim input value to remove leading/trailing spaces

// Send the message to the server via Axios POST request
axios
.post(‘http://localhost:5050/v1/chat/completions/’, {
messages: [{ role: role, content: messageContent }]
})
.then((response) => {
// Handle the response if needed (e.g., update UI with server’s response)
console.log(‘Message sent successfully:’, response.data);

// Clear the input value after sending the message
setInputValue(”);
})
.catch((error) => {
console.error(‘Error sending message:’, error);
// Handle network or other errors
});
};

return (

);
};

export default ChatInput;
“`

In this updated code:

– The `role` for the message sent to the server is set to `’user’`, which is typically used for user messages.
– The `content` is derived directly from the `inputValue` after trimming any leading or trailing spaces using `trim()`.
– When the send button is clicked (`handleSendMessage` function), it constructs the message object with the correct `role` and `content`, then sends it to the server using Axios.
– After sending the message successfully, it clears the `inputValue` to prepare for the next input.

Make sure to adjust the `role` value based on the server’s requirements and the role of the message being sent (e.g., `’user’` for user input). This should resolve the issue you encountered with the server rejecting the message due to an incorrect `role` field.
How then do I add the pre-prompt or system prompt

How then do I add the pre-prompt or system prompt?

To incorporate a pre-prompt or system message into your chat input component, you can modify the `handleSendMessage` function to handle different
To incorporate a system prompt or pre-prompt into your chat input component, you can modify the component to handle different types of messages (such as system messages) alongside user messages. Here’s an updated version of your `ChatInput` component that supports sending system prompts and user messages:

“`javascript
import React, { useState } from ‘react’;
import axios from ‘axios’;

interface Props {
onSendMessage: (message: { role: string; content: string }) => void;
}

const ChatInput: React.FC = ({ onSendMessage }) => {
const [inputValue, setInputValue] = useState(”);

const handleSendMessage = (event, isFirstMessage = false) => {
event.preventDefault();

// Determine the role based on whether it’s the first system message or a user message
const role = isFirstMessage ? ‘system’ : ‘user’;

// Construct the message content
let messageContent;
if (isFirstMessage) {
messageContent = `As EVA (Eccentric Virtual Assistant), I’m here to assist you. Type your message below.`;
} else {
messageContent = inputValue.trim(); // Trim input value to remove leading/trailing spaces
}

// Send the message to the server via Axios POST request
axios
.post(‘http://localhost:5050/v1/chat/completions/’, {
messages: [{ role: role, content: messageContent }]
})
.then((response) => {
// Handle the response if needed (e.g., update UI with server’s response)
console.log(‘Message sent successfully:’, response.data);

// Clear the input value after sending the message
if (!isFirstMessage) {
setInputValue(”);
}
})
.catch((error) => {
console.error(‘Error sending message:’, error);
// Handle network or other errors
});
};

// Initial system message when the component mounts
React.useEffect(() => {
handleSendMessage(null, true); // Send the initial system prompt message
}, []); // Run only once when the component mounts

return (

);
};

export default ChatInput;
“`

In this updated code:

– The `handleSendMessage` function now accepts an optional `isFirstMessage` parameter. This parameter indicates whether the message being sent is the initial system prompt (true) or a user input message (false).
– When the component mounts (`useEffect` with an empty dependency array `[]`), it automatically sends the initial system prompt message by calling `handleSendMessage(null, true)`.
– The `role` and `content` of the message are constructed differently based on whether it’s the first message or a user input message.
– For the initial system prompt message, the `role` is set to `’system’` and the `content` is a predefined prompt.
– For user input messages, the `role` is set to `’user’` and the `content` is derived from the `inputValue` after trimming any leading/trailing spaces.

This setup allows your `ChatInput` component to handle both system prompts and user messages appropriately when sending requests to the server. Adjust the system prompt message content (`systemMessage`) as needed to fit your application’s requirements.
Uncaught runtime errors:
×
ERROR
Cannot read properties of null (reading ‘preventDefault’)
TypeError: Cannot read properties of null (reading ‘preventDefault’)
at handleSendMessage
The error you’re encountering indicates that the `event` object is `null` when the `handleSendMessage` function is invoked. This error typically occurs when the event handler is not properly bound or triggered.

To resolve this issue, you can modify the `onClick` event handler for the “Send” button in your `ChatInput` component to ensure that the `event` object is properly passed and handled. Here’s how you can adjust the `onClick` handler:

“`javascript
import React, { useState } from ‘react’;
import axios from ‘axios’;

interface Props {
onSendMessage: (message: { role: string; content: string }) => void;
}

const ChatInput: React.FC = ({ onSendMessage }) => {
const [inputValue, setInputValue] = useState(”);

const handleSendMessage = (event: React.MouseEvent, isFirstMessage = false) => {
event.preventDefault();

// Determine the role based on whether it’s the first system message or a user message
const role = isFirstMessage ? ‘system’ : ‘user’;

// Construct the message content
let messageContent;
if (isFirstMessage) {
messageContent = `As EVA (Eccentric Virtual Assistant), I’m here to assist you. Type your message below.`;
} else {
messageContent = inputValue.trim(); // Trim input value to remove leading/trailing spaces
}

// Send the message to the server via Axios POST request
axios
.post(‘http://localhost:5050/v1/chat/completions/’, {
messages: [{ role: role, content: messageContent }]
})
.then((response) => {
// Handle the response if needed (e.g., update UI with server’s response)
console.log(‘Message sent successfully:’, response.data);

// Clear the input value after sending the message
if (!isFirstMessage) {
setInputValue(”);
}
})
.catch((error) => {
console.error(‘Error sending message:’, error);
// Handle network or other errors
});
};

// Initial system message when the component mounts
React.useEffect(() => {
handleSendMessage(null, true); // Send the initial system prompt message
}, []); // Run only once when the component mounts

return (

);
};

export default ChatInput;
“`

In this updated code:

– The `handleSendMessage` function now specifies `event: React.MouseEvent` as the type for the `event` parameter of the `onClick` handler. This ensures that the event object passed to the function is of the correct type.
– The `event.preventDefault()` method is called within `handleSendMessage` to prevent the default behavior of the button click event (e.g., form submission).
– When invoking the `handleSendMessage` function in the `onClick` handler of the “Send” button (``), we explicitly pass the `event` object (`e`) to ensure that it is properly handled within the function.

By making these adjustments, you should no longer encounter the “Cannot read properties of null” error when clicking the “Send” button in your `ChatInput` component. This ensures that the event object is correctly handled within the event handler function.
**Integration with react-grid-layout:**

To integrate the react-grid-layout library into a React application to enable users to arrange and customize a dashboard layout with resizable and draggable widgets, you can follow these steps:

1. **Installation:** Install the library using npm or yarn by executing the following command in your project directory:
“`
npm install react-grid-layout
“`

2. **Import the grid components:** In your main application component file (`App.js`), import the `Grid, GridLayout, and Responsive` components from the installed `react-grid-layout` module. Below is a sample code snippet that shows an example of how to do this:
“`js
import { Grid, GridLayout, Responsive } from ‘react-grid-layout’;
“`

3. **Wrap the grid items:** Wrap each of your widgets (grid item) within the `Grid` component. You can define the column width and row height in the `layout` property by using the `GridLayout` component or use a pre-defined layout with `Responsive`. Below is an example code snippet that shows an example of how to do this:
“`jsx

{
widgets.map((widget) => (

{/* your widget component here */}

))
}

“`

4. **Widget Interactions:** Implement an event handler for the onDrag and onResize events by defining a function to handle these events in the `componentDidMount` lifecycle method of each grid item component. Below is a sample code snippet that shows how you can do this:
“`js
import React, { useEffect } from ‘react’;
import { useGridLayout } from ‘react-grid-layout’;

const App = () => {
const [width, setWidth] = useState(0);

// Create the grid items.
const widgets = [
{
id: 1,
name: “Widget 1”,
position: {
x: 0,
y: 0
}
},
{
id: 2,
name: “Widget 2”,
position: {
x: 3,
y: 0
}
}
];

// Define the grid layout.
const [grid, setGrid] = useGridLayout({
rowHeight: 30,
cols: 12,
});

useEffect(() => {
const handleOnDragStart = (event) => {
console.log(“Handle on drag start”);
};

const handleOnDragStop = () => {
console.log(“Handle on drag stop”);
};

// Define the event handlers for the grid items.
const eventHandlers = {
onDragStart: handleOnDragStart,
onDragStop: handleOnDragStop
};

setGrid(grid);
}, [width]);

return (

{widgets.map((widget) => (

{/* your widget component here */}

))
}

“`

5. **Rendering the grid layout:** In your main `App.js` file, import the `GridLayout` and use it as a parent element for all of the grid items you created in step 3. This will allow users to interactively resize and rearrange the widgets.

**Example with widget and event handling:**

Here is an example of how you can set up the grid items and provide event handlers for them:
“`jsx
import React, { useState } from ‘react’;
import ‘./styles.css’;
import { GridLayout, Grid, Responsive } from ‘react-grid-layout’;

const App = () => {
const [width, setWidth] = useState(0);
const widgets = [
{
id: 1,
name: “Widget 1”,
position: {
x: 0,
y: 0
}
},
{
id: 2,
name: “Widget 2”,
position: {
x: 3,
y: 0
}
}
];

// Define the grid layout.
const [grid, setGrid] = useGridLayout({
rowHeight: 30,
cols: 12,
});

useEffect(() => {
const handleOnDragStart = (event) => {
console.log(“Handle on drag start”);
};

const handleOnDragStop = () => {
console.log(“Handle on drag stop”);
};

// Define the event handlers for the grid items.
const eventHandlers = {
onDragStart: handleOnDragStart,
onDragStop: handleOnDragStop
};

setGrid(grid);
}, [width]);

return (

{widgets.map((widget) => (

{/* your widget component here */}

))}

);
};
“`

**Using the context API for communication between components:**

To use React’s Context API to facilitate communication between dashboard widgets, you can create a provider and consumer that holds and updates an application-wide state. Below is a sample code snippet that shows how you can do this:

1. **Provider:** The `DashboardProvider` component will hold the state and make it available to any of its children in the tree through context:
“`js
const DashboardContext = React.createContext();
“`

2. **Consumer:** Create a consumer for the application-wide state held by your provider:
“`js
function DashboardConsumer({ children }) {
return {children};
}
“`
3. **The Provider-Consumer pattern:** Wrap any of your widgets within this consumer to gain access to the context and share a value between components. Below is a sample code snippet that shows an example of how you can do this:
“`js
const DashboardProvider = ({ children }) => {
return {children};
};
“`

4. **Updating the state:** In order to update the context with a value, you need to pass a function `updateWidget` that updates the widget’s name using this code:
“`js
const handleUpdate = () => {
setWidgetName((previousState) =>
(Math.floor(Math.random() * 100)) + 1);
};
“`

5. **Accessing and updating the state:** Within a component that is wrapped in the `DashboardConsumer`, you can access the value of the context using the `useContext` hook:
“`js
import { useContext } from ‘react’;
import { setWidgetName } from ‘./context.js’;

const Context = useContext(DashboardContext);
“`

6. **Updating and receiving information from another widget:** Using the code above, you can make a call to `setWidgetName` with any information that you want to share between components. This will be accessible in all of the components that are wrapped in `DashboardConsumer`. Below is an example of how you might use this:
“`js
import { useContext } from ‘react’;
import setWidgetName from ‘./context.js’;

const Context = useContext(DashboardContext);

export default () => {
// Update widget 1’s name by clicking the button.
function updateWidgets() {
handleUpdate();
}

return (

Widget 1

{/* Button to update the state */}

Click me!

{/* Context.Consumer will be used below. It provides access to any of the context values in your application tree, starting at where you wrap it. In this case, we are wrapping the ‘DashboardProvider’ so all components within have access to the updated state. */}

{({ widgetName }) => (

Widget 1’s name: {widgetName}

{/* You can get the value and use it below */}

Here is a paragraph of text with some details about this information that was shared to all components.

{‘ ‘}
{
setWidgetName((widget) => widget + 1);
}}
>
Click me!

)}

Current Widget: {currentWidget}

{/* You could even use an API call here to update the widget’s information. */}

{‘ ‘}
setCurrentWidget(2)}
>
Click me!

Current Widget: {currentWidget}

{/* You could even use an API call here to update the widget’s information. */}

{‘ ‘}
setCurrentWidget(1)}
>
Click me!

handleWidgetSelect(‘widget1’)}>Select Widget 1
handleWidgetSelect(‘widget2’)}>Select Widget 2

Selected Widget: {selectedWidget}