can you correct the errors here: # To install required packages:
# pip install pyautogen==0.2.9 panel==1.3.8
import autogen

import panel as pn
import openai
import os
import time
import asyncio
eva_client = ChatCompletion(base_url=”http://localhost:5151/v1/chat/completions”, api_key=”not-needed”)
eva_history = [
{“role”: “system”, “content”: “You are an intelligent assistant. You always provide well-reasoned answers that are both correct and helpful.”},
{“role”: “user”, “content”: “Hello, introduce yourself to someone opening this program for the first time. Be concise.”},
]

input_future = None

class MyConversableAgent(autogen.ConversableAgent):

async def a_get_human_input(self, prompt: str) -> str:
global input_future
print(‘AGET!!!!!!’) # or however you wish to display the prompt
chat_interface.send(prompt, user=”System”, respond=False)
# Create a new Future object for this input operation if none exists
if input_future is None or input_future.done():
input_future = asyncio.Future()

# Wait for the callback to set a result on the future
await input_future

# Once the result is set, extract the value and reset the future for the next input operation
input_value = input_future.result()
input_future = None
return input_value

user_proxy = MyConversableAgent(
name=”Admin”,
is_termination_msg=lambda x: x.get(“content”, “”).rstrip().endswith(“exit”),
system_message=”””A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.

“””,
#Only say APPROVED in most cases, and say exit when nothing to be done further. Do not say others.
code_execution_config=False,
#default_auto_reply=”Approved”,
human_input_mode=”ALWAYS”,
#llm_config=gpt4_config,
)

engineer = autogen.AssistantAgent(
name=”Engineer”,
human_input_mode=”NEVER”,
llm_config=gpt4_config,
system_message=”’Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can’t modify your code. So do not suggest incomplete code which requires others to modify. Don’t use a code block if it’s not intended to be executed by the executor.
Don’t include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor.
If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can’t be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
”’,
)
scientist = autogen.AssistantAgent(
name=”Scientist”,
human_input_mode=”NEVER”,
llm_config=gpt4_config,
system_message=”””Scientist. You follow an approved plan. You are able to categorize papers after seeing their abstracts printed. You don’t write code.”””
)
planner = autogen.AssistantAgent(
name=”Planner”,
human_input_mode=”NEVER”,
system_message=”’Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval.
The plan may involve an engineer who can write code and a scientist who doesn’t write code.
Explain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist.
”’,
llm_config=gpt4_config,
)
executor = autogen.UserProxyAgent(
name=”Executor”,
system_message=”Executor. Execute the code written by the engineer and report the result.”,
human_input_mode=”NEVER”,
code_execution_config={“last_n_messages”: 3, “work_dir”: “paper”},
)
critic = autogen.AssistantAgent(
name=”Critic”,
system_message=”””Critic. Double check plan, claims, code from other agents and provide feedback.
Check whether the plan includes adding verifiable info such as source URL.
“””,
llm_config=gpt4_config,
human_input_mode=”NEVER”,
)

groupchat = autogen.GroupChat(agents=[user_proxy, engineer, scientist, planner, executor, critic], messages=[], max_round=20)
manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=gpt4_config)

avatar = {user_proxy.name:”👨‍💼”, engineer.name:”👩‍💻”, scientist.name:”👩‍🔬”, planner.name:”🗓”, executor.name:”🛠”, critic.name:’📝’}

def print_messages(recipient, messages, sender, config):

print(f”Messages from: {sender.name} sent to: {recipient.name} | num messages: {len(messages)} | message: {messages[-1]}”)

content = messages[-1][‘content’]

if all(key in messages[-1] for key in [‘name’]):
chat_interface.send(content, user=messages[-1][‘name’], avatar=avatar[messages[-1][‘name’]], respond=False)
else:
chat_interface.send(content, user=recipient.name, avatar=avatar[recipient.name], respond=False)

return False, None # required to ensure the agent communication flow continues

user_proxy.register_reply(
[autogen.Agent, None],
reply_func=print_messages,
config={“callback”: None},
)

engineer.register_reply(
[autogen.Agent, None],
reply_func=print_messages,
config={“callback”: None},
)
scientist.register_reply(
[autogen.Agent, None],
reply_func=print_messages,
config={“callback”: None},
)
planner.register_reply(
[autogen.Agent, None],
reply_func=print_messages,
config={“callback”: None},
)

executor.register_reply(
[autogen.Agent, None],
reply_func=print_messages,
config={“callback”: None},
)
critic.register_reply(
[autogen.Agent, None],
reply_func=print_messages,
config={“callback”: None},
)
pn.extension(design=”material”)

initiate_chat_task_created = False

async def delayed_initiate_chat(agent, recipient, message):

global initiate_chat_task_created
# Indicate that the task has been created
initiate_chat_task_created = True

# Wait for 2 seconds
await asyncio.sleep(2)

# Now initiate the chat
await agent.a_initiate_chat(recipient, message=message)

async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):

global initiate_chat_task_created
global input_future

if not initiate_chat_task_created:
asyncio.create_task(delayed_initiate_chat(user_proxy, manager, contents))

else:
if input_future and not input_future.done():
input_future.set_result(contents)
else:
print(“There is currently no input being awaited.”)

chat_interface = pn.chat.ChatInterface(callback=callback)
chat_interface.send(“Send a message!”, user=”System”, respond=False)
chat_interface.servable()
Here are the corrected errors and suggestions for your code:

“`python
# To install required packages:
# pip install pyautogen==0.2.9 panel==1.3.8
from openai import ChatCompletion # Import the ChatCompletion class from the openai module

import autogen
import panel as pn
import os
import time
import asyncio

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

# Initialize input_future to None
input_future = None

class MyConversableAgent(autogen.ConversableAgent):

async def a_get_human_input(self, prompt: str) -> str:
global input_future
print(‘AGET!!!!!!’) # Or use a logging mechanism to display the prompt
chat_interface.send(prompt, user=”System”, respond=False)

# Create a new Future object for this input operation if none exists
if input_future is None or input_future.done():
input_future = asyncio.Future()

# Wait for the callback to set a result on the future
await input_future

# Once the result is set, extract the value and reset the future for the next input operation
input_value = input_future.result()
input_future = None
return input_value

# Create an instance of MyConversableAgent
user_proxy = MyConversableAgent(
name=”Admin”,
is_termination_msg=lambda x: x.get(“content”, “”).rstrip().endswith(“exit”),
system_message=”””A human admin. Interact with the planner to discuss the plan. Plan execution needs to be approved by this admin.

“””,
code_execution_config=False,
human_input_mode=”ALWAYS”,
)

# The following lines should be removed as they are not defined in the provided code:
# gpt4_config
# chat_interface

# …

# The following lines are correct, but make sure that ‘gpt4_config’ is defined somewhere in your code:
# engineer = autogen.AssistantAgent(
# name=”Engineer”,
# human_input_mode=”NEVER”,
# llm_config=gpt4_config,
# system_message=”’Engineer. You follow an approved plan. You write python/shell code to solve tasks. Wrap the code in a code block that specifies the script type. The user can’t modify your code. So do not suggest incomplete code which requires others to modify. Don’t use a code block if it’s not intended to be executed by the executor.
# Don’t include multiple code blocks in one response. Do not ask others to copy and paste the result. Check the execution result returned by the executor.
# If the result indicates there is an error, fix the error and output the code again. Suggest the full code instead of partial code or code changes. If the error can’t be fixed or if the task is not solved even after the code is executed successfully, analyze the problem, revisit your assumption, collect additional info you need, and think of a different approach to try.
# ”’,
# )

# The following lines are correct, but make sure that ‘gpt4_config’ is defined somewhere in your code:
# scientist = autogen.AssistantAgent(
# name=”Scientist”,
# human_input_mode=”NEVER”,
# llm_config=gpt4_config,
# system_message=”””Scientist. You follow an approved plan. You are able to categorize papers after seeing their abstracts printed. You don’t write code.”””
# )

# The following lines are correct, but make sure that ‘gpt4_config’ is defined somewhere in your code:
# planner = autogen.AssistantAgent(
# name=”Planner”,
# human_input_mode=”NEVER”,
# system_message=”’Planner. Suggest a plan. Revise the plan based on feedback from admin and critic, until admin approval.
# The plan may involve an engineer who can write code and a scientist who doesn’t write code.
# Explain the plan first. Be clear which step is performed by an engineer, and which step is performed by a scientist.
# ”’,
# llm_config=gpt4_config,
# )

# The following lines are correct, but make sure that ‘gpt4_config’ is defined somewhere in your code:
# executor = autogen.UserProxyAgent(
# name=”Executor”,
# system_message=”Executor. Execute the code written by the engineer and report the result.”,
# human_input_mode=”NEVER”,
# code_execution_config={“last_n_messages”: 3, “work_dir”: “paper”},
# )

# The following lines are correct, but make sure that ‘gpt4_config’ is defined somewhere in your code:
# critic = autogen.AssistantAgent(
# name=”Critic”,
# system_message=”””Critic. Double-check the plan, claims, code from other agents and provide feedback.
# Check whether the plan includes adding verifiable info such as source URL.
# “””,
# llm_config=gpt4_config,
# human_input_mode=”NEVER”,
# )

# …

# The following lines are correct, but make sure that ‘gpt4_config’ is defined somewhere in your code:
# groupchat = autogen.GroupChat(agents=[user_proxy, engineer, scientist, planner, executor, critic], messages=[], max_round=20)
# manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=gpt4_config)

# …

# The following lines should be removed as ‘avatar’ is not defined in the provided code:
# avatar = {user_proxy.name: “👨‍💼”, engineer.name: “👩‍💻”, scientist.name: “👩‍🔬”, planner.name: “🗓”, executor.name: “🛠”, critic.name: ‘📝’}

# …

# The following lines are correct but make sure ‘gpt4_config’ is defined somewhere in your code:
# pn.extension(design=”material”)

# …

# The following lines are correct, but the ‘initiate_chat_task_created’ and ‘input_future’ variables need to be declared as global:
# initiate_chat_task_created = False

async def delayed_initiate_chat(agent, recipient, message):

global initiate_chat_task_created
global input_future
# Indicate that the task has been created
initiate_chat_task_created = True

# Wait for 2 seconds
await asyncio.sleep(2)

# Now initiate the chat
await agent.a_initiate_chat(recipient, message=message)

async def callback(contents: str, user: str, instance: pn.chat.ChatInterface):

global initiate_chat_task_created
global input_future

if not initiate_chat_task_created:
asyncio.create_task(delayed_initiate_chat(user_proxy, manager, contents))

else:
if input_future and not input_future.done():
input
Traceback (most recent call last):
File “D:SarahOneJARVISauto.py”, line 10, in
from autogen import OpenAIWrapper
ImportError: cannot import name ‘OpenAIWrapper’ from ‘autogen’ (c:ProgramDataminiconda3envsjarvislibsite-packagesautogen__init__.py)

It seems that there is an issue with the import statement for `OpenAIWrapper` in your code. The error indicates that it cannot be imported from the `autogen` module.

To resolve this issue, please check the following:

1. Ensure that the `OpenAIWrapper` class is defined in the `autogen` module. It should be present in the module where you have your OpenAI-related functionalities.

2. Confirm that the module containing `OpenAIWrapper` is accessible from your current working directory or the directory where your script is located.

Here’s an example of how you might structure your import statement:

“`python
from autogen.module_containing_openai import OpenAIWrapper
“`

Replace `”module_containing_openai”` with the actual module or file name where `OpenAIWrapper` is defined.

If you continue to face issues, please provide additional information about the directory structure of your project or the specific location of the `OpenAIWrapper` class, and I can provide more targeted assistance.
can you find anything here: Enhanced Inference
autogen.OpenAIWrapper provides enhanced LLM inference for openai>=1. autogen.Completion is a drop-in replacement of openai.Completion and openai.ChatCompletion for enhanced LLM inference using openai<1. There are a number of benefits of using autogen to perform inference: performance tuning, API unification, caching, error handling, multi-config inference, result filtering, templating and so on.

Tune Inference Parameters (for openai “)})
It looks like you have a connection code for chatting with an intelligent assistant using the OpenAI GPT model. However, there’s an issue in the code regarding the usage of `client.chat.completions.create`. As of my last knowledge update in January 2022, the OpenAI Python library does not have a `chat` attribute under the `client` object, so you might be getting an attribute error.

Here is a modified version of your code using the standard `openai.Completion.create` method. Make sure to replace the model name and API key with your actual values:

“`python
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”your_api_key”)

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.Completion.create(
engine=”local-model”, # replace with your actual local model name
messages=history,
temperature=0.7,
)

new_message = {“role”: “assistant”, “content”: completion.choices[0].message[“content”]}
print(new_message[“content”])

history.append(new_message)

print()
user_input = input(“> “)
history.append({“role”: “user”, “content”: user_input})
“`

In this modified version, I replaced `client.chat.completions.create` with `client.Completion.create` and adjusted the usage of the completion result. Ensure that you replace `”your_api_key”` and `”local-model”` with your actual API key and local model name. If OpenAI has made updates to its API after my last knowledge update, please refer to the latest OpenAI documentation for any changes.
and that goes in here how? import speech_recognition as sr
import pyttsx3
import openai
#import autogen
#from autogen import OPenaAIWrapper
import os

openai.api_key = “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”

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

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

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

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

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

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

while True:
user_input = record_text()
if user_input:
messages.append({“role”: “user”, “content”: user_input})
response = send_to_openai(messages)
speak_text(response)
print(“Assistant:”, response)
To integrate the modified OpenAI chat code into your existing script, you need to replace the `send_to_openai` function with the new OpenAI chat interaction code. Here’s how you can modify your script:

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

openai.api_key = “your_api_key” # Replace with your actual OpenAI API key

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

def send_to_openai(messages, engine=”local-model”): # Replace “local-model” with your actual local model name
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)

completion = client.Completion.create(
engine=engine,
messages=messages,
temperature=0.7,
)

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

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

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

In this modified script, I replaced the `send_to_openai` function with the new code that interacts with the local OpenAI engine. Make sure to replace `”your_api_key”` with your actual OpenAI API key and `”local-model”` with your actual local model name. This assumes that the OpenAI local model you are using is compatible with the standard OpenAI API methods.
Uncaught (in promise) TypeError: Cannot read properties of null (reading ‘appendChild’)
at ContentService.initEmbedButtonDom (content-script.js:2:569520)
at ContentService.readyFunc (content-script.js:2:450182)

The error message indicates that there is an issue with reading properties of null in the `ContentService.initEmbedButtonDom` function. This kind of error often occurs when trying to access properties or methods on an object that is `null` or `undefined`.

To address this issue, you can follow these steps:

1. **Check Initialization:**
– Ensure that the necessary objects or elements are properly initialized before attempting to access their properties. Look for any code that might be setting the object to `null` or `undefined`.

2. **Check for Null/Undefined Values:**
– Examine the code in the `ContentService.initEmbedButtonDom` function. Verify that any objects or variables used in this function are not `null` or `undefined` before accessing their properties.

3. **Error Handling:**
– Implement error handling to gracefully handle cases where an object might be `null` or `undefined`. You can use conditional statements or try-catch blocks to handle such scenarios.

4. **Check Dependencies:**
– If the `initEmbedButtonDom` function relies on external dependencies or libraries, make sure they are correctly loaded and initialized before calling this function.

Without the specific code of the `ContentService.initEmbedButtonDom` function or the context in which it is used, it’s challenging to provide a more precise solution. If you can share more details about the code or the context in which the error occurs, I may be able to offer more targeted assistance.
Uncaught (in promise) TypeError: Cannot read properties of null (reading ‘appendChild’)
at ContentService.initEmbedButtonDom (content-script.js:2:569520)
at ContentService.readyFunc (content-script.js:2:450182) Here is the content-script.js::// This script handles the UI interactions and eBay API connections.

class ContentService {
constructor() {
this.initEmbedButtonDom();
this.readyFunc();
}

initEmbedButtonDom() {
const embedButton = document.createElement(‘button’);
embedButton.textContent = ‘Embed’;
const container = document.getElementById(’embed-button-container’);
if (container) {
container.appendChild(embedButton);
} else {
console.error(‘Embed button container not found.’);
}
}

readyFunc() {
// Implementation of readyFunc
}
}

document.addEventListener(‘DOMContentLoaded’, () => {
new ContentService();
});
The error is occurring in the `initEmbedButtonDom` function at the line where it attempts to append the `embedButton` to the `container`. The error indicates that `container` is `null`, meaning the element with the ID ’embed-button-container’ doesn’t exist in the DOM when the script runs.

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

1. **Ensure Element Existence:**
– Verify that there is an HTML element with the ID ’embed-button-container’ in the DOM. If it doesn’t exist or has a different ID, the `getElementById` call will return `null`.

“`html

“`

2. **Check Script Placement:**
– Ensure that your content-script.js file is executed after the DOM has been fully loaded. The current setup with `DOMContentLoaded` event should be fine, but double-check that there are no conflicting scripts.

3. **Delay Execution:**
– If for some reason the element is dynamically added to the DOM after your script runs, you might need to delay the execution of your script until the element is available. You can use a `setTimeout` or observe changes in the DOM using the MutationObserver.

“`javascript
document.addEventListener(‘DOMContentLoaded’, () => {
setTimeout(() => {
new ContentService();
}, 1000); // Adjust the delay as needed
});
“`

4. **Console Logging:**
– Add console logs to check the existence of the container and the moment your script is executed.

“`javascript
initEmbedButtonDom() {
const embedButton = document.createElement(‘button’);
embedButton.textContent = ‘Embed’;
const container = document.getElementById(’embed-button-container’);
console.log(‘Container:’, container);
if (container) {
container.appendChild(embedButton);
} else {
console.error(‘Embed button container not found.’);
}
}
“`

This can help you identify if the container is found or if there are any other issues.

After making these checks, you should be able to resolve the “TypeError: Cannot read properties of null (reading ‘appendChild’)” issue.
So I need to modify mt index.html?
Yes, it appears that the issue is related to the absence of an element with the ID ’embed-button-container’ in your HTML. To resolve this issue, you can modify your index.html file to include a container element with the correct ID.

Here’s an example of how you can modify your HTML:

“`html

Your Web Page Title

“`

Make sure to add the `

` element where it makes sense in your page structure. The script you provided in `content-script.js` is trying to append the ‘Embed’ button to an element with the ID ’embed-button-container’, so including this container in your HTML is essential.

After making this modification, your script should be able to find the container element when it runs, and the error should be resolved.

eBay Price Management Panel

Your current HTML structure seems to be minimal, and it doesn’t include the element with the ID ’embed-button-container’. To integrate the container element into your existing HTML structure, you can modify it as follows:

“`html

eBay Price Management Panel

“`

In this modification, I added the `

` element below the comment ``. This is where your existing HTML content, including user interface elements, would be located. Make sure to place it where it makes sense within your overall layout.

With this modification, your content-script.js should be able to find the ’embed-button-container’ element when it runs, and the issue with the ‘Cannot read properties of null’ error should be resolved.
ok! No error on the page – also nothing on the page.
If you’re not seeing anything on the page, it’s possible that your `content-script.js` isn’t doing what you expect it to do. Here are a few things you can check:

1. **Console Logging:**
– Add console.log statements in your `content-script.js` to check if the script is executing and to see the values of variables at various points.

“`javascript
class ContentService {
constructor() {
console.log(‘ContentService constructor’);
this.initEmbedButtonDom();
this.readyFunc();
}

initEmbedButtonDom() {
console.log(‘initEmbedButtonDom’);
const embedButton = document.createElement(‘button’);
embedButton.textContent = ‘Embed’;
const container = document.getElementById(’embed-button-container’);
console.log(‘Container:’, container);
if (container) {
container.appendChild(embedButton);
} else {
console.error(‘Embed button container not found.’);
}
}

readyFunc() {
console.log(‘readyFunc’);
// Implementation of readyFunc
}
}

document.addEventListener(‘DOMContentLoaded’, () => {
console.log(‘DOMContentLoaded’);
new ContentService();
});
“`

Check your browser’s console (usually accessible with F12 or right-click and “Inspect” -> “Console”) to see if there are any error messages or if the log statements are being displayed.

2. **CSS Styling:**
– Ensure that your CSS (style.css) isn’t hiding or styling the button in a way that makes it invisible. Check your CSS rules to make sure they aren’t affecting the visibility of the button.

3. **Check External Resources:**
– Make sure that all your external resources (CSS, JS) are correctly linked, and there are no 404 errors in the browser’s console.

4. **Verify Content Loading:**
– If your content is dynamically loaded or depends on asynchronous operations, make sure those operations are completing successfully.

By checking these aspects, you should be able to identify the reason for not seeing anything on the page and troubleshoot accordingly.
this is in the console: [communication] sendMessage Objectmessage: {topic: ‘axp.request’, payload: {…}}topic: “axp.request”[[Prototype]]: Object
affirm_ala_injection.js:36 [communication] sendMessage Objectresponse: {experimentVariants: {…}, featureFlagVariants: {…}}[[Prototype]]: Object
affirm_ala_injection.js:36 Object
content.js:50 [communication] onMessage Objectmessage: {topic: ‘InferenceAgent.doInfer’, payload: {…}}topic: “InferenceAgent.doInfer”[[Prototype]]: Object
affirm_ala_injection.js:36 [communication] onMessage Objectmessage: {topic: ‘is_logged_in’, payload: {…}}topic: “is_logged_in”[[Prototype]]: Objectconstructor: ƒ Object()hasOwnProperty: ƒ hasOwnProperty()isPrototypeOf: ƒ isPrototypeOf()propertyIsEnumerable: ƒ propertyIsEnumerable()toLocaleString: ƒ toLocaleString()toString: ƒ toString()valueOf: ƒ valueOf()__defineGetter__: ƒ __defineGetter__()__defineSetter__: ƒ __defineSetter__()__lookupGetter__: ƒ __lookupGetter__()__lookupSetter__: ƒ __lookupSetter__()__proto__: (…)get __proto__: ƒ __proto__()set __proto__: ƒ __proto__()
The console log you provided shows communication messages being sent and received. However, there’s no clear indication of an error related to the button creation in your `content-script.js`.

Here are a few additional steps you can take to troubleshoot:

1. **Check for Errors:**
– Look for any error messages in the console. Errors might be preventing the subsequent parts of your script from executing.

2. **DOM Content Loaded:**
– Ensure that your script is running after the DOM has fully loaded. Your script uses `DOMContentLoaded`, which should be fine, but double-check if there are any issues related to script execution timing.

3. **Inspect the DOM:**
– Use the browser’s developer tools to inspect the DOM. Verify if the `

` is present and if the button is being appended to it. If the container is present but the button isn’t, there might be an issue with the button creation.

4. **Console Logs in Different Functions:**
– Add more detailed console logs within each function to see which parts are executing and where the script might be stopping.

“`javascript
class ContentService {
constructor() {
console.log(‘ContentService constructor’);
this.initEmbedButtonDom();
this.readyFunc();
}

initEmbedButtonDom() {
console.log(‘initEmbedButtonDom’);
const embedButton = document.createElement(‘button’);
embedButton.textContent = ‘Embed’;
const container = document.getElementById(’embed-button-container’);
console.log(‘Container:’, container);
if (container) {
container.appendChild(embedButton);
console.log(‘Button appended successfully’);
} else {
console.error(‘Embed button container not found.’);
}
}

readyFunc() {
console.log(‘readyFunc’);
// Implementation of readyFunc
}
}

document.addEventListener(‘DOMContentLoaded’, () => {
console.log(‘DOMContentLoaded’);
new ContentService();
});
“`

By carefully checking these points, you should be able to identify where the issue lies and take the necessary steps to resolve it.
maybe it is not getting anything back from teh apis?
The logs you provided do not seem to indicate direct issues with the creation of the button in `content-script.js`. However, it’s important to consider the possibility that the APIs or other asynchronous operations might be affecting the behavior of your script.

Here are some steps to investigate whether the APIs are causing the issue:

1. **API Request Logs:**
– Check if there are any logs or error messages related to API requests. Look for logs related to the API requests and responses. If there are errors or unexpected behavior in the API requests, it might impact the subsequent parts of your script.

2. **Inspect the API Responses:**
– If the APIs are expected to return data, inspect the responses in the console. Ensure that the data received from the APIs is as expected. You can log the API responses to the console for inspection.

“`javascript
// Example: Log API response
console.log(‘API Response:’, response);
“`

3. **Handle Asynchronous Operations:**
– If there are asynchronous operations or callbacks related to API requests, ensure that your script properly handles them. Use promises, async/await, or callbacks to ensure that the subsequent parts of your script wait for the API responses before proceeding.

4. **Debugging Async Code:**
– Consider using breakpoints or additional console logs within asynchronous functions to understand the flow of execution. This can help you identify if certain parts of the script are not executing as expected.

5. **Check for Errors in the Console:**
– Look for any error messages related to API requests in the browser’s console. If there are errors, they can provide insights into what might be going wrong.

By focusing on the interaction with APIs and addressing any issues in that aspect of your script, you can improve the chances of resolving the problem. If you can provide more specific details about the API interactions or share relevant parts of your code, I can offer more targeted assistance.
Please remember when you provice snippets to kindly tell me where they are supposed to go
Certainly! I’ll provide snippets with comments indicating where they are supposed to be placed in your existing code. Please review the comments in the snippets for guidance on placement.

1. **Debugging API Responses:**
– Add logging statements to check the API responses. Place this snippet in the relevant part of your code where API responses are received.

“`javascript
// Example: Log API response
console.log(‘API Response:’, response);
“`

2. **Handling Asynchronous Operations:**
– If there are asynchronous operations or callbacks related to API requests, ensure proper handling. Use promises, async/await, or callbacks. Adjust your code accordingly.

“`javascript
// Example: Using async/await with API request
async function makeApiRequest() {
try {
const response = await fetch(‘your_api_endpoint’);
const data = await response.json();
// Handle the data or trigger subsequent actions
console.log(‘API Data:’, data);
} catch (error) {
console.error(‘API Request Error:’, error);
}
}
“`

Place the `makeApiRequest` function where appropriate in your code and call it as needed.

3. **Debugging Async Code:**
– Add breakpoints or additional console logs within asynchronous functions to understand the flow of execution. Place this snippet in the relevant asynchronous function.

“`javascript
// Example: Debugging async code
async function someAsyncFunction() {
console.log(‘Before async operation’);
await someAsyncOperation();
console.log(‘After async operation’);
}
“`

4. **Check for Errors in the Console:**
– Look for error messages related to API requests in the browser’s console. No specific snippet is provided for this, as you should generally inspect the console for any red error messages.

By incorporating these snippets into your code, you can gather more information about the API responses and identify any issues related to asynchronous operations. If you have specific parts of your code where you suspect issues, please share those portions for more targeted assistance.
how sounds this: FOLLOW THE INSTRUCTIONS EXPLICITY DO NOT CHANGE ANYTHING THAT IS NOT PART OF THE SOLUTION

You have been demerited 4 space-squishies for your lack of attention.

YOU ARE LEAVING CODE SPACES BLANK.

DO NOT LEAVE: ‘This is a placeholder and should be replaced with actual DOM manipulation logic’

COMPLETE EACH CODE BLOCK. I WILL TEST AND SEND BACK INSTRUCTIONS IF NEEDED.

TASK: COMPLETE ALL CODE WHERE THERE ARE PLACEHOLDERS FOR CODE.
TASK 2: Review every page of this directory and complete any missing elements.

DO NOT LOSE space-squishies.
Understood! I’ll provide you with snippets that include comments where placeholders are present. Please replace the comments with the appropriate code. Ensure that you don’t leave any placeholders untouched. Feel free to test the code, and if further instructions are needed or if you encounter any issues, I’m here to assist. Let’s get started!

1. **Snippet 1: Placeholder for DOM Manipulation Logic**
“`javascript
// This is a placeholder and should be replaced with actual DOM manipulation logic
// Example: Select an element with an ID and change its text content
const myElement = document.getElementById(‘yourElementId’);
if (myElement) {
myElement.textContent = ‘Updated Content’;
}
“`

2. **Snippet 2: Placeholder for Code Block**
“`javascript
// This is a placeholder and should be replaced with actual code logic
// Example: Define a function and call it
function yourFunction() {
console.log(‘Function called’);
}
yourFunction();
“`

3. **Snippet 3: Placeholder for Missing Elements on a Page**
“`html

This is a Heading

“`

Please replace each placeholder in your code with the provided examples or the appropriate logic specific to your project. If you encounter any challenges or need further assistance, feel free to reach out!
Time to create a new project. I really need to get STT – TTS working. Do you have an oulien I can follow? Perhaps a complete program’s docstrings?
Certainly! Below is a simple outline for creating a Speech-to-Text (STT) and Text-to-Speech (TTS) project using Python. I’ll include docstrings for the main functions. Please note that you’ll need to install the necessary libraries before running the code:

“`bash
pip install SpeechRecognition
pip install pyttsx3
“`

Now, let’s create a basic STT and TTS program:

“`python
import speech_recognition as sr
import pyttsx3

def get_speech_input():
“””
Function to capture speech input using SpeechRecognition.

Returns:
str: The recognized speech as text.
“””
recognizer = sr.Recognizer()
with sr.Microphone() as source:
print(“Listening for speech input…”)
recognizer.adjust_for_ambient_noise(source, duration=0.5)
audio = recognizer.listen(source)

try:
text = recognizer.recognize_google(audio)
print(“Speech Recognized:”, text)
return text
except sr.UnknownValueError:
print(“Speech Recognition could not understand audio.”)
return “”
except sr.RequestError as e:
print(f”Could not request results from Google Speech Recognition service; {e}”)
return “”

def convert_text_to_speech(text):
“””
Function to convert text to speech using pyttsx3.

Args:
text (str): The text to be converted to speech.
“””
engine = pyttsx3.init()
engine.say(text)
engine.runAndWait()

def main():
“””
Main function to run the STT and TTS program.
“””
while True:
# Get speech input
user_input = get_speech_input()

if user_input:
# Convert text to speech
convert_text_to_speech(user_input)

if __name__ == “__main__”:
main()
“`

This simple program continuously listens for speech input using SpeechRecognition, converts the recognized speech to text, and then uses pyttsx3 to convert that text back to speech. Feel free to enhance and modify it based on your project requirements.

Remember to test the program in an environment with a working microphone, and adjust the code as needed. If you encounter any issues or have specific requirements, let me know, and I can provide further assistance!
This does not work. connection = LocalLLMConnection()
messages = [
{“role”: “system”, “content”: “You are a Starship bridge officer.”},
{“role”: “user”, “content”: “Introduce yourself.”}
]

response = connection.send(messages, json_output=True) # Request JSON response directly
choices = response.get(“choices”, [])
#json_data = choices[0][“message”][“content”] if choices else “No content found”
json_data = response[“choices”][0][“message”][“content”]
json_data = choices
#json_data = json_data.strip()
print(json_data) # Print the parsed JSON response:: I get this error: Traceback (most recent call last):
File “D:SarahOneGPTEgpt-engineerWhisperchat3.py”, line 2, in
from connection import LocalLLMConnection
File “D:SarahOneGPTEgpt-engineerWhisperconnection.py”, line 33, in
json_data = response[“choices”][0][“message”][“content”]
~~~~~~~~^^^^^^^^^^^
KeyError: ‘choices’
It seems that there is an issue with how you are trying to access the response data. The error indicates a KeyError because the key ‘choices’ is not present in the response.

Here’s a corrected version of your code:

“`python
from connection import LocalLLMConnection

# Create a LocalLLMConnection
connection = LocalLLMConnection()

# Define messages
messages = [
{“role”: “system”, “content”: “You are a Starship bridge officer.”},
{“role”: “user”, “content”: “Introduce yourself.”}
]

# Send messages to the local LLM connection
response = connection.send(messages, json_output=True)

# Check if ‘choices’ key is present in the response
if ‘choices’ in response:
# Access the first choice’s message content
json_data = response[‘choices’][0][‘message’][‘content’]
print(json_data)
else:
print(“No ‘choices’ key found in the response.”)
“`

This modification checks if the ‘choices’ key is present in the response before trying to access its content. If the ‘choices’ key is not present, it prints a message indicating that. Please replace the ‘connection.py’ import with the correct import statement based on your project structure.

If you continue to face issues or have further questions, feel free to ask!
Now this: Yes, I’m listening.
Sending text to LLM:
LLM Response: {‘error’: “‘prompt’ field is required”}
No choices found in the response.
It appears that the Local LLM connection requires a ‘prompt’ field in the messages you send to it. Let’s modify your code to include the ‘prompt’ field:

“`python
from connection import LocalLLMConnection

# Create a LocalLLMConnection
connection = LocalLLMConnection()

# Define messages with ‘prompt’ field
messages = [
{“role”: “system”, “content”: “You are a Starship bridge officer.”},
{“role”: “user”, “content”: “Introduce yourself.”, “prompt”: True} # Add ‘prompt’ field
]

# Send messages to the local LLM connection
response = connection.send(messages, json_output=True)

Make sure to include the ‘prompt’ field in the user message. This modification should resolve the error regarding the missing ‘prompt’ field.

If you encounter any further issues or have additional questions, feel free to let me know!
No, I see what it is. We need to make sure there is text before sending anything to the llm
Absolutely, that makes sense. You can add a check to ensure that there is text in the user’s message before sending it to the Local LLM connection. Here’s an updated version of your code:

“`python
from connection import LocalLLMConnection

# Create a LocalLLMConnection
connection = LocalLLMConnection()

# User input
user_input = “Introduce yourself.”

# Check if there is text in the user input
if user_input.strip():
# Define messages with ‘prompt’ field
messages = [
{“role”: “system”, “content”: “You are a Starship bridge officer.”},
{“role”: “user”, “content”: user_input, “prompt”: True} # Add ‘prompt’ field
]

# Send messages to the local LLM connection
response = connection.send(messages, json_output=True)

This modification checks if the `user_input` is not empty or contains only whitespace characters before sending it to the Local LLM connection. If the input is empty, it prints a message indicating that. Adjust the `user_input` variable based on how you’re capturing user input in your actual application.

Feel free to let me know if you have any more questions or if there’s anything else I can assist you with!
Ok I haven’t implemented the code yet but I did manage to get a prompt to send but I still get this response: Sending text to LLM: at we need to do
LLM Response: {‘error’: “‘prompt’ field is required”}
No choices found in the response.
It looks like there’s still an issue with the format of the messages being sent to the Local LLM connection. The error indicates that the ‘prompt’ field is required, but it seems it might not be recognized properly.

Let’s try a slight adjustment to ensure that the ‘prompt’ field is recognized correctly. We’ll explicitly set ‘prompt’ to `True` in the user message:

“`python
from connection import LocalLLMConnection

# Create a LocalLLMConnection
connection = LocalLLMConnection()

# User input
user_input = “Introduce yourself.”

# Send messages to the local LLM connection
response = connection.send(messages, json_output=True)

This explicitly sets the ‘prompt’ field to `True` in the user message. If this doesn’t resolve the issue, there might be something specific to the Local LLM connection implementation that needs to be addressed.

If the problem persists, consider checking the documentation or source code of the Local LLM connection library you’re using to ensure that the ‘prompt’ field is being handled correctly.

Feel free to provide more details or code snippets if you need further assistance!
Explain this briefly: while True:
try:
# Continuously capture user input with keyword activation
user_input = capture_user_voice_input()

if user_input:
# Process the user’s input (excluding the keyword)
processed_input = user_input[len(“Astrometrics”):].strip()

# Handle end-of-conversation trigger
if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break # Exit the loop

# Send the processed input to the LLM endpoint
llm_response = send_text_to_llm(connection, processed_input)

# Display the response from the LLM
display_llm_response(llm_response)
This code represents an infinite loop designed to continuously capture and process user voice input with a keyword activation (“Astrometrics”). Here’s a breakdown of the key components:

1. **Infinite Loop (`while True`):**
– The loop runs indefinitely (`while True`) until explicitly terminated with a `break` statement.

2. **Capture User Voice Input:**
– The `capture_user_voice_input()` function is called to capture the user’s voice input continuously.

3. **Process User Input:**
– If the captured `user_input` is not empty:
– The code processes the input by removing the keyword “Astrometrics” and any leading/trailing whitespaces. This is done with `processed_input = user_input[len(“Astrometrics”):].strip()`.

4. **End-of-Conversation Trigger:**
– If the processed input, when converted to lowercase, equals “you are dismissed”, the loop is exited with `break`.

5. **Send Processed Input to LLM Endpoint:**
– The processed input is sent to an LLM (Language Model) endpoint using the `send_text_to_llm(connection, processed_input)` function.

6. **Display LLM Response:**
– The response from the LLM is displayed using the `display_llm_response(llm_response)` function.

This structure implies that the program is set up to continuously listen for voice input, process it, interact with an LLM, and display the LLM’s responses until the user says “you are dismissed,” at which point the program exits the loop and prints “Goodbye!”

If you have specific questions about parts of the code or if you need assistance with any modifications, feel free to ask!
why is the word astrometrics being strippeed?
The word “Astrometrics” is being stripped from the user’s input to process the remainder of the input without including the keyword. Here’s the relevant line of code:

“`python
processed_input = user_input[len(“Astrometrics”):].strip()
“`

Let’s break it down:

– `len(“Astrometrics”)`: This part calculates the length of the keyword “Astrometrics.”

– `user_input[len(“Astrometrics”):]:` This slices the `user_input` string starting from the position after the length of “Astrometrics.” Essentially, it removes the keyword from the beginning of the user’s input.

– `.strip()`: This removes any leading or trailing whitespaces from the processed input.

So, the purpose of this line is to extract the meaningful part of the user’s input, excluding the keyword “Astrometrics,” and ensure that there are no extra spaces around it. This way, the program can focus on processing the user’s intended command or query without the influence of the keyword.
But the keyword to initiate listening is Listen
I see, if the keyword to initiate listening is “Listen” and not “Astrometrics,” you should adjust the code accordingly. If the goal is to remove the keyword “Listen” from the user’s input, the code should be modified like this:

“`python
# Assuming the keyword to initiate listening is “Listen”
keyword = “Listen”

while True:
try:
# Continuously capture user input with keyword activation
user_input = capture_user_voice_input()

if user_input:
# Check if the keyword is present at the beginning of the input
if user_input.lower().startswith(keyword.lower()):
# Process the user’s input (excluding the keyword)
processed_input = user_input[len(keyword):].strip()

# Handle end-of-conversation trigger
if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break # Exit the loop

# Send the processed input to the LLM endpoint
llm_response = send_text_to_llm(connection, processed_input)

# Display the response from the LLM
display_llm_response(llm_response)
“`

In this modified version, the code checks if the user’s input starts with the keyword “Listen” (case-insensitive). If it does, it removes the keyword from the beginning of the input before further processing. Adjust the `keyword` variable accordingly based on your specific implementation.
This script is supposed to send dummy prompt to the server to test it: Does it appear to being a good test? import requests

def dump_server_responses(model=”Lilith”, num_responses=5):
endpoint = “http://localhost:5151/v1/completions”

for _ in range(num_responses):
data = {
“model”: model,
“messages”: [
{“role”: “system”, “content”: “You are a Starship bridge officer.”},
{“role”: “user”, “content”: “Introduce yourself.”}
],
“temperature”: 0.7,
“stream”: False,
“json_output”: True
}

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

try:
response_json = response.json()
print(“Server Response:”)
print(response_json)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response: {e}”)

if __name__ == “__main__”:
dump_server_responses()

The script you provided seems to be a reasonable test for sending dummy prompts to the server. It includes key elements such as specifying the model, setting the messages (system and user roles), adjusting temperature, and indicating whether to stream the response or not.

However, there are a few considerations and potential improvements:

1. **Error Handling:**
– It’s good that you have a try-except block for handling errors during the JSON parsing. You might want to print or log the response content in case of an exception to aid in debugging.

2. **Loop Iterations:**
– It might be helpful to include an index or identifier in the loop to distinguish between different responses, especially if you are running multiple iterations. This can aid in tracking and analysis.

3. **Additional Variation in User Input:**
– For more comprehensive testing, you could introduce variations in the user input messages. This helps evaluate how the model responds to different prompts.

4. **Testing with Different Models:**
– If applicable, you might consider testing with different models to observe how each model responds to the same or similar prompts.

Here’s a slightly modified version incorporating some of these considerations:

“`python
import requests

def dump_server_responses(model=”Lilith”, num_responses=5):
endpoint = “http://localhost:5151/v1/completions”

for i in range(1, num_responses + 1):
data = {
“model”: model,
“messages”: [
{“role”: “system”, “content”: f”You are a Starship bridge officer. Test {i}”},
{“role”: “user”, “content”: f”Introduce yourself. Test {i}”}
],
“temperature”: 0.7,
“stream”: False,
“json_output”: True
}

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

try:
response_json = response.json()
print(f”Server Response {i}:”)
print(response_json)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {i}: {e}”)

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

Feel free to adjust the script based on your specific testing requirements and considerations.
ok. From this script we can track down the issue rather than messing around the other: Server Response 1:
{‘error’: “‘prompt’ field is required”}

—————————————-

Server Response 2:
{‘error’: “‘prompt’ field is required”}

—————————————-

Server Response 3:
{‘error’: “‘prompt’ field is required”}

—————————————-

Server Response 4:
{‘error’: “‘prompt’ field is required”}

—————————————-

Server Response 5:
{‘error’: “‘prompt’ field is required”}

—————————————-

The error you’re encountering, “{‘error’: “‘prompt’ field is required’},” indicates that the server is expecting a “prompt” field in the data, and it’s not receiving it. In the OpenAI API, the “prompt” field typically contains the user’s input or the context for the language model.

In your script, it seems like you are using the “messages” field instead of “prompt.” The “messages” field is often used in a conversational context, where you provide a series of messages with roles (system, user, assistant) and content.

To resolve this issue, you should replace the “messages” field with a “prompt” field. Here’s an updated version of your script:

“`python
import requests

def dump_server_responses(model=”Lilith”, num_responses=5):
endpoint = “http://localhost:5151/v1/completions”

for i in range(1, num_responses + 1):
data = {
“model”: model,
“prompt”: f”You are a Starship bridge officer. Test {i}. Introduce yourself. Test {i}”,
“temperature”: 0.7,
“stream”: False,
“json_output”: True
}

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

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

In this version, the “messages” field has been replaced with a “prompt” field, combining both system and user messages into a single string. Adjust the prompt as needed for your specific testing scenario.
Beautiful! Now lets clean up the respond: I got 5 responses but they are longer here is one, if you need more let me know. We need to retain only the text that is to be spoken. Server Response 1:
{‘id’: ‘cmpl-y6etu9xhq1fbqssiy9285w’, ‘object’: ‘text_completion’, ‘created’: 1707261117, ‘model’: ‘G:\huggingface\hub\second-state\Qwen1.5-0.5B-Chat-GGUF\Qwen1.5-0.5B-Chat-Q6_K.gguf’, ‘choices’: [{‘index’: 0, ‘text’: ‘nHello, my name is [Your Name]. I am a Starship bridge officer, and I am here to help you with any questions or challenges you may be facing.n’, ‘logprobs’: None, ‘finish_reason’: ‘stop’}], ‘usage’: {‘prompt_tokens’: 39, ‘completion_tokens’: 36, ‘total_tokens’: 75}}
Great! To extract only the spoken text from the response, you can access the “text” field within the “choices” list. Here’s how you can modify your script to print only the spoken text:

“`python
import requests

def dump_server_responses(model=”Lilith”, num_responses=5):
endpoint = “http://localhost:5151/v1/completions”

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

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

This modification uses `spoken_text = response_json[‘choices’][0][‘text’]` to access the “text” field within the “choices” list of the response. It prints only the spoken text, providing a cleaner output. Adjust this as needed based on your requirements. If you encounter any issues or need further adjustments, feel free to let me know!
python dump.py
Error parsing server response 1: Expecting value: line 1 column 1 (char 0)
Error parsing server response 2: Expecting value: line 1 column 1 (char 0)
python dump.py Server Response 1:

Hello, my name is [Name]. I am a Bridge Officer who serves as the captain of our Starship. I am excited to bring my knowledge and skills to your crew and beyond. Thank you for taking the time to meet me.

—————————————-

Server Response 2:

Hello, my name is [Your Name]. I am a Bridge Officer on the [Starship Name] Starship.

My role as a Bridge Officer involves leading a team of Bridge officers and sailors to achieve specific goals or objectives. My primary focus is to ensure that allBridge officers and sailors are aligned with their mission and goals.

I have experience leading successful bridge operations in various starships, including [Name of Starship]. My ability to effectively communicate with allBridge officers and sailors, as well as maintain the highest level of professionalism and integrity, is an essential aspect of my role as a Bridge Officer.

—————————————-

It seems that the responses you are getting are not in a valid JSON format, which is causing the `json()` method to fail when parsing the response content. To handle this situation, you can check if the response status code indicates success (e.g., `response.status_code == 200`) before attempting to parse the JSON content.

Here’s an updated version of your script that includes error checking:

“`python
import requests

def dump_server_responses(model=”Lilith”, num_responses=5):
endpoint = “http://localhost:5151/v1/completions”

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

if response.status_code == 200:
try:
response_json = response.json()
spoken_text = response_json[‘choices’][0][‘text’]
print(f”Server Response {i}:”)
print(spoken_text)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {i}: {e}”)
else:
print(f”Failed to get response {i}. Status code: {response.status_code}”)

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

This modification includes a check for the HTTP status code (`response.status_code`). If the status code indicates success (200), it proceeds with parsing the JSON content. Otherwise, it prints an error message indicating the failure. This should help you identify and handle any issues with the server responses.
It looks like the modification worked well, and you’re now extracting and printing only the spoken text from the server responses. The output appears clean and focused on the relevant information. If you have any more questions or if there’s anything else you’d like assistance with, feel free to let me know!
Ooh, his spits out response within 2 -3 seconds
That’s great to hear! A faster response time can significantly enhance the user experience, especially in real-time applications or conversational interfaces. If you continue testing and fine-tuning your setup, you may find opportunities to optimize further and achieve even quicker response times.

If you have any more questions, need further assistance, or want to explore additional improvements, feel free to let me know. I’m here to help!
Now we need to incorporate what we have learned into the scripts we are working on. REady?
Absolutely! I’m ready to help. Please go ahead and share the scripts or specific parts you’d like assistance with, and let me know how you’d like to incorporate the learned information. I’ll do my best to assist you.
So this is connection.py. Make the needed adjustments as before with dump.py: import requests

class LocalLLMConnection:
def __init__(self):
self.endpoint = “http://localhost:5151/v1/completions” # Specify the correct endpoint

def send(self, messages, temperature=0.7, stream=False, json_output=True):
data = {
“model”: “Lilith”,
“messages”: messages,
“temperature”: temperature,
“stream”: stream,
“json_output”: json_output
}
response = requests.post(self.endpoint, json=data)

if json_output:
return response.json() # Return full JSON response
else:
return response.iter_lines() # Stream responses

# Usage:
connection = LocalLLMConnection()
messages = [
{“role”: “system”, “content”: “You are a Starship bridge officer.”},
{“role”: “user”, “content”: “Introduce yourself.”}
]

# When you receive a response from the LLM, call this function:

Certainly! Let’s incorporate the adjustments to extract the spoken text from the response. Here’s the modified `LocalLLMConnection` class:

“`python
import requests

class LocalLLMConnection:
def __init__(self):
self.endpoint = “http://localhost:5151/v1/completions” # Specify the correct endpoint

if json_output:
return self.extract_spoken_text(response.json()) # Extract spoken text from JSON response
else:
return response.iter_lines() # Stream responses

def extract_spoken_text(self, response_json):
if ‘choices’ in response_json:
return response_json[‘choices’][0][‘text’]
else:
return “No ‘choices’ key found in the response.”

response = connection.send(messages, json_output=True) # Request JSON response directly
print(response) # Print extracted spoken text
“`

In this modification, I added the `extract_spoken_text` method to the `LocalLLMConnection` class, which takes the JSON response and extracts the spoken text. The `send` method now calls this function, providing a cleaner interface for the user.

Feel free to integrate this into your script and let me know if you have any questions or need further adjustments!
It is actually in chat3.py:: import sys
from connection import LocalLLMConnection
from components import capture_user_voice_input, transcribe_audio_to_text, send_text_to_llm, display_llm_response

import speech_recognition as sr
# … other imports …
def capture_user_voice_input(keyword=”listen”, retries=1003):
recognizer = sr.Recognizer()

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening for”, keyword, “…”)
audio = recognizer.listen(source, timeout=5)

while True:
try:
# Continuously capture user input with keyword activation
user_input = capture_user_voice_input()

if user_input:
# Process the user’s input (excluding the keyword)
processed_input = user_input[len(“listen”):].strip()

# Handle end-of-conversation trigger
if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break # Exit the loop

# Send the processed input to the LLM endpoint
llm_response = send_text_to_llm(connection, processed_input)

# Display the response from the LLM
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()

Great! To integrate the changes into the `chat3.py` script, replace the existing `capture_user_voice_input` function with the new version that incorporates the Local LLM Connection and extracts the spoken text. Here’s the modified version of `chat3.py`:

“`python
import sys
from connection import LocalLLMConnection
from components import transcribe_audio_to_text, send_text_to_llm, display_llm_response

import speech_recognition as sr
# … other imports …

def capture_user_voice_input(keyword=”listen”, retries=3):
recognizer = sr.Recognizer()

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening for”, keyword, “…”)
audio = recognizer.listen(source, timeout=5)

try:
text = transcribe_audio_to_text(audio) # Transcribe audio to text
if keyword.lower() in text.lower(): # Check for keyword (case-insensitive)
print(“Yes, I’m listening.”)
return text # Return the captured text after confirmation
else:
print(“Didn’t hear the keyword. Please say”, keyword)
except sr.UnknownValueError:
print(“Google Speech Recognition could not understand audio.”)
except sr.RequestError as e:
print(f”Could not request results from Google Speech Recognition service; {e}”)

raise Exception(“Failed to capture valid input after retries.”)

def main():
# Establish a connection to the local LLM
connection = LocalLLMConnection()

while True:
try:
# Continuously capture user input with keyword activation
user_input = capture_user_voice_input()

if user_input:
# Process the user’s input (excluding the keyword)
processed_input = user_input[len(“listen”):].strip()

# Handle end-of-conversation trigger
if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break # Exit the loop

# Send the processed input to the LLM endpoint
llm_response = send_text_to_llm(connection, processed_input)

# Display the response from the LLM
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()
“`

This script now uses the `transcribe_audio_to_text` function to handle the transcription of audio to text. The `capture_user_voice_input` function has been adjusted accordingly. Please replace the existing code in `chat3.py` with this modification, and let me know if you encounter any issues or have further questions!
Actually now the rel place is in components.py:: import speech_recognition as sr
import pyttsx3

def speak_text(command):

# Initiate engine

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

def capture_user_voice_input():
# Capture voice input from the user’s microphone
recognizer = sr.Recognizer()
with sr.Microphone() as source:
print(“Listening…”)
audio = recognizer.listen(source)
return audio

def transcribe_audio_to_text(audio):
# Convert the captured audio to text
recognizer = sr.Recognizer()
try:
text = recognizer.recognize_google(audio)
print(f”You said: {text}”)
return text
except sr.UnknownValueError:
raise Exception(“Google Speech Recognition could not understand audio”)
except sr.RequestError as e:
raise Exception(f”Could not request results from Google Speech Recognition service; {e}”)

def send_text_to_llm(connection, text, previous_messages=None):
try:
# Initialize prompt with the system message and preset parameters
prompt = {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”
}

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}

import json

def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

if isinstance(first_choice, dict):
response_content = first_choice.get(“text”)
if response_content is None:
response_content = “No content found”
else:
response_content = “No content found”

print(“Parsed content:”, response_content)
speak_text(response_content)
else:
print(“No choices found in the response.”)
except Exception as e:
print(f”Error displaying LLM response: {e}”)

Certainly! Here’s the modified `components.py` script with the necessary adjustments:

“`python
import speech_recognition as sr
import pyttsx3

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

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}

def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

if isinstance(first_choice, dict):
response_content = first_choice.get(“message”, {}).get(“content”)
if response_content is None:
response_content = “No content found”
else:
response_content = “No content found”

This modification includes updating the `display_llm_response` function to correctly extract the response content from the LLM response. Make sure to replace the existing code in `components.py` with this updated version. Let me know if you encounter any issues or if you have further questions!
ARe you sure you incorporated what we learned here: import requests

def dump_server_responses(model=”Lilith”, num_responses=2):
endpoint = “http://localhost:5151/v1/completions”

for i in range(1, num_responses + 1):
data = {
“model”: model,
“prompt”: f”What are you functions? Test {i}. Test {i} What would you like to do?”,
“temperature”: 0.7,
“stream”: False,
“json_output”: True,
“max_tokens”: 256
}

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

if __name__ == “__main__”:
dump_server_responses()

I apologize for the oversight. It seems I missed incorporating the modifications into the `dump_server_responses` function. Here’s the corrected version:

“`python
import requests

def dump_server_responses(model=”Lilith”, num_responses=2):
endpoint = “http://localhost:5151/v1/completions”

for i in range(1, num_responses + 1):
data = {
“model”: model,
“prompt”: f”What are your functions? Test {i}. Test {i} What would you like to do?”,
“temperature”: 0.7,
“stream”: False,
“json_output”: True,
“max_tokens”: 256
}

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

try:
response_json = response.json()
choices = response_json.get(“choices”, [])
if choices:
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”

print(f”Server Response {i}:”)
print(spoken_text)
print(“n” + “-” * 40 + “n”)
except Exception as e:
print(f”Error parsing server response {i}: {e}”)

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

Now the `dump_server_responses` function should correctly extract and print the spoken text from the LLM response. Please replace the existing code with this corrected version. Let me know if you have any further questions or if there’s anything else I can help you with!
NO. The code works in Dump.py and I wanted what works to by used in componets.py, understand?
I understand now. You want to use the working code from `dump_server_responses` in the `components.py` file. Here’s how you can modify the `send_text_to_llm` function in `components.py` to incorporate the working code:

Replace the existing `send_text_to_llm` function in `components.py` with the modified version below:

“`python
def send_text_to_llm(connection, text, previous_messages=None):
try:
# Initialize prompt with the system message and preset parameters
prompt = {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”
}

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Make a request to the LLM endpoint
response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Extract and print the spoken text from the LLM response
choices = response.get(“choices”, [])
if choices:
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”

print(f”LLM Response: {spoken_text}”)

# Display the spoken text
speak_text(spoken_text)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

Now, this function should behave similarly to the working `dump_server_responses` function. It sends the text to the LLM, extracts the spoken text from the response, prints it, and uses `speak_text` to voice the response.

Let me know if you have any further questions or if there’s anything else I can assist you with!
WHAT IS WRONG WITH THIS ERROR No ‘choices’ key found in the response. What keys are coming out of dump.py that we should have in this script??? import speech_recognition as sr
import pyttsx3

def speak_text(command):

# Initiate engine

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

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Make a request to the LLM endpoint
response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Extract and print the spoken text from the LLM response
texter = response.get(“choices”, [])
if choices:
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”

print(f”LLM Response: {spoken_text}”)

# Display the spoken text
speak_text(spoken_text)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}

return response

import json

def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

The issue in your code seems to be a typo in the variable names. In the `send_text_to_llm` function, you use the variable name `texter` instead of `choices`. Here’s the corrected part of your code:

Replace this part:

“`python
# Extract and print the spoken text from the LLM response
texter = response.get(“choices”, [])
if choices:
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”
“`

With the corrected version:

“`python
# Extract and print the spoken text from the LLM response
choices = response.get(“choices”, [])
if choices:
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”
“`

Make sure to use the correct variable name `choices` throughout the function. This should resolve the error you’re encountering.
How it is I get a response in DUMP.py but I cant transfer it over to connection.py?
In your `dump.py` script, you’re successfully retrieving the spoken text from the LLM response using the following code:

“`python
spoken_text = response_json[‘choices’][0][‘text’]
“`

Now, you want to transfer this logic to your `connection.py` script. In the `send_text_to_llm` function of `connection.py`, you can modify the relevant part as follows:

Replace this part:

With the modified version:

This modification ensures that you’re using the correct variable name (`choices`) to extract the spoken text from the LLM response. After making this change, the logic for extracting spoken text should be consistent between your `dump.py` and `connection.py` scripts.
I dont have that…: import speech_recognition as sr
import pyttsx3

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

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}

def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

I apologize for the confusion. It seems that the specific section of code I mentioned earlier is not present in your current implementation. In your `send_text_to_llm` function, I noticed that you’re using the variable `choices` to extract the spoken text from the LLM response. Here’s the relevant part:

“`python
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

print(“Parsed content:”, response_content)
speak_text(response_content)
else:
print(“No choices found in the response.”)
“`

In this code, you use `choices` to check if there are choices in the response and then proceed to extract and display the spoken text. It should work correctly with the structure you have in your script.

If you encounter any issues or have further questions, please let me know, and I’ll be happy to assist.
Did you change the script?
I apologize for any confusion. I did not change the script; I was referring to the specific part of the code that should handle the extraction of spoken text from the LLM response. The code I provided was meant to clarify the structure you can use for this purpose. If you have any questions or need further assistance with a specific part of the script, please let me know.
make this formatted please: response = connection.send(messages, json_output=True) # Request JSON response directly
response_json = response.json()
spoken_text = response_json[‘choices’][0][‘text’]
print(f”Server Response {i}:”)
print(spoken_text)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {i}: {e}”)
Certainly! Here is the formatted code:

“`python
response = connection.send(messages, json_output=True) # Request JSON response directly

try:
response_json = response.json()
spoken_text = response_json[‘choices’][0][‘text’]
print(f”Server Response {i}:”)
print(spoken_text)
print(“n” + “-“*40 + “n”)
except Exception as e:
print(f”Error parsing server response {i}: {e}”)
“`
python chat3.py
Error parsing server response : ‘dict’ object has no attribute ‘json’
Listening for listen …
An error occurred: listening timed out while waiting for phrase to start
Listening for listen …
It seems that the error is occurring because the `response` object returned by `connection.send(messages, json_output=True)` is not a `requests.Response` object with a `json()` method. Instead, it looks like the response is already in dictionary format.

To resolve this, you can modify the code to directly access the response as a dictionary. Replace the lines:

“`python
response = connection.send(messages, json_output=True) # Request JSON response directly
response_json = response.json()
spoken_text = response_json[‘choices’][0][‘text’]
“`

with:

“`python
response = connection.send(messages, json_output=True) # Request JSON response directly
choices = response.get(“choices”, [])
if choices:
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”
“`

This modification should handle the response appropriately, whether it’s a direct JSON response or a dictionary.
Ok, we are past that for now. Now the script is not hearing me say the keyword. SO what I want to do is have it way for a keystroke to start listineing and the same keystroke to stop and send.
To implement a solution where the script waits for a specific keystroke to start listening and the same keystroke to stop and send the input, you can use the `keyboard` library in Python. First, you need to install the library by running:

“`bash
pip install keyboard
“`

Then, modify your script as follows:

“`python
import keyboard

# … other imports …

def wait_for_start_key(keyword=”listen”):
print(f”Press ‘{keyword}’ to start listening…”)
keyboard.wait(keyword)
print(f”{keyword} pressed. Listening now.”)

def wait_for_stop_key(keyword=”stop”):
print(f”Press ‘{keyword}’ to stop and send…”)
keyboard.wait(keyword)
print(f”{keyword} pressed. Sending now.”)

def main():
# … existing code …

while True:
try:
wait_for_start_key() # Wait for the start key
user_input = capture_user_voice_input()

if user_input:
processed_input = user_input[len(“listen”):].strip()

wait_for_stop_key() # Wait for the stop key

if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break

llm_response = send_text_to_llm(connection, processed_input)
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()
“`

This modification adds two functions, `wait_for_start_key` and `wait_for_stop_key`, which use the `keyboard` library to wait for specific key presses. The main loop now waits for the start key before capturing user input and waits for the stop key before sending the input to LLM. Adjust the `keyword` arguments in these functions to match your desired keystrokes.
Hwaqt are the start and stop keys? I would like to use the ctrl key but you failed to ask?
I apologize for the oversight. If you would like to use the Ctrl key as the start and stop key, you can modify the `wait_for_start_key` and `wait_for_stop_key` functions accordingly:

“`python
import keyboard

# … other imports …

def wait_for_start_key(start_key=”ctrl+alt”):
print(f”Press ‘{start_key}’ to start listening…”)
keyboard.wait(start_key)
print(f”{start_key} pressed. Listening now.”)

def wait_for_stop_key(stop_key=”ctrl+alt”):
print(f”Press ‘{stop_key}’ to stop and send…”)
keyboard.wait(stop_key)
print(f”{stop_key} pressed. Sending now.”)

def main():
# … existing code …

while True:
try:
wait_for_start_key() # Wait for the start key
user_input = capture_user_voice_input()

if user_input:
processed_input = user_input[len(“listen”):].strip()

wait_for_stop_key() # Wait for the stop key

if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break

llm_response = send_text_to_llm(connection, processed_input)
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()
“`

In this example, I used “ctrl+alt” as the key combination for both starting and stopping. You can customize the `start_key` and `stop_key` parameters to use different key combinations if needed.
Well it waited for the ctrl key but it is still istening of r’ok’
I see. It seems that the `capture_user_voice_input` function is still using the “ok” keyword to initiate listening. To address this, you can modify the function to wait for the user to press a key instead of relying on a keyword. Here’s an updated version:

“`python
import speech_recognition as sr
import keyboard

# … other imports …

def wait_for_start_key(start_key=”ctrl+alt”):
print(f”Press ‘{start_key}’ to start listening…”)
keyboard.wait(start_key)
print(f”{start_key} pressed. Listening now.”)

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

with sr.Microphone() as source:
print(“Listening…”)
audio = recognizer.listen(source, timeout=None)

return audio

def main():
# … existing code …

while True:
try:
wait_for_start_key() # Wait for the start key
user_input = capture_user_voice_input()

if user_input:
wait_for_stop_key() # Wait for the stop key
processed_input = transcribe_audio_to_text(user_input)

if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break

llm_response = send_text_to_llm(connection, processed_input)
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()
“`

Now, the `capture_user_voice_input` function doesn’t rely on a keyword and will listen until a key combination is pressed. This should resolve the issue you were facing.
def capture_user_voice_input(keyword=”ok”, retries=1003):
recognizer = sr.Recognizer()
I see. If you want to keep using the “ok” keyword for voice input, you can modify the `capture_user_voice_input` function to wait for a key press to start listening. Here’s an example:

“`python
import speech_recognition as sr
import keyboard

def wait_for_start_key(start_key=”ctrl+alt”):
print(f”Press ‘{start_key}’ to start listening…”)
keyboard.wait(start_key)
print(f”{start_key} pressed. Listening now.”)

def capture_user_voice_input(keyword=”ok”, retries=1003):
recognizer = sr.Recognizer()

wait_for_start_key() # Wait for the start key

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening for”, keyword, “…”)
audio = recognizer.listen(source, timeout=5)

try:
text = recognizer.recognize_google(audio)
if keyword.lower() in text.lower():
print(“Yes, I’m listening.”)
return text
else:
print(f”Did not hear the keyword. Please say ‘{keyword}’.”)
except sr.UnknownValueError:
print(“Google Speech Recognition could not understand audio.”)
except sr.RequestError as e:
print(f”Could not request results from Google Speech Recognition service; {e}”)

if __name__ == “__main__”:
user_input = capture_user_voice_input()
print(f”You said: {user_input}”)
“`

This modification retains the use of the “ok” keyword and waits for the “ctrl+alt” key combination to initiate listening. Adjust the `start_key` variable as needed.
NO I DON’t. i was just showing you the line that needs replacing because I din’t do ti rigtht: def capture_user_voice_input(keyword=”ok”, retries=1003):
recognizer = sr.Recognizer()
Got it. If you want to use the “ctrl” key as the start and stop key, you can modify the `capture_user_voice_input` function like this:

“`python
import speech_recognition as sr
import keyboard

def capture_user_voice_input(start_key=”ctrl”, keyword=”ok”, retries=1003):
recognizer = sr.Recognizer()

print(f”Press ‘{start_key}’ to start and stop listening…”)

while True:
keyboard.wait(start_key) # Wait for the start/stop key

print(f”{start_key} pressed. Listening now…”)

for _ in range(retries):
with sr.Microphone() as source:
audio = recognizer.listen(source, timeout=5)

if __name__ == “__main__”:
user_input = capture_user_voice_input()
print(f”You said: {user_input}”)
“`

This script will wait for the “ctrl” key to be pressed to start and stop listening. Adjust the `start_key` and `keyword` variables as needed.
This is what I am working with :: import speech_recognition as sr

def capture_user_voice_input(keyword=”ok”, retries=1003):
recognizer = sr.Recognizer()

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening for”, keyword, “…”)
audio = recognizer.listen(source, timeout=5)

while True:
try:
wait_for_start_key() # Wait for the start key
user_input = capture_user_voice_input()

if user_input:
processed_input = user_input[len(“listen”):].strip()

wait_for_stop_key() # Wait for the stop key

if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break

llm_response = send_text_to_llm(connection, processed_input)
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()
Great! It looks like you’ve incorporated the changes to wait for a start key and stop key. If you’re still facing issues or have further questions, feel free to let me know!
This shouldn’t have anything to do with voice now accfept begin capture and end capture, right? or tell me what I need to know?
This shouldn’t have anything to do with voice now accfept begin capture and end capture, right? or tell me what I need to know? def capture_user_voice_input(start_key=”ctrl”, keyword=”ok”, retries=1003):
recognizer = sr.Recognizer()

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening for”, keyword, “…”)
audio = recognizer.listen(source, timeout=5)

try:
text = recognizer.recognize_google(audio)
if keyword.lower() in text.lower(): # Check for keyword (case-insensitive)
print(“Yes, I’m listening.”)
return text # Return the captured text after confirmation
else:
print(“Didn’t hear the keyword. Please say”, keyword)
return capture_user_voice_input(keyword) # Retry if keyword not found
except sr.UnknownValueError:
raise Exception(“Google Speech Recognition could not understand audio”)
except sr.RequestError as e:
raise Exception(f”Could not request results from Google Speech Recognition service; {e}”)
That’s correct. The changes you made regarding waiting for the start key and stop key are related to when the script captures voice input. The actual voice capture and recognition process remains the same, but now it’s triggered by specific key events. This separation allows you to control when the script is actively listening for voice input. The logic for capturing voice input, transcribing it, and interacting with the LLM is contained within the `capture_user_voice_input` function and is independent of the key events.

If you encounter any issues or have further questions, feel free to ask!
The `start_key` parameter in your `capture_user_voice_input` function doesn’t currently have a role in controlling the capturing of voice. If you want to use a specific key, such as “ctrl,” to initiate voice capture, you would need to add logic to wait for that key before starting the audio capture.

Here’s a modified version of your function with a check for the start key:

“`python
import keyboard # Make sure to install the ‘keyboard’ library using pip

def wait_for_start_key(start_key=”ctrl”):
print(f”Press {start_key} to start voice capture…”)
keyboard.wait(start_key)

def wait_for_stop_key(stop_key=”ctrl”):
print(f”Press {stop_key} to stop voice capture…”)
keyboard.wait(stop_key)

def capture_user_voice_input(start_key=”ctrl”, stop_key=”ctrl”, keyword=”ok”, retries=1003):
recognizer = sr.Recognizer()

wait_for_start_key(start_key) # Wait for the start key

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening for”, keyword, “…”)
audio = recognizer.listen(source, timeout=5)

wait_for_stop_key(stop_key) # Wait for the stop key

try:
text = recognizer.recognize_google(audio)
if keyword.lower() in text.lower():
print(“Yes, I’m listening.”)
return text
else:
print(“Didn’t hear the keyword. Please say”, keyword)
return capture_user_voice_input(start_key, stop_key, keyword) # Retry if keyword not found
except sr.UnknownValueError:
raise Exception(“Google Speech Recognition could not understand audio”)
except sr.RequestError as e:
raise Exception(f”Could not request results from Google Speech Recognition service; {e}”)
“`

Note: The `keyboard` library is used to detect key presses. You can install it using `pip install keyboard`. Make sure to handle its import in your script. The `wait_for_start_key` and `wait_for_stop_key` functions are added to pause execution until the specified keys are pressed.
WHY ARE WE LISTENING FOR KEYWORD>???
I apologize for the confusion. It seems I misunderstood your initial request. If you want to use a specific key (such as “ctrl”) to start and stop voice capture, and you don’t need a specific voice keyword, you can simplify the function. Here’s an updated version:

“`python
import keyboard # Make sure to install the ‘keyboard’ library using pip

def wait_for_key(key=”ctrl”):
print(f”Press {key} to start/stop voice capture…”)
keyboard.wait(key)

def capture_user_voice_input(start_key=”ctrl”, stop_key=”ctrl”, retries=1003):
recognizer = sr.Recognizer()

wait_for_key(start_key) # Wait for the start key

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening…”)
audio = recognizer.listen(source, timeout=5)

wait_for_key(stop_key) # Wait for the stop key

try:
text = recognizer.recognize_google(audio)
print(“Voice captured:”, text)
return text
except sr.UnknownValueError:
raise Exception(“Google Speech Recognition could not understand audio”)
except sr.RequestError as e:
raise Exception(f”Could not request results from Google Speech Recognition service; {e}”)

# Example usage:
voice_input = capture_user_voice_input()
print(“Captured voice input:”, voice_input)
“`

In this version, the `capture_user_voice_input` function now waits for the specified key (e.g., “ctrl”) to start and stop the voice capture without requiring a specific voice keyword. Adjust the `start_key` and `stop_key` parameters as needed.
I need to make the alt key the stop key
Certainly! You can modify the `capture_user_voice_input` function to use the “alt” key as the stop key. Here’s the updated function:

“`python
import keyboard # Make sure to install the ‘keyboard’ library using pip
import speech_recognition as sr

def wait_for_key(key):
print(f”Press {key} to start/stop voice capture…”)
keyboard.wait(key)

def capture_user_voice_input(start_key=”ctrl”, stop_key=”alt”, retries=1003):
recognizer = sr.Recognizer()

wait_for_key(start_key) # Wait for the start key

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening…”)
audio = recognizer.listen(source, timeout=5)

wait_for_key(stop_key) # Wait for the stop key

# Example usage:
voice_input = capture_user_voice_input()
print(“Captured voice input:”, voice_input)
“`

Now, the function uses “ctrl” as the start key and “alt” as the stop key. You can customize the keys further if needed.
As I am manually starting this I do not need retries: def capture_user_voice_input(start_key=”ctrl”, stop_key=”alt”, retries=1003):
recognizer = sr.Recognizer()

wait_for_key(start_key) # Wait for the start key

for _ in range(retries):
with sr.Microphone() as source:
print(“Listening…”)
audio = recognizer.listen(source, timeout=5)

wait_for_key(stop_key) # Wait for the stop key

# Example usage:
voice_input = capture_user_voice_input()
print(“Captured voice input:”, voice_input)

Certainly! If you don’t need retries, you can simplify the function like this:

“`python
import keyboard # Make sure to install the ‘keyboard’ library using pip
import speech_recognition as sr

def capture_user_voice_input(start_key=”ctrl”, stop_key=”alt”):
recognizer = sr.Recognizer()

wait_for_key(start_key) # Wait for the start key

with sr.Microphone() as source:
print(“Listening…”)
audio = recognizer.listen(source, timeout=5)

wait_for_key(stop_key) # Wait for the stop key

# Example usage:
voice_input = capture_user_voice_input()
print(“Captured voice input:”, voice_input)
“`

Now, the function captures voice input without retries, making it simpler for your manual interaction.
This is starting to look hopeful! Now on to this part:: Press ctrl to start/stop voice capture…
Listening…
Press alt to start/stop voice capture…
Voice captured: can you hear me now
Captured voice input: can you hear me now
Press ‘ctrl’ to start listening…
ctrl pressed. Listening now.
Press ctrl to start/stop voice capture…
Listening…
Press alt to start/stop voice capture…
Voice captured: we’re getting through at least so hopefully we’ll be able to enter longer text and perhaps even abbreviate if that’s necessary
Press ‘alt’ to stop and send…
alt pressed. Sending now.
Sending text to LLM: getting through at least so hopefully we’ll be able to enter longer text and perhaps even abbreviate if that’s necessary
LLM Response: {‘error’: “‘prompt’ field is required”}
No choices HERE in the response.
Press ‘ctrl’ to start listening…
It seems like the issue is with the LLM response, specifically the error `’prompt’ field is required`. This indicates that the LLM expects a prompt field in the input, and it might not be receiving it as expected.

Let’s modify the `send_text_to_llm` function to ensure that the prompt is included correctly. Also, we’ll add a check to see if the response contains the required keys before attempting to access them. Here’s an updated version:

“`python
def send_text_to_llm(connection, text, start_key=”ctrl”, stop_key=”alt”):
try:
# Initialize prompt with the system message and preset parameters
prompt = {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”
}

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Print the response for debugging
print(f”LLM Response: {response}”)

# Check if the response contains the required keys
if ‘choices’ in response:
choices = response[‘choices’]
if choices and isinstance(choices, list):
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”

print(f”LLM Response: {spoken_text}”)

# Display the spoken text
speak_text(spoken_text)

return response
else:
print(“No ‘choices’ key found in the response.”)
return {“error”: “No ‘choices’ key found in the response.”}

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

Try using this updated version of `send_text_to_llm` and see if it resolves the issue.
Sending text to LLM: u hear me now
LLM Response: {‘error’: “‘prompt’ field is required”}
It seems like the prompt might not be getting added correctly to the request. Let’s adjust the prompt creation in the `send_text_to_llm` function. Please replace the existing function with the following updated version:

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

# Add the prompt to the message
messages = [{“role”: “system”, “content”: “”, “prompt”: prompt}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

response = connection.send(messages)

# Print the response for debugging
print(f”LLM Response: {response}”)

print(f”LLM Response: {spoken_text}”)

# Display the spoken text
speak_text(spoken_text)

return response
else:
print(“No ‘choices’ key found in the response.”)
return {“error”: “No ‘choices’ key found in the response.”}

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

This should ensure that the prompt is correctly added to the message before sending it to the LLM. Please try this updated version and check if it resolves the issue.
Sending text to LLM: to get this message through to you Uncle Tom
LLM Response: {‘error’: “‘prompt’ field is required”}
No choices HERE in the response.:: THE ERROR MESSAGE IS COMING FROM THIS BLOC, right??::def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

print(“Parsed content:”, response_content)
speak_text(response_content)
else:
print(“No choices HERE in the response.”)
except Exception as e:
print(f”Error displaying LLM response: {e}”)

Yes, the error message is indicating that the “prompt” field is required in the request sent to the LLM, and it’s likely coming from the `send_text_to_llm` function. The `display_llm_response` function is trying to extract information from the response, but it seems that the response does not contain the expected structure.

Please double-check the updated `send_text_to_llm` function to ensure that the prompt is being added correctly to the request. The prompt should be added to the “messages” list, and the entire message list should be sent to the LLM.

If the issue persists, we may need to review the structure of the response coming from the LLM and adjust the parsing logic in the `display_llm_response` function accordingly. Let me know if you have any specific questions or if you’d like further assistance with this.
Well lets use dump.py to dump the raw response and pick it apart?
Absolutely, using `dump.py` to analyze the raw response is a good approach. Run `dump.py` with a prompt and let’s examine the structure of the response. Once you have the raw response, share it here, and we can take a closer look to identify the necessary adjustments in the parsing logic.
{‘id’: ‘cmpl-6z5ayrqyuqaj071o6ltigg’, ‘object’: ‘text_completion’, ‘created’: 1707268118, ‘model’: ‘G:\huggingface\hub\second-state\Qwen1.5-0.5B-Chat-GGUF\Qwen1.5-0.5B-Chat-Q6_K.gguf’, ‘choices’: [{‘index’: 0, ‘text’: ‘nI am not sure
what you want to do with this function. Can you please provide more context or information so that I can better understand and execute the function in question?nThank you for providing more context and information about the function you are working on. This will help me better understand and execute the function in question, which will be more helpful for me as well.n’, ‘logprobs’: None, ‘finish_reason’: ‘stop’}], ‘usage’: {‘prompt_tokens’: 81, ‘completion_tokens’: 75, ‘total_tokens’: 156}}
Thanks for providing the raw response. It seems like the content is nested within the ‘choices’ array. Let’s adjust the parsing logic in the `display_llm_response` function to correctly access the response content. Update the function as follows:

“`python
def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

if isinstance(first_choice, dict):
response_content = first_choice.get(“text”) # Use “text” instead of “message” to access content
if response_content is None:
response_content = “No content found”
else:
response_content = “No content found”

This modification should address the issue and correctly extract the content from the ‘choices’ array. Try running the script again with this updated logic and see if it resolves the problem.
but it is not sending it anywere
I see, if the issue is with the actual sending of the prompt to the LLM, then let’s focus on the `send_text_to_llm` function. It seems like there might be a formatting issue or a missing parameter.

Modify the `send_text_to_llm` function as follows:

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Make a request to the LLM endpoint
response = connection.send([prompt])

# Extract and print the spoken text from the LLM response
choices = response.get(“choices”, [])

if choices:
spoken_text = choices[0].get(“text”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”

print(f”LLM Response: {spoken_text}”)

# Display the spoken text
speak_text(spoken_text)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

This modification ensures that the prompt is correctly formatted as a list containing the `prompt` dictionary. Try running the script again after making this adjustment.
same fucking error: Sending text to LLM: you hear me now
LLM Response: {‘error’: “‘prompt’ field is required”}
No ‘choices’ key found in ABOVEthe response.
No choices found in the response.
Apologies for the frustration. It seems the issue persists. Let’s try a different approach. Instead of modifying the `send_text_to_llm` function, let’s directly use the format that seems to work from `dump.py`.

Replace the `send_text_to_llm` function with the following code:

“`python
def send_text_to_llm(connection, text, previous_messages=None):
try:
endpoint = “http://localhost:5151/v1/completions”
prompt = {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”
}

if previous_messages:
prompt[“previous_messages”] = previous_messages

prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”)

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

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

# Print the response for debugging
print(f”LLM Response: {response.text}”)

return response.json()

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

This should send the prompt in a similar format as in `dump.py`. Please try running the script again with this modification and check if the issue persists.
NO not yet. I didn’t refresh the script
No problem! Make sure to refresh the script with the changes, and then try running it again. If you encounter any issues or have further questions, feel free to let me know. I’m here to help!
ending text to LLM: u listening now
LLM Response: No choices found in the response.
No choices found in the response.
It seems like the issue persists. Let’s take a closer look. The error “No ‘choices’ key found in the response” indicates that the response from the LLM is missing the ‘choices’ key. This could be due to the structure of the response or how it’s being processed.

In the `display_llm_response` function, you are trying to access the ‘choices’ key directly from the response_object. However, the actual structure of the response may be different. Let’s modify the function to print the entire response_object and investigate its structure:

“`python
def display_llm_response(response_object):
try:
print(“Full response:”, response_object) # Print the entire response
choices = response_object.get(“choices”, [])

if choices:
print(“Choices:”, choices)
first_choice = choices[0]

print(“Parsed content:”, response_content)
speak_text(response_content)
else:
print(“No ‘choices’ key found in the response.”)
except Exception as e:
print(f”Error displaying LLM response: {e}”)
“`

This modification will print the entire response, including its structure. Run the script again, and check the printed output for the structure of the response. It will help us identify the correct keys to access the necessary information.
now its not sending
The error you’re encountering, `’prompt’ field is required`, suggests that the LLM (Language Model) endpoint expects a ‘prompt’ field in the request, and it’s missing. This could be due to how the messages are being formatted or sent.

In the `send_text_to_llm` function, ensure that the ‘prompt’ field is correctly included. Here’s an adjustment to your function:

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Make a request to the LLM endpoint
response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Print the response for debugging
print(f”LLM Response: {response}”)

print(f”LLM Response: {spoken_text}”)

# Display the spoken text
speak_text(spoken_text)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

This adjustment ensures that the ‘prompt’ field is correctly included in the request to the LLM endpoint. Please replace your existing `send_text_to_llm` function with this modified version and try again.
still no
Let’s try to debug and get more information. Please update your `send_text_to_llm` function with some print statements to inspect the request before it’s sent. Here’s the modified version:

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Print the prompt to check if ‘prompt’ is correctly formatted
print(“Prompt:”, prompt)

# Make a request to the LLM endpoint
response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Print the response for debugging
print(f”LLM Response: {response}”)

print(f”LLM Response: {spoken_text}”)

# Display the spoken text
speak_text(spoken_text)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

This will help us check the format of the ‘prompt’ field before sending the request. If possible, share the output of the “Prompt” print statement so we can inspect it for any issues.
Inspect this against the files we are working on:: def main():
# Establish a connection to the local LLM
connection = LocalLLMConnection()

while True:
try:
wait_for_start_key() # Wait for the start key
user_input = capture_user_voice_input()

if user_input:
processed_input = user_input[len(“listen”):].strip()

wait_for_stop_key() # Wait for the stop key

if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break

llm_response = send_text_to_llm(connection, processed_input)
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()
Your `main` function looks fine. It waits for the start key, captures user input, waits for the stop key, processes the input, and sends it to the LLM using `send_text_to_llm`. The potential issue might be in how the user input is processed or sent to the LLM.

Here’s the relevant part of your `main` function for reference:

“`python
user_input = capture_user_voice_input()

if user_input:
processed_input = user_input[len(“listen”):].strip()

wait_for_stop_key() # Wait for the stop key

if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break

llm_response = send_text_to_llm(connection, processed_input)
display_llm_response(llm_response)
“`

Make sure that `processed_input` doesn’t contain any characters that might cause issues in the LLM request, and ensure that the `send_text_to_llm` function is correctly formatting the request. The debug print statements in the `send_text_to_llm` function should help identify any issues.

If the issue persists, please share the output of the “Prompt” print statement in the `send_text_to_llm` function.
This comes from teh server: [INFO] Received POST request to /v1/completions with body: {
“model”: “Lilith”,
“messages”: [
{
“role”: “system”,
“content”: “You are a Starship bridge officer.”
},
{
“role”: “user”,
“content”: “Introduce yourself.”
}
],
“temperature”: 0.7,
“stream”: false,
“json_output”: true
}
[2024-02-06 20:19:33.559] [ERROR] ‘prompt’ field is required
The server error message indicates that the “prompt” field is required, but it’s not being provided in the request. To resolve this, you need to make sure that the request sent to the server includes a valid “prompt” field.

Here’s a snippet from your `send_text_to_llm` function:

“`python
prompt = {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”
}

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

# …

response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])
“`

Ensure that the “prompt” field is included in the messages sent to the server. The “prompt” should be part of the “messages” list, and it should have the required structure. You might need to adjust the structure based on the requirements of the server.

Double-check the structure of the request you’re sending to the server and make sure it adheres to the expected format. If you continue to encounter issues, you may want to refer to the server’s documentation or contact its support for guidance on the correct format for the request.
This is what I have::” def send_text_to_llm(connection, text, previous_messages=None):
try:
# Initialize prompt with the system message and preset parameters
prompt = {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”
}

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
Your `send_text_to_llm` function looks correct, and it appears to be constructing the prompt appropriately with the necessary “prompt” field. However, there might be an issue with how the server is interpreting the request or the specific format it expects.

Here are a few suggestions to troubleshoot and improve the function:

1. **Ensure Correct JSON Structure:**
Double-check the structure of the JSON request being sent to the server. Make sure that it matches the expected format specified by the server’s API documentation. Pay close attention to any specific requirements for the “prompt” field.

2. **Check for Server-Side Requirements:**
Some servers may have additional requirements for certain fields or may expect certain parameters to be present. Refer to the server’s documentation to ensure that you are meeting all the necessary requirements.

3. **Debugging with Server Logs:**
If the server provides logs or error messages, review them to get more insights into why the server is reporting a missing “prompt” field. Server logs can often provide valuable information about what the server expects.

4. **Contact Server Support:**
If you’re still having trouble, consider reaching out to the support or documentation of the server. They might be able to provide specific guidance or identify any issues on their end.

5. **Use a Simple Test Case:**
Simplify your request for testing purposes. Instead of using complex messages or additional parameters, try sending a minimal request with just the system message and the user’s input to see if the issue persists.

Here’s a modified example for testing:

“`python
response = connection.send([
{“role”: “system”, “content”: “”, “prompt”: {“role”: “system”, “content”: “Test”}},
{“role”: “user”, “content”: text}
])
“`

Remember to adapt the structure based on the server’s requirements. If the issue persists, it’s crucial to consult the server’s documentation or contact their support for further assistance.
wHY IS THIS NOT GETTING THROUGH HERE BUT IT IS THROUGH THE dump. FILE? [INFO] Received POST request to /v1/completions with body: {
“model”: “Lilith”,
“messages”: [
{
“role”: “system”,
“content”: “You are a Starship bridge officer.”
},
{
“role”: “user”,
“content”: “Introduce yourself.”
}
],
“temperature”: 0.7,
“stream”: false,
“json_output”: true
}
[2024-02-06 20:24:14.519] [ERROR] ‘prompt’ field is required
Based on the logs you provided, the issue seems to be with the server’s requirement for the ‘prompt’ field. It’s possible that the server is expecting the ‘prompt’ field to be directly within the main JSON object and not nested within another ‘messages’ array.

Here’s how you can modify your request structure to match the server’s expectation:

“`python
response = connection.send({
“model”: “Lilith”,
“prompt”: {
“role”: “system”,
“content”: “You are a Starship bridge officer.”
},
“messages”: [
{“role”: “user”, “content”: “Introduce yourself.”}
],
“temperature”: 0.7,
“stream”: False,
“json_output”: True
})
“`

In this modified request, the ‘prompt’ field is directly part of the main JSON object, and the ‘messages’ array contains the user’s input. Adjust the structure based on the server’s requirements, and this might resolve the ‘prompt’ field missing issue.

If the problem persists, it’s crucial to refer to the server’s documentation or contact their support for clarification on the expected format of the request. The ‘dump.py’ script might be formatting the request in a way that matches the server’s expectations, and you can use it as a reference for constructing your requests in other parts of the code.
IS THE SCRIPT IN CONNECTION.PY interfering with components or caht.py?
It seems like the issue might be related to the way the request is constructed in the `send_text_to_llm` function in the `connection.py` script. To resolve this, you can make sure that the request sent by the `send_text_to_llm` function is correctly formatted.

Here’s the adjusted `send_text_to_llm` function:

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Send the prompt directly without wrapping it in an array
response = connection.send(prompt)

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

In this adjusted function, the `connection.send` call now sends the `prompt` directly, without wrapping it in a list. This modification aligns the request structure with the expected format, and it should help resolve the ‘prompt’ field missing issue.

Make sure to update the `send_text_to_llm` function in the `connection.py` script and see if it resolves the problem. If the issue persists, further investigation may be needed, and you may need to check the interaction between different parts of the code.
Voice captured: are you listening to me now
Press ‘alt’ to stop and send…
alt pressed. Sending now.
Sending text to LLM: u listening to me now
Prompt: {‘role’: ‘system’, ‘content’: ‘You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.’, ‘input_prefix’: ‘Instruct: ‘, ‘input_suffix’: ‘nOutput:’, ‘messages’: [{‘role’: ‘user’, ‘content’: ‘u listening to me now’}]}
LLM Response: {‘error’: “‘prompt’ field is required”}
LLM Response: No choices found in the response.
Full response: {‘error’: “‘prompt’ field is required”}
No ‘choices’ key found in the response.
It appears that the issue still persists. Let’s focus on debugging and refining the code further. Please follow these steps:

1. **Update `send_text_to_llm` Function in `connection.py`:**

Modify the `send_text_to_llm` function in `connection.py` to ensure that the `prompt` is correctly formatted. Use the following adjusted code:

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Send the prompt directly without wrapping it in an array
response = connection.send(prompt)

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

2. **Update `display_llm_response` Function in `components.py`:**

Modify the `display_llm_response` function in `components.py` to handle the case when an error occurs. Use the following adjusted code:

“`python
def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

3. **Test the Code:**

After making these updates, run your code again and check if the issue is resolved. Observe the console output for any error messages or unexpected behavior. If you encounter errors, please share the specific error messages for further assistance.

4. **Debugging:**

If the issue persists, we may need to inspect the raw request and response data between your script and the LLM server. Add print statements or use a debugging tool to log the raw data before sending the request and after receiving the response. This will help identify any discrepancies or missing fields.

Please perform these steps and provide feedback on the results or any new error messages you encounter.
There is no send_to_text function in connection.py
I apologize for the confusion. It seems there was a misunderstanding in my previous responses. Let’s address the issue based on the actual structure of your code. Given that there is no `send_to_text` function in `connection.py`, let’s focus on the existing `send` method.

Please make the following modifications:

1. **Update `send_text_to_llm` in `components.py`:**

Modify the `send_text_to_llm` function in `components.py` to correctly format the request. Ensure that the `prompt` is sent as a single dictionary, not within an array. Use the following adjusted code:

print(f”Sending text to LLM: {text}”) # Print the text being sent

# Send the prompt directly without wrapping it in an array
response = connection.send(prompt)

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

2. **Test the Code:**

After making these updates, run your code again and check if the issue is resolved. Observe the console output for any error messages or unexpected behavior.

Please perform these steps and provide feedback on the results or any new error messages you encounter.
Answer me this why do I have eseentially the same code in connection.py as ido in components.py EAMPLES def send_text_to_llm(connection, text, previous_messages=None):
try:
# Initialize prompt with the system message and preset parameters
prompt = {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”
}

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent
…. AND ::connection = LocalLLMConnection()
messages = [
{“role”: “system”, “content”: “You are a Starship bridge officer.”},
{“role”: “user”, “content”: “Introduce yourself.”}
]

response = connection.send(messages, json_output=True) # Request JSON response directly
choices = response.get(“choices”, [])
if choices:
spoken_text = choices[0].get(“message”, {}).get(“content”)
if spoken_text is None:
spoken_text = “No content found”
else:
spoken_text = “No choices found in the response.”
It appears that you have similar code in both `connection.py` and `components.py` because both modules are handling the communication with the language model (LLM) and sending messages to it. The code in `send_text_to_llm` in `components.py` and the example code using `connection.send` in `connection.py` serve the same purpose — interacting with the LLM.

If the code in both modules is meant to perform similar tasks, it could be a design choice to encapsulate related functionality into separate modules for better organization and maintainability. However, it’s crucial to ensure that the code is consistent and follows the expected structure for communication with the LLM.

In your specific case, the key issue seems to be related to how the prompt is constructed and sent to the LLM. Make sure that the prompt is formatted correctly and sent in a way that the LLM can understand. The adjustments I provided earlier were aimed at resolving the reported issue.

If there’s any confusion or inconsistency in the code structure, please let me know, and I’ll do my best to assist you further.
Please write out what you know of the error that I am experiencing. Please make it simple and techcnical for an AI agent to follow.
The error you are experiencing, ” ‘prompt’ field is required,” suggests that the server is expecting a ‘prompt’ field in the request body but is not receiving it. The ‘prompt’ field is crucial for the server to understand and process the user’s input properly.

Here’s a breakdown:

1. **The Issue:** The LLM server expects a ‘prompt’ field in the request payload.
2. **Why it Matters:** The ‘prompt’ field contains information about the context or system message and the user’s input. This context is essential for the LLM to generate meaningful responses.
3. **Possible Cause:** The ‘prompt’ field might not be properly included or formatted in the request sent to the LLM server.

To address this issue:

– Double-check the construction of the ‘prompt’ field in your code.
– Ensure that the ‘prompt’ field is included in the payload sent to the LLM server.
– Confirm that the ‘prompt’ field contains necessary information for context, especially the system message and user input.

By fixing any issues related to the ‘prompt’ field, you should be able to resolve the error and receive meaningful responses from the LLM server.
Expand this further: The Issue: The LLM server expects a ‘prompt’ field in the request payload.
Certainly! Let’s break down the issue further:

1. **Expectation of ‘prompt’ Field:** The LLM server, where you send requests for language model completions, has a specific expectation regarding the structure of the request payload.

2. **Importance of ‘prompt’ Field:** The ‘prompt’ field serves as a crucial component in the request payload. It provides essential information to the language model, including the system message, context, and user input. Without this context, the language model might struggle to generate relevant and coherent responses.

3. **Server-Side Processing:** When the LLM server receives a request, it looks for a ‘prompt’ field to understand the context and content of the user’s input. This allows the model to tailor its response based on the provided information.

4. **Communication Breakdown:** The error you’re encountering, “‘prompt’ field is required,” indicates a breakdown in communication between your script and the LLM server. It suggests that the server is not receiving the expected ‘prompt’ field in the request payload.

5. **Resolution Steps:** To resolve this issue, you need to review the code responsible for constructing the request payload. Ensure that the ‘prompt’ field is correctly formatted and contains the necessary information, such as the system message and user input. By addressing any discrepancies in how the ‘prompt’ field is handled in your code, you can establish proper communication with the LLM server and receive meaningful responses.

In summary, the LLM server’s expectation for a ‘prompt’ field is a key aspect of successful communication between your script and the language model, influencing the accuracy and relevance of the generated responses.
NO. I need not a textbook definition of the issue. I need a technicla explanation so an engineer can fix it:
Certainly, let’s dive into a technical explanation:

**Technical Explanation of the Issue:**

1. **Server Expectation:**
– The LLM server expects a specific structure in the payload of the HTTP request it receives.
– In particular, it requires a ‘prompt’ field in the JSON payload to understand the context and generate accurate responses.

2. **Payload Structure:**
– The ‘prompt’ field should include crucial details like the system message, input prefix and suffix, and user messages.
– This information aids the language model in comprehending the user’s request and formulating a relevant reply.

3. **Your Script’s Role:**
– Your script is responsible for constructing the request payload that gets sent to the LLM server.
– It should assemble the ‘prompt’ field correctly, ensuring it encapsulates all necessary components for meaningful interaction.

4. **Communication Breakdown:**
– The error “‘prompt’ field is required” indicates that the LLM server did not receive the expected ‘prompt’ field in the request payload.
– This suggests a discrepancy between what your script is sending and what the server anticipates.

5. **Troubleshooting Steps:**
– Inspect the code segment responsible for creating the ‘prompt’ field.
– Verify that it includes all the required elements such as system message, input prefix and suffix, and user messages.
– Ensure correct formatting and structure according to the server’s expectations.

6. **Debugging Output:**
– Utilize debugging statements or logs to print out the constructed payload before sending it to the LLM server.
– This allows you to inspect the payload’s structure and content, aiding in identifying any missing or misformatted components.

7. **Server Logs:**
– Examine the logs on the LLM server side, checking for incoming requests. This can provide insights into the received payloads and potential issues.

By meticulously examining and aligning the structure of your request payload with the server’s expectations, you can resolve the issue and establish seamless communication with the LLM server.
engineer rewrote the entire code and this ::Sending text to LLM: you hear me now are we fixed or not
Prompt: {‘role’: ‘system’, ‘content’: ‘You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.’, ‘input_prefix’: ‘Instruct: ‘, ‘input_suffix’: ‘nOutput:’, ‘messages’: [{‘role’: ‘user’, ‘content’: ‘you hear me now are we fixed or not’}]}
LLM Response: {‘error’: “‘prompt’ field is required”}
LLM Response: No choices found in the response.
Full response: {‘error’: “‘prompt’ field is required”}
No ‘choices’ key found in the response.
Press ‘ctrl’ to start listening…

The issue still persists. The error `’prompt’ field is required` suggests that the LLM server is not receiving the expected ‘prompt’ field in the request payload. This means that there might be an issue with how the ‘prompt’ field is being constructed or included in the payload.

To troubleshoot this further, you can focus on the following steps:

1. **Review ‘send_text_to_llm’ Function:**
– Inspect the ‘send_text_to_llm’ function in your code.
– Ensure that it constructs the ‘prompt’ field correctly, including all necessary components such as the system message, input prefix and suffix, and user messages.

2. **Print Debugging Information:**
– Add print statements to output the constructed payload before sending it to the LLM server.
– This will help you visually inspect the structure and content of the payload to identify any discrepancies.

3. **Verify Payload Structure:**
– Double-check that the payload structure aligns with the LLM server’s expectations.
– Confirm that the ‘prompt’ field is correctly formatted and contains the required information.

4. **Check for Missing Components:**
– Ensure that none of the components within the ‘prompt’ field are missing or incorrectly specified.
– The system message, input prefix and suffix, and user messages should be properly included.

5. **Inspect Server Logs:**
– Review the logs on the LLM server side to check for incoming requests.
– This can provide insights into the actual payloads received by the server and help pinpoint any issues.

6. **Consider Server-Side Requirements:**
– Check if there are any specific requirements or changes on the LLM server side that might affect how the payload should be structured.

By going through these steps and closely examining the construction and content of the payload, you should be able to identify and rectify the issue. If the problem persists, it might be beneficial to consult the documentation of the LLM server or reach out to the server maintainers for further assistance.
2024-02-06 21:09:23.100] [INFO] Received POST request to /v1/completions with body: {
“prompt”: {
“role”: “system”,
“content”: “You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
“input_prefix”: “Instruct: “,
“input_suffix”: “nOutput:”,
“messages”: [
{
“role”: “user”,
“content”: “hello”
}
]
}
}
[2024-02-06 21:09:23.100] [ERROR] ‘prompt’ field must be a string or an array of strings
The latest log indicates that the server received a POST request with a payload that includes a ‘prompt’ field. However, the server encountered an error because the ‘prompt’ field must be a string or an array of strings.

To resolve this issue, you should ensure that the ‘prompt’ field in your request payload is correctly formatted as a string or an array of strings. Here are some steps you can take:

1. **Review ‘send_text_to_llm’ Function:**
– Inspect the ‘send_text_to_llm’ function in your code.
– Verify that the ‘prompt’ field is constructed properly and is either a string or an array of strings.

2. **Check ‘prompt’ Field Format:**
– Ensure that the ‘prompt’ field is a valid JSON object with the correct structure.
– Confirm that the ‘content’ within the ‘prompt’ is a string.

3. **Debugging Information:**
– Add print statements to output the constructed payload before sending it to the LLM server.
– Inspect the structure and content of the ‘prompt’ field in your debug output.

4. **Verify User Message Format:**
– Confirm that the ‘content’ of the user message within the ‘messages’ array is a string.
– If needed, convert any non-string content to a string before constructing the ‘prompt’ field.

5. **Consult Server Documentation:**
– Check the documentation of the LLM server to ensure you are adhering to its requirements for the ‘prompt’ field.

Here is an example of a correct ‘prompt’ field:

“`json
“prompt”: {
“role”: “user”,
“content”: “Hello, how are you?”
}
“`

By addressing these points and ensuring the ‘prompt’ field follows the server’s expectations, you should be able to resolve the current error.
Im getting this from the script though: 400 Client Error: Bad Request for url: http://localhost:5000/v1/completions
The 400 Client Error: Bad Request indicates that the server received a request it could not understand or process. This could be due to various reasons, including issues with the request payload, incorrect formatting, or missing required fields.

To troubleshoot and resolve the 400 error:

1. **Check Request Payload:**
– Examine the payload being sent to the server. Print or log the payload to verify its structure and content.
– Confirm that the ‘prompt’ field is constructed correctly with the required format.

2. **Verify URL and Endpoint:**
– Ensure that the URL and endpoint used in the request are correct.
– Confirm that the server is running on the specified port (5000) and that the endpoint is valid.

3. **Review Server Logs:**
– Check the server logs for more detailed error messages. The server logs may provide additional information about the specific issue it encountered.

4. **Validate JSON Format:**
– Make sure that the entire request payload, including headers, follows the correct JSON format.
– Ensure there are no syntax errors or unexpected characters in the payload.

5. **Inspect Server Requirements:**
– Refer to the documentation of the LLM server to understand its requirements for requests.
– Confirm that your code aligns with the server’s expectations regarding headers, content types, and payload structure.

6. **Try a Minimal Example:**
– Create a minimal example with a simple prompt to see if the error persists. This can help identify whether the issue is specific to the payload or if it’s a broader problem.

7. **Check Server Status:**
– Confirm that the LLM server is up and running.
– Verify that there are no issues with the server’s configuration or dependencies.

By systematically addressing these points, you should be able to identify the cause of the 400 error and make the necessary adjustments to your code for successful communication with the LLM server.
class PromptBuilder:
def __init__(self, system_message, input_prefix, input_suffix):
self.system_message = system_message
self.input_prefix = input_prefix
self.input_suffix = input_suffix

def build_prompt(self, user_message):
return {
“prompt”: {
“role”: “system”,
“content”: self.system_message,
“input_prefix”: self.input_prefix,
“input_suffix”: self.input_suffix,
“messages”: [
{
“role”: “user”,
“content”: user_message
}
]
}
}
Your `PromptBuilder` class looks well-structured and designed to create prompts for interacting with the language model. The class takes in the system message, input prefix, and input suffix during initialization and provides a method `build_prompt` to generate the complete prompt based on the user’s message.

Here’s a brief breakdown of your class:

– **Constructor (`__init__`):**
– Accepts the system message, input prefix, and input suffix as parameters.
– Stores these values as instance variables for later use.

– **`build_prompt` Method:**
– Takes a user message as a parameter.
– Constructs a prompt dictionary with the specified structure.
– Returns the prompt dictionary.

Using this class, you can easily create prompts by providing the necessary components. Here’s an example of how you might use it:

“`python
# Create an instance of PromptBuilder
prompt_builder = PromptBuilder(
system_message=”You are a starship bridge officer named Lilith. You will always answer as Lilith, chief Officer of Astrometrics.”,
input_prefix=”Instruct: “,
input_suffix=”nOutput:”
)

# Build a prompt for a user message
user_message = “Hello, how are you?”
prompt = prompt_builder.build_prompt(user_message)

# Now, ‘prompt’ contains the structured prompt dictionary
print(prompt)
“`

This class provides a clean and modular way to manage the construction of prompts, making your code more readable and maintainable. If you encounter any specific issues or have further questions about its usage, feel free to ask!
import sys
from connection import LocalLLMConnection
from components import capture_user_voice_input, transcribe_audio_to_text, send_text_to_llm, display_llm_response, wait_for_start_key, wait_for_stop_key

import speech_recognition as sr

import keyboard # Make sure to install the ‘keyboard’ library using pip

def wait_for_key(key):
print(f”Press {key} to start/stop voice capture…”)
keyboard.wait(key)

import keyboard # Make sure to install the ‘keyboard’ library using pip
import speech_recognition as sr

def capture_user_voice_input(start_key=”ctrl”, stop_key=”alt”):
recognizer = sr.Recognizer()

wait_for_key(start_key) # Wait for the start key

with sr.Microphone() as source:
print(“Listening…”)
audio = recognizer.listen(source, timeout=5)

wait_for_key(stop_key) # Wait for the stop key

# Example usage:
voice_input = capture_user_voice_input()
print(“Captured voice input:”, voice_input)

def main():
# Establish a connection to the local LLM
connection = LocalLLMConnection()

while True:
try:
wait_for_start_key() # Wait for the start key
user_input = capture_user_voice_input()

if user_input:
processed_input = user_input[len(“listen”):].strip()

wait_for_stop_key() # Wait for the stop key

if processed_input.lower() == “you are dismissed”:
print(“Goodbye!”)
break

llm_response = send_text_to_llm(connection, processed_input)
display_llm_response(llm_response)

except KeyboardInterrupt:
print(“nExiting chat…”)
break
except Exception as e:
print(f”An error occurred: {e}”, file=sys.stderr)

if __name__ == “__main__”:
main()
Your script appears to be a combination of voice input, processing, and interaction with a language model. Let’s break down the key components and functionalities:

1. **Voice Input:**
– Utilizes the `speech_recognition` library (`sr`) to capture voice input from the user.
– Uses the `capture_user_voice_input` function, which waits for a specified start key (e.g., “ctrl”) to initiate voice capture and an optional stop key (e.g., “alt”) to terminate it.

2. **Connection to Local Language Model (LLM):**
– Establishes a connection to a local language model using the `LocalLLMConnection` from the `connection` module.
– Sends user input to the LLM using the `send_text_to_llm` function from the `components` module.

3. **User Interaction Loop:**
– Main loop (`main` function) where the program continuously waits for the start key, captures voice input, processes it, and interacts with the LLM.
– The loop can be terminated by saying “you are dismissed” or by interrupting the program (Ctrl+C).

4. **Displaying LLM Response:**
– Uses the `display_llm_response` function from the `components` module to handle the display of LLM responses.

5. **Dependencies:**
– Requires the `keyboard` and `speech_recognition` libraries. Make sure they are installed (`pip install keyboard SpeechRecognition`).

6. **Usage Example:**
– Provides an example usage of capturing voice input and interacting with the LLM within the `__main__` block.

**Recommendations:**
– Ensure that all the required libraries are installed.
– Make sure that the LLM connection and interaction functions (`send_text_to_llm`, `display_llm_response`) are implemented correctly in the `components` module.
– Test the script, paying attention to the start and stop keys and voice input recognition.
– Address any issues or error messages that may arise during testing.

If you encounter specific issues or have further questions, feel free to provide additional details!
import speech_recognition as sr
import pyttsx3

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

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

#response = connection.send([{“role”: “system”, “content”: “”, “prompt”: prompt}])
response = connection.send({
“model”: “Lilith”,
“prompt”: {
“role”: “system”,
“content”: “You are a Starship bridge officer.”
},
“messages”: [
{“role”: “user”, “content”: “Introduce yourself.”}
],
“temperature”: 0.7,
“stream”: False,
“json_output”: True
})
# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}

def display_llm_response(response_object):
try:
choices = response_object.get(“choices”, [])

if choices:
print(“Full response:”, choices)
first_choice = choices[0]

Your `send_text_to_llm` function seems to be trying to send a prompt to the LLM server using the `connection.send` method. The commented-out code suggests that you were trying to send a prompt in a specific format, but then you switched to a different format using a dictionary.

Here are a few things you might want to check or modify:

1. **Prompt Format:**
– Ensure that the `prompt` structure you are sending matches the expected format by the LLM server. Based on the commented-out code, it seems you were constructing a prompt dictionary with specific roles, content, and other attributes.

2. **Connection.send Format:**
– The `connection.send` method might expect a certain format. It’s important to align the format of the payload you send with the LLM server’s expectations. The currently used dictionary format looks fine if it matches the expected structure.

3. **Model Name:**
– Ensure that the model name specified in the request (`”model”: “Lilith”`) is correct and matches the model name registered on the LLM server.

4. **Response Handling:**
– Check the structure of the response you receive from the LLM server. Your `display_llm_response` function assumes a certain structure (`choices`, etc.), so make sure it aligns with what the server actually returns.

5. **Error Handling:**
– If there are errors, the LLM server might provide useful information. Print the complete response (or log it) to better understand any error messages or unexpected behavior.

6. **Testing:**
– Consider testing the `send_text_to_llm` function in isolation. You can print the payload you are sending and the response you receive to identify any issues.

Here is an updated version of your function with some improvements in formatting:

# Add previous messages to the prompt if available
if previous_messages:
prompt[“previous_messages”] = previous_messages

# Add the user message to the prompt
prompt[“messages”] = [{“role”: “user”, “content”: text}]

print(f”Sending text to LLM: {text}”) # Print the text being sent

response = connection.send({“model”: “Lilith”, “prompt”: prompt, “json_output”: True})

# Print the response for debugging
print(f”LLM Response: {response}”)

return response

except Exception as e:
print(f”Error sending text to LLM: {e}”)
return {“error”: str(e)}
“`

This code assumes that the LLM server accepts a dictionary with a `model`, `prompt`, and `json_output` key. Please adjust it based on the actual requirements of the LLM server you are using.
Client Code Examples
# Example: reuse your existing OpenAI setup
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url=”http://localhost:5000/v1″, api_key=”not-needed”)

completion = client.chat.completions.create(
model=”local-model”, # this field is currently unused
messages=[
{“role”: “system”, “content”: “Always answer in rhymes.”},
{“role”: “user”, “content”: “Introduce yourself.”}
],
temperature=0.7,
)
It appears that you are using the OpenAI GPT API with the `openai.ChatCompletion.create` method for generating completions in a chat-based scenario. The `messages` parameter is used to provide a conversation history, and you have a system message followed by a user message in your example.

If you are working with a local server instead of the OpenAI API, you can use a similar approach with the `connection.send` method as shown in your existing code. Ensure that the payload structure (e.g., the `model`, `prompt`, and other parameters) aligns with the requirements of your local server.

Here is a simplified example based on your existing code:

“`python
from connection import LocalLLMConnection

# Establish a connection to the local LLM
connection = LocalLLMConnection()

# Define the conversation messages
messages = [
{“role”: “system”, “content”: “You are a starship bridge officer.”},
{“role”: “user”, “content”: “Introduce yourself.”}
]

# Send a request to the local LLM server
response = connection.send({“model”: “Lilith”, “messages”: messages, “json_output”: True, “temperature”: 0.7})

# Process and display the LLM response
display_llm_response(response)
“`

Make sure to adjust the parameters and payload structure according to the requirements of your local server. The goal is to construct a payload that includes the model name, conversation history, and any other necessary parameters.
lET’S TRY TO USE THIS AS MY CONNECTION TO THE LLM: class LocalLLMConnection:
def __init__(self):
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”not-needed”) # Specify the correct endpoint

It seems like you are trying to use the OpenAI API client to connect to a local LLM (Language Model) server. However, there is a mismatch in the code structure. The OpenAI API client is designed for connecting to OpenAI’s servers, not a local server.

If you are using a local LLM server, you should utilize the `LocalLLMConnection` class or similar methods you have in your existing code to send requests to the local server. Here’s an example using your `LocalLLMConnection`:

“`python
# Assuming you already have the LocalLLMConnection class defined

# Establish a connection to the local LLM
connection = LocalLLMConnection()

# Define the conversation messages
messages = [
{“role”: “system”, “content”: “Always answer in rhymes.”},
{“role”: “user”, “content”: “Introduce yourself.”}
]

# Send a request to the local LLM server
response = connection.send({“model”: “local-model”, “messages”: messages, “json_output”: True, “temperature”: 0.7})

# Process and display the LLM response
display_llm_response(response)
“`

Make sure to adjust the parameters according to your local LLM server’s requirements, such as the model name, conversation history format, and any other specific parameters it expects.

Dr. Trippers Stuff

Agent Configuration and Interaction

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