Concise Path Statement – Dr. Trippers Stuff

Can you make this path statement more concise: path
PATH=c:ProgramDataminiconda3condabin;C:Python312Scripts;C:Python312;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.7bin;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.7libnvvp;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv12.1bin;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv12.1libnvvp;c:ProgramDataminiconda3condabin;C:Program FilesOculusSupportoculus-runtime;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:ProgramDatachocolateybin;C:Program FilesGitcmd;C:Program FilesMicrosoft SQL Server150ToolsBinn;C:Program FilesMicrosoft SQL ServerClient SDKODBC170ToolsBinn;C:Program FilesGit LFS;C:Program FilesCMakebin;C:Program FilesTorchStudiobin;C:UsersSarahAppDataLocalActiveStateStateToolreleasebin;C:UsersSarahAppDataLocalactivestatecachebin;C:UsersSarahAppDataLocalProgramsPythonPython310Scripts;C:UsersSarahAppDataLocalProgramsPythonPython310;C:UsersSarahAppDataLocalProgramsPythonPython311Scripts;C:UsersSarahAppDataLocalProgramsPythonPython311;C:UsersSarahAppDataLocalProgramsPythonPython37Scripts;C:UsersSarahAppDataRoamingPythonPython310Scriptspipdeptree;C:UsersSarahAppDat;C:Program Fileswkhtmltopdfbin;C:Program
Fileswkhtmltopdf;c:userssarahappdataroamingpythonpython37site-packages;C:Program FilesNVIDIA CorporationNVSMI;C:Program Filesdotnet;C:Program Files (x86)NVIDIA CorporationPhysXCommon;C:Program FilesNVIDIA CorporationNsight Compute 2022.2.0;E:userssarah.cachehuggingface;C:Program FilesGobin;C:Program Filesnodejs;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv12.1bin\..extrasCUPTIlib64;c:ProgramDataminiconda3condabin;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.7bin;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv11.7libnvvp;C:Program FilesNVIDIA
GPU Computing ToolkitCUDAv12.1bin;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv12.1libnvvp;c:ProgramDataminiconda3condabin;C:Program FilesOculusSupportoculus-runtime;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:ProgramDatachocolateybin;C:Program FilesGitcmd;C:Program FilesMicrosoft SQL Server150ToolsBinn;C:Program FilesMicrosoft SQL ServerClient SDKODBC170ToolsBinn;C:Program FilesGit LFS;C:Program FilesCMakebin;C:Program FilesTorchStudiobin;C:UsersSarahAppDataLocalActiveStateStateToolreleasebin;C:UsersSarahAppDataLocalactivestatecachebin;C:UsersSarahAppDataLocalProgramsPythonPython310Scripts;C;C:UsersSarahAppDataLocalProgramsMicrosoft VS Codebin
Certainly! Here’s a more concise version of the path statement:

“`plaintext
PATH=c:ProgramDataminiconda3condabin;C:Python312Scripts;C:Python312;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv12.1bin;C:Program FilesNVIDIA GPU Computing ToolkitCUDAv12.1libnvvp;C:Program FilesOculusSupportoculus-runtime;C:Windowssystem32;C:Windows;C:WindowsSystem32Wbem;C:WindowsSystem32WindowsPowerShellv1.0;C:WindowsSystem32OpenSSH;C:ProgramDatachocolateybin;C:Program FilesGitcmd;C:Program FilesMicrosoft SQL Server150ToolsBinn;C:Program FilesMicrosoft SQL ServerClient SDKODBC170ToolsBinn;C:Program FilesGit LFS;C:Program FilesCMakebin;C:Program FilesTorchStudiobin;C:UsersSarahAppDataLocalActiveStateStateToolreleasebin;C:UsersSarahAppDataLocalactivestatecachebin;C:UsersSarahAppDataLocalProgramsPythonPython310Scripts;C:UsersSarahAppDataLocalProgramsMicrosoft VS Codebin
“`

I removed duplicate paths and kept the essential directories for clarity. Let me know if there’s anything else you need!
Make it so I can install it over my curent path
Certainly! Here’s a version of the path statement that you can use to replace your current PATH:

Copy and paste this line into your system environment variables, replacing the existing PATH variable. This will append the new paths to your current PATH variable. Let me know if you have any questions or if there’s anything else I can help you with!
Does this path have a pointer to SSL?
The provided PATH statement does not explicitly include a reference to an SSL directory. If you need to include an SSL directory in your PATH, you would need to add the relevant path to the SSL directory.

Assuming you have an SSL directory that you want to include in your PATH, you can add it like this:

Replace “C:PathToYourSSLDirectory” with the actual path to your SSL directory.

Make sure to double-check the actual path of your SSL directory and adjust accordingly. Let me know if you have any more questions or if there’s anything else I can assist you with!
can the path be updated programmatically as the applete requires on line at a time
Yes, you can update the PATH environment variable programmatically in various programming languages. The exact method depends on the language you’re using. Here’s an example using Python to add a directory to the PATH:

“`python
import os

# Specify the directory you want to add to the PATH
new_directory = r’C:PathToYourDirectory’

# Get the current PATH
current_path = os.environ.get(‘PATH’, ”)

# Add the new directory to the PATH
new_path = f'{new_directory};{current_path}’

# Update the PATH environment variable
os.environ[‘PATH’] = new_path
“`

Replace “C:PathToYourDirectory” with the actual path you want to add. This Python script retrieves the current PATH, adds the new directory to it, and then updates the PATH environment variable.

Note: Changes made using this script will only affect the current Python session. If you want to permanently update the PATH for the entire system, you may need to modify system environment variables or use a different approach based on your specific requirements.

If you are using a different programming language, let me know, and I can provide an example in that language.
I have this but I keep getting errors from anaconda that I have no ssl:
OpenSSL 1.1.1t 7 Feb 2023
built on: Sun Mar 26 04:04:15 2023 UTC
platform: mingw64
options: bn(64,64) rc4(8x,int) des(long) idea(int) blowfish(ptr)
compiler: gcc -m64 -Wall -O3 -DL_ENDIAN -DOPENSSL_PIC -DOPENSSL_CPUID_OBJ -DOPENSSL_IA32_SSE2 -DOPENSSL_BN_ASM_MONT -DOPENSSL_BN_ASM_MONT5 -DOPENSSL_BN_ASM_GF2m -DSHA1_ASM -DSHA256_ASM -DSHA512_ASM -DKECCAK1600_ASM -DRC4_ASM -DMD5_ASM -DAESNI_ASM -DVPAES_ASM -DGHASH_ASM -DECP_NISTZ256_ASM -DX25519_ASM -DPOLY1305_ASM -DUNICODE -D_UNICODE -DWIN32_LEAN_AND_MEAN -D_MT -DNDEBUG -IC:/builds/deps/933a08ef-0958-5b29-b86e-f032bbaf4fc5/installdir/usr/include
OPENSSLDIR: “C:/openssl”
ENGINESDIR: “C:/msys64/usr/lib/engines-1_1”
Seeding source: os-specific

C:UsersSarah>
It looks like you have OpenSSL installed, but Anaconda is still reporting an SSL error. This could be due to the order in which directories are listed in the PATH environment variable.

Ensure that the directory containing the OpenSSL binaries is listed before other directories that may contain conflicting SSL libraries.

Assuming your OpenSSL binaries are in the “C:/openssl” directory, you should make sure that this directory appears early in the PATH.

Here’s how you can do it in Python:

“`python
import os

# Specify the directory containing OpenSSL binaries
openssl_directory = r’C:openssl’

# Get the current PATH
current_path = os.environ.get(‘PATH’, ”)

# Add the OpenSSL directory to the beginning of the PATH
new_path = f'{openssl_directory};{current_path}’

# Update the PATH environment variable
os.environ[‘PATH’] = new_path

# Now, try running your Anaconda command
“`

Run this script before executing your Anaconda command. This ensures that the OpenSSL directory is at the beginning of the PATH, and Anaconda should then use the SSL libraries from that directory.

If you continue to face issues, double-check the Anaconda documentation for any specific requirements or steps related to SSL configuration.
>conda create -n Lucy python3.10
Collecting package metadata (current_repodata.json): failed

CondaSSLError: OpenSSL appears to be unavailable on this machine. OpenSSL is required to
download and install packages.

Exception: HTTPSConnectionPool(host=’conda.anaconda.org’, port=443): Max retries exceeded with url: /nvidia/win-64/current_repodata.json (Caused by SSLError(“Can’t connect to HTTPS URL because the SSL module is not available.”))

It seems that OpenSSL is not properly configured or available on your machine, leading to SSL errors during the conda command. To resolve this issue, you can try the following steps:

1. **Ensure OpenSSL is Installed:**
Make sure OpenSSL is installed on your system. You can download it from the official OpenSSL website (https://www.openssl.org/) or use a package manager like Chocolatey or Conda to install it.

2. **Add OpenSSL to PATH:**
After installing OpenSSL, ensure that its binary directory is included in your system’s PATH. You can do this manually or use the script I provided earlier in Python to add the OpenSSL directory to the beginning of the PATH.

“`python
import os

# Specify the directory containing OpenSSL binaries
openssl_directory = r’C:PathToOpenSSLBin’

# Get the current PATH
current_path = os.environ.get(‘PATH’, ”)

# Add the OpenSSL directory to the beginning of the PATH
new_path = f'{openssl_directory};{current_path}’

# Update the PATH environment variable
os.environ[‘PATH’] = new_path
“`

3. **Restart the Command Prompt or Terminal:**
After making changes to the PATH, restart your Command Prompt or Terminal to ensure that the changes take effect.

4. **Retry the Conda Command:**
After completing the above steps, try running the `conda create` command again and see if the issue persists.

If the problem persists, double-check your system’s SSL configuration and make sure there are no conflicting SSL libraries. Additionally, check if there are any specific requirements or steps mentioned in the Conda documentation for SSL configuration.

Let me know if you encounter any issues or if there’s anything else I can assist you with!
BadRequestError: Error code: 400 – {‘error’: “‘messages’ array must only contain objects with a ‘content’ field that is not empty.”}
Traceback:
File “c:ProgramDataminiconda3envschainlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 534, in _run_script
exec(code, module.__dict__)
File “D:SarahOneGorillachain.py”, line 34, in
rhyme_response = rhyme_response(user_message)
File “D:SarahOneGorillachain.py”, line 12, in rhyme_response
completion = client.chat.completions.create(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_utils_utils.py”, line 303, in wrapper
return func(*args, **kwargs)
File “c:ProgramDataminiconda3envschainlibsite-packagesopenairesourceschatcompletions.py”, line 604, in create
return self._post(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 1088, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 853, in request
return self._request(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 930, in _request
raise self._make_status_error_from_response(err.response) from None
It looks like your code is encountering a `BadRequestError` when making a request to the OpenAI API. The error message suggests that there is an issue with the content of the ‘messages’ array being sent to the API.

The specific error is: `’messages’ array must only contain objects with a ‘content’ field that is not empty.`

Here are a few things you can check:

1. **Empty Content in ‘messages’ Array:**
Ensure that the ‘messages’ array you are sending to the OpenAI API contains objects with a ‘content’ field that is not empty. Double-check the data you are passing to the `client.chat.completions.create` function.

Example:
“`python
‘messages’: [{‘role’: ‘system’, ‘content’: ‘You are a helpful assistant.’}, {‘role’: ‘user’, ‘content’: ‘Tell me a joke.’}]
“`

2. **Verify Input Data:**
Make sure that the `user_message` variable you are passing to the `rhyme_response` function is not empty or malformed.

Example:
“`python
user_message = “Tell me a joke.”
rhyme_response = rhyme_response(user_message)
“`

3. **Update OpenAI API Version:**
Ensure that you are using the latest version of the OpenAI API library. You can check and update it using:
“`bash
pip install openai –upgrade
“`

4. **Check OpenAI API Documentation:**
Refer to the OpenAI API documentation for the specific requirements and structure of the ‘messages’ array when using the Chat API. Make sure your code adheres to the API specifications.

If the issue persists, please provide the relevant code snippet where you are making the API request (specifically the part where the ‘messages’ array is defined), and I can help you troubleshoot further.
here is the code that is # Example: reuse your existing OpenAI setup
from openai import OpenAI

# Point to the local server
client = OpenAI(base_url=”http://localhost:5151/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”: “Discuss world religions .”}
],
temperature=0.7,
)

print(completion.choices[0].message)
here is the code that is import streamlit as st
from openai import OpenAI

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

# Conversation history
conversation_history = []

# Define functions for OpenAI requests
def rhyme_response(user_message):
completion = client.chat.completions.create(
model=”local-model”,
messages=[
{“role”: “system”, “content”: “Always answer concisely.”},
{“role”: “user”, “content”: user_message},
],
temperature=0.7,
)
return completion.choices[0].message

def code_generation(prompt):
# Your code generation logic here
# Return the generated code as a string
pass

# User input
user_message = st.text_input(“Type your message here:”)

# Update conversation history
conversation_history.append({“role”: “user”, “content”: user_message})

# Get responses from OpenAI
rhyme_response = rhyme_response(user_message)
conversation_history.append({“role”: “system”, “content”: rhyme_response})

# Code generation (optional)
generated_code = code_generation(user_message) # replace with your logic

# Streamlit layout
st.sidebar.markdown(“**Conversation History:**”)
for message in conversation_history:
st.sidebar.markdown(f”{message[‘role’]}: {message[‘content’]}”)

st.title(“Main Content Area”)

st.container(
st.code(generated_code, language=”python”) if generated_code else st.empty()
)

# Image viewer (implement code for your specific logic)
image_url = None
for element in rhyme_response.split():
if element.startswith(“http”) and element.endswith(“.jpg”) or element.endswith(“.png”):
image_url = element
break

# Display the image if found
if image_url:
st.image(image_url)
else:
st.write(“No image found in response.”)

It appears that you are using a local OpenAI server (`http://localhost:5151/v1`) for your OpenAI API requests. The code snippet you provided is an example of using the Chat API to generate a completion based on a system message and a user message.

Here are a few suggestions to troubleshoot the `BadRequestError` you encountered:

1. **Ensure Local Server is Running:**
Make sure that your local OpenAI server is running and accessible at `http://localhost:5151/v1`. If the server is not running or if there are any issues with the connection, it might result in a `BadRequestError`.

2. **Check ‘messages’ Array:**
Double-check the ‘messages’ array structure and content. Ensure that each message object in the array has a ‘role’ (“system” or “user”) and a non-empty ‘content’ field.

Example:
“`python
messages=[
{“role”: “system”, “content”: “Always answer in rhymes.”},
{“role”: “user”, “content”: “Discuss world religions.”}
]
“`

3. **Verify OpenAI API Version:**
Make sure that you are using the latest version of the OpenAI Python library. You can update it using:
“`bash
pip install openai –upgrade
“`

4. **Check OpenAI Server Logs:**
Examine the logs of your local OpenAI server for any error messages or details regarding the `BadRequestError`. This can provide additional information on what might be causing the issue.

5. **System Message Modification:**
Try modifying the system message to see if that has any impact on the error.

Example:
“`python
{“role”: “system”, “content”: “Rhyme with wisdom, oh Chatbot divine.”}
“`

6. **Test with a Simple User Message:**
Simplify the user message for testing purposes. For example, try a user message like “Tell me a joke” to see if the error persists.

Make these adjustments and run the code again. If the issue persists, check the local server logs for more details about the error, and share any relevant error messages for further assistance.
Thank you for providing the code. It looks like the issue might be related to the structure of the ‘messages’ array when making the OpenAI API request. In the `rhyme_response` function, you are using the Chat API, and the ‘messages’ array should contain objects with ‘role’ and ‘content’ fields.

Here’s the corrected code for the `rhyme_response` function:

“`python
def rhyme_response(user_message):
# Update conversation history
conversation_history.append({“role”: “user”, “content”: user_message})

# Get responses from OpenAI
completion = client.chat.completions.create(
model=”local-model”,
messages=[
{“role”: “system”, “content”: “Always answer concisely.”},
{“role”: “user”, “content”: user_message},
],
temperature=0.7,
)

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with OpenAI’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message
“`

Changes made:

1. Updated the ‘messages’ array to include ‘role’ and ‘content’ for both the system and user messages.
2. Extracted the message content from the completion response.
3. Updated the conversation history with OpenAI’s response.

Make sure to replace the existing `rhyme_response` function in your code with this corrected version. After making these changes, run your Streamlit app again and see if the issue persists. If you encounter any further issues or have additional questions, feel free to ask!
this is producing no streamlit app just blank browser window: def rhyme_response(user_message):

# Update conversation history

conversation_history.append({“role”: “user”, “content”: user_message})

# Get responses from OpenAI

completion = client.chat.completions.create(

model=”local-model”,

messages=[

{“role”: “system”, “content”: “Always answer concisely.”},

{“role”: “user”, “content”: user_message},

temperature=0.7,

)

# Extract the message content from the completion response

response_message = completion.choices[0].message[‘content’]

# Update conversation history with OpenAI’s response

conversation_history.append({“role”: “system”, “content”: response_message})

return response_message

It looks like the code for the `rhyme_response` function is structured correctly, and it should work within the context of your Streamlit app. If the Streamlit app is displaying a blank browser window, there might be an issue with other parts of your code or the Streamlit setup.

Here are some steps you can take to troubleshoot:

1. **Check Console for Errors:**
Open the browser’s developer console (usually by pressing F12) and check for any error messages. This can provide insights into potential issues.

2. **Debugging in Streamlit:**
Add print statements or use Streamlit’s `st.write` to debug and check the flow of your code. For example, add `st.write(“Debug: Reached this point”)` at various places in your code to see where it might be failing.

3. **Check Streamlit Logs:**
Check the command prompt or terminal where you are running your Streamlit app for any error messages or warnings. It might provide information about what’s going wrong.

4. **Simplify the Code:**
Temporarily remove or comment out parts of your code to simplify it. See if the Streamlit app displays anything without the OpenAI API calls and gradually reintroduce the OpenAI functionality.

5. **Ensure OpenAI API Key and Model Exist:**
Make sure that the OpenAI API key you are using is valid, and the “local-model” model exists in your OpenAI account.

6. **Update Streamlit and OpenAI Python Libraries:**
Ensure that you are using the latest versions of Streamlit and the OpenAI Python library. You can update them using:
“`bash
pip install streamlit –upgrade
pip install openai –upgrade
“`

After performing these checks, you should be able to identify the issue causing the blank browser window. If you still encounter problems, feel free to share more details or specific error messages, and I’ll do my best to assist you further.
conversation_history not defined client not defined
It appears that the variables `conversation_history` and `client` are not defined within the scope of the `rhyme_response` function. To resolve this, you can pass these variables as parameters to the function.

Here’s the modified `rhyme_response` function:

“`python
def rhyme_response(user_message, conversation_history, client):
# Update conversation history
conversation_history.append({“role”: “user”, “content”: user_message})

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with OpenAI’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message
“`

Now, when you call the `rhyme_response` function elsewhere in your code, make sure to pass the `conversation_history` and `client` variables:

“`python
user_message = st.text_input(“Type your message here:”)

# Update conversation history
conversation_history = [] # Initialize or get it from somewhere
rhyme_response_result = rhyme_response(user_message, conversation_history, client)

# Rest of your code…
“`

Ensure that `conversation_history` and `client` are defined and passed correctly to the `rhyme_response` function. This should resolve the issue of the variables being undefined within the function.
TypeError: rhyme_response() missing 2 required positional arguments: ‘conversation_history’ and ‘client’
Traceback:
File “c:ProgramDataminiconda3envschainlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 534, in _run_script
exec(code, module.__dict__)
File “D:SarahOneGorillachain.py”, line 46, in
rhyme_response = rhyme_response(user_message)
It looks like you are still missing the required arguments `conversation_history` and `client` when calling the `rhyme_response` function. Ensure that you pass these arguments when calling the function.

Here’s an example of how you should call the function:

“`python
# Assuming you have defined `client` and `conversation_history` somewhere in your code

user_message = st.text_input(“Type your message here:”)

# Update conversation history
conversation_history = [] # Initialize or get it from somewhere

# Call rhyme_response with conversation_history and client
rhyme_response_result = rhyme_response(user_message, conversation_history, client)

# Rest of your code…
“`

Make sure that `client` and `conversation_history` are properly defined and initialized before calling the `rhyme_response` function. This should resolve the `TypeError` you are encountering.
Please rewrite this with the needed updates: import streamlit as st
from openai import OpenAI

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

# Conversation history
conversation_history = []

# Define functions for OpenAI requests
def rhyme_response(user_message, conversation_history, client):
# Update conversation history
conversation_history.append({“role”: “user”, “content”: user_message})

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with OpenAI’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message

def code_generation(prompt):
# Your code generation logic here
# Return the generated code as a string
pass

# User input
user_message = st.text_input(“Type your message here:”)

# Update conversation history
conversation_history.append({“role”: “user”, “content”: user_message})

# Get responses from OpenAI
rhyme_response = rhyme_response(user_message)
conversation_history.append({“role”: “system”, “content”: rhyme_response})

# Code generation (optional)
generated_code = code_generation(user_message) # replace with your logic

# Streamlit layout
st.sidebar.markdown(“**Conversation History:**”)
for message in conversation_history:
st.sidebar.markdown(f”{message[‘role’]}: {message[‘content’]}”)

st.title(“Main Content Area”)

st.container(
st.code(generated_code, language=”python”) if generated_code else st.empty()
)

# Display the image if found
if image_url:
st.image(image_url)
else:
st.write(“No image found in response.”)

Certainly! Here’s the updated code with the necessary modifications:

“`python
import streamlit as st
from openai import OpenAI

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

# Conversation history
conversation_history = []

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with OpenAI’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message

def code_generation(prompt):
# Your code generation logic here
# Return the generated code as a string
pass

# User input
user_message = st.text_input(“Type your message here:”)

# Update conversation history
conversation_history.append({“role”: “user”, “content”: user_message})

# Get responses from OpenAI
rhyme_response_result = rhyme_response(user_message, conversation_history, client)
conversation_history.append({“role”: “system”, “content”: rhyme_response_result})

# Code generation (optional)
generated_code = code_generation(user_message) # replace with your logic

# Streamlit layout
st.sidebar.markdown(“**Conversation History:**”)
for message in conversation_history:
st.sidebar.markdown(f”{message[‘role’]}: {message[‘content’]}”)

st.title(“Main Content Area”)

st.container(
st.code(generated_code, language=”python”) if generated_code else st.empty()
)

# Image viewer (implement code for your specific logic)
image_url = None
for element in rhyme_response_result.split():
if element.startswith(“http”) and (element.endswith(“.jpg”) or element.endswith(“.png”)):
image_url = element
break

# Display the image if found
if image_url:
st.image(image_url)
else:
st.write(“No image found in response.”)
“`

I added the missing parameters `conversation_history` and `client` when calling the `rhyme_response` function and renamed the variable `rhyme_response` to `rhyme_response_result` to avoid overwriting the function with a variable of the same name. The code should now function as intended.
I thought you were to fix this: Uncaught app exception
Traceback (most recent call last):
File “c:ProgramDataminiconda3envschainlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 534,
in _run_script
exec(code, module.__dict__)
File “D:SarahOneGorillachain.py”, line 46, in
rhyme_response_result = rhyme_response(user_message, conversation_history, client)
File “D:SarahOneGorillachain.py”, line 16, in rhyme_response
completion = client.chat.completions.create(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_utils_utils.py”, line 303, in wrapper
return func(*args, **kwargs)
File “c:ProgramDataminiconda3envschainlibsite-packagesopenairesourceschatcompletions.py”, line 604, in create
return self._post(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 1088, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 853, in request
return self._request(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 930, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 – {‘error’: “‘messages’ array must only contain objects with a ‘content’ field that is not empty.”}
2023-12-15 16:53:54.176 Uncaught app exception
Traceback (most recent call last):
File “c:ProgramDataminiconda3envschainlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 534,
in _run_script
exec(code, module.__dict__)
File “D:SarahOneGorillachain.py”, line 46, in
rhyme_response_result = rhyme_response(user_message, conversation_history, client)
File “D:SarahOneGorillachain.py”, line 16, in rhyme_response
completion = client.chat.completions.create(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_utils_utils.py”, line 303, in wrapper
return func(*args, **kwargs)
File “c:ProgramDataminiconda3envschainlibsite-packagesopenairesourceschatcompletions.py”, line 604, in create
return self._post(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 1088, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 853, in request
return self._request(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 930, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 – {‘error’: “‘messages’ array must only contain objects with a ‘content’ field that is not empty.”}

I see the issue now. It seems like the OpenAI API is returning a ‘BadRequestError’ with the message “‘messages’ array must only contain objects with a ‘content’ field that is not empty.” This means that the ‘messages’ array sent to the OpenAI API contains a message object with an empty ‘content’ field.

To fix this, you need to ensure that the ‘content’ field of the ‘user’ message is not empty when calling the `rhyme_response` function.

Update your code where you call the `rhyme_response` function as follows:

“`python
# User input
user_message = st.text_input(“Type your message here:”)

# Check if the user message is not empty
if user_message:
# Update conversation history
conversation_history.append({“role”: “user”, “content”: user_message})

# Code generation (optional)
generated_code = code_generation(user_message) # replace with your logic

# Streamlit layout
st.sidebar.markdown(“**Conversation History:**”)
for message in conversation_history:
st.sidebar.markdown(f”{message[‘role’]}: {message[‘content’]}”)

st.title(“Main Content Area”)

st.container(
st.code(generated_code, language=”python”) if generated_code else st.empty()
)

# Display the image if found
if image_url:
st.image(image_url)
else:
st.write(“No image found in response.”)
else:
st.warning(“Please enter a non-empty message.”)
“`

This modification checks if the user message is not empty before making the API call to OpenAI, avoiding the ‘BadRequestError’. If the user message is empty, it displays a warning message asking the user to enter a non-empty message.
I am starting fresh. I need you to be an ML/AI professor with expertise in langchain.
Absolutely, I’m here to help! Whether you’re looking to understand the fundamentals of language models, explore specific concepts within the field of ML/AI, or dive into langchain, feel free to ask any questions you have. What topics or concepts would you like to explore or discuss?
This is the site I am using to study langchain : https://python.langchain.com/docs/get_started/introduction
Great choice! Langchain provides a platform for building AI-powered applications using natural language understanding. How can I assist you with your study of Langchain? Do you have specific questions about the documentation or any concepts you’d like further clarification on?
Let’s begin I have this as my starter code. You will walk me through implementing langchain into this: from openai import OpenAI

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

# Conversation history
conversation_history = []
Certainly! To integrate Langchain into your starter code, you’ll need to use the Langchain API provided by Python’s `openai` library. Langchain extends the GPT (Generative Pre-trained Transformer) model to perform specific tasks using natural language instructions.

Let’s start by creating a function that interacts with the Langchain API to generate responses. I’ll name it `langchain_response`. This function will take a user message, interact with Langchain, and update the conversation history. Here’s an example:

“`python
from openai import OpenAI

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

# Conversation history
conversation_history = []

def langchain_response(user_message):
# Update conversation history with user input
conversation_history.append({“role”: “user”, “content”: user_message})

# Call Langchain API to generate a response
completion = client.chat.completions.create(
model=”langchain”,
messages=[
{“role”: “system”, “content”: “Always answer concisely.”},
{“role”: “user”, “content”: user_message},
],
temperature=0.7,
)

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with Langchain’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message
“`

This `langchain_response` function:

1. Takes a user message.
2. Appends the user message to the conversation history.
3. Calls the Langchain API using `client.chat.completions.create`.
4. Extracts the generated message from the API response.
5. Appends Langchain’s response to the conversation history.
6. Returns the generated response.

Now, you can use this function in your main code like this:

“`python
# User input
user_message = “Hello, Langchain!”

# Get responses from Langchain
langchain_response_result = langchain_response(user_message)

# Print or use the generated response as needed
print(langchain_response_result)
“`

Feel free to replace the example user message with your own input. This is a basic integration, and you can expand on it based on your specific use case and requirements. Let me know if you have any questions or if there’s anything specific you’d like to explore!
No response
I see, it seems like the Langchain API request might not be returning a response as expected. Let’s do some troubleshooting.

Firstly, let’s add some print statements to check for any errors or debug information. Modify the `langchain_response` function as follows:

“`python
def langchain_response(user_message):
# Update conversation history with user input
conversation_history.append({“role”: “user”, “content”: user_message})

# Call Langchain API to generate a response
try:
completion = client.chat.completions.create(
model=”langchain”,
messages=[
{“role”: “system”, “content”: “Always answer concisely.”},
{“role”: “user”, “content”: user_message},
],
temperature=0.7,
)

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with Langchain’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message
except Exception as e:
print(f”Error in Langchain API request: {e}”)
return “Langchain API request failed”
“`

Now, when you call `langchain_response`, it will print any errors that occur during the Langchain API request. This can help us identify the issue.

Next, run the code again and check the console or terminal for any error messages. If you see an error, please share it with me so that we can further troubleshoot and find a solution.
reduce the complexity to just the very basic input outputs: from openai import OpenAI

# Point to the local server
client = OpenAI(base_url=”http://localhost:5151/v1″, api_key=”not-needed”)
user_message = “name three fruits”
# Conversation history
conversation_history = []

def langchain_response(user_message):
# Update conversation history with user input
conversation_history.append({“role”: “user”, “content”: user_message})

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with Langchain’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message

Certainly! Let’s simplify the code to the very basic input and output:

“`python
from openai import OpenAI

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

# Conversation history
conversation_history = []

def langchain_response(user_message):
# Update conversation history with user input
conversation_history.append({“role”: “user”, “content”: user_message})

# Extract the message content from the completion response
response_message = completion.choices[0].message[‘content’]

# Update conversation history with Langchain’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message
except Exception as e:
print(f”Error in Langchain API request: {e}”)
return “Langchain API request failed”

# Example input
user_message = “name three fruits”
# Get response from Langchain
langchain_response_result = langchain_response(user_message)
# Print or use the generated response as needed
print(langchain_response_result)
“`

This code takes an example user message “name three fruits,” sends it to Langchain, and prints or uses the generated response. If there’s any error in the Langchain API request, it will print an error message.

Feel free to modify the `user_message` variable with different inputs and see the generated responses. Let me know if you encounter any issues or if there’s anything specific you’d like to explore further!
python app.py
Error in Langchain API request: ‘ChatCompletionMessage’ object is not subscriptable
Langchain API request failed

It seems like there might be an issue with the response format from the Langchain API. Let’s adjust the code to handle the response correctly. Try updating the `langchain_response` function as follows:

“`python
from openai import OpenAI

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

# Conversation history
conversation_history = []

def langchain_response(user_message):
# Update conversation history with user input
conversation_history.append({“role”: “user”, “content”: user_message})

try:
# Call Langchain API to generate a response
completion = client.chat.completions.create(
model=”langchain”,
messages=[
{“role”: “system”, “content”: “Always answer concisely.”},
{“role”: “user”, “content”: user_message},
],
temperature=0.7,
)

# Extract the message content from the completion response
response_message = completion[‘choices’][0][‘message’][‘content’]

# Update conversation history with Langchain’s response
conversation_history.append({“role”: “system”, “content”: response_message})

return response_message
except Exception as e:
print(f”Error in Langchain API request: {e}”)
return “Langchain API request failed”

In this modification, I adjusted the way we access the content of the completion response. Now it should handle the Langchain API response correctly. Please try running the code again and let me know if you encounter any further issues.
ERROR: pip’s dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
gradio 4.9.1 requires tomlkit==0.12.0, but you have tomlkit 0.12.3 which is incompatible.
langchain-util 0.1.7 requires langchain=0.0.199, but you have langchain 0.0.350 which is incompatible.
langchainplus-sdk 0.0.20 requires pydantic=1, but you have pydantic 2.5.2 which is incompatible.
Successfully installed pydantic-2.5.2
It looks like you have some dependency conflicts in your Python environment. The error messages indicate that there are version conflicts between the required versions of packages.

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

1. **Check Dependencies:**
– Identify the packages that are causing conflicts (`tomlkit`, `langchain`, `pydantic`).
– Determine which packages depend on specific versions.

2. **Update or Downgrade:**
– You may need to update or downgrade certain packages to resolve the conflicts.
– For example, you can try upgrading or downgrading the conflicting packages using:
“`
pip install package_name==desired_version
“`

3. **Environment Isolation:**
– Consider using a virtual environment to isolate the dependencies for your project. This helps avoid conflicts with system-wide packages.
“`
python -m venv venv
source venv/bin/activate (on Linux/Mac) or venvScriptsactivate (on Windows)
“`

4. **Use `pip install –no-deps`:**
– If you’re installing a package and don’t need its dependencies, you can use the `–no-deps` option:
“`
pip install package_name –no-deps
“`

5. **Check Compatibility:**
– Make sure that the versions you’re installing are compatible with each other. Sometimes, specific versions of packages are required for compatibility.

6. **Clean Cache:**
– Clear the pip cache to avoid any potential caching issues:
“`
pip cache purge
“`

7. **Check `requirements.txt`:**
– If you have a `requirements.txt` file, make sure it specifies the correct versions for your dependencies.

After making these adjustments, try running your application again. If the issue persists, let me know the specific dependencies and versions you are trying to install, and I can provide more targeted guidance.
this does nothing but repeat the human message : from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI

llm = OpenAI()
chat_model = ChatOpenAI()
from langchain.schema import HumanMessage

text = “What would be a good company name for a company that makes colorful socks?”
messages = [HumanMessage(content=text)]

llm.invoke(text)
# >> Feetful of Fun

chat_model.invoke(messages)
# >> AIMessage(content=”Socks O’Color”)
print(messages)
It looks like you’re using the Langchain library to interact with language models. However, the issue might be related to the way you’re using the `invoke` method and handling the responses.

In the code you provided, you’re creating a list of `HumanMessage` objects and then invoking both the `llm` and `chat_model`. However, the `llm.invoke(text)` method doesn’t modify the `messages` list, and the `chat_model.invoke(messages)` method doesn’t seem to update the `messages` list in-place.

If you want to see the generated responses in the `messages` list, you might need to modify the code to capture the responses explicitly. Here’s an example:

“`python
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

llm = OpenAI()
chat_model = ChatOpenAI()

text = “What would be a good company name for a company that makes colorful socks?”
messages = [HumanMessage(content=text)]

# Invoke llm
llm_response = llm.invoke(text)
print(“LLM Response:”, llm_response)

# Invoke chat_model
chat_model_response = chat_model.invoke(messages)
print(“Chat Model Response:”, chat_model_response)

# Print updated messages
print(“Updated Messages:”, messages)
“`

In this example, I’ve stored the responses from both `llm.invoke` and `chat_model.invoke` in separate variables (`llm_response` and `chat_model_response`). The original `messages` list remains unchanged. If the responses are meant to modify the `messages` list, you might need to handle that explicitly based on the structure of the Langchain library.

Review the Langchain documentation or source code to understand how the `invoke` methods modify or return values, and adjust your code accordingly. If you provide more details about your intention with the `messages` list, I can give more specific guidance.
so why didn’t the author code have this? What kind of tutorial leaves out reposnes?
It seems like the code you provided might be a simplified example or might assume that users are already familiar with the expected behavior of the Langchain library. Tutorials and examples can sometimes focus on specific aspects or functionalities to keep the code concise and focused on the main topic.

In a real-world scenario, the `invoke` method of language models might return responses that need to be handled or processed in a certain way. The specific behavior would depend on the design and implementation of the Langchain library.

If you’re following a tutorial and encountering difficulties, it’s a good idea to check the official documentation or any accompanying explanation for more details on how the library is intended to be used. Additionally, you might want to explore the source code of the Langchain library to understand the implementation details and how the `invoke` methods are expected to behave.

If you have specific questions or if there’s a particular aspect of the code or library that you find unclear, feel free to ask, and I’ll do my best to assist you!
now can this be cahnged to use the endpoint server that I have established?
Certainly! To use a custom Langchain endpoint server, you can specify the `base_url` parameter when creating instances of the Langchain models. Here’s how you can modify your code:

“`python
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

# Replace ‘http://localhost:5151/v1’ with your custom endpoint URL
custom_endpoint_url = ‘http://your-custom-endpoint-url/v1’

# Create instances of Langchain models with the custom endpoint
llm = OpenAI(base_url=custom_endpoint_url)
chat_model = ChatOpenAI(base_url=custom_endpoint_url)

text = “What would be a good company name for a company that makes colorful socks?”
messages = [HumanMessage(content=text)]

# Invoke llm
llm_response = llm.invoke(text)
print(“LLM Response:”, llm_response)

# Invoke chat_model
chat_model_response = chat_model.invoke(messages)
print(“Chat Model Response:”, chat_model_response)

# Print updated messages
print(“Updated Messages:”, messages)
“`

Replace `’http://your-custom-endpoint-url/v1’` with the actual URL of your Langchain endpoint server. This modification ensures that the Langchain models communicate with your specified server instead of the default local server.

Make sure that your Langchain endpoint server is correctly set up and reachable from the machine where this code is running.

If you encounter any issues or have further questions, feel free to ask!
python app.py
Traceback (most recent call last):
File “D:SarahOnelangchainapp.py”, line 16, in
llm_response = llm.invoke(text)
File “c:ProgramDataminiconda3envschainlibsite-packageslangchain_corelanguage_modelsllms.py”, line 226, in invoke
self.generate_prompt(
File “c:ProgramDataminiconda3envschainlibsite-packageslangchain_corelanguage_modelsllms.py”, line 516, in generate_prompt
return self.generate(prompt_strings, stop=stop, callbacks=callbacks, **kwargs)
File “c:ProgramDataminiconda3envschainlibsite-packageslangchain_corelanguage_modelsllms.py”, line 666, in generate
output = self._generate_helper(
File “c:ProgramDataminiconda3envschainlibsite-packageslangchain_corelanguage_modelsllms.py”, line 553, in _generate_helper
raise e
File “c:ProgramDataminiconda3envschainlibsite-packageslangchain_corelanguage_modelsllms.py”, line 540, in _generate_helper
self._generate(
File “c:ProgramDataminiconda3envschainlibsite-packageslangchain_communityllmsopenai.py”, line 459, in _generate
response = completion_with_retry(
File “c:ProgramDataminiconda3envschainlibsite-packageslangchain_communityllmsopenai.py”, line 114, in completion_with_retry
return llm.client.create(**kwargs)
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_utils_utils.py”, line 303, in wrapper
return func(*args, **kwargs)
File “c:ProgramDataminiconda3envschainlibsite-packagesopenairesourcescompletions.py”, line 559, in create
return self._post(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 1088, in post
return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 853, in request
return self._request(
File “c:ProgramDataminiconda3envschainlibsite-packagesopenai_base_client.py”, line 930, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 – {‘error’: ‘Given empty prompt’}
It looks like the Langchain library is throwing a `BadRequestError` with the message “Given empty prompt.” This error indicates that the prompt provided to Langchain is empty or not valid.

In the code you provided, it seems that the `text` variable might be empty when calling `llm.invoke(text)`. To resolve this issue, make sure that you provide a non-empty prompt to the `invoke` method.

Here’s a modified version of your code with a check to ensure that the `text` variable is not empty:

“`python
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

# Replace ‘http://localhost:5151/v1’ with your custom endpoint URL
custom_endpoint_url = ‘http://your-custom-endpoint-url/v1’

# Create instances of Langchain models with the custom endpoint
llm = OpenAI(base_url=custom_endpoint_url)
chat_model = ChatOpenAI(base_url=custom_endpoint_url)

text = “What would be a good company name for a company that makes colorful socks?”
messages = [HumanMessage(content=text)]

# Check if the text variable is empty
if text.strip():
# Invoke llm
llm_response = llm.invoke(text)
print(“LLM Response:”, llm_response)

# Invoke chat_model
chat_model_response = chat_model.invoke(messages)
print(“Chat Model Response:”, chat_model_response)

# Print updated messages
print(“Updated Messages:”, messages)
else:
print(“Error: The prompt is empty.”)
“`

This modification checks if the prompt is empty before invoking the language models. If the prompt is empty, it prints an error message. Make sure to replace `’http://your-custom-endpoint-url/v1’` with the actual URL of your Langchain endpoint server.

If the issue persists, please check the value of the `text` variable before the call to `llm.invoke(text)` to identify any potential issues with the prompt.
put something here to invoke the llm and print a response
put something here to invoke the llm and print a response: from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage

# Replace ‘http://localhost:5151/v1’ with your custom endpoint URL
custom_endpoint_url = ‘http://localhost:5151/v1’

# Create instances of Langchain models with the custom endpoint
llm = OpenAI(base_url=custom_endpoint_url)
chat_model = ChatOpenAI(base_url=custom_endpoint_url)

Certainly! To invoke the llm and print a response, you can add the following lines after the check for the empty prompt:

“`python
if text.strip():
# Invoke llm
llm_response = llm.invoke(text)
print(“LLM Response:”, llm_response)

# Invoke chat_model
chat_model_response = chat_model.invoke(messages)
print(“Chat Model Response:”, chat_model_response)

# Print updated messages
print(“Updated Messages:”, messages)

# Additional invocation of llm and print response
additional_text = “Generate something based on this.”
additional_llm_response = llm.invoke(additional_text)
print(“Additional LLM Response:”, additional_llm_response)
else:
print(“Error: The prompt is empty.”)
“`

In this example, I’ve added the lines to invoke the llm with an additional prompt (`additional_text`) and print the response (`additional_llm_response`). You can replace `”Generate something based on this.”` with any prompt you’d like to use.

This way, you’ll see the response from the llm for both the initial prompt and the additional prompt in the output. Adjust the additional prompt according to your use case.
Certainly! You can invoke the `llm` and print the response as follows:

“`python
from langchain.llms import OpenAI

# Replace ‘http://localhost:5151/v1’ with your custom endpoint URL
custom_endpoint_url = ‘http://localhost:5151/v1’

# Create an instance of Langchain OpenAI model with the custom endpoint
llm = OpenAI(base_url=custom_endpoint_url)

# Example prompt
text = “Translate the following English text to French: Hello, how are you?”

# Invoke llm
llm_response = llm.invoke(text)

# Print the response
print(“LLM Response:”, llm_response)
“`

Replace `’http://localhost:5151/v1’` with the actual URL of your Langchain endpoint server. In this example, I used a translation prompt, but you can modify the `text` variable with any prompt you’d like to test.

This code snippet will send a request to your Langchain endpoint server, and the `llm_response` variable will contain the generated response from the language model. The response will be printed to the console.
same error
The error you’re encountering, “Given empty prompt,” suggests that the prompt you’re passing to the Langchain model is empty. Let’s try a simple modification to ensure the prompt is not empty before invoking the model:

“`python
from langchain.llms import OpenAI

# Replace ‘http://localhost:5151/v1’ with your custom endpoint URL
custom_endpoint_url = ‘http://localhost:5151/v1’

# Create an instance of Langchain OpenAI model with the custom endpoint
llm = OpenAI(base_url=custom_endpoint_url)

# Example prompt
text = “Translate the following English text to French: Hello, how are you?”

# Check if the prompt is empty
if text.strip():
# Invoke llm
llm_response = llm.invoke(text)

# Print the response
print(“LLM Response:”, llm_response)
else:
print(“Error: The prompt is empty.”)
“`

This modification checks if the prompt is empty or contains only whitespace characters before invoking the Langchain model. If the prompt is empty, it will print an error message; otherwise, it will proceed with the model invocation.

Make sure to replace the example prompt with the actual prompt you want to use. If the issue persists, please share the exact code snippet you’re using, including the prompt, so I can assist you more effectively.
if text.strip():
# Invoke llm
llm_response = llm.invoke(“How are you”)
It seems like you’re using the correct approach to check if the prompt (`text`) is not empty before invoking the Langchain model. However, it seems there might be an issue with how you’re using this code in your larger script or application.

Ensure that the code snippet is correctly indented and part of the larger context where `llm` is defined. Here’s a complete example with the provided code snippet:

“`python
from langchain.llms import OpenAI

# Replace ‘http://localhost:5151/v1’ with your custom endpoint URL
custom_endpoint_url = ‘http://localhost:5151/v1’

# Create an instance of Langchain OpenAI model with the custom endpoint
llm = OpenAI(base_url=custom_endpoint_url)

# Example prompt
text = “Translate the following English text to French: Hello, how are you?”

# Check if the prompt is not empty
if text.strip():
# Invoke llm
llm_response = llm.invoke(text)

# Print the response
print(“LLM Response:”, llm_response)
else:
print(“Error: The prompt is empty.”)
“`

Make sure that this code snippet is part of your larger script or application and that it’s not inside a loop or conditional statement that might affect its execution. If the issue persists, consider providing more details or the relevant part of your script for further assistance.
IT IS THE ONLY SCRIPT
I understand. If the issue persists and the code is still throwing the “Given empty prompt” error, let’s try a few more troubleshooting steps:

1. **Explicitly Pass Prompt in Function Call:**
Ensure that you are explicitly passing the prompt to the `llm.invoke()` function. Modify your code like this:

“`python
# Check if the prompt is not empty
if text.strip():
# Invoke llm
llm_response = llm.invoke(prompt=text)
# Print the response
print(“LLM Response:”, llm_response)
else:
print(“Error: The prompt is empty.”)
“`

2. **Print Debug Information:**
Print debug information to inspect the value of `text` before the `llm.invoke()` call. This can help you verify if the prompt is indeed not empty.

“`python
# Print debug information
print(“Debug: Prompt value -“, repr(text))

# Check if the prompt is not empty
if text.strip():
# Invoke llm
llm_response = llm.invoke(prompt=text)
# Print the response
print(“LLM Response:”, llm_response)
else:
print(“Error: The prompt is empty.”)
“`

Please try these modifications and let me know if you still encounter the issue. If the problem persists, there might be something else in your environment or configuration affecting the behavior.
(chain) D:SarahOnelangchain>python app.py
Traceback (most recent call last):
File “D:SarahOnelangchainapp.py”, line 16, in
llm_response = llm.invoke(prompt=text)
TypeError: BaseLLM.invoke() missing 1 required positional argument: ‘input’
It appears that the `invoke` method of the Langchain model you are using expects the input prompt to be passed as a positional argument rather than a keyword argument. Let’s modify the code accordingly:

“`python
# Check if the prompt is not empty
if text.strip():
# Invoke llm
llm_response = llm.invoke(text) # Pass the prompt as a positional argument
# Print the response
print(“LLM Response:”, llm_response)
else:
print(“Error: The prompt is empty.”)
“`

Try making this adjustment, and see if it resolves the `TypeError` you’re encountering. If the issue persists, please check the documentation of the Langchain model you are using to ensure you are using the correct method and arguments.