Flask OpenAI Web App – Dr. Trippers Stuff

help fix this : from flask import Flask, render_template, request
from langchain.llms import OpenAI

app = Flask(__name__)

# Function to load OpenAI model and get responses

def get_openai_response(question):
llm = OpenAI(
openai_api_key=’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’,
model_name=”text-davinci-003″,
temperature=0.5
)
response = llm(question)
return response

@app.route(‘/’, methods=[‘GET’, ‘POST’])
def index():
if request.method == ‘POST’:
input_question = request.form[‘input’]
response = get_openai_response(input_question)
return render_template(‘index.html’, input_question=input_question, response=response)

return render_template(‘index.html’)

CMD [“gunicorn”, “-b”, “0.0.0.0:7860”, “main:app”]
It looks like you’re working on a Flask web application that uses the OpenAI language model. Everything seems fine, but I noticed a couple of things that might help you improve the code.

1. **Security Concerns:**
– Avoid hardcoding sensitive information like API keys directly in your code. Consider using environment variables to store your API key securely.

2. **Code Structure:**
– It’s a good practice to separate your configuration settings from the main application code. You might want to consider moving the OpenAI model configuration to a separate file or use a configuration object.

3. **Error Handling:**
– Add error handling to your code. What if there’s an issue with the OpenAI API or the model loading fails? You might want to catch exceptions and provide appropriate feedback or log the errors for debugging.

4. **Documentation:**
– Consider adding comments or docstrings to explain the purpose and functionality of your functions. This will make it easier for others (or even yourself in the future) to understand the code.

Here’s a revised version with these suggestions:

“`python
from flask import Flask, render_template, request
from langchain.llms import OpenAI

app = Flask(__name__)

class OpenAIConfig:
API_KEY = ‘your_api_key’
MODEL_NAME = ‘text-davinci-003’
TEMPERATURE = 0.5

# Function to load OpenAI model and get responses
def get_openai_response(question):
try:
llm = OpenAI(
openai_api_key=OpenAIConfig.API_KEY,
model_name=OpenAIConfig.MODEL_NAME,
temperature=OpenAIConfig.TEMPERATURE
)
response = llm(question)
return response
except Exception as e:
# Add proper error handling (e.g., logging, return an error message)
return f”Error: {str(e)}”

return render_template(‘index.html’)

# Consider using a more secure method for managing secrets, e.g., python-decouple, python-dotenv
if __name__ == ‘__main__’:
app.run(debug=True)
“`

This revised code separates the configuration into a class, adds a try-except block for error handling, and includes comments for better readability. Remember to replace ‘your_api_key’ with your actual OpenAI API key.
How to drop in my own server here? from flask import Flask, render_template, request
from langchain.llms import OpenAI

app = Flask(__name__)

class OpenAIConfig:
API_KEY = ‘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’
MODEL_NAME = ‘text-davinci-003’
TEMPERATURE = 0.5

return render_template(‘index.html’)

# Consider using a more secure method for managing secrets, e.g., python-decouple, python-dotenv
if __name__ == ‘__main__’:
app.run(debug=True)

#if __name__ == ‘__main__’:
# Replace with your OpenAI API key
# app.config[‘OPENAI_API_KEY’] = ‘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’
# app.run(debug=True)
It looks like you’re trying to add your OpenAI API key to the Flask app configuration. You were on the right track with `app.config[‘OPENAI_API_KEY’]`, but you need to modify the `get_openai_response` function to retrieve the API key from the app configuration.

Here’s the modified code:

“`python
from flask import Flask, render_template, request
from langchain.llms import OpenAI

app = Flask(__name__)

class OpenAIConfig:
API_KEY = ‘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’
MODEL_NAME = ‘text-davinci-003’
TEMPERATURE = 0.5

# Function to load OpenAI model and get responses
def get_openai_response(question):
try:
llm = OpenAI(
openai_api_key=app.config[‘OPENAI_API_KEY’],
model_name=OpenAIConfig.MODEL_NAME,
temperature=OpenAIConfig.TEMPERATURE
)
response = llm(question)
return response
except Exception as e:
# Add proper error handling (e.g., logging, return an error message)
return f”Error: {str(e)}”

return render_template(‘index.html’)

# Set the OpenAI API key in the app configuration
app.config[‘OPENAI_API_KEY’] = OpenAIConfig.API_KEY

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

This way, you set the OpenAI API key in the app configuration before running the app. Also, make sure to replace ‘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’ with your actual OpenAI API key.
No, I want to use my own server as the llm.
Ah, got it! If you want to use your own server as the language model (llm), you’ll need to modify the `get_openai_response` function to make a request to your server instead of using the OpenAI library directly. Here’s an example assuming your server is running and has an API endpoint to handle the language model requests:

“`python
from flask import Flask, render_template, request
import requests

app = Flask(__name__)

class ServerConfig:
SERVER_URL = ‘http://your-server-url’
MODEL_ENDPOINT = ‘/llm’

# Function to send a request to your server and get responses
def get_server_response(question):
try:
# Make a POST request to your server’s language model endpoint
response = requests.post(
f”{ServerConfig.SERVER_URL}{ServerConfig.MODEL_ENDPOINT}”,
json={“question”: question}
)

if response.status_code == 200:
return response.json().get(‘response’)
else:
return f”Error: {response.status_code} – {response.text}”
except Exception as e:
# Add proper error handling (e.g., logging, return an error message)
return f”Error: {str(e)}”

@app.route(‘/’, methods=[‘GET’, ‘POST’])
def index():
if request.method == ‘POST’:
input_question = request.form[‘input’]
response = get_server_response(input_question)
return render_template(‘index.html’, input_question=input_question, response=response)

return render_template(‘index.html’)

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

In this example, the `get_server_response` function uses the `requests` library to make a POST request to your server’s language model endpoint. You’ll need to replace ‘http://your-server-url’ with the actual URL of your server and make sure your server’s endpoint is set up to handle language model requests.

Adjust the code according to the specifics of your server’s API and how it expects questions to be sent and responses to be received.
[2023-12-15 18:03:14.868] [ERROR] Unexpected endpoint or method. (POST /v1). Returning 200 anyway.
It looks like your server is returning a 200 status code even when it encounters an unexpected endpoint or method. In a typical scenario, a server would return a 404 status code for an unexpected endpoint, indicating that the requested resource could not be found.

However, if your server is explicitly designed to return a 200 status code for any request, you might need to check the server’s documentation or code to understand how it handles unexpected endpoints or methods.

Here are a few steps you can take to troubleshoot:

1. **Check Server Documentation:**
– Review the documentation or source code of your server to see how it handles unexpected endpoints or methods. Look for any custom error handling.

2. **Verify Endpoint and Method:**
– Double-check that you are using the correct endpoint and method in your `requests.post` call. The error message suggests that the server is receiving a request to an unexpected endpoint (“/v1”). Ensure that the endpoint you are using matches the expected endpoint on your server.

3. **Update Server Configuration:**
– If your server is not behaving as expected, you may need to update its configuration to handle requests to the “/llm” endpoint properly. Make sure your server is configured to accept POST requests to the “/llm” endpoint.

4. **Server Logs:**
– Check the logs of your server for any additional error messages or information. The error message you provided seems to be a generic error, and there might be more details in the server logs.

If you can provide more information about your server’s configuration or code related to handling the “/llm” endpoint, I might be able to offer more specific guidance.
I changed the endpoint to /chat/completions and this is the reposnse: /chat/completions
I changed the endpoint to /chat/completions and this is the reposnse: The Response is

Error: 400 – {“error”:”‘messages’ field is required”}
It seems like your server is responding with the string “/chat/completions” when it receives a POST request to the “/chat/completions” endpoint. If this is the expected behavior according to your server’s logic, then it seems to be working correctly.

If you are expecting a language model response from your server and instead receiving “/chat/completions” as the response, there might be an issue with how your server is processing the request or generating the response. Here are a few steps to investigate:

1. **Server Implementation:**
– Review the code on your server that handles requests to the “/chat/completions” endpoint. Ensure that it is correctly processing the incoming question and generating the desired language model response.

2. **Check Server Logs:**
– Look into the logs of your server for any additional information or error messages. The logs might provide insights into why the response is not what you expect.

3. **Verify Server Expectations:**
– Confirm that your server is set up to handle language model requests and is configured to respond with the actual language model output rather than a static string.

If you can share a snippet of the relevant code from your server that handles requests to “/chat/completions” or provide more details about the server implementation, I may be able to offer more targeted assistance.
The error message indicates that a “messages” field is required in the request payload, but it seems like you haven’t included it in your current implementation. The OpenAI API typically expects a list of messages to generate a response.

Here’s how you can modify your `get_server_response` function to include a “messages” field in the request payload:

“`python
from flask import Flask, render_template, request, jsonify
import requests

app = Flask(__name__)

class ServerConfig:
SERVER_URL = ‘http://your-server-url’
MODEL_ENDPOINT = ‘/chat/completions’

# Function to send a request to your server and get responses
def get_server_response(question):
try:
# Construct the messages list with a single message
messages = [{“role”: “system”, “content”: “You are a helpful assistant.”},
{“role”: “user”, “content”: question}]

# Make a POST request to your server’s language model endpoint
response = requests.post(
f”{ServerConfig.SERVER_URL}{ServerConfig.MODEL_ENDPOINT}”,
json={“messages”: messages}
)

if response.status_code == 200:
return response.json().get(‘choices’)[0].get(‘message’).get(‘content’)
else:
return f”Error: {response.status_code} – {response.text}”
except Exception as e:
# Add proper error handling (e.g., logging, return an error message)
return f”Error: {str(e)}”

return render_template(‘index.html’)

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

In this example, I’ve created a list of messages with a system message indicating that “You are a helpful assistant” and a user message with the input question. The messages list is then included in the request payload.

Make sure to adapt the message structure based on your server’s requirements, as the format may vary depending on the specific implementation of your language model endpoint.
were getting somehwere Now the answer is: The Response is

I’m sorry, I don’t know how to help you with that. Please ask your teacher. You are a helpful assistant
It looks like the response you’re getting indicates that the language model is generating a message saying, “I’m sorry, I don’t know how to help you with that. Please ask your teacher. You are a helpful assistant.”

This response suggests that the model might not have enough context or information to provide a meaningful answer. It’s also possible that the model is designed to encourage the user to seek help from a teacher.

To improve the model’s responses, you can experiment with adjusting the input messages and context provided to the language model. You might want to include more context or specify the role of the assistant more explicitly in the system message.

Here’s an example of how you can modify the messages to provide more context:

“`python
# Construct the messages list with a system message and a user message
messages = [
{“role”: “system”, “content”: “You are a helpful assistant.”},
{“role”: “user”, “content”: f”Can you provide information about {question}?”}
]
“`

This modification explicitly asks the assistant to provide information about the user’s input question.

Remember to experiment with different approaches to find the best way to structure your input messages and system message to get the desired responses from the language model.
I have its context window at 2048 of 32k. is that a limiting factor
A context window of 2048 tokens within a larger context of 32,000 tokens is quite substantial, and it should generally provide enough context for the language model to generate meaningful responses. However, there are a few considerations:

1. **Token Limit:**
– Keep in mind that GPT models, including those from OpenAI, have a maximum token limit. If the total number of tokens in your input exceeds this limit, you may need to truncate or omit parts of the text to fit within the model’s constraints.

2. **Model Understanding:**
– While a large context window can capture a lot of information, the model’s ability to understand and generate coherent responses depends on the quality and relevance of the provided context. Ensure that the context you provide is informative and relevant to the user’s query.

3. **Experimentation:**
– Experiment with different context window sizes and see how it impacts the model’s responses. Sometimes, adjusting the context size can lead to better results, especially if certain information is critical for understanding the context.

4. **System Message:**
– The system message, which sets the behavior or role of the assistant, can also influence responses. Experiment with different system message formulations to guide the model’s behavior appropriately.

If you’re facing challenges with the model’s responses, try varying the input context, adjusting the system message, and experimenting with different context window sizes to find the optimal configuration for your specific use case. Additionally, you can refer to OpenAI’s documentation for any specific recommendations related to the use of their models.
with the context windows at 32K I get this: The Response is

1. The moon has an orbital period of 27 days, or 4022960 minutes per revolution 2. Its orbital velocity is 80 miles per second 3. It takes about 255 Earth days to orbit the Sun 4. It’s a moon 5. 14.7 light years away from the sun
It looks like the response you provided includes various statements about the moon and its characteristics. If this is the output from the language model given a certain input, it suggests that the model is generating information related to the moon.

To assess the quality of the response and its relevance, consider the following:

1. **Accuracy:** Verify the accuracy of the information provided by the model. Check if the details about the moon’s orbital period, velocity, orbit around the Sun, and distance from the sun are correct.

2. **Relevance:** Ensure that the generated information is relevant to the input question or prompt. If the user’s question was specific, check if the model’s response addresses that question appropriately.

3. **Consistency:** Review the consistency of the information. Make sure that the details provided by the model align with known scientific facts and are logically consistent.

If the information generated by the model is accurate and relevant to the user’s query, it indicates that the model is successfully using the context provided to generate informative responses. If there are inaccuracies or the information is not relevant, you may need to adjust the input context or experiment with different approaches to improve the model’s output.

Remember that language models like GPT-3 generate responses based on patterns learned during training and the context provided. Adjusting the context, tweaking the system message, and experimenting with different approaches can help you fine-tune the model for your specific use case.
What is limiting the output to 130 tokens?
The limitation on the output to 130 tokens is likely due to the default maximum token limit set by the OpenAI GPT-3 model. GPT-3 has a maximum token limit that restricts the length of both input and output sequences. This limit ensures that the model’s computational resources are used efficiently.

In the context of OpenAI’s API, the maximum token limit for GPT-3 is 4096 tokens. This limit encompasses both input and output tokens. If your input context and the generated response together exceed this limit, you may encounter truncation or incomplete responses.

To address this limitation, you can try the following:

1. **Reduce Input Size:**
– If the input context is lengthy, consider shortening it to reduce the total number of tokens. This can create more room for the generated response within the token limit.

2. **Adjust Output Length:**
– You can explicitly set the `max_tokens` parameter in your API call to limit the length of the generated response. However, be cautious with very low values, as it might result in truncated or incomplete answers.

Here’s an example of how to set `max_tokens` in your request:

“`python
response = requests.post(
f”{ServerConfig.SERVER_URL}{ServerConfig.MODEL_ENDPOINT}”,
json={“messages”: messages, “max_tokens”: 150} # Adjust the value as needed
)
“`

Keep in mind that while reducing the output length can help stay within the token limit, it may also affect the completeness and coherence of the responses. Finding the right balance between input context, output length, and the token limit is important for optimal results.
I am using my own model on my own server and it hasw a context of 32k tokens
If you are using your own model on your own server with a context of 32k tokens, and you are still observing a limitation to 130 tokens in the output, there might be a few reasons for this behavior:

1. **Response Truncation:**
– The server or client handling the response may truncate the output for display or processing purposes. Check the code responsible for handling and displaying the response to ensure that it is not imposing a length restriction.

2. **Client-Side Limitations:**
– The application or interface consuming the response may have its own limitations on the length of displayed text. Verify if there are any restrictions on the client side that could be affecting the rendering of the complete response.

3. **Incomplete API Call:**
– Double-check that the API call to your server is correctly sending and receiving the entire response. Ensure that the code responsible for making the API request and processing the response is working as expected.

4. **Server-Side Processing:**
– Examine the server-side code that handles the response from your language model. Verify that it is correctly processing and returning the entire output from your model.

Without more specific details about your server setup, it’s challenging to pinpoint the exact cause. If the issue persists after reviewing the above points, you may want to inspect the server logs and client-side code for any indications of where the truncation might be occurring.

If you provide more information about the components involved, such as the server code, client code, and any relevant configurations, I may be able to offer more targeted assistance.
[2023-12-15 18:11:00.527] [INFO] Generated prediction: {
“id”: “chatcmpl-xzopbm6shwneel02xicm8l”,
“object”: “chat.completion”,
“created”: 1702681851,
“model”: “D:\SarahOne\Mistral\Mistral-7B-v0.1-GGUF\mistral-7b-v0.1.Q3_K_S.gguf”,
“choices”: [
{
“index”: 0,
“message”: {
“role”: “assistant”,
“content”: “1. The moon has an orbital period of 27 days, or 4022960 minutes per revolutionn2. Its orbital velocity is 80 miles per secondn3. It takes about 255 Earth days to orbit the Sunn4. It’s a moonn5. 14.7 light years away from the sunn”
},
“finish_reason”: “stop”
}
],
“usage”: {
“prompt_tokens”: 50,
“completion_tokens”: 80,
“total_tokens”: 130
}
}
The information provided in the generated prediction log indicates that the response has a total of 130 tokens. This includes both the input (prompt) tokens and the tokens in the completion (response) generated by the model. Here’s a breakdown of the token usage:

– `prompt_tokens`: 50 tokens
– `completion_tokens`: 80 tokens
– `total_tokens`: 130 tokens

Based on this log, it appears that the complete response from your server is being generated, and there is no explicit truncation happening on the server side. The response is provided under the “message” field with the role of “assistant,” and it contains the detailed information about the moon.

If you are experiencing truncation or incomplete output on the client side, you may want to investigate the client code, the display logic, or any limitations imposed by the application or interface consuming the response.

Additionally, you can adjust the client-side code or interface to display the entire response, or you may consider implementing pagination or other strategies to handle longer responses if displaying them in full is not feasible.
lets ask a question and ask that it provide an answer close to 500 tokens
Certainly! You can explicitly set the `max_tokens` parameter in your request to instruct the language model to generate a response with a target length close to 500 tokens. Here’s an example:

“`python
import requests

# Assuming your server configuration
server_url = ‘http://your-server-url’
model_endpoint = ‘/chat/completions’

# Construct the input messages
question = “What is the meaning of life?”
messages = [{“role”: “user”, “content”: question}]

# Make a POST request to your server’s language model endpoint
response = requests.post(
f”{server_url}{model_endpoint}”,
json={“messages”: messages, “max_tokens”: 500} # Adjust the value as needed
)

# Extract the generated response
generated_response = response.json().get(‘choices’)[0].get(‘message’).get(‘content’)

# Print or use the generated response
print(generated_response)
“`

In this example, the `max_tokens` parameter is set to 500, indicating that you want the response to be close to 500 tokens in length. Adjust the value based on your specific requirements.

Keep in mind that setting a high `max_tokens` value may result in longer response times and could potentially impact the quality of the generated content. Experiment with different values to find a balance between response length and content quality that suits your needs.
here is the models inspector: {
“name”: “mistralai_mistral-7b-v0.1”,
“arch”: “llama”,
“quant”: “Q3_K_S”,
“context_length”: 32768,
“embedding_length”: 4096,
“num_layers”: 32,
“rope”: {
“freq_base”: 10000,
“dimension_count”: 128
},
“head_count”: 32,
“head_count_kv”: 8,
“parameters”: “7B”
}
Thanks for sharing the details of your language model! Here’s a brief overview of some key characteristics:

– **Name:** mistralai_mistral-7b-v0.1
– **Architecture:** llama
– **Quantization:** Q3_K_S
– **Context Length:** 32,768 tokens
– **Embedding Length:** 4,096
– **Number of Layers:** 32
– **Rope:**
– **Frequency Base:** 10,000
– **Dimension Count:** 128
– **Head Count:** 32
– **Head Count (Key-Value):** 8
– **Parameters:** 7B

This information provides insights into the architecture, quantization, context length, and various parameters of your custom language model. The context length of 32,768 tokens is quite substantial, allowing the model to consider a large amount of information when generating responses.

If you have any specific questions or if there’s anything specific you’d like assistance with regarding this model, feel free to let me know!
Yes, I want to fully utlize all of its tokens – and then I want to know how to limit them. But first I want a very loing response.
To fully utilize the tokens provided by your model, you can set the `max_tokens` parameter to a value close to the maximum limit supported by your model. In this case, you mentioned that the context length is 32,768 tokens. While the overall token limit is typically higher, using the context length as a starting point should allow you to generate a response that fully utilizes the available tokens.

Here’s an example of how you can set up the request for a very long response:

“`python
import requests

# Assuming your server configuration
server_url = ‘http://your-server-url’
model_endpoint = ‘/chat/completions’

# Construct the input messages
question = “What are the details of the mistralai_mistral-7b-v0.1 model?”
messages = [{“role”: “user”, “content”: question}]

# Set max_tokens to the context length (32,768) or slightly lower to ensure completeness
max_tokens = 32750 # Adjust the value as needed

# Make a POST request to your server’s language model endpoint
response = requests.post(
f”{server_url}{model_endpoint}”,
json={“messages”: messages, “max_tokens”: max_tokens}
)

# Extract the generated response
generated_response = response.json().get(‘choices’)[0].get(‘message’).get(‘content’)

# Print or use the generated response
print(generated_response)
“`

In this example, `max_tokens` is set close to the context length. Feel free to adjust the value based on your specific requirements. Note that generating very long responses may result in longer response times and increased resource usage. Experiment with different values to find the right balance for your use case.
Q”Discus the religions of the world” AI Answer: [2023-12-15 18:19:03.608] [INFO] Generated prediction: {
“id”: “chatcmpl-u14g0maj85rxex87v3c1n”,
“object”: “chat.completion”,
“created”: 1702682335,
“model”: “D:\SarahOne\Mistral\Mistral-7B-v0.1-GGUF\mistral-7b-v0.1.Q3_K_S.gguf”,
“choices”: [
{
“index”: 0,
“message”: {
“role”: “assistant”,
“content”: “nReligion is the practice and belief in a system of supernatural forces that exist beyond material reality. Religious practitioners may also believe that their gods or saints are immortal, and/or that they interact with the universe and may be able to alter it directly by using their powers”
},
“finish_reason”: “stop”
}
],
“usage”: {
“prompt_tokens”: 49,
“completion_tokens”: 62,
“total_tokens”: 111
}
}
It looks like the response generated by the model provides a brief overview of religion, emphasizing the practice and belief in supernatural forces beyond material reality. However, the response seems to be cut off, and the generated content is limited to 111 tokens.

If you want a longer response, you can adjust the `max_tokens` parameter in your request to a higher value. For example:

“`python
# Set max_tokens to a higher value to get a longer response
max_tokens = 500 # Adjust the value as needed

# Make a POST request to your server’s language model endpoint
response = requests.post(
f”{server_url}{model_endpoint}”,
json={“messages”: messages, “max_tokens”: max_tokens}
)
“`

By setting `max_tokens` to a higher value, you can increase the length of the generated response. Experiment with different values until you achieve the desired length for the response. Keep in mind that very long responses may result in longer processing times.
I changed the endpoint to /completions and now it is error Error: 400 – {“error”:”‘prompt’ field is required”}
It seems that changing the endpoint to `/completions` has altered the expected request format, and now the server is expecting a “prompt” field instead of “messages.” The error message indicates that the “prompt” field is required.

Here’s an example of how you can modify the request to include a “prompt” field:

“`python
import requests

# Assuming your server configuration
server_url = ‘http://your-server-url’
model_endpoint = ‘/completions’

# Construct the input prompt
prompt = “Discuss the religions of the world.”

# Set max_tokens to a higher value to get a longer response
max_tokens = 500 # Adjust the value as needed

# Make a POST request to your server’s language model endpoint
response = requests.post(
f”{server_url}{model_endpoint}”,
json={“prompt”: prompt, “max_tokens”: max_tokens}
)
“`

In this example, the input is provided as a single “prompt” string, and the `max_tokens` parameter is set to control the length of the generated response. Adjust the values as needed for your specific use case.

Remember to consult your server’s documentation or code to ensure you are using the correct format for the request, as it can vary based on the implementation of the language model endpoint.
can this be a good place for langchain prompt templates?
Absolutely! Creating a template for interacting with language models through LangChain can be a great way to streamline the process and ensure consistency in your requests. It helps to define a structure that can be easily reused across different queries. Here’s an example of how you might structure a LangChain prompt template:

“`python
import requests

class LangChainPromptTemplate:
def __init__(self, server_url, model_endpoint):
self.server_url = server_url
self.model_endpoint = model_endpoint

def generate_response(self, prompt, max_tokens=500):
# Make a POST request to the language model endpoint
response = requests.post(
f”{self.server_url}{self.model_endpoint}”,
json={“prompt”: prompt, “max_tokens”: max_tokens}
)

# Extract the generated response
generated_response = response.json().get(‘choices’)[0].get(‘message’).get(‘content’)

return generated_response

# Example usage
template = LangChainPromptTemplate(‘http://your-server-url’, ‘/completions’)
prompt = “Discuss the religions of the world.”
max_tokens = 500
response = template.generate_response(prompt, max_tokens)
print(response)
“`

This example demonstrates how you can encapsulate the logic for making requests to your LangChain server using a template class. It provides a clean and reusable way to structure your interactions with the language model.

Feel free to customize the template based on your specific needs, and you can expand it to handle various aspects of the interaction, such as error handling, dynamic endpoint selection, or different types of prompts.