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ChatGPT API Bible

Chapter 3 - Basic Usage of ChatGPT API

3.3. Managing API Rate Limits

When using the ChatGPT API, it's important to be aware of and manage the API rate limits. Rate limiting is a mechanism used by APIs to control the amount of traffic sent to the server at any given time. The ChatGPT API has a limit on the number of requests that can be made in a given time period. Therefore, it's important to use the API efficiently to prevent hitting these limits and avoid any interruptions in service.

One way to manage API rate limits is by implementing caching. Caching stores the API response locally and retrieves it from the cache instead of making a new request to the server. This can help reduce the number of API requests made and, in turn, reduce the likelihood of hitting the rate limits.

Another strategy for efficient API usage is to batch requests. Instead of making multiple requests for each individual task, batching allows you to combine multiple tasks into a single request. This can also help reduce the number of API requests made, which can help prevent hitting the rate limits.

By understanding these strategies and employing them in your use of the ChatGPT API, you can ensure a smooth experience while interacting with the API, even when dealing with large amounts of data.

3.3.1. Understanding Rate Limiting

Rate limiting is a crucial mechanism used by APIs to regulate the number of requests a user can send within a specific time frame. This helps to ensure that OpenAI's services are used in a fair and optimal manner. The rate limits for ChatGPT API can vary depending on your subscription tier and can differ across various engines.

For instance, free trial users are typically provided with a rate limit of 20 requests per minute (RPM) and 40,000 tokens per minute (TPM). However, pay-as-you-go users may have different limits during their first 48 hours, with a rate limit of 60 RPM and 60,000 TPM. After this period, the limits could increase to 3,500 RPM and 90,000 TPM, which is quite a significant difference from the free trial limit.

It's important to note that while these limits may seem restrictive, they are put in place to ensure that the API remains accessible and available for all users. By limiting the number of requests that can be made, OpenAI can better manage the resources available to them and provide a smoother experience to their users.

Example:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    response = None
    while response is None:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

    return response.choices[0].text.strip()

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

This example demonstrates how to handle a RateLimitError when calling the ChatGPT API. When the rate limit is exceeded, the program prints a message and waits for the recommended time before retrying the request.

3.3.2. Strategies for Efficient API Usage

To manage rate limits effectively and make the most of your available tokens, consider the following strategies:

Batching requests

If you have multiple prompts to process, you can use the n parameter to generate multiple responses in a single API call. This can help you reduce the number of requests and make better use of your available rate limit.

Additionally, batching requests can help reduce the amount of time it takes to process a large number of prompts. By sending multiple prompts in a single API call, you can streamline your workflow and improve your overall efficiency.

Furthermore, using the n parameter can also help you better manage your resources. Instead of making multiple API calls and potentially exceeding your rate limit, you can consolidate your requests and make more efficient use of your available resources. This can be especially useful if you are working with a large dataset or processing a high volume of prompts.

In summary, batching requests using the n parameter is a powerful technique for improving your workflow and making better use of your available resources. By consolidating multiple prompts into a single API call, you can save time, reduce the number of requests you need to make, and improve your overall efficiency.

Handling rate limit errors

When making requests to an API, it is important to keep in mind that the server might limit the number of requests you can make over a certain period of time. If you exceed this limit, the API will return a 429 Too Many Requests error. In order to avoid this error, it is important to implement error handling in your code that can intelligently deal with these rate limit errors.

One way to do this is to catch the 429 Too Many Requests error and pause for an appropriate duration before retrying the request. An appropriate duration can be calculated based on the rate limit information provided by the API. Some APIs might return the duration of the rate limit as part of the error response, while others might require you to make a separate request to retrieve this information.

Another way to deal with rate limit errors is to implement a queuing system that can throttle your requests to ensure that you don't exceed the rate limit. This can be especially useful if you need to make a large number of requests or if you are working with a slow API that requires long pauses between requests.

Regardless of the method you choose to deal with rate limit errors, it is important to make sure that your code is robust and can handle unexpected errors that might arise. By implementing error handling and rate limiting strategies, you can ensure that your code is reliable and can handle the demands of working with APIs over the long term.

Here's an example of handling rate limit errors using Python and the time module:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    while True:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
            return response.choices[0].text.strip()
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

Here's another code example that demonstrates a simple technique to track the number of tokens used in your requests to avoid exceeding your tokens per minute (TPM) limit:

import openai

openai.api_key = "your_api_key"

def count_tokens(text):
    return len(openai.Tokenizer().encode(text))

def generate_text(prompt, token_budget):
    tokens_used = count_tokens(prompt)

    if tokens_used > token_budget:
        print("Token budget exceeded.")
        return None

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used += response.choices[0].usage["total_tokens"]

    if tokens_used > token_budget:
        print("Token budget exceeded after generating response.")
        return None

    return response.choices[0].text.strip(), tokens_used

token_budget = 10000
prompt = "What are the benefits of exercise?"
generated_text, tokens_used = generate_text(prompt, token_budget)

if generated_text is not None:
    print(f"Generated text: {generated_text}")
    print(f"Tokens used: {tokens_used}")

In this example, we define a token_budget to represent the maximum number of tokens we want to use in a certain period. We then use the count_tokens function to count the tokens in both the prompt and the response. If the combined tokens exceed our budget, we print a message and return None.

Token tracking is a crucial aspect of managing your token usage, especially if you're working with TPM limits. By tracking your tokens, you can keep a closer eye on your token usage and prevent accidental overuse.

Furthermore, you can identify patterns in your token usage and optimize your code accordingly. This can help you not only stay under your TPM limit, but also improve the performance of your code. Overall, token tracking is a simple yet powerful tool that can make a big difference in your token usage and overall code quality.

3.3.3. Monitoring and Managing Token Usage

Tracking your token usage is one of the most important things you can do to ensure that you are using APIs effectively. By carefully monitoring your token usage, you can avoid the risk of encountering unexpected errors caused by exceeding rate limits, which can cause significant delays and even result in the temporary suspension of your account.

In addition, taking the time to understand how your API tokens are being used can help you to identify areas where your application may be overutilizing certain APIs, allowing you to fine-tune your usage and optimize performance.

Overall, making a habit of tracking your token usage is a simple yet effective way to ensure that you are getting the most out of your API integration and avoiding any potential issues down the line.

Here are a few tips to help you monitor and manage your token usage:

  1. Check token usage in API responses

To ensure you have a clear understanding of your token consumption when using the ChatGPT API, the response object includes a usage attribute that provides detailed information on token usage. This attribute can be accessed by users to monitor their token usage, and ensure they have sufficient tokens available for their needs. By keeping a close eye on token usage, users can ensure they have the necessary resources to use the ChatGPT API effectively and efficiently, without running into any issues or limitations.

Example:

import openai

openai.api_key = "your_api_key"

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt="What are the benefits of exercise?",
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

tokens_used = response.choices[0].usage["total_tokens"]
print(f"Tokens used: {tokens_used}")
  1. Implement token usage alerts

One of the most important things to do when working with tokens is to set up alerts that tell you when your token usage approaches a certain threshold. By doing this, you can avoid hitting rate limits unexpectedly and proactively manage your consumption.

There are several ways to set up these alerts, including email notifications or automated messages in your code. You can also consider creating a dashboard that provides real-time information about your token usage, so you can quickly identify any potential issues.

Additionally, it's important to regularly review your token usage and adjust your alerts as needed. By taking these steps, you can ensure that your token usage is always optimized and that you have the information you need to make informed decisions about your API integration.

Example:

In this example, we'll set up an alert to notify when the total token usage reaches a certain threshold:

import openai

openai.api_key = "your_api_key"

# Set a token usage threshold
token_threshold = 10000
total_tokens_used = 0

# Example prompts
prompts = ["What are the benefits of exercise?",
           "What is the difference between aerobic and anaerobic exercise?",
           "How often should one exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used = response.choices[0].usage["total_tokens"]
    total_tokens_used += tokens_used

    # Check if the total token usage exceeds the threshold
    if total_tokens_used >= token_threshold:
        print(f"Token usage threshold reached: {total_tokens_used}/{token_threshold}")

    print(f"Response: {response.choices[0].text.strip()}")
  1. Optimize token usage

One thing that can really help when designing your application is taking a close look at your prompts and responses. By optimizing these to be more concise, you can help to minimize the number of tokens used in each request.

For instance, you might consider using shorter prompts or carefully setting max_tokens values that will limit the length of each response. This can help to ensure that your application is running smoothly and efficiently, while also making it easier for users to interact with and enjoy.

Example:

In this example, we'll demonstrate how to optimize token usage by using concise prompts and limiting response length with the max_tokens parameter:

import openai

openai.api_key = "your_api_key"

# Example prompts
prompts = ["Benefits of exercise?",
           "Aerobic vs anaerobic exercise?",
           "How often to exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=30,  # Limit response length
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

3.3.4. Handling Long Conversations

When working with ChatGPT, you may need to handle long conversations with multiple back-and-forth exchanges. To ensure that you stay within rate limits and manage tokens effectively in such scenarios, you can adopt the following strategies:

  1. Truncate or omit less relevant parts

If a conversation exceeds the maximum token limit for a single API call (e.g., 4096 tokens for some engines), you may need to truncate or omit parts of the conversation that are less relevant. However, it is important to note that removing a message might cause the model to lose context about that message. This can lead to inaccurate responses or misunderstandings.

Therefore, it is recommended to carefully consider which parts of the conversation to truncate or omit and to do so in a way that preserves the key ideas and context of the conversation. Additionally, in some cases, it may be useful to split the conversation into multiple API calls to ensure that all the relevant information is included.

By doing so, you can ensure that the model has access to all the necessary context and can provide accurate responses.

Example:

In this example, we truncate the conversation to fit within the token limit:

import openai

openai.api_key = "your_api_key"

def truncate_conversation(conversation, max_tokens):
    tokens = openai.Tokenizer().encode(conversation)
    if len(tokens) > max_tokens:
        tokens = tokens[-max_tokens:]
        truncated_conversation = openai.Tokenizer().decode(tokens)
        return truncated_conversation
    return conversation

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens = 4096

truncated_conversation = truncate_conversation(conversation, max_tokens)

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt=truncated_conversation,
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

print(response.choices[0].text.strip())
  1. Use continuation tokens

To prevent exceeding token limits, it is always a good idea to break long conversations into smaller segments. By using continuation tokens, you can ensure that the conversation can be resumed where it left off, even if it crosses the token limit. When the conversation continues beyond the token limit, you can store the last few tokens from the current response and use them as a starting point for the next API call.

This way, the conversation can continue seamlessly without any interruption or loss of data. It is important to note that using continuation tokens not only helps prevent token limits but also ensures that the conversation is more manageable and easier to work with.

Example:

In this example, we demonstrate breaking a long conversation into smaller segments using continuation tokens:

import openai

openai.api_key = "your_api_key"

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens_per_call = 1000
continuation_length = 5

tokens = openai.Tokenizer().encode(conversation)
num_segments = (len(tokens) + max_tokens_per_call - 1) // max_tokens_per_call

responses = []

for i in range(num_segments):
    start = i * max_tokens_per_call
    end = (i + 1) * max_tokens_per_call

    if i > 0:
        start -= continuation_length

    segment = openai.Tokenizer().decode(tokens[start:end])

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=segment,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )
    responses.append(response.choices[0].text.strip())

print("\n".join(responses))
  1. Minimize tokens in prompts

 It can be beneficial to keep prompts and instructions brief when engaging in conversation in order to preserve tokens for more meaningful content. However, it is important to strike a balance between brevity and thoroughness. By providing clear and detailed prompts and instructions, you can ensure that all necessary information is conveyed and that everyone involved in the conversation is on the same page.

Additionally, taking the time to explain things in depth can help to foster a deeper understanding and promote more productive discussions. Therefore, while it is important to be concise, it is equally important to be thorough and provide enough information to facilitate effective communication.

Example:

In this example, we demonstrate how to minimize tokens in prompts:

import openai

openai.api_key = "your_api_key"

concise_prompts = [
    "Benefits of exercise?",
    "Aerobic vs anaerobic?",
    "How often to exercise?",
]

for prompt in concise_prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

When it comes to managing long conversations, it's important to have a few strategies in place to ensure that you don't run into any issues with rate limits or token usage. One approach is to break up the conversation into smaller, more manageable chunks. This can be done by setting a maximum message length or by limiting the number of messages that can be sent in a given amount of time.

Another strategy is to use more efficient communication methods, such as sending condensed or summarized messages that still convey the main ideas. Additionally, it's important to be aware of any external factors that could impact the conversation, such as network connectivity or server downtime, and to plan accordingly. By implementing these strategies, you can ensure that your long conversations are both effective and efficient, without running into any unnecessary roadblocks or limitations.

3.3. Managing API Rate Limits

When using the ChatGPT API, it's important to be aware of and manage the API rate limits. Rate limiting is a mechanism used by APIs to control the amount of traffic sent to the server at any given time. The ChatGPT API has a limit on the number of requests that can be made in a given time period. Therefore, it's important to use the API efficiently to prevent hitting these limits and avoid any interruptions in service.

One way to manage API rate limits is by implementing caching. Caching stores the API response locally and retrieves it from the cache instead of making a new request to the server. This can help reduce the number of API requests made and, in turn, reduce the likelihood of hitting the rate limits.

Another strategy for efficient API usage is to batch requests. Instead of making multiple requests for each individual task, batching allows you to combine multiple tasks into a single request. This can also help reduce the number of API requests made, which can help prevent hitting the rate limits.

By understanding these strategies and employing them in your use of the ChatGPT API, you can ensure a smooth experience while interacting with the API, even when dealing with large amounts of data.

3.3.1. Understanding Rate Limiting

Rate limiting is a crucial mechanism used by APIs to regulate the number of requests a user can send within a specific time frame. This helps to ensure that OpenAI's services are used in a fair and optimal manner. The rate limits for ChatGPT API can vary depending on your subscription tier and can differ across various engines.

For instance, free trial users are typically provided with a rate limit of 20 requests per minute (RPM) and 40,000 tokens per minute (TPM). However, pay-as-you-go users may have different limits during their first 48 hours, with a rate limit of 60 RPM and 60,000 TPM. After this period, the limits could increase to 3,500 RPM and 90,000 TPM, which is quite a significant difference from the free trial limit.

It's important to note that while these limits may seem restrictive, they are put in place to ensure that the API remains accessible and available for all users. By limiting the number of requests that can be made, OpenAI can better manage the resources available to them and provide a smoother experience to their users.

Example:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    response = None
    while response is None:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

    return response.choices[0].text.strip()

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

This example demonstrates how to handle a RateLimitError when calling the ChatGPT API. When the rate limit is exceeded, the program prints a message and waits for the recommended time before retrying the request.

3.3.2. Strategies for Efficient API Usage

To manage rate limits effectively and make the most of your available tokens, consider the following strategies:

Batching requests

If you have multiple prompts to process, you can use the n parameter to generate multiple responses in a single API call. This can help you reduce the number of requests and make better use of your available rate limit.

Additionally, batching requests can help reduce the amount of time it takes to process a large number of prompts. By sending multiple prompts in a single API call, you can streamline your workflow and improve your overall efficiency.

Furthermore, using the n parameter can also help you better manage your resources. Instead of making multiple API calls and potentially exceeding your rate limit, you can consolidate your requests and make more efficient use of your available resources. This can be especially useful if you are working with a large dataset or processing a high volume of prompts.

In summary, batching requests using the n parameter is a powerful technique for improving your workflow and making better use of your available resources. By consolidating multiple prompts into a single API call, you can save time, reduce the number of requests you need to make, and improve your overall efficiency.

Handling rate limit errors

When making requests to an API, it is important to keep in mind that the server might limit the number of requests you can make over a certain period of time. If you exceed this limit, the API will return a 429 Too Many Requests error. In order to avoid this error, it is important to implement error handling in your code that can intelligently deal with these rate limit errors.

One way to do this is to catch the 429 Too Many Requests error and pause for an appropriate duration before retrying the request. An appropriate duration can be calculated based on the rate limit information provided by the API. Some APIs might return the duration of the rate limit as part of the error response, while others might require you to make a separate request to retrieve this information.

Another way to deal with rate limit errors is to implement a queuing system that can throttle your requests to ensure that you don't exceed the rate limit. This can be especially useful if you need to make a large number of requests or if you are working with a slow API that requires long pauses between requests.

Regardless of the method you choose to deal with rate limit errors, it is important to make sure that your code is robust and can handle unexpected errors that might arise. By implementing error handling and rate limiting strategies, you can ensure that your code is reliable and can handle the demands of working with APIs over the long term.

Here's an example of handling rate limit errors using Python and the time module:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    while True:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
            return response.choices[0].text.strip()
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

Here's another code example that demonstrates a simple technique to track the number of tokens used in your requests to avoid exceeding your tokens per minute (TPM) limit:

import openai

openai.api_key = "your_api_key"

def count_tokens(text):
    return len(openai.Tokenizer().encode(text))

def generate_text(prompt, token_budget):
    tokens_used = count_tokens(prompt)

    if tokens_used > token_budget:
        print("Token budget exceeded.")
        return None

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used += response.choices[0].usage["total_tokens"]

    if tokens_used > token_budget:
        print("Token budget exceeded after generating response.")
        return None

    return response.choices[0].text.strip(), tokens_used

token_budget = 10000
prompt = "What are the benefits of exercise?"
generated_text, tokens_used = generate_text(prompt, token_budget)

if generated_text is not None:
    print(f"Generated text: {generated_text}")
    print(f"Tokens used: {tokens_used}")

In this example, we define a token_budget to represent the maximum number of tokens we want to use in a certain period. We then use the count_tokens function to count the tokens in both the prompt and the response. If the combined tokens exceed our budget, we print a message and return None.

Token tracking is a crucial aspect of managing your token usage, especially if you're working with TPM limits. By tracking your tokens, you can keep a closer eye on your token usage and prevent accidental overuse.

Furthermore, you can identify patterns in your token usage and optimize your code accordingly. This can help you not only stay under your TPM limit, but also improve the performance of your code. Overall, token tracking is a simple yet powerful tool that can make a big difference in your token usage and overall code quality.

3.3.3. Monitoring and Managing Token Usage

Tracking your token usage is one of the most important things you can do to ensure that you are using APIs effectively. By carefully monitoring your token usage, you can avoid the risk of encountering unexpected errors caused by exceeding rate limits, which can cause significant delays and even result in the temporary suspension of your account.

In addition, taking the time to understand how your API tokens are being used can help you to identify areas where your application may be overutilizing certain APIs, allowing you to fine-tune your usage and optimize performance.

Overall, making a habit of tracking your token usage is a simple yet effective way to ensure that you are getting the most out of your API integration and avoiding any potential issues down the line.

Here are a few tips to help you monitor and manage your token usage:

  1. Check token usage in API responses

To ensure you have a clear understanding of your token consumption when using the ChatGPT API, the response object includes a usage attribute that provides detailed information on token usage. This attribute can be accessed by users to monitor their token usage, and ensure they have sufficient tokens available for their needs. By keeping a close eye on token usage, users can ensure they have the necessary resources to use the ChatGPT API effectively and efficiently, without running into any issues or limitations.

Example:

import openai

openai.api_key = "your_api_key"

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt="What are the benefits of exercise?",
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

tokens_used = response.choices[0].usage["total_tokens"]
print(f"Tokens used: {tokens_used}")
  1. Implement token usage alerts

One of the most important things to do when working with tokens is to set up alerts that tell you when your token usage approaches a certain threshold. By doing this, you can avoid hitting rate limits unexpectedly and proactively manage your consumption.

There are several ways to set up these alerts, including email notifications or automated messages in your code. You can also consider creating a dashboard that provides real-time information about your token usage, so you can quickly identify any potential issues.

Additionally, it's important to regularly review your token usage and adjust your alerts as needed. By taking these steps, you can ensure that your token usage is always optimized and that you have the information you need to make informed decisions about your API integration.

Example:

In this example, we'll set up an alert to notify when the total token usage reaches a certain threshold:

import openai

openai.api_key = "your_api_key"

# Set a token usage threshold
token_threshold = 10000
total_tokens_used = 0

# Example prompts
prompts = ["What are the benefits of exercise?",
           "What is the difference between aerobic and anaerobic exercise?",
           "How often should one exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used = response.choices[0].usage["total_tokens"]
    total_tokens_used += tokens_used

    # Check if the total token usage exceeds the threshold
    if total_tokens_used >= token_threshold:
        print(f"Token usage threshold reached: {total_tokens_used}/{token_threshold}")

    print(f"Response: {response.choices[0].text.strip()}")
  1. Optimize token usage

One thing that can really help when designing your application is taking a close look at your prompts and responses. By optimizing these to be more concise, you can help to minimize the number of tokens used in each request.

For instance, you might consider using shorter prompts or carefully setting max_tokens values that will limit the length of each response. This can help to ensure that your application is running smoothly and efficiently, while also making it easier for users to interact with and enjoy.

Example:

In this example, we'll demonstrate how to optimize token usage by using concise prompts and limiting response length with the max_tokens parameter:

import openai

openai.api_key = "your_api_key"

# Example prompts
prompts = ["Benefits of exercise?",
           "Aerobic vs anaerobic exercise?",
           "How often to exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=30,  # Limit response length
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

3.3.4. Handling Long Conversations

When working with ChatGPT, you may need to handle long conversations with multiple back-and-forth exchanges. To ensure that you stay within rate limits and manage tokens effectively in such scenarios, you can adopt the following strategies:

  1. Truncate or omit less relevant parts

If a conversation exceeds the maximum token limit for a single API call (e.g., 4096 tokens for some engines), you may need to truncate or omit parts of the conversation that are less relevant. However, it is important to note that removing a message might cause the model to lose context about that message. This can lead to inaccurate responses or misunderstandings.

Therefore, it is recommended to carefully consider which parts of the conversation to truncate or omit and to do so in a way that preserves the key ideas and context of the conversation. Additionally, in some cases, it may be useful to split the conversation into multiple API calls to ensure that all the relevant information is included.

By doing so, you can ensure that the model has access to all the necessary context and can provide accurate responses.

Example:

In this example, we truncate the conversation to fit within the token limit:

import openai

openai.api_key = "your_api_key"

def truncate_conversation(conversation, max_tokens):
    tokens = openai.Tokenizer().encode(conversation)
    if len(tokens) > max_tokens:
        tokens = tokens[-max_tokens:]
        truncated_conversation = openai.Tokenizer().decode(tokens)
        return truncated_conversation
    return conversation

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens = 4096

truncated_conversation = truncate_conversation(conversation, max_tokens)

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt=truncated_conversation,
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

print(response.choices[0].text.strip())
  1. Use continuation tokens

To prevent exceeding token limits, it is always a good idea to break long conversations into smaller segments. By using continuation tokens, you can ensure that the conversation can be resumed where it left off, even if it crosses the token limit. When the conversation continues beyond the token limit, you can store the last few tokens from the current response and use them as a starting point for the next API call.

This way, the conversation can continue seamlessly without any interruption or loss of data. It is important to note that using continuation tokens not only helps prevent token limits but also ensures that the conversation is more manageable and easier to work with.

Example:

In this example, we demonstrate breaking a long conversation into smaller segments using continuation tokens:

import openai

openai.api_key = "your_api_key"

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens_per_call = 1000
continuation_length = 5

tokens = openai.Tokenizer().encode(conversation)
num_segments = (len(tokens) + max_tokens_per_call - 1) // max_tokens_per_call

responses = []

for i in range(num_segments):
    start = i * max_tokens_per_call
    end = (i + 1) * max_tokens_per_call

    if i > 0:
        start -= continuation_length

    segment = openai.Tokenizer().decode(tokens[start:end])

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=segment,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )
    responses.append(response.choices[0].text.strip())

print("\n".join(responses))
  1. Minimize tokens in prompts

 It can be beneficial to keep prompts and instructions brief when engaging in conversation in order to preserve tokens for more meaningful content. However, it is important to strike a balance between brevity and thoroughness. By providing clear and detailed prompts and instructions, you can ensure that all necessary information is conveyed and that everyone involved in the conversation is on the same page.

Additionally, taking the time to explain things in depth can help to foster a deeper understanding and promote more productive discussions. Therefore, while it is important to be concise, it is equally important to be thorough and provide enough information to facilitate effective communication.

Example:

In this example, we demonstrate how to minimize tokens in prompts:

import openai

openai.api_key = "your_api_key"

concise_prompts = [
    "Benefits of exercise?",
    "Aerobic vs anaerobic?",
    "How often to exercise?",
]

for prompt in concise_prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

When it comes to managing long conversations, it's important to have a few strategies in place to ensure that you don't run into any issues with rate limits or token usage. One approach is to break up the conversation into smaller, more manageable chunks. This can be done by setting a maximum message length or by limiting the number of messages that can be sent in a given amount of time.

Another strategy is to use more efficient communication methods, such as sending condensed or summarized messages that still convey the main ideas. Additionally, it's important to be aware of any external factors that could impact the conversation, such as network connectivity or server downtime, and to plan accordingly. By implementing these strategies, you can ensure that your long conversations are both effective and efficient, without running into any unnecessary roadblocks or limitations.

3.3. Managing API Rate Limits

When using the ChatGPT API, it's important to be aware of and manage the API rate limits. Rate limiting is a mechanism used by APIs to control the amount of traffic sent to the server at any given time. The ChatGPT API has a limit on the number of requests that can be made in a given time period. Therefore, it's important to use the API efficiently to prevent hitting these limits and avoid any interruptions in service.

One way to manage API rate limits is by implementing caching. Caching stores the API response locally and retrieves it from the cache instead of making a new request to the server. This can help reduce the number of API requests made and, in turn, reduce the likelihood of hitting the rate limits.

Another strategy for efficient API usage is to batch requests. Instead of making multiple requests for each individual task, batching allows you to combine multiple tasks into a single request. This can also help reduce the number of API requests made, which can help prevent hitting the rate limits.

By understanding these strategies and employing them in your use of the ChatGPT API, you can ensure a smooth experience while interacting with the API, even when dealing with large amounts of data.

3.3.1. Understanding Rate Limiting

Rate limiting is a crucial mechanism used by APIs to regulate the number of requests a user can send within a specific time frame. This helps to ensure that OpenAI's services are used in a fair and optimal manner. The rate limits for ChatGPT API can vary depending on your subscription tier and can differ across various engines.

For instance, free trial users are typically provided with a rate limit of 20 requests per minute (RPM) and 40,000 tokens per minute (TPM). However, pay-as-you-go users may have different limits during their first 48 hours, with a rate limit of 60 RPM and 60,000 TPM. After this period, the limits could increase to 3,500 RPM and 90,000 TPM, which is quite a significant difference from the free trial limit.

It's important to note that while these limits may seem restrictive, they are put in place to ensure that the API remains accessible and available for all users. By limiting the number of requests that can be made, OpenAI can better manage the resources available to them and provide a smoother experience to their users.

Example:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    response = None
    while response is None:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

    return response.choices[0].text.strip()

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

This example demonstrates how to handle a RateLimitError when calling the ChatGPT API. When the rate limit is exceeded, the program prints a message and waits for the recommended time before retrying the request.

3.3.2. Strategies for Efficient API Usage

To manage rate limits effectively and make the most of your available tokens, consider the following strategies:

Batching requests

If you have multiple prompts to process, you can use the n parameter to generate multiple responses in a single API call. This can help you reduce the number of requests and make better use of your available rate limit.

Additionally, batching requests can help reduce the amount of time it takes to process a large number of prompts. By sending multiple prompts in a single API call, you can streamline your workflow and improve your overall efficiency.

Furthermore, using the n parameter can also help you better manage your resources. Instead of making multiple API calls and potentially exceeding your rate limit, you can consolidate your requests and make more efficient use of your available resources. This can be especially useful if you are working with a large dataset or processing a high volume of prompts.

In summary, batching requests using the n parameter is a powerful technique for improving your workflow and making better use of your available resources. By consolidating multiple prompts into a single API call, you can save time, reduce the number of requests you need to make, and improve your overall efficiency.

Handling rate limit errors

When making requests to an API, it is important to keep in mind that the server might limit the number of requests you can make over a certain period of time. If you exceed this limit, the API will return a 429 Too Many Requests error. In order to avoid this error, it is important to implement error handling in your code that can intelligently deal with these rate limit errors.

One way to do this is to catch the 429 Too Many Requests error and pause for an appropriate duration before retrying the request. An appropriate duration can be calculated based on the rate limit information provided by the API. Some APIs might return the duration of the rate limit as part of the error response, while others might require you to make a separate request to retrieve this information.

Another way to deal with rate limit errors is to implement a queuing system that can throttle your requests to ensure that you don't exceed the rate limit. This can be especially useful if you need to make a large number of requests or if you are working with a slow API that requires long pauses between requests.

Regardless of the method you choose to deal with rate limit errors, it is important to make sure that your code is robust and can handle unexpected errors that might arise. By implementing error handling and rate limiting strategies, you can ensure that your code is reliable and can handle the demands of working with APIs over the long term.

Here's an example of handling rate limit errors using Python and the time module:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    while True:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
            return response.choices[0].text.strip()
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

Here's another code example that demonstrates a simple technique to track the number of tokens used in your requests to avoid exceeding your tokens per minute (TPM) limit:

import openai

openai.api_key = "your_api_key"

def count_tokens(text):
    return len(openai.Tokenizer().encode(text))

def generate_text(prompt, token_budget):
    tokens_used = count_tokens(prompt)

    if tokens_used > token_budget:
        print("Token budget exceeded.")
        return None

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used += response.choices[0].usage["total_tokens"]

    if tokens_used > token_budget:
        print("Token budget exceeded after generating response.")
        return None

    return response.choices[0].text.strip(), tokens_used

token_budget = 10000
prompt = "What are the benefits of exercise?"
generated_text, tokens_used = generate_text(prompt, token_budget)

if generated_text is not None:
    print(f"Generated text: {generated_text}")
    print(f"Tokens used: {tokens_used}")

In this example, we define a token_budget to represent the maximum number of tokens we want to use in a certain period. We then use the count_tokens function to count the tokens in both the prompt and the response. If the combined tokens exceed our budget, we print a message and return None.

Token tracking is a crucial aspect of managing your token usage, especially if you're working with TPM limits. By tracking your tokens, you can keep a closer eye on your token usage and prevent accidental overuse.

Furthermore, you can identify patterns in your token usage and optimize your code accordingly. This can help you not only stay under your TPM limit, but also improve the performance of your code. Overall, token tracking is a simple yet powerful tool that can make a big difference in your token usage and overall code quality.

3.3.3. Monitoring and Managing Token Usage

Tracking your token usage is one of the most important things you can do to ensure that you are using APIs effectively. By carefully monitoring your token usage, you can avoid the risk of encountering unexpected errors caused by exceeding rate limits, which can cause significant delays and even result in the temporary suspension of your account.

In addition, taking the time to understand how your API tokens are being used can help you to identify areas where your application may be overutilizing certain APIs, allowing you to fine-tune your usage and optimize performance.

Overall, making a habit of tracking your token usage is a simple yet effective way to ensure that you are getting the most out of your API integration and avoiding any potential issues down the line.

Here are a few tips to help you monitor and manage your token usage:

  1. Check token usage in API responses

To ensure you have a clear understanding of your token consumption when using the ChatGPT API, the response object includes a usage attribute that provides detailed information on token usage. This attribute can be accessed by users to monitor their token usage, and ensure they have sufficient tokens available for their needs. By keeping a close eye on token usage, users can ensure they have the necessary resources to use the ChatGPT API effectively and efficiently, without running into any issues or limitations.

Example:

import openai

openai.api_key = "your_api_key"

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt="What are the benefits of exercise?",
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

tokens_used = response.choices[0].usage["total_tokens"]
print(f"Tokens used: {tokens_used}")
  1. Implement token usage alerts

One of the most important things to do when working with tokens is to set up alerts that tell you when your token usage approaches a certain threshold. By doing this, you can avoid hitting rate limits unexpectedly and proactively manage your consumption.

There are several ways to set up these alerts, including email notifications or automated messages in your code. You can also consider creating a dashboard that provides real-time information about your token usage, so you can quickly identify any potential issues.

Additionally, it's important to regularly review your token usage and adjust your alerts as needed. By taking these steps, you can ensure that your token usage is always optimized and that you have the information you need to make informed decisions about your API integration.

Example:

In this example, we'll set up an alert to notify when the total token usage reaches a certain threshold:

import openai

openai.api_key = "your_api_key"

# Set a token usage threshold
token_threshold = 10000
total_tokens_used = 0

# Example prompts
prompts = ["What are the benefits of exercise?",
           "What is the difference between aerobic and anaerobic exercise?",
           "How often should one exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used = response.choices[0].usage["total_tokens"]
    total_tokens_used += tokens_used

    # Check if the total token usage exceeds the threshold
    if total_tokens_used >= token_threshold:
        print(f"Token usage threshold reached: {total_tokens_used}/{token_threshold}")

    print(f"Response: {response.choices[0].text.strip()}")
  1. Optimize token usage

One thing that can really help when designing your application is taking a close look at your prompts and responses. By optimizing these to be more concise, you can help to minimize the number of tokens used in each request.

For instance, you might consider using shorter prompts or carefully setting max_tokens values that will limit the length of each response. This can help to ensure that your application is running smoothly and efficiently, while also making it easier for users to interact with and enjoy.

Example:

In this example, we'll demonstrate how to optimize token usage by using concise prompts and limiting response length with the max_tokens parameter:

import openai

openai.api_key = "your_api_key"

# Example prompts
prompts = ["Benefits of exercise?",
           "Aerobic vs anaerobic exercise?",
           "How often to exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=30,  # Limit response length
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

3.3.4. Handling Long Conversations

When working with ChatGPT, you may need to handle long conversations with multiple back-and-forth exchanges. To ensure that you stay within rate limits and manage tokens effectively in such scenarios, you can adopt the following strategies:

  1. Truncate or omit less relevant parts

If a conversation exceeds the maximum token limit for a single API call (e.g., 4096 tokens for some engines), you may need to truncate or omit parts of the conversation that are less relevant. However, it is important to note that removing a message might cause the model to lose context about that message. This can lead to inaccurate responses or misunderstandings.

Therefore, it is recommended to carefully consider which parts of the conversation to truncate or omit and to do so in a way that preserves the key ideas and context of the conversation. Additionally, in some cases, it may be useful to split the conversation into multiple API calls to ensure that all the relevant information is included.

By doing so, you can ensure that the model has access to all the necessary context and can provide accurate responses.

Example:

In this example, we truncate the conversation to fit within the token limit:

import openai

openai.api_key = "your_api_key"

def truncate_conversation(conversation, max_tokens):
    tokens = openai.Tokenizer().encode(conversation)
    if len(tokens) > max_tokens:
        tokens = tokens[-max_tokens:]
        truncated_conversation = openai.Tokenizer().decode(tokens)
        return truncated_conversation
    return conversation

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens = 4096

truncated_conversation = truncate_conversation(conversation, max_tokens)

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt=truncated_conversation,
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

print(response.choices[0].text.strip())
  1. Use continuation tokens

To prevent exceeding token limits, it is always a good idea to break long conversations into smaller segments. By using continuation tokens, you can ensure that the conversation can be resumed where it left off, even if it crosses the token limit. When the conversation continues beyond the token limit, you can store the last few tokens from the current response and use them as a starting point for the next API call.

This way, the conversation can continue seamlessly without any interruption or loss of data. It is important to note that using continuation tokens not only helps prevent token limits but also ensures that the conversation is more manageable and easier to work with.

Example:

In this example, we demonstrate breaking a long conversation into smaller segments using continuation tokens:

import openai

openai.api_key = "your_api_key"

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens_per_call = 1000
continuation_length = 5

tokens = openai.Tokenizer().encode(conversation)
num_segments = (len(tokens) + max_tokens_per_call - 1) // max_tokens_per_call

responses = []

for i in range(num_segments):
    start = i * max_tokens_per_call
    end = (i + 1) * max_tokens_per_call

    if i > 0:
        start -= continuation_length

    segment = openai.Tokenizer().decode(tokens[start:end])

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=segment,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )
    responses.append(response.choices[0].text.strip())

print("\n".join(responses))
  1. Minimize tokens in prompts

 It can be beneficial to keep prompts and instructions brief when engaging in conversation in order to preserve tokens for more meaningful content. However, it is important to strike a balance between brevity and thoroughness. By providing clear and detailed prompts and instructions, you can ensure that all necessary information is conveyed and that everyone involved in the conversation is on the same page.

Additionally, taking the time to explain things in depth can help to foster a deeper understanding and promote more productive discussions. Therefore, while it is important to be concise, it is equally important to be thorough and provide enough information to facilitate effective communication.

Example:

In this example, we demonstrate how to minimize tokens in prompts:

import openai

openai.api_key = "your_api_key"

concise_prompts = [
    "Benefits of exercise?",
    "Aerobic vs anaerobic?",
    "How often to exercise?",
]

for prompt in concise_prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

When it comes to managing long conversations, it's important to have a few strategies in place to ensure that you don't run into any issues with rate limits or token usage. One approach is to break up the conversation into smaller, more manageable chunks. This can be done by setting a maximum message length or by limiting the number of messages that can be sent in a given amount of time.

Another strategy is to use more efficient communication methods, such as sending condensed or summarized messages that still convey the main ideas. Additionally, it's important to be aware of any external factors that could impact the conversation, such as network connectivity or server downtime, and to plan accordingly. By implementing these strategies, you can ensure that your long conversations are both effective and efficient, without running into any unnecessary roadblocks or limitations.

3.3. Managing API Rate Limits

When using the ChatGPT API, it's important to be aware of and manage the API rate limits. Rate limiting is a mechanism used by APIs to control the amount of traffic sent to the server at any given time. The ChatGPT API has a limit on the number of requests that can be made in a given time period. Therefore, it's important to use the API efficiently to prevent hitting these limits and avoid any interruptions in service.

One way to manage API rate limits is by implementing caching. Caching stores the API response locally and retrieves it from the cache instead of making a new request to the server. This can help reduce the number of API requests made and, in turn, reduce the likelihood of hitting the rate limits.

Another strategy for efficient API usage is to batch requests. Instead of making multiple requests for each individual task, batching allows you to combine multiple tasks into a single request. This can also help reduce the number of API requests made, which can help prevent hitting the rate limits.

By understanding these strategies and employing them in your use of the ChatGPT API, you can ensure a smooth experience while interacting with the API, even when dealing with large amounts of data.

3.3.1. Understanding Rate Limiting

Rate limiting is a crucial mechanism used by APIs to regulate the number of requests a user can send within a specific time frame. This helps to ensure that OpenAI's services are used in a fair and optimal manner. The rate limits for ChatGPT API can vary depending on your subscription tier and can differ across various engines.

For instance, free trial users are typically provided with a rate limit of 20 requests per minute (RPM) and 40,000 tokens per minute (TPM). However, pay-as-you-go users may have different limits during their first 48 hours, with a rate limit of 60 RPM and 60,000 TPM. After this period, the limits could increase to 3,500 RPM and 90,000 TPM, which is quite a significant difference from the free trial limit.

It's important to note that while these limits may seem restrictive, they are put in place to ensure that the API remains accessible and available for all users. By limiting the number of requests that can be made, OpenAI can better manage the resources available to them and provide a smoother experience to their users.

Example:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    response = None
    while response is None:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

    return response.choices[0].text.strip()

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

This example demonstrates how to handle a RateLimitError when calling the ChatGPT API. When the rate limit is exceeded, the program prints a message and waits for the recommended time before retrying the request.

3.3.2. Strategies for Efficient API Usage

To manage rate limits effectively and make the most of your available tokens, consider the following strategies:

Batching requests

If you have multiple prompts to process, you can use the n parameter to generate multiple responses in a single API call. This can help you reduce the number of requests and make better use of your available rate limit.

Additionally, batching requests can help reduce the amount of time it takes to process a large number of prompts. By sending multiple prompts in a single API call, you can streamline your workflow and improve your overall efficiency.

Furthermore, using the n parameter can also help you better manage your resources. Instead of making multiple API calls and potentially exceeding your rate limit, you can consolidate your requests and make more efficient use of your available resources. This can be especially useful if you are working with a large dataset or processing a high volume of prompts.

In summary, batching requests using the n parameter is a powerful technique for improving your workflow and making better use of your available resources. By consolidating multiple prompts into a single API call, you can save time, reduce the number of requests you need to make, and improve your overall efficiency.

Handling rate limit errors

When making requests to an API, it is important to keep in mind that the server might limit the number of requests you can make over a certain period of time. If you exceed this limit, the API will return a 429 Too Many Requests error. In order to avoid this error, it is important to implement error handling in your code that can intelligently deal with these rate limit errors.

One way to do this is to catch the 429 Too Many Requests error and pause for an appropriate duration before retrying the request. An appropriate duration can be calculated based on the rate limit information provided by the API. Some APIs might return the duration of the rate limit as part of the error response, while others might require you to make a separate request to retrieve this information.

Another way to deal with rate limit errors is to implement a queuing system that can throttle your requests to ensure that you don't exceed the rate limit. This can be especially useful if you need to make a large number of requests or if you are working with a slow API that requires long pauses between requests.

Regardless of the method you choose to deal with rate limit errors, it is important to make sure that your code is robust and can handle unexpected errors that might arise. By implementing error handling and rate limiting strategies, you can ensure that your code is reliable and can handle the demands of working with APIs over the long term.

Here's an example of handling rate limit errors using Python and the time module:

import openai
import time

openai.api_key = "your_api_key"

def generate_text(prompt):
    while True:
        try:
            response = openai.Completion.create(
                engine="text-davinci-002",
                prompt=prompt,
                max_tokens=50,
                n=1,
                stop=None,
                temperature=0.5,
            )
            return response.choices[0].text.strip()
        except openai.error.RateLimitError as e:
            print(f"Rate limit exceeded. Retrying in {e.retry_after} seconds.")
            time.sleep(e.retry_after + 1)

generated_text = generate_text("What are the benefits of exercise?")
print(generated_text)

Here's another code example that demonstrates a simple technique to track the number of tokens used in your requests to avoid exceeding your tokens per minute (TPM) limit:

import openai

openai.api_key = "your_api_key"

def count_tokens(text):
    return len(openai.Tokenizer().encode(text))

def generate_text(prompt, token_budget):
    tokens_used = count_tokens(prompt)

    if tokens_used > token_budget:
        print("Token budget exceeded.")
        return None

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used += response.choices[0].usage["total_tokens"]

    if tokens_used > token_budget:
        print("Token budget exceeded after generating response.")
        return None

    return response.choices[0].text.strip(), tokens_used

token_budget = 10000
prompt = "What are the benefits of exercise?"
generated_text, tokens_used = generate_text(prompt, token_budget)

if generated_text is not None:
    print(f"Generated text: {generated_text}")
    print(f"Tokens used: {tokens_used}")

In this example, we define a token_budget to represent the maximum number of tokens we want to use in a certain period. We then use the count_tokens function to count the tokens in both the prompt and the response. If the combined tokens exceed our budget, we print a message and return None.

Token tracking is a crucial aspect of managing your token usage, especially if you're working with TPM limits. By tracking your tokens, you can keep a closer eye on your token usage and prevent accidental overuse.

Furthermore, you can identify patterns in your token usage and optimize your code accordingly. This can help you not only stay under your TPM limit, but also improve the performance of your code. Overall, token tracking is a simple yet powerful tool that can make a big difference in your token usage and overall code quality.

3.3.3. Monitoring and Managing Token Usage

Tracking your token usage is one of the most important things you can do to ensure that you are using APIs effectively. By carefully monitoring your token usage, you can avoid the risk of encountering unexpected errors caused by exceeding rate limits, which can cause significant delays and even result in the temporary suspension of your account.

In addition, taking the time to understand how your API tokens are being used can help you to identify areas where your application may be overutilizing certain APIs, allowing you to fine-tune your usage and optimize performance.

Overall, making a habit of tracking your token usage is a simple yet effective way to ensure that you are getting the most out of your API integration and avoiding any potential issues down the line.

Here are a few tips to help you monitor and manage your token usage:

  1. Check token usage in API responses

To ensure you have a clear understanding of your token consumption when using the ChatGPT API, the response object includes a usage attribute that provides detailed information on token usage. This attribute can be accessed by users to monitor their token usage, and ensure they have sufficient tokens available for their needs. By keeping a close eye on token usage, users can ensure they have the necessary resources to use the ChatGPT API effectively and efficiently, without running into any issues or limitations.

Example:

import openai

openai.api_key = "your_api_key"

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt="What are the benefits of exercise?",
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

tokens_used = response.choices[0].usage["total_tokens"]
print(f"Tokens used: {tokens_used}")
  1. Implement token usage alerts

One of the most important things to do when working with tokens is to set up alerts that tell you when your token usage approaches a certain threshold. By doing this, you can avoid hitting rate limits unexpectedly and proactively manage your consumption.

There are several ways to set up these alerts, including email notifications or automated messages in your code. You can also consider creating a dashboard that provides real-time information about your token usage, so you can quickly identify any potential issues.

Additionally, it's important to regularly review your token usage and adjust your alerts as needed. By taking these steps, you can ensure that your token usage is always optimized and that you have the information you need to make informed decisions about your API integration.

Example:

In this example, we'll set up an alert to notify when the total token usage reaches a certain threshold:

import openai

openai.api_key = "your_api_key"

# Set a token usage threshold
token_threshold = 10000
total_tokens_used = 0

# Example prompts
prompts = ["What are the benefits of exercise?",
           "What is the difference between aerobic and anaerobic exercise?",
           "How often should one exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    tokens_used = response.choices[0].usage["total_tokens"]
    total_tokens_used += tokens_used

    # Check if the total token usage exceeds the threshold
    if total_tokens_used >= token_threshold:
        print(f"Token usage threshold reached: {total_tokens_used}/{token_threshold}")

    print(f"Response: {response.choices[0].text.strip()}")
  1. Optimize token usage

One thing that can really help when designing your application is taking a close look at your prompts and responses. By optimizing these to be more concise, you can help to minimize the number of tokens used in each request.

For instance, you might consider using shorter prompts or carefully setting max_tokens values that will limit the length of each response. This can help to ensure that your application is running smoothly and efficiently, while also making it easier for users to interact with and enjoy.

Example:

In this example, we'll demonstrate how to optimize token usage by using concise prompts and limiting response length with the max_tokens parameter:

import openai

openai.api_key = "your_api_key"

# Example prompts
prompts = ["Benefits of exercise?",
           "Aerobic vs anaerobic exercise?",
           "How often to exercise?"]

for prompt in prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=30,  # Limit response length
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

3.3.4. Handling Long Conversations

When working with ChatGPT, you may need to handle long conversations with multiple back-and-forth exchanges. To ensure that you stay within rate limits and manage tokens effectively in such scenarios, you can adopt the following strategies:

  1. Truncate or omit less relevant parts

If a conversation exceeds the maximum token limit for a single API call (e.g., 4096 tokens for some engines), you may need to truncate or omit parts of the conversation that are less relevant. However, it is important to note that removing a message might cause the model to lose context about that message. This can lead to inaccurate responses or misunderstandings.

Therefore, it is recommended to carefully consider which parts of the conversation to truncate or omit and to do so in a way that preserves the key ideas and context of the conversation. Additionally, in some cases, it may be useful to split the conversation into multiple API calls to ensure that all the relevant information is included.

By doing so, you can ensure that the model has access to all the necessary context and can provide accurate responses.

Example:

In this example, we truncate the conversation to fit within the token limit:

import openai

openai.api_key = "your_api_key"

def truncate_conversation(conversation, max_tokens):
    tokens = openai.Tokenizer().encode(conversation)
    if len(tokens) > max_tokens:
        tokens = tokens[-max_tokens:]
        truncated_conversation = openai.Tokenizer().decode(tokens)
        return truncated_conversation
    return conversation

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens = 4096

truncated_conversation = truncate_conversation(conversation, max_tokens)

response = openai.Completion.create(
    engine="text-davinci-002",
    prompt=truncated_conversation,
    max_tokens=50,
    n=1,
    stop=None,
    temperature=0.5,
)

print(response.choices[0].text.strip())
  1. Use continuation tokens

To prevent exceeding token limits, it is always a good idea to break long conversations into smaller segments. By using continuation tokens, you can ensure that the conversation can be resumed where it left off, even if it crosses the token limit. When the conversation continues beyond the token limit, you can store the last few tokens from the current response and use them as a starting point for the next API call.

This way, the conversation can continue seamlessly without any interruption or loss of data. It is important to note that using continuation tokens not only helps prevent token limits but also ensures that the conversation is more manageable and easier to work with.

Example:

In this example, we demonstrate breaking a long conversation into smaller segments using continuation tokens:

import openai

openai.api_key = "your_api_key"

conversation = "A long conversation that exceeds the maximum token limit..."
max_tokens_per_call = 1000
continuation_length = 5

tokens = openai.Tokenizer().encode(conversation)
num_segments = (len(tokens) + max_tokens_per_call - 1) // max_tokens_per_call

responses = []

for i in range(num_segments):
    start = i * max_tokens_per_call
    end = (i + 1) * max_tokens_per_call

    if i > 0:
        start -= continuation_length

    segment = openai.Tokenizer().decode(tokens[start:end])

    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=segment,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )
    responses.append(response.choices[0].text.strip())

print("\n".join(responses))
  1. Minimize tokens in prompts

 It can be beneficial to keep prompts and instructions brief when engaging in conversation in order to preserve tokens for more meaningful content. However, it is important to strike a balance between brevity and thoroughness. By providing clear and detailed prompts and instructions, you can ensure that all necessary information is conveyed and that everyone involved in the conversation is on the same page.

Additionally, taking the time to explain things in depth can help to foster a deeper understanding and promote more productive discussions. Therefore, while it is important to be concise, it is equally important to be thorough and provide enough information to facilitate effective communication.

Example:

In this example, we demonstrate how to minimize tokens in prompts:

import openai

openai.api_key = "your_api_key"

concise_prompts = [
    "Benefits of exercise?",
    "Aerobic vs anaerobic?",
    "How often to exercise?",
]

for prompt in concise_prompts:
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=prompt,
        max_tokens=50,
        n=1,
        stop=None,
        temperature=0.5,
    )

    print(f"Response: {response.choices[0].text.strip()}")

When it comes to managing long conversations, it's important to have a few strategies in place to ensure that you don't run into any issues with rate limits or token usage. One approach is to break up the conversation into smaller, more manageable chunks. This can be done by setting a maximum message length or by limiting the number of messages that can be sent in a given amount of time.

Another strategy is to use more efficient communication methods, such as sending condensed or summarized messages that still convey the main ideas. Additionally, it's important to be aware of any external factors that could impact the conversation, such as network connectivity or server downtime, and to plan accordingly. By implementing these strategies, you can ensure that your long conversations are both effective and efficient, without running into any unnecessary roadblocks or limitations.