500 An internal error has occurred

[papireddy903]

Description of the bug:

Quickstart in Google Colab went successfully, but when I try to setup this locally. I got an error
InternalServerError: 500 An internal error has occurred. Please retry or report in https://developers.generativeai.google/guide/troubleshooting

Actual vs expected behavior:

It must run locally as it run in Google colab

It ran successfully in colab but not in local jupyter server

Any other information you'd like to share?

No response

[TTMOR]

API Credentials and Jupyter Troubleshooting

Hello @papireddy903! It seems that you are facing issues with your API credentials or authentication method. Let's check the possible solutions:

1. Is your API key in restricted mode?
2. Have you sent a request to check if your API is running? You can also check it on your Google Dashboard.
3. Is your Jupyter notebook open to forward and receive data from the web?
4. Have you set up your notebook as both a client and host?
5. Is the SDK in the same location where you are running Gemini?
6. Do you have the necessary privileges to install on this notebook?
7. What is the Python version you are using?

It's highly likely that the issue lies with your Jupyter and how you are managing it. From the provided screenshot, it appears to be related to your Jupyter connection. Try to:

Credentials Verification in Python and Necessary Libraries:

Before getting started, make sure the required libraries are installed. If you don't have them, install them using the following command in the notebook:

!pip install requests

Use the requests Library to Make an API Call:

Assuming you are using an API that requires an API key, you can use the requests library to make a test call and check if the credentials are valid.

import requests

url = 'https://api.example.com/endpoint'
headers = {
    'Authorization': 'Bearer YOUR_API_KEY_HERE'  # Replace with your API key
}

try:
    response = requests.get(url, headers=headers)

    # Check the response status code
    if response.status_code == 200:
        print('Valid credentials. Successful connection!')
    else:
        print('Error in API call. Status code:', response.status_code)
        print('Response:', response.text)

except Exception as e:
    print('Error during API call:', e)

Local Environment Setup

To run locally, ensure that your development environment meets the following requirements:

• Python 3.9+
• An installation of Jupyter to run the notebook.

Setup

Install the Python SDK

The Python SDK for the Gemini API is contained in the google-generativeai package. Install the dependency using pip:

!pip install google-generativeai

import pathlib
import textwrap

import google.generativeai as genai  # Ensure that the correct module/package name is used
from IPython.display import display
from IPython.display import Markdown

def to_markdown(text):
  text = text.replace('•', '  *')  # Replacing '•' with '  *' for bullet points
  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))

Configure your API and Model

genai.configure(api_key='put your api here')

Models

for m in genai.list_models():
  if 'generateContent' in m.supported_generation_methods:
    print(m.name)

gemini-pro/gemini-pro-vision

model = genai.GenerativeModel('gemini-pro')

Chating

response = model.generate_content("bla bla bla...say something")
to_markdown(response.text)

Done!

If you get the response, its all fine. Be sure to run line per line on Jupyter to check errors!

Regards.
(:

[email protected]

[hafizuriu]

Hi @TTMOR

I faced the same error- 500 An internal error has occurred. Please retry or report in https://developers.generativeai.google/guide/troubleshooting.

Here is my script.

GOOGLE_API_KEY='my api key'

genai.configure(api_key=GOOGLE_API_KEY)

gemini_model = genai.GenerativeModel('gemini-pro')
chat = gemini_model.start_chat()

for i in range(50):
    response = chat.send_message(messages)
    generated_text = response.text
    messages= 'my prompt'

It shows an error after responding for around 15 iterations.

Could you please suggest me a solution?

[TTMOR]

Hello @hafizuriu! Well, this code seems not about only for Genai pro. Lets check it:

if you do not import the parts of the code related to specific libraries or modules, such as google.generativeai and IPython.display, you will encounter issues when trying to execute functions that depend on these libraries. The code may result in import errors or undefined references if the dependencies are not available.

To avoid problems, make sure to import the necessary libraries before executing functions that use them. In your Jupyter Notebook environment, you can create separate cells for importing libraries and then another cell to execute the remaining parts of the code.

execute de code line by line!

here a code that works.

Copy and Paste. Run Line by Line!

Import cell

import pathlib
import textwrap

import google.generativeai as genai
from IPython.display import display
from IPython.display import Markdown

def to_markdown(text):
  text = text.replace('•', '  *')
  return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True))

Line 2

genai.configure(api_key='put your api here')

Line 3

for m in genai.list_models():
  if 'generateContent' in m.supported_generation_methods:
    print(m.name)

Line 4 Now you are start to run " Chat" commands.

model = genai.GenerativeModel('gemini-pro')
chat = model.start_chat(history=[])
chat

<google.generativeai.generative_models.ChatSession at 0x_your_session_ will appear_here>

line 5

response = chat.send_message('Your_questions_here')
to_markdown(response.text)

Done!

If you've reached this point and generated a response, you can proceed to modify your code. In the code you sent, you didn't import the libraries and didn't activate the session. If you are using a Jupyter virtual environment, you need to do it line by line. After being certain, assemble a single code that performs each step together. Don't put separate parts before testing and knowing which dependencies are required.

---

Your code:

GOOGLE_API_KEY='my api key'  ok as Global variable!

genai.configure(api_key=GOOGLE_API_KEY) # ok! But this line will not work alone. Need steps that i said above

gemini_model = genai.GenerativeModel('gemini-pro') #   model=   you are using "gemini_model"= have you tested it yet? 
chat = gemini_model.start_chat()    # ok! 

for i in range(50):             #   loop repeat the process of sending a message, receiving a response, and updating the input  50 times. Have you checked if this number has exceeded the limit per minute?? 

    response = chat.send_message(messages) # ok 
    generated_text = response.text    # ok 
    messages= 'my prompt' # ok 

If this work, just comment here. Good luck!

[hafizuriu]

Thank you so much for your reply. Could you please tell me how I can check if the number of requests I sent exceeded the limit per minute or not? Thanks again for your time and support. Best, Hafizur

…

On Tue, Dec 26, 2023 at 5:48 AM TTMOR ***@***.***> wrote: Hello @hafizuriu <https://github.com/hafizuriu>! Well, this code seems not about only for Genai pro. Lets check it: *if you do not import the parts of the code related to specific libraries or modules*, such as google.generativeai and IPython.display, you will encounter *issues when trying to execute functions that depend on these libraries*. The code may result in import errors or undefined references if the dependencies are not available. To avoid problems, *make sure to import the necessary libraries before executing functions that use them*. In your Jupyter Notebook environment, you can create separate cells for importing libraries and then another cell to execute the remaining parts of the code. *execute de code line by line!* here a code that works. Copy and Paste. Run Line by Line! Import cell import pathlib import textwrap import google.generativeai as genai from IPython.display import display from IPython.display import Markdown def to_markdown(text): text = text.replace('•', ' *') return Markdown(textwrap.indent(text, '> ', predicate=lambda _: True)) Line 2 genai.configure(api_key='put your api here') Line 3 for m in genai.list_models(): if 'generateContent' in m.supported_generation_methods: print(m.name) Line 4 Now you are start to run " Chat" commands. model = genai.GenerativeModel('gemini-pro') chat = model.start_chat(history=[]) chat *<google.generativeai.generative_models.ChatSession at 0x_your_session_ will appear_here>* line 5 response = chat.send_message('Your_questions_here') to_markdown(response.text) Done! If you've reached this point and generated a response, you can proceed to modify your code. In the code you sent, you didn't import the libraries and didn't activate the session. If you are using a Jupyter virtual environment, you need to do it line by line. After being certain, assemble a single code that performs each step together. Don't put separate parts before testing and knowing which dependencies are required. ------------------------------ Your code: GOOGLE_API_KEY='my api key' ok as Global variable! genai.configure(api_key=GOOGLE_API_KEY) # ok! But this line will not work alone. Need steps that i said above gemini_model = genai.GenerativeModel('gemini-pro') # model= you are using "gemini_model"= have you tested it yet? chat = gemini_model.start_chat() # ok! for i in range(50): # loop repeat the process of sending a message, receiving a response, and updating the input 50 times. Have you checked if this number has exceeded the limit per minute?? response = chat.send_message(messages) # ok generated_text = response.text # ok messages= 'my prompt' # ok If this work, just comment here. Good luck! — Reply to this email directly, view it on GitHub <#204 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AU6ONRYJVQO5I62XABGTGO3YLKTPRAVCNFSM6AAAAABA52Q6T6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQNRZGQ2TENZXGI> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[TTMOR]

Hello @hafizuriu! After reading the Rate limits and Token limit, I believe that if you run 50 executions in less than 1 minute, you got HTTP 500 internal Server Error every time!

Max output tokens: Specifies the maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words.

Your code:

for i in range(50):
    response = chat.send_message(messages)
    generated_text = response.text
    messages = 'my prompt'

Now, let's use the provided information for Gemini-Pro:

1. Input token limit: 30720 Tokens
2. Output token limit: 2048 Tokens
3. Rate Limit: 60 requests per minute.

Let's calculate the number of input and output tokens.

1. Number of input tokens:
   {Number of input tokens} = [{Number of characters in the text}/{4} ]

[ {Number of input tokens} = {465}/{4} ~~ approx 116.25tokens.

Since the input is limited to 30720 tokens, you are well below that limit.

2. Number of output tokens:

{Number of output tokens} = {text{Number of characters in the generated text}}/{4}

Now, considering the generated text during the conversation, you need to account for the output tokens as well. Let's continue the calculation based on the length of the generated text that i did today on Gemini-Pro

Me> Hello! Can you help me with some papers about AI?

Model Demini Pro>

1. Attention Is All You Need

• Authors: Vaswani, Ashish et al.
• Abstract: "The Transformer is a novel neural network architecture that lends itself naturally to parallelization, making it scalable to long sequences. Unlike recurrent neural networks (RNNs), which process data sequentially, the Transformer processes all of the data in parallel, attending to different parts of the data as needed. This makes the Transformer more efficient than RNNs for long sequences, and it also allows the Transformer to learn global dependencies in the data."

2. Generative Adversarial Networks

• Authors: Goodfellow, Ian et al.
• Abstract: "Generative adversarial networks (GANs) are a class of deep learning models that can be used to generate new data instances that are indistinguishable from real data. GANs consist of two neural networks, a generator and a discriminator. The generator creates new data instances, and the discriminator tries to distinguish between real and generated data. The two networks are trained in a competitive way, with the generator trying to fool the discriminator and the discriminator trying to correctly identify real and generated data."

3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

• Authors: Devlin, Jacob et al.
• Abstract: "BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can be fine-tuned for a variety of natural language processing tasks. BERT is based on the Transformer architecture, which is a neural network architecture that is particularly well-suited for natural language processing tasks. BERT is pre-trained on a massive corpus of text data, and it learns to represent words and phrases in a way that is useful for a variety of downstream tasks, such as question answering, text classification, and machine translation."

4. Deep Reinforcement Learning with Double Q-learning

• Authors: van Hasselt, Hado et al.
• Abstract: "Q-learning is a reinforcement learning algorithm that can be used to learn optimal policies for sequential decision-making problems. Double Q-learning is a variant of Q-learning that reduces the bias in Q-learning's estimates of the optimal action-value function. This can lead to improved performance in domains where the state space is large or where the rewards are sparse."

5. Learning to Communicate with Deep Multi-Agent Reinforcement Learning

• Authors: Foerster, Jakob et al.
• Abstract: "Multi-agent reinforcement learning (MARL) is a challenging problem in which multiple agents learn to coordinate their actions in order to achieve a common goal. Deep multi-agent reinforcement learning (DMARL) is a subfield of MARL that uses deep neural networks to learn the agents' policies. In this paper, we introduce a new DMARL algorithm called COMMA (Communication-based Multi-Agent Reinforcement Learning). COMMA allows agents to communicate with each other using a shared language, which can help them to coordinate their actions and achieve better performance."

The text has 1633 characters. Given that each token is approximately four characters, we can estimate the number of tokens:

{Number of tokens} = {Total characters}}/Characters per token}}]
{Number of tokens} = {1633}/{4} ~~approx 408.25 Tokens Output.

Therefore, the estimated number of output tokens for the given text is approximately 408 tokens.

If each iteration consumes approximately 36 tokens and the total output was about 408 tokens, we can calculate how many iterations would be possible with that amount of tokens.

{Number of possible iterations} = {Total tokens}}/{Tokens per iteration}}
{Number of possible iterations} = {408}/{36}~~approx 11.33 Iterations.

So, with the generated output of approximately 408 tokens, it would be possible to complete about 11 full iterations and a part of another.

So, no way no how! haha! WE need to talk to Google to increase this rate! And the Model have lots of bugs...

Well, hope that helps!

Good luck bro!

[hafizuriu]

Thank you so much for your explanation on how to calculate the tokens. I'm waiting for the paid version of Gemini where the request limit will be increased. Best, Hafizur

…

On Wed, Dec 27, 2023 at 1:42 PM TTMOR ***@***.***> wrote: Hello @hafizuriu <https://github.com/hafizuriu>! After reading the Rate limits and Token limit, I believe that if you run 50 executions in less than 1 minute, you got HTTP 500 internal Server Error every time! *Max output tokens:* Specifies the maximum number of tokens that can be generated in the response. A token is approximately four characters. 100 tokens correspond to roughly 60-80 words. *Your code:* for i in range(50): response = chat.send_message(messages) generated_text = response.text messages = 'my prompt' Now, let's use the provided information for Gemini-Pro: 1. *Input token limit:* 30720 Tokens 2. *Output token limit:* 2048 Tokens 3. *Rate Limit:* 60 requests per minute. Let's calculate the number of input and output tokens. 1. *Number of input tokens:* {Number of input tokens} = [{Number of characters in the text}/{4} ] [ {Number of input tokens} = {465}/{4} ~~ approx 116.25tokens. Since the input is limited to 30720 tokens, you are well below that limit. 1. *Number of output tokens:* {Number of output tokens} = {text{Number of characters in the generated text}}/{4} Now, considering the generated text during the conversation, you need to account for the output tokens as well. Let's continue the calculation based on the length of the generated text that i did today on Gemini-Pro Me> Hello! Can you help me with some papers about AI? Model Demini Pro> *1. Attention Is All You Need <https://arxiv.org/abs/1706.03762>* - *Authors:* Vaswani, Ashish et al. - *Abstract:* "The Transformer is a novel neural network architecture that lends itself naturally to parallelization, making it scalable to long sequences. Unlike recurrent neural networks (RNNs), which process data sequentially, the Transformer processes all of the data in parallel, attending to different parts of the data as needed. This makes the Transformer more efficient than RNNs for long sequences, and it also allows the Transformer to learn global dependencies in the data." *2. Generative Adversarial Networks <https://arxiv.org/abs/1406.2661>* - *Authors:* Goodfellow, Ian et al. - *Abstract:* "Generative adversarial networks (GANs) are a class of deep learning models that can be used to generate new data instances that are indistinguishable from real data. GANs consist of two neural networks, a generator and a discriminator. The generator creates new data instances, and the discriminator tries to distinguish between real and generated data. The two networks are trained in a competitive way, with the generator trying to fool the discriminator and the discriminator trying to correctly identify real and generated data." *3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>* - *Authors:* Devlin, Jacob et al. - *Abstract:* "BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that can be fine-tuned for a variety of natural language processing tasks. BERT is based on the Transformer architecture, which is a neural network architecture that is particularly well-suited for natural language processing tasks. BERT is pre-trained on a massive corpus of text data, and it learns to represent words and phrases in a way that is useful for a variety of downstream tasks, such as question answering, text classification, and machine translation." *4. Deep Reinforcement Learning with Double Q-learning <https://arxiv.org/abs/1509.06461>* - *Authors:* van Hasselt, Hado et al. - *Abstract:* "Q-learning is a reinforcement learning algorithm that can be used to learn optimal policies for sequential decision-making problems. Double Q-learning is a variant of Q-learning that reduces the bias in Q-learning's estimates of the optimal action-value function. This can lead to improved performance in domains where the state space is large or where the rewards are sparse." *5. Learning to Communicate with Deep Multi-Agent Reinforcement Learning <https://arxiv.org/abs/1706.05296>* - *Authors:* Foerster, Jakob et al. - *Abstract:* "Multi-agent reinforcement learning (MARL) is a challenging problem in which multiple agents learn to coordinate their actions in order to achieve a common goal. Deep multi-agent reinforcement learning (DMARL) is a subfield of MARL that uses deep neural networks to learn the agents' policies. In this paper, we introduce a new DMARL algorithm called COMMA (Communication-based Multi-Agent Reinforcement Learning). COMMA allows agents to communicate with each other using a shared language, which can help them to coordinate their actions and achieve better performance." The text has 1633 characters. Given that each token is approximately four characters, we can estimate the number of tokens: {Number of tokens} = {Total characters}}/Characters per token}}] {Number of tokens} = {1633}/{4} ~~approx 408.25 Tokens Output. Therefore, the estimated number of output tokens for the given text is approximately 408 tokens. If each iteration consumes approximately 36 tokens and the total output was about 408 tokens, we can calculate how many iterations would be possible with that amount of tokens. {Number of possible iterations} = {Total tokens}}/{Tokens per iteration}} {Number of possible iterations} = {408}/{36}~~approx 11.33 Iterations. So, with the generated output of approximately 408 tokens, it would be possible to complete about 11 full iterations and a part of another. So, no way no how! haha! WE need to talk to Google to increase this rate! And the Model have lots of bugs... Well, hope that helps! Good luck bro! — Reply to this email directly, view it on GitHub <#204 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AU6ONRZ2YSHXOQY3PCMV5MLYLRTXVAVCNFSM6AAAAABA52Q6T6VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQNZQGU2DGNJVHA> . You are receiving this because you were mentioned.Message ID: ***@***.***>

[TTMOR]

HI Sir @hafizuriu! you are Welcome! yes, too much bug....wait the better models. For some kind of stuffs actually is good: I have seen some pappers on Enselvier and Sciensce Direct. He did it good. I am using mine to check about. For some reason, when ask complex questions( Kirchhoff, Chebyshev etc...) the model have nice responses. hahah. Regards!

[MarkDaoust]

Hi, also note that we were having capacity issues recently that were generating a lot of 500 errors.

But if you exceed the individual rate limit of 60/minute, you should get some sort of quota-exceeded error, not just a 500.

I'm closing this as a duplicate of #211

[bmounim]

Hi, I had the same issue and I think the solution is to decrease the max_output_tokens to 100-500 or less , I guess the error was due to the model generation limits.

[MarkDaoust]

I guess the error was due to the model generation limits.

The service should still return the response with a clear finish_reason.

[Chrisma-98]

Hi, I had the same issue and I think the solution is to decrease the max_output_tokens to 100-500 or less , I guess the error was due to the model generation limits.

Not work, even max_output_tokens = 5. This appears to be a random error. Maybe due to the server response?

[Jammode]

But if you exceed the individual rate limit of 60/minute, you should get somw sort of quota-exceeded error, not just a 500.

This does not make if clear if the 500 error is a token limit response or a rate limit response?

What response would the rate limit of 60/min provide to distinguish between a token limit?

[Wiselnn570]

Hello, I have been encountering this error recently while using Gemini's API. I would like to know if the quota for the account is still being consumed when this error occurs.

[MarkDaoust]

I would like to know if the quota for the account is still being consumed when this error occurs.

The API should not charge you if it returns an error code like a 500 or a 400.

But note that if the api call succeeds, but the SDK throws the error, that will still charge you (shouldn't happen, but raise an issue if it does).

[naarkhoo]

this seems to be an internal error from Google - just re-run the code.