Connection error on embedding

[andreabrgit]

Hi,

Sunddenly my aplication started generate a error when I call the method chat from ChatEngine.

Retrying llama_index.embeddings.openai.base.OpenAIEmbedding._get_query_embedding.._retryable_get_embedding in 0.8407925745186211 seconds as it raised APIConnectionError: Connection error..

I already try reinstall all packages, run commands of certifieds (pip install --upgrade certifi), but no success.

Anyone could help me? Thank you in advance

[dosubot[bot]]

I found some similar threads that might help you resolve the APIConnectionError issue you're experiencing:

1. Open Issue: Consistent Connection error when using LlamaIndex w/RAG. The suggested solutions include:

   • Checking the status of the OpenAI API to ensure it's operational.
   • Ensuring your API key is valid and has the necessary permissions.
   • Customizing the retry mechanism or adding additional error handling.
   • Double-checking for any network restrictions or firewalls.
   • Ensuring all dependencies are up-to-date and compatible.
   • Enabling debugging in the OpenAI client for more detailed logs.
   • Updating to the latest version of LlamaIndex for any bug fixes or improvements ^[1].

2. Open Issue: APIConnectionError in llama_index.llms.openai.base.OpenAI._achat. Suggested solutions include increasing the timeout and max_retries parameters when initializing the OpenAI class to mitigate connection issues ^[2].

Additionally, you can implement a retry mechanism using the create_retry_decorator function from the utils.py file in the LlamaIndex library. This function handles retry logic for various OpenAI API errors, including APIConnectionError. Here's an example of how to use it:

from llama_index.embeddings.openai.utils import create_retry_decorator
import openai

# Define your retry decorator with desired parameters
retry_decorator = create_retry_decorator(
    max_retries=5,  # Number of retries
    random_exponential=True,  # Use random exponential backoff
    stop_after_delay_seconds=30  # Maximum delay in seconds
)

# Use the retry decorator on your function that calls the OpenAI API
@retry_decorator
def call_openai_chat_method():
    # Your code to call the OpenAI chat method
    response = openai.ChatCompletion.create(
        model="your-model",
        messages=[{"role": "user", "content": "Hello, world!"}]
    )
    return response

# Call the function
try:
    result = call_openai_chat_method()
    print(result)
except openai.APIConnectionError as e:
    print(f"Failed to connect to OpenAI API: {e}")

This setup will attempt to reconnect to the OpenAI API in case of connection errors, using exponential backoff to manage the timing of retries ^[3].

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[andreabrgit]

@dosu

The log contains this information

map_httpcore_exceptions
raise mapped_exc(message) from exc
httpx.ConnectError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000)

[dosubot[bot]]

To resolve the SSL certificate verification error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1000), you can disable SSL certificate verification by setting the verify_ssl_certs parameter to False. Here's an example using the GigaChatLLM class:

from llama_index.llms.gigachat import GigaChatLLM

llm = GigaChatLLM(
    credentials="your_credentials_here",
    verify_ssl_certs=False,  # Disable SSL certificate verification
)
resp = llm.complete("What is the capital of France?")
print(resp)

Keep in mind that disabling SSL verification can expose your application to security risks, such as man-in-the-middle attacks. If possible, consider using a valid certificate or adding the self-signed certificate to your trusted certificate store instead ^[1][2].

To continue talking to Dosu, mention @dosu.

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[andreabrgit]

@dosu

How can I do that on OpenAI for the llama_index.llms.openai OpenAI?

[dosubot[bot]]

I couldn't find specific information on how to disable SSL certificate verification directly within the llama_index.llms.openai module. However, you can typically handle SSL verification issues by configuring the HTTP client used by the module. If the module uses httpx or a similar library, you can create a custom client with SSL verification disabled and pass it to the module, if supported.

Here's a general example using httpx:

import httpx

# Create a custom HTTP client with SSL verification disabled
client = httpx.Client(verify=False)

# Use this client in your API calls, if the module allows passing a custom client
# Example: openai_api_call(client=client)

Keep in mind that disabling SSL verification can expose your application to security risks. It's safer to add the self-signed certificate to your trusted certificate store if possible.

To continue talking to Dosu, mention @dosu.

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