Troubleshooting Connection Issues with Arize Phoenix on Azure | Arize AI Community

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Troubleshooting Connection Issues with Arize Phoenix on Azure | Arize AI Community

RunLLM
APP
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The errors you're encountering (StatusCode.DEADLINE_EXCEEDED and StatusCode.UNAVAILABLE) suggest that your application is having trouble communicating with the Phoenix instance. Here are a few steps to troubleshoot and resolve the issue:
1. Verify Endpoint Configuration: Ensure that the PHOENIX_COLLECTOR_ENDPOINT is correctly set in your environment variables. The endpoint should include the full URL to the Phoenix server's trace collection endpoint.
2. Check Network Accessibility: Make sure that your application can reach the Phoenix server. You can test this by making a simple HTTP request to the Phoenix server's endpoint from within your application environment.
3. Adjust Timeout Settings: The DEADLINE_EXCEEDED error might be due to a timeout. You can increase the timeout settings for the trace exporter. According to this GitHub issue, you can adjust the timeout parameter.
4. Ensure Correct Instrumentation: Verify that the instrumentation code is correctly set up and that the PHOENIX_COLLECTOR_ENDPOINT is being used properly. Here is an example of how to set up the instrumentation:
```
import os from openai import AzureOpenAI from dotenv import load_dotenv from phoenix.otel import register from openinference.instrumentation.openai import OpenAIInstrumentor from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace.export import SimpleSpanProcessor from opentelemetry.sdk import trace as trace_sdk from opentelemetry.sdk.resources import Resource
load_dotenv()
os.environ['PHOENIX_PROJECT_NAME'] = "phoenix-test" os.environ['PHOENIX_COLLECTOR_ENDPOINT'] = os.getenv("PHOENIX_COLLECTOR_ENDPOINT")
resource = Resource(attributes={}) tracer_provider = trace_sdk.TracerProvider(resource=resource) span_exporter = OTLPSpanExporter(endpoint=os.getenv("PHOENIX_COLLECTOR_ENDPOINT")) tracer_provider.add_span_processor(SimpleSpanProcessor(span_exporter))
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider, skip_dep_check=True)
client = AzureOpenAI( api_key=os.getenv("AZURE_OPENAI_API_KEY"), api_version="2024-02-15-preview", azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT") )
response = client.chat.completions.create( messages=[ { "role": "system", "content": "You are a helpful assistant.", }, { "role": "user", "content": "Write a poem about a phoenix." } ], model="gpt-4o-2024-08-06", temperature=0, max_tokens=200 )
RunLLM
APP
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5. Check Phoenix Server Logs: Look at the logs of the Phoenix server to see if there are any errors or warnings that might indicate why traces are not being collected.
Would you like more detailed steps on any of these points?
Sources: - Phoenix Tracing Core Concepts - Phoenix OpenAI Integration - GitHub Issue on Timeout Error
💡 Hint: Mention RunLLM in the thread for followups.
epistoteles
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No problem! It's the end of my workday in this timezone anyway. I'll check back tomorrow, thank you for your help! :)
1
epistoteles
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This works! Thanks 🙂 Maybe as feedback, I find the combination of a PHOENIX_COLLECTOR_ENDPOINT and COLLECTOR_ENDPOINT that is essentially the same just with /v1/traces at the end a bit unintuitive. In fact my code seems to work even if I remove the PHOENIX_COLLECTOR_ENDPOINT from my .env completely. When I deployed phoenix to Azure I was told to store both variables in the CLI. What is PHOENIX_COLLECTOR_ENDPOINT used for?
epistoteles
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Also, I don't see any kind of authentication happening between my client and the phoenix deployment - can anyone that knows my endpoint URL write traces into my deployment (and, potentially, read all previous conversations or download my datasets via the API)?
Mikyo
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hey epistoteles - on the front of auth we need to update phoenix-on-azure to use Phoenix 5.0's native authentication. It currently relies on basic auth but phoenix has a robust authentication system that can be configured. https://docs.arize.com/phoenix/setup/authentication We will integrate it into the template in the coming weeks but if auth is a big need definitely deploy our containers directly on Azure!