import os
# Add Phoenix API Key for tracing
PHOENIX_API_KEY = "ADD YOUR API KEY"
os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={PHOENIX_API_KEY}"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com"
Your Phoenix API key can be found on the Keys section of your dashboard.
Launch your local Phoenix instance:
pip install arize-phoenix
phoenix serve
For details on customizing a local terminal deployment, see Terminal Setup.
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
docker run -p 6006:6006 arizephoenix/phoenix:latest
This will expose the Phoenix on localhost:6006
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
For more info on using Phoenix with Docker, see Docker
Install packages:
pip install arize-phoenix
Launch Phoenix:
import phoenix as px
px.launch_app()
By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See Persistence or use one of the other deployment options to retain traces.
Use the register function to connect your application to Phoenix:
from phoenix.otel import register
# configure the Phoenix tracer
tracer_provider = register(
project_name="my-llm-app", # Default is 'default'
auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)
Run Haystack
Phoenix's auto-instrumentor collects any traces from Haystack Pipelines. If you are using Haystack but not using Pipelines, you won't see traces appearing in Phoenix automatically.
If you don't want to use Haystack pipelines but still want tracing in Phoenix, you can use Instrument: Python instead of this auto-instrumentor.
From here, you can set up your Haystack app as normal:
from haystack import Pipeline
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders.prompt_builder import PromptBuilder
prompt_template = """
Answer the following question.
Question: {{question}}
Answer:
"""
# Initialize the pipeline
pipeline = Pipeline()
# Initialize the OpenAI generator component
llm = OpenAIGenerator(model="gpt-3.5-turbo")
prompt_builder = PromptBuilder(template=prompt_template)
# Add the generator component to the pipeline
pipeline.add_component("prompt_builder", prompt_builder)
pipeline.add_component("llm", llm)
pipeline.connect("prompt_builder", "llm")
# Define the question
question = "What is the location of the Hanging Gardens of Babylon?"
Observe
Now that you have tracing setup, all invocations of pipelines will be streamed to your running Phoenix for observability and evaluation.
