is a data framework for your LLM application. It's a powerful framework by which you can build an application that leverages RAG (retrieval-augmented generation) to super-charge an LLM with your own data. RAG is an extremely powerful LLM application model because it lets you harness the power of LLMs such as OpenAI's GPT but tuned to your data and use-case.
For LlamaIndex, tracing instrumentation is added via an OpenTelemetry instrumentor aptly named the LlamaIndexInstrumentor . This callback is what is used to create spans and send them to the Phoenix collector.
Launch Phoenix
Sign up for Phoenix:
Phoenix Developer Edition is another name for LlamaTrace
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint and API Key:
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"
Launch your local Phoenix instance:
pip install arize-phoenix
phoenix serve
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint:
import os
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"
docker pull arizephoenix/phoenix:latest
Run your containerized instance:
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"
Initialize the LlamaIndexInstrumentor before your application code.
from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from phoenix.otel import register
tracer_provider = register()
LlamaIndexInstrumentor().instrument(tracer_provider=tracer_provider)
Run LlamaIndex
You can now use LlamaIndex as normal, and tracing will be automatically captured and sent to your Phoenix instance.
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
import os
os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("Some question about the data should go here")
print(response)
Observe
View your traces in Phoenix:
Resources
Legacy Integrations (<0.10.43)
Legacy One-Click (<0.10.43)
Using phoenix as a callback requires an install of `llama-index-callbacks-arize-phoenix>0.1.3'
llama-index 0.10 introduced modular sub-packages. To use llama-index's one click, you must install the small integration first:
# Phoenix can display in real time the traces automatically
# collected from your LlamaIndex application.
import phoenix as px
# Look for a URL in the output to open the App in a browser.
px.launch_app()
# The App is initially empty, but as you proceed with the steps below,
# traces will appear automatically as your LlamaIndex application runs.
from llama_index.core import set_global_handler
set_global_handler("arize_phoenix")
# Run all of your LlamaIndex applications as usual and traces
# will be collected and displayed in Phoenix.
Legacy (<0.10.0)
If you are using an older version of llamaIndex (pre-0.10), you can still use phoenix. You will have to be using arize-phoenix>3.0.0 and downgrade openinference-instrumentation-llama-index<1.0.0
# Phoenix can display in real time the traces automatically
# collected from your LlamaIndex application.
import phoenix as px
# Look for a URL in the output to open the App in a browser.
px.launch_app()
# The App is initially empty, but as you proceed with the steps below,
# traces will appear automatically as your LlamaIndex application runs.
import llama_index
llama_index.set_global_handler("arize_phoenix")
# Run all of your LlamaIndex applications as usual and traces
# will be collected and displayed in Phoenix.
Phoenix supports LlamaIndex's latest paradigm. This paradigm requires LlamaIndex >= 0.10.43. For legacy support, see below.
Sign up for an Arize Phoenix account at or
Your Phoenix API key can be found on the Keys section of your .
For details on customizing a local terminal deployment, see .
See for more details
Pull latest Phoenix image from:
For more info on using Phoenix with Docker, see .
By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See or use one of the other deployment options to retain traces.
