LlamaIndex

How to use the python LlamaIndexInstrumentor to trace LlamaIndex

Google Colaboratory

Troubleshooting an LLM application using the OpenInferenceTraceCallback

LlamaIndex 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.

We recommend using llama_index >= 0.11.0

Launch Phoenix

Phoenix supports LlamaIndex's latest instrumentation paradigm. This paradigm requires LlamaIndex >= 0.10.43. For legacy support, see below.

Phoenix Developer Edition

Sign up for Phoenix:

Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login or https://llamatrace.com

Phoenix Developer Edition is another name for LlamaTrace

Install packages:

pip install arize-phoenix-otel

Connect your application to your cloud instance:

import os
from phoenix.otel import register

# 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"

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
) 

Your Phoenix API key can be found on the Keys section of your dashboard.

Command Line

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

Connect your application to your instance using:

from phoenix.otel import register

tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  endpoint="http://localhost:6006/v1/traces",
)

See Quickstart: Deployment for more details

Docker

Pull latest Phoenix image from Docker Hub:

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

Connect your application to your instance using:

from phoenix.otel import register

tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
  endpoint="http://localhost:6006/v1/traces",
)

For more info on using Phoenix with Docker, see Docker

Notebook

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()

Connect your notebook to Phoenix:

from phoenix.otel import register

tracer_provider = register(
  project_name="my-llm-app", # Default is 'default'
)

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.

Install

pip install openinference-instrumentation-llama_index llama-index>=0.11.0

Setup

Initialize the LlamaIndexInstrumentor before your application code.

from openinference.instrumentation.llama_index import LlamaIndexInstrumentor

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

• Example notebook

• Instrumentation Package

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:

pip install 'llama-index-callbacks-arize-phoenix>0.1.3'

# 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.