openinference-instrumentation-llama-index 1.3.0

OpenInference LlamaIndex Instrumentation

OpenInference LlamaIndex Instrumentation

Python auto-instrumentation library for LlamaIndex.

These traces are fully OpenTelemetry compatible and can be sent to an OpenTelemetry collector for viewing, such as arize-phoenix.

Installation

pip install openinference-instrumentation-llama-index

Compatibility

llama-index version	openinference-instrumentation-llama-index version
>=0.10.0	>=1.0.0
<0.10.0, >=0.9.14	0.1.3

Quickstart

Install packages needed for this demonstration.

pip install openinference-instrumentation-llama-index llama-index arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

Start the Phoenix app in the background as a collector. By default, it listens on http://localhost:6006. You can visit the app via a browser at the same address.

The Phoenix app does not send data over the internet. It only operates locally on your machine.

python -m phoenix.server.main serve

The following Python code sets up the LlamaIndexInstrumentor to trace llama-index and send the traces to Phoenix at the endpoint shown below.

from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
from opentelemetry import trace as trace_api
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk import trace as trace_sdk
from opentelemetry.sdk.trace.export import SimpleSpanProcessor

endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = trace_sdk.TracerProvider()
trace_api.set_tracer_provider(tracer_provider)
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))

LlamaIndexInstrumentor().instrument()

To demonstrate tracing, we'll use LlamaIndex below to query a document.

First, download a text file.

import tempfile
from urllib.request import urlretrieve
from llama_index.core import SimpleDirectoryReader

url = "https://raw.githubusercontent.com/Arize-ai/phoenix-assets/main/data/paul_graham/paul_graham_essay.txt"
with tempfile.NamedTemporaryFile() as tf:
    urlretrieve(url, tf.name)
    documents = SimpleDirectoryReader(input_files=[tf.name]).load_data()

Next, we'll query using OpenAI. To do that you need to set up your OpenAI API key in an environment variable.

import os

os.environ["OPENAI_API_KEY"] = "<your openai key>"

Now we can query the indexed documents.

from llama_index.core import VectorStoreIndex

query_engine = VectorStoreIndex.from_documents(documents).as_query_engine()
print(query_engine.query("What did the author do growing up?"))

Visit the Phoenix app at http://localhost:6006 to see the traces.

More Info

More details about tracing with OpenInference and Phoenix can be found in the Phoenix documentation.

For AI/ML observability solutions in production, including a cloud-based trace collector, visit Arize.