Skip to main content
Python auto-instrumentation library for LLM applications implemented with Haystack. Haystack Pipelines and Components (ex. PromptBuilder, OpenAIGenerator, etc.) are fully OpenTelemetry-compatible and can be sent to an OpenTelemetry collector for monitoring, such as Arize Phoenix.

​
Installation

pip install openinference-instrumentation-haystack

​
Quickstart

This quickstart shows you how to instrument your Haystack-orchestrated LLM application. Install required packages: 
pip install openinference-instrumentation-haystack haystack-ai arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

​
Start Phoenix server

pip install arize-phoenix
python -m phoenix.server.main serve
Start Phoenix 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. (Phoenix does not send data over the internet. It only operates locally on your machine.)

​
Setup instrumentation

Set up HaystackInstrumentor to trace your application and send traces to Phoenix: 
from openinference.instrumentation.haystack import HaystackInstrumentor
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
import os

# Set your OpenAI API key
os.environ["OPENAI_API_KEY"] = "YOUR_KEY_HERE"

# Set up the tracer, using Arize Phoenix as the endpoint
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)))

# Instrument the Haystack application
HaystackInstrumentor().instrument()

​
Set up a simple Pipeline

from haystack import Pipeline
from haystack.components.generators import OpenAIGenerator

# Initialize the pipeline
pipeline = Pipeline()

# Initialize the OpenAI generator component
llm = OpenAIGenerator(model="gpt-3.5-turbo")

# Add the generator component to the pipeline
pipeline.add_component("llm", llm)

# Define the question
question = "What is the location of the Hanging Gardens of Babylon?"

# Run the pipeline with the question
response = pipeline.run({"llm": {"prompt": question}})

print(response)
Now, on the Phoenix UI in your browser, you should see the traces from your Haystack application. Specifically, you can see attributes from the execution of the OpenAIGenerator.

​
More Info

Build docs developers (and LLMs) love

Get started for freeTalk to us