OpenInference Haystack Instrumentation

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

Through your terminal, install required packages.

pip install openinference-instrumentation-haystack haystack-ai arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

You can install Phoenix and start it with the following terminal commands:

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.)

Try the following in a Python file.

Set up HaystackInstrumentor to trace your application and sends the traces to Phoenix at the endpoint defined below.

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 with a template using OpenAIGenerator.

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 on your browser, you should see the traces from your Haystack application. Specifically, you can see attributes from the execution of the OpenAIGenerator.

More Info

Metadata

Release files for openinference-instrumentation-haystack 0.1.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for openinference-instrumentation-haystack 0.1.8
File	Size	Uploaded
openinference_instrumentation_haystack-0.1.8.tar.gz	12.4 kB	Aug 15, 2024	Details

Built distribution (wheel)

Table of built distributions (wheels) for openinference-instrumentation-haystack 0.1.8
File	Interpreter	ABI	Platform	Reset
openinference_instrumentation_haystack-0.1.8-py3-none-any.whl 13.7 kB Aug 15, 2024	Python 3	none	any	Details

Total release size: 26.1 kB

Release files / openinference_instrumentation_haystack-0.1.8.tar.gz

Download URL	openinference_instrumentation_haystack-0.1.8.tar.gz
Size	12.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`93e50b6ffbc210e3bac28ffd0f41f3b01249ec05bc4e1dc38438f66fc2f56214`
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Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.12.4`

Release files / openinference_instrumentation_haystack-0.1.8-py3-none-any.whl

Download URL	openinference_instrumentation_haystack-0.1.8-py3-none-any.whl
Size	13.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`b0fd219701eaedcc2d0147596c14fed3fc3138ba348f0691e8993d1be8ca8213`
BLAKE2b-256 checksum How to use checksums	`4895093a40e985535354f86623e7951598c9ee15639cafded934dbad24a16f5b`
Upload date	Aug 15, 2024
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/5.1.1 CPython/3.12.4`