openinference
OpenInference Instructor Instrumentation
Python auto-instrumentation library for the (Instructor)[https://github.com/jxnl/instructor] library
Installation
pip install openinference-instrumentation-instructor
Quickstart
This quickstart shows you how to instrument Instructor
Install required packages.
pip install instructor arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp
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.)
python -m phoenix.server.main serve
Set up InstructorInstrumentor to trace your application and send the traces to Phoenix at the endpoint defined below.
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import SimpleSpanProcessor
endpoint = "http://127.0.0.1:6006/v1/traces"
tracer_provider = TracerProvider()
tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint)))
from openinference.instrumentation.instructor import InstructorInstrumentor
from openinference.instrumentation.openai import OpenAIInstrumentor
InstructorInstrumentor().instrument(tracer_provider=tracer_provider)
# Optionally instrument the OpenAI SDK to get additional observability
OpenAIInstrumentor().instrument(tracer_provider=tracer_provider)
Simple Instructor example
import instructor
from pydantic import BaseModel
from openai import OpenAI
# Define your desired output structure
class UserInfo(BaseModel):
name: str
age: int
# Patch the OpenAI client
client = instructor.from_openai(OpenAI())
# Extract structured data from natural language
user_info = client.chat.completions.create(
model="gpt-3.5-turbo",
response_model=UserInfo,
messages=[{"role": "user", "content": "John Doe is 30 years old."}],
)