Skip to main content
Python auto-instrumentation library for DSPy. 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-dspy

​
Quickstart

This quickstart shows you how to instrument your DSPy application. It is adapted from the DSPy quickstart. Install required packages: 
pip install openinference-instrumentation-dspy dspy-ai arize-phoenix opentelemetry-sdk opentelemetry-exporter-otlp

​
Start Phoenix server

Start Phoenix in the background as a collector. By default, it listens on http://localhost:6006: 
python -m phoenix.server.main serve
(Phoenix does not send data over the internet. It only operates locally on your machine.)

​
Setup instrumentation

Set up DSPyInstrumentor to trace your DSPy application and send traces to Phoenix: 
from openinference.instrumentation.dspy import DSPyInstrumentor
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)))

DSPyInstrumentor().instrument()

​
Import DSPy and configure language model

import dspy
from dspy.datasets.gsm8k import GSM8K, gsm8k_metric

turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct', max_tokens=250)
dspy.settings.configure(lm=turbo)
gms8k = GSM8K()
gsm8k_trainset, gsm8k_devset = gms8k.train[:10], gms8k.dev[:10]

​
Define a custom program

Define a program that utilizes the ChainOfThought module to perform step-by-step reasoning: 
class CoT(dspy.Module):
    def __init__(self):
        super().__init__()
        self.prog = dspy.ChainOfThought("question -> answer")
    
    def forward(self, question):
        return self.prog(question=question)

​
Optimize your program

Optimize your program using the BootstrapFewShot teleprompter: 
from dspy.teleprompt import BootstrapFewShot

config = dict(max_bootstrapped_demos=4, max_labeled_demos=4)
teleprompter = BootstrapFewShot(metric=gsm8k_metric, **config)
optimized_cot = teleprompter.compile(CoT(), trainset=gsm8k_trainset, valset=gsm8k_devset)

​
Evaluate performance

from dspy.evaluate import Evaluate

evaluate = Evaluate(devset=gsm8k_devset, metric=gsm8k_metric, num_threads=4, display_progress=True, display_table=0)
evaluate(optimized_cot)
Visit the Phoenix app at http://localhost:6006 to see your traces.

​
More Info

Build docs developers (and LLMs) love

Get started for freeTalk to us