clank-so-openinference-instrumentation-dspy 0.1.10

OpenInference DSPy Instrumentation (clank.so fork)

OpenInference DSPy Instrumentation

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 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 DSPyInstrumentor to trace your DSPy application and sends the traces to Phoenix at the endpoint defined below.

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 your 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 that utilizes the ChainOfThought module to perform step-by-step reasoning to generate answers.

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 using the BootstrapFewShotWithRandomSearch 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 on the dev dataset.

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

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

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