elasticdash 0.0.4

A client library for accessing elasticdash

ElasticDash Python SDK

Installation

pip install elasticdash

Then set your ElasticDash credentials as environment variables so the SDK knows who you are.

ELASTICDASH_SECRET_KEY = "sk-lf-..."
ELASTICDASH_PUBLIC_KEY = "pk-lf-..."
ELASTICDASH_BASE_URL = "https://devserver-logger.elasticdash.com"

At the moment, the Secret Key and Public Key will be provided by ElasticDash directly.

Usage

OpenAI SDK

Swap the regular OpenAI import to Langfuse’s OpenAI drop-in. It behaves like the regular OpenAI client while also recording each call for you.

from elasticdash.openai import openai

Use the OpenAI SDK as you normally would. The wrapper captures the prompt, model and output and forwards everything to ElasticDash.

completion = openai.chat.completions.create(
  name="test-chat",
  model="gpt-4o",
  messages=[
      {"role": "system", "content": "You are a very accurate calculator. You output only the result of the calculation."},
      {"role": "user", "content": "1 + 1 = "}],
  metadata={"someMetadataKey": "someValue"},
)

Manual Observation

This can help you cover the LLM models that we are not currently supporting.

from elasticdash import get_client
 
elasticdash = get_client()
 
# Create a span using a context manager
with elasticdash.start_as_current_observation(as_type="span", name="process-request") as span:
    # Your processing logic here
    span.update(input="Llm input here")
 
    # Create a nested generation for an LLM call
    with elasticdash.start_as_current_observation(as_type="generation", name="llm-response", model="gpt-3.5-turbo") as generation:
        generation.update(input="Llm input here")
        # Your LLM call logic here
        span.update(output="Processing complete")
        generation.update(output="Generated response")
 
# All spans are automatically closed when exiting their context blocks
 
 
# Flush events in short-lived applications
elasticdash.flush()