dokumetry 0.1.2

A Python library for tracking LLM and GenAI usage and sending the usage data to Doku

Doku Python SDK - dokumetry

Doku Python SDK (dokumetry) is your workhorse for collecting and transmitting language learning model (LLM) usage data and metrics with zero added latency. Simplicity is at the core of dokumetry, enabling you to kickstart comprehensive LLM observability with just two lines of code. It’s designed to blend seamlessly into your projects, supporting integration with leading LLM platforms:

• ✅ OpenAI
• ✅ Anthropic
• ✅ Cohere
• ✅ Mistral
• ✅ Azure OpenAI

Deployed as the backbone for all your LLM monitoring needs, dokumetry channels crucial usage data directly to Doku, streamlining the tracking process. Unlock efficient and effective observability for your LLM applications with DokuMetry.

🔥 Features

• Effortless Integration: With dokumetry, observability comes easy. Elevate your LLM observability by integrating this powerhouse into your projects using just two lines of code.

• Zero Latency Impact: We value the performance of your applications. dokumetry is engineered to capture and send data without hampering your application’s speed, ensuring a seamless user experience.

• • Customizable Data Labeling: Enhance your LLM analytics with customizable environment and application tags. dokumetry allows you to append these labels to your data, offering you the capability to sift through your observability data with ease. Drill down and view metrics in Doku, segmented by these specific tags for a more insightful analysis.

💿 Installation

pip install dokumetry

⚡️ Quick Start

OpenAI

from openai import OpenAI
import dokumetry

client = OpenAI(
    api_key="YOUR_OPENAI_KEY"
)

# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_INGESTER_DOKU_URL", api_key="YOUR_DOKU_TOKEN")

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "What is LLM Observability and Monitoring?",
        }
    ],
    model="gpt-3.5-turbo",
)

Anthropic

from anthropic import Anthropic
import dokumetry

client = Anthropic(
  # This is the default and can be omitted
  api_key="YOUR_ANTHROPIC_API_KEY",
)

# Pass the above `client` object along with your Doku Ingester URL and API key and this will make sure that all Anthropic calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_INGESTER_DOKU_URL", api_key="YOUR_DOKU_TOKEN")

message = client.messages.create(
  max_tokens=1024,
  messages=[
      {
          "role": "user",
          "content": "What is LLM Observability and Monitoring?",
      }
  ],
  model="claude-3-opus-20240229",
)
print(message.content)

Cohere

import cohere
import dokumetry

# initialize the Cohere Client with an API Key
co = cohere.Client('YOUR_COHERE_API_KEY')

# Pass the above `co` object along with your Doku Ingester URL and API key and this will make sure that all Cohere calls are automatically tracked.
dokumetry.init(llm=co, doku_url="YOUR_INGESTER_DOKU_URL", api_key="YOUR_DOKU_TOKEN")

# generate a prediction for a prompt
prediction = co.chat(message='What is LLM Observability and Monitoring?', model='command')

# print the predicted text
print(f'Chatbot: {prediction.text}')

Supported Parameters

Parameter	Description	Required
llm	Language Learning Model (LLM) Object to track	Yes
doku_url	URL of your Doku Instance	Yes
api_key	Your Doku API key	Yes
environment	Custom environment tag to include in your metrics	Optional
application_name	Custom application name tag for your metrics	Optional
skip_resp	Skip response from the Doku Ingester for faster execution	Optional

Semantic Versioning

This package generally follows SemVer conventions, though certain backwards-incompatible changes may be released as minor versions:

Changes that only affect static types, without breaking runtime behavior. Changes to library internals which are technically public but not intended or documented for external use. (Please open a GitHub issue to let us know if you are relying on such internals). Changes that we do not expect to impact the vast majority of users in practice. We take backwards-compatibility seriously and work hard to ensure you can rely on a smooth upgrade experience.

Requirements

Python >= 3.7 is supported.

If you are interested in other runtime environments, please open or upvote an issue on GitHub.

Security

Doku Python Library (dokumetry) sends the observability data over HTTP/HTTPS to the Doku Ingester which uses key based authentication mechanism to ensure the security of your data. Be sure to keep your API keys confidential and manage permissions diligently. Refer to our Security Policy

Contributing

We welcome contributions to the Doku Python Library (dokumetry) project. Please refer to CONTRIBUTING for detailed guidelines on how you can participate.

License

Doku Python Library (dokumetry) is available under the Apache-2.0 license.

Support

For support, issues, or feature requests, submit an issue through the GitHub issues associated with the Doku Repository and add dokumetry-python label.