dokumetry 0.0.3

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

Doku Python SDK - dokumetry

Doku Python SDK (dokumetry) empowers you to effortlessly track and monitor language learning model (LLM) usage data and metrics from your Python code. It seamlessly integrates with major LLM Platforms:

• ✅ OpenAI
• ✅ Anthropic
• ✅ Cohere

All LLM observability usage data is sent directly to the Doku Platform for streamlined tracking. Get started with Doku Python SDK for simplified and effective observability.

Features

• User-friendly UI Logs: Log all your LLM requests in just two lines of code.

• Cost and Latency Tracking: Track costs and latencies based on users and custom properties for better analysis.

• Prompt and Response Feedback: Iterate on prompts and chat conversations directly in the UI.

• Collaboration and Sharing: Share results and collaborate with friends or teammates for more effective teamwork.

• Very Low Latency Impact We know latency of your Large-Language Model usage is important to your application's success, that's why we designed Doku SDKs to impact latency as little as possible.

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 URL and API key and this will make sure that all OpenAI calls are automatically tracked.
dokumetry.init(llm=client, doku_url="YOUR_DOKU_URL", api_key="YOUR_DOKU_TOKEN")

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

Anthropic

from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
import dokumetry

anthropic = Anthropic(
    # defaults to os.environ.get("ANTHROPIC_API_KEY")
    api_key="my api key",
)

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

completion = anthropic.completions.create(
    model="claude-2",
    max_tokens_to_sample=300,
    prompt=f"{HUMAN_PROMPT} What is LLM Observability?{AI_PROMPT}",
)
print(completion.completion)

Cohere

import cohere
import dokumetry

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

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

# generate a prediction for a prompt
prediction = co.chat(message='What is LLM Observability?', 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 GPL-3.0 License.

Support

For support, issues, or feature requests, submit an issue through the GitHub issues associated with this repository.