nebuly 0.2.2

The SDK for instrumenting applications for tracking AI costs.

Nebuly SDK

The SDK for instrumenting applications for tracking AI costs.

TODO

1. Nice to have: semantic versioning expansion in package (maybe OSS library or Poetry?)
2. Check the publisher doens't crash, and if it does re start it somehow
3. batch processing
4. if publishing message fails retry (discuss how many retries before dropping the message)
5. limit queue size (discuss how long it should be and what to do if it gets full)

Design

classDiagram
    Init --> Config
    Config --> Package
    Init --> MonkeyPatching
    MonkeyPatching --> Observer_T
    Init --> Observer
    Init --> Consumer
    Init --> DevelopmentPhase
    Init --> SpanWatch
    Init --> Observer_T
    MonkeyPatching --> Package
    MonkeyPatching --> SpanWatch
    Observer --> DevelopmentPhase
    Observer --> SpanWatch
    Observer --> Publisher_T
    Observer --> SpanWatch
    Consumer --> SpanWatch
    Consumer --> Publisher_T
    Observer --|> Observer_T
    Consumer --|> Publisher_T
    Publisher_T --> SpanWatch
    Observer_T --> SpanWatch
    <<Interface>> Publisher_T
    <<Interface>> Observer_T
    <<Entity>> Package
    <<Entity>> SpanWatch
    <<Enum>> DevelopmentPhase

Code Quality Checks

This section provides guidelines and instructions on how to perform code quality checks using Black as a formatter, **Ruff ** as a linter, and **SonarCloud ** as a code quality checker. These tools assist in ensuring that the codebase adheres to a consistent style, follows best practices, and meets predefined quality standards.

Setup

To set up the code quality checks for this project:

1. Clone the repository
2. Run the setup command to install the necessary requirements, including Poetry for handling dependencies

make setup

Code Formatting and Linting

The code formatting and linting checks help maintain consistent style and identify potential issues. Black and Ruff are automatically invoked with each commit, but they can also be utilized independently without committing changes:

• To display the issues detected by the linter

make lint

• To automatically apply the formatter changes and the suggested changes by the linter, use the following command

make lint-fix

Supported Providers

- OpenAI
- Azure OpenAI
- Cohere
- Anthropic
- HuggingFace pipelines
- HuggingFace HUB
- LangChain
- Amazon Bedrock
- Amazon SageMaker
- Google PALM API
- Google VertexAI

Usage

Make sure you initialize Nebuly before importing other libraries like openai, cohere, huggingface, etc.

Simple usage

In the simple case, you can just import nebuly and call the init function with your API key. This will automatically setup all the tracking for you. After that, you can call the other libraries as normal.

Example with OpenAI

import os
import nebuly

api_key = os.getenv("NEBULY_API_KEY")
nebuly.init(api_key=api_key)

import openai

openai.api_key = os.getenv("OPENAI_API_KEY")
completion = openai.ChatCompletion.create(
    model="gpt-3.5-turbo",
    messages=[
        {"role": "user", "content": "Hello world"}
    ],
    user_id="test_user",
    user_group_profile="test_group",
)

Example with LangChain

import os
import nebuly

api_key = os.getenv("NEBULY_API_KEY")
nebuly.init(api_key=api_key)

# Setup LangChain
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)

chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run(
    "colorful socks",
    user_id="test_user",
    user_group_profile="test_group",
)

Advanced usage: Context managers

In the simple case, each call will be stored as a separate Interaction, you can use context managers to group more calls in a single Interaction:

Example with OpenAI and Cohere

import os
import nebuly
from nebuly.contextmanager import new_interaction

api_key = os.getenv("NEBULY_API_KEY")
nebuly.init(api_key=api_key)

# Setup OpenAI
import openai

openai.api_key = os.getenv("OPENAI_API_KEY")

# Setup Cohere
import cohere

co = cohere.Client(os.getenv("COHERE_API_KEY"))

with new_interaction(user_id="test_user", user_group_profile="test_group") as interaction:
    # interaction.set_input("Some custom input")
    # interaction.set_history([{"role": "user/assistant", "content": "sample content"}}])
    completion_1 = openai.ChatCompletion.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system", "content": "You are an helpful assistant"},
            {"role": "user", "content": "Hello world"}
        ]
    )
    completion_2 = co.generate(
        prompt='Please explain to me how LLMs work',
    )
    # interaction.set_output("Some custom output")

Example with LangChain

import os
import nebuly
from nebuly.contextmanager import new_interaction

api_key = os.getenv("NEBULY_API_KEY")
nebuly.init(api_key=api_key)

# Setup LangChain
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

llm = OpenAI(temperature=0.9)
prompt = PromptTemplate(
    input_variables=["product"],
    template="What is a good name for a company that makes {product}?",
)
chain = LLMChain(llm=llm, prompt=prompt)

with new_interaction(user_id="test_user", user_group_profile="test_group") as interaction:
    interaction.set_input("What is a good name for a company that makes colorful socks?")
    # interaction.set_history(...)
    result = chain.run("colorful socks")
    interaction.set_output("colorful socks spa")