interactiveai 1.0.7

A client library for accessing InteractiveAI

InteractiveAI Python SDK

The official Python SDK for InteractiveAI - an observability platform for LLM applications. This SDK provides tracing, automatic instrumentation for popular LLM frameworks, and direct API access to InteractiveAI's features.

Installation

From PyPI (Production)

Install the latest stable release:

pip install interactiveai

With optional integrations:

# With OpenAI instrumentation
pip install "interactiveai[openai]"

In requirements.txt:

interactiveai>=4.0.0
# or with extras
interactiveai[openai]>=4.0.0

In pyproject.toml (Poetry):

[tool.poetry.dependencies]
interactiveai = "^4.0.0"
# or with extras
interactiveai = { version = "^4.0.0", extras = ["openai"] }

In pyproject.toml (pip/uv):

[project]
dependencies = [
    "interactiveai>=4.0.0",
]

# or with extras
[project.optional-dependencies]
llm = ["interactiveai[openai]>=4.0.0"]

From GitHub (Internal Development)

Install a specific tagged version:

pip install git+https://github.com/InteractiveAI/[e-mail]1

Install from the main branch:

pip install git+https://github.com/InteractiveAI/interactiveai-python-sdk.git@main

In requirements.txt:

# Specific tag
interactiveai @ git+https://github.com/InteractiveAI/[e-mail]1

# Main branch (latest)
interactiveai @ git+https://github.com/InteractiveAI/interactiveai-python-sdk.git@main

In pyproject.toml (Poetry):

[tool.poetry.dependencies]
# Specific tag
interactiveai = { git = "https://github.com/InteractiveAI/interactiveai-python-sdk.git", tag = "v4.1.0.dev1" }

# Main branch
interactiveai = { git = "https://github.com/InteractiveAI/interactiveai-python-sdk.git", branch = "main" }

Quick Start

Basic Usage

from interactiveai import Interactive

# Initialize the client (uses environment variables by default)
client = Interactive()

# Create a trace
trace = client.trace(name="my-llm-app")

# Create a span within the trace
span = trace.span(name="llm-call")

# ... your LLM operations ...

# End the span with output
span.end(output={"response": "Hello, world!"})

# Flush to ensure all data is sent
client.flush()

Using the @observe Decorator

from interactiveai import observe

@observe()
def my_llm_function(prompt: str) -> str:
    # Your LLM logic here
    response = call_llm(prompt)
    return response

# The function is automatically traced
result = my_llm_function("Hello!")

OpenAI Integration

from interactiveai.openai import openai

# Use openai as normal - all calls are automatically traced
response = openai.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)

Configuration

The SDK is configured via environment variables:

Variable	Description	Default
INTERACTIVEAI_PUBLIC_KEY	Your InteractiveAI public API key	Required
INTERACTIVEAI_SECRET_KEY	Your InteractiveAI secret API key	Required
INTERACTIVEAI_HOST	API endpoint URL	https://cloud.interactive.ai
INTERACTIVEAI_DEBUG	Enable debug logging	false
INTERACTIVEAI_TRACING_ENABLED	Enable/disable tracing	true
INTERACTIVEAI_SAMPLE_RATE	Sampling rate for traces (0.0-1.0)	1.0

Documentation

For detailed documentation, guides, and API reference, visit:

• SDK Documentation
• API Reference
• Migration Guide - For users migrating from other observability SDKs

License

MIT License - see LICENSE for details.