easier-openai 5.1

Utilities for orchestrating OpenAI chat, tool calling, search, audio, and images from one helper package.

Easier OpenAI

Easier OpenAI wraps the official OpenAI Python SDK so you can drive modern assistants, manage tool selection, search files, and work with speech from one helper package -- all with minimal boilerplate.

Features

• High level Assistant wrapper with conversation memory, tool toggles, and streaming helpers.
• Temporary vector store ingestion to ground answers in local notes or documents.
• Text to speech and speech to text bridges designed for quick experiments or internal tooling.
• Built-in helper for defining and executing OpenAI function tools without leaving Python.
• Lazy module loading so import easier_openai stays fast even as optional helpers expand.
• Type hints and comprehensive inline docstrings across the project for easier discovery.

Installation

pip install easier-openai

Optional extras:

pip install "easier-openai[function_tools]"   # decorator helpers
pip install "easier-openai[speech_models]"    # whisper speech recognition models

Set the OPENAI_API_KEY environment variable or pass api_key directly when instantiating Assistant.

Quick Start

from easier_openai import Assistant

assistant = Assistant(model="gpt-4o-mini", system_prompt="You are concise.")
response_text = assistant.chat("Summarize Rayleigh scattering in one sentence.")
print(response_text)

Tool Calling Made Simple

Use Assistant.openai_function to convert regular functions into structured tool definitions and hand them to chat:

from easier_openai import Assistant

assistant = Assistant()

@assistant.openai_function
def look_up_fact(topic: str) -> dict:
    """Return a knowledge base lookup result for the given topic."""
    return {"topic": topic}

assistant.chat(
    "Tell me about the ozone layer using the fact tool.",
    custom_tools=[look_up_fact],
)

Stream Responses with Tools

stream = assistant.chat(
    "Summarise launch blockers for the robotics demo",
    custom_tools=[look_up_fact],
    text_stream=True,
)
for delta in stream:
    if delta == "done":
        break
    print(delta, end="", flush=True)

Ground Responses With Your Files

notes = ["notes/overview.md", "notes/data-sheet.pdf"]
reply = assistant.chat(
    "Highlight key risks from the attached docs",
    file_search=notes,
    tools_required="auto",
)
print(reply)

Speech I/O

Generate audio output directly from assistant responses:

assistant.full_text_to_speech(
    "Ship a status update that sounds upbeat",
    model="gpt-4o-mini-tts",
    voice="alloy",
    play=True,
)

full_text_to_speech accepts the same keyword arguments as chat, so you can pass custom_tools, file_search, or web_search before the reply is spoken.

Or capture short dictated prompts without leaving the terminal:

transcript = assistant.speech_to_text(mode="vad", model="base.en")
print(transcript)

Image Utilities

Openai_Images extends the assistant with helpers that accept URLs, file paths, or base64 payloads and normalise them for the Images API:

from easier_openai import Openai_Images

image_client = Openai_Images("samples/promenade.jpg")
# Generated metadata is stored on image_client.image for re-use in calls.

Configuration Reference

• model: Default model used for chat, tool calls, and reasoning workflows.
• system_prompt: Injected once per conversation to shape assistant behaviour.
• reasoning_effort and summary_length: Fine tune reasoning models via the official API semantics.
• temperature: Pass through value mapped to OpenAI responses for deterministic vs creative answers.
• function_call_list: Pre-register decorated tool callables that should accompany every chat request.
• default_conversation: Set to False if you prefer to supply conversation IDs manually.
• mass_update(**kwargs): Bulk update configuration attributes using keyword arguments validated by type hints.

assistant.mass_update(model="gpt-4o-mini", temperature=0.2)
assistant.mass_update(reasoning_effort="high", summary_length="concise")

Developer Notes

• Every public function and class ships with contextual docstrings to make the codebase self-documenting.
• The repository includes unit tests under tests/ that exercise tool-calling flows; run them with pytest.
• Generated artifacts in build/ mirror the source package and inherit the same documentation updates.
• Issues and pull requests are welcome; please run checks locally before submitting changes.

License

Licensed under the Apache License 2.0.