Skip to content

Getting Started

Welcome to Draive, a Haiway-powered toolkit for building reliable, observable AI applications. This guide gives you the context you need before diving into installation and hands-on tutorials.

Why Draive

  • Composable foundations: Build on immutable State objects and scoped context managers, so every component remains testable and predictable.
  • Provider flexibility: Switch between OpenAI, Anthropic, Gemini, Mistral, local models, and more without rewriting your pipelines.
  • Multimodal-first: Work with text, audio, images, and artifacts through a unified content API.
  • Production guardrails: Apply moderation, privacy, validation, and telemetry with the same abstractions you use for generation.

What You Will Build

Throughout the getting-started journey you will assemble:

  • A basic assistant that can call your own Python tools.
  • A multimodal workflow that combines text and image inputs.
  • A retrieval-augmented generation (RAG) flow over your documents.
  • A structured extraction pipeline that yields typed outputs you can store or downstream.
  • A test and evaluation harness to keep regressions out of production.

Prerequisites

  • Python 3.12+ with uv managing the virtual environment at ./.venv (this repository includes a ready-to-use setup).
  • Familiarity with async/await and running pytest from the command line.
  • Access tokens for the model providers you plan to use (store them in environment variables—never hard-code secrets).

Core Concepts

  1. State management – mutable-looking, immutable-under-the-hood State classes represent configuration and runtime data. You update them with methods like State.updated(...) to keep histories, snapshots, and metrics consistent.
  2. Context scoping – ctx.scope(...) activates a stack of State instances and disposables for a logical unit of work, ensuring structured concurrency and clean teardown.
  3. Generation flows – typed facades in draive.generation orchestrate text, image, and audio calls, while provider adapters translate the request to each backend.
  4. Tools and multimodal content – MultimodalContent, ResourceContent, and tool abstractions let you stream artifacts, call Python functions, or chain agents without sacrificing type safety.
  5. Guardrails and observability – moderation, privacy, metrics, and logging integrations keep your application auditable. Use ctx.log_* for structured logs and ctx.record for metrics.

Next Steps

  1. Follow the Installation guide to set up dependencies and the runtime environment.
  2. Walk through the quickstart notebooks and examples under docs/cookbooks/ to see Draive in action.
  3. Explore provider-specific instructions in docs/guides/ when you are ready to connect to production endpoints.

You now have the core mental model for Draive. Continue with installation to bring the toolkit to life.