Gaik Project Gaik Toolkit Gaik Toolkit

GAIK Toolkit

Current PyPI version: !python ${CLAUDE_SKILL_DIR}/scripts/fetch_pypi_readme.py --version

Python toolkit for knowledge extraction, capture, and generation. Use when working with:

• Structured data extraction from documents, PDFs, images, or audio
• Schema generation from natural language requirements
• Document parsing (PDF, DOCX, images)
• Audio/video transcription with Whisper + local Whisper backends (Finnish fine-tuned model)
• Transcript enhancement — two-pass LLM error correction
• Parallel transcription with FFmpeg chunking
• Text-to-speech generation
• Document classification
• RAG pipelines: embedder, vector store (Chroma / PostgreSQL), retriever, answer generator
• End-to-end pipelines: AudioToStructuredData, DocumentsToStructuredData, RAGWorkflow

Quick Links

• Documentation: https://gaik-project.github.io/gaik-toolkit/
• Live Demo: https://gaik-demo.2.rahtiapp.fi/ (registration required)
• GitHub: https://github.com/GAIK-project/gaik-toolkit
• Source Code: implementation_layer/src/gaik/
• PyPI: https://pypi.org/project/gaik/

Repository Structure

Toolkit Demo App

Interactive web app at implementation_layer/toolkit_demo_app/. Next.js 16 + FastAPI (bun + uv).

• Live: https://gaik-demo.2.rahtiapp.fi/ (registration required)
• Dev: bun run dev:all (runs both frontend and API)
• See: Demo App Reference for full architecture, routes, and conventions

Documentation Website

Fumadocs/Next.js site at guidance_layer/website/. Content in .mdx files under content/docs/.

• Live: https://gaik-project.github.io/gaik-toolkit/
• Dev: pnpm dev (from guidance_layer/website/ -- uses pnpm, not bun)
• See: Docs Website Reference for content structure and editing guide

Installation

Install via pip with optional extras: pip install "gaik[extract]", pip install "gaik[all-cpu]", etc. See Installation Reference for all available extras and setup.

Environment Variables

Azure OpenAI (recommended):

AZURE_API_KEY=your-key
AZURE_ENDPOINT=https://your-resource.openai.azure.com/
AZURE_DEPLOYMENT=gpt-5.1
AZURE_API_VERSION=2025-03-01-preview

OpenAI:

OPENAI_API_KEY=your-key
OPENAI_MODEL=gpt-5.1

Configuration Pattern

All components use get_openai_config():

from gaik.software_components.config import get_openai_config, create_openai_client

config = get_openai_config(use_azure=True)   # Azure OpenAI
config = get_openai_config(use_azure=False)  # Standard OpenAI
client = create_openai_client(config)        # OpenAI/AzureOpenAI client

Building Blocks

Core classes in gaik.software_components.*. For detailed API and constructor parameters, see Building Blocks Reference.

Transcriber notes

• Models: "whisper", "whisper-1", "gpt-4o-transcribe", "whisper_local"
• enhanced_transcript=True runs output through TranscriptEnhancer (two-pass LLM correction)
• whisper_local requires local_api_base + local_api_key; language="fi" selects Finnish fine-tuned model
• ParallelTranscriber uses FFmpeg chunking; requires ffmpeg + ffprobe on $PATH

SRT/VTT Utilities

from gaik.software_components.transcriber import segments_to_srt, segments_to_vtt, parse_srt, chunk_segments

Video Search Helpers

from gaik.software_components.RAG.pg_vector_store import PgVectorStore, ingest_video_segments, format_search_results

RAG Building Blocks

Core RAG classes in gaik.software_components.RAG.*. For full API, see RAG Reference.

End-to-End Pipelines

Composed pipelines in gaik.software_modules.*. For full API, see Software Components Reference.

All pipelines follow: pipeline = Pipeline(use_azure=True) → result = pipeline.run(file_path, user_requirements, ...).

Architecture Overview

Use Cases

Documented in guidance_layer/website/content/docs/use-cases/: incident reporting, dental transcription & captioning, semantic dental video search, construction diary, dental learning assistant, purchase order processing, report writing, sales proposal generation, customer onboarding.

Detailed References

• Building Blocks API - Constructor params, return types, all options
• RAG Building Blocks - RAG components: Embedder, stores, Retriever, AnswerGenerator
• Software Components - Pipeline patterns, schema persistence, batch processing
• Examples - Complete working examples (invoice extraction, RAG, parallel transcription, etc.)
• Demo App - Demo app architecture, routes, env vars, deployment
• Docs Website - Documentation site structure and editing guide
• Installation - All pip install extras and system dependencies
• Maintenance - Skill maintenance and PyPI fetch script

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