gaik-toolkit

name	gaik-toolkit
description	>- Use when this capability is needed.

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

Path	Description
implementation_layer/src/gaik/	Python package source (building blocks + software modules)
implementation_layer/toolkit_demo_app/	Next.js + FastAPI interactive demo app (bun + uv)
guidance_layer/website/	Documentation website (Fumadocs/Next.js, deployed to GitHub Pages)
guidance_layer/website/content/docs/	Documentation source (.mdx files)
implementation_layer/no-code-assets/	Prompt templates and agent skills for no-code usage
strategy_layer/	Value evaluation framework, AI maturity assessment
business_layer/	GenAI product canvas templates

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.

Component	Import	Key Method
SchemaGenerator	from gaik.software_components.extractor import SchemaGenerator	generate_schema(user_requirements)
DataExtractor	from gaik.software_components.extractor import DataExtractor	extract(extraction_model, requirements, ...)
VisionParser	from gaik.software_components.parsers import VisionParser	convert_pdf(path) → list[str] per page
PyMuPDFParser	from gaik.software_components.parsers import PyMuPDFParser	parse_pdf(path) → str
DocxParser	from gaik.software_components.parsers import DocxParser	parse_docx(path) → str
DoclingParser	from gaik.software_components.parsers import DoclingParser	parse(path) → str
Transcriber	from gaik.software_components.transcriber import Transcriber	transcribe(path) → TranscriptionResult
TranscriptEnhancer	from gaik.software_components.enhance_transcript import TranscriptEnhancer	enhance_text(text) / enhance_file(path)
ParallelTranscriber	from gaik.software_components.parallel_transcriber import ParallelTranscriber	transcribe(path) → TranscriptionResult
TextToSpeech	from gaik.software_components.text_to_speech import TextToSpeech	synthesize(text) → SpeechSynthesisResult
DocumentClassifier	from gaik.software_components.doc_classifier import DocumentClassifier	classify(file_or_dir, classes)

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.

Component	Import	Key Method
Embedder	from gaik.software_components.RAG.embedder import Embedder	embed(docs), embed_query(text)
VectorStore	from gaik.software_components.RAG.vector_store import VectorStore	add(docs, embeddings), search(vec, top_k)
PgVectorStore	from gaik.software_components.RAG.pg_vector_store import PgVectorStore	search_hybrid(vec, text, top_k)
Retriever	from gaik.software_components.RAG.retriever import Retriever	search(query, top_k, hybrid_search, re_rank)
AnswerGenerator	from gaik.software_components.RAG.answer_generator import AnswerGenerator	generate(query, documents, stream)
VisionRagParser	from gaik.software_components.RAG.rag_parser_vision import VisionRagParser	convert_doc_to_chunks_with_vision(path)
DoclingRagParser	from gaik.software_components.RAG.rag_parser_docling import DoclingRagParser	convert_pdf_to_chunks_with_metadata(path)

End-to-End Pipelines

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

Pipeline	Flow	Import
AudioToStructuredData	Audio → Transcript → Schema → JSON	from gaik.software_modules.audio_to_structured_data import AudioToStructuredData
DocumentsToStructuredData	PDF/DOCX → Parse → Schema → JSON	from gaik.software_modules.documents_to_structured_data import DocumentsToStructuredData
RAGWorkflow	PDF → Parse → Embed → Store → Retrieve → Answer	from gaik.software_modules.RAG_workflow import RAGWorkflow

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

Architecture Overview

Level	Concept	Examples
Service	Logical capability	speech_to_text, document_parsing, information_extraction, rag
Building block	Atomic toolkit class/function	Transcriber, ParallelTranscriber, TranscriptEnhancer, TextToSpeech, SchemaGenerator, DataExtractor, VisionParser, Embedder, VectorStore, PgVectorStore, Retriever, AnswerGenerator
Software component	Composed, workflow-ready unit	AudioToStructuredData, DocumentsToStructuredData, RAGWorkflow

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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