# Gaik Project Gaik Toolkit Gaik Toolkit > GAIK Toolkit - Skill: `tomevault-io/gaik-project-gaik-toolkit-gaik-toolkit` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/gaik-project-gaik-toolkit-gaik-toolkit` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/gaik-project-gaik-toolkit-gaik-toolkit/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/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 | 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](references/demo-app.md) 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](references/docs-website.md) 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](references/installation.md) for all available extras and setup. ## Environment Variables **Azure OpenAI (recommended):** ```bash 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:** ```bash OPENAI_API_KEY=your-key OPENAI_MODEL=gpt-5.1 ``` ## Configuration Pattern All components use `get_openai_config()`: ```python 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](references/building-blocks.md). | 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 ```python from gaik.software_components.transcriber import segments_to_srt, segments_to_vtt, parse_srt, chunk_segments ``` ### Video Search Helpers ```python 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](references/rag.md). | 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](references/software-components.md). | 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](references/building-blocks.md) - Constructor params, return types, all options - [RAG Building Blocks](references/rag.md) - RAG components: Embedder, stores, Retriever, AnswerGenerator - [Software Components](references/software-components.md) - Pipeline patterns, schema persistence, batch processing - [Examples](references/examples.md) - Complete working examples (invoice extraction, RAG, parallel transcription, etc.) - [Demo App](references/demo-app.md) - Demo app architecture, routes, env vars, deployment - [Docs Website](references/docs-website.md) - Documentation site structure and editing guide - [Installation](references/installation.md) - All pip install extras and system dependencies - [Maintenance](references/maintenance.md) - Skill maintenance and PyPI fetch script --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/gaik-project) — claim your Tome and manage your conversions.