---
type: Note
---
# BUdowa folderów

Poniżej masz **wierne przepisanie (OCR) struktury z obrazka** w formie tekstowej:

***

# **GENERATIVE AI PROJECT STRUCTURE**

```text
generative_ai_project/
├── config/
│   ├── model_config.yaml
│   └── logging_config.yaml
│
├── data/
│   ├── cache/
│   ├── embeddings/
│   └── vectordb/
│
├── src/
│   ├── core/
│   │   ├── base_llm.py
│   │   ├── gpt_client.py
│   │   ├── claude_client.py
│   │   ├── local_llm.py
│   │   └── model_factory.py
│   │
│   ├── prompts/
│   │   ├── templates.py
│   │   └── chain.py
│   │
│   ├── rag/
│   │   ├── embedder.py
│   │   ├── retriever.py
│   │   ├── vector_store.py
│   │   └── indexer.py
│   │
│   ├── processing/
│   │   ├── chunking.py
│   │   ├── tokenizer.py
│   │   └── preprocessor.py
│   │
│   └── inference/
│       ├── inference_engine.py
│       └── response_parser.py
│
├── tests/
│   ├── unit/
│   │   ├── test_llm_clients.py
│   │   └── test_prompts.py
│   │
│   └── integration/
│       ├── test_end_to_end.py
│       └── test_api_integration.py
│
├── scripts/
│   ├── setup_env.sh
│   ├── run_tests.sh
│   ├── build_embeddings.py
│   └── cleanup.py
│
├── .gitignore
├── Dockerfile
├── docker-compose.yml
└── requirements.txt
```

***

# **Opis sekcji (z prawej strony obrazka)**

### config/ – Project configuration files:

- model_config.yaml: Model settings and API keys
- logging_config.yaml: Logging configuration

***

### data/ – Stores all project data:

- cache/: Cached and processed data
- embeddings/: Vector embeddings
- vectordb/: Vector database files

***

### src/ – Main application code

#### core/ – LLM abstraction and integrations:

- base_llm.py: Common interface for all LLMs
- gpt_client.py: OpenAI GPT client
- claude_client.py: Anthropic Claude client
- local_llm.py: Local/self-hosted models
- model_factory.py: Model selection factory

***

#### prompts/ – Prompt design utilities:

- templates.py: Prompt templates
- chain.py: Multi-step prompt chaining

***

#### rag/ – Retrieval-Augmented Generation module:

- embedder.py: Embedding creation
- retriever.py: Document search
- vector_store.py: Vector DB wrapper
- indexer.py: Document indexing

***

#### processing/ – Data preparation pipeline:

- chunking.py: Text splitting
- tokenizer.py: Tokenization utilities
- preprocessor.py: Data cleaning

***

#### inference/ – Model execution flow:

- inference_engine.py: Inference engine
- response_parser.py: Output formatting

***

### tests/ – Testing modules

#### unit/ – Function-level tests:

- test_llm_clients.py
- test_prompts.py

#### integration/ – End-to-end tests:

- test_end_to_end.py
- test_api_integration.py

***

### scripts/ – Automation scripts for common tasks:

- setup_env.sh: Environment setup
- run_tests.sh: Test execution
- build_embeddings.py: Embedding generation
- cleanup.py: Cleanup tasks

***

### Configuration Files:

- .gitignore: Git exclusions
- Dockerfile: Container configuration
- docker-compose.yml: Multi-service setup
- requirements.txt: Dependencies

***

Jeśli chcesz — mogę:

- przerobić to na **org-mode (pod Twój workflow)**
- zrobić **template repo (ZIP / Git skeleton)**
- albo rozbić to na **architekturę pod Twoje use-case’y (DBA + AI + automatyzacja)**