DBAdmin/inbox/todo/budowa-folderów.md

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Note

BUdowa folderów

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

---

GENERATIVE AI PROJECT STRUCTURE

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)