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The Settings class is the central configuration backbone of RAGLight. It provides a single source of truth for:
  • Provider Identifiers (LLMs, embeddings, vector stores)
  • Default Models (optimized for local use)
  • Environment Configuration (loading .env automatically)
Philosophy: RAGLight avoids “magic values”. All defaults are exposed as constants in Settings, making pipelines predictable, debuggable, and easy to override.

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Global Setup

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Logging

Before building any pipeline, it is recommended to initialize the logging system. This ensures that RAGLight’s internal logs (ingestion progress, retrieval stats) are formatted correctly. 
from raglight.config.settings import Settings

# Call this once at the start of your script to enable structured logging
Settings.setup_logging()

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Provider Identifiers

RAGLight uses string constants to identify providers. Using these constants prevents typo-related errors when configuring your RAGConfig.

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LLM & Embeddings

These constants are used in RAGConfig.provider and VectorStoreConfig.provider.
ConstantValueDescription
Settings.OLLAMA"ollama"Recommended for local. Requires an Ollama instance.
Settings.OPENAI"openai"Uses OpenAI API (requires key).
Settings.MISTRAL"mistral"Uses Mistral API (requires key).
Settings.VLLM"vllm"Connects to a vLLM server (high throughput).
Settings.LMSTUDIO"lmstudio"Connects to LM Studio local server.
Settings.GOOGLE_GEMINI"google_gemini"Uses Google’s Gemini API.
Settings.HUGGINGFACE"huggingface"Embeddings only. Runs locally via sentence-transformers.

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Vector Stores

ConstantValueDescription
Settings.CHROMA"chroma"Default. Local, persistent vector database.

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Default Configuration

RAGLight ships with “batteries-included” defaults, optimized for a standard local laptop (Apple Silicon / NVIDIA GPU). 
# Default Model (usually "llama3")
Settings.DEFAULT_LLM

# Reasoning / Agentic Model

Settings.DEFAULT_REASONING_LLM

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System Prompts

You can inspect or override the default system prompts used by the pipelines. These are designed to be readable and inspectable. 
# Standard RAG prompt
print(Settings.DEFAULT_SYSTEM_PROMPT)

# Agentic RAG prompt (Tool use & reasoning rules)
print(Settings.DEFAULT_AGENT_PROMPT)

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Environment Variables

Settings automatically looks for a .env file in your working directory using python-dotenv. This is the secure way to manage API keys and custom endpoints without hardcoding them.

API Keys

Supported keys for remote providers: * OPENAI_API_KEY * MISTRAL_API_KEY
  • GEMINI_API_KEY * ANTHROPIC_API_KEY

Custom Endpoints

URLs for local/custom servers: * OLLAMA_CLIENT_URL (default: http://localhost:11434) * LMSTUDIO_CLIENT * OPENAI_CLIENT_URL (useful for vLLM/compatible APIs)

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Example .env File

Create a .env file in your project root:
.env
# Use a custom Ollama instance on another machine
OLLAMA_CLIENT_URL=http://192.168.1.50:11434

# OpenAI Fallback
OPENAI_API_KEY=sk-proj-12345...

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Ignore Folders

When using FolderSource to ingest data, RAGLight automatically ignores common build, cache, and system directories to keep your index clean and relevant. 
# Returns a list of ignored directories
ignored_list = Settings.DEFAULT_IGNORE_FOLDERS
Includes by default:
  • .git, .vscode, .idea
  • __pycache__, venv, env
  • node_modules
  • dist, build
  • .DS_Store