Configuration

ragit is configured through environment variables, which can be set directly or loaded from a .env file.

Environment Variables

Ollama Connection

Variable	Default	Description
`OLLAMA_BASE_URL`	`http://localhost:11434`	Base URL for Ollama LLM API
`OLLAMA_EMBEDDING_URL`	Same as `OLLAMA_BASE_URL`	URL for embedding API (useful for split deployments)
`OLLAMA_API_KEY`	None	API key for cloud Ollama providers
`OLLAMA_TIMEOUT`	`120`	Request timeout in seconds

Model Defaults

Variable	Default	Description
`RAGIT_DEFAULT_LLM_MODEL`	`qwen3-vl:235b-instruct`	Default LLM model for generation
`RAGIT_DEFAULT_EMBEDDING_MODEL`	`mxbai-embed-large`	Default model for embeddings

Logging

Variable	Default	Description
`RAGIT_LOG_LEVEL`	`INFO`	Logging level (DEBUG, INFO, WARNING, ERROR)

Using a .env File

Create a .env file in your project root:

# .env file
OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_TIMEOUT=180

RAGIT_DEFAULT_LLM_MODEL=llama3
RAGIT_DEFAULT_EMBEDDING_MODEL=mxbai-embed-large

ragit automatically loads this file using python-dotenv.

Configuration Examples

Local Development

Minimal configuration for local Ollama:

# .env
OLLAMA_BASE_URL=http://localhost:11434
RAGIT_DEFAULT_LLM_MODEL=llama3

Cloud Ollama Provider

Configuration for cloud-hosted Ollama:

# .env
OLLAMA_BASE_URL=https://api.your-ollama-cloud.com
OLLAMA_API_KEY=your-api-key-here
OLLAMA_TIMEOUT=300

# Cloud might not support embeddings, use local for that
OLLAMA_EMBEDDING_URL=http://localhost:11434

Split LLM and Embedding Servers

Use different servers for LLM and embeddings:

# .env
# Powerful cloud server for LLM generation
OLLAMA_BASE_URL=https://llm-server.example.com
OLLAMA_API_KEY=llm-api-key

# Local server for embeddings (faster, no API costs)
OLLAMA_EMBEDDING_URL=http://localhost:11434

Production Deployment

Production configuration with longer timeouts:

# .env
OLLAMA_BASE_URL=https://ollama.internal.company.com
OLLAMA_API_KEY=${OLLAMA_SECRET}  # From secrets manager
OLLAMA_TIMEOUT=300

RAGIT_DEFAULT_LLM_MODEL=llama3:70b
RAGIT_DEFAULT_EMBEDDING_MODEL=mxbai-embed-large
RAGIT_LOG_LEVEL=WARNING

Accessing Configuration in Code

Access configuration values programmatically:

from ragit.config import config

print(f"Ollama URL: {config.OLLAMA_BASE_URL}")
print(f"Default LLM: {config.DEFAULT_LLM_MODEL}")
print(f"Default Embedding: {config.DEFAULT_EMBEDDING_MODEL}")
print(f"Timeout: {config.OLLAMA_TIMEOUT}")

Overriding Configuration

Override defaults when creating instances:

from ragit import RAGAssistant

# Override model defaults
assistant = RAGAssistant(
    "docs/",
    llm_model="mistral",                  # Override LLM model
    embedding_model="nomic-embed-text"    # Override embedding model
)

Provider-level overrides:

from ragit.providers import OllamaProvider

# Use different base URL
provider = OllamaProvider(
    base_url="http://other-server:11434",
    api_key="different-key",
    timeout=60
)

Model Recommendations

LLM Models

For different use cases:

Model	Size	Use Case
`llama3`	8B	General purpose, fast responses
`llama3:70b`	70B	Higher quality, complex questions
`mistral`	7B	Fast, good for simple queries
`codellama`	7B-34B	Code-related questions

Embedding Models

Model	Dimensions	Characteristics
`mxbai-embed-large`	1024	High quality, general purpose
`nomic-embed-text`	768	Good for long documents
`all-minilm`	384	Lightweight, fast

Troubleshooting Configuration

Connection Issues

If ragit can’t connect to Ollama:

from ragit.providers import OllamaProvider

provider = OllamaProvider()
if not provider.is_available():
    print("Cannot connect to Ollama")
    print(f"URL: {provider.base_url}")
    print("Check that Ollama is running: ollama serve")

Timeout Issues

For large models or slow connections:

# Increase timeout
export OLLAMA_TIMEOUT=300

Model Not Found

If a model isn’t available:

# List available models
ollama list

# Pull missing model
ollama pull llama3
ollama pull mxbai-embed-large