# Configuration

Configuration is done through YAML configuration files.

!!! note
    haiku.rag enforces one hard rule on existing databases: the embedding `vector_dim` in your config must match the value stored in the db. A mismatch exits with `ConfigMismatchError` and you must **rebuild** to apply the change (see [Rebuild Database](../cli.md#rebuild-database)).

    Changing only `provider` or `name` (e.g. switching from Ollama to vLLM serving the same model) is treated as soft drift: haiku.rag logs a one-time warning and updates the stored settings to match your config. Subsequent opens are silent. If the change was unintentional, revert your config to match the previously stored settings before running anything that depends on retrieval quality.

## Getting Started

Generate a configuration file with defaults:

```bash
haiku-rag init-config
```

This creates a `haiku.rag.yaml` file in your current directory with all available settings.

## Configuration File Locations

`haiku.rag` searches for configuration files in this order:

1. Path specified via `--config` flag: `haiku-rag --config /path/to/config.yaml <command>`
2. `./haiku.rag.yaml` (current directory)
3. Platform-specific user directory:
    - **Linux**: `~/.local/share/haiku.rag/haiku.rag.yaml`
    - **macOS**: `~/Library/Application Support/haiku.rag/haiku.rag.yaml`
    - **Windows**: `C:/Users/<USER>/AppData/Roaming/haiku.rag/haiku.rag.yaml`

## Minimal Configuration

A minimal configuration file with defaults:

```yaml
# haiku.rag.yaml
environment: production

embeddings:
  model:
    provider: ollama
    name: qwen3-embedding:4b
    vector_dim: 2560

qa:
  model:
    provider: ollama
    name: gpt-oss
    enable_thinking: true
```

## Complete Configuration Example

```yaml
# haiku.rag.yaml
environment: production

storage:
  data_dir: ""  # Empty = use default platform location
  vacuum_retention_seconds: 86400

ingester:
  sources:
    - type: fs
      id: local-docs
      root: /path/to/documents
      ignore_patterns: []  # Gitignore-style patterns to exclude
      include_patterns: []  # Gitignore-style patterns to include
      delete_orphans: true

lancedb:
  uri: ""  # Empty for local, or db://, s3://, az://, gs://
  api_key: ""
  region: ""

embeddings:
  model:
    provider: ollama
    name: qwen3-embedding:4b
    vector_dim: 2560

reranking:
  model:
    provider: ""  # Empty to disable, or mxbai, cohere, zeroentropy, vllm
    name: ""

qa:
  model:
    provider: ollama
    name: gpt-oss
    enable_thinking: true
    temperature: 0.3
  max_searches: 3

search:
  limit: 10                    # Default number of results to return
  max_context_chars: 10000     # Maximum characters in expanded context
  vector_index_metric: cosine  # cosine, l2, or dot
  vector_refine_factor: 30

prompts:
  domain_preamble: ""  # Prepended to skill instructions

processing:
  converter: docling-local  # docling-local or docling-serve
  chunker: docling-local    # docling-local or docling-serve
  chunker_type: hybrid      # hybrid or hierarchical
  chunk_size: 256
  chunking_tokenizer: "Qwen/Qwen3-Embedding-0.6B"
  chunking_merge_peers: true
  chunking_use_markdown_tables: false
  auto_title: false              # Auto-generate titles on ingestion
  title_model:
    provider: ollama
    name: gpt-oss
    enable_thinking: false
    temperature: 0.3
    max_tokens: 100
  conversion_options:
    do_ocr: true
    force_ocr: false
    ocr_lang: []
    do_table_structure: true
    table_mode: accurate
    table_cell_matching: true
    images_scale: 2.0

providers:
  ollama:
    base_url: http://localhost:11434

  docling_serve:
    base_url: http://localhost:5001
    api_key: ""
    timeout: 300
```

## Programmatic Configuration

When using haiku.rag as a Python library, you can pass configuration directly to the `HaikuRAG` client:

```python
from haiku.rag.config import AppConfig
from haiku.rag.config.models import EmbeddingModelConfig, ModelConfig, QAConfig, EmbeddingsConfig
from haiku.rag.client import HaikuRAG

# Create custom configuration
custom_config = AppConfig(
    qa=QAConfig(
        model=ModelConfig(
            provider="openai",
            name="gpt-4o",
            temperature=0.3
        )
    ),
    embeddings=EmbeddingsConfig(
        model=EmbeddingModelConfig(
            provider="ollama",
            name="qwen3-embedding:4b",
            vector_dim=2560
        )
    ),
    processing={"chunk_size": 512}
)

# Pass configuration to the client
async with HaikuRAG(config=custom_config) as client:
    ...
```

If you don't pass a config, the client uses the global configuration loaded from your YAML file or defaults.

This is useful for:
- Jupyter notebooks
- Python scripts
- Testing with different configurations
- Applications that need multiple clients with different configurations

## Configuration Topics

For detailed configuration of specific topics, see:

- **[Providers](providers.md)** - Model settings and provider-specific configuration (embeddings, reranking)
- **[Search and Question Answering](qa.md)** - Search settings and question answering
- **[Document Processing](processing.md)** - Document conversion and chunking
- **[Ingester](../ingester.md)** - Continuous ingestion from filesystem, HTTP, S3, and WebDAV sources
- **[Storage](storage.md)** - Database, remote storage, and vector indexing
- **[Prompts](prompts.md)** - Customize agent prompts for your domain