Update and restructure docs
This commit is contained in:
Yiorgis Gozadinos
parent
1bb7b6d5bd
commit
41ea675964
14 changed files with 978 additions and 840 deletions
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@ -69,7 +69,7 @@ haiku-rag add-src /path/to/documents/
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```
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!!! note
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When adding a directory, the same content filters configured for [file monitoring](configuration.md#filtering-monitored-files) are applied. This means `ignore_patterns` and `include_patterns` from your configuration will be used to filter which files are added.
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When adding a directory, the same content filters configured for [file monitoring](processing.md#filtering-monitored-files) are applied. This means `ignore_patterns` and `include_patterns` from your configuration will be used to filter which files are added.
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!!! note
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As you add documents to `haiku.rag` the database keeps growing. By default, LanceDB supports versioning
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@ -158,7 +158,7 @@ haiku-rag research "How does haiku.rag organize and query documents?" --verbose
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Flags:
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- `--verbose`: Show planning, searching previews, evaluation summary, and stop reason
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Research parameters like `max_iterations`, `confidence_threshold`, and `max_concurrency` are configured in your [configuration file](configuration.md) under the `research` section.
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Research parameters like `max_iterations`, `confidence_threshold`, and `max_concurrency` are configured in your [configuration file](config-index.md) under the `research` section.
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When `--verbose` is set, the CLI consumes the research graph's AG-UI event stream, displaying step events and activity snapshots as agents progress through planning, search, evaluation, and synthesis. Without `--verbose`, only the final research report is displayed.
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195
docs/config-index.md
Normal file
195
docs/config-index.md
Normal file
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@ -0,0 +1,195 @@
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# Configuration
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Configuration is done through YAML configuration files.
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!!! note
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If you create a db with certain settings and later change them, `haiku.rag` will detect incompatibilities (for example, if you change embedding provider) and will exit. You can **rebuild** the database to apply the new settings, see [Rebuild Database](./cli.md#rebuild-database).
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## Getting Started
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Generate a configuration file with defaults:
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```bash
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haiku-rag init-config
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```
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This creates a `haiku.rag.yaml` file in your current directory with all available settings.
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## Configuration File Locations
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`haiku.rag` searches for configuration files in this order:
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1. Path specified via `--config` flag: `haiku-rag --config /path/to/config.yaml <command>`
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2. `./haiku.rag.yaml` (current directory)
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3. Platform-specific user directory:
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- **Linux**: `~/.local/share/haiku.rag/haiku.rag.yaml`
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- **macOS**: `~/Library/Application Support/haiku.rag/haiku.rag.yaml`
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- **Windows**: `C:/Users/<USER>/AppData/Roaming/haiku.rag/haiku.rag.yaml`
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## Minimal Configuration
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A minimal configuration file with defaults:
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```yaml
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# haiku.rag.yaml
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environment: production
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embeddings:
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model:
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provider: ollama
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model: qwen3-embedding:4b
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vector_dim: 2560
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qa:
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model:
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provider: ollama
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model: gpt-oss
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enable_thinking: false
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```
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## Complete Configuration Example
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```yaml
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# haiku.rag.yaml
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environment: production
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storage:
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data_dir: "" # Empty = use default platform location
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vacuum_retention_seconds: 86400
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monitor:
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directories:
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- /path/to/documents
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- /another/path
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ignore_patterns: [] # Gitignore-style patterns to exclude
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include_patterns: [] # Gitignore-style patterns to include
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lancedb:
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uri: "" # Empty for local, or db://, s3://, az://, gs://
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api_key: ""
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region: ""
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embeddings:
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model:
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provider: ollama
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model: qwen3-embedding:4b
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vector_dim: 2560
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reranking:
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model:
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provider: "" # Empty to disable, or mxbai, cohere, zeroentropy, vllm
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model: ""
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qa:
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model:
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provider: ollama
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model: gpt-oss
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enable_thinking: false
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max_sub_questions: 3
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max_iterations: 2
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max_concurrency: 1
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research:
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model:
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provider: "" # Empty to use qa settings
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model: ""
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enable_thinking: true
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max_iterations: 3
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confidence_threshold: 0.8
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max_concurrency: 1
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search:
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vector_index_metric: cosine # cosine, l2, or dot
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vector_refine_factor: 30
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agui:
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host: "0.0.0.0"
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port: 8000
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cors_origins: ["*"]
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cors_credentials: true
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cors_methods: ["GET", "POST", "OPTIONS"]
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cors_headers: ["*"]
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processing:
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converter: docling-local # docling-local or docling-serve
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chunker: docling-local # docling-local or docling-serve
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chunker_type: hybrid # hybrid or hierarchical
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chunk_size: 256
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context_chunk_radius: 0
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chunking_tokenizer: "Qwen/Qwen3-Embedding-0.6B"
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chunking_merge_peers: true
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chunking_use_markdown_tables: false
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markdown_preprocessor: ""
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conversion_options:
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do_ocr: true
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force_ocr: false
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ocr_lang: []
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do_table_structure: true
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table_mode: accurate
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table_cell_matching: true
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images_scale: 2.0
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providers:
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ollama:
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base_url: http://localhost:11434
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vllm:
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embeddings_base_url: ""
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rerank_base_url: ""
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qa_base_url: ""
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research_base_url: ""
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docling_serve:
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base_url: http://localhost:5001
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api_key: ""
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timeout: 300
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```
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## Programmatic Configuration
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When using haiku.rag as a Python library, you can pass configuration directly to the `HaikuRAG` client:
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```python
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from haiku.rag.config import AppConfig
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from haiku.rag.config.models import ModelConfig, QAConfig, EmbeddingsConfig
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from haiku.rag.client import HaikuRAG
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# Create custom configuration
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custom_config = AppConfig(
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qa=QAConfig(
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model=ModelConfig(
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provider="openai",
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model="gpt-4o",
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temperature=0.7
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)
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),
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embeddings=EmbeddingsConfig(
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model=ModelConfig(
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provider="ollama",
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model="qwen3-embedding:4b"
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),
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vector_dim=2560
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),
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processing={"chunk_size": 512}
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)
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# Pass configuration to the client
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client = HaikuRAG(config=custom_config)
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```
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If you don't pass a config, the client uses the global configuration loaded from your YAML file or defaults.
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This is useful for:
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- Jupyter notebooks
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- Python scripts
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- Testing with different configurations
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- Applications that need multiple clients with different configurations
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## Configuration Topics
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For detailed configuration of specific topics, see:
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- **[Providers](providers.md)** - Model settings and provider-specific configuration (embeddings, QA, reranking)
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- **[QA and Research](qa-research.md)** - Question answering and research workflow configuration
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- **[Storage](storage.md)** - Database, remote storage, and vector indexing
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- **[Document Processing](processing.md)** - Document conversion, chunking, and file monitoring
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@ -1,829 +0,0 @@
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# Configuration
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Configuration is done through YAML configuration files.
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!!! note
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If you create a db with certain settings and later change them, `haiku.rag` will detect incompatibilities (for example, if you change embedding provider) and will exit. You can **rebuild** the database to apply the new settings, see [Rebuild Database](./cli.md#rebuild-database).
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## Getting Started
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Generate a configuration file with defaults:
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```bash
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haiku-rag init-config
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```
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This creates a `haiku.rag.yaml` file in your current directory with all available settings.
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## Configuration File Locations
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`haiku.rag` searches for configuration files in this order:
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1. Path specified via `--config` flag: `haiku-rag --config /path/to/config.yaml <command>`
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2. `./haiku.rag.yaml` (current directory)
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3. Platform-specific user directory:
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- **Linux**: `~/.local/share/haiku.rag/haiku.rag.yaml`
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- **macOS**: `~/Library/Application Support/haiku.rag/haiku.rag.yaml`
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- **Windows**: `C:/Users/<USER>/AppData/Roaming/haiku.rag/haiku.rag.yaml`
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|
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## Minimal Configuration
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A minimal configuration file with defaults:
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```yaml
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# haiku.rag.yaml
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environment: production
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embeddings:
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provider: ollama
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model: qwen3-embedding:4b
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vector_dim: 2560
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qa:
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provider: ollama
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model: gpt-oss
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```
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## Complete Configuration Example
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```yaml
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# haiku.rag.yaml
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environment: production
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storage:
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data_dir: "" # Empty = use default platform location
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vacuum_retention_seconds: 86400
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monitor:
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directories:
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- /path/to/documents
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- /another/path
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ignore_patterns: [] # Gitignore-style patterns to exclude
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include_patterns: [] # Gitignore-style patterns to include
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lancedb:
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uri: "" # Empty for local, or db://, s3://, az://, gs://
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api_key: ""
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region: ""
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embeddings:
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provider: ollama
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model: qwen3-embedding:4b
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vector_dim: 2560
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reranking:
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provider: "" # Empty to disable, or mxbai, cohere, zeroentropy, vllm
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model: ""
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qa:
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provider: ollama
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model: gpt-oss
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research:
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provider: "" # Empty to use qa settings
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model: ""
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max_iterations: 3
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confidence_threshold: 0.8
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max_concurrency: 1
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search:
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vector_index_metric: cosine # cosine, l2, or dot
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vector_refine_factor: 30
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agui:
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host: "0.0.0.0"
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port: 8000
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cors_origins: ["*"]
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cors_credentials: true
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cors_methods: ["GET", "POST", "OPTIONS"]
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cors_headers: ["*"]
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processing:
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converter: docling-local # docling-local or docling-serve
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chunker: docling-local # docling-local or docling-serve
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chunker_type: hybrid # hybrid or hierarchical
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chunk_size: 256
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context_chunk_radius: 0
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chunking_tokenizer: "Qwen/Qwen3-Embedding-0.6B"
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chunking_merge_peers: true
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chunking_use_markdown_tables: false
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markdown_preprocessor: ""
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conversion_options:
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do_ocr: true
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force_ocr: false
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ocr_lang: []
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do_table_structure: true
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table_mode: accurate
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table_cell_matching: true
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images_scale: 2.0
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providers:
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ollama:
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base_url: http://localhost:11434
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vllm:
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embeddings_base_url: ""
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rerank_base_url: ""
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qa_base_url: ""
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research_base_url: ""
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docling_serve:
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base_url: http://localhost:5001
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api_key: ""
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timeout: 300
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```
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## Programmatic Configuration
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|
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When using haiku.rag as a Python library, you can pass configuration directly to the `HaikuRAG` client:
|
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|
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```python
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from haiku.rag.config import AppConfig
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from haiku.rag.client import HaikuRAG
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# Create custom configuration
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custom_config = AppConfig(
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qa={"provider": "openai", "model": "gpt-4o"},
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embeddings={"provider": "ollama", "model": "qwen3-embedding:4b", "vector_dim": 2560},
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processing={"chunk_size": 512}
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)
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# Pass configuration to the client
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client = HaikuRAG(config=custom_config)
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```
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If you don't pass a config, the client uses the global configuration loaded from your YAML file or defaults.
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|
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This is useful for:
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- Jupyter notebooks
|
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- Python scripts
|
||||
- Testing with different configurations
|
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- Applications that need multiple clients with different configurations
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|
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## File Monitoring
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Set directories to monitor for automatic indexing:
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```yaml
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monitor:
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directories:
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- /path/to/documents
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- /another_path/to/documents
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```
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### Filtering Monitored Files
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Use gitignore-style patterns to control which files are monitored:
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```yaml
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monitor:
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directories:
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- /path/to/documents
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# Exclude specific files or directories
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ignore_patterns:
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- "*draft*" # Ignore files with "draft" in the name
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- "temp/" # Ignore temp directory
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- "**/archive/**" # Ignore all archive directories
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- "*.backup" # Ignore backup files
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# Only include specific files (whitelist mode)
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include_patterns:
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- "*.md" # Only markdown files
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- "*.pdf" # Only PDF files
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- "**/docs/**" # Only files in docs directories
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```
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**How patterns work:**
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1. **Extension filtering** - Only supported file types are considered
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2. **Include patterns** - If specified, only matching files are included (whitelist)
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3. **Ignore patterns** - Matching files are excluded (blacklist)
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4. **Combining both** - Include patterns are applied first, then ignore patterns
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**Common patterns:**
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```yaml
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# Only monitor markdown documentation, but ignore drafts
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monitor:
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include_patterns:
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- "*.md"
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ignore_patterns:
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- "*draft*"
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- "*WIP*"
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# Monitor all supported files except in specific directories
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monitor:
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ignore_patterns:
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- "node_modules/"
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- ".git/"
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- "**/test/**"
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- "**/temp/**"
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```
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Patterns follow [gitignore syntax](https://git-scm.com/docs/gitignore#_pattern_format):
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- `*` matches anything except `/`
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- `**` matches zero or more directories
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- `?` matches any single character
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- `[abc]` matches any character in the set
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## Document Processing
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Configure how documents are converted and chunked:
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```yaml
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processing:
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# Chunking configuration
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chunk_size: 256 # Maximum tokens per chunk
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context_chunk_radius: 0 # Context radius for chunk expansion
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markdown_preprocessor: "" # Optional preprocessor script
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# Converter selection
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converter: docling-local # docling-local or docling-serve
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# Chunker selection and configuration
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chunker: docling-local # docling-local or docling-serve
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chunker_type: hybrid # hybrid or hierarchical
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chunking_tokenizer: "Qwen/Qwen3-Embedding-0.6B" # HuggingFace model for tokenization
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chunking_merge_peers: true # Merge undersized successive chunks
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chunking_use_markdown_tables: false # Use markdown tables vs narrative format
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# Conversion options (works with both local and remote converters)
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conversion_options:
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# OCR settings
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do_ocr: true # Enable OCR for bitmap content
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force_ocr: false # Replace existing text with OCR
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ocr_lang: [] # OCR languages (e.g., ["en", "fr", "de"])
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# Table extraction
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do_table_structure: true # Extract table structure
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table_mode: accurate # fast or accurate
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table_cell_matching: true # Match table cells back to PDF cells
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# Image settings
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images_scale: 2.0 # Image scale factor
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```
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### Conversion Options
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The `conversion_options` section allows fine-grained control over document conversion. These options work with both `docling-local` and `docling-serve` converters.
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|
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#### OCR Settings
|
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```yaml
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conversion_options:
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do_ocr: true # Enable OCR for bitmap/scanned content
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force_ocr: false # Replace all text with OCR output
|
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ocr_lang: [] # List of OCR languages, e.g., ["en", "fr", "de"]
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```
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- **do_ocr**: When `true`, applies OCR to images and scanned pages. Disable for faster processing if documents contain only native text.
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- **force_ocr**: When `true`, replaces existing text layers with OCR output. Useful for documents with poor text extraction.
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- **ocr_lang**: List of language codes for OCR. Empty list uses default language detection. Examples: `["en"]`, `["en", "fr", "de"]`.
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#### Table Extraction
|
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|
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```yaml
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conversion_options:
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do_table_structure: true # Extract structured table data
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table_mode: accurate # fast or accurate
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table_cell_matching: true # Match cells back to PDF
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```
|
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|
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- **do_table_structure**: When `true`, extracts table structure. Disable for faster processing if tables aren't important.
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- **table_mode**:
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- `accurate`: Better table structure recognition (slower)
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- `fast`: Faster processing with simpler table detection
|
||||
- **table_cell_matching**: When `true`, matches detected table cells back to PDF cells. Disable if tables have merged cells across columns.
|
||||
|
||||
#### Image Settings
|
||||
|
||||
```yaml
|
||||
conversion_options:
|
||||
images_scale: 2.0 # Image resolution scale factor
|
||||
```
|
||||
|
||||
- **images_scale**: Scale factor for extracted images. Higher values = better quality but larger size. Typical range: 1.0-3.0.
|
||||
|
||||
### Local vs Remote Processing
|
||||
|
||||
**Local processing** (default):
|
||||
|
||||
- Uses `docling` library locally
|
||||
- No external dependencies
|
||||
- Good for development and small workloads
|
||||
|
||||
**Remote processing** (docling-serve):
|
||||
|
||||
- Offloads processing to docling-serve API
|
||||
- Better for heavy workloads and production
|
||||
- Requires docling-serve instance (see [Remote processing setup](remote-processing.md))
|
||||
|
||||
To use remote processing:
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
converter: docling-serve
|
||||
chunker: docling-serve
|
||||
|
||||
providers:
|
||||
docling_serve:
|
||||
base_url: http://localhost:5001
|
||||
api_key: "your-api-key" # Optional
|
||||
timeout: 300 # Request timeout in seconds
|
||||
```
|
||||
|
||||
Conversion options work identically for both local and remote processing.
|
||||
|
||||
### Chunking Strategies
|
||||
|
||||
**Hybrid chunking** (default):
|
||||
- Structure-aware chunking
|
||||
- Respects document boundaries
|
||||
- Best for most use cases
|
||||
|
||||
**Hierarchical chunking**:
|
||||
- Creates hierarchical chunk structure
|
||||
- Preserves document hierarchy
|
||||
- Useful for complex documents
|
||||
|
||||
### Table Serialization
|
||||
|
||||
Control how tables are represented in chunks:
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
chunking_use_markdown_tables: false # Default: narrative format
|
||||
```
|
||||
|
||||
- `false`: Tables as narrative text ("Value A, Column 2 = Value B")
|
||||
- `true`: Tables as markdown (preserves table structure)
|
||||
|
||||
## Embedding Providers
|
||||
|
||||
If you use Ollama, you can use any pulled model that supports embeddings.
|
||||
|
||||
### Ollama (Default)
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
provider: ollama
|
||||
model: mxbai-embed-large
|
||||
vector_dim: 1024
|
||||
```
|
||||
|
||||
The Ollama base URL can be configured in your config file or via environment variable:
|
||||
|
||||
```yaml
|
||||
providers:
|
||||
ollama:
|
||||
base_url: http://localhost:11434
|
||||
```
|
||||
|
||||
Or via environment variable:
|
||||
|
||||
```bash
|
||||
export OLLAMA_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
If not configured, it defaults to `http://localhost:11434`.
|
||||
|
||||
!!! note
|
||||
You can use a `.env` file in your project directory to set environment variables like `OLLAMA_BASE_URL` and API keys (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`). These will be automatically loaded when running `haiku-rag` commands.
|
||||
|
||||
### VoyageAI
|
||||
|
||||
If you installed `haiku.rag` (full package), VoyageAI is already included. If you installed `haiku.rag-slim`, install with VoyageAI extras:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[voyageai]
|
||||
```
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
provider: voyageai
|
||||
model: voyage-3.5
|
||||
vector_dim: 1024
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export VOYAGE_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### OpenAI
|
||||
|
||||
OpenAI embeddings are included in the default installation:
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
provider: openai
|
||||
model: text-embedding-3-small # or text-embedding-3-large
|
||||
vector_dim: 1536
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### vLLM
|
||||
|
||||
For high-performance local inference, you can use vLLM to serve embedding models with OpenAI-compatible APIs:
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
provider: vllm
|
||||
model: mixedbread-ai/mxbai-embed-large-v1
|
||||
vector_dim: 512
|
||||
|
||||
providers:
|
||||
vllm:
|
||||
embeddings_base_url: http://localhost:8000
|
||||
```
|
||||
|
||||
**Note:** You need to run a vLLM server separately with an embedding model loaded.
|
||||
|
||||
## Question Answering Providers
|
||||
|
||||
Configure which LLM provider to use for question answering. Any provider and model supported by [Pydantic AI](https://ai.pydantic.dev/models/) can be used.
|
||||
|
||||
### Ollama (Default)
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
provider: ollama
|
||||
model: gpt-oss
|
||||
```
|
||||
|
||||
The Ollama base URL can be configured via the `OLLAMA_BASE_URL` environment variable, config file, or defaults to `http://localhost:11434`:
|
||||
|
||||
```bash
|
||||
export OLLAMA_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
Or in your config file:
|
||||
|
||||
```yaml
|
||||
providers:
|
||||
ollama:
|
||||
base_url: http://localhost:11434
|
||||
```
|
||||
|
||||
### OpenAI
|
||||
|
||||
OpenAI QA is included in the default installation:
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
provider: openai
|
||||
model: gpt-4o-mini # or gpt-4, gpt-3.5-turbo, etc.
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### Anthropic
|
||||
|
||||
Anthropic QA is included in the default installation:
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
provider: anthropic
|
||||
model: claude-3-5-haiku-20241022 # or claude-3-5-sonnet-20241022, etc.
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export ANTHROPIC_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### vLLM
|
||||
|
||||
For high-performance local inference:
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
provider: vllm
|
||||
model: Qwen/Qwen3-4B # Any model with tool support in vLLM
|
||||
|
||||
providers:
|
||||
vllm:
|
||||
qa_base_url: http://localhost:8002
|
||||
```
|
||||
|
||||
**Note:** You need to run a vLLM server separately with a model that supports tool calling loaded. Consult the specific model's documentation for proper vLLM serving configuration.
|
||||
|
||||
### Other Providers
|
||||
|
||||
Any provider supported by Pydantic AI can be used. Examples:
|
||||
|
||||
```yaml
|
||||
# Google Gemini
|
||||
qa:
|
||||
provider: gemini
|
||||
model: gemini-1.5-flash
|
||||
|
||||
# Groq
|
||||
qa:
|
||||
provider: groq
|
||||
model: llama-3.3-70b-versatile
|
||||
|
||||
# Mistral
|
||||
qa:
|
||||
provider: mistral
|
||||
model: mistral-small-latest
|
||||
```
|
||||
|
||||
See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the complete list of supported providers and models.
|
||||
|
||||
## Reranking
|
||||
|
||||
Reranking improves search quality by re-ordering the initial search results using specialized models. When enabled, the system retrieves more candidates (10x the requested limit) and then reranks them to return the most relevant results.
|
||||
|
||||
Reranking is **disabled by default** (`provider: ""`) for faster searches. You can enable it by configuring one of the providers below.
|
||||
|
||||
### MixedBread AI
|
||||
|
||||
If you installed `haiku.rag` (full package), MxBAI is already included. If you installed `haiku.rag-slim`, add the mxbai extra:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[mxbai]
|
||||
```
|
||||
|
||||
Then configure:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
provider: mxbai
|
||||
model: mixedbread-ai/mxbai-rerank-base-v2
|
||||
```
|
||||
|
||||
### Cohere
|
||||
|
||||
If you installed `haiku.rag` (full package), Cohere is already included. If you installed `haiku.rag-slim`, add the cohere extra:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[cohere]
|
||||
```
|
||||
|
||||
Then configure:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
provider: cohere
|
||||
model: rerank-v3.5
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export CO_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### Zero Entropy
|
||||
|
||||
If you installed `haiku.rag` (full package), Zero Entropy is already included. If you installed `haiku.rag-slim`, add the zeroentropy extra:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[zeroentropy]
|
||||
```
|
||||
|
||||
Then configure:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
provider: zeroentropy
|
||||
model: zerank-1 # Currently the only available model
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export ZEROENTROPY_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### vLLM
|
||||
|
||||
For high-performance local reranking using dedicated reranking models:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
provider: vllm
|
||||
model: mixedbread-ai/mxbai-rerank-base-v2
|
||||
|
||||
providers:
|
||||
vllm:
|
||||
rerank_base_url: http://localhost:8001
|
||||
```
|
||||
|
||||
**Note:** vLLM reranking uses the `/rerank` API endpoint. You need to run a vLLM server separately with a reranking model loaded. Consult the specific model's documentation for proper vLLM serving configuration.
|
||||
|
||||
## Research Configuration
|
||||
|
||||
Configure the multi-agent research workflow:
|
||||
|
||||
```yaml
|
||||
research:
|
||||
provider: "" # Empty to use qa settings
|
||||
model: "" # Empty to use qa model
|
||||
max_iterations: 3 # Maximum search/evaluate cycles
|
||||
confidence_threshold: 0.8 # Stop when confidence meets/exceeds this
|
||||
max_concurrency: 1 # Sub-questions searched in parallel per iteration
|
||||
```
|
||||
|
||||
- **provider/model**: LLM provider and model for research. Leave empty to use the same settings as `qa`.
|
||||
- **max_iterations**: Maximum number of search/evaluate cycles before stopping (default: 3)
|
||||
- **confidence_threshold**: Stop research when evaluation confidence score meets or exceeds this threshold (default: 0.8)
|
||||
- **max_concurrency**: Number of sub-questions to search in parallel during each iteration (default: 1)
|
||||
|
||||
The research workflow plans sub-questions, searches in parallel batches, evaluates findings, and iterates until reaching the confidence threshold or max iterations.
|
||||
|
||||
## AG-UI Server Configuration
|
||||
|
||||
Configure the AG-UI HTTP server for streaming graph execution events:
|
||||
|
||||
```yaml
|
||||
agui:
|
||||
host: "0.0.0.0"
|
||||
port: 8000
|
||||
cors_origins: ["*"]
|
||||
cors_credentials: true
|
||||
cors_methods: ["GET", "POST", "OPTIONS"]
|
||||
cors_headers: ["*"]
|
||||
```
|
||||
|
||||
Start the AG-UI server with:
|
||||
|
||||
```bash
|
||||
haiku-rag serve --agui
|
||||
```
|
||||
|
||||
The server exposes:
|
||||
- `GET /health` - Health check endpoint
|
||||
- `POST /v1/agent/stream` - Research graph streaming endpoint (Server-Sent Events)
|
||||
|
||||
See [Server Mode](server.md) for more details.
|
||||
|
||||
## Other Settings
|
||||
|
||||
### Database and Storage
|
||||
|
||||
By default, `haiku.rag` uses a local LanceDB database:
|
||||
|
||||
```yaml
|
||||
storage:
|
||||
data_dir: /path/to/data # Empty = use default platform location
|
||||
```
|
||||
|
||||
For remote storage, use the `lancedb` settings with various backends:
|
||||
|
||||
```yaml
|
||||
# LanceDB Cloud
|
||||
lancedb:
|
||||
uri: db://your-database-name
|
||||
api_key: your-api-key
|
||||
region: us-west-2 # optional
|
||||
|
||||
# Amazon S3
|
||||
lancedb:
|
||||
uri: s3://my-bucket/my-table
|
||||
# Use AWS credentials or IAM roles
|
||||
|
||||
# Azure Blob Storage
|
||||
lancedb:
|
||||
uri: az://my-container/my-table
|
||||
# Use Azure credentials
|
||||
|
||||
# Google Cloud Storage
|
||||
lancedb:
|
||||
uri: gs://my-bucket/my-table
|
||||
# Use GCP credentials
|
||||
|
||||
# HDFS
|
||||
lancedb:
|
||||
uri: hdfs://namenode:port/path/to/table
|
||||
```
|
||||
|
||||
Authentication is handled through standard cloud provider credentials (AWS CLI, Azure CLI, gcloud, etc.) or by setting `api_key` for LanceDB Cloud.
|
||||
|
||||
**Note:** Table optimization is automatically handled by LanceDB Cloud (`db://` URIs) and is disabled for better performance. For object storage backends (S3, Azure, GCS), optimization is still performed locally.
|
||||
|
||||
#### Database Auto-creation
|
||||
|
||||
haiku.rag intelligently handles database creation based on operation type:
|
||||
|
||||
- **Write operations** (add, add-src, delete, rebuild): Automatically create the database and required tables if they don't exist
|
||||
- **Read operations** (list, get, search, ask, research): Fail with a clear error if the database doesn't exist
|
||||
|
||||
This prevents the common mistake where a search query accidentally creates an empty database. To initialize your database, simply add your first document using `haiku-rag add` or `haiku-rag add-src`.
|
||||
|
||||
### Vector Indexing
|
||||
|
||||
Configure vector indexing behavior for efficient similarity search:
|
||||
|
||||
```yaml
|
||||
search:
|
||||
vector_index_metric: cosine # cosine, l2, or dot
|
||||
vector_refine_factor: 30 # Re-ranking factor for accuracy
|
||||
```
|
||||
|
||||
- **vector_index_metric**: Distance metric for vector similarity:
|
||||
- `cosine`: Cosine similarity (default, best for most embeddings)
|
||||
- `l2`: Euclidean distance
|
||||
- `dot`: Dot product similarity
|
||||
- **vector_refine_factor**: Improves accuracy when using a vector index by retrieving `refine_factor * limit` candidates (using approximate search) and re-ranking them with exact distances. Higher values increase accuracy but slow down queries. Default: 30
|
||||
- **Only applies with a vector index** - has no effect on brute-force search, which already returns exact results
|
||||
|
||||
!!! note
|
||||
Vector indexes are only necessary for large datasets with over 100,000 chunks. For smaller datasets, LanceDB's brute-force kNN search provides exact results with good performance. Only create an index if you notice search performance degradation on large datasets.
|
||||
|
||||
**Index creation:**
|
||||
|
||||
Vector indexes are **not created automatically** during document ingestion to avoid slowing down the process. After you've added documents (at least 256 chunks required), create the index manually:
|
||||
|
||||
```bash
|
||||
haiku-rag create-index
|
||||
```
|
||||
|
||||
This command:
|
||||
- Checks if you have enough data (minimum 256 chunks)
|
||||
- Creates an IVF_PQ index for fast approximate nearest neighbor (ANN) search
|
||||
- Uses LanceDB's automatic parameter calculation based on your dataset size and vector dimensions
|
||||
|
||||
**Re-indexing:**
|
||||
|
||||
Indexes are not automatically updated when you add new documents. After adding a significant amount of new data:
|
||||
|
||||
```bash
|
||||
haiku-rag create-index # Rebuilds the index with all data
|
||||
```
|
||||
|
||||
Searches still work with stale indexes - LanceDB uses the index for old data (fast ANN) and brute-force kNN for new unindexed rows, then combines the results. However, performance degrades as more unindexed data accumulates.
|
||||
|
||||
For datasets with fewer than 256 chunks, searches use brute-force kNN scans (exact nearest neighbors, 100% recall) which work well for small datasets but don't scale beyond a few hundred thousand vectors.
|
||||
|
||||
### Document Processing
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
# Chunk size for document processing
|
||||
chunk_size: 256
|
||||
|
||||
# Number of adjacent chunks to include before/after retrieved chunks for context
|
||||
# 0 = no expansion (default), 1 = include 1 chunk before and after, etc.
|
||||
# When expanded chunks overlap or are adjacent, they are automatically merged
|
||||
# into single chunks with continuous content to eliminate duplication
|
||||
context_chunk_radius: 0
|
||||
|
||||
# Optional dotted path or file path to a callable that preprocesses
|
||||
# markdown content before chunking
|
||||
markdown_preprocessor: ""
|
||||
|
||||
storage:
|
||||
# Vacuum retention threshold (seconds) for automatic cleanup
|
||||
# When documents are added/updated, old table versions older than this are removed
|
||||
# Default: 86400 seconds (1 day, safe for concurrent connections)
|
||||
# Set to 0 for aggressive cleanup (removes all old versions immediately)
|
||||
vacuum_retention_seconds: 86400
|
||||
```
|
||||
|
||||
#### Markdown Preprocessor
|
||||
|
||||
Optionally preprocess Markdown before chunking by pointing to a callable that receives and returns Markdown text. This is useful for normalizing content, stripping boilerplate, or applying custom transformations before chunk boundaries are computed.
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
# A callable path in one of these formats:
|
||||
# - package.module:func
|
||||
# - package.module.func
|
||||
# - /abs/or/relative/path/to/file.py:func
|
||||
markdown_preprocessor: my_pkg.preprocess:clean_md
|
||||
```
|
||||
|
||||
!!! note
|
||||
- The function signature should be `def clean_md(text: str) -> str` or `async def clean_md(text: str) -> str`.
|
||||
- If the function raises or returns a non-string, haiku.rag logs a warning and proceeds without preprocessing.
|
||||
- The preprocessor affects only the chunking pipeline. The stored document content remains unchanged.
|
||||
|
||||
Example implementation:
|
||||
|
||||
```python
|
||||
# my_pkg/preprocess.py
|
||||
def clean_md(text: str) -> str:
|
||||
# strip HTML comments and collapse multiple blank lines
|
||||
lines = [line for line in text.splitlines() if not line.strip().startswith("<!--")]
|
||||
out = []
|
||||
for line in lines:
|
||||
if line.strip() == "" and (out and out[-1] == ""):
|
||||
continue
|
||||
out.append(line)
|
||||
return "\n".join(out)
|
||||
```
|
||||
|
|
@ -55,7 +55,7 @@ haiku-rag ask "Who is the author of haiku.rag?"
|
|||
|
||||
- [Getting started](tutorial.md) - Tutorial
|
||||
- [Installation](installation.md) - Install haiku.rag with different providers
|
||||
- [Configuration](configuration.md) - Environment variables and settings
|
||||
- [Configuration](config-index.md) - Environment variables and settings
|
||||
- [CLI](cli.md) - Command line interface usage
|
||||
- [Server](server.md) - File monitoring and server mode
|
||||
- [MCP](mcp.md) - Model Context Protocol integration
|
||||
|
|
|
|||
|
|
@ -43,7 +43,7 @@ The slim package has minimal dependencies and lets you install only what you nee
|
|||
- **OpenAI** (GPT models for QA and embeddings)
|
||||
- **Anthropic** (Claude models for QA)
|
||||
|
||||
See [Configuration](configuration.md) for configuring providers including advanced options like vLLM.
|
||||
See [Configuration](config-index.md) for configuring providers including advanced options like vLLM.
|
||||
|
||||
## Requirements
|
||||
|
||||
|
|
@ -71,7 +71,7 @@ When using `haiku.rag-slim`, you can skip installing the `docling` extra and ins
|
|||
- Offloading heavy document processing to a dedicated service
|
||||
- Production deployments with separate processing infrastructure
|
||||
|
||||
See [Remote processing](remote-processing.md) for setup instructions and [Document Processing](configuration.md#document-processing) for configuration options.
|
||||
See [Remote processing](remote-processing.md) for setup instructions and [Document Processing](processing.md) for configuration options.
|
||||
|
||||
## Docker
|
||||
|
||||
|
|
|
|||
250
docs/processing.md
Normal file
250
docs/processing.md
Normal file
|
|
@ -0,0 +1,250 @@
|
|||
# Document Processing & Monitoring
|
||||
|
||||
This guide covers how haiku.rag converts, chunks, and monitors documents.
|
||||
|
||||
## Document Processing
|
||||
|
||||
Configure how documents are converted and chunked:
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
# Chunking configuration
|
||||
chunk_size: 256 # Maximum tokens per chunk
|
||||
context_chunk_radius: 0 # Context radius for chunk expansion
|
||||
markdown_preprocessor: "" # Optional preprocessor script
|
||||
|
||||
# Converter selection
|
||||
converter: docling-local # docling-local or docling-serve
|
||||
|
||||
# Chunker selection and configuration
|
||||
chunker: docling-local # docling-local or docling-serve
|
||||
chunker_type: hybrid # hybrid or hierarchical
|
||||
chunking_tokenizer: "Qwen/Qwen3-Embedding-0.6B" # HuggingFace model for tokenization
|
||||
chunking_merge_peers: true # Merge undersized successive chunks
|
||||
chunking_use_markdown_tables: false # Use markdown tables vs narrative format
|
||||
|
||||
# Conversion options (works with both local and remote converters)
|
||||
conversion_options:
|
||||
# OCR settings
|
||||
do_ocr: true # Enable OCR for bitmap content
|
||||
force_ocr: false # Replace existing text with OCR
|
||||
ocr_lang: [] # OCR languages (e.g., ["en", "fr", "de"])
|
||||
|
||||
# Table extraction
|
||||
do_table_structure: true # Extract table structure
|
||||
table_mode: accurate # fast or accurate
|
||||
table_cell_matching: true # Match table cells back to PDF cells
|
||||
|
||||
# Image settings
|
||||
images_scale: 2.0 # Image scale factor
|
||||
```
|
||||
|
||||
### Conversion Options
|
||||
|
||||
The `conversion_options` section allows fine-grained control over document conversion. These options work with both `docling-local` and `docling-serve` converters.
|
||||
|
||||
#### OCR Settings
|
||||
|
||||
```yaml
|
||||
conversion_options:
|
||||
do_ocr: true # Enable OCR for bitmap/scanned content
|
||||
force_ocr: false # Replace all text with OCR output
|
||||
ocr_lang: [] # List of OCR languages, e.g., ["en", "fr", "de"]
|
||||
```
|
||||
|
||||
- **do_ocr**: When `true`, applies OCR to images and scanned pages. Disable for faster processing if documents contain only native text.
|
||||
- **force_ocr**: When `true`, replaces existing text layers with OCR output. Useful for documents with poor text extraction.
|
||||
- **ocr_lang**: List of language codes for OCR. Empty list uses default language detection. Examples: `["en"]`, `["en", "fr", "de"]`.
|
||||
|
||||
#### Table Extraction
|
||||
|
||||
```yaml
|
||||
conversion_options:
|
||||
do_table_structure: true # Extract structured table data
|
||||
table_mode: accurate # fast or accurate
|
||||
table_cell_matching: true # Match cells back to PDF
|
||||
```
|
||||
|
||||
- **do_table_structure**: When `true`, extracts table structure. Disable for faster processing if tables aren't important.
|
||||
- **table_mode**:
|
||||
- `accurate`: Better table structure recognition (slower)
|
||||
- `fast`: Faster processing with simpler table detection
|
||||
- **table_cell_matching**: When `true`, matches detected table cells back to PDF cells. Disable if tables have merged cells across columns.
|
||||
|
||||
#### Image Settings
|
||||
|
||||
```yaml
|
||||
conversion_options:
|
||||
images_scale: 2.0 # Image resolution scale factor
|
||||
```
|
||||
|
||||
- **images_scale**: Scale factor for extracted images. Higher values = better quality but larger size. Typical range: 1.0-3.0.
|
||||
|
||||
### Local vs Remote Processing
|
||||
|
||||
**Local processing** (default):
|
||||
|
||||
- Uses `docling` library locally
|
||||
- No external dependencies
|
||||
- Good for development and small workloads
|
||||
|
||||
**Remote processing** (docling-serve):
|
||||
|
||||
- Offloads processing to docling-serve API
|
||||
- Better for heavy workloads and production
|
||||
- Requires docling-serve instance (see [Remote processing setup](remote-processing.md))
|
||||
|
||||
To use remote processing:
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
converter: docling-serve
|
||||
chunker: docling-serve
|
||||
|
||||
providers:
|
||||
docling_serve:
|
||||
base_url: http://localhost:5001
|
||||
api_key: "your-api-key" # Optional
|
||||
timeout: 300 # Request timeout in seconds
|
||||
```
|
||||
|
||||
Conversion options work identically for both local and remote processing.
|
||||
|
||||
### Chunking Strategies
|
||||
|
||||
**Hybrid chunking** (default):
|
||||
- Structure-aware chunking
|
||||
- Respects document boundaries
|
||||
- Best for most use cases
|
||||
|
||||
**Hierarchical chunking**:
|
||||
- Creates hierarchical chunk structure
|
||||
- Preserves document hierarchy
|
||||
- Useful for complex documents
|
||||
|
||||
### Table Serialization
|
||||
|
||||
Control how tables are represented in chunks:
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
chunking_use_markdown_tables: false # Default: narrative format
|
||||
```
|
||||
|
||||
- `false`: Tables as narrative text ("Value A, Column 2 = Value B")
|
||||
- `true`: Tables as markdown (preserves table structure)
|
||||
|
||||
### Chunk Size and Context
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
# Chunk size for document processing
|
||||
chunk_size: 256
|
||||
|
||||
# Number of adjacent chunks to include before/after retrieved chunks for context
|
||||
# 0 = no expansion (default), 1 = include 1 chunk before and after, etc.
|
||||
# When expanded chunks overlap or are adjacent, they are automatically merged
|
||||
# into single chunks with continuous content to eliminate duplication
|
||||
context_chunk_radius: 0
|
||||
```
|
||||
|
||||
### Markdown Preprocessor
|
||||
|
||||
Optionally preprocess Markdown before chunking by pointing to a callable that receives and returns Markdown text. This is useful for normalizing content, stripping boilerplate, or applying custom transformations before chunk boundaries are computed.
|
||||
|
||||
```yaml
|
||||
processing:
|
||||
# A callable path in one of these formats:
|
||||
# - package.module:func
|
||||
# - package.module.func
|
||||
# - /abs/or/relative/path/to/file.py:func
|
||||
markdown_preprocessor: my_pkg.preprocess:clean_md
|
||||
```
|
||||
|
||||
!!! note
|
||||
- The function signature should be `def clean_md(text: str) -> str` or `async def clean_md(text: str) -> str`.
|
||||
- If the function raises or returns a non-string, haiku.rag logs a warning and proceeds without preprocessing.
|
||||
- The preprocessor affects only the chunking pipeline. The stored document content remains unchanged.
|
||||
|
||||
Example implementation:
|
||||
|
||||
```python
|
||||
# my_pkg/preprocess.py
|
||||
def clean_md(text: str) -> str:
|
||||
# strip HTML comments and collapse multiple blank lines
|
||||
lines = [line for line in text.splitlines() if not line.strip().startswith("<!--")]
|
||||
out = []
|
||||
for line in lines:
|
||||
if line.strip() == "" and (out and out[-1] == ""):
|
||||
continue
|
||||
out.append(line)
|
||||
return "\n".join(out)
|
||||
```
|
||||
|
||||
## File Monitoring
|
||||
|
||||
Set directories to monitor for automatic indexing:
|
||||
|
||||
```yaml
|
||||
monitor:
|
||||
directories:
|
||||
- /path/to/documents
|
||||
- /another_path/to/documents
|
||||
```
|
||||
|
||||
### Filtering Monitored Files
|
||||
|
||||
Use gitignore-style patterns to control which files are monitored:
|
||||
|
||||
```yaml
|
||||
monitor:
|
||||
directories:
|
||||
- /path/to/documents
|
||||
|
||||
# Exclude specific files or directories
|
||||
ignore_patterns:
|
||||
- "*draft*" # Ignore files with "draft" in the name
|
||||
- "temp/" # Ignore temp directory
|
||||
- "**/archive/**" # Ignore all archive directories
|
||||
- "*.backup" # Ignore backup files
|
||||
|
||||
# Only include specific files (whitelist mode)
|
||||
include_patterns:
|
||||
- "*.md" # Only markdown files
|
||||
- "*.pdf" # Only PDF files
|
||||
- "**/docs/**" # Only files in docs directories
|
||||
```
|
||||
|
||||
**How patterns work:**
|
||||
|
||||
1. **Extension filtering** - Only supported file types are considered
|
||||
2. **Include patterns** - If specified, only matching files are included (whitelist)
|
||||
3. **Ignore patterns** - Matching files are excluded (blacklist)
|
||||
4. **Combining both** - Include patterns are applied first, then ignore patterns
|
||||
|
||||
**Common patterns:**
|
||||
|
||||
```yaml
|
||||
# Only monitor markdown documentation, but ignore drafts
|
||||
monitor:
|
||||
include_patterns:
|
||||
- "*.md"
|
||||
ignore_patterns:
|
||||
- "*draft*"
|
||||
- "*WIP*"
|
||||
|
||||
# Monitor all supported files except in specific directories
|
||||
monitor:
|
||||
ignore_patterns:
|
||||
- "node_modules/"
|
||||
- ".git/"
|
||||
- "**/test/**"
|
||||
- "**/temp/**"
|
||||
```
|
||||
|
||||
Patterns follow [gitignore syntax](https://git-scm.com/docs/gitignore#_pattern_format):
|
||||
|
||||
- `*` matches anything except `/`
|
||||
- `**` matches zero or more directories
|
||||
- `?` matches any single character
|
||||
- `[abc]` matches any character in the set
|
||||
343
docs/providers.md
Normal file
343
docs/providers.md
Normal file
|
|
@ -0,0 +1,343 @@
|
|||
# Providers
|
||||
|
||||
haiku.rag supports multiple AI providers for embeddings, question answering, and reranking. This guide covers provider-specific configuration and setup.
|
||||
|
||||
!!! note
|
||||
You can use a `.env` file in your project directory to set environment variables like `OLLAMA_BASE_URL` and API keys (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`). These will be automatically loaded when running `haiku-rag` commands.
|
||||
|
||||
## Model Settings
|
||||
|
||||
Configure model behavior for `qa` and `research` workflows. These settings apply to any provider that supports them.
|
||||
|
||||
### Basic Settings
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
model:
|
||||
provider: ollama
|
||||
model: gpt-oss
|
||||
temperature: 0.7
|
||||
max_tokens: 500
|
||||
```
|
||||
|
||||
**Available options:**
|
||||
|
||||
- **temperature**: Sampling temperature (0.0-1.0+)
|
||||
- Lower (0.0-0.3): Deterministic, focused responses
|
||||
- Medium (0.4-0.7): Balanced
|
||||
- Higher (0.8-1.0+): Creative, varied responses
|
||||
- **max_tokens**: Maximum tokens in response
|
||||
- **enable_thinking**: Control reasoning behavior (see below)
|
||||
|
||||
### Thinking Control
|
||||
|
||||
The `enable_thinking` setting controls whether models use explicit reasoning steps before answering.
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
model:
|
||||
enable_thinking: false # Faster responses
|
||||
|
||||
research:
|
||||
model:
|
||||
enable_thinking: true # Deeper reasoning
|
||||
```
|
||||
|
||||
**Values:**
|
||||
- `false`: Disable reasoning for faster responses
|
||||
- `true`: Enable reasoning for complex tasks
|
||||
- Not set: Use model defaults
|
||||
|
||||
**Provider support:**
|
||||
|
||||
See the [Pydantic AI thinking documentation](https://ai.pydantic.dev/thinking/) for detailed provider support. haiku.rag supports thinking control for:
|
||||
|
||||
- **OpenAI**: Reasoning models (o1, o3, gpt-oss)
|
||||
- **Anthropic**: All Claude models
|
||||
- **Google**: Gemini models with thinking support
|
||||
- **Groq**: Models with reasoning capabilities
|
||||
- **Bedrock**: Claude, OpenAI, and Qwen models
|
||||
- **Ollama**: Models supporting reasoning (gpt-oss, etc.)
|
||||
- **vLLM**: Models supporting reasoning (gpt-oss, etc.)
|
||||
|
||||
**When to use:**
|
||||
- Disable for simple queries, RAG workflows, speed-critical applications
|
||||
- Enable for complex reasoning, mathematical problems, research tasks
|
||||
|
||||
## Embedding Providers
|
||||
|
||||
If you use Ollama, you can use any pulled model that supports embeddings.
|
||||
|
||||
### Ollama (Default)
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
model:
|
||||
provider: ollama
|
||||
model: mxbai-embed-large
|
||||
vector_dim: 1024
|
||||
```
|
||||
|
||||
The Ollama base URL can be configured in your config file or via environment variable:
|
||||
|
||||
```yaml
|
||||
providers:
|
||||
ollama:
|
||||
base_url: http://localhost:11434
|
||||
```
|
||||
|
||||
Or via environment variable:
|
||||
|
||||
```bash
|
||||
export OLLAMA_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
If not configured, it defaults to `http://localhost:11434`.
|
||||
|
||||
### VoyageAI
|
||||
|
||||
If you installed `haiku.rag` (full package), VoyageAI is already included. If you installed `haiku.rag-slim`, install with VoyageAI extras:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[voyageai]
|
||||
```
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
model:
|
||||
provider: voyageai
|
||||
model: voyage-3.5
|
||||
vector_dim: 1024
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export VOYAGE_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### OpenAI
|
||||
|
||||
OpenAI embeddings are included in the default installation:
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
model:
|
||||
provider: openai
|
||||
model: text-embedding-3-small # or text-embedding-3-large
|
||||
vector_dim: 1536
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### vLLM
|
||||
|
||||
For high-performance local inference, you can use vLLM to serve embedding models with OpenAI-compatible APIs:
|
||||
|
||||
```yaml
|
||||
embeddings:
|
||||
model:
|
||||
provider: vllm
|
||||
model: mixedbread-ai/mxbai-embed-large-v1
|
||||
vector_dim: 512
|
||||
|
||||
providers:
|
||||
vllm:
|
||||
embeddings_base_url: http://localhost:8000
|
||||
```
|
||||
|
||||
**Note:** You need to run a vLLM server separately with an embedding model loaded.
|
||||
|
||||
## Question Answering Providers
|
||||
|
||||
Configure which LLM provider to use for question answering. Any provider and model supported by [Pydantic AI](https://ai.pydantic.dev/models/) can be used.
|
||||
|
||||
### Ollama (Default)
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
model:
|
||||
provider: ollama
|
||||
model: gpt-oss
|
||||
```
|
||||
|
||||
The Ollama base URL can be configured via the `OLLAMA_BASE_URL` environment variable, config file, or defaults to `http://localhost:11434`:
|
||||
|
||||
```bash
|
||||
export OLLAMA_BASE_URL=http://localhost:11434
|
||||
```
|
||||
|
||||
Or in your config file:
|
||||
|
||||
```yaml
|
||||
providers:
|
||||
ollama:
|
||||
base_url: http://localhost:11434
|
||||
```
|
||||
|
||||
### OpenAI
|
||||
|
||||
OpenAI QA is included in the default installation:
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
model:
|
||||
provider: openai
|
||||
model: gpt-4o-mini # or gpt-4, gpt-3.5-turbo, etc.
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export OPENAI_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### Anthropic
|
||||
|
||||
Anthropic QA is included in the default installation:
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
model:
|
||||
provider: anthropic
|
||||
model: claude-3-5-haiku-20241022 # or claude-3-5-sonnet-20241022, etc.
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export ANTHROPIC_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### vLLM
|
||||
|
||||
For high-performance local inference:
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
model:
|
||||
provider: vllm
|
||||
model: Qwen/Qwen3-4B # Any model with tool support in vLLM
|
||||
|
||||
providers:
|
||||
vllm:
|
||||
qa_base_url: http://localhost:8002
|
||||
```
|
||||
|
||||
**Note:** You need to run a vLLM server separately with a model that supports tool calling loaded. Consult the specific model's documentation for proper vLLM serving configuration.
|
||||
|
||||
### Other Providers
|
||||
|
||||
Any provider supported by Pydantic AI can be used. Examples:
|
||||
|
||||
```yaml
|
||||
# Google Gemini
|
||||
qa:
|
||||
model:
|
||||
provider: gemini
|
||||
model: gemini-1.5-flash
|
||||
|
||||
# Groq
|
||||
qa:
|
||||
model:
|
||||
provider: groq
|
||||
model: llama-3.3-70b-versatile
|
||||
|
||||
# Mistral
|
||||
qa:
|
||||
model:
|
||||
provider: mistral
|
||||
model: mistral-small-latest
|
||||
```
|
||||
|
||||
See the [Pydantic AI documentation](https://ai.pydantic.dev/models/) for the complete list of supported providers and models.
|
||||
|
||||
## Reranking Providers
|
||||
|
||||
Reranking improves search quality by re-ordering the initial search results using specialized models. When enabled, the system retrieves more candidates (10x the requested limit) and then reranks them to return the most relevant results.
|
||||
|
||||
Reranking is **disabled by default** (`provider: ""`) for faster searches. You can enable it by configuring one of the providers below.
|
||||
|
||||
### MixedBread AI
|
||||
|
||||
If you installed `haiku.rag` (full package), MxBAI is already included. If you installed `haiku.rag-slim`, add the mxbai extra:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[mxbai]
|
||||
```
|
||||
|
||||
Then configure:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
model:
|
||||
provider: mxbai
|
||||
model: mixedbread-ai/mxbai-rerank-base-v2
|
||||
```
|
||||
|
||||
### Cohere
|
||||
|
||||
If you installed `haiku.rag` (full package), Cohere is already included. If you installed `haiku.rag-slim`, add the cohere extra:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[cohere]
|
||||
```
|
||||
|
||||
Then configure:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
model:
|
||||
provider: cohere
|
||||
model: rerank-v3.5
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export CO_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### Zero Entropy
|
||||
|
||||
If you installed `haiku.rag` (full package), Zero Entropy is already included. If you installed `haiku.rag-slim`, add the zeroentropy extra:
|
||||
|
||||
```bash
|
||||
uv pip install haiku.rag-slim[zeroentropy]
|
||||
```
|
||||
|
||||
Then configure:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
model:
|
||||
provider: zeroentropy
|
||||
model: zerank-1 # Currently the only available model
|
||||
```
|
||||
|
||||
Set your API key via environment variable:
|
||||
|
||||
```bash
|
||||
export ZEROENTROPY_API_KEY=your-api-key
|
||||
```
|
||||
|
||||
### vLLM
|
||||
|
||||
For high-performance local reranking using dedicated reranking models:
|
||||
|
||||
```yaml
|
||||
reranking:
|
||||
model:
|
||||
provider: vllm
|
||||
model: mixedbread-ai/mxbai-rerank-base-v2
|
||||
|
||||
providers:
|
||||
vllm:
|
||||
rerank_base_url: http://localhost:8001
|
||||
```
|
||||
|
||||
**Note:** vLLM reranking uses the `/rerank` API endpoint. You need to run a vLLM server separately with a reranking model loaded. Consult the specific model's documentation for proper vLLM serving configuration.
|
||||
|
|
@ -321,6 +321,6 @@ answer = await client.ask(
|
|||
|
||||
The QA agent will search your documents for relevant information and use the configured LLM to generate a comprehensive answer. With `cite=True`, responses include citations showing which documents were used as sources. Citations prefer the document title when present, otherwise they use the URI.
|
||||
|
||||
The QA provider and model are configured in `haiku.rag.yaml` or can be passed directly to the client (see [Configuration](configuration.md)).
|
||||
The QA provider and model are configured in `haiku.rag.yaml` or can be passed directly to the client (see [Configuration](config-index.md)).
|
||||
|
||||
See also: [Agents](agents.md) for details on the QA agent and the multi‑agent research workflow.
|
||||
|
|
|
|||
71
docs/qa-research.md
Normal file
71
docs/qa-research.md
Normal file
|
|
@ -0,0 +1,71 @@
|
|||
# QA and Research Configuration
|
||||
|
||||
## Question Answering Configuration
|
||||
|
||||
Configure the QA workflow:
|
||||
|
||||
```yaml
|
||||
qa:
|
||||
model:
|
||||
provider: ollama
|
||||
model: gpt-oss
|
||||
enable_thinking: false
|
||||
max_sub_questions: 3 # Maximum sub-questions for deep QA
|
||||
max_iterations: 2 # Maximum search iterations per sub-question
|
||||
max_concurrency: 1 # Sub-questions processed in parallel
|
||||
```
|
||||
|
||||
- **model**: LLM configuration (see [Providers](providers.md#model-settings))
|
||||
- **max_sub_questions**: For deep QA mode, maximum number of sub-questions to generate (default: 3)
|
||||
- **max_iterations**: Maximum search/evaluate cycles per sub-question (default: 2)
|
||||
- **max_concurrency**: Number of sub-questions to process in parallel (default: 1)
|
||||
|
||||
Deep QA mode (`haiku-rag ask --deep`) decomposes complex questions into sub-questions, processes them in parallel batches, and synthesizes the results.
|
||||
|
||||
## Research Configuration
|
||||
|
||||
Configure the multi-agent research workflow:
|
||||
|
||||
```yaml
|
||||
research:
|
||||
model:
|
||||
provider: "" # Empty to use qa settings
|
||||
model: "" # Empty to use qa model
|
||||
enable_thinking: true
|
||||
max_iterations: 3
|
||||
confidence_threshold: 0.8
|
||||
max_concurrency: 1
|
||||
```
|
||||
|
||||
- **model**: LLM configuration. Leave provider/model empty to inherit from `qa` (see [Providers](providers.md#model-settings))
|
||||
- **max_iterations**: Maximum search/evaluate cycles (default: 3)
|
||||
- **confidence_threshold**: Stop when confidence score meets/exceeds this (default: 0.8)
|
||||
- **max_concurrency**: Sub-questions searched in parallel per iteration (default: 1)
|
||||
|
||||
The research workflow plans sub-questions, searches in parallel batches, evaluates findings, and iterates until reaching the confidence threshold or max iterations.
|
||||
|
||||
## AG-UI Server Configuration
|
||||
|
||||
Configure the AG-UI HTTP server for streaming graph execution events:
|
||||
|
||||
```yaml
|
||||
agui:
|
||||
host: "0.0.0.0"
|
||||
port: 8000
|
||||
cors_origins: ["*"]
|
||||
cors_credentials: true
|
||||
cors_methods: ["GET", "POST", "OPTIONS"]
|
||||
cors_headers: ["*"]
|
||||
```
|
||||
|
||||
Start the AG-UI server with:
|
||||
|
||||
```bash
|
||||
haiku-rag serve --agui
|
||||
```
|
||||
|
||||
The server exposes:
|
||||
- `GET /health` - Health check endpoint
|
||||
- `POST /v1/agent/stream` - Research graph streaming endpoint (Server-Sent Events)
|
||||
|
||||
See [Server Mode](server.md) for more details.
|
||||
|
|
@ -44,7 +44,7 @@ docker run -p 5001:5001 -e DOCLING_SERVE_ENABLE_UI=1 quay.io/docling-project/doc
|
|||
|
||||
### Configuration
|
||||
|
||||
Configure haiku.rag to use docling-serve. See the [Document Processing section in Configuration](configuration.md#document-processing) for all available options.
|
||||
Configure haiku.rag to use docling-serve. See the [Document Processing](processing.md) guide for all available options.
|
||||
|
||||
```yaml
|
||||
# haiku.rag.yaml
|
||||
|
|
|
|||
|
|
@ -122,7 +122,7 @@ agui:
|
|||
cors_credentials: true
|
||||
```
|
||||
|
||||
See [Configuration](configuration.md#ag-ui-server-configuration) for all available options.
|
||||
See [Configuration](qa-research.md#ag-ui-server-configuration) for all available options.
|
||||
|
||||
### Using the Streaming Endpoints
|
||||
|
||||
|
|
|
|||
103
docs/storage.md
Normal file
103
docs/storage.md
Normal file
|
|
@ -0,0 +1,103 @@
|
|||
# Database and Storage
|
||||
|
||||
## Local Storage
|
||||
|
||||
By default, `haiku.rag` uses a local LanceDB database:
|
||||
|
||||
```yaml
|
||||
storage:
|
||||
data_dir: /path/to/data # Empty = use default platform location
|
||||
vacuum_retention_seconds: 86400 # Cleanup threshold in seconds
|
||||
```
|
||||
|
||||
- **data_dir**: Directory for local database storage. When empty, uses platform-specific default locations
|
||||
- **vacuum_retention_seconds**: When documents are added/updated, old table versions older than this are removed. Default: 86400 seconds (1 day, safe for concurrent connections). Set to 0 for aggressive cleanup (removes all old versions immediately)
|
||||
|
||||
## Remote Storage
|
||||
|
||||
For remote storage, use the `lancedb` settings with various backends:
|
||||
|
||||
```yaml
|
||||
# LanceDB Cloud
|
||||
lancedb:
|
||||
uri: db://your-database-name
|
||||
api_key: your-api-key
|
||||
region: us-west-2 # optional
|
||||
|
||||
# Amazon S3
|
||||
lancedb:
|
||||
uri: s3://my-bucket/my-table
|
||||
# Use AWS credentials or IAM roles
|
||||
|
||||
# Azure Blob Storage
|
||||
lancedb:
|
||||
uri: az://my-container/my-table
|
||||
# Use Azure credentials
|
||||
|
||||
# Google Cloud Storage
|
||||
lancedb:
|
||||
uri: gs://my-bucket/my-table
|
||||
# Use GCP credentials
|
||||
|
||||
# HDFS
|
||||
lancedb:
|
||||
uri: hdfs://namenode:port/path/to/table
|
||||
```
|
||||
|
||||
Authentication is handled through standard cloud provider credentials (AWS CLI, Azure CLI, gcloud, etc.) or by setting `api_key` for LanceDB Cloud.
|
||||
|
||||
**Note:** Table optimization is automatically handled by LanceDB Cloud (`db://` URIs) and is disabled for better performance. For object storage backends (S3, Azure, GCS), optimization is still performed locally.
|
||||
|
||||
## Database Auto-creation
|
||||
|
||||
haiku.rag intelligently handles database creation based on operation type:
|
||||
|
||||
- **Write operations** (add, add-src, delete, rebuild): Automatically create the database and required tables if they don't exist
|
||||
- **Read operations** (list, get, search, ask, research): Fail with a clear error if the database doesn't exist
|
||||
|
||||
This prevents the common mistake where a search query accidentally creates an empty database. To initialize your database, simply add your first document using `haiku-rag add` or `haiku-rag add-src`.
|
||||
|
||||
## Vector Indexing
|
||||
|
||||
Configure vector indexing behavior for efficient similarity search:
|
||||
|
||||
```yaml
|
||||
search:
|
||||
vector_index_metric: cosine # cosine, l2, or dot
|
||||
vector_refine_factor: 30 # Re-ranking factor for accuracy
|
||||
```
|
||||
|
||||
- **vector_index_metric**: Distance metric for vector similarity:
|
||||
- `cosine`: Cosine similarity (default, best for most embeddings)
|
||||
- `l2`: Euclidean distance
|
||||
- `dot`: Dot product similarity
|
||||
- **vector_refine_factor**: Improves accuracy when using a vector index by retrieving `refine_factor * limit` candidates (using approximate search) and re-ranking them with exact distances. Higher values increase accuracy but slow down queries. Default: 30
|
||||
- **Only applies with a vector index** - has no effect on brute-force search, which already returns exact results
|
||||
|
||||
!!! note
|
||||
Vector indexes are only necessary for large datasets with over 100,000 chunks. For smaller datasets, LanceDB's brute-force kNN search provides exact results with good performance. Only create an index if you notice search performance degradation on large datasets.
|
||||
|
||||
**Index creation:**
|
||||
|
||||
Vector indexes are **not created automatically** during document ingestion to avoid slowing down the process. After you've added documents (at least 256 chunks required), create the index manually:
|
||||
|
||||
```bash
|
||||
haiku-rag create-index
|
||||
```
|
||||
|
||||
This command:
|
||||
- Checks if you have enough data (minimum 256 chunks)
|
||||
- Creates an IVF_PQ index for fast approximate nearest neighbor (ANN) search
|
||||
- Uses LanceDB's automatic parameter calculation based on your dataset size and vector dimensions
|
||||
|
||||
**Re-indexing:**
|
||||
|
||||
Indexes are not automatically updated when you add new documents. After adding a significant amount of new data:
|
||||
|
||||
```bash
|
||||
haiku-rag create-index # Rebuilds the index with all data
|
||||
```
|
||||
|
||||
Searches still work with stale indexes - LanceDB uses the index for old data (fast ANN) and brute-force kNN for new unindexed rows, then combines the results. However, performance degrades as more unindexed data accumulates.
|
||||
|
||||
For datasets with fewer than 256 chunks, searches use brute-force kNN scans (exact nearest neighbors, 100% recall) which work well for small datasets but don't scale beyond a few hundred thousand vectors.
|
||||
|
|
@ -50,7 +50,7 @@ export OPENAI_API_KEY="<your OpenAI API key>"
|
|||
|
||||
For the list of available OpenAI models and their vector dimensions, see the [OpenAI documentation](https://platform.openai.com/docs/guides/embeddings).
|
||||
|
||||
See [Configuration](configuration.md) for all available options.
|
||||
See [Configuration](config-index.md) for all available options.
|
||||
|
||||
## Adding the first documents
|
||||
|
||||
|
|
@ -215,4 +215,4 @@ The following people are presenting talks at PyCon Finland 2025:
|
|||
|
||||
## Configuration
|
||||
|
||||
See [Configuration page](./configuration.md) for complete documentation on YAML configuration and all available options.
|
||||
See [Configuration page](./config-index.md) for complete documentation on YAML configuration and all available options.
|
||||
|
|
|
|||
|
|
@ -59,7 +59,12 @@ nav:
|
|||
- index.md
|
||||
- Getting started: tutorial.md
|
||||
- Installation: installation.md
|
||||
- Configuration: configuration.md
|
||||
- Configuration:
|
||||
- config-index.md
|
||||
- Providers: providers.md
|
||||
- QA and Research: qa-research.md
|
||||
- Document Processing: processing.md
|
||||
- Storage: storage.md
|
||||
- CLI: cli.md
|
||||
- Python: python.md
|
||||
- Agents: agents.md
|
||||
|
|
|
|||
Loading…
Reference in a new issue