haiku.rag/docs/cli.md

Command Line Interface

The haiku-rag CLI provides complete document management functionality.

!!! note All commands support:

- `--db` - Specify custom database path
- `-h` - Show help for specific command

Example:
```bash
haiku-rag list --db /path/to/custom.db
haiku-rag add -h
```

Document Management

List Documents

haiku-rag list

Filter documents by properties:

# Filter by URI pattern
haiku-rag list --filter "uri LIKE '%arxiv%'"

# Filter by exact title
haiku-rag list --filter "title = 'My Document'"

# Combine multiple conditions
haiku-rag list --filter "uri LIKE '%.pdf' AND title LIKE '%paper%'"

Add Documents

From text:

haiku-rag add "Your document content here"

# Attach metadata (repeat --meta for multiple entries)
haiku-rag add "Your document content here" --meta author=alice --meta topic=notes

From file or URL:

haiku-rag add-src /path/to/document.pdf
haiku-rag add-src https://example.com/article.html

# Optionally set a human‑readable title stored in the DB schema
haiku-rag add-src /mnt/data/doc1.pdf --title "Q3 Financial Report"

# Optionally attach metadata (repeat --meta). Values use JSON parsing if possible:
# numbers, booleans, null, arrays/objects; otherwise kept as strings.
haiku-rag add-src /mnt/data/doc1.pdf --meta source=manual --meta page_count=12 --meta published=true

From directory (recursively adds all supported files):

haiku-rag add-src /path/to/documents/

!!! note When adding a directory, the same content filters configured for file monitoring are applied. This means ignore_patterns and include_patterns from your configuration will be used to filter which files are added.

!!! note As you add documents to haiku.rag the database keeps growing. By default, LanceDB supports versioning of your data. Create/update operations are atomic‑feeling: if anything fails during chunking or embedding, the database rolls back to the pre‑operation snapshot using LanceDB table versioning. You can optimize and compact the database by running the vacuum command.

Get Document

haiku-rag get 3f4a...   # document ID

Delete Document

haiku-rag delete 3f4a...   # document ID
haiku-rag rm 3f4a...       # alias

Use this when you want to change things like the embedding model or chunk size for example.

Search

Basic search:

haiku-rag search "machine learning"

With options:

haiku-rag search "python programming" --limit 10

With filters (filter by document properties):

# Filter by URI pattern
haiku-rag search "neural networks" --filter "uri LIKE '%arxiv%'"

# Filter by exact title
haiku-rag search "transformers" --filter "title = 'Deep Learning Guide'"

# Combine multiple conditions
haiku-rag search "AI" --filter "uri LIKE '%.pdf' AND title LIKE '%paper%'"

Question Answering

Ask questions about your documents:

haiku-rag ask "Who is the author of haiku.rag?"

Ask questions with citations showing source documents:

haiku-rag ask "Who is the author of haiku.rag?" --cite

Use deep QA for complex questions (multi-agent decomposition):

haiku-rag ask "What are the main features and architecture of haiku.rag?" --deep --cite

Show verbose output with deep QA:

haiku-rag ask "What are the main features and architecture of haiku.rag?" --deep --verbose

The QA agent will search your documents for relevant information and provide a comprehensive answer. With --cite, responses include citations showing which documents were used. With --deep, the question is decomposed into sub-questions that are answered in parallel before synthesizing a final answer. With --verbose (only with --deep), you'll see the planning, searching, evaluation, and synthesis steps as they happen. When available, citations use the document title; otherwise they fall back to the URI.

Research

Run the multi-step research graph:

haiku-rag research "How does haiku.rag organize and query documents?" \
  --max-iterations 2 \
  --confidence-threshold 0.8 \
  --max-concurrency 3 \
  --verbose

Flags:

• --max-iterations, -n: maximum search/evaluate cycles (default: 3)
• --confidence-threshold: stop once evaluation confidence meets/exceeds this (default: 0.8)
• --max-concurrency: number of sub-questions searched in parallel each iteration (default: 3)
• --verbose: show planning, searching previews, evaluation summary, and stop reason

When --verbose is set the CLI also consumes the internal research stream, printing every log event as agents progress through planning, search, evaluation, and synthesis. If you build your own integration, call stream_research_graph to access the same log, report, and error events and render them however you like while the graph is running.

Server

Start services (requires at least one flag):

# MCP server only (HTTP transport)
haiku-rag serve --mcp

# MCP server (stdio transport)
haiku-rag serve --mcp --stdio

# A2A server only
haiku-rag serve --a2a

# File monitoring only
haiku-rag serve --monitor

# All services
haiku-rag serve --monitor --mcp --a2a

# Custom ports
haiku-rag serve --mcp --mcp-port 9000 --a2a --a2a-port 9001

See Server Mode for details on available services.

A2A Interactive Client

Connect to and chat with haiku.rag's A2A server:

# Connect to local server
haiku-rag a2aclient

# Connect to remote server
haiku-rag a2aclient --url https://example.com:8000

The interactive client provides:

• Rich markdown rendering of agent responses
• Multi-turn conversation with context
• Agent card discovery and display
• Compact artifact summaries

See A2A documentation for more details.

Settings

View current configuration settings:

haiku-rag settings

Maintenance

Info (Read-only)

Display database metadata without upgrading or modifying it:

haiku-rag info [--db /path/to/your.lancedb]

Shows:

• path to the database
• stored haiku.rag version (from settings)
• embeddings provider/model and vector dimension
• number of documents
• table versions per table (documents, chunks)

At the end, a separate “Versions” section lists runtime package versions:

• haiku.rag
• lancedb
• docling

Vacuum (Optimize and Cleanup)

Reduce disk usage by optimizing and pruning old table versions across all tables:

haiku-rag vacuum

Automatic Cleanup: Vacuum runs automatically in the background after document operations. By default, it removes versions older than 60 seconds (configurable via storage.vacuum_retention_seconds), preserving recent versions for concurrent connections. Manual vacuum can be useful for cleanup after bulk operations or to free disk space immediately.

Rebuild Database

Rebuild the database by deleting all chunks & embeddings and re-indexing all documents. This is useful when want to switch embeddings provider or model:

haiku-rag rebuild

Download Models

Download required runtime models:

haiku-rag download-models

This command:

• Downloads Docling OCR/conversion models (no-op if already present).
• Pulls Ollama models referenced in your configuration (embeddings, QA, research, rerank).