Chat

The chat TUI runs conversational RAG against your database from the terminal. Streaming responses, expandable citations with visual grounding, multi-turn sessions, and a command palette for filtering and inspection.

Run it

haiku-rag chat
haiku-rag chat --db /path/to/database.lancedb
haiku-rag chat --model openai:gpt-4o

How it works

The chat is a Pydantic AI agent with the RAG capability attached by default. A single capability loads eagerly; when both RAG and analysis are enabled, they remain deferred until the model chooses which one to load. Each turn the agent decides which tool to call next, runs hybrid search against your documents, expands context around the hits, may issue further searches, and answers with citations. You see streaming text and native tool events directly from Pydantic AI.

The session is in-memory for the lifetime of the TUI. Conversation history is kept across turns so follow-up questions reuse prior context. Citations are tracked per turn and inspectable via the command palette. Clearing the chat resets the session and the agent's memory.

Citations and visual grounding

Each answer cites the chunks the agent used, with source document, page numbers, and section headings. Citations are expandable inline. Picture citations render the figure directly underneath the text snippet.

For visual grounding of a text chunk (the chunk highlighted on its source page image), open the command palette and pick "Show visual grounding". This requires:

• Documents processed via Docling with page images (default for PDFs).
• A terminal that supports inline images (iTerm2, WezTerm, Kitty).
• A stored DoclingDocument on the document. Plain text added via haiku-rag add doesn't have it.

You can also render visual grounding from the CLI without launching the TUI:

haiku-rag visualize <chunk_id>

Attaching images

Press Ctrl+I to open the image picker: a directory tree filtered to image files with a live preview. Selecting an image inserts an [Image #N] token at the cursor and attaches the image to your next message. Tokens delete as a unit with backspace or delete, and you can place them anywhere in the text to control where each image appears relative to your words.

Retrieval stays text-based; the images are sent to the model alongside your message, so the driving model needs vision: true in its configuration.

Command palette

Ctrl+P opens the palette.

Capabilities

The default capability is rag. Enable analysis when the question needs computation, aggregation, comparison across documents, or section-scoped reading that a single search can't deliver:

# both capabilities (the agent routes between them)
haiku-rag chat -c rag -c analysis

# analysis only
haiku-rag chat -c analysis

The analysis capability mounts every document as a virtual filesystem at /documents/{id}/ (with metadata.json, content.txt, items.jsonl, and toc.json) and runs Python in a sandboxed interpreter with search and list_documents as awaitable functions. It's the right choice for questions like:

• "How many of these documents mention X?"
• "Summarize Section 5 of paper Y."
• "Compare the experimental sections across these three reports."
• "Which section discusses the proof of Theorem 4.10?"

For everyday Q&A, RAG alone is faster and cheaper. Attaching both lets the agent pick. See Analysis capability.

Document filter

Run "Filter documents" from the command palette to restrict searches to a subset. The filter applies to every search the agent runs for the rest of the session.

Chat also honors the global --read-only flag. See the CLI reference for details.