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Desktop app product overview

This page is the product-level description of the VERA desktop app (moved from the repository root). For install and first-run steps see Getting started; for internals see Architecture.

What This App Is

This application is a grounded document assistant built around the .vera format.
It helps users search long source documents, get useful answers, and verify those answers against the original pages.

In short: it turns document Q&A into a transparent, source-backed workflow.

Who It Is For

  • Teams working with large manuals, policies, standards, or reports
  • Analysts and compliance users who need citation-ready answers
  • Engineers and operators who need fast lookup in technical documents
  • AI-assisted workflows that require grounded, auditable outputs

Core Value

  • Better search over long documents
  • Answers tied back to source pages and sections
  • Fewer hallucinated responses through grounding and citation
  • One reusable .vera file that works across tools and sessions

Key Capabilities

1. Source Document Viewer

  • View original document pages
  • Navigate by page and section
  • Jump from answer citations directly to source location
  • Work in a two-pane layout with Ask on the left and Source Document on the right
  • Open documents from the native File menu and show file metrics in the bottom status bar
  • Drag the Source Document divider to resize the grounded PDF review area

2. Prompt Input + Retrieval

  • Accept natural language user prompts
  • Retrieve relevant chunks from .vera using keyword, semantic, or hybrid search
  • Pass retrieved context into response generation

3. Visual Grounding

  • Highlight retrieved passages in the source view
  • Show where each claim came from (page and heading path)
  • Keep selected citations focused on the source PDF, with metadata and retrieval details available on demand

4. Session Management

  • Save conversations and retrieval state
  • Revisit prior prompts, results, and citations
  • Support iterative research and comparison across runs

5. Configurable Instructions

  • Layered instructions for response behavior
  • Configurable augmentation that combines system/app instructions, retrieved context, and user prompt
  • Optional domain-specific response templates

6. LLM Ask

  • Connect to one or more LLM providers under File > Settings
  • Select model by task profile (speed, quality, cost)
  • Stream grounded answers with citation links
  • Search remains fully local when no provider is configured

7. External Tool Connectivity

  • Integrate useful supporting tools (search, APIs, data sources, utilities)
  • Use tool outputs as additional context
  • Keep provenance so users can see what informed the answer

How It Works (High Level)

  1. User asks a question
  2. App retrieves relevant context from .vera
  3. App composes prompt with instructions + context + user input
  4. App returns a grounded cited answer from the configured LLM provider, or Search-only results when no provider is set
  5. Citation clicks open the Source Document viewer and highlight the supporting region

Why .vera Matters in This App

A .vera file is a portable retrieval archive that packages:

  • source document content
  • structured text blocks and chunks
  • keyword index
  • embeddings
  • citation metadata
  • visual grounding regions

This lets the app deliver faster, more consistent, and more explainable answers than querying raw PDFs alone.

Design Principles

  • Grounded first: answers should be traceable to sources
  • Transparent by default: show evidence and retrieval path
  • Configurable behavior: adapt to team and domain needs
  • Tool-agnostic architecture: integrate models and utilities safely
  • Reproducible sessions: preserve context and configuration history

Example Use Cases

  • Compliance question answering with page-level citations
  • Technical operations lookup in large manuals
  • Policy interpretation with source traceability
  • Analyst research workflows with persistent sessions

Success Criteria

  • Users can find relevant answers quickly
  • Answers include clear evidence paths
  • Teams trust outputs because sources are visible
  • Configuration and session history support repeatable workflows

Future Enhancements

Libraries (folder-scoped Search/Ask with persistent collection indexes) and LLM Ask are already shipped. Remaining product follow-ups:

  • Voyage and Ollama embeddings after an optional query/document hint on EmbeddingFunction. OpenAI already ships as vera-embed-openai.
  • Advanced reranking and confidence scoring
  • Evaluation dashboard for groundedness and retrieval quality
  • Role-based governance and audit trails
  • Stronger tool orchestration and approval policies