vector-inspector 0.4.2

A comprehensive desktop application for visualizing, querying, and managing vector database data

Release Notes (0.4.2)

This release finalizes the UI refactor and AppState migration. Key changes focus on centralizing application state in AppState, restoring robust singleton semantics for core services, and updating documentation and tests to reflect the new patterns.

Highlights

• AppState: Introduced as the canonical, required application state container for UI views and services. Views now receive app_state: AppState and access shared services via app_state.* properties.
• Singleton core services: SettingsService, CacheManager, and EmbeddingModelRegistry now implement a safe singleton pattern. The initialization guard checks the instance dictionary to avoid false positives from class-level resets used in tests.
• Backward compatibility: Deprecated helper getters (e.g. get_cache_manager()) still return the same singleton instances to avoid state fragmentation, but new code should prefer app_state.*.
• Cache management: CacheManager is documented and examples updated to use app_state.cache_manager for get/set/invalidate operations.
• Settings: SettingsService persists to ~/.vector-inspector/settings.json. Access via app_state.settings_service; signals available on the service for reactive UI updates.
• Tests: Full test suite run — all tests passing (281 passed, 2 skipped).
• Docs: Updated .github/copilot-instructions.md to describe the AppState pattern, singleton usage, preferred access patterns, and testing notes.

Migration notes

• All UI components should now require app_state (no Optional[AppState] fallbacks remain). If any legacy code still instantiates services directly, it will receive the same singleton instance to preserve behavior.

---

Vector Inspector

A comprehensive desktop application for visualizing, querying, and managing vector database data. Similar to SQL database viewers, Vector Inspector provides an intuitive GUI for exploring vector embeddings, metadata, and performing similarity searches across multiple vector database providers.

Quick Demo: See Vector Inspector in action!

🟦 Quick Install (recommended)

These installers work on macOS, Linux, and Windows (PowerShell or Git Bash).

macOS & Linux

curl -fsSL https://vector-inspector.divinedevops.com/install.sh | bash

Windows (PowerShell)

powershell -c "iwr https://vector-inspector.divinedevops.com/install.ps1 -UseBasicParsing | iex"

Windows (Git Bash)

curl -fsSL https://vector-inspector.divinedevops.com/install.sh | bash

These scripts:

• install Vector Inspector
• create a desktop shortcut
• launch the app immediately

This is the easiest and most reliable way to get started.

---

🟩 Run the App

If you installed via pip or prefer manual launch:

vector-inspector

This opens the full desktop application.

From PyPI (Recommended)

pip install vector-inspector
vector-inspector

From a Downloaded Wheel or Tarball (e.g., GitHub Release)

Download the .whl or .tar.gz file from the GitHub Releases page, then install with:

pip install <your-filename.whl>
# or
pip install <your-filename.tar.gz>

After installation, run the application with:

vector-inspector

Note: pip install does not create a desktop shortcut.
Use the bootstrap installer for the full experience.

From Source

# Clone the repository
git clone https://github.com/anthonypdawson/vector-inspector.git
cd vector-inspector

# Install dependencies using PDM
pdm install

# Launch application
scripts/run.sh     # Linux/macOS
scripts/run.bat    # Windows

---

Overview

Vector Inspector bridges the gap between vector databases and user-friendly data exploration tools. While vector databases are powerful for semantic search and AI applications, they often lack the intuitive inspection and management tools that traditional SQL databases have. This project aims to provide that missing layer.

Table of Contents

• Overview
• Key Features
• Architecture
• Use Cases
• Feature Access
• Roadmap
• Configuration
• Development Setup
• Contributing
• License
• Acknowledgments

Key Features

Note: Some features listed below may be not started or currently in progress.

1. Multi-Provider Support

• Connect to vector databases:
  • ChromaDB (persistent local storage)
  • Qdrant (remote server or embedded local)
  • Pinecone (cloud-hosted)
  • Milvus (remote server or Milvus Lite) (Only on MacOs/Linux, experimental)
  • LanceDB (persistent local storage)
  • PgVector/PostgreSQL (remote server)
• Unified interface regardless of backend provider
• Automatically saves last connection configuration
• Secure API key storage for cloud providers

2. Data Visualization

• Metadata Explorer: Browse and filter vector entries by metadata fields
• Vector Dimensionality Reduction: Visualize high-dimensional vectors in 2D/3D using:
  • t-SNE
  • UMAP
  • PCA
• Cluster Visualization: Color-code vectors by metadata categories or clustering results
• Interactive Plots: Zoom, pan, and select vectors for detailed inspection
• Data Distribution Charts: Histograms and statistics for metadata fields

3. Search & Query Interface

• Similarity Search:
  • Text-to-vector search (with embedding model integration)
  • Vector-to-vector search
  • Find similar items to selected entries
  • Adjustable top-k results and similarity thresholds
• Metadata Filtering:
  • SQL-like query builder for metadata
  • Combine vector similarity with metadata filters
  • Advanced filtering: ranges, IN clauses, pattern matching
• Hybrid Search: Combine semantic search with keyword search
• Query History: Save and reuse frequent queries

4. Data Management

• Browse Collections/Indexes: View all available collections with statistics
• CRUD Operations:
  • View individual vectors and their metadata
  • Add new vectors (with auto-embedding options)
  • Update metadata fields
  • Delete vectors (single or batch)
• Bulk Import/Export:
  • Import from CSV, JSON, Parquet
  • Export query results to various formats
  • Backup and restore collections
• Schema Inspector: View collection configuration, vector dimensions, metadata schema

5. SQL-Like Experience

• Query Console: Write queries in a familiar SQL-like syntax (where supported)
• Results Grid:
  • Sortable, filterable table view
  • Pagination for large result sets
  • Column customization
• Data Inspector: Click any row to see full details including raw vector
• Query Execution Plans: Understand how queries are executed
• Auto-completion: Intelligent suggestions for collection names, fields, and operations

6. Advanced Features

• Embedding Model Integration:
  • Use OpenAI, Cohere, HuggingFace models for text-to-vector conversion
  • Local model support (sentence-transformers)
  • Custom model integration
• Vector Analysis:
  • Compute similarity matrices
  • Identify outliers and anomalies
  • Cluster analysis with k-means, DBSCAN
• Embedding Inspector:
  • For similar collections or items, automatically identify which vector dimensions (activations) most contribute to the similarity
  • Map key activations to interpretable concepts (e.g., 'humor', 'sadness', 'anger') using metadata or labels
  • Generate human-readable explanations for why items are similar
• Performance Monitoring:
  • Query latency tracking
  • Index performance metrics
  • Connection health monitoring

Architecture

Vector Inspector is built with PySide6 (Qt for Python) for the GUI, providing a native desktop experience. The backend uses Python with support for multiple vector database providers through a unified interface.

For detailed architecture information, see docs/architecture.md.

Use Cases

1. AI/ML Development: Inspect embeddings generated during model development
2. RAG System Debugging: Verify what documents are being retrieved
3. Data Quality Assurance: Identify poorly embedded or outlier vectors
4. Production Monitoring: Check vector database health and data consistency
5. Data Migration: Transfer data between vector database providers
6. Education: Learn and experiment with vector databases interactively

Feature Access

Vector Inspector follows a user-friendly monetization model:

• All vector database providers are free — Try the full app with any database
• Core workflows remain free — Connect, browse, search, visualize, and manage your data
• Pro adds power tools — Advanced analytics, enterprise formats, workflow automation, and collaboration

Nothing currently in Free will ever move to Pro. See FEATURES.md for a detailed comparison.

Roadmap

Current Status: ✅ Phase 2 Complete

See ROADMAP.md for the complete development roadmap and planned features.

Configuration

Paths are resolved relative to the project root (where pyproject.toml is). For example, entering ./data/chroma_db will use the absolute path resolved from the project root.

The application automatically saves your last connection configuration to ~/.vector-viewer/settings.json. The next time you launch the application, it will attempt to reconnect using the last saved settings.

Example settings structure:

{
  "last_connection": {
    "provider": "chromadb",
    "connection_type": "persistent",
    "path": "./data/chroma_db"
  }
}

Development Setup

# Install PDM if you haven't already
pip install pdm

# Install dependencies with development tools (PDM will create venv automatically)
pdm install -d

# Run tests
pdm run pytest

# Run application in development mode
./run.sh     # Linux/macOS
./run.bat    # Windows

# Or use Python module directly from src directory:
cd src
pdm run python -m vector_viewer

Contributing

Contributions are welcome! Areas where help is needed:

• Additional vector database provider integrations
• UI/UX improvements
• Performance optimizations
• Documentation
• Test coverage

Please see CONTRIBUTING.md for guidelines.

License

MIT License - See LICENSE file for details.

Acknowledgments

This project draws inspiration from:

• DBeaver (SQL database viewer)
• MongoDB Compass (NoSQL database GUI)
• Pinecone Console
• Various vector database management tools

---

See CHANGELOG.md for the latest status and what's new in each release.

See GETTING_STARTED.md for usage instructions and IMPLEMENTATION_SUMMARY.md for technical details.

Contact: Anthony Dawson