vector-inspector 0.8.2

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

Release Notes (0.8.2) — May 25, 2026

Bootstrap installer for systems without Python installed, or for users who prefer not to use the command line.

Packaging & Distribution

• Added a pip-enabled Windows bootstrap installer flow via scripts/bootstrap_installer.py. It creates an app-local virtual environment, installs vector-inspector from pip, and writes app/pip launchers so users can install extras after setup.
• Added scripts/build_installer.py to build the bootstrap installer executable with Nuitka.

Docs

• Added docs/BOOTSTRAP_INSTALLER.md documenting end-to-end build, install, update, pip usage, and troubleshooting for the new bootstrap installer flow.

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Vector Inspector

The ultimate toolkit for vector databases - a comprehensive desktop app to inspect, query, and visualize your embeddings across Chroma, Qdrant, Weaviate, Pinecone, LanceDB, pgvector and more.

Similar to SQL viewers (DBeaver/TablePlus) but built for vector databases, Vector Inspector offers an intuitive GUI for exploring embeddings, metadata, similarity search, and CRUD across multiple providers.

Quick Demo: See Vector Inspector in action!

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.

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Homepage

https://vector-inspector.divinedevops.com

🟦 Installation

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.

From PyPI

Fast Install (Minimal)

Start with just the core app, then add database providers as needed:

# Install core app only (fast, ~30 seconds)
pip install vector-inspector

# Launch the app
vector-inspector

When you connect to a database, the app will prompt you to install the needed provider.

Recommended Install

Install with commonly used databases and features:

pip install vector-inspector[recommended]
# Includes: ChromaDB, Qdrant, embeddings, and visualization tools

Full Install

Install everything (all database providers and features):

pip install vector-inspector[all]
# Same as previous versions - includes all providers

Individual Providers

Install specific database providers:

pip install vector-inspector[chromadb]    # ChromaDB only
pip install vector-inspector[qdrant]      # Qdrant only
pip install vector-inspector[pinecone]    # Pinecone only
pip install vector-inspector[pgvector]    # PostgreSQL/pgvector
pip install vector-inspector[weaviate]    # Weaviate
pip install vector-inspector[lancedb]     # LanceDB
pip install vector-inspector[milvus]      # Milvus

Optional Features

Add embedding models, visualization, or document processing:

pip install vector-inspector[embeddings]  # Text embedding models
pip install vector-inspector[clip]        # Image/text embeddings
pip install vector-inspector[viz]         # UMAP, t-SNE, clustering
pip install vector-inspector[documents]   # PDF, DOCX support

You can combine multiple extras:

pip install vector-inspector[chromadb,qdrant,embeddings,viz]

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 (PDM)

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

# Install core dependencies only (fast)
pdm install

# OR install with recommended providers
pdm install -G recommended

# OR install everything (all providers and features)
pdm install -G all

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

Note for developers: The dependency groups in pyproject.toml can be installed with PDM using -G <group>. Available groups: chromadb, qdrant, pinecone, lancedb, pgvector, weaviate, milvus, embeddings, clip, viz, documents, llm, recommended, all.

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Provider Compatibility Matrix

Minimum tested library versions and platform support for each database provider. These correspond to the version constraints in pyproject.toml.

Python: >=3.11

Provider	Install Extra	Package(s)	Min Version(s)	Platform	Connection Mode	Status
ChromaDB	[chromadb]	chromadb	0.4.22	Windows, macOS, Linux	Persistent / HTTP / Ephemeral	✅ Stable
Qdrant	[qdrant]	qdrant-client	1.7.0	Windows, macOS, Linux	Persistent / HTTP	✅ Stable
Pinecone	[pinecone]	pinecone	8.0.0	Windows, macOS, Linux	HTTP (Cloud)	✅ Stable
LanceDB	[lancedb]	lancedb, pyarrow	0.27.0, 14.0.0	Windows, macOS, Linux	Persistent	✅ Stable
PgVector	[pgvector]	psycopg2-binary, pgvector	2.9.11, 0.4.2	Windows, macOS, Linux	HTTP (PostgreSQL)	✅ Stable
Weaviate	[weaviate]	weaviate-client	4.19.2	Windows, macOS, Linux	Persistent / HTTP	✅ Stable
Milvus	[milvus]	pymilvus	2.6.8	macOS, Linux	Lite (file) / HTTP (server)	⚠️ Experimental

Milvus modes: Use Milvus Lite (file-based) on macOS/Linux, or connect to a remote Milvus server via HTTP. Milvus Lite is ideal for development and testing; use a Milvus server for production workloads. Windows support is experimental — some Milvus Lite operations (e.g. dropping collections) are known to fail due to OS-level file-locking limitations.

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🟩 Running Vector Inspector

vector-inspector

Note: The Quick Install script launches the app automatically. If you installed via pip or from source, use the command above. This opens the full desktop application.

Optional LLM runtime (llama-cpp-python)

llama-cpp-python is optional and only needed for the in-process LLM provider (llama-cpp).

• Install via PDM dependency group (developer / recommended):

pdm install -G llm

• Platform-specific pip install (end users / PyPI):

Windows (pre-built CPU wheel index):

pip install llama-cpp-python --prefer-binary \
  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu

Linux / macOS (official wheels / source build):

pip install llama-cpp-python

CUDA / GPU wheels (pick matching CUDA version):

pip install llama-cpp-python --prefer-binary \
  --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121

• Scripts included in this repo:
  • scripts/install-llm-windows.ps1 — PowerShell helper for Windows
  • scripts/install-llm-unix.sh — bash helper for Linux/macOS

Notes:

• If you cannot build native wheels on Windows, use the Windows pre-built index above.
• Vector Studio users can use Settings → LLM → "Download default model" to automatically download the default Phi-3-mini GGUF model into the local cache. This button is disabled in the free Vector Inspector tier.
• Free-tier users should download a GGUF model manually (or use the scripts above) and set the path in Settings → LLM, or configure Ollama (local server) or an OpenAI-compatible API instead.

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Table of Contents

• 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) - In Progress
  • LanceDB (persistent local storage) — requires lancedb>=0.27.0, pyarrow>=14.0.0
  • PgVector/PostgreSQL (remote server)
  • Weaviate (Local/Remote + WCD/Cloud)
• 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-inspector/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_inspector

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

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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