vectrixdb 1.9.2

Where vectors come alive - A lightweight, visual-first vector database with embedded ML models

VectrixDB

Where vectors come alive.

A lightweight vector database with embedded ML models, beautiful dashboard, and GraphRAG - no API keys required.

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Features

• 4 Search Modes - Dense, Hybrid, Ultimate, and Graph (GraphRAG)
• Embedded Models - Works offline with bundled ONNX models
• Model Selection - Choose from bundled, HuggingFace, or GitHub release models
• Visual Dashboard - Built-in web UI for managing collections
• Zero Config - Just pip install and start using

Installation

pip install vectrixdb

Quick Start

from vectrixdb import Vectrix

db = Vectrix("my_docs")
db.add(["Python is great", "JavaScript powers the web", "Rust is fast"])

results = db.search("programming")
print(results.top.text)

Search Modes

VectrixDB offers 4 search modes, each building on the previous:

Mode	Components	Best For
dense	Vector similarity	Fast semantic search
hybrid	Dense + Sparse + Reranker	Keyword + semantic matching
ultimate	Hybrid + ColBERT	Maximum accuracy
graph	Ultimate + Knowledge Graph	Complex reasoning (GraphRAG)

# Choose your mode
db = Vectrix("docs", mode="dense")     # Fastest
db = Vectrix("docs", mode="hybrid")    # Balanced
db = Vectrix("docs", mode="ultimate")  # Best quality
db = Vectrix("docs", mode="graph")     # GraphRAG

Model Selection

Customize models for each component. Models load from 3 sources:

1. Bundled Models (Offline)

Pre-packaged ONNX models that work without internet (~100MB total):

db = Vectrix(
    "docs",
    mode="ultimate",
    dense_model="e5-small",
    sparse_model="bm25",
    reranker_model="L12",
    late_interaction_model="colbert",
)

Component	Alias	Model	Size
Dense	e5-small	intfloat/e5-small-v2	33MB
Sparse	bm25	BM25 vocabulary	1MB
Reranker	L12	ms-marco-MiniLM-L12-v2	33MB
ColBERT	colbert	answerai-colbert-small-v1	33MB

2. HuggingFace Models

Use any compatible model from HuggingFace (downloads on first use):

db = Vectrix(
    "docs",
    mode="hybrid",
    dense_model="BAAI/bge-large-en-v1.5",
    sparse_model="naver/splade-cocondenser-ensembledistil",
    reranker_model="cross-encoder/ms-marco-MiniLM-L-12-v2",
)

Compatible models:

• Dense: BAAI/bge-large-en-v1.5, intfloat/e5-large-v2, sentence-transformers/all-mpnet-base-v2
• Sparse: naver/splade-cocondenser-ensembledistil
• Reranker: cross-encoder/ms-marco-MiniLM-L-12-v2, BAAI/bge-reranker-base
• ColBERT: jinaai/jina-colbert-v2, colbert-ir/colbertv2.0

3. GitHub Release Models

Larger models hosted on GitHub releases (auto-downloaded on first use):

db = Vectrix(
    "docs",
    mode="ultimate",
    dense_model="github:bge-small",
    sparse_model="github:splade",
    reranker_model="github:reranker-l6",
    late_interaction_model="github:bge-m3",
)

Tag	Model	Type	Languages	Size
github:bge-small	BAAI/bge-small-en-v1.5	Dense	EN	127MB
github:e5-small	intfloat/e5-small-v2 FP32	Dense	EN	127MB
github:dense-multi	multilingual-e5-small	Dense	100+	113MB
github:splade	SPLADE++	Sparse	EN	508MB
github:reranker-l6	ms-marco-MiniLM-L6-v2	Reranker	EN	87MB
github:reranker-multi	mMiniLMv2-L12	Reranker	15+	113MB
github:bge-m3	BGE-M3	ColBERT	100+	563MB

Metadata & Filtering

db.add(
    texts=["iPhone 15", "Galaxy S24", "Pixel 8"],
    metadata=[
        {"brand": "Apple", "price": 999},
        {"brand": "Samsung", "price": 899},
        {"brand": "Google", "price": 699}
    ]
)

results = db.search("smartphone", filter={"brand": "Apple"})

Storage Backends

Use external storage backends (Lakebase, DeltaLake, CosmosDB) with full search mode support:

from vectrixdb import Vectrix, VectrixDB

# Connect to Lakebase (PostgreSQL + pgvector)
lakebase = VectrixDB.with_lakebase(
    host="your-lakebase-host",
    database="vectrixdb",
    user="your-user",
    password="your-password",
)

# Use Vectrix with storage backend + ultimate mode
db = Vectrix(
    "products",
    mode="ultimate",
    dense_model="bge-small",
    sparse_model="splade",
    reranker_model="L6",
    late_interaction_model="colbert",
    storage_backend=lakebase,
)

db.add(texts=["Product A", "Product B"])
results = db.search("query")  # Full ultimate search from Lakebase

Adaptive Schema

Schema adapts based on selected mode:

Mode	Columns Created
dense	dense_embedding
hybrid	dense_embedding + sparse_embedding
ultimate	dense_embedding + sparse_embedding + late_interaction_embedding
graph	Same as ultimate + graph tables

All modes store text_content for reranker (computed at query time).

REST API

Start the server:

VECTRIXDB_API_KEY=your_secret vectrixdb serve --port 7337

Open the dashboard at http://localhost:7337/dashboard

API Examples

# Create collection
curl -X POST http://localhost:7337/api/v1/collections \
  -H "Content-Type: application/json" \
  -H "api-key: your_secret" \
  -d '{"name": "docs", "dimension": 384}'

# Add documents (auto-embedding)
curl -X POST http://localhost:7337/api/v1/collections/docs/text-upsert \
  -H "Content-Type: application/json" \
  -H "api-key: your_secret" \
  -d '{"points": [{"id": "1", "text": "Hello world"}]}'

# Search
curl -X POST http://localhost:7337/api/v1/collections/docs/text-search \
  -H "Content-Type: application/json" \
  -H "api-key: your_secret" \
  -d '{"query_text": "greeting", "limit": 10}'

Project Structure

VectrixDB/
├── vectrixdb/
│   ├── core/           # Vector index, storage, search
│   │   ├── graphrag/   # Knowledge graph
│   │   └── search/     # Search algorithms
│   ├── api/            # FastAPI server
│   ├── models/         # Embedded ONNX models
│   ├── dashboard/      # Web UI
│   └── cli.py          # Command line
├── tests/
└── requirements.txt

Install from Source

git clone https://github.com/knowusuboaky/VectrixDB.git
cd VectrixDB
pip install -e .

Requirements

• Python 3.9+
• No API keys needed
• Models are bundled or auto-downloaded

License

Apache 2.0

Author

Kwadwo Daddy Nyame Owusu - Boakye

GitHub: @knowusuboaky