vectrixdb 2.1.0

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)
• 8 Storage Backends - Memory, SQLite, Lakebase, DeltaLake, CosmosDB, PostgreSQL, OpenSearch, Aurora PostgreSQL
• Embedded Models - Works offline with bundled ONNX models
• Model Selection - Choose from bundled, HuggingFace, or GitHub release models
• Document Index - Hierarchical document storage with chunking
• Visual Dashboard - Built-in web UI for managing collections
• Zero Config - Just pip install and start using

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Installation

From PyPI (Recommended)

pip install vectrixdb

From GitHub (Latest)

pip install git+https://github.com/knowusuboaky/VectrixDB.git

Specific Version from GitHub

pip install git+https://github.com/knowusuboaky/VectrixDB.git@v2.0.0

From Source

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

Optional Dependencies

# HuggingFace sentence-transformers
pip install vectrixdb[hf]

# FastEmbed (lightweight ONNX embeddings)
pip install vectrixdb[fastembed]

# All embedding providers
pip install vectrixdb[embeddings]

# Visualization (UMAP)
pip install vectrixdb[viz]

# Everything
pip install vectrixdb[all]

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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)

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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

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Model Selection

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

1. Bundled Models (Offline, No Downloads)

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

db = Vectrix(
    "docs",
    mode="ultimate",
    dense_model="e5-small",            # 384 dim, 33MB
    sparse_model="bm25",               # 1MB
    reranker_model="L12",              # 33MB
    late_interaction_model="colbert",  # 33MB
)

Component	Alias	Model	Dimension	Size
Dense	e5-small	intfloat/e5-small-v2	384	33MB
Sparse	bm25	BM25 vocabulary	-	1MB
Reranker	L12	ms-marco-MiniLM-L12-v2	-	33MB
ColBERT	colbert	answerai-colbert-small-v1	128	33MB

2. GitHub Release Models (Auto-Downloaded)

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

db = Vectrix(
    "docs",
    mode="ultimate",
    dense_model="bge-base",            # 768 dim, higher quality
    sparse_model="bm25",
    reranker_model="bge-reranker",     # Higher quality
    late_interaction_model="colbert-v2",
)

Alias	Model	Dimension	Size
bge-base	BAAI/bge-base-en-v1.5	768	110MB
bge-small	BAAI/bge-small-en-v1.5	384	127MB
bge-reranker	BAAI/bge-reranker-base	-	212MB
colbert-v2	colbert-ir/colbertv2.0	128	67MB
splade	SPLADE++	-	508MB

3. HuggingFace Models

Use any compatible model from HuggingFace (requires pip install vectrixdb[hf]):

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

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Storage Backends

VectrixDB supports 8 storage backends:

Backend	Type	Persistence	Modes	Best For
memory	In-Memory	No	All	Testing, small datasets
sqlite	File-based	Yes	All	Local development
lakebase	PostgreSQL + pgvector	Yes	All	Databricks Lakebase
delta_lake	Delta Lake	Yes	All	Databricks Unity Catalog
cosmosdb	Azure CosmosDB	Yes	All	Azure cloud
postgresql	PostgreSQL + pgvector	Yes	All	Self-hosted PostgreSQL
opensearch	AWS OpenSearch	Yes	Dense, Hybrid	AWS managed search
aurora_postgresql	AWS Aurora + pgvector	Yes	All	AWS managed PostgreSQL

Memory Storage (Default)

from vectrixdb import VectrixDB, StorageConfig, StorageBackend

# In-memory (default, no persistence)
db = VectrixDB()

# Or explicitly
config = StorageConfig(backend=StorageBackend.MEMORY)
db = VectrixDB(storage_config=config)

SQLite Storage (Local Persistence)

from vectrixdb import VectrixDB

# SQLite with file path
db = VectrixDB(path="./my_vectors")

# Creates: ./my_vectors/vectrix.db

Lakebase Storage (Databricks)

from vectrixdb import Vectrix, VectrixDB

# Connect to Lakebase (PostgreSQL + pgvector)
lakebase = VectrixDB.with_lakebase(
    host="your-lakebase-host.cloud.databricks.com",
    database="databricks_postgres",
    user="your-user",
    password="your-oauth-token",  # OAuth JWT from Lakebase Connect
    port=5432,
    schema="public",  # Optional, defaults to "public"
)

# Use with Vectrix
db = Vectrix(
    "products",
    mode="ultimate",
    storage_backend=lakebase,
)

db.add(texts=["Product A", "Product B"])
results = db.search("query")

Delta Lake Storage (Databricks Unity Catalog)

from vectrixdb import VectrixDB

# Connect to Delta Lake via Databricks SQL
delta = VectrixDB.with_delta_lake(
    workspace_url="https://your-workspace.cloud.databricks.com",
    token="dapi_your_token",
    catalog="main",
    schema="vectrixdb",
    warehouse_id="your_warehouse_id",
)

# Use with Vectrix
db = Vectrix("products", mode="hybrid", storage_backend=delta)

CosmosDB Storage (Azure)

from vectrixdb import VectrixDB, StorageConfig, StorageBackend

config = StorageConfig(
    backend=StorageBackend.COSMOSDB,
    cosmos_endpoint="https://your-account.documents.azure.com:443/",
    cosmos_key="your-primary-key",
    cosmos_database="vectrixdb",
)

db = VectrixDB(storage_config=config)

OpenSearch Storage (AWS)

AWS OpenSearch Serverless with native k-NN vector search.

Note: OpenSearch supports dense and hybrid modes only.

from vectrixdb import VectrixDB

opensearch = VectrixDB.with_opensearch(
    endpoint="https://xxx.us-east-1.aoss.amazonaws.com",
    region="us-east-1",
)

Aurora PostgreSQL Storage (AWS)

AWS Aurora PostgreSQL with pgvector. Supports all modes including ultimate.

from vectrixdb import VectrixDB

aurora = VectrixDB.with_aurora_postgresql(
    host="cluster.xxx.us-east-1.rds.amazonaws.com",
    database="vectrixdb",
    user="admin",
    password="password",
)

Adaptive Schema

Schema adapts based on selected mode:

Mode	Columns Created
dense	id, dense_embedding, metadata, text_content, created_at, updated_at
hybrid	+ sparse_embedding
ultimate	+ late_interaction_embedding
graph	Same as ultimate + graph tables

---

Document Index

Hierarchical document storage with automatic chunking:

from vectrixdb import DocumentIndex, chunk_text, chunk_with_context

# Create document index
doc_index = DocumentIndex("./docs_index")

# Chunk text (simple)
chunks = chunk_text(
    "Your long document text here...",
    chunk_size=1000,
    chunk_overlap=200,
)

# Chunk markdown with context (preserves headings)
chunks = chunk_with_context(
    markdown_text,
    chunk_size=1200,
    chunk_overlap=200,
)
# Returns: [{"content": "...", "heading": "Section Title", "level": 2}, ...]

# Build tree from markdown
from vectrixdb import build_tree_from_markdown, build_tree_from_pdf

tree = build_tree_from_markdown(markdown_content)
tree = build_tree_from_pdf(pdf_path)

Document Index with Storage Backend

from vectrixdb import DocumentIndex, VectrixDB

# Connect to storage
lakebase = VectrixDB.with_lakebase(...)

# Document index uses storage backend
doc_index = DocumentIndex(storage=lakebase)

# Save documents and nodes
doc_index.save_document({
    "doc_id": "doc_001",
    "title": "My Document",
    "doc_type": "markdown",
    "page_count": 5,
})

# Query documents
docs = doc_index.list_documents()
nodes = doc_index.get_document_nodes("doc_001")

---

Metadata & Filtering

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

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

# Complex filters
results = db.search("phone", filter={
    "brand": {"$in": ["Apple", "Samsung"]},
    "price": {"$lt": 1000}
})

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Advanced API

For full control, use the VectrixDB class directly:

from vectrixdb import VectrixDB, Collection

# Create database
db = VectrixDB(path="./my_db")

# Create collection with specific dimension
coll = db.create_collection("products", dimension=384)

# Add vectors directly
coll.add(
    ids=["p1", "p2"],
    vectors=[[0.1, 0.2, ...], [0.3, 0.4, ...]],
    metadata=[{"name": "Product A"}, {"name": "Product B"}],
)

# Search with vectors
results = coll.search(query=[0.1, 0.2, ...], limit=10)

# List collections
collections = db.list_collections()

# Delete collection
db.delete_collection("products")

---

Embedded Models API

Use embedding models directly:

from vectrixdb import (
    DenseEmbedder,
    SparseEmbedder,
    RerankerEmbedder,
    LateInteractionEmbedder,
)

# Dense embeddings
dense = DenseEmbedder(model="e5-small")
vectors = dense.embed(["Hello world", "How are you?"])

# Sparse embeddings (BM25)
sparse = SparseEmbedder()
sparse_vectors = sparse.embed(["Hello world"])

# Reranker
reranker = RerankerEmbedder(model="L12")
scores = reranker.rerank("query", ["doc1", "doc2", "doc3"])

# Late interaction (ColBERT)
colbert = LateInteractionEmbedder(model="colbert")
token_embeddings = colbert.embed(["Hello world"])

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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}'

---

GraphRAG

Build knowledge graphs from documents:

from vectrixdb import Vectrix, create_openai_config

# Create with graph mode
db = Vectrix("docs", mode="graph")

# Or with custom LLM config
config = create_openai_config(
    api_key="your-openai-key",
    model="gpt-4o-mini",
)

db = Vectrix(
    "docs",
    mode="graph",
    graphrag_config=config,
)

# Add documents (extracts entities & relationships)
db.add(["Apple announced the iPhone 15 in September 2023."])

# Search with graph reasoning
results = db.search("What products did Apple release?")

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Project Structure

VectrixDB/
├── vectrixdb/
│   ├── core/           # Vector index, storage, search
│   │   ├── storage.py  # All storage backends
│   │   ├── collection.py
│   │   ├── database.py
│   │   ├── document_index.py
│   │   ├── graphrag/   # Knowledge graph
│   │   └── search/     # Search algorithms
│   ├── api/            # FastAPI server
│   ├── models/         # Embedded ONNX models
│   │   └── data/       # Bundled model files
│   ├── dashboard/      # Web UI
│   ├── easy.py         # Vectrix simple API
│   └── cli.py          # Command line
├── tests/
└── pyproject.toml

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Requirements

• Python 3.9+
• No API keys needed (for bundled models)
• Models are bundled or auto-downloaded

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License

Apache 2.0

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Author

Kwadwo Daddy Nyame Owusu - Boakye

GitHub: @knowusuboaky