anywidget-vector 0.2.8

Interactive 3D vector visualization with query UI for vector databases

anywidget-vector

Interactive 3D vector visualization for Python notebooks.

Works with Marimo, Jupyter, VS Code, Colab, anywhere anywidget runs.

Features

• Universal: One widget, every notebook environment
• 6D Visualization: X, Y, Z position + Color, Shape, Size encoding
• Backend-agnostic: NumPy, pandas, Qdrant, Chroma, Pinecone, Weaviate, LanceDB, or raw dicts
• Interactive: Orbit, pan, zoom, click, hover, box select
• Customizable: Color scales, shapes, sizes, themes
• Performant: Instanced rendering for large point clouds

Installation

uv add anywidget-vector

Quick Start

from anywidget_vector import VectorSpace

widget = VectorSpace(points=[
    {"id": "a", "x": 0.5, "y": 0.3, "z": 0.8, "label": "Point A", "cluster": 0},
    {"id": "b", "x": -0.2, "y": 0.7, "z": 0.1, "label": "Point B", "cluster": 1},
    {"id": "c", "x": 0.1, "y": -0.4, "z": 0.6, "label": "Point C", "cluster": 0},
])

widget

Data Sources

Dictionary

widget = VectorSpace.from_dict({
    "points": [
        {"id": "a", "x": 0, "y": 0, "z": 0},
        {"id": "b", "x": 1, "y": 1, "z": 1},
    ]
})

NumPy Arrays

import numpy as np

positions = np.random.randn(100, 3)
widget = VectorSpace.from_numpy(positions)

pandas DataFrame

import pandas as pd

df = pd.DataFrame({
    "x": [0.1, 0.5, 0.9],
    "y": [0.2, 0.6, 0.3],
    "z": [0.3, 0.1, 0.7],
    "cluster": ["A", "B", "A"],
    "size": [0.5, 1.0, 0.8],
})

widget = VectorSpace.from_dataframe(
    df,
    color_col="cluster",
    size_col="size",
)

UMAP / t-SNE / PCA

import umap

embedding = umap.UMAP(n_components=3).fit_transform(high_dim_data)
widget = VectorSpace.from_umap(embedding, labels=labels)

Qdrant

from qdrant_client import QdrantClient

client = QdrantClient("localhost", port=6333)
widget = VectorSpace.from_qdrant(client, "my_collection", limit=5000)

ChromaDB

import chromadb

client = chromadb.Client()
collection = client.get_collection("embeddings")
widget = VectorSpace.from_chroma(collection)

Pinecone

from pinecone import Pinecone

pc = Pinecone(api_key="...")
index = pc.Index("my-index")
widget = VectorSpace.from_pinecone(index, limit=5000)

Weaviate

import weaviate

client = weaviate.Client("http://localhost:8080")
widget = VectorSpace.from_weaviate(client, "Article", limit=5000)

LanceDB

import lancedb

db = lancedb.connect("~/.lancedb")
table = db.open_table("vectors")
widget = VectorSpace.from_lancedb(table, limit=5000)

Visual Encoding

6 Dimensions

Dimension	Visual Channel	Example
X	Horizontal position	x coordinate
Y	Vertical position	y coordinate
Z	Depth position	z coordinate
Color	Hue/gradient	Cluster, score
Shape	Geometry	Category, type
Size	Scale	Importance, count

Color Scales

widget = VectorSpace(
    points=data,
    color_field="score",           # Field to map
    color_scale="viridis",         # Scale: viridis, plasma, inferno, magma, cividis, turbo
    color_domain=[0, 100],         # Optional: explicit range
)

Shapes

widget = VectorSpace(
    points=data,
    shape_field="category",
    shape_map={
        "type_a": "sphere",        # Available: sphere, cube, cone,
        "type_b": "cube",          #            tetrahedron, octahedron, cylinder
        "type_c": "cone",
    }
)

Size

widget = VectorSpace(
    points=data,
    size_field="importance",
    size_range=[0.02, 0.15],       # Min/max point size
)

Interactivity

Events

widget = VectorSpace(points=data)

@widget.on_click
def handle_click(point_id, point_data):
    print(f"Clicked: {point_id}")

@widget.on_hover
def handle_hover(point_id, point_data):
    if point_id:
        print(f"Hovering: {point_id}")

@widget.on_selection
def handle_selection(point_ids, points_data):
    print(f"Selected {len(point_ids)} points")

Selection

widget.selected_points              # Current selection
widget.select(["a", "b"])           # Select points
widget.clear_selection()            # Clear
widget.selection_mode = "box"       # Switch to box-select mode

Camera

widget.camera_position              # Get position [x, y, z]
widget.camera_target                # Get target [x, y, z]
widget.reset_camera()               # Reset to default
widget.focus_on(["a", "b"])         # Focus on specific points

Distance Metrics

Compute distances and visualize similarity relationships between points.

Supported Metrics

Metric	Description
euclidean	Straight-line distance (L2 norm)
cosine	Angle-based distance (1 - cosine similarity)
manhattan	Sum of absolute differences (L1 norm)
dot_product	Negative dot product (higher = closer)

Color by Distance

widget.color_by_distance("point_a")
widget.color_by_distance("point_a", metric="cosine")

Find Neighbors

neighbors = widget.find_neighbors("point_a", k=5)
# Returns: [("point_b", 0.1), ("point_c", 0.2), ...]

neighbors = widget.find_neighbors("point_a", threshold=0.5)

Show Connections

widget.show_neighbors("point_a", k=5)
widget.show_neighbors("point_a", threshold=0.3)

# Manual connection settings
widget = VectorSpace(
    points=data,
    show_connections=True,
    k_neighbors=3,
    distance_metric="cosine",
    connection_color="#00ff00",
    connection_opacity=0.5,
)

Compute Distances

distances = widget.compute_distances("point_a")
# Returns: {"point_b": 0.1, "point_c": 0.5, ...}

# Use high-dimensional vectors (not just x,y,z)
distances = widget.compute_distances(
    "point_a",
    metric="cosine",
    vector_field="embedding"
)

Options

widget = VectorSpace(
    points=data,
    width=1000,
    height=700,
    background="#1a1a2e",         # Dark theme default
    show_axes=True,
    show_grid=True,
    axis_labels={"x": "PC1", "y": "PC2", "z": "PC3"},
    show_tooltip=True,
    tooltip_fields=["label", "x", "y", "z", "cluster"],
    selection_mode="click",       # "click", "multi", or "box"
    use_instancing=True,          # Performance: instanced rendering
)

Backends

Configure a backend for interactive querying:

widget.set_backend("chroma", client=collection)
widget.set_backend("lancedb", client=table)
widget.set_backend("grafeo", client=db)

Export

widget.to_json()                            # Points as JSON string
widget.to_html()                            # Self-contained HTML string
widget.to_html(title="My Vectors")          # Custom title
widget.save_html("vectors.html")            # Write HTML to file

Environment Support

Environment	Supported
Marimo	Yes
JupyterLab	Yes
Jupyter Notebook	Yes
VS Code	Yes
Google Colab	Yes
Databricks	Yes

Related

• anywidget, custom Jupyter widgets made easy
• anywidget-graph, graph visualization widget
• Three.js, 3D JavaScript library

License

Apache-2.0