Visualize Embeddings in 3D
Jupyter notebook workflow that reduces text-embedding-3-small vectors from 1536 to 3 dimensions using PCA and plots them in 3D scatter plots.
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Why it matters
Understand complex data relationships by visualizing high-dimensional embeddings in an intuitive 3D space. This asset helps you explore patterns and clusters within your data.
Outcomes
What it gets done
Load and query embeddings from a dataset.
Reduce embedding dimensionality using PCA.
Plot the reduced-dimension embeddings in 3D.
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Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/oai-visualizingembeddingsin3d | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Visualizing embeddings in 3D
This Jupyter notebook workflow takes text embeddings from OpenAI's text-embedding-3-small model (1536 dimensions) and uses Principal Component Analysis to reduce them to 3 dimensions for visualization. It then generates an interactive 3D scatter plot using matplotlib where each point represents a text sample, colored by its category from the DBpedia dataset. Use this when you need to visually explore how well your embeddings separate different categories or when presenting embedding relationships to stakeholders. It is ideal for debugging classification tasks and validating that semantically similar texts cluster together in embedding space.
What it does
This workflow demonstrates how to visualize high-dimensional text embeddings in three-dimensional space. It takes text embeddings with 1536 dimensions, applies Principal Component Analysis (PCA) to reduce them to 3 dimensions, and renders a 3D scatter plot where each point represents a text sample colored by category. The example uses a curated dataset of 200 samples randomly drawn from the DBpedia validation dataset.
When to use - and when NOT to
Use this workflow when you need to visually explore clustering patterns in your embedding space, debug category separation in classification tasks, or present embedding relationships to stakeholders who need intuitive visual explanations. It is particularly useful for datasets with known categories where you want to verify that semantically similar items cluster together in embedding space.
Do not use this approach for production similarity search or when you need to preserve the full fidelity of embedding dimensions for downstream tasks. PCA dimensionality reduction discards information, so the 3D visualization is for exploratory analysis only, not for actual retrieval or classification operations.
Inputs and outputs
The workflow expects a JSONL dataset where each record contains at least a "text" field for embedding generation and a "category" field for color-coding the visualization. The example demonstrates this with the dbpedia_samples.jsonl file containing 200 samples.
The workflow produces a 3D matplotlib plot where each axis represents a principal component, points are positioned according to their reduced embeddings, and colors distinguish different categories. A DataFrame with 3-dimensional embedding coordinates is created with coordinates stored in an "embed_vis" column.
Integrations
The workflow uses the text-embedding-3-small model through the get_embeddings function, sending batch queries of up to 200 samples. It uses scikit-learn's PCA implementation for dimensionality reduction and matplotlib for 3D plotting with the "tab20" colormap. The example loads data using pandas from JSONL format.
from utils.embeddings_utils import get_embeddings
# NOTE: The following code will send a query of batch size 200 to /embeddings
matrix = get_embeddings(samples["text"].to_list(), model="text-embedding-3-small")
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
vis_dims = pca.fit_transform(matrix)
samples["embed_vis"] = vis_dims.tolist()
Who it's for
This workflow is for anyone working with text embeddings who needs to validate that their embedding model captures semantic relationships appropriately. It is also valuable for presenting embedding-based systems to non-technical audiences, as the 3D visualization makes abstract vector spaces tangible. Anyone building classification systems on top of embeddings can use this to diagnose whether categories are well-separated in the embedding space before investing in more complex modeling.
Source README
Visualizing embeddings in 3D
The example uses PCA to reduce the dimensionality of the embeddings from 1536 to 3. Then we can visualize the data points in a 3D plot. The small dataset dbpedia_samples.jsonl is curated by randomly sampling 200 samples from DBpedia validation dataset.
1. Load the dataset and query embeddings
import pandas as pd
samples = pd.read_json("data/dbpedia_samples.jsonl", lines=True)
categories = sorted(samples["category"].unique())
print("Categories of DBpedia samples:", samples["category"].value_counts())
samples.head()
from utils.embeddings_utils import get_embeddings
# NOTE: The following code will send a query of batch size 200 to /embeddings
matrix = get_embeddings(samples["text"].to_list(), model="text-embedding-3-small")
2. Reduce the embedding dimensionality
from sklearn.decomposition import PCA
pca = PCA(n_components=3)
vis_dims = pca.fit_transform(matrix)
samples["embed_vis"] = vis_dims.tolist()
3. Plot the embeddings of lower dimensionality
%matplotlib widget
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(projection='3d')
cmap = plt.get_cmap("tab20")
# Plot each sample category individually such that we can set label name.
for i, cat in enumerate(categories):
sub_matrix = np.array(samples[samples["category"] == cat]["embed_vis"].to_list())
x=sub_matrix[:, 0]
y=sub_matrix[:, 1]
z=sub_matrix[:, 2]
colors = [cmap(i/len(categories))] * len(sub_matrix)
ax.scatter(x, y, zs=z, zdir='z', c=colors, label=cat)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.legend(bbox_to_anchor=(1.1, 1))
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