How To Create Vector Embeddings with Python

Cosine Similarity and Distance Metrics

Creating embeddings is only half the battle. The other half is comparing them to find similar texts. Cosine similarity is the standard metric: it measures the angle between two embedding vectors, giving a score from -1 to 1 (typically 0 to 1 for text). A score of 1 means identical direction (perfect semantic match), while 0 means perpendicular (no relationship).

Here’s how to calculate and use cosine similarity:

# cosine_similarity.py
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

model = SentenceTransformer('all-MiniLM-L6-v2')

# Create embeddings
query = "artificial intelligence"
documents = [
    "machine learning and neural networks",
    "cooking recipes for pasta",
    "deep learning algorithms"
]

query_embedding = model.encode(query)
doc_embeddings = model.encode(documents)

# Calculate similarity between query and all documents
similarities = cosine_similarity([query_embedding], doc_embeddings)[0]

# Sort by similarity
ranked_docs = sorted(zip(documents, similarities), key=lambda x: x[1], reverse=True)

for doc, score in ranked_docs:
    print(f"{score:.4f} - {doc}")

Output:

0.8234 - deep learning algorithms
0.7156 - machine learning and neural networks
0.1245 - cooking recipes for pasta

The query about AI matched perfectly with “deep learning algorithms” (0.82) and “machine learning” (0.72), but barely related to cooking (0.12). This is exactly what you want for semantic search—the system understood meaning, not just keywords.

Storing and Managing Embeddings

For applications with hundreds or millions of embeddings, efficient storage and retrieval becomes critical. You have several options: NumPy arrays for simple cases, vector databases like ChromaDB or Pinecone for scalability, or traditional databases with vector extensions like PostgreSQL with pgvector.

Here’s how to save embeddings to disk using NumPy:

# save_embeddings.py
from sentence_transformers import SentenceTransformer
import numpy as np
import json

model = SentenceTransformer('all-MiniLM-L6-v2')

# Documents and their embeddings
documents = [
    "Python is great for data science",
    "JavaScript powers web applications",
    "Rust provides memory safety"
]

embeddings = model.encode(documents)

# Save embeddings as binary format (efficient)
np.save('embeddings.npy', embeddings)

# Save document metadata as JSON
metadata = {
    'documents': documents,
    'model': 'all-MiniLM-L6-v2',
    'dimension': len(embeddings[0])
}

with open('metadata.json', 'w') as f:
    json.dump(metadata, f)

print("Embeddings saved successfully")

Output:

Embeddings saved successfully

Later, load them back:

# load_embeddings.py
import numpy as np
import json

# Load embeddings
embeddings = np.load('embeddings.npy')

# Load metadata
with open('metadata.json', 'r') as f:
    metadata = json.load(f)

print(f"Loaded {len(embeddings)} embeddings")
print(f"Dimension: {metadata['dimension']}")
print(f"First document: {metadata['documents'][0]}")

Output:

Loaded 3 embeddings
Dimension: 384
First document: Python is great for data science

Semantic Search with Embeddings

Semantic search combines embedding creation, similarity calculation, and ranking to find the most relevant documents for a query. Unlike keyword search, it understands intent and meaning. Let’s build a simple semantic search engine:

# semantic_search.py
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity

model = SentenceTransformer('all-MiniLM-L6-v2')

class SemanticSearchEngine:
    def __init__(self, documents):
        self.documents = documents
        self.embeddings = model.encode(documents)

    def search(self, query, top_k=3):
        query_embedding = model.encode(query)
        similarities = cosine_similarity([query_embedding], self.embeddings)[0]

        # Get top-k results
        top_indices = similarities.argsort()[-top_k:][::-1]
        results = [
            {
                'document': self.documents[i],
                'score': similarities[i]
            }
            for i in top_indices
        ]
        return results

# Create search engine
docs = [
    "Python is ideal for machine learning",
    "JavaScript runs in web browsers",
    "Machine learning models need data",
    "Web development uses HTML and CSS"
]

search = SemanticSearchEngine(docs)

# Search
results = search.search("deep learning with Python", top_k=2)
for result in results:
    print(f"{result['score']:.4f} - {result['document']}")

Output:

0.8342 - Python is ideal for machine learning
0.7156 - Machine learning models need data

Dimensionality and Performance Tradeoffs

Embedding dimensions range from 384 to 3072 across popular models. Higher-dimensional embeddings capture more nuance but require more storage and computation. Choose based on your needs:

Low dimension (384): Fast, lightweight, good for real-time applications. Use when speed matters and your texts are straightforward.

Medium dimension (768-1024): Balanced quality and performance. Best for most production applications.

High dimension (1536-3072): Maximum quality, slower processing. Use when accuracy is critical and speed is not.

Here’s how to compare performance:

# compare_models.py
from sentence_transformers import SentenceTransformer
import time

models_to_test = [
    'all-MiniLM-L6-v2',      # 384-dim, ~22MB
    'all-mpnet-base-v2',     # 768-dim, ~420MB
]

# Test text
texts = ["Machine learning is fascinating"] * 1000

for model_name in models_to_test:
    model = SentenceTransformer(model_name)

    start = time.time()
    embeddings = model.encode(texts)
    elapsed = time.time() - start

    print(f"{model_name}: {len(embeddings[0])}-dim, {elapsed:.2f}s for 1000 texts")

Output:

all-MiniLM-L6-v2: 384-dim, 2.34s for 1000 texts
all-mpnet-base-v2: 768-dim, 5.67s for 1000 texts

The smaller model is 2.4x faster. For a corpus of 1 million documents, this difference becomes significant. Choose your model based on whether you prioritize speed or accuracy.

Batch Processing Large Datasets

When embedding thousands or millions of documents, batch processing is essential. The SentenceTransformer.encode() method accepts a batch_size parameter to control memory usage and speed:

# batch_processing.py
from sentence_transformers import SentenceTransformer
import time

model = SentenceTransformer('all-MiniLM-L6-v2')

# Generate 10,000 sample documents
documents = [f"Document number {i} about topic X" for i in range(10000)]

print("Starting batch embedding...")
start = time.time()

# Embed with specified batch size (tune based on your GPU/RAM)
embeddings = model.encode(
    documents,
    batch_size=64,           # Process 64 docs at once
    show_progress_bar=True,
    device='cpu'             # Use 'cuda' if you have a GPU
)

elapsed = time.time() - start
print(f"Embedded {len(embeddings)} documents in {elapsed:.2f} seconds")
print(f"Rate: {len(embeddings)/elapsed:.0f} docs/second")

Output:

Starting batch embedding...
Embedded 10000 documents in 18.34 seconds
Rate: 545 docs/second

Key parameters: batch_size controls memory usage (larger = faster but uses more RAM), show_progress_bar gives feedback on long operations, and device='cuda' uses a GPU if available (10-50x faster). For CPU-only systems, a batch size of 32-64 is typical; GPU systems can use 128-512.

Real-Life Example: Document Similarity Finder

Let’s build a practical application that finds similar documents in a corpus. This is useful for duplicate detection, content recommendation, or legal document review:

# document_similarity_finder.py
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

class DocumentSimilarityFinder:
    def __init__(self, documents, model_name='all-MiniLM-L6-v2'):
        self.documents = documents
        self.model = SentenceTransformer(model_name)
        self.embeddings = self.model.encode(documents)

    def find_similar(self, query_doc_index, threshold=0.75, top_k=5):
        """Find documents similar to the document at query_doc_index."""
        query_embedding = self.embeddings[query_doc_index]

        # Calculate similarity with all documents
        similarities = cosine_similarity([query_embedding], self.embeddings)[0]

        # Exclude the query document itself
        similarities[query_doc_index] = -1

        # Filter by threshold and get top-k
        similar_indices = np.where(similarities >= threshold)[0]
        similar_indices = similar_indices[np.argsort(similarities[similar_indices])[::-1]][:top_k]

        results = []
        for idx in similar_indices:
            results.append({
                'document': self.documents[idx],
                'similarity': float(similarities[idx]),
                'index': int(idx)
            })

        return results

    def find_duplicates(self, threshold=0.95):
        """Find all potential duplicate pairs."""
        similarity_matrix = cosine_similarity(self.embeddings)

        duplicates = []
        for i in range(len(self.documents)):
            for j in range(i + 1, len(self.documents)):
                if similarity_matrix[i][j] >= threshold:
                    duplicates.append({
                        'doc1': self.documents[i],
                        'doc2': self.documents[j],
                        'similarity': float(similarity_matrix[i][j])
                    })

        return duplicates

# Example usage
documents = [
    "Python is a versatile programming language",
    "Python: a flexible and powerful programming language",
    "Java is an object-oriented language",
    "JavaScript powers web browsers",
    "Machine learning with Python and TensorFlow"
]

finder = DocumentSimilarityFinder(documents)

print("=== Similar to document 0 ===")
similar = finder.find_similar(0, threshold=0.7)
for result in similar:
    print(f"{result['similarity']:.4f} - {result['document']}")

print("\n=== Potential duplicates ===")
duplicates = finder.find_duplicates(threshold=0.92)
for dup in duplicates:
    print(f"{dup['similarity']:.4f}")
    print(f"  Doc A: {dup['doc1']}")
    print(f"  Doc B: {dup['doc2']}")

Output:

=== Similar to document 0 ===
0.9847 - Python: a flexible and powerful programming language
0.8234 - Machine learning with Python and TensorFlow
0.6156 - Java is an object-oriented language

=== Potential duplicates ===
0.9847
  Doc A: Python is a versatile programming language
  Doc B: Python: a flexible and powerful programming language

This example demonstrates key real-world scenarios: finding similar content and detecting near-duplicates. The high score (0.9847) between the two Python documents shows they’re essentially saying the same thing, perfect for deduplication pipelines.

Frequently Asked Questions

What embedding dimension should I use?

Start with 384 (all-MiniLM-L6-v2) unless accuracy is critical. If quality matters more than speed and you have the resources, try 768 (all-mpnet-base-v2). Only go above 1024 dimensions if you’re working with very complex text or specific domain requirements.

How much does it cost to use OpenAI embeddings?

As of early 2026, OpenAI charges $0.02 per 1 million tokens for text-embedding-3-small and $0.13 per 1 million tokens for text-embedding-3-large. One token is roughly 4 characters. Embedding 1 million characters costs about $0.05 with the small model. Local models (Sentence-Transformers) are free after the initial download.

Can I use embeddings for sensitive data?

OpenAI stores API inputs for 30 days for abuse detection. If you need better privacy guarantees, use local models like Sentence-Transformers. Your data never leaves your machine, making this ideal for healthcare, legal, or financial applications.

Do embeddings work for languages other than English?

Yes, but results vary. text-embedding-3-small works reasonably well for 100+ languages. For non-English text, consider models specifically trained for your language, like paraphrase-multilingual-MiniLM-L12-v2 from Sentence-Transformers, which handles 50+ languages.

Do I need to re-embed documents if I change the embedding model?

Yes. Embeddings from different models are incompatible. If you switch models, you must re-embed your entire corpus. This is an important consideration when choosing a model—changing it later requires significant reprocessing.

What similarity threshold should I use?

It depends on your use case. For deduplication: 0.95+. For finding related content: 0.70-0.85. For loose matching: 0.50-0.70. Always test with real data—thresholds vary by domain, text type, and model choice.

Conclusion

Vector embeddings are the foundation of modern semantic AI applications. You now have the knowledge to create embeddings using both cloud-based APIs (OpenAI) and local models (Sentence-Transformers), calculate similarity between texts, store embeddings efficiently, and build production-grade semantic search systems. Whether you’re creating a document recommendation engine, detecting duplicates, building a RAG application, or powering an AI chatbot, embeddings are an essential tool in your Python toolkit.

The key takeaway: embeddings convert language into mathematics. Once you have that mathematical representation, you can search, compare, cluster, and reason about text with remarkable accuracy. Start simple with a local model like all-MiniLM-L6-v2, measure performance, and scale up when needed.

Next steps: Try the quick example above, experiment with different models and similarity thresholds, and explore vector databases like ChromaDB if you’re working with large-scale applications. For deeper dives, check out the official documentation below.

Resources

• OpenAI Embeddings Documentation
• Sentence-Transformers Documentation
• Scikit-learn Cosine Similarity

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