Text Embeddings with OpenAI: Unlocking Insights from Technical Text

Why Use Text Embeddings with OpenAI?

Text embeddings are vector representations of text—numerical arrays (e.g., 1536 dimensions with OpenAI’s text-embedding-ada-002) that capture semantic meaning, turning words, sentences, or documents into a form machines can process. Imagine a code doc snippet—“print(): Outputs text to console”—its embedding distills its essence into a 1536D vector, placing it near similar concepts like “input(): Reads user data” in a latent space. OpenAI excels here—its transformer-based models, trained on vast datasets up to April 2023, generate contextual embeddings—e.g., “print” in code vs. “print” in publishing differs—unlike static models like Word2Vec—see What Is Generative AI and Why Use It?.

The value lies in versatility and power. Embeddings enable semantic search (find similar docs), clustering (group related content), or recommendation (suggest tech terms)—all with a free tier ($5 credit) or low cost (~$0.0001/1000 tokens for ada-002). Processing a technical corpus—like code docs—unlocks domain-specific insights, while NumPy and similarity metrics make analysis practical—explained with purpose—let’s process a corpus.

Step 1: Process a Technical Corpus (e.g., Code Docs)

A technical corpus—like code documentation—feeds your embeddings with real-world, domain-specific text, setting the stage for meaningful vectors.

Preparing Your Python Environment

You’ll need Python (3.8+) and pip—core tools for scripting and libraries. Open a terminal (e.g., in VS Code, a robust editor with integrated tools) and verify:

Copy codepython --version

Expect “Python 3.11.7” (stable as of April 2025)—if missing, download from python.org, ensuring “Add to PATH” for terminal access—sets python globally. Then:

Copy codepip --version

See “pip 23.3.1” or similar—if absent, run python -m ensurepip --upgrade then python -m pip install --upgrade pip. Pip connects to PyPI—a library hub.

Install required libraries:

Copy codepip install openai numpy python-dotenv

• openai: Links to OpenAI API—version 0.28.1+—~1 MB—explained for API access.
• numpy: Handles vector math—fast, C-based—~20 MB—explained for array management.
• python-dotenv: Loads .env—secure key storage—~100 KB—explained for security.

Verify with pip show openai—explained to confirm setup readiness.

Setting Up OpenAI API Access

An API key authenticates your calls—visit platform.openai.com, sign up (free tier: $5 credit), and under “API Keys,” create one—e.g., “EmbedTech2025”—copy: sk-abc123xyz. This key—your access pass—covers ~50 million tokens free—see OpenAI Pricing.

Create a folder—e.g., “TechEmbedBot” with mkdir TechEmbedBot && cd TechEmbedBot—add .env:

Copy codeOPENAI_API_KEY=sk-abc123xyz

Building a Technical Corpus

A technical corpus—code docs—represents real-world tech text. Define a sample:

Copy codecorpus = [
    "print(): Outputs text to the console for display or debugging.",
    "def function(): Defines a reusable block of code with a name.",
    "class Object: Creates a blueprint for instances with attributes.",
    "import module: Loads external libraries or modules into the script.",
    "for loop: Iterates over a sequence to perform repeated actions."
]

• Content: 5 snippets—~20-25 words each—mimic Python docs—e.g., from Python Docs—explained for relevance.
• Size: Small but representative—~100 words total—fits OpenAI’s 8192-token context—explained for practicality.

This isn’t arbitrary—tech-specific—next, generate embeddings.

Step 2: Generate Embeddings (See /generative-ai/core-tools/generate-embeddings-openai)

Let’s generate embeddings with OpenAI—referencing Generate Embeddings with OpenAI API for setup depth—turning our corpus into 1536D vectors.

Coding the Embedding Generation

Create embed_tech.py:

Copy codeimport openai
import numpy as np
from dotenv import load_dotenv
import os

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Technical corpus
corpus = [
    "print(): Outputs text to the console for display or debugging.",
    "def function(): Defines a reusable block of code with a name.",
    "class Object: Creates a blueprint for instances with attributes.",
    "import module: Loads external libraries or modules into the script.",
    "for loop: Iterates over a sequence to perform repeated actions."
]

# Generate embeddings
response = openai.Embedding.create(
    model="text-embedding-ada-002",
    input=corpus
)

# Extract embeddings
embeddings = [np.array(item["embedding"]) for item in response["data"]]

# Display results
print("=== Generated Embeddings ===")
for i, text in enumerate(corpus):
    print(f"Text {i+1}: {text}")
    print(f"Embedding (first 5 values): {embeddings[i][:5]}")
    print(f"Dimensions: {len(embeddings[i])}")
    print("-" * 20)

Run python embed_tech.py—expect:

Copy code=== Generated Embeddings ===
Text 1: print(): Outputs text to the console for display or debugging.
Embedding (first 5 values): [-0.0123  0.0456 -0.0789  0.0012  0.0345]
Dimensions: 1536
--------------------
Text 2: def function(): Defines a reusable block of code with a name.
Embedding (first 5 values): [ 0.0234 -0.0678  0.0123 -0.0890  0.0567]
Dimensions: 1536
...

How Embeddings Are Generated

• openai.Embedding.create: Calls the embeddings endpoint (/v1/embeddings)—text-embedding-ada-002—fast, cost-effective (~$0.0001/1000 tokens)—explained for API choice.
• model="text-embedding-ada-002": Transformer-based—1536D output—captures context—e.g., “print” as function—trained to April 2023—see OpenAI Models—explained for model detail.
• input=corpus: Sends list of texts—processes all at once—8192-token limit fits ~6000 words—explained for batch efficiency.
• embeddings = [np.array(item["embedding"])]: Extracts 1536D vectors—np.array converts to NumPy—each ~12 KB—explained for data format.

This isn’t guesswork—ada-002 ensures semantic richness—next, store them.

Step 3: Store 1536D Vectors in a NumPy Array

NumPy arrays store your 1536D embeddings—fast, efficient, and ready for analysis.

Storing in NumPy

Update embed_tech.py:

Copy codeimport openai
import numpy as np
from dotenv import load_dotenv
import os

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Technical corpus
corpus = [
    "print(): Outputs text to the console for display or debugging.",
    "def function(): Defines a reusable block of code with a name.",
    "class Object: Creates a blueprint for instances with attributes.",
    "import module: Loads external libraries or modules into the script.",
    "for loop: Iterates over a sequence to perform repeated actions."
]

# Generate embeddings
response = openai.Embedding.create(
    model="text-embedding-ada-002",
    input=corpus
)

# Store embeddings in NumPy array
embeddings = np.array([item["embedding"] for item in response["data"]])

# Save to file and display
np.save("tech_embeddings.npy", embeddings)
print("=== Embedding Storage ===")
print(f"Array shape: {embeddings.shape}")
print(f"First embedding (5 values): {embeddings[0][:5]}")
print(f"Saved to 'tech_embeddings.npy' for later use!")

Run it—output:

Copy code=== Embedding Storage ===
Array shape: (5, 1536)
First embedding (5 values): [-0.0123  0.0456 -0.0789  0.0012  0.0345]
Saved to 'tech_embeddings.npy' for later use!

How Storage Works

• embeddings = np.array([...]): Converts list to 2D NumPy array—shape (5, 1536)—5 texts, 1536D each—NumPy—C-based, fast—explained for structure.
• np.save("tech_embeddings.npy", embeddings): Saves as .npy—binary, ~30 KB—reload with np.load—explained for persistence.
• embeddings.shape: Confirms dimensions—5 rows, 1536 columns—explained for verification.

This isn’t vague—NumPy ensures efficiency—next, compute similarities.

Step 4: Compute Pairwise Similarities with Python

Pairwise similarities—using cosine similarity—measure semantic closeness between embeddings, testing their relationship.

Coding Similarity

Update embed_tech.py:

Copy codeimport openai
import numpy as np
from scipy.spatial import distance
from dotenv import load_dotenv
import os

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Technical corpus
corpus = [
    "print(): Outputs text to the console for display or debugging.",
    "def function(): Defines a reusable block of code with a name.",
    "class Object: Creates a blueprint for instances with attributes.",
    "import module: Loads external libraries or modules into the script.",
    "for loop: Iterates over a sequence to perform repeated actions."
]

# Generate embeddings
response = openai.Embedding.create(
    model="text-embedding-ada-002",
    input=corpus
)
embeddings = np.array([item["embedding"] for item in response["data"]])

# Compute pairwise similarities
similarities = []
for i in range(len(corpus)):
    for j in range(i + 1, len(corpus)):
        sim = 1 - distance.cosine(embeddings[i], embeddings[j])
        similarities.append((i, j, sim))

# Display similarities
print("=== Pairwise Similarities ===")
for i, j, sim in similarities:
    print(f"Text {i+1} vs Text {j+1}: {sim:.3f}")
    print(f"  {corpus[i]}")
    print(f"  {corpus[j]}")
    print("-" * 20)

Run pip install scipy—then python embed_tech.py—expect:

Copy code=== Pairwise Similarities ===
Text 1 vs Text 2: 0.845
  print(): Outputs text to the console for display or debugging.
  def function(): Defines a reusable block of code with a name.
--------------------
Text 1 vs Text 3: 0.792
  print(): Outputs text to the console for display or debugging.
  class Object: Creates a blueprint for instances with attributes.
...

How Similarity Works

• from scipy.spatial import distance: Uses SciPy—scientific computing—distance.cosine computes cosine distance—explained for tool choice.
• sim = 1 - distance.cosine: Cosine similarity—1 (identical), 0 (unrelated)—1 - distance converts—explained for metric.
• similarities.append((i, j, sim)): Stores pairs—e.g., print() vs def—0.845—high similarity—explained for tracking.
• Display: Shows texts and scores—0.845—code output vs function—related—explained for interpretation.

This isn’t fluff—similarities quantify meaning—next, test quality.

Step 5: Test Embedding Quality with Tech Terms

Let’s test embedding quality—comparing tech terms to ensure semantic accuracy.

Testing with Tech Terms

Update embed_tech.py:

Copy codeimport openai
import numpy as np
from scipy.spatial import distance
from dotenv import load_dotenv
import os

# Load API key
load_dotenv()
openai.api_key = os.getenv("OPENAI_API_KEY")

# Technical corpus with test term
corpus = [
    "print(): Outputs text to the console for display or debugging.",
    "def function(): Defines a reusable block of code with a name.",
    "class Object: Creates a blueprint for instances with attributes.",
    "import module: Loads external libraries or modules into the script.",
    "for loop: Iterates over a sequence to perform repeated actions."
]
test_term = "while loop: Controls repetition with a condition."

# Generate embeddings
response = openai.Embedding.create(
    model="text-embedding-ada-002",
    input=corpus + [test_term]
)
embeddings = np.array([item["embedding"] for item in response["data"]])

# Test similarity with "for loop" (index 4) vs "while loop" (index 5)
for_embedding = embeddings[4]
while_embedding = embeddings[5]
sim = 1 - distance.cosine(for_embedding, while_embedding)

# Display test result
print("=== Embedding Quality Test ===")
print(f"Text: {corpus[4]}")
print(f"Test Term: {test_term}")
print(f"Similarity: {sim:.3f}")
print("==============================")

Run it—expect:

Copy code=== Embedding Quality Test ===
Text: for loop: Iterates over a sequence to perform repeated actions.
Test Term: while loop: Controls repetition with a condition.
Similarity: 0.917
==============================

How Testing Works

• test_term: Adds “while loop”—similar concept—tests semantic closeness—explained for relevance.
• corpus + [test_term]: Combines—batch processing—saves API calls—explained for efficiency.
• sim = 1 - distance.cosine: Compares for vs while—0.917—high similarity—both loops—explained for validation.
• Output: Similarity score—0.917—close meaning—ada-002 captures tech nuance—explained for quality proof.

This isn’t random—high similarity confirms embedding accuracy—your vectors shine.

Next Steps: Leveraging Your Embeddings

Your embeddings are ready—processed, stored, compared, tested! Scale with Setup Pinecone Vector Storage or analyze with Social Media Posts with AI. You’ve mastered text embeddings—keep exploring!

FAQ: Common Questions About Text Embeddings with OpenAI

1. Do I need a big corpus?

No—5 texts work—8192 tokens (~6000 words) max—explained for flexibility.

2. Why ada-002 over other models?

Fast, cheap—1536D—balanced—see OpenAI Models—explained for choice.

3. What if similarities are low?

Check corpus quality—vague texts skew—retry—explained for troubleshooting.

4. How does cosine similarity work?

Angle-based—1 (same), 0 (unrelated)—see SciPy Docs—explained for mechanics.

5. Can I use other storage?

Yes—CSV, HDF5—NumPy—fast, simple—explained for options.

6. Why test with tech terms?

Validates domain—e.g., loops—ensures tech fit—explained for purpose.

Your queries answered—embed with confidence!