How to generate code embeddings with OpenAI's API in Python
Code embeddings transform pieces of code into numerical sequences, making it simpler for computers to comprehend the relationships between various segments of code. This technique is beneficial for functions such as searching for code, matching relevance, among others. OpenAI’s API delivers an uncomplicated method to produce code embeddings. In this Answer, we will see how to achieve this using Python.
Understanding code embeddings
Code embeddings are vector representations of code that capture the
OpenAI’s code embedding models
OpenAI offers particular models for the task of searching for code. They are fashioned to locate relevant code using plain language inquiries. The available models include:
code-search-ada-{code, text}-001code-search-babbage-{code, text}-001
These models were tested on the CodeSearchNet evaluation platform, demonstrating notably superior performance compared to earlier techniques.
Generating code embeddings with OpenAI’s API
Here’s a guide to creating code embeddings via OpenAI’s API using Python.
Install OpenAI’s Python library
You’ll start by installing OpenAI’s Python library, which can be done using pip:
pip install openai
Don’t forget to secure an API key from OpenAI for the necessary authentication.
Import the OpenAI library
Next, you’ll need to import the OpenAI library into your Python script.
import openai
Create code embeddings
By calling the Embedding.create method and defining the input code and the chosen model, you can form code embeddings. Here’s how it can be done:
import openaiimport osopenai.api_key = os.environ["SECRET_KEY"]response = openai.Embedding.create(input="function add(a, b) { return a + b; }",model="code-search-ada-code-001")print(response)
The returned response will encompass the code embedding in a high-dimensional space.
Finding similar code snippets
You can harness code embeddings to track down similar code snippets within a collection of code segments. Here’s an example:
import openaifrom sklearn.metrics.pairwise import cosine_similarity# Define a collection of code snippetscode_snippets = ["function add(a, b) { return a + b; }","function subtract(a, b) { return a - b; }","function multiply(a, b) { return a * b; }"]openai.api_key = os.environ["SECRET_KEY"]# Generate embeddings for each code snippetembeddings = [openai.Embedding.create(input=code, model="code-search-ada-code-001")['embedding'] for code in code_snippets]# Convert the embeddings to a format suitable for cosine similarityembeddings_matrix = [embedding.tolist() for embedding in embeddings]# Compute the cosine similarity between the embeddingssimilarity_matrix = cosine_similarity(embeddings_matrix)# Print the similarity matrixprint("Cosine Similarity Matrix:")print(similarity_matrix)
Conclusion
Code embeddings stand as an influential instrument for comprehending and interacting with code. OpenAI’s API simplifies the generation of code embeddings in Python, unlocking various applications like code searching and relevance matching. By adhering to the procedures described in this Answer, you can leverage code embeddings in your undertakings, improving efficiency and accuracy in your coding projects.
