Creating OpenAI Embeddings Without Chroma

While Chroma is a powerful tool for managing and querying vector databases, it’s not always necessary to use a dedicated vector database for embedding generation and storage. In this guide, we’ll explore how to create OpenAI embeddings directly and leverage them for various applications.

Understanding OpenAI Embeddings

OpenAI offers a suite of models capable of generating high-quality embeddings for text, images, and audio. These embeddings capture the semantic or visual features of the data, allowing for efficient similarity search and other applications.

Prerequisites

• OpenAI API Key: Obtain an OpenAI API key from the OpenAI platform.
• Python: Ensure you have Python installed on your system.
• OpenAI Python Library: Install the OpenAI Python library using pip: pip install openai

Creating Text Embeddings

Import Necessary Libraries:

Python

import openai

Set Your API Key:

Python

openai.api_key = “YOUR_API_KEY”

Generate Embeddings:

Python

text = “This is a sample text.”
embedding = openai.Embedding.create(
input=text,
engine=”text-davinci-003″
)

Using Embeddings for Similarity Search

1. Calculate Distance: Use a suitable distance metric (e.g., cosine similarity, Euclidean distance) to calculate the distance between the query embedding and the embeddings of your dataset.
2. Find Closest Matches: Sort the results by distance to find the most similar items.

Example

Python

from scipy.spatial.distance import cosine

# ... (code to generate embeddings for your dataset)

query_text = "What is the capital of France?"
query_embedding = openai.Embedding.create(
    input=query_text,
    engine="text-davinci-003"
)

# Calculate cosine similarity for each item in your dataset
for embedding in dataset_embeddings:
    similarity = 1 - cosine(query_embedding["data"][0], embedding["data"][0])
    print(f"Similarity: {similarity}")

While Chroma provides a convenient and efficient way to manage vector databases, you can also create OpenAI embeddings directly and perform similarity search using custom implementations. This approach gives you more flexibility and control over your embedding generation and storage processes.