Vector Embedding Generator & Semantic Similarity Calculator for AI Optimization

This tool calculates the **semantic similarity** between two pieces of text or two web pages using advanced AI embedding models from Google's Gemini or OpenAI. Here's how to get started:

1. **Select API Provider:** Choose either **Gemini** or **OpenAI**. You'll need an API key for your chosen provider.
2. **Enter API Key:** Paste your API key into the "Enter your API Key here" field. Click "Lock API Key" to save it securely in your browser's local storage. This key is never sent to our servers.
3. **Input Sources:** For each "Source 1" and "Source 2", you have two options:
   • **Page URL:** Enter the full URL of a webpage. The calculator will attempt to fetch and extract the main content from the page.
   • **Direct Text Input:** Paste any text directly into the textarea. There's a character counter to help you manage length.
4. **Get Embeddings:** Click the "Get Embedding from URL" or "Get Embedding from Text" button for each source. This sends your content to the selected AI model, which converts it into a numerical vector (embedding).
5. **Calculate Similarity:** Once both embeddings are generated, click the "Calculate Similarity" button. The tool will then compute the cosine similarity between the two embeddings, providing a score between -1 (opposite meaning) and 1 (identical meaning).
6. **Review History:** Your past calculations are saved in "Calculation History" at the bottom of the page. You can clear this history anytime.

This calculator leverages state-of-the-art **large language models (LLMs)** from Google (Gemini) or OpenAI to understand the meaning of text. Here's a simplified breakdown:

1. **Text/URL Input:** You provide either raw text or a URL. If it's a URL, a proxy is used to bypass browser CORS restrictions and extract the primary textual content from that page.
2. **Embedding Generation (AI Magic!):** The extracted text is sent to the chosen AI model (Gemini or OpenAI). These models use a process called "embedding" to convert human-readable text into high-dimensional numerical vectors. Texts with similar meanings will have vectors that are "closer" to each other in this multi-dimensional space.
3. **Cosine Similarity:** Once you have two such vectors (embeddings), the calculator uses a mathematical formula called **cosine similarity**. This formula measures the cosine of the angle between two vectors.
   • A cosine similarity of **1** means the vectors are perfectly aligned (texts are semantically identical).
   • A score of **0** means the vectors are orthogonal (no semantic relationship).
   • A score of **-1** means the vectors point in opposite directions (texts have opposite meanings).
   The closer the score is to 1, the more similar the texts are.
4. **Client-Side Processing:** All API key handling, API calls, and similarity calculations are performed directly in your browser using JavaScript. Your API key is stored securely in your browser's local storage and is never transmitted to any external server other than the respective AI provider's API.

This embedding similarity calculator is a powerful tool with diverse applications, especially for understanding content from an AI's perspective:

• AI & SEO Content Optimization: This is arguably one of the most important uses!
  Determine how closely related your website content (e.g., a blog post, product description) is to a target keyword, competitor's page, or an ideal content brief. By understanding semantic similarity from an AI's viewpoint, you can:
  • **Optimize for AI Ranking:** Ensure your content aligns with what AI models "understand" as relevant to a topic, which is increasingly crucial for how search engines and AI assistants process and rank information.
  • **Improve SEO Performance:** Create content that is semantically rich and comprehensive for its topic, helping you rank better for a wider array of related keywords and satisfy user intent more effectively.
• **Content Duplication Check:** Quickly assess how similar two articles, blog posts, or product descriptions are.
• **Plagiarism Detection (Basic):** Get an initial indicator of text similarity between a source and a submitted document. (Note: For robust plagiarism detection, dedicated tools are recommended).
• **Information Retrieval & Search:** Understand if different search queries or documents are semantically related, even if they don't share exact keywords.
• **Customer Support & FAQs:** Determine if a new customer query is similar to an existing FAQ answer or support ticket.
• **Semantic Clustering:** Group similar documents or pieces of text together based on their meaning.
• **Recommendation Systems:** Identify items (e.g., movies, products) that are semantically similar to what a user has previously enjoyed.
• **Learning & Experimentation:** A hands-on way to understand how text embeddings work and their practical applications in Natural Language Processing (NLP).

Ultimately, it's a foundational tool for tasks requiring an understanding of text meaning beyond just keyword matching.