SvelteSearch is an intelligent search engine specifically designed for Svelte documentation. It uses RAG techniques and AI to understand and answer your questions about Svelte development with high accuracy and contextual awareness.

How It Works

Vector Embeddings

At the heart of SvelteSearch is a system that converts Svelte's documentation into mathematical representations called embeddings. I use OpenAI's text-embedding-3-large model to transform documentation text into numerical vectors that capture the semantic meaning of the content.

Vector Database

These embeddings are stored in Qdrant, a vector database designed for similarity search. When you search for something, your query is compared against these stored vectors to find the most relevant documentation sections.

Search Process

1. When you enter a question, it's converted into a vector embedding using the same text-embedding-3-large model
2. This query vector is used to perform a similarity search in Qdrant
3. The most relevant documentation sections are retrieved
4. These sections are passed to the Deepseek API, which generates a natural, contextually accurate response

Current Status and Future Development

Current Implementation

The system currently uses 256-dimensional vectors for document embeddings, providing a balance between accuracy and computational efficiency.

Roadmap

Few things I'll work on improving SvelteSearch's capabilities:

• Vector Dimensionality: Evaluating the impact of increasing vector dimensions beyond 256 to potentially improve search accuracy
• Performance Optimization: Ongoing improvements to search speed and accuracy

Technical Architecture

Embedding Generation

Search Flow