> ## Documentation Index
> Fetch the complete documentation index at: https://docs.jabrod.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Embeddings

> How text is converted into numbers

# Embeddings

An embedding is a list of floating-point numbers (a vector) that represents the semantic meaning of a piece of text.

By comparing these vectors using distance metrics (like Cosine Similarity), Jabrod can determine which chunks of text are most relevant to a user's query, even if they don't share the exact same keywords.

## Supported Models

Jabrod integrates with several embedding providers to give you flexibility between cost, speed, and quality.

| Model                       | Dimensions | Provider                | Tier       | Notes                                   |
| --------------------------- | ---------- | ----------------------- | ---------- | --------------------------------------- |
| `text-embedding-3-small`    | 1536       | OpenAI (via OpenRouter) | Free / Pro | Fast, cheap, industry standard.         |
| `nomic-embed-text`          | 768        | Nomic (via OpenRouter)  | Free / Pro | Open source alternative.                |
| `cohere/embed-english-v3.0` | 1024       | Cohere (via OpenRouter) | Pro        | Excellent for English RAG.              |
| `voyage/voyage-large-2`     | 1536       | Voyage (via OpenRouter) | Pro        | State-of-the-art retrieval performance. |

## The Embedding Process

1. When a Data Source is chunked, Jabrod sends the chunks in batches to the selected embedding model.
2. The model returns a vector for each chunk.
3. Jabrod stores both the original text chunk and the vector in the Vector Database (Upstash).
4. When you submit a Query, Jabrod embeds the query text using the *same* model, and asks the Vector Database to find the closest matching vectors.