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
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
- When a Data Source is chunked, Jabrod sends the chunks in batches to the selected embedding model.
- The model returns a vector for each chunk.
- Jabrod stores both the original text chunk and the vector in the Vector Database (Upstash).
- 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.