Spice Embedding Models

Embedding models transform text into vectors for similarity search and RAG applications.

Basic Configuration

embeddings:

• from: <provider>:<model_id> name: <embedding_name> params: <provider>_api_key: ${ secrets:API_KEY }

Provider Prefixes

Provider From Format Example

openai

openai:<model_id>

openai:text-embedding-3-large

huggingface

huggingface:huggingface.co/...

huggingface:huggingface.co/sentence-transformers/all-MiniLM-L6-v2

azure

azure:<deployment>

azure:my-embedding-deployment

bedrock

bedrock:<model_id>

bedrock:amazon.titan-embed-text-v1

google

google:<model_id>

google:text-embedding-004

file

file:<path>

file:./models/embed.safetensors

Embedding Columns on Datasets

Add vector embeddings to dataset columns for search:

embeddings:

• from: openai:text-embedding-3-small name: embed_model params: openai_api_key: ${ secrets:OPENAI_API_KEY }

datasets:

• from: postgres:documents name: docs acceleration: enabled: true columns:
  • name: content embeddings:
    • from: embed_model row_id: id chunking: enabled: true target_chunk_size: 512

Chunking Configuration

For long text, enable chunking to split into smaller segments:

columns:

• name: body embeddings:
  • from: embed_model chunking: enabled: true target_chunk_size: 512 # tokens per chunk overlap_size: 64 # overlap between chunks

Search API

Query embeddings via the search endpoint:

curl http://localhost:8090/v1/search
-H "Content-Type: application/json"
-d '{"datasets": ["docs"], "text": "search query", "limit": 10}'

Documentation

• Embedding Models Overview

• Embeddings Reference

• Vector Search

• Search API