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Embedding Providers

pgEdge Vectorizer supports multiple embedding providers. Each provider has different configuration requirements and capabilities.

OpenAI

OpenAI provides industry-leading embedding models; these models offer excellent quality and are widely used for semantic search applications. Save your OpenAI API key from https://platform.openai.com/api-keys; specify the path in the pgedge_vectorizer.api_key_file configuration parameter.

Configuration: 

pgedge_vectorizer.provider = 'openai'
pgedge_vectorizer.api_url = 'https://api.openai.com/v1'
pgedge_vectorizer.api_key_file = '/path/to/api-key-file'
pgedge_vectorizer.model = 'text-embedding-3-small'

Available Models: - text-embedding-3-small - 1536 dimensions, cost-effective - text-embedding-3-large - 3072 dimensions, higher quality - text-embedding-ada-002 - 1536 dimensions, legacy model

Voyage AI

Voyage AI provides high-quality embeddings optimized for retrieval tasks. The API is OpenAI-compatible, making it easy to switch between providers. Save your Voyage AI API key from https://www.voyageai.com/; specify the path in the pgedge_vectorizer.api_key_file configuration parameter.

Configuration: 

pgedge_vectorizer.provider = 'voyage'
pgedge_vectorizer.api_url = 'https://api.voyageai.com/v1'
pgedge_vectorizer.api_key_file = '/path/to/api-key-file'
pgedge_vectorizer.model = 'voyage-2'

Available Models: - voyage-2 - 1024 dimensions, general purpose - voyage-large-2 - 1536 dimensions, higher quality - voyage-code-2 - 1536 dimensions, optimized for code

Ollama (Local)

Ollama allows you to run embedding models locally without API keys or internet connectivity. This option is ideal for development, testing, or environments with strict data privacy requirements.

Configuration: 

pgedge_vectorizer.provider = 'ollama'
pgedge_vectorizer.api_url = 'http://localhost:11434'
pgedge_vectorizer.model = 'nomic-embed-text'
# No API key needed for Ollama

Available Models (install via ollama pull <model>): - nomic-embed-text - 768 dimensions, good quality - mxbai-embed-large - 1024 dimensions, high quality - all-minilm - 384 dimensions, fast and small

Install Ollama from https://ollama.ai and pull your desired embedding model: 

ollama pull nomic-embed-text

Note that Ollama doesn't support batch processing, so each text is embedded individually.