grepai-embeddings-openai

You can use OpenAI embeddings to generate high-quality vector representations for code and text, enabling accurate semantic search across large repositories without maintaining local embedding infrastructure. This requires setting your API key and choosing a model.

Yes, Azure OpenAI deployments are supported for enterprise setups. This integration allows you to generate vector representations for large codebases while utilizing your existing Azure environment configuration and credentials securely.

You can choose between text-embedding-3-small or text-embedding-3-large to balance cost and accuracy. These models generate the vector representations required for improving semantic matching across various programming languages and formats.

Yes, an OpenAI API key is required for cloud-based vector indexing. You must securely supply this credential through proper environment configuration to authenticate requests when generating vector representations for your code search workflows.

Yes, parallel processing is supported to efficiently generate vector representations for large codebases. This operational flexibility allows you to index extensive repositories faster while maintaining high-quality semantic matching for code search.