Issue is WIP and will be further refined.

LLM and embedding model sources are currently defined in GUC, e.g. vectorize.openai_service_url = https://api.openai.com/v1 contains the base url for OpenAI. This implementation introduces at least two limitatations:

1. two vectorize projects cannot use two different urls for the same source. for example, project_a wants vectorize.openai_service_url = https://api.openai.com/v1 and project_b wants vectorize.openai_service_url = https://myapi.mydomain.com/v1. The pg vectorize background worker reads the model source from the job's values in the vectorize.job table in order to know which guc to use.
2. adding a new embedding source is cumbersome. adding GUCs requires a code change, and a new project relase.

Proposal: move GUCs into a table such as vectorize.model_sources to contain information such as the base url, schema, etc.

Considerations:

• How to handle API keys for each record in model source. Can this be a superuser only table, or does API key need to be a GUC?
• API request/response schemas. These are currently defined in code, but we could also define these in json mapping using a framework like jolt . There is an experiment of this implementation here. Using this framework would allow for adding arbitrary embedding and LLM sources via inserts to this model sources table.