The rag use case uses candidate documents that are inserted into a LLM’s context to ground the generated response to those documents instead of generating an answer from details stored in the LLM’s trained weights. This type of search adds guardrails so the LLM can search private data collections.
The RAG search can perform queries against external documents passed in as part of the request.
Bearer token used for authentication. Format: Authorization: Bearer ACCESS_TOKEN.
application/json
"application/json"
Unique identifier for the model.
OK