Support Oracle Cloud Infrastructure (OCI) Generative AI as a provider

[fede-kamel]

Summary

NeMo Relay ships provider codecs for OpenAI Chat, OpenAI Responses, and Anthropic Messages, so middleware (PII redaction, guardrails, policy) and observability subscribers can reason over those providers' payloads through the normalized annotated shapes. There is currently no coverage for Oracle Cloud Infrastructure (OCI) Generative AI, whose chat API uses its own request envelope (ChatDetails with servingMode and chatRequest) and two wire formats (GENERIC and COHERE).

OCI Generative AI serves managed on-demand models (Meta Llama, Cohere Command, xAI Grok, Google Gemini, OpenAI) and dedicated AI cluster endpoints, including imported open-weights models such as NVIDIA Nemotron 3 — so agents calling those models today are invisible to Relay's request intercepts and get no normalized response annotations.

Proposal

1. A supported-integrations guide for observing LangChain agents backed by langchain-oci (plus a langchain-oci optional-dependency extra) with callback tests.
2. LlmCodec / LlmResponseCodec implementations for the OCI Generative AI chat API covering both the GENERIC and COHERE formats, meeting the Provider Codecs guide's guarantees (encode(decode(original), original) == original, unmodeled-field preservation via baseline-compare-and-patch), with unit tests and an opt-in live integration test against a real OCI endpoint.

Follow-ups under discussion with the maintainers: OCI model pricing entries for cost accounting, and Switchyard routing support.

I have a branch implementing 1 and 2, validated end-to-end against a dedicated AI cluster endpoint hosting an imported NVIDIA Nemotron 3 model; PR to follow referencing this issue.

[fede-kamel]

Plan updated after review in #549: the OCI codec will be implemented in the Rust core (crates/types/src/codec) with exhaustive tests and Python/Node binding exposure, pinned by the wire-format fixtures validated live in #549. The LangChain guide is deferred unless it has genuine value-add beyond examples.

[fede-kamel]

Series plan of record (supersedes the single-PR approach in #552)

The OCI Generative AI provider lands as a four-part stacked series, foundation-ordered, each layer independently green (full workspace build + tests + clippy + fmt on every branch), with the combined content byte-identical to the end-to-end-validated original from #552 (encoded payload posted verbatim to the signed OCI REST endpoint, answered by a dedicated AI cluster serving an imported NVIDIA Nemotron 3 model).

#	PR	Content	Size
1	#554 (open, ready for review)	Typed ApiSpecific OCI variants + LlmResponseCodec (GENERIC + COHERE ChatResult decode, usage/finish-reason normalization)	+562
2	feat/oci-codec-2-request-codec — opens when #554 merges	LlmCodec decode + merge-not-replace encode with encode(decode(x), x) == x identity and unmodeled-field preservation	+1,414
3	feat/oci-codec-3-resolver-surfaces	Provider-surface registration + detection, OCIGenAIStreamingCodec (descriptor requires it at this layer), guardrails / PII-redaction overlay / adaptive support, parity cases	+1,060
4	feat/oci-codec-4-bindings — closes this issue	PyOCIGenAIChatCodec (pyo3), napi class, .pyi/codecs.py exposure, binding tests	+202

Each later branch is a pure superset of the previous, so review comments on layer N apply cleanly to N+1..4 via rebase.