ai-proxy and ai-proxy-advanced: Gemini driver drops cachedContent field during OpenAI-to-Gemini transformation

### Is there an existing issue for this? - [x] I have searched the existing issues ### Kong version (`$ kong version`) Kong 3.13.x (ai-proxy-advanced plugin) ### Current Behavior When sending a request to the `llm/v1/chat` route with `provider: gemini`, the `cachedContent` field in the request body is silently dropped during the OpenAI-to-Gemini transformation. The request succeeds, but the Vertex AI context cache is not used. The response shows `prompt_tokens` reflecting only the user message (e.g. 8 tokens), with no `cachedContentTokenCount` — confirming the cache was ignored. ### Expected Behavior The `cachedContent` field should be preserved through the OpenAI-to-Gemini transformation and included in the outgoing request to Vertex AI, so that users can leverage [Vertex AI context caching](https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-create) from the OpenAI-compatible `/v1/chat/completions` route. The response should include `cachedContentTokenCount` in `usageMetadata`, and `promptTokenCount` should reflect the cached tokens plus the user message. ### Steps To Reproduce 1. Create a Vertex AI context cache via the `cachedContents` API with a large system prompt (32,768+ tokens) 2. Configure ai-proxy-advanced with `provider: gemini` and `route_type: llm/v1/chat` 3. Send a request to `/v1/chat/completions` with `cachedContent` in the body (e.g. via `extra_body` in the OpenAI SDK): ```json { "model": "gemini-2.0-flash-001", "cachedContent": "projects/123456789/locations/us-central1/cachedContents/987654321", "messages": [ {"role": "user", "content": "What company are you an expert on?"} ] } ``` 4. Observe that the response `usage.prompt_tokens` only reflects the user message size (e.g. 8), not the cached content. The cache is not being used. 5. For comparison, send the same request via the native `generateContent` endpoint (with `llm_format: gemini`, which bypasses transformation) — this works correctly and returns `cachedContentTokenCount` in the response. ### Anything else? **Root cause** is in [`kong/llm/drivers/gemini.lua` lines 272-383](https://github.com/Kong/kong/blob/master/kong/llm/drivers/gemini.lua#L272-L383). The `to_gemini_chat_openai` function builds a new request body from scratch: ```lua local function to_gemini_chat_openai(request_table, model_info, route_type) local new_r = {} -- only populates: new_r.contents, new_r.systemInstruction, -- new_r.generationConfig, new_r.tools, new_r.tool_config return new_r, "application/json", nil end ``` `request_table.cachedContent` is never read or assigned to `new_r`. **Suggested fix** — add one line before the return: ```lua new_r.cachedContent = request_table.cachedContent ``` Context caching is a first-class Vertex AI feature for reducing latency and cost on large static prompts. Supporting it on the OpenAI-compatible route would let users access it without switching to the native Gemini API format.