Turn on Kong's own AI telemetry instead of improvising it

Four settings, all of them off, which is why the AI gateway produced
HTTP-level telemetry and nothing about the model.

  logging.log_payloads     the prompt and the completion, in the log
                           records and — since OTel instrumentation is on
                           here — in the spans too
  logging.log_statistics   model usage and token counts
  prometheus.ai_metrics    kong_ai_llm_* : requests, tokens by type, cost,
                           provider latency, per model and per consumer
  input_cost / output_cost cost per 1M tokens, which is what makes
                           kong_ai_llm_cost_total mean anything

Also removes this morning's pre-function/post-function body capture from
this gateway. It was the wrong tool for the LLM route: Kong's own
implementation reassembles a streamed SSE answer, while accumulating raw
chunks in kong.ctx.shared does not. The five business domains keep theirs
— their MCP traffic is not streamed the same way.

The costs are a snapshot, checked against OpenRouter's price list for
google/gemini-3.7-flash on 2026-08-27: $0.375 in, $1.875 out per 1M
tokens. They are substitution variables rather than literals precisely
because a stale figure produces confidently wrong money rather than an
error.

Note what this does NOT fix: usage is missing on streamed responses
because the OpenAI-compatible API only reports it when the caller asks
for stream_options.include_usage, and that is LibreChat's request to
make, not Kong's. Expect token counts to stay partial until then.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

kong/ai-proxy-advanced.yaml

@@ -10,9 +10,28 @@ config:
       auth:
         header_name: Authorization
         header_value: ${OPENROUTER_BEARER}
+      # dev only, like the rest of this gateway's audit trail: log_payloads
+      # puts the prompt and the completion in the log stream, and -- because
+      # OpenTelemetry instrumentation is on here -- in the spans as well.
+      #
+      # This is what the pre-function/post-function capture in
+      # observability.yaml stood in for on this route. Kong's own
+      # implementation reassembles a streamed SSE answer; accumulating raw
+      # chunks does not.
+      logging:
+        log_payloads: true
+        # Model usage and token counts in the log records. With this off Kong
+        # counted nothing of its own, which is half of why usage was missing.
+        log_statistics: true
       model:
         provider: openai
         # name deliberately unset: the client's own `model` field is passed
         # through to OpenRouter.
         options:
           upstream_url: ${OPENROUTER_UPSTREAM_URL}
+          # Cost per 1M tokens, in the provider's currency. Kong multiplies
+          # these by the token counts to produce kong_ai_llm_cost_total, so a
+          # stale figure yields confidently wrong money rather than an error.
+          # Re-check them whenever the model or the contract changes.
+          input_cost: ${LLM_INPUT_COST}
+          output_cost: ${LLM_OUTPUT_COST}