fix: metrics reporting when LLM plugin is not enabled

[qiqizjl]

Summary

Fix incorrect metrics reporting when LLM plugin is not enabled.

Problem

When the LLM plugin is not enabled, the nginx variables for LLM metrics were defaulting to '0', which caused the metrics to be incorrectly reported as active even when no LLM functionality was being used.

Solution

Changed the default values from '0' to '' (empty string) for the following nginx variables:

• llm_time_to_first_token
• llm_prompt_tokens
• llm_completion_tokens

Also updated the condition check in openai-base.lua to properly detect when LLM functionality is not active.

Changes

• apisix/cli/ngx_tpl.lua: Changed default values from '0' to ''
• apisix/plugins/ai-drivers/openai-base.lua: Updated condition check

🤖 Generated with Claude Code

[Baoyuantop]

Hi @qiqizjl, we made this change in #12549. Could you explain the original requirements of this PR? Changing the default value from '0' to '' may affect users' existing JSON log format.

[qiqizjl]

Hi @qiqizjl, we made this change in #12549. Could you explain the original requirements of this PR? Changing the default value from '0' to '' may affect users' existing JSON log format.

We found in apisix/plugins/prometheus/exporter.lua#L376-L394 that metrics are reported when the value is not empty. When the LLM plugin is not enabled, the default value is 0, which results in invalid metrics.

[qiqizjl]

Another possible solution is to check if request_type is ai_stream or ai_chat, and report accordingly. I tried changing the check to report if ctx.var.llm_time_to_first_token != 0, but this might affect abnormal requests. If possible, I can continue to refine this issue based on the discussion.

[flearc]

Another possible solution is to check if request_type is ai_stream or ai_chat, and report accordingly. I tried changing the check to report if ctx.var.llm_time_to_first_token != 0, but this might affect abnormal requests. If possible, I can continue to refine this issue based on the discussion.

    if llm_time_to_first_token ~= "0" then
        metrics.llm_latency:observe(tonumber(llm_time_to_first_token),
            gen_arr(route_id, service_id, consumer_name, balancer_ip,
            vars.request_type, vars.request_llm_model, vars.llm_model,
            unpack(extra_labels("llm_latency", ctx))))
    end
    if vars.llm_prompt_tokens ~= "0" then
        metrics.llm_prompt_tokens:inc(tonumber(vars.llm_prompt_tokens),
            gen_arr(route_id, service_id, consumer_name, balancer_ip,
            vars.request_type, vars.request_llm_model, vars.llm_model,
            unpack(extra_labels("llm_prompt_tokens", ctx))))
    end
    if vars.llm_completion_tokens ~= "0" then
        metrics.llm_completion_tokens:inc(tonumber(vars.llm_completion_tokens),
            gen_arr(route_id, service_id, consumer_name, balancer_ip,
            vars.request_type, vars.request_llm_model, vars.llm_model,
            unpack(extra_labels("llm_completion_tokens", ctx))))
    end

Adjust the if statement might help

[Baoyuantop]

Hi @qiqizjl, the proposed changes in this PR are incomplete and will cause a regression. We recommend creating an issue first to discuss a solution for this problem.