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4fb6767c83
Adds Prometheus metrics for the discourse-ai plugin to track LLM usage, performance, and costs. When discourse-prometheus is installed, exports 4 metrics for all LLM API calls: - `discourse_ai_llm_calls_total` - Total API calls with success/error tracking - `discourse_ai_llm_request_tokens_total` - Input tokens sent to LLMs - `discourse_ai_llm_response_tokens_total` - Output tokens received from LLMs - `discourse_ai_llm_duration_seconds` - API call latency All metrics include labels: `db`, `provider`, `model_name`, `feature`, `seeded`, `status` Implementation follows the SpamMetric pattern with instrumentation in the base endpoint class to capture all providers. --------- Co-authored-by: Keegan George <kgeorge13@gmail.com>
46 lines
1.1 KiB
Ruby
Vendored
46 lines
1.1 KiB
Ruby
Vendored
# frozen_string_literal: true
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RSpec.describe DiscourseAi::Completions::LlmMetric do
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fab!(:llm_model)
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describe ".record" do
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it "does not raise an error when DiscoursePrometheus is not defined" do
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expect do
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described_class.record(
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llm_model: llm_model,
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feature_name: "test_feature",
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request_tokens: 100,
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response_tokens: 50,
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duration_ms: 1500,
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status: :success,
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)
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end.not_to raise_error
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end
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it "accepts nil feature_name" do
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expect do
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described_class.record(
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llm_model: llm_model,
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feature_name: nil,
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request_tokens: 100,
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response_tokens: 50,
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duration_ms: 1500.0,
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status: :success,
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)
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end.not_to raise_error
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end
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it "accepts error status" do
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expect do
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described_class.record(
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llm_model: llm_model,
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feature_name: "test_feature",
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request_tokens: 0,
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response_tokens: 0,
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duration_ms: 500.0,
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status: :error,
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)
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end.not_to raise_error
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end
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end
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end
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