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## 🔍 Overview This update some changes when showcasing feature cost for LLMs that has credit allocations ## 📸 Screenshots _Included in plan instead of cost when a feature comes solely from a credit allocation model_ <img width="1107" height="463" alt="Screenshot 2025-12-08 at 16 47 53" src="https://github.com/user-attachments/assets/24391a07-2af2-4d56-b8b9-b770ebdb132d" /> _A subtable in a tooltip with line items for mixed costs_ <img width="1110" height="460" alt="Screenshot 2025-12-08 at 16 48 19" src="https://github.com/user-attachments/assets/ceb94f00-9a1a-49f8-9e46-7ff544113d55" />
128 lines
4.3 KiB
Ruby
Vendored
128 lines
4.3 KiB
Ruby
Vendored
# frozen_string_literal: true
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RSpec.describe AiUsageSerializer do
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fab!(:user)
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fab!(:claude_model) do
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Fabricate(
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:llm_model,
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name: "claude-3-opus",
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provider: "anthropic",
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input_cost: 15.0,
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output_cost: 75.0,
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)
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end
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fab!(:gpt_model) do
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Fabricate(:llm_model, name: "gpt-4", provider: "open_ai", input_cost: 10.0, output_cost: 30.0)
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end
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before { enable_current_plugin }
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describe "#feature_models" do
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it "returns a hash mapping feature names to model breakdowns" do
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AiApiRequestStat.create!(
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provider_id: AiApiAuditLog::Provider::Anthropic,
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user_id: user.id,
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llm_id: claude_model.id,
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language_model: "claude-3-opus",
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feature_name: "ai_bot",
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request_tokens: 1000,
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response_tokens: 500,
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created_at: 1.day.ago,
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)
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AiApiRequestStat.create!(
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provider_id: AiApiAuditLog::Provider::OpenAI,
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user_id: user.id,
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llm_id: gpt_model.id,
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language_model: "gpt-4",
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feature_name: "ai_bot",
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request_tokens: 2000,
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response_tokens: 1000,
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created_at: 1.day.ago,
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)
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report = DiscourseAi::Completions::Report.new(start_date: 2.days.ago, end_date: Time.current)
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serialized = described_class.new(report, root: false)
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json = JSON.parse(serialized.to_json)
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expect(json).to have_key("feature_models")
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expect(json["feature_models"]).to be_a(Hash)
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expect(json["feature_models"]).to have_key("ai_bot")
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expect(json["feature_models"]["ai_bot"].size).to eq(2)
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end
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it "includes credit_allocation for models with LlmCreditAllocation" do
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seeded_model = Fabricate(:seeded_model)
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Fabricate(:llm_credit_allocation, llm_model: seeded_model)
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AiApiRequestStat.create!(
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provider_id: AiApiAuditLog::Provider::Anthropic,
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user_id: user.id,
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llm_id: seeded_model.id,
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language_model: seeded_model.name,
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feature_name: "ai_helper",
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request_tokens: 500,
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response_tokens: 250,
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created_at: 1.day.ago,
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)
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report = DiscourseAi::Completions::Report.new(start_date: 2.days.ago, end_date: Time.current)
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serialized = described_class.new(report, root: false)
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json = JSON.parse(serialized.to_json)
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ai_helper_models = json["feature_models"]["ai_helper"]
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seeded_model_data = ai_helper_models.find { |m| m["llm_id"].to_i == seeded_model.id }
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expect(seeded_model_data).to have_key("credit_allocation")
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expect(seeded_model_data["credit_allocation"]).to be_present
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end
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it "does not include credit_allocation for models without LlmCreditAllocation" do
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AiApiRequestStat.create!(
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provider_id: AiApiAuditLog::Provider::Anthropic,
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user_id: user.id,
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llm_id: claude_model.id,
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language_model: "claude-3-opus",
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feature_name: "ai_bot",
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request_tokens: 1000,
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response_tokens: 500,
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created_at: 1.day.ago,
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)
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report = DiscourseAi::Completions::Report.new(start_date: 2.days.ago, end_date: Time.current)
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serialized = described_class.new(report, root: false)
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json = JSON.parse(serialized.to_json)
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ai_bot_models = json["feature_models"]["ai_bot"]
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claude_model_data = ai_bot_models.find { |m| m["llm_id"].to_i == claude_model.id }
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expect(claude_model_data).not_to have_key("credit_allocation")
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end
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it "includes spending data for each model in the breakdown" do
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AiApiRequestStat.create!(
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provider_id: AiApiAuditLog::Provider::Anthropic,
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user_id: user.id,
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llm_id: claude_model.id,
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language_model: "claude-3-opus",
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feature_name: "ai_bot",
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request_tokens: 1000,
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response_tokens: 500,
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created_at: 1.day.ago,
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)
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report = DiscourseAi::Completions::Report.new(start_date: 2.days.ago, end_date: Time.current)
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serialized = described_class.new(report, root: false)
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json = JSON.parse(serialized.to_json)
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ai_bot_models = json["feature_models"]["ai_bot"]
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model_data = ai_bot_models.first
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expect(model_data).to have_key("input_spending")
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expect(model_data).to have_key("output_spending")
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expect(model_data).to have_key("total_tokens")
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expect(model_data).to have_key("usage_count")
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end
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end
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end
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