wehub-resource-sync
94 lines
3.6 KiB
TypeScript
94 lines
3.6 KiB
TypeScript
/**
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* Extract usage from non-streaming response body
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* Handles different provider response formats
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*/
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export function extractUsageFromResponse(responseBody, provider) {
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if (!responseBody || typeof responseBody !== "object") return null;
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const providerId = typeof provider === "string" ? provider.toLowerCase() : "";
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const isClaudeProvider =
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providerId === "claude" ||
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providerId === "anthropic" ||
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providerId.startsWith("anthropic-compatible");
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// OpenAI format (has prompt_tokens / completion_tokens)
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if (
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responseBody.usage &&
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typeof responseBody.usage === "object" &&
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responseBody.usage.prompt_tokens !== undefined
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) {
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return {
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prompt_tokens: responseBody.usage.prompt_tokens || 0,
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completion_tokens: responseBody.usage.completion_tokens || 0,
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// DeepSeek native API uses flat prompt_cache_hit_tokens (NOT
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// prompt_tokens_details.cached_tokens). Fall back to it so V4 cache
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// gets surfaced into kanban call_logs alongside the OpenAI/Claude paths.
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cached_tokens:
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responseBody.usage.prompt_tokens_details?.cached_tokens ??
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responseBody.usage.input_tokens_details?.cached_tokens ??
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responseBody.usage.prompt_cache_hit_tokens ??
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responseBody.usage.cached_tokens,
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reasoning_tokens:
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responseBody.usage.completion_tokens_details?.reasoning_tokens ??
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responseBody.usage.output_tokens_details?.reasoning_tokens ??
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responseBody.usage.reasoning_tokens,
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};
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}
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// Claude format
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if (
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isClaudeProvider &&
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responseBody.usage &&
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typeof responseBody.usage === "object" &&
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(responseBody.usage.input_tokens !== undefined ||
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responseBody.usage.output_tokens !== undefined)
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) {
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const inputTokens = responseBody.usage.input_tokens || 0;
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const cacheRead = responseBody.usage.cache_read_input_tokens || 0;
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const cacheCreation = responseBody.usage.cache_creation_input_tokens || 0;
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// Total prompt tokens = input + cache_read + cache_creation (per Claude API docs)
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const promptTokens = inputTokens + cacheRead + cacheCreation;
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return {
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prompt_tokens: promptTokens,
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completion_tokens: responseBody.usage.output_tokens || 0,
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cache_read_input_tokens: cacheRead,
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cache_creation_input_tokens: cacheCreation,
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};
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}
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// OpenAI Responses API format (input_tokens / output_tokens)
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const responsesUsage = responseBody.response?.usage || responseBody.usage;
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if (
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responsesUsage &&
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typeof responsesUsage === "object" &&
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(responsesUsage.input_tokens !== undefined || responsesUsage.output_tokens !== undefined)
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) {
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return {
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prompt_tokens: responsesUsage.input_tokens || 0,
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completion_tokens: responsesUsage.output_tokens || 0,
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cache_read_input_tokens: responsesUsage.cache_read_input_tokens,
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cached_tokens:
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responsesUsage.input_tokens_details?.cached_tokens ??
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responsesUsage.prompt_tokens_details?.cached_tokens ??
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responsesUsage.cache_read_input_tokens,
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cache_creation_input_tokens: responsesUsage.cache_creation_input_tokens,
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reasoning_tokens:
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responsesUsage.output_tokens_details?.reasoning_tokens ??
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responsesUsage.completion_tokens_details?.reasoning_tokens ??
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responsesUsage.reasoning_tokens,
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};
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}
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// Gemini format
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if (responseBody.usageMetadata && typeof responseBody.usageMetadata === "object") {
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return {
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prompt_tokens: responseBody.usageMetadata.promptTokenCount || 0,
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completion_tokens: responseBody.usageMetadata.candidatesTokenCount || 0,
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reasoning_tokens: responseBody.usageMetadata.thoughtsTokenCount,
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};
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}
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return null;
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}
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