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15a9b88fc8
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15a9b88fc8 | ||
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03eb7203ec | ||
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e38cd6819b |
@@ -484,9 +484,8 @@ class ChatHandlerBase(BaseMessageHandler, ABC):
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stream_response.raise_for_status()
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# 使用字节流迭代器(避免 aiter_lines 的性能问题)
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# aiter_raw() 返回原始数据块,无缓冲,实现真正的流式传输
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byte_iterator = stream_response.aiter_raw()
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# 使用字节流迭代器(避免 aiter_lines 的性能问题, aiter_bytes 会自动解压 gzip/deflate)
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byte_iterator = stream_response.aiter_bytes()
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# 预读检测嵌套错误
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prefetched_chunks = await stream_processor.prefetch_and_check_error(
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@@ -476,8 +476,8 @@ class CliMessageHandlerBase(BaseMessageHandler):
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stream_response.raise_for_status()
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# 使用字节流迭代器(避免 aiter_lines 的性能问题)
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byte_iterator = stream_response.aiter_raw()
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# 使用字节流迭代器(避免 aiter_lines 的性能问题, aiter_bytes 会自动解压 gzip/deflate)
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byte_iterator = stream_response.aiter_bytes()
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# 预读第一个数据块,检测嵌套错误(HTTP 200 但响应体包含错误)
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prefetched_chunks = await self._prefetch_and_check_embedded_error(
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@@ -531,7 +531,7 @@ class CliMessageHandlerBase(BaseMessageHandler):
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# 检查是否需要格式转换
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needs_conversion = self._needs_format_conversion(ctx)
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async for chunk in stream_response.aiter_raw():
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async for chunk in stream_response.aiter_bytes():
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# 在第一次输出数据前更新状态为 streaming
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if not streaming_status_updated:
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self._update_usage_to_streaming_with_ctx(ctx)
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@@ -4,17 +4,28 @@ Handler 基础工具函数
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from typing import Any, Dict, Optional
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from src.core.logger import logger
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def extract_cache_creation_tokens(usage: Dict[str, Any]) -> int:
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"""
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提取缓存创建 tokens(兼容新旧格式)
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提取缓存创建 tokens(兼容三种格式)
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Claude API 在不同版本中使用了不同的字段名来表示缓存创建 tokens:
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- 新格式(2024年后):使用 claude_cache_creation_5_m_tokens 和
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claude_cache_creation_1_h_tokens 分别表示 5 分钟和 1 小时缓存
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- 旧格式:使用 cache_creation_input_tokens 表示总的缓存创建 tokens
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根据 Anthropic API 文档,支持三种格式(按优先级):
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此函数自动检测并适配两种格式,优先使用新格式。
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1. **嵌套格式(优先级最高)**:
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usage.cache_creation.ephemeral_5m_input_tokens
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usage.cache_creation.ephemeral_1h_input_tokens
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2. **扁平新格式(优先级第二)**:
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usage.claude_cache_creation_5_m_tokens
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usage.claude_cache_creation_1_h_tokens
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3. **旧格式(优先级第三)**:
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usage.cache_creation_input_tokens
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优先使用嵌套格式,如果嵌套格式字段存在但值为 0,则智能 fallback 到旧格式。
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扁平格式和嵌套格式互斥,按顺序检查。
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Args:
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usage: API 响应中的 usage 字典
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@@ -22,20 +33,63 @@ def extract_cache_creation_tokens(usage: Dict[str, Any]) -> int:
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Returns:
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缓存创建 tokens 总数
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"""
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# 检查新格式字段是否存在(而非值是否为 0)
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# 如果字段存在,即使值为 0 也是合法的,不应 fallback 到旧格式
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has_new_format = (
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# 1. 检查嵌套格式(最新格式)
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cache_creation = usage.get("cache_creation")
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if isinstance(cache_creation, dict):
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cache_5m = int(cache_creation.get("ephemeral_5m_input_tokens", 0))
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cache_1h = int(cache_creation.get("ephemeral_1h_input_tokens", 0))
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total = cache_5m + cache_1h
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if total > 0:
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logger.debug(
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f"Using nested cache_creation: 5m={cache_5m}, 1h={cache_1h}, total={total}"
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)
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return total
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# 嵌套格式存在但为 0,fallback 到旧格式
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old_format = int(usage.get("cache_creation_input_tokens", 0))
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if old_format > 0:
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logger.debug(
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f"Nested cache_creation is 0, using old format: {old_format}"
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)
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return old_format
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# 都是 0,返回 0
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return 0
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# 2. 检查扁平新格式
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has_flat_format = (
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"claude_cache_creation_5_m_tokens" in usage
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or "claude_cache_creation_1_h_tokens" in usage
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)
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if has_new_format:
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cache_5m = usage.get("claude_cache_creation_5_m_tokens", 0)
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cache_1h = usage.get("claude_cache_creation_1_h_tokens", 0)
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return int(cache_5m) + int(cache_1h)
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if has_flat_format:
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cache_5m = int(usage.get("claude_cache_creation_5_m_tokens", 0))
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cache_1h = int(usage.get("claude_cache_creation_1_h_tokens", 0))
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total = cache_5m + cache_1h
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# 回退到旧格式
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return int(usage.get("cache_creation_input_tokens", 0))
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if total > 0:
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logger.debug(
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f"Using flat new format: 5m={cache_5m}, 1h={cache_1h}, total={total}"
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)
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return total
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# 扁平格式存在但为 0,fallback 到旧格式
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old_format = int(usage.get("cache_creation_input_tokens", 0))
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if old_format > 0:
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logger.debug(
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f"Flat cache_creation is 0, using old format: {old_format}"
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)
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return old_format
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# 都是 0,返回 0
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return 0
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# 3. 回退到旧格式
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old_format = int(usage.get("cache_creation_input_tokens", 0))
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if old_format > 0:
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logger.debug(f"Using old format: cache_creation_input_tokens={old_format}")
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return old_format
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def build_sse_headers(extra_headers: Optional[Dict[str, str]] = None) -> Dict[str, str]:
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@@ -8,86 +8,116 @@ from src.api.handlers.base.utils import build_sse_headers, extract_cache_creatio
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class TestExtractCacheCreationTokens:
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"""测试 extract_cache_creation_tokens 函数"""
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def test_new_format_only(self) -> None:
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"""测试只有新格式字段"""
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# === 嵌套格式测试(优先级最高)===
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def test_nested_cache_creation_format(self) -> None:
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"""测试嵌套格式正常情况"""
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usage = {
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"cache_creation": {
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"ephemeral_5m_input_tokens": 456,
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"ephemeral_1h_input_tokens": 100,
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}
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}
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assert extract_cache_creation_tokens(usage) == 556
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def test_nested_cache_creation_with_old_format_fallback(self) -> None:
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"""测试嵌套格式为 0 时回退到旧格式"""
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usage = {
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"cache_creation": {
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"ephemeral_5m_input_tokens": 0,
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"ephemeral_1h_input_tokens": 0,
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},
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"cache_creation_input_tokens": 549,
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}
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assert extract_cache_creation_tokens(usage) == 549
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def test_nested_has_priority_over_flat(self) -> None:
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"""测试嵌套格式优先于扁平格式"""
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usage = {
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"cache_creation": {
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"ephemeral_5m_input_tokens": 100,
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"ephemeral_1h_input_tokens": 200,
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},
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"claude_cache_creation_5_m_tokens": 999, # 应该被忽略
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"claude_cache_creation_1_h_tokens": 888, # 应该被忽略
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"cache_creation_input_tokens": 777, # 应该被忽略
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}
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assert extract_cache_creation_tokens(usage) == 300
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# === 扁平格式测试(优先级第二)===
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def test_flat_new_format_still_works(self) -> None:
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"""测试扁平新格式兼容性"""
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usage = {
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"claude_cache_creation_5_m_tokens": 100,
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"claude_cache_creation_1_h_tokens": 200,
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}
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assert extract_cache_creation_tokens(usage) == 300
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def test_new_format_5m_only(self) -> None:
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"""测试只有 5 分钟缓存"""
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def test_flat_new_format_with_old_format_fallback(self) -> None:
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"""测试扁平格式为 0 时回退到旧格式"""
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usage = {
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"claude_cache_creation_5_m_tokens": 0,
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"claude_cache_creation_1_h_tokens": 0,
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"cache_creation_input_tokens": 549,
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}
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assert extract_cache_creation_tokens(usage) == 549
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def test_flat_new_format_5m_only(self) -> None:
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"""测试只有 5 分钟扁平缓存"""
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usage = {
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"claude_cache_creation_5_m_tokens": 150,
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"claude_cache_creation_1_h_tokens": 0,
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}
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assert extract_cache_creation_tokens(usage) == 150
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def test_new_format_1h_only(self) -> None:
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"""测试只有 1 小时缓存"""
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def test_flat_new_format_1h_only(self) -> None:
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"""测试只有 1 小时扁平缓存"""
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usage = {
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"claude_cache_creation_5_m_tokens": 0,
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"claude_cache_creation_1_h_tokens": 250,
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}
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assert extract_cache_creation_tokens(usage) == 250
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# === 旧格式测试(优先级第三)===
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def test_old_format_only(self) -> None:
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"""测试只有旧格式字段"""
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"""测试只有旧格式"""
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usage = {
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"cache_creation_input_tokens": 500,
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"cache_creation_input_tokens": 549,
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}
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assert extract_cache_creation_tokens(usage) == 500
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assert extract_cache_creation_tokens(usage) == 549
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def test_both_formats_prefers_new(self) -> None:
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"""测试同时存在时优先使用新格式"""
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usage = {
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"claude_cache_creation_5_m_tokens": 100,
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"claude_cache_creation_1_h_tokens": 200,
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"cache_creation_input_tokens": 999, # 应该被忽略
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}
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assert extract_cache_creation_tokens(usage) == 300
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# === 边界情况测试 ===
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def test_empty_usage(self) -> None:
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"""测试空字典"""
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def test_no_cache_creation_tokens(self) -> None:
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"""测试没有任何缓存字段"""
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usage = {}
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assert extract_cache_creation_tokens(usage) == 0
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def test_all_zeros(self) -> None:
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"""测试所有字段都为 0"""
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def test_all_formats_zero(self) -> None:
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"""测试所有格式都为 0"""
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usage = {
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"cache_creation": {
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"ephemeral_5m_input_tokens": 0,
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"ephemeral_1h_input_tokens": 0,
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},
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"claude_cache_creation_5_m_tokens": 0,
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"claude_cache_creation_1_h_tokens": 0,
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"cache_creation_input_tokens": 0,
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}
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assert extract_cache_creation_tokens(usage) == 0
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def test_partial_new_format_with_old_format_fallback(self) -> None:
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"""测试新格式字段不存在时回退到旧格式"""
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usage = {
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"cache_creation_input_tokens": 123,
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}
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assert extract_cache_creation_tokens(usage) == 123
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def test_new_format_zero_should_not_fallback(self) -> None:
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"""测试新格式字段存在但为 0 时,不应 fallback 到旧格式"""
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usage = {
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"claude_cache_creation_5_m_tokens": 0,
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"claude_cache_creation_1_h_tokens": 0,
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"cache_creation_input_tokens": 456,
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}
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# 新格式字段存在,即使值为 0 也应该使用新格式(返回 0)
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# 而不是 fallback 到旧格式(返回 456)
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assert extract_cache_creation_tokens(usage) == 0
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def test_unrelated_fields_ignored(self) -> None:
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"""测试忽略无关字段"""
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usage = {
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"input_tokens": 1000,
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"output_tokens": 2000,
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"cache_read_input_tokens": 300,
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"claude_cache_creation_5_m_tokens": 50,
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"claude_cache_creation_1_h_tokens": 75,
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"cache_creation": {
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"ephemeral_5m_input_tokens": 50,
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"ephemeral_1h_input_tokens": 75,
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},
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}
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assert extract_cache_creation_tokens(usage) == 125
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