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feat(provider): show cache hit rate in /status (#2645)
This commit is contained in:
chengyongru
chengyongru
parent
4741026538
commit
0334fa9944
@ -97,6 +97,15 @@ class _LoopHook(AgentHook):
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logger.info("Tool call: {}({})", tc.name, args_str[:200])
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self._loop._set_tool_context(self._channel, self._chat_id, self._message_id)
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async def after_iteration(self, context: AgentHookContext) -> None:
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u = context.usage or {}
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logger.debug(
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"LLM usage: prompt={} completion={} cached={}",
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u.get("prompt_tokens", 0),
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u.get("completion_tokens", 0),
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u.get("cached_tokens", 0),
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)
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def finalize_content(self, context: AgentHookContext, content: str | None) -> str | None:
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return self._loop._strip_think(content)
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@ -60,7 +60,7 @@ class AgentRunner:
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messages = list(spec.initial_messages)
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final_content: str | None = None
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tools_used: list[str] = []
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usage = {"prompt_tokens": 0, "completion_tokens": 0}
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usage: dict[str, int] = {}
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error: str | None = None
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stop_reason = "completed"
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tool_events: list[dict[str, str]] = []
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@ -92,13 +92,15 @@ class AgentRunner:
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response = await self.provider.chat_with_retry(**kwargs)
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raw_usage = response.usage or {}
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usage = {
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"prompt_tokens": int(raw_usage.get("prompt_tokens", 0) or 0),
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"completion_tokens": int(raw_usage.get("completion_tokens", 0) or 0),
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}
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context.response = response
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context.usage = usage
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context.usage = raw_usage
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context.tool_calls = list(response.tool_calls)
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# Accumulate standard fields into result usage.
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usage["prompt_tokens"] = usage.get("prompt_tokens", 0) + int(raw_usage.get("prompt_tokens", 0) or 0)
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usage["completion_tokens"] = usage.get("completion_tokens", 0) + int(raw_usage.get("completion_tokens", 0) or 0)
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cached = raw_usage.get("cached_tokens")
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if cached:
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usage["cached_tokens"] = usage.get("cached_tokens", 0) + int(cached)
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if response.has_tool_calls:
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if hook.wants_streaming():
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@ -379,6 +379,10 @@ class AnthropicProvider(LLMProvider):
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val = getattr(response.usage, attr, 0)
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if val:
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usage[attr] = val
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# Normalize to cached_tokens for downstream consistency.
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cache_read = usage.get("cache_read_input_tokens", 0)
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if cache_read:
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usage["cached_tokens"] = cache_read
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return LLMResponse(
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content="".join(content_parts) or None,
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@ -308,6 +308,13 @@ class OpenAICompatProvider(LLMProvider):
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@classmethod
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def _extract_usage(cls, response: Any) -> dict[str, int]:
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"""Extract token usage from an OpenAI-compatible response.
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Handles both dict-based (raw JSON) and object-based (SDK Pydantic)
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responses. Provider-specific ``cached_tokens`` fields are normalised
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under a single key; see the priority chain inside for details.
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"""
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# --- resolve usage object ---
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usage_obj = None
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response_map = cls._maybe_mapping(response)
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if response_map is not None:
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@ -317,19 +324,53 @@ class OpenAICompatProvider(LLMProvider):
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usage_map = cls._maybe_mapping(usage_obj)
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if usage_map is not None:
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return {
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result = {
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"prompt_tokens": int(usage_map.get("prompt_tokens") or 0),
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"completion_tokens": int(usage_map.get("completion_tokens") or 0),
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"total_tokens": int(usage_map.get("total_tokens") or 0),
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}
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if usage_obj:
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return {
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elif usage_obj:
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result = {
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"prompt_tokens": getattr(usage_obj, "prompt_tokens", 0) or 0,
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"completion_tokens": getattr(usage_obj, "completion_tokens", 0) or 0,
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"total_tokens": getattr(usage_obj, "total_tokens", 0) or 0,
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}
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return {}
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else:
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return {}
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# --- cached_tokens (normalised across providers) ---
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# Try nested paths first (dict), fall back to attribute (SDK object).
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# Priority order ensures the most specific field wins.
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for path in (
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("prompt_tokens_details", "cached_tokens"), # OpenAI/Zhipu/MiniMax/Qwen/Mistral/xAI
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("cached_tokens",), # StepFun/Moonshot (top-level)
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("prompt_cache_hit_tokens",), # DeepSeek/SiliconFlow
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):
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cached = cls._get_nested_int(usage_map, path)
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if not cached and usage_obj:
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cached = cls._get_nested_int(usage_obj, path)
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if cached:
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result["cached_tokens"] = cached
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break
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return result
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@staticmethod
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def _get_nested_int(obj: Any, path: tuple[str, ...]) -> int:
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"""Drill into *obj* by *path* segments and return an ``int`` value.
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Supports both dict-key access and attribute access so it works
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uniformly with raw JSON dicts **and** SDK Pydantic models.
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"""
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current = obj
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for segment in path:
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if current is None:
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return 0
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if isinstance(current, dict):
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current = current.get(segment)
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else:
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current = getattr(current, segment, None)
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return int(current or 0) if current is not None else 0
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def _parse(self, response: Any) -> LLMResponse:
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if isinstance(response, str):
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@ -255,14 +255,18 @@ def build_status_content(
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)
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last_in = last_usage.get("prompt_tokens", 0)
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last_out = last_usage.get("completion_tokens", 0)
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cached = last_usage.get("cached_tokens", 0)
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ctx_total = max(context_window_tokens, 0)
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ctx_pct = int((context_tokens_estimate / ctx_total) * 100) if ctx_total > 0 else 0
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ctx_used_str = f"{context_tokens_estimate // 1000}k" if context_tokens_estimate >= 1000 else str(context_tokens_estimate)
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ctx_total_str = f"{ctx_total // 1024}k" if ctx_total > 0 else "n/a"
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token_line = f"\U0001f4ca Tokens: {last_in} in / {last_out} out"
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if cached and last_in:
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token_line += f" ({cached * 100 // last_in}% cached)"
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return "\n".join([
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f"\U0001f408 nanobot v{version}",
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f"\U0001f9e0 Model: {model}",
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f"\U0001f4ca Tokens: {last_in} in / {last_out} out",
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token_line,
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f"\U0001f4da Context: {ctx_used_str}/{ctx_total_str} ({ctx_pct}%)",
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f"\U0001f4ac Session: {session_msg_count} messages",
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f"\u23f1 Uptime: {uptime}",
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@ -333,3 +333,82 @@ async def test_subagent_max_iterations_announces_existing_fallback(tmp_path, mon
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args = mgr._announce_result.await_args.args
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assert args[3] == "Task completed but no final response was generated."
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assert args[5] == "ok"
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@pytest.mark.asyncio
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async def test_runner_accumulates_usage_and_preserves_cached_tokens():
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"""Runner should accumulate prompt/completion tokens across iterations
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and preserve cached_tokens from provider responses."""
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from nanobot.agent.runner import AgentRunSpec, AgentRunner
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provider = MagicMock()
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call_count = {"n": 0}
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async def chat_with_retry(*, messages, **kwargs):
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call_count["n"] += 1
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if call_count["n"] == 1:
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return LLMResponse(
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content="thinking",
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tool_calls=[ToolCallRequest(id="call_1", name="read_file", arguments={"path": "x"})],
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usage={"prompt_tokens": 100, "completion_tokens": 10, "cached_tokens": 80},
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)
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return LLMResponse(
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content="done",
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tool_calls=[],
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usage={"prompt_tokens": 200, "completion_tokens": 20, "cached_tokens": 150},
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)
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provider.chat_with_retry = chat_with_retry
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tools = MagicMock()
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tools.get_definitions.return_value = []
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tools.execute = AsyncMock(return_value="file content")
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runner = AgentRunner(provider)
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result = await runner.run(AgentRunSpec(
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initial_messages=[{"role": "user", "content": "do task"}],
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tools=tools,
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model="test-model",
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max_iterations=3,
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))
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# Usage should be accumulated across iterations
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assert result.usage["prompt_tokens"] == 300 # 100 + 200
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assert result.usage["completion_tokens"] == 30 # 10 + 20
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assert result.usage["cached_tokens"] == 230 # 80 + 150
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@pytest.mark.asyncio
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async def test_runner_passes_cached_tokens_to_hook_context():
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"""Hook context.usage should contain cached_tokens."""
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from nanobot.agent.hook import AgentHook, AgentHookContext
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from nanobot.agent.runner import AgentRunSpec, AgentRunner
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provider = MagicMock()
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captured_usage: list[dict] = []
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class UsageHook(AgentHook):
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async def after_iteration(self, context: AgentHookContext) -> None:
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captured_usage.append(dict(context.usage))
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async def chat_with_retry(**kwargs):
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return LLMResponse(
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content="done",
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tool_calls=[],
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usage={"prompt_tokens": 200, "completion_tokens": 20, "cached_tokens": 150},
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)
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provider.chat_with_retry = chat_with_retry
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tools = MagicMock()
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tools.get_definitions.return_value = []
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runner = AgentRunner(provider)
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await runner.run(AgentRunSpec(
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initial_messages=[],
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tools=tools,
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model="test-model",
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max_iterations=1,
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hook=UsageHook(),
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))
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assert len(captured_usage) == 1
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assert captured_usage[0]["cached_tokens"] == 150
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@ -152,10 +152,12 @@ class TestRestartCommand:
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])
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await loop._run_agent_loop([])
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assert loop._last_usage == {"prompt_tokens": 9, "completion_tokens": 4}
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assert loop._last_usage["prompt_tokens"] == 9
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assert loop._last_usage["completion_tokens"] == 4
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await loop._run_agent_loop([])
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assert loop._last_usage == {"prompt_tokens": 0, "completion_tokens": 0}
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assert loop._last_usage["prompt_tokens"] == 0
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assert loop._last_usage["completion_tokens"] == 0
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@pytest.mark.asyncio
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async def test_status_falls_back_to_last_usage_when_context_estimate_missing(self):
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231
tests/providers/test_cached_tokens.py
Normal file
231
tests/providers/test_cached_tokens.py
Normal file
@ -0,0 +1,231 @@
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"""Tests for cached token extraction from OpenAI-compatible providers."""
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from __future__ import annotations
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from nanobot.providers.openai_compat_provider import OpenAICompatProvider
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class FakeUsage:
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"""Mimics an OpenAI SDK usage object (has attributes, not dict keys)."""
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def __init__(self, **kwargs):
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for k, v in kwargs.items():
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setattr(self, k, v)
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class FakePromptDetails:
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"""Mimics prompt_tokens_details sub-object."""
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def __init__(self, cached_tokens=0):
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self.cached_tokens = cached_tokens
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class _FakeSpec:
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supports_prompt_caching = False
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model_id_prefix = None
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strip_model_prefix = False
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max_completion_tokens = False
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reasoning_effort = None
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def _provider():
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from unittest.mock import MagicMock
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p = OpenAICompatProvider.__new__(OpenAICompatProvider)
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p.client = MagicMock()
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p.spec = _FakeSpec()
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return p
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# Minimal valid choice so _parse reaches _extract_usage.
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_DICT_CHOICE = {"message": {"content": "Hello"}}
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class _FakeMessage:
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content = "Hello"
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tool_calls = None
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class _FakeChoice:
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message = _FakeMessage()
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finish_reason = "stop"
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# --- dict-based response (raw JSON / mapping) ---
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def test_extract_usage_openai_cached_tokens_dict():
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"""prompt_tokens_details.cached_tokens from a dict response."""
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p = _provider()
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response = {
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"choices": [_DICT_CHOICE],
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"usage": {
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"prompt_tokens": 2000,
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"completion_tokens": 300,
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"total_tokens": 2300,
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"prompt_tokens_details": {"cached_tokens": 1200},
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}
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}
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result = p._parse(response)
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assert result.usage["cached_tokens"] == 1200
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assert result.usage["prompt_tokens"] == 2000
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def test_extract_usage_deepseek_cached_tokens_dict():
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"""prompt_cache_hit_tokens from a DeepSeek dict response."""
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p = _provider()
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response = {
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"choices": [_DICT_CHOICE],
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"usage": {
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"prompt_tokens": 1500,
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"completion_tokens": 200,
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"total_tokens": 1700,
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"prompt_cache_hit_tokens": 1200,
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"prompt_cache_miss_tokens": 300,
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}
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}
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result = p._parse(response)
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assert result.usage["cached_tokens"] == 1200
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def test_extract_usage_no_cached_tokens_dict():
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"""Response without any cache fields -> no cached_tokens key."""
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p = _provider()
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response = {
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"choices": [_DICT_CHOICE],
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"usage": {
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"prompt_tokens": 1000,
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"completion_tokens": 200,
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"total_tokens": 1200,
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}
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}
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result = p._parse(response)
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assert "cached_tokens" not in result.usage
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def test_extract_usage_openai_cached_zero_dict():
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"""cached_tokens=0 should NOT be included (same as existing fields)."""
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p = _provider()
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response = {
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"choices": [_DICT_CHOICE],
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"usage": {
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"prompt_tokens": 2000,
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"completion_tokens": 300,
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"total_tokens": 2300,
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"prompt_tokens_details": {"cached_tokens": 0},
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}
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}
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result = p._parse(response)
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assert "cached_tokens" not in result.usage
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# --- object-based response (OpenAI SDK Pydantic model) ---
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def test_extract_usage_openai_cached_tokens_obj():
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"""prompt_tokens_details.cached_tokens from an SDK object response."""
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p = _provider()
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usage_obj = FakeUsage(
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prompt_tokens=2000,
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completion_tokens=300,
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total_tokens=2300,
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prompt_tokens_details=FakePromptDetails(cached_tokens=1200),
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)
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response = FakeUsage(choices=[_FakeChoice()], usage=usage_obj)
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result = p._parse(response)
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assert result.usage["cached_tokens"] == 1200
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def test_extract_usage_deepseek_cached_tokens_obj():
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"""prompt_cache_hit_tokens from a DeepSeek SDK object response."""
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p = _provider()
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usage_obj = FakeUsage(
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prompt_tokens=1500,
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completion_tokens=200,
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total_tokens=1700,
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prompt_cache_hit_tokens=1200,
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)
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response = FakeUsage(choices=[_FakeChoice()], usage=usage_obj)
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result = p._parse(response)
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assert result.usage["cached_tokens"] == 1200
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def test_extract_usage_stepfun_top_level_cached_tokens_dict():
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"""StepFun/Moonshot: usage.cached_tokens at top level (not nested)."""
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p = _provider()
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response = {
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"choices": [_DICT_CHOICE],
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"usage": {
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"prompt_tokens": 591,
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"completion_tokens": 120,
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"total_tokens": 711,
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"cached_tokens": 512,
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}
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}
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result = p._parse(response)
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assert result.usage["cached_tokens"] == 512
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def test_extract_usage_stepfun_top_level_cached_tokens_obj():
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"""StepFun/Moonshot: usage.cached_tokens as SDK object attribute."""
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p = _provider()
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usage_obj = FakeUsage(
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prompt_tokens=591,
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completion_tokens=120,
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total_tokens=711,
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cached_tokens=512,
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)
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response = FakeUsage(choices=[_FakeChoice()], usage=usage_obj)
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result = p._parse(response)
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assert result.usage["cached_tokens"] == 512
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def test_extract_usage_priority_nested_over_top_level_dict():
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"""When both nested and top-level cached_tokens exist, nested wins."""
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p = _provider()
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response = {
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"choices": [_DICT_CHOICE],
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"usage": {
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"prompt_tokens": 2000,
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"completion_tokens": 300,
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"total_tokens": 2300,
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"prompt_tokens_details": {"cached_tokens": 100},
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"cached_tokens": 500,
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}
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}
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result = p._parse(response)
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assert result.usage["cached_tokens"] == 100
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def test_anthropic_maps_cache_fields_to_cached_tokens():
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"""Anthropic's cache_read_input_tokens should map to cached_tokens."""
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from nanobot.providers.anthropic_provider import AnthropicProvider
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usage_obj = FakeUsage(
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input_tokens=800,
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output_tokens=200,
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cache_creation_input_tokens=0,
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cache_read_input_tokens=1200,
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)
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content_block = FakeUsage(type="text", text="hello")
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response = FakeUsage(
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id="msg_1",
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type="message",
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stop_reason="end_turn",
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content=[content_block],
|
||||
usage=usage_obj,
|
||||
)
|
||||
result = AnthropicProvider._parse_response(response)
|
||||
assert result.usage["cached_tokens"] == 1200
|
||||
assert result.usage["prompt_tokens"] == 800
|
||||
|
||||
|
||||
def test_anthropic_no_cache_fields():
|
||||
"""Anthropic response without cache fields should not have cached_tokens."""
|
||||
from nanobot.providers.anthropic_provider import AnthropicProvider
|
||||
|
||||
usage_obj = FakeUsage(input_tokens=800, output_tokens=200)
|
||||
content_block = FakeUsage(type="text", text="hello")
|
||||
response = FakeUsage(
|
||||
id="msg_1",
|
||||
type="message",
|
||||
stop_reason="end_turn",
|
||||
content=[content_block],
|
||||
usage=usage_obj,
|
||||
)
|
||||
result = AnthropicProvider._parse_response(response)
|
||||
assert "cached_tokens" not in result.usage
|
||||
59
tests/test_build_status.py
Normal file
59
tests/test_build_status.py
Normal file
@ -0,0 +1,59 @@
|
||||
"""Tests for build_status_content cache hit rate display."""
|
||||
|
||||
from nanobot.utils.helpers import build_status_content
|
||||
|
||||
|
||||
def test_status_shows_cache_hit_rate():
|
||||
content = build_status_content(
|
||||
version="0.1.0",
|
||||
model="glm-4-plus",
|
||||
start_time=1000000.0,
|
||||
last_usage={"prompt_tokens": 2000, "completion_tokens": 300, "cached_tokens": 1200},
|
||||
context_window_tokens=128000,
|
||||
session_msg_count=10,
|
||||
context_tokens_estimate=5000,
|
||||
)
|
||||
assert "60% cached" in content
|
||||
assert "2000 in / 300 out" in content
|
||||
|
||||
|
||||
def test_status_no_cache_info():
|
||||
"""Without cached_tokens, display should not show cache percentage."""
|
||||
content = build_status_content(
|
||||
version="0.1.0",
|
||||
model="glm-4-plus",
|
||||
start_time=1000000.0,
|
||||
last_usage={"prompt_tokens": 2000, "completion_tokens": 300},
|
||||
context_window_tokens=128000,
|
||||
session_msg_count=10,
|
||||
context_tokens_estimate=5000,
|
||||
)
|
||||
assert "cached" not in content.lower()
|
||||
assert "2000 in / 300 out" in content
|
||||
|
||||
|
||||
def test_status_zero_cached_tokens():
|
||||
"""cached_tokens=0 should not show cache percentage."""
|
||||
content = build_status_content(
|
||||
version="0.1.0",
|
||||
model="glm-4-plus",
|
||||
start_time=1000000.0,
|
||||
last_usage={"prompt_tokens": 2000, "completion_tokens": 300, "cached_tokens": 0},
|
||||
context_window_tokens=128000,
|
||||
session_msg_count=10,
|
||||
context_tokens_estimate=5000,
|
||||
)
|
||||
assert "cached" not in content.lower()
|
||||
|
||||
|
||||
def test_status_100_percent_cached():
|
||||
content = build_status_content(
|
||||
version="0.1.0",
|
||||
model="glm-4-plus",
|
||||
start_time=1000000.0,
|
||||
last_usage={"prompt_tokens": 1000, "completion_tokens": 100, "cached_tokens": 1000},
|
||||
context_window_tokens=128000,
|
||||
session_msg_count=5,
|
||||
context_tokens_estimate=3000,
|
||||
)
|
||||
assert "100% cached" in content
|
||||
Loading…
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Reference in New Issue
Block a user