mirror of
https://github.com/katanemo/plano.git
synced 2026-04-25 00:36:34 +02:00
add planoai obs: live LLM observability TUI
This commit is contained in:
Adil Hafeez
parent
1f701258cb
commit
d30018cf35
19 changed files with 1736 additions and 5 deletions
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@ -37,6 +37,7 @@ from planoai.core import (
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)
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from planoai.init_cmd import init as init_cmd
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from planoai.trace_cmd import trace as trace_cmd, start_trace_listener_background
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from planoai.obs_cmd import obs as obs_cmd
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from planoai.consts import (
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DEFAULT_OTEL_TRACING_GRPC_ENDPOINT,
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DEFAULT_NATIVE_OTEL_TRACING_GRPC_ENDPOINT,
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@ -714,6 +715,7 @@ main.add_command(cli_agent)
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main.add_command(generate_prompt_targets)
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main.add_command(init_cmd, name="init")
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main.add_command(trace_cmd, name="trace")
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main.add_command(obs_cmd, name="obs")
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if __name__ == "__main__":
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main()
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6
cli/planoai/obs/__init__.py
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6
cli/planoai/obs/__init__.py
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@ -0,0 +1,6 @@
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"""Plano observability console: in-memory live view of LLM traffic."""
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from planoai.obs.collector import LLMCall, LLMCallStore, ObsCollector
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from planoai.obs.pricing import PricingCatalog
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__all__ = ["LLMCall", "LLMCallStore", "ObsCollector", "PricingCatalog"]
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281
cli/planoai/obs/collector.py
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281
cli/planoai/obs/collector.py
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@ -0,0 +1,281 @@
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"""In-memory collector for LLM calls, fed by OTLP/gRPC spans from brightstaff."""
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from __future__ import annotations
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import threading
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from collections import deque
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from concurrent import futures
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from typing import Any, Iterable
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import grpc
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from opentelemetry.proto.collector.trace.v1 import (
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trace_service_pb2,
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trace_service_pb2_grpc,
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)
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DEFAULT_GRPC_PORT = 4317
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DEFAULT_CAPACITY = 1000
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@dataclass
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class LLMCall:
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"""One LLM call as reconstructed from a brightstaff LLM span.
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Fields default to ``None`` when the underlying span attribute was absent.
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"""
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request_id: str
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timestamp: datetime
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model: str
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provider: str | None = None
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request_model: str | None = None
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session_id: str | None = None
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route_name: str | None = None
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is_streaming: bool | None = None
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status_code: int | None = None
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prompt_tokens: int | None = None
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completion_tokens: int | None = None
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total_tokens: int | None = None
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cached_input_tokens: int | None = None
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cache_creation_tokens: int | None = None
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reasoning_tokens: int | None = None
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ttft_ms: float | None = None
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duration_ms: float | None = None
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routing_strategy: str | None = None
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routing_reason: str | None = None
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cost_usd: float | None = None
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@property
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def tpt_ms(self) -> float | None:
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if self.duration_ms is None or self.completion_tokens in (None, 0):
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return None
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ttft = self.ttft_ms or 0.0
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generate_ms = max(0.0, self.duration_ms - ttft)
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if generate_ms <= 0:
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return None
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return generate_ms / self.completion_tokens
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@property
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def tokens_per_sec(self) -> float | None:
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tpt = self.tpt_ms
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if tpt is None or tpt <= 0:
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return None
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return 1000.0 / tpt
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class LLMCallStore:
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"""Thread-safe ring buffer of recent LLM calls."""
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def __init__(self, capacity: int = DEFAULT_CAPACITY) -> None:
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self._capacity = capacity
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self._calls: deque[LLMCall] = deque(maxlen=capacity)
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self._lock = threading.Lock()
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@property
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def capacity(self) -> int:
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return self._capacity
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def add(self, call: LLMCall) -> None:
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with self._lock:
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self._calls.append(call)
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def clear(self) -> None:
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with self._lock:
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self._calls.clear()
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def snapshot(self) -> list[LLMCall]:
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with self._lock:
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return list(self._calls)
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def __len__(self) -> int:
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with self._lock:
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return len(self._calls)
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# Attribute keys mirror crates/brightstaff/src/tracing/constants.rs.
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_LLM_MODEL = "llm.model"
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_LLM_PROVIDER = "llm.provider"
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_LLM_IS_STREAMING = "llm.is_streaming"
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_LLM_DURATION_MS = "llm.duration_ms"
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_LLM_TTFT_MS = "llm.time_to_first_token"
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_LLM_PROMPT_TOKENS = "llm.usage.prompt_tokens"
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_LLM_COMPLETION_TOKENS = "llm.usage.completion_tokens"
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_LLM_TOTAL_TOKENS = "llm.usage.total_tokens"
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_LLM_CACHED_INPUT_TOKENS = "llm.usage.cached_input_tokens"
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_LLM_CACHE_CREATION_TOKENS = "llm.usage.cache_creation_tokens"
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_LLM_REASONING_TOKENS = "llm.usage.reasoning_tokens"
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_HTTP_STATUS = "http.status_code"
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_MODEL_REQUESTED = "model.requested"
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_PLANO_SESSION_ID = "plano.session_id"
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_PLANO_ROUTE_NAME = "plano.route.name"
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_ROUTING_STRATEGY = "routing.strategy"
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_ROUTING_SELECTION_REASON = "routing.selection_reason"
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_REQUEST_ID_KEYS = ("request_id", "http.request_id")
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def _anyvalue_to_python(value: Any) -> Any: # AnyValue from OTLP
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kind = value.WhichOneof("value")
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if kind == "string_value":
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return value.string_value
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if kind == "bool_value":
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return value.bool_value
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if kind == "int_value":
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return value.int_value
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if kind == "double_value":
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return value.double_value
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return None
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def _attrs_to_dict(attrs: Iterable[Any]) -> dict[str, Any]:
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out: dict[str, Any] = {}
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for kv in attrs:
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py = _anyvalue_to_python(kv.value)
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if py is not None:
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out[kv.key] = py
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return out
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def _maybe_int(value: Any) -> int | None:
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if value is None:
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return None
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try:
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return int(value)
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except (TypeError, ValueError):
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return None
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def _maybe_float(value: Any) -> float | None:
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if value is None:
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return None
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try:
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return float(value)
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except (TypeError, ValueError):
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return None
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def span_to_llm_call(
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span: Any, service_name: str, pricing: Any | None = None
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) -> LLMCall | None:
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"""Convert an OTLP span into an LLMCall, or return None if it isn't one.
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A span is considered an LLM call iff it carries the ``llm.model`` attribute.
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"""
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attrs = _attrs_to_dict(span.attributes)
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model = attrs.get(_LLM_MODEL)
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if not model:
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return None
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# Prefer explicit span attributes; fall back to likely aliases.
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request_id = next(
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(
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str(attrs[key])
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for key in _REQUEST_ID_KEYS
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if key in attrs and attrs[key] is not None
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),
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span.span_id.hex() if span.span_id else "",
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)
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start_ns = span.start_time_unix_nano or 0
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ts = (
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datetime.fromtimestamp(start_ns / 1_000_000_000, tz=timezone.utc).astimezone()
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if start_ns
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else datetime.now().astimezone()
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)
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call = LLMCall(
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request_id=str(request_id),
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timestamp=ts,
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model=str(model),
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provider=str(attrs[_LLM_PROVIDER]) if _LLM_PROVIDER in attrs else service_name,
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request_model=(
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str(attrs[_MODEL_REQUESTED]) if _MODEL_REQUESTED in attrs else None
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),
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session_id=(
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str(attrs[_PLANO_SESSION_ID]) if _PLANO_SESSION_ID in attrs else None
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),
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route_name=(
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str(attrs[_PLANO_ROUTE_NAME]) if _PLANO_ROUTE_NAME in attrs else None
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),
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is_streaming=bool(attrs[_LLM_IS_STREAMING])
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if _LLM_IS_STREAMING in attrs
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else None,
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status_code=_maybe_int(attrs.get(_HTTP_STATUS)),
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prompt_tokens=_maybe_int(attrs.get(_LLM_PROMPT_TOKENS)),
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completion_tokens=_maybe_int(attrs.get(_LLM_COMPLETION_TOKENS)),
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total_tokens=_maybe_int(attrs.get(_LLM_TOTAL_TOKENS)),
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cached_input_tokens=_maybe_int(attrs.get(_LLM_CACHED_INPUT_TOKENS)),
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cache_creation_tokens=_maybe_int(attrs.get(_LLM_CACHE_CREATION_TOKENS)),
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reasoning_tokens=_maybe_int(attrs.get(_LLM_REASONING_TOKENS)),
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ttft_ms=_maybe_float(attrs.get(_LLM_TTFT_MS)),
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duration_ms=_maybe_float(attrs.get(_LLM_DURATION_MS)),
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routing_strategy=(
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str(attrs[_ROUTING_STRATEGY]) if _ROUTING_STRATEGY in attrs else None
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),
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routing_reason=(
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str(attrs[_ROUTING_SELECTION_REASON])
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if _ROUTING_SELECTION_REASON in attrs
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else None
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),
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)
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if pricing is not None:
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call.cost_usd = pricing.cost_for_call(call)
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return call
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class _ObsServicer(trace_service_pb2_grpc.TraceServiceServicer):
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def __init__(self, store: LLMCallStore, pricing: Any | None) -> None:
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self._store = store
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self._pricing = pricing
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def Export(self, request, context): # noqa: N802 — gRPC generated name
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for resource_spans in request.resource_spans:
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service_name = "unknown"
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for attr in resource_spans.resource.attributes:
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if attr.key == "service.name":
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val = _anyvalue_to_python(attr.value)
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if val is not None:
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service_name = str(val)
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break
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for scope_spans in resource_spans.scope_spans:
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for span in scope_spans.spans:
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call = span_to_llm_call(span, service_name, self._pricing)
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if call is not None:
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self._store.add(call)
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return trace_service_pb2.ExportTraceServiceResponse()
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@dataclass
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class ObsCollector:
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"""Owns the OTLP/gRPC server and the in-memory LLMCall ring buffer."""
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store: LLMCallStore = field(default_factory=LLMCallStore)
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pricing: Any | None = None
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host: str = "0.0.0.0"
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port: int = DEFAULT_GRPC_PORT
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_server: grpc.Server | None = field(default=None, init=False, repr=False)
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def start(self) -> None:
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if self._server is not None:
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return
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server = grpc.server(futures.ThreadPoolExecutor(max_workers=4))
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trace_service_pb2_grpc.add_TraceServiceServicer_to_server(
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_ObsServicer(self.store, self.pricing), server
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)
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address = f"{self.host}:{self.port}"
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bound = server.add_insecure_port(address)
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if bound == 0:
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raise OSError(
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f"Failed to bind OTLP listener on {address}: port already in use. "
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"Stop `planoai trace listen` or pick another port with --port."
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)
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server.start()
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self._server = server
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def stop(self, grace: float = 2.0) -> None:
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if self._server is not None:
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self._server.stop(grace)
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self._server = None
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276
cli/planoai/obs/pricing.py
Normal file
276
cli/planoai/obs/pricing.py
Normal file
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@ -0,0 +1,276 @@
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"""DigitalOcean Gradient pricing catalog for the obs console.
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Ported loosely from ``crates/brightstaff/src/router/model_metrics.rs::fetch_do_pricing``.
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Single-source: one fetch at startup, cached for the life of the process.
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"""
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from __future__ import annotations
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import logging
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import threading
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from dataclasses import dataclass
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from typing import Any
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import requests
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DEFAULT_PRICING_URL = "https://api.digitalocean.com/v2/gen-ai/models/catalog"
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FETCH_TIMEOUT_SECS = 5.0
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logger = logging.getLogger(__name__)
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@dataclass(frozen=True)
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class ModelPrice:
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"""Input/output $/token rates. Token counts are multiplied by these."""
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input_per_token_usd: float
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output_per_token_usd: float
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cached_input_per_token_usd: float | None = None
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class PricingCatalog:
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"""In-memory pricing lookup keyed by model id.
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DO's catalog uses ids like ``openai-gpt-5.4``; Plano's resolved model names
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may arrive as ``do/openai-gpt-5.4`` or bare ``openai-gpt-5.4``. We strip the
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leading provider prefix when looking up.
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"""
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def __init__(self, prices: dict[str, ModelPrice] | None = None) -> None:
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self._prices: dict[str, ModelPrice] = prices or {}
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self._lock = threading.Lock()
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def __len__(self) -> int:
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with self._lock:
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return len(self._prices)
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def sample_models(self, n: int = 5) -> list[str]:
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with self._lock:
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return list(self._prices.keys())[:n]
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@classmethod
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def fetch(
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cls,
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url: str = DEFAULT_PRICING_URL,
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api_key: str | None = None,
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) -> "PricingCatalog":
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"""Fetch pricing from DO's catalog endpoint. On failure, returns an
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empty catalog (cost column will be blank).
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The catalog endpoint requires a DigitalOcean Personal Access Token —
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this is *not* the same as the inference ``MODEL_ACCESS_KEY`` used at
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runtime. We check ``DIGITALOCEAN_TOKEN`` first (standard DO CLI env
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var), then ``DO_PAT``, then fall back to ``DO_API_KEY``.
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"""
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import os
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headers = {}
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token = (
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api_key
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or os.environ.get("DIGITALOCEAN_TOKEN")
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or os.environ.get("DO_PAT")
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or os.environ.get("DO_API_KEY")
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)
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if token:
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headers["Authorization"] = f"Bearer {token}"
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try:
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resp = requests.get(url, headers=headers, timeout=FETCH_TIMEOUT_SECS)
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resp.raise_for_status()
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data = resp.json()
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except Exception as exc: # noqa: BLE001 — best-effort; never fatal
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logger.warning(
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"DO pricing fetch failed: %s; cost column will be blank. "
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"Set DIGITALOCEAN_TOKEN with a DO Personal Access Token to "
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"enable cost.",
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exc,
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)
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return cls()
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prices = _parse_do_pricing(data)
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if not prices:
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# Dump the first entry's raw shape so we can see which fields DO
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# actually returned — helps when the catalog adds new fields or
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# the response doesn't match our parser.
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import json as _json
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sample_items = _coerce_items(data)
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sample = sample_items[0] if sample_items else data
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logger.warning(
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"DO pricing response had no parseable entries; cost column "
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"will be blank. Sample entry: %s",
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_json.dumps(sample, default=str)[:400],
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)
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return cls(prices)
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def price_for(self, model_name: str | None) -> ModelPrice | None:
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if not model_name:
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return None
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with self._lock:
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# Try the full name first, then stripped prefix, then lowercased variants.
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for candidate in _model_key_candidates(model_name):
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hit = self._prices.get(candidate)
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if hit is not None:
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return hit
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return None
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def cost_for_call(self, call: Any) -> float | None:
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"""Compute USD cost for an LLMCall. Returns None when pricing is unknown."""
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price = self.price_for(getattr(call, "model", None)) or self.price_for(
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getattr(call, "request_model", None)
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)
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if price is None:
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return None
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prompt = int(getattr(call, "prompt_tokens", 0) or 0)
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completion = int(getattr(call, "completion_tokens", 0) or 0)
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cached = int(getattr(call, "cached_input_tokens", 0) or 0)
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# Cached input tokens are priced separately at the cached rate when known;
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# otherwise they're already counted in prompt tokens at the regular rate.
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fresh_prompt = prompt
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if price.cached_input_per_token_usd is not None and cached:
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fresh_prompt = max(0, prompt - cached)
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cost_cached = cached * price.cached_input_per_token_usd
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else:
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cost_cached = 0.0
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cost = (
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fresh_prompt * price.input_per_token_usd
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+ completion * price.output_per_token_usd
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+ cost_cached
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)
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return round(cost, 6)
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|
||||
|
||||
def _model_key_candidates(model_name: str) -> list[str]:
|
||||
base = model_name.strip()
|
||||
out = [base]
|
||||
if "/" in base:
|
||||
out.append(base.split("/", 1)[1])
|
||||
out.extend([v.lower() for v in list(out)])
|
||||
# Dedup while preserving order.
|
||||
seen: set[str] = set()
|
||||
uniq = []
|
||||
for key in out:
|
||||
if key not in seen:
|
||||
seen.add(key)
|
||||
uniq.append(key)
|
||||
return uniq
|
||||
|
||||
|
||||
def _parse_do_pricing(data: Any) -> dict[str, ModelPrice]:
|
||||
"""Parse DO catalog response into a ModelPrice map keyed by model id.
|
||||
|
||||
DO's shape (as of 2026-04):
|
||||
{
|
||||
"data": [
|
||||
{"model_id": "openai-gpt-5.4",
|
||||
"pricing": {"input_price_per_million": 5.0,
|
||||
"output_price_per_million": 15.0}},
|
||||
...
|
||||
]
|
||||
}
|
||||
|
||||
Older/alternate shapes are also accepted (flat top-level fields, or the
|
||||
``id``/``model``/``name`` key).
|
||||
"""
|
||||
prices: dict[str, ModelPrice] = {}
|
||||
items = _coerce_items(data)
|
||||
for item in items:
|
||||
model_id = (
|
||||
item.get("model_id")
|
||||
or item.get("id")
|
||||
or item.get("model")
|
||||
or item.get("name")
|
||||
)
|
||||
if not model_id:
|
||||
continue
|
||||
|
||||
# DO nests rates under `pricing`; try that first, then fall back to
|
||||
# top-level fields for alternate response shapes.
|
||||
sources = [item]
|
||||
if isinstance(item.get("pricing"), dict):
|
||||
sources.insert(0, item["pricing"])
|
||||
|
||||
input_rate = _extract_rate_from_sources(
|
||||
sources,
|
||||
["input_per_token", "input_token_price", "price_input"],
|
||||
["input_price_per_million", "input_per_million", "input_per_mtok"],
|
||||
)
|
||||
output_rate = _extract_rate_from_sources(
|
||||
sources,
|
||||
["output_per_token", "output_token_price", "price_output"],
|
||||
["output_price_per_million", "output_per_million", "output_per_mtok"],
|
||||
)
|
||||
cached_rate = _extract_rate_from_sources(
|
||||
sources,
|
||||
[
|
||||
"cached_input_per_token",
|
||||
"cached_input_token_price",
|
||||
"prompt_cache_read_per_token",
|
||||
],
|
||||
[
|
||||
"cached_input_price_per_million",
|
||||
"cached_input_per_million",
|
||||
"cached_input_per_mtok",
|
||||
],
|
||||
)
|
||||
|
||||
if input_rate is None or output_rate is None:
|
||||
continue
|
||||
# Treat 0-rate entries as "unknown" so cost falls back to `—` rather
|
||||
# than showing a misleading $0.0000. DO's catalog sometimes omits
|
||||
# rates for promo/open-weight models.
|
||||
if input_rate == 0 and output_rate == 0:
|
||||
continue
|
||||
prices[str(model_id)] = ModelPrice(
|
||||
input_per_token_usd=input_rate,
|
||||
output_per_token_usd=output_rate,
|
||||
cached_input_per_token_usd=cached_rate,
|
||||
)
|
||||
return prices
|
||||
|
||||
|
||||
def _coerce_items(data: Any) -> list[dict]:
|
||||
if isinstance(data, list):
|
||||
return [x for x in data if isinstance(x, dict)]
|
||||
if isinstance(data, dict):
|
||||
for key in ("data", "models", "pricing", "items"):
|
||||
val = data.get(key)
|
||||
if isinstance(val, list):
|
||||
return [x for x in val if isinstance(x, dict)]
|
||||
return []
|
||||
|
||||
|
||||
def _extract_rate_from_sources(
|
||||
sources: list[dict],
|
||||
per_token_keys: list[str],
|
||||
per_million_keys: list[str],
|
||||
) -> float | None:
|
||||
"""Return a per-token rate in USD, or None if unknown.
|
||||
|
||||
Some DO catalog responses put per-token values under a field whose name
|
||||
says ``_per_million`` (e.g. ``input_price_per_million: 5E-8`` — that's
|
||||
$5e-8 per token, not per million). Heuristic: values < 1 are already
|
||||
per-token (real per-million rates are ~0.1 to ~100); values >= 1 are
|
||||
treated as per-million and divided by 1,000,000.
|
||||
"""
|
||||
for src in sources:
|
||||
for key in per_token_keys:
|
||||
if key in src and src[key] is not None:
|
||||
try:
|
||||
return float(src[key])
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
for key in per_million_keys:
|
||||
if key in src and src[key] is not None:
|
||||
try:
|
||||
v = float(src[key])
|
||||
except (TypeError, ValueError):
|
||||
continue
|
||||
if v >= 1:
|
||||
return v / 1_000_000
|
||||
return v
|
||||
return None
|
||||
324
cli/planoai/obs/render.py
Normal file
324
cli/planoai/obs/render.py
Normal file
|
|
@ -0,0 +1,324 @@
|
|||
"""Rich TUI renderer for the observability console."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections import Counter
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime, timezone
|
||||
|
||||
from rich.box import SIMPLE
|
||||
from rich.columns import Columns
|
||||
from rich.console import Group
|
||||
from rich.panel import Panel
|
||||
from rich.table import Table
|
||||
from rich.text import Text
|
||||
|
||||
from planoai.obs.collector import LLMCall
|
||||
|
||||
|
||||
@dataclass
|
||||
class AggregateStats:
|
||||
count: int
|
||||
total_cost_usd: float
|
||||
total_input_tokens: int
|
||||
total_output_tokens: int
|
||||
distinct_sessions: int
|
||||
current_session: str | None
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelRollup:
|
||||
model: str
|
||||
requests: int
|
||||
input_tokens: int
|
||||
output_tokens: int
|
||||
cache_write: int
|
||||
cache_read: int
|
||||
cost_usd: float
|
||||
|
||||
|
||||
def _now() -> datetime:
|
||||
return datetime.now(tz=timezone.utc).astimezone()
|
||||
|
||||
|
||||
def aggregates(calls: list[LLMCall]) -> AggregateStats:
|
||||
total_cost = sum((c.cost_usd or 0.0) for c in calls)
|
||||
total_input = sum(int(c.prompt_tokens or 0) for c in calls)
|
||||
total_output = sum(int(c.completion_tokens or 0) for c in calls)
|
||||
session_ids = {c.session_id for c in calls if c.session_id}
|
||||
current = next(
|
||||
(c.session_id for c in reversed(calls) if c.session_id is not None), None
|
||||
)
|
||||
return AggregateStats(
|
||||
count=len(calls),
|
||||
total_cost_usd=total_cost,
|
||||
total_input_tokens=total_input,
|
||||
total_output_tokens=total_output,
|
||||
distinct_sessions=len(session_ids),
|
||||
current_session=current,
|
||||
)
|
||||
|
||||
|
||||
def model_rollups(calls: list[LLMCall]) -> list[ModelRollup]:
|
||||
buckets: dict[str, dict[str, float | int]] = {}
|
||||
for c in calls:
|
||||
key = c.model
|
||||
b = buckets.setdefault(
|
||||
key,
|
||||
{
|
||||
"requests": 0,
|
||||
"input": 0,
|
||||
"output": 0,
|
||||
"cache_write": 0,
|
||||
"cache_read": 0,
|
||||
"cost": 0.0,
|
||||
},
|
||||
)
|
||||
b["requests"] = int(b["requests"]) + 1
|
||||
b["input"] = int(b["input"]) + int(c.prompt_tokens or 0)
|
||||
b["output"] = int(b["output"]) + int(c.completion_tokens or 0)
|
||||
b["cache_write"] = int(b["cache_write"]) + int(c.cache_creation_tokens or 0)
|
||||
b["cache_read"] = int(b["cache_read"]) + int(c.cached_input_tokens or 0)
|
||||
b["cost"] = float(b["cost"]) + (c.cost_usd or 0.0)
|
||||
|
||||
rollups: list[ModelRollup] = []
|
||||
for model, b in buckets.items():
|
||||
rollups.append(
|
||||
ModelRollup(
|
||||
model=model,
|
||||
requests=int(b["requests"]),
|
||||
input_tokens=int(b["input"]),
|
||||
output_tokens=int(b["output"]),
|
||||
cache_write=int(b["cache_write"]),
|
||||
cache_read=int(b["cache_read"]),
|
||||
cost_usd=float(b["cost"]),
|
||||
)
|
||||
)
|
||||
rollups.sort(key=lambda r: r.cost_usd, reverse=True)
|
||||
return rollups
|
||||
|
||||
|
||||
def route_hits(calls: list[LLMCall]) -> list[tuple[str, int, float]]:
|
||||
counts: Counter[str] = Counter()
|
||||
for c in calls:
|
||||
if c.route_name:
|
||||
counts[c.route_name] += 1
|
||||
total = sum(counts.values())
|
||||
if total == 0:
|
||||
return []
|
||||
return [(r, n, (n / total) * 100.0) for r, n in counts.most_common()]
|
||||
|
||||
|
||||
def _fmt_cost(v: float | None) -> str:
|
||||
if v is None:
|
||||
return "—"
|
||||
if v == 0:
|
||||
return "$0"
|
||||
# Adaptive precision so tiny costs ($3.8e-5) remain readable.
|
||||
if abs(v) < 0.0001:
|
||||
return f"${v:.8f}".rstrip("0").rstrip(".")
|
||||
if abs(v) < 0.01:
|
||||
return f"${v:.6f}".rstrip("0").rstrip(".")
|
||||
return f"${v:.4f}"
|
||||
|
||||
|
||||
def _fmt_ms(v: float | None) -> str:
|
||||
if v is None:
|
||||
return "—"
|
||||
if v >= 1000:
|
||||
return f"{v / 1000:.1f}s"
|
||||
return f"{v:.0f}ms"
|
||||
|
||||
|
||||
def _fmt_int(v: int | None) -> str:
|
||||
if v is None or v == 0:
|
||||
return "—"
|
||||
return f"{v:,}"
|
||||
|
||||
|
||||
def _fmt_tokens(v: int | None) -> str:
|
||||
if v is None:
|
||||
return "—"
|
||||
return f"{v:,}"
|
||||
|
||||
|
||||
def _request_panel(last: LLMCall | None) -> Panel:
|
||||
if last is None:
|
||||
body = Text("no requests yet", style="dim")
|
||||
else:
|
||||
t = Table.grid(padding=(0, 1))
|
||||
t.add_column(style="bold cyan")
|
||||
t.add_column()
|
||||
t.add_row("Endpoint", "chat/completions")
|
||||
status = "—" if last.status_code is None else str(last.status_code)
|
||||
t.add_row("Status", status)
|
||||
t.add_row("Model", last.model)
|
||||
if last.request_model and last.request_model != last.model:
|
||||
t.add_row("Req model", last.request_model)
|
||||
if last.route_name:
|
||||
t.add_row("Route", last.route_name)
|
||||
body = t
|
||||
return Panel(body, title="[bold]Request[/]", border_style="cyan", box=SIMPLE)
|
||||
|
||||
|
||||
def _cost_panel(last: LLMCall | None) -> Panel:
|
||||
if last is None:
|
||||
body = Text("—", style="dim")
|
||||
else:
|
||||
t = Table.grid(padding=(0, 1))
|
||||
t.add_column(style="bold green")
|
||||
t.add_column()
|
||||
t.add_row("Request", _fmt_cost(last.cost_usd))
|
||||
t.add_row("Input", _fmt_tokens(last.prompt_tokens))
|
||||
t.add_row("Output", _fmt_tokens(last.completion_tokens))
|
||||
if last.cached_input_tokens:
|
||||
t.add_row("Cached", _fmt_tokens(last.cached_input_tokens))
|
||||
body = t
|
||||
return Panel(body, title="[bold]Cost[/]", border_style="green", box=SIMPLE)
|
||||
|
||||
|
||||
def _totals_panel(stats: AggregateStats) -> Panel:
|
||||
t = Table.grid(padding=(0, 1))
|
||||
t.add_column(style="bold magenta")
|
||||
t.add_column()
|
||||
t.add_column(style="bold magenta")
|
||||
t.add_column()
|
||||
t.add_row(
|
||||
"Total cost",
|
||||
_fmt_cost(stats.total_cost_usd),
|
||||
"Requests",
|
||||
str(stats.count),
|
||||
)
|
||||
t.add_row(
|
||||
"Input",
|
||||
_fmt_tokens(stats.total_input_tokens),
|
||||
"Output",
|
||||
_fmt_tokens(stats.total_output_tokens),
|
||||
)
|
||||
t.add_row(
|
||||
"Sessions",
|
||||
str(stats.distinct_sessions),
|
||||
"Current session",
|
||||
stats.current_session or "—",
|
||||
)
|
||||
return Panel(t, title="[bold]Totals[/]", border_style="magenta", box=SIMPLE)
|
||||
|
||||
|
||||
def _model_rollup_table(rollups: list[ModelRollup]) -> Table:
|
||||
table = Table(
|
||||
title="Totals by model",
|
||||
box=SIMPLE,
|
||||
header_style="bold",
|
||||
expand=True,
|
||||
)
|
||||
table.add_column("Model", style="cyan")
|
||||
table.add_column("Req", justify="right")
|
||||
table.add_column("Input", justify="right")
|
||||
table.add_column("Output", justify="right", style="green")
|
||||
table.add_column("Cache write", justify="right", style="yellow")
|
||||
table.add_column("Cache read", justify="right", style="yellow")
|
||||
table.add_column("Cost", justify="right", style="green")
|
||||
if not rollups:
|
||||
table.add_row("—", "—", "—", "—", "—", "—", "—")
|
||||
for r in rollups:
|
||||
table.add_row(
|
||||
r.model,
|
||||
str(r.requests),
|
||||
_fmt_tokens(r.input_tokens),
|
||||
_fmt_tokens(r.output_tokens),
|
||||
_fmt_int(r.cache_write),
|
||||
_fmt_int(r.cache_read),
|
||||
_fmt_cost(r.cost_usd),
|
||||
)
|
||||
return table
|
||||
|
||||
|
||||
def _route_hit_table(hits: list[tuple[str, int, float]]) -> Table:
|
||||
table = Table(
|
||||
title="Route hit %",
|
||||
box=SIMPLE,
|
||||
header_style="bold",
|
||||
expand=True,
|
||||
)
|
||||
table.add_column("Route", style="cyan")
|
||||
table.add_column("Hits", justify="right")
|
||||
table.add_column("%", justify="right")
|
||||
for route, n, pct in hits:
|
||||
table.add_row(route, str(n), f"{pct:.1f}")
|
||||
return table
|
||||
|
||||
|
||||
def _recent_table(calls: list[LLMCall], limit: int = 15) -> Table:
|
||||
show_route = any(c.route_name for c in calls)
|
||||
table = Table(
|
||||
title="Recent requests",
|
||||
box=SIMPLE,
|
||||
header_style="bold",
|
||||
expand=True,
|
||||
)
|
||||
table.add_column("time")
|
||||
table.add_column("model", style="cyan")
|
||||
if show_route:
|
||||
table.add_column("route", style="yellow")
|
||||
table.add_column("in", justify="right")
|
||||
table.add_column("cache", justify="right", style="yellow")
|
||||
table.add_column("out", justify="right", style="green")
|
||||
table.add_column("rsn", justify="right")
|
||||
table.add_column("cost", justify="right", style="green")
|
||||
table.add_column("TTFT", justify="right")
|
||||
table.add_column("lat", justify="right")
|
||||
table.add_column("st")
|
||||
|
||||
recent = list(reversed(calls))[:limit]
|
||||
for c in recent:
|
||||
status_cell = "ok" if c.status_code and 200 <= c.status_code < 400 else str(c.status_code or "—")
|
||||
row = [
|
||||
c.timestamp.strftime("%H:%M:%S"),
|
||||
c.model,
|
||||
]
|
||||
if show_route:
|
||||
row.append(c.route_name or "—")
|
||||
row.extend(
|
||||
[
|
||||
_fmt_tokens(c.prompt_tokens),
|
||||
_fmt_int(c.cached_input_tokens),
|
||||
_fmt_tokens(c.completion_tokens),
|
||||
_fmt_int(c.reasoning_tokens),
|
||||
_fmt_cost(c.cost_usd),
|
||||
_fmt_ms(c.ttft_ms),
|
||||
_fmt_ms(c.duration_ms),
|
||||
status_cell,
|
||||
]
|
||||
)
|
||||
table.add_row(*row)
|
||||
if not recent:
|
||||
table.add_row(*(["no requests yet"] + ["—"] * (10 if show_route else 9)))
|
||||
return table
|
||||
|
||||
|
||||
def render(calls: list[LLMCall]) -> Group:
|
||||
last = calls[-1] if calls else None
|
||||
stats = aggregates(calls)
|
||||
rollups = model_rollups(calls)
|
||||
hits = route_hits(calls)
|
||||
|
||||
header = Columns(
|
||||
[_request_panel(last), _cost_panel(last), _totals_panel(stats)],
|
||||
expand=True,
|
||||
equal=True,
|
||||
)
|
||||
parts = [
|
||||
header,
|
||||
_model_rollup_table(rollups),
|
||||
]
|
||||
if hits:
|
||||
parts.append(_route_hit_table(hits))
|
||||
parts.append(_recent_table(calls))
|
||||
parts.append(
|
||||
Text(
|
||||
"q quit · c clear · waiting for spans on OTLP :4317 — brightstaff needs "
|
||||
"tracing.opentracing_grpc_endpoint=localhost:4317",
|
||||
style="dim",
|
||||
)
|
||||
)
|
||||
return Group(*parts)
|
||||
99
cli/planoai/obs_cmd.py
Normal file
99
cli/planoai/obs_cmd.py
Normal file
|
|
@ -0,0 +1,99 @@
|
|||
"""`planoai obs` — live observability TUI."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import time
|
||||
|
||||
import rich_click as click
|
||||
from rich.console import Console
|
||||
from rich.live import Live
|
||||
|
||||
from planoai.consts import PLANO_COLOR
|
||||
from planoai.obs.collector import (
|
||||
DEFAULT_CAPACITY,
|
||||
DEFAULT_GRPC_PORT,
|
||||
LLMCallStore,
|
||||
ObsCollector,
|
||||
)
|
||||
from planoai.obs.pricing import PricingCatalog
|
||||
from planoai.obs.render import render
|
||||
|
||||
|
||||
@click.command(name="obs", help="Live observability console for Plano LLM traffic.")
|
||||
@click.option(
|
||||
"--port",
|
||||
type=int,
|
||||
default=DEFAULT_GRPC_PORT,
|
||||
show_default=True,
|
||||
help="OTLP/gRPC port to listen on. Must match the brightstaff tracing endpoint.",
|
||||
)
|
||||
@click.option(
|
||||
"--host",
|
||||
type=str,
|
||||
default="0.0.0.0",
|
||||
show_default=True,
|
||||
help="Host to bind the OTLP listener.",
|
||||
)
|
||||
@click.option(
|
||||
"--capacity",
|
||||
type=int,
|
||||
default=DEFAULT_CAPACITY,
|
||||
show_default=True,
|
||||
help="Max LLM calls kept in memory; older calls evicted FIFO.",
|
||||
)
|
||||
@click.option(
|
||||
"--refresh-ms",
|
||||
type=int,
|
||||
default=500,
|
||||
show_default=True,
|
||||
help="TUI refresh interval.",
|
||||
)
|
||||
def obs(port: int, host: str, capacity: int, refresh_ms: int) -> None:
|
||||
console = Console()
|
||||
console.print(
|
||||
f"[bold {PLANO_COLOR}]planoai obs[/] — loading DO pricing catalog...",
|
||||
end="",
|
||||
)
|
||||
pricing = PricingCatalog.fetch()
|
||||
if len(pricing):
|
||||
sample = ", ".join(pricing.sample_models(3))
|
||||
console.print(
|
||||
f" [green]{len(pricing)} models loaded[/] [dim]({sample}, ...)[/]"
|
||||
)
|
||||
else:
|
||||
console.print(
|
||||
" [yellow]no pricing loaded[/] — "
|
||||
"[dim]set DIGITALOCEAN_TOKEN (DO Personal Access Token) to enable cost[/]"
|
||||
)
|
||||
|
||||
store = LLMCallStore(capacity=capacity)
|
||||
collector = ObsCollector(store=store, pricing=pricing, host=host, port=port)
|
||||
try:
|
||||
collector.start()
|
||||
except OSError as exc:
|
||||
console.print(f"[red]{exc}[/]")
|
||||
raise SystemExit(1)
|
||||
|
||||
console.print(
|
||||
f"Listening for OTLP spans on [bold]{host}:{port}[/]. "
|
||||
"Ensure plano config has [cyan]tracing.opentracing_grpc_endpoint: http://localhost:4317[/] "
|
||||
"and [cyan]tracing.random_sampling: 100[/] (or run [bold]planoai up[/] "
|
||||
"with no config — it wires this automatically)."
|
||||
)
|
||||
console.print("Press [bold]Ctrl-C[/] to exit.\n")
|
||||
|
||||
refresh = max(0.05, refresh_ms / 1000.0)
|
||||
try:
|
||||
with Live(
|
||||
render(store.snapshot()),
|
||||
console=console,
|
||||
refresh_per_second=1.0 / refresh,
|
||||
screen=False,
|
||||
) as live:
|
||||
while True:
|
||||
time.sleep(refresh)
|
||||
live.update(render(store.snapshot()))
|
||||
except KeyboardInterrupt:
|
||||
console.print("\n[dim]obs stopped[/]")
|
||||
finally:
|
||||
collector.stop()
|
||||
|
|
@ -61,7 +61,7 @@ def configure_rich_click(plano_color: str) -> None:
|
|||
},
|
||||
{
|
||||
"name": "Observability",
|
||||
"commands": ["trace"],
|
||||
"commands": ["trace", "obs"],
|
||||
},
|
||||
{
|
||||
"name": "Utilities",
|
||||
|
|
|
|||
141
cli/test/test_obs_collector.py
Normal file
141
cli/test/test_obs_collector.py
Normal file
|
|
@ -0,0 +1,141 @@
|
|||
import time
|
||||
from datetime import datetime, timezone
|
||||
from types import SimpleNamespace
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
|
||||
from planoai.obs.collector import LLMCall, LLMCallStore, span_to_llm_call
|
||||
|
||||
|
||||
def _mk_attr(key: str, value):
|
||||
v = MagicMock()
|
||||
if isinstance(value, bool):
|
||||
v.WhichOneof.return_value = "bool_value"
|
||||
v.bool_value = value
|
||||
elif isinstance(value, int):
|
||||
v.WhichOneof.return_value = "int_value"
|
||||
v.int_value = value
|
||||
elif isinstance(value, float):
|
||||
v.WhichOneof.return_value = "double_value"
|
||||
v.double_value = value
|
||||
else:
|
||||
v.WhichOneof.return_value = "string_value"
|
||||
v.string_value = str(value)
|
||||
kv = MagicMock()
|
||||
kv.key = key
|
||||
kv.value = v
|
||||
return kv
|
||||
|
||||
|
||||
def _mk_span(attrs: dict, start_ns: int | None = None, span_id_hex: str = "ab") -> MagicMock:
|
||||
span = MagicMock()
|
||||
span.attributes = [_mk_attr(k, v) for k, v in attrs.items()]
|
||||
span.start_time_unix_nano = start_ns or int(time.time() * 1_000_000_000)
|
||||
span.span_id.hex.return_value = span_id_hex
|
||||
return span
|
||||
|
||||
|
||||
def test_span_without_llm_model_is_ignored():
|
||||
span = _mk_span({"http.method": "POST"})
|
||||
assert span_to_llm_call(span, "plano(llm)") is None
|
||||
|
||||
|
||||
def test_span_with_full_llm_attrs_produces_call():
|
||||
span = _mk_span(
|
||||
{
|
||||
"llm.model": "openai-gpt-5.4",
|
||||
"model.requested": "router:software-engineering",
|
||||
"plano.session_id": "sess-abc",
|
||||
"plano.route.name": "software-engineering",
|
||||
"llm.is_streaming": False,
|
||||
"llm.duration_ms": 1234,
|
||||
"llm.time_to_first_token": 210,
|
||||
"llm.usage.prompt_tokens": 100,
|
||||
"llm.usage.completion_tokens": 50,
|
||||
"llm.usage.total_tokens": 150,
|
||||
"llm.usage.cached_input_tokens": 30,
|
||||
"llm.usage.cache_creation_tokens": 5,
|
||||
"llm.usage.reasoning_tokens": 200,
|
||||
"http.status_code": 200,
|
||||
"request_id": "req-42",
|
||||
}
|
||||
)
|
||||
call = span_to_llm_call(span, "plano(llm)")
|
||||
assert call is not None
|
||||
assert call.request_id == "req-42"
|
||||
assert call.model == "openai-gpt-5.4"
|
||||
assert call.request_model == "router:software-engineering"
|
||||
assert call.session_id == "sess-abc"
|
||||
assert call.route_name == "software-engineering"
|
||||
assert call.is_streaming is False
|
||||
assert call.duration_ms == 1234.0
|
||||
assert call.ttft_ms == 210.0
|
||||
assert call.prompt_tokens == 100
|
||||
assert call.completion_tokens == 50
|
||||
assert call.total_tokens == 150
|
||||
assert call.cached_input_tokens == 30
|
||||
assert call.cache_creation_tokens == 5
|
||||
assert call.reasoning_tokens == 200
|
||||
assert call.status_code == 200
|
||||
|
||||
|
||||
def test_pricing_lookup_attaches_cost():
|
||||
class StubPricing:
|
||||
def cost_for_call(self, call):
|
||||
# Simple: 2 * prompt + 3 * completion, in cents
|
||||
return 0.02 * (call.prompt_tokens or 0) + 0.03 * (call.completion_tokens or 0)
|
||||
|
||||
span = _mk_span(
|
||||
{
|
||||
"llm.model": "do/openai-gpt-5.4",
|
||||
"llm.usage.prompt_tokens": 10,
|
||||
"llm.usage.completion_tokens": 2,
|
||||
}
|
||||
)
|
||||
call = span_to_llm_call(span, "plano(llm)", pricing=StubPricing())
|
||||
assert call is not None
|
||||
assert call.cost_usd == pytest.approx(0.26)
|
||||
|
||||
|
||||
def test_tpt_and_tokens_per_sec_derived():
|
||||
call = LLMCall(
|
||||
request_id="x",
|
||||
timestamp=datetime.now(tz=timezone.utc),
|
||||
model="m",
|
||||
duration_ms=1000,
|
||||
ttft_ms=200,
|
||||
completion_tokens=80,
|
||||
)
|
||||
# (1000 - 200) / 80 = 10ms per token => 100 tokens/sec
|
||||
assert call.tpt_ms == 10.0
|
||||
assert call.tokens_per_sec == 100.0
|
||||
|
||||
|
||||
def test_tpt_returns_none_when_no_completion_tokens():
|
||||
call = LLMCall(
|
||||
request_id="x",
|
||||
timestamp=datetime.now(tz=timezone.utc),
|
||||
model="m",
|
||||
duration_ms=1000,
|
||||
ttft_ms=200,
|
||||
completion_tokens=0,
|
||||
)
|
||||
assert call.tpt_ms is None
|
||||
assert call.tokens_per_sec is None
|
||||
|
||||
|
||||
def test_store_evicts_fifo_at_capacity():
|
||||
store = LLMCallStore(capacity=3)
|
||||
now = datetime.now(tz=timezone.utc)
|
||||
for i in range(5):
|
||||
store.add(
|
||||
LLMCall(
|
||||
request_id=f"r{i}",
|
||||
timestamp=now,
|
||||
model="m",
|
||||
)
|
||||
)
|
||||
snap = store.snapshot()
|
||||
assert len(snap) == 3
|
||||
assert [c.request_id for c in snap] == ["r2", "r3", "r4"]
|
||||
103
cli/test/test_obs_pricing.py
Normal file
103
cli/test/test_obs_pricing.py
Normal file
|
|
@ -0,0 +1,103 @@
|
|||
from datetime import datetime, timezone
|
||||
|
||||
from planoai.obs.collector import LLMCall
|
||||
from planoai.obs.pricing import ModelPrice, PricingCatalog
|
||||
|
||||
|
||||
def _call(model: str, prompt: int, completion: int, cached: int = 0) -> LLMCall:
|
||||
return LLMCall(
|
||||
request_id="r",
|
||||
timestamp=datetime.now(tz=timezone.utc),
|
||||
model=model,
|
||||
prompt_tokens=prompt,
|
||||
completion_tokens=completion,
|
||||
cached_input_tokens=cached,
|
||||
)
|
||||
|
||||
|
||||
def test_lookup_matches_bare_and_prefixed():
|
||||
prices = {
|
||||
"openai-gpt-5.4": ModelPrice(
|
||||
input_per_token_usd=0.000001, output_per_token_usd=0.000002
|
||||
)
|
||||
}
|
||||
catalog = PricingCatalog(prices)
|
||||
assert catalog.price_for("openai-gpt-5.4") is not None
|
||||
# do/openai-gpt-5.4 should resolve after stripping the provider prefix.
|
||||
assert catalog.price_for("do/openai-gpt-5.4") is not None
|
||||
assert catalog.price_for("unknown-model") is None
|
||||
|
||||
|
||||
def test_cost_computation_without_cache():
|
||||
prices = {
|
||||
"m": ModelPrice(input_per_token_usd=0.000001, output_per_token_usd=0.000002)
|
||||
}
|
||||
cost = PricingCatalog(prices).cost_for_call(_call("m", 1000, 500))
|
||||
assert cost == 0.002 # 1000 * 1e-6 + 500 * 2e-6
|
||||
|
||||
|
||||
def test_cost_computation_with_cached_discount():
|
||||
prices = {
|
||||
"m": ModelPrice(
|
||||
input_per_token_usd=0.000001,
|
||||
output_per_token_usd=0.000002,
|
||||
cached_input_per_token_usd=0.0000001,
|
||||
)
|
||||
}
|
||||
# 800 fresh @ 1e-6 = 8e-4; 200 cached @ 1e-7 = 2e-5; 500 out @ 2e-6 = 1e-3
|
||||
cost = PricingCatalog(prices).cost_for_call(_call("m", 1000, 500, cached=200))
|
||||
assert cost == round(0.0008 + 0.00002 + 0.001, 6)
|
||||
|
||||
|
||||
def test_empty_catalog_returns_none():
|
||||
assert PricingCatalog().cost_for_call(_call("m", 100, 50)) is None
|
||||
|
||||
|
||||
def test_parse_do_catalog_treats_small_values_as_per_token():
|
||||
"""DO's real catalog uses per-token values under the `_per_million` key
|
||||
(e.g. 5E-8 for GPT-oss-20b). We treat values < 1 as already per-token."""
|
||||
from planoai.obs.pricing import _parse_do_pricing
|
||||
|
||||
sample = {
|
||||
"data": [
|
||||
{
|
||||
"model_id": "openai-gpt-oss-20b",
|
||||
"pricing": {
|
||||
"input_price_per_million": 5e-8,
|
||||
"output_price_per_million": 4.5e-7,
|
||||
},
|
||||
},
|
||||
{
|
||||
"model_id": "openai-gpt-oss-120b",
|
||||
"pricing": {
|
||||
"input_price_per_million": 1e-7,
|
||||
"output_price_per_million": 7e-7,
|
||||
},
|
||||
},
|
||||
]
|
||||
}
|
||||
prices = _parse_do_pricing(sample)
|
||||
# Values < 1 are assumed to already be per-token — no extra division.
|
||||
assert prices["openai-gpt-oss-20b"].input_per_token_usd == 5e-8
|
||||
assert prices["openai-gpt-oss-20b"].output_per_token_usd == 4.5e-7
|
||||
assert prices["openai-gpt-oss-120b"].input_per_token_usd == 1e-7
|
||||
|
||||
|
||||
def test_parse_do_catalog_divides_large_values_as_per_million():
|
||||
"""A provider that genuinely reports $5-per-million in that field gets divided."""
|
||||
from planoai.obs.pricing import _parse_do_pricing
|
||||
|
||||
sample = {
|
||||
"data": [
|
||||
{
|
||||
"model_id": "mystery-model",
|
||||
"pricing": {
|
||||
"input_price_per_million": 5.0, # > 1 → treated as per-million
|
||||
"output_price_per_million": 15.0,
|
||||
},
|
||||
},
|
||||
]
|
||||
}
|
||||
prices = _parse_do_pricing(sample)
|
||||
assert prices["mystery-model"].input_per_token_usd == 5.0 / 1_000_000
|
||||
assert prices["mystery-model"].output_per_token_usd == 15.0 / 1_000_000
|
||||
73
cli/test/test_obs_render.py
Normal file
73
cli/test/test_obs_render.py
Normal file
|
|
@ -0,0 +1,73 @@
|
|||
from datetime import datetime, timedelta, timezone
|
||||
|
||||
from planoai.obs.collector import LLMCall
|
||||
from planoai.obs.render import aggregates, model_rollups, route_hits
|
||||
|
||||
|
||||
def _call(model: str, ts: datetime, prompt=0, completion=0, cost=None, route=None, session=None, cache_read=0, cache_write=0):
|
||||
return LLMCall(
|
||||
request_id="r",
|
||||
timestamp=ts,
|
||||
model=model,
|
||||
prompt_tokens=prompt,
|
||||
completion_tokens=completion,
|
||||
cached_input_tokens=cache_read,
|
||||
cache_creation_tokens=cache_write,
|
||||
cost_usd=cost,
|
||||
route_name=route,
|
||||
session_id=session,
|
||||
)
|
||||
|
||||
|
||||
def test_aggregates_sum_and_session_counts():
|
||||
now = datetime.now(tz=timezone.utc).astimezone()
|
||||
calls = [
|
||||
_call("m1", now - timedelta(seconds=50), prompt=10, completion=5, cost=0.001, session="s1"),
|
||||
_call("m2", now - timedelta(seconds=40), prompt=20, completion=10, cost=0.002, session="s1"),
|
||||
_call("m1", now - timedelta(seconds=30), prompt=30, completion=15, cost=0.003, session="s2"),
|
||||
]
|
||||
stats = aggregates(calls)
|
||||
assert stats.count == 3
|
||||
assert stats.total_cost_usd == 0.006
|
||||
assert stats.total_input_tokens == 60
|
||||
assert stats.total_output_tokens == 30
|
||||
assert stats.distinct_sessions == 2
|
||||
assert stats.current_session == "s2"
|
||||
|
||||
|
||||
def test_rollups_split_by_model_and_cache():
|
||||
now = datetime.now(tz=timezone.utc).astimezone()
|
||||
calls = [
|
||||
_call("m1", now, prompt=10, completion=5, cost=0.001, cache_write=3, cache_read=7),
|
||||
_call("m1", now, prompt=20, completion=10, cost=0.002, cache_read=1),
|
||||
_call("m2", now, prompt=30, completion=15, cost=0.004),
|
||||
]
|
||||
rollups = model_rollups(calls)
|
||||
by_model = {r.model: r for r in rollups}
|
||||
assert by_model["m1"].requests == 2
|
||||
assert by_model["m1"].input_tokens == 30
|
||||
assert by_model["m1"].cache_write == 3
|
||||
assert by_model["m1"].cache_read == 8
|
||||
assert by_model["m2"].input_tokens == 30
|
||||
|
||||
|
||||
def test_route_hits_only_for_routed_calls():
|
||||
now = datetime.now(tz=timezone.utc).astimezone()
|
||||
calls = [
|
||||
_call("m", now, route="code"),
|
||||
_call("m", now, route="code"),
|
||||
_call("m", now, route="summarization"),
|
||||
_call("m", now), # no route
|
||||
]
|
||||
hits = route_hits(calls)
|
||||
# Only calls with route names are counted.
|
||||
assert sum(n for _, n, _ in hits) == 3
|
||||
hits_by_name = {name: (n, pct) for name, n, pct in hits}
|
||||
assert hits_by_name["code"][0] == 2
|
||||
assert hits_by_name["summarization"][0] == 1
|
||||
|
||||
|
||||
def test_route_hits_empty_when_no_routes():
|
||||
now = datetime.now(tz=timezone.utc).astimezone()
|
||||
calls = [_call("m", now), _call("m", now)]
|
||||
assert route_hits(calls) == []
|
||||
|
|
@ -33,7 +33,8 @@ use crate::streaming::{
|
|||
ObservableStreamProcessor, StreamProcessor,
|
||||
};
|
||||
use crate::tracing::{
|
||||
collect_custom_trace_attributes, llm as tracing_llm, operation_component, set_service_name,
|
||||
collect_custom_trace_attributes, llm as tracing_llm, operation_component,
|
||||
plano as tracing_plano, set_service_name,
|
||||
};
|
||||
use model_selection::router_chat_get_upstream_model;
|
||||
|
||||
|
|
@ -102,15 +103,36 @@ async fn llm_chat_inner(
|
|||
.and_then(|hdr| request_headers.get(hdr))
|
||||
.and_then(|v| v.to_str().ok())
|
||||
.map(|s| s.to_string());
|
||||
let pinned_model: Option<String> = if let Some(ref sid) = session_id {
|
||||
let cached_route = if let Some(ref sid) = session_id {
|
||||
state
|
||||
.orchestrator_service
|
||||
.get_cached_route(sid, tenant_id.as_deref())
|
||||
.await
|
||||
.map(|c| c.model_name)
|
||||
} else {
|
||||
None
|
||||
};
|
||||
let (pinned_model, pinned_route_name): (Option<String>, Option<String>) = match cached_route {
|
||||
Some(c) => (Some(c.model_name), c.route_name),
|
||||
None => (None, None),
|
||||
};
|
||||
|
||||
// Record session id on the LLM span for the observability console.
|
||||
if let Some(ref sid) = session_id {
|
||||
get_active_span(|span| {
|
||||
span.set_attribute(opentelemetry::KeyValue::new(
|
||||
tracing_plano::SESSION_ID,
|
||||
sid.clone(),
|
||||
));
|
||||
});
|
||||
}
|
||||
if let Some(ref route_name) = pinned_route_name {
|
||||
get_active_span(|span| {
|
||||
span.set_attribute(opentelemetry::KeyValue::new(
|
||||
tracing_plano::ROUTE_NAME,
|
||||
route_name.clone(),
|
||||
));
|
||||
});
|
||||
}
|
||||
|
||||
let full_qualified_llm_provider_url = format!("{}{}", state.llm_provider_url, request_path);
|
||||
|
||||
|
|
@ -311,6 +333,18 @@ async fn llm_chat_inner(
|
|||
alias_resolved_model.clone()
|
||||
};
|
||||
|
||||
// Record route name on the LLM span (only when the orchestrator produced one).
|
||||
if let Some(ref rn) = route_name {
|
||||
if !rn.is_empty() && rn != "none" {
|
||||
get_active_span(|span| {
|
||||
span.set_attribute(opentelemetry::KeyValue::new(
|
||||
tracing_plano::ROUTE_NAME,
|
||||
rn.clone(),
|
||||
));
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
if let Some(ref sid) = session_id {
|
||||
state
|
||||
.orchestrator_service
|
||||
|
|
@ -671,6 +705,36 @@ async fn send_upstream(
|
|||
// Propagate upstream headers and status
|
||||
let response_headers = llm_response.headers().clone();
|
||||
let upstream_status = llm_response.status();
|
||||
|
||||
// Upstream routers (e.g. DigitalOcean Gradient) may return an
|
||||
// `x-model-router-selected-route` header indicating which task-level
|
||||
// route the request was classified into (e.g. "Code Generation"). Surface
|
||||
// it as `plano.route.name` so the obs console's Route hit % panel can
|
||||
// show the breakdown even when Plano's own orchestrator wasn't in the
|
||||
// routing path. Any value from Plano's orchestrator already set earlier
|
||||
// takes precedence — this only fires when the span doesn't already have
|
||||
// a route name.
|
||||
if let Some(upstream_route) = response_headers
|
||||
.get("x-model-router-selected-route")
|
||||
.and_then(|v| v.to_str().ok())
|
||||
{
|
||||
if !upstream_route.is_empty() {
|
||||
get_active_span(|span| {
|
||||
span.set_attribute(opentelemetry::KeyValue::new(
|
||||
crate::tracing::plano::ROUTE_NAME,
|
||||
upstream_route.to_string(),
|
||||
));
|
||||
});
|
||||
}
|
||||
}
|
||||
// Record the upstream HTTP status on the span for the obs console.
|
||||
get_active_span(|span| {
|
||||
span.set_attribute(opentelemetry::KeyValue::new(
|
||||
crate::tracing::http::STATUS_CODE,
|
||||
upstream_status.as_u16() as i64,
|
||||
));
|
||||
});
|
||||
|
||||
let mut response = Response::builder().status(upstream_status);
|
||||
if let Some(headers) = response.headers_mut() {
|
||||
for (name, value) in response_headers.iter() {
|
||||
|
|
|
|||
|
|
@ -16,10 +16,131 @@ use tracing_opentelemetry::OpenTelemetrySpanExt;
|
|||
use crate::handlers::agents::pipeline::{PipelineError, PipelineProcessor};
|
||||
|
||||
const STREAM_BUFFER_SIZE: usize = 16;
|
||||
/// Cap on accumulated response bytes kept for usage extraction.
|
||||
/// Most chat responses are well under this; pathological ones are dropped without
|
||||
/// affecting pass-through streaming to the client.
|
||||
const USAGE_BUFFER_MAX: usize = 2 * 1024 * 1024;
|
||||
use crate::signals::{InteractionQuality, SignalAnalyzer, TextBasedSignalAnalyzer, FLAG_MARKER};
|
||||
use crate::tracing::{llm, set_service_name, signals as signal_constants};
|
||||
use hermesllm::apis::openai::Message;
|
||||
|
||||
/// Parsed usage + resolved-model details from a provider response.
|
||||
#[derive(Debug, Default, Clone)]
|
||||
struct ExtractedUsage {
|
||||
prompt_tokens: Option<i64>,
|
||||
completion_tokens: Option<i64>,
|
||||
total_tokens: Option<i64>,
|
||||
cached_input_tokens: Option<i64>,
|
||||
cache_creation_tokens: Option<i64>,
|
||||
reasoning_tokens: Option<i64>,
|
||||
/// The model the upstream actually used. For router aliases (e.g.
|
||||
/// `router:software-engineering`), this differs from the request model.
|
||||
resolved_model: Option<String>,
|
||||
}
|
||||
|
||||
impl ExtractedUsage {
|
||||
fn is_empty(&self) -> bool {
|
||||
self.prompt_tokens.is_none()
|
||||
&& self.completion_tokens.is_none()
|
||||
&& self.total_tokens.is_none()
|
||||
&& self.resolved_model.is_none()
|
||||
}
|
||||
|
||||
fn from_json(value: &serde_json::Value) -> Self {
|
||||
let mut out = Self::default();
|
||||
if let Some(model) = value.get("model").and_then(|v| v.as_str()) {
|
||||
if !model.is_empty() {
|
||||
out.resolved_model = Some(model.to_string());
|
||||
}
|
||||
}
|
||||
if let Some(u) = value.get("usage") {
|
||||
// OpenAI-shape usage
|
||||
out.prompt_tokens = u.get("prompt_tokens").and_then(|v| v.as_i64());
|
||||
out.completion_tokens = u.get("completion_tokens").and_then(|v| v.as_i64());
|
||||
out.total_tokens = u.get("total_tokens").and_then(|v| v.as_i64());
|
||||
out.cached_input_tokens = u
|
||||
.get("prompt_tokens_details")
|
||||
.and_then(|d| d.get("cached_tokens"))
|
||||
.and_then(|v| v.as_i64());
|
||||
out.reasoning_tokens = u
|
||||
.get("completion_tokens_details")
|
||||
.and_then(|d| d.get("reasoning_tokens"))
|
||||
.and_then(|v| v.as_i64());
|
||||
|
||||
// Anthropic-shape fallbacks
|
||||
if out.prompt_tokens.is_none() {
|
||||
out.prompt_tokens = u.get("input_tokens").and_then(|v| v.as_i64());
|
||||
}
|
||||
if out.completion_tokens.is_none() {
|
||||
out.completion_tokens = u.get("output_tokens").and_then(|v| v.as_i64());
|
||||
}
|
||||
if out.total_tokens.is_none() {
|
||||
if let (Some(p), Some(c)) = (out.prompt_tokens, out.completion_tokens) {
|
||||
out.total_tokens = Some(p + c);
|
||||
}
|
||||
}
|
||||
if out.cached_input_tokens.is_none() {
|
||||
out.cached_input_tokens = u.get("cache_read_input_tokens").and_then(|v| v.as_i64());
|
||||
}
|
||||
if out.cached_input_tokens.is_none() {
|
||||
out.cached_input_tokens =
|
||||
u.get("cached_content_token_count").and_then(|v| v.as_i64());
|
||||
}
|
||||
out.cache_creation_tokens = u
|
||||
.get("cache_creation_input_tokens")
|
||||
.and_then(|v| v.as_i64());
|
||||
if out.reasoning_tokens.is_none() {
|
||||
out.reasoning_tokens = u.get("thoughts_token_count").and_then(|v| v.as_i64());
|
||||
}
|
||||
}
|
||||
out
|
||||
}
|
||||
}
|
||||
|
||||
/// Try to pull usage out of an accumulated response body.
|
||||
/// Handles both a single JSON object (non-streaming) and SSE streams where the
|
||||
/// final `data: {...}` event carries the `usage` field.
|
||||
fn extract_usage_from_bytes(buf: &[u8]) -> ExtractedUsage {
|
||||
if buf.is_empty() {
|
||||
return ExtractedUsage::default();
|
||||
}
|
||||
|
||||
// Fast path: full-body JSON (non-streaming).
|
||||
if let Ok(value) = serde_json::from_slice::<serde_json::Value>(buf) {
|
||||
let u = ExtractedUsage::from_json(&value);
|
||||
if !u.is_empty() {
|
||||
return u;
|
||||
}
|
||||
}
|
||||
|
||||
// SSE path: scan from the end for a `data:` line containing a usage object.
|
||||
let text = match std::str::from_utf8(buf) {
|
||||
Ok(t) => t,
|
||||
Err(_) => return ExtractedUsage::default(),
|
||||
};
|
||||
for line in text.lines().rev() {
|
||||
let trimmed = line.trim_start();
|
||||
let payload = match trimmed.strip_prefix("data:") {
|
||||
Some(p) => p.trim_start(),
|
||||
None => continue,
|
||||
};
|
||||
if payload == "[DONE]" || payload.is_empty() {
|
||||
continue;
|
||||
}
|
||||
if !payload.contains("\"usage\"") {
|
||||
continue;
|
||||
}
|
||||
if let Ok(value) = serde_json::from_str::<serde_json::Value>(payload) {
|
||||
let u = ExtractedUsage::from_json(&value);
|
||||
if !u.is_empty() {
|
||||
return u;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
ExtractedUsage::default()
|
||||
}
|
||||
|
||||
/// Trait for processing streaming chunks
|
||||
/// Implementors can inject custom logic during streaming (e.g., hallucination detection, logging)
|
||||
pub trait StreamProcessor: Send + 'static {
|
||||
|
|
@ -60,6 +181,10 @@ pub struct ObservableStreamProcessor {
|
|||
start_time: Instant,
|
||||
time_to_first_token: Option<u128>,
|
||||
messages: Option<Vec<Message>>,
|
||||
/// Accumulated response bytes used only for best-effort usage extraction
|
||||
/// on `on_complete`. Capped at `USAGE_BUFFER_MAX`; excess chunks are dropped
|
||||
/// from the buffer (they still pass through to the client).
|
||||
response_buffer: Vec<u8>,
|
||||
}
|
||||
|
||||
impl ObservableStreamProcessor {
|
||||
|
|
@ -93,6 +218,7 @@ impl ObservableStreamProcessor {
|
|||
start_time,
|
||||
time_to_first_token: None,
|
||||
messages,
|
||||
response_buffer: Vec::new(),
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -101,6 +227,13 @@ impl StreamProcessor for ObservableStreamProcessor {
|
|||
fn process_chunk(&mut self, chunk: Bytes) -> Result<Option<Bytes>, String> {
|
||||
self.total_bytes += chunk.len();
|
||||
self.chunk_count += 1;
|
||||
// Accumulate for best-effort usage extraction; drop further chunks once
|
||||
// the cap is reached so we don't retain huge response bodies in memory.
|
||||
if self.response_buffer.len() < USAGE_BUFFER_MAX {
|
||||
let remaining = USAGE_BUFFER_MAX - self.response_buffer.len();
|
||||
let take = chunk.len().min(remaining);
|
||||
self.response_buffer.extend_from_slice(&chunk[..take]);
|
||||
}
|
||||
Ok(Some(chunk))
|
||||
}
|
||||
|
||||
|
|
@ -124,6 +257,52 @@ impl StreamProcessor for ObservableStreamProcessor {
|
|||
);
|
||||
}
|
||||
|
||||
// Record total duration on the span for the observability console.
|
||||
let duration_ms = self.start_time.elapsed().as_millis() as i64;
|
||||
{
|
||||
let span = tracing::Span::current();
|
||||
let otel_context = span.context();
|
||||
let otel_span = otel_context.span();
|
||||
otel_span.set_attribute(KeyValue::new(llm::DURATION_MS, duration_ms));
|
||||
otel_span.set_attribute(KeyValue::new(llm::RESPONSE_BYTES, self.total_bytes as i64));
|
||||
}
|
||||
|
||||
// Best-effort usage extraction + emission (works for both streaming
|
||||
// SSE and non-streaming JSON responses that include a `usage` object).
|
||||
let usage = extract_usage_from_bytes(&self.response_buffer);
|
||||
if !usage.is_empty() {
|
||||
let span = tracing::Span::current();
|
||||
let otel_context = span.context();
|
||||
let otel_span = otel_context.span();
|
||||
if let Some(v) = usage.prompt_tokens {
|
||||
otel_span.set_attribute(KeyValue::new(llm::PROMPT_TOKENS, v));
|
||||
}
|
||||
if let Some(v) = usage.completion_tokens {
|
||||
otel_span.set_attribute(KeyValue::new(llm::COMPLETION_TOKENS, v));
|
||||
}
|
||||
if let Some(v) = usage.total_tokens {
|
||||
otel_span.set_attribute(KeyValue::new(llm::TOTAL_TOKENS, v));
|
||||
}
|
||||
if let Some(v) = usage.cached_input_tokens {
|
||||
otel_span.set_attribute(KeyValue::new(llm::CACHED_INPUT_TOKENS, v));
|
||||
}
|
||||
if let Some(v) = usage.cache_creation_tokens {
|
||||
otel_span.set_attribute(KeyValue::new(llm::CACHE_CREATION_TOKENS, v));
|
||||
}
|
||||
if let Some(v) = usage.reasoning_tokens {
|
||||
otel_span.set_attribute(KeyValue::new(llm::REASONING_TOKENS, v));
|
||||
}
|
||||
// Override `llm.model` with the model the upstream actually ran
|
||||
// (e.g. `openai-gpt-5.4` resolved from `router:software-engineering`).
|
||||
// Cost lookup keys off the real model, not the alias.
|
||||
if let Some(resolved) = usage.resolved_model.clone() {
|
||||
otel_span.set_attribute(KeyValue::new(llm::MODEL_NAME, resolved));
|
||||
}
|
||||
}
|
||||
// Release the buffered bytes early; nothing downstream needs them.
|
||||
self.response_buffer.clear();
|
||||
self.response_buffer.shrink_to_fit();
|
||||
|
||||
// Analyze signals if messages are available and record as span attributes
|
||||
if let Some(ref messages) = self.messages {
|
||||
let analyzer: Box<dyn SignalAnalyzer> = Box::new(TextBasedSignalAnalyzer::new());
|
||||
|
|
@ -404,3 +583,55 @@ pub fn truncate_message(message: &str, max_length: usize) -> String {
|
|||
message.to_string()
|
||||
}
|
||||
}
|
||||
|
||||
#[cfg(test)]
|
||||
mod usage_extraction_tests {
|
||||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn non_streaming_openai_with_cached() {
|
||||
let body = br#"{"id":"x","model":"gpt-4o","choices":[],"usage":{"prompt_tokens":12,"completion_tokens":34,"total_tokens":46,"prompt_tokens_details":{"cached_tokens":5}}}"#;
|
||||
let u = extract_usage_from_bytes(body);
|
||||
assert_eq!(u.prompt_tokens, Some(12));
|
||||
assert_eq!(u.completion_tokens, Some(34));
|
||||
assert_eq!(u.total_tokens, Some(46));
|
||||
assert_eq!(u.cached_input_tokens, Some(5));
|
||||
assert_eq!(u.reasoning_tokens, None);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn non_streaming_anthropic_with_cache_creation() {
|
||||
let body = br#"{"id":"x","model":"claude","usage":{"input_tokens":100,"output_tokens":50,"cache_creation_input_tokens":20,"cache_read_input_tokens":30}}"#;
|
||||
let u = extract_usage_from_bytes(body);
|
||||
assert_eq!(u.prompt_tokens, Some(100));
|
||||
assert_eq!(u.completion_tokens, Some(50));
|
||||
assert_eq!(u.total_tokens, Some(150));
|
||||
assert_eq!(u.cached_input_tokens, Some(30));
|
||||
assert_eq!(u.cache_creation_tokens, Some(20));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn streaming_openai_final_chunk_has_usage() {
|
||||
let sse = b"data: {\"choices\":[{\"delta\":{\"content\":\"hi\"}}]}
|
||||
|
||||
data: {\"choices\":[{\"delta\":{}, \"finish_reason\":\"stop\"}],\"usage\":{\"prompt_tokens\":7,\"completion_tokens\":3,\"total_tokens\":10}}
|
||||
|
||||
data: [DONE]
|
||||
|
||||
";
|
||||
let u = extract_usage_from_bytes(sse);
|
||||
assert_eq!(u.prompt_tokens, Some(7));
|
||||
assert_eq!(u.completion_tokens, Some(3));
|
||||
assert_eq!(u.total_tokens, Some(10));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn empty_returns_default() {
|
||||
assert!(extract_usage_from_bytes(b"").is_empty());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn no_usage_in_body_returns_default() {
|
||||
assert!(extract_usage_from_bytes(br#"{"ok":true}"#).is_empty());
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -80,6 +80,18 @@ pub mod llm {
|
|||
/// Total tokens used (prompt + completion)
|
||||
pub const TOTAL_TOKENS: &str = "llm.usage.total_tokens";
|
||||
|
||||
/// Tokens served from a prompt cache read
|
||||
/// (OpenAI `prompt_tokens_details.cached_tokens`, Anthropic `cache_read_input_tokens`,
|
||||
/// Google `cached_content_token_count`)
|
||||
pub const CACHED_INPUT_TOKENS: &str = "llm.usage.cached_input_tokens";
|
||||
|
||||
/// Tokens used to write a prompt cache entry (Anthropic `cache_creation_input_tokens`)
|
||||
pub const CACHE_CREATION_TOKENS: &str = "llm.usage.cache_creation_tokens";
|
||||
|
||||
/// Reasoning tokens for reasoning models
|
||||
/// (OpenAI `completion_tokens_details.reasoning_tokens`, Google `thoughts_token_count`)
|
||||
pub const REASONING_TOKENS: &str = "llm.usage.reasoning_tokens";
|
||||
|
||||
/// Temperature parameter used
|
||||
pub const TEMPERATURE: &str = "llm.temperature";
|
||||
|
||||
|
|
@ -119,6 +131,22 @@ pub mod routing {
|
|||
pub const SELECTION_REASON: &str = "routing.selection_reason";
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// Span Attributes - Plano-specific
|
||||
// =============================================================================
|
||||
|
||||
/// Attributes specific to Plano (session affinity, routing decisions).
|
||||
pub mod plano {
|
||||
/// Session identifier propagated via the `x-model-affinity` header.
|
||||
/// Absent when the client did not send the header.
|
||||
pub const SESSION_ID: &str = "plano.session_id";
|
||||
|
||||
/// Matched route name from routing (e.g. "code", "summarization",
|
||||
/// "software-engineering"). Absent when the client routed directly
|
||||
/// to a concrete model.
|
||||
pub const ROUTE_NAME: &str = "plano.route.name";
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// Span Attributes - Error Handling
|
||||
// =============================================================================
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ mod init;
|
|||
mod service_name_exporter;
|
||||
|
||||
pub use constants::{
|
||||
error, http, llm, operation_component, routing, signals, OperationNameBuilder,
|
||||
error, http, llm, operation_component, plano, routing, signals, OperationNameBuilder,
|
||||
};
|
||||
pub use custom_attributes::collect_custom_trace_attributes;
|
||||
pub use init::init_tracer;
|
||||
|
|
|
|||
|
|
@ -435,6 +435,12 @@ impl TokenUsage for MessagesResponse {
|
|||
fn total_tokens(&self) -> usize {
|
||||
(self.usage.input_tokens + self.usage.output_tokens) as usize
|
||||
}
|
||||
fn cached_input_tokens(&self) -> Option<usize> {
|
||||
self.usage.cache_read_input_tokens.map(|t| t as usize)
|
||||
}
|
||||
fn cache_creation_tokens(&self) -> Option<usize> {
|
||||
self.usage.cache_creation_input_tokens.map(|t| t as usize)
|
||||
}
|
||||
}
|
||||
|
||||
impl ProviderResponse for MessagesResponse {
|
||||
|
|
|
|||
|
|
@ -596,6 +596,18 @@ impl TokenUsage for Usage {
|
|||
fn total_tokens(&self) -> usize {
|
||||
self.total_tokens as usize
|
||||
}
|
||||
|
||||
fn cached_input_tokens(&self) -> Option<usize> {
|
||||
self.prompt_tokens_details
|
||||
.as_ref()
|
||||
.and_then(|d| d.cached_tokens.map(|t| t as usize))
|
||||
}
|
||||
|
||||
fn reasoning_tokens(&self) -> Option<usize> {
|
||||
self.completion_tokens_details
|
||||
.as_ref()
|
||||
.and_then(|d| d.reasoning_tokens.map(|t| t as usize))
|
||||
}
|
||||
}
|
||||
|
||||
/// Implementation of ProviderRequest for ChatCompletionsRequest
|
||||
|
|
|
|||
|
|
@ -710,6 +710,18 @@ impl crate::providers::response::TokenUsage for ResponseUsage {
|
|||
fn total_tokens(&self) -> usize {
|
||||
self.total_tokens as usize
|
||||
}
|
||||
|
||||
fn cached_input_tokens(&self) -> Option<usize> {
|
||||
self.input_tokens_details
|
||||
.as_ref()
|
||||
.map(|d| d.cached_tokens.max(0) as usize)
|
||||
}
|
||||
|
||||
fn reasoning_tokens(&self) -> Option<usize> {
|
||||
self.output_tokens_details
|
||||
.as_ref()
|
||||
.map(|d| d.reasoning_tokens.max(0) as usize)
|
||||
}
|
||||
}
|
||||
|
||||
/// Token details
|
||||
|
|
|
|||
|
|
@ -23,6 +23,31 @@ pub trait TokenUsage {
|
|||
fn completion_tokens(&self) -> usize;
|
||||
fn prompt_tokens(&self) -> usize;
|
||||
fn total_tokens(&self) -> usize;
|
||||
/// Tokens served from a prompt cache read (OpenAI `prompt_tokens_details.cached_tokens`,
|
||||
/// Anthropic `cache_read_input_tokens`, Google `cached_content_token_count`).
|
||||
fn cached_input_tokens(&self) -> Option<usize> {
|
||||
None
|
||||
}
|
||||
/// Tokens used to write a cache entry (Anthropic `cache_creation_input_tokens`).
|
||||
fn cache_creation_tokens(&self) -> Option<usize> {
|
||||
None
|
||||
}
|
||||
/// Reasoning tokens for reasoning models (OpenAI `completion_tokens_details.reasoning_tokens`,
|
||||
/// Google `thoughts_token_count`).
|
||||
fn reasoning_tokens(&self) -> Option<usize> {
|
||||
None
|
||||
}
|
||||
}
|
||||
|
||||
/// Rich usage breakdown extracted from a provider response.
|
||||
#[derive(Debug, Clone, Copy, Default, PartialEq, Eq)]
|
||||
pub struct UsageDetails {
|
||||
pub prompt_tokens: usize,
|
||||
pub completion_tokens: usize,
|
||||
pub total_tokens: usize,
|
||||
pub cached_input_tokens: Option<usize>,
|
||||
pub cache_creation_tokens: Option<usize>,
|
||||
pub reasoning_tokens: Option<usize>,
|
||||
}
|
||||
|
||||
pub trait ProviderResponse: Send + Sync {
|
||||
|
|
@ -34,6 +59,18 @@ pub trait ProviderResponse: Send + Sync {
|
|||
self.usage()
|
||||
.map(|u| (u.prompt_tokens(), u.completion_tokens(), u.total_tokens()))
|
||||
}
|
||||
|
||||
/// Extract a rich usage breakdown including cached/cache-creation/reasoning tokens.
|
||||
fn extract_usage_details(&self) -> Option<UsageDetails> {
|
||||
self.usage().map(|u| UsageDetails {
|
||||
prompt_tokens: u.prompt_tokens(),
|
||||
completion_tokens: u.completion_tokens(),
|
||||
total_tokens: u.total_tokens(),
|
||||
cached_input_tokens: u.cached_input_tokens(),
|
||||
cache_creation_tokens: u.cache_creation_tokens(),
|
||||
reasoning_tokens: u.reasoning_tokens(),
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
impl ProviderResponse for ProviderResponseType {
|
||||
|
|
|
|||
|
|
@ -340,6 +340,42 @@ And to get the list of supported currencies:
|
|||
"Here is a list of the currencies that are supported for conversion from USD, along with their symbols:\n\n1. AUD - Australian Dollar\n2. BGN - Bulgarian Lev\n3. BRL - Brazilian Real\n4. CAD - Canadian Dollar\n5. CHF - Swiss Franc\n6. CNY - Chinese Renminbi Yuan\n7. CZK - Czech Koruna\n8. DKK - Danish Krone\n9. EUR - Euro\n10. GBP - British Pound\n11. HKD - Hong Kong Dollar\n12. HUF - Hungarian Forint\n13. IDR - Indonesian Rupiah\n14. ILS - Israeli New Sheqel\n15. INR - Indian Rupee\n16. ISK - Icelandic Króna\n17. JPY - Japanese Yen\n18. KRW - South Korean Won\n19. MXN - Mexican Peso\n20. MYR - Malaysian Ringgit\n21. NOK - Norwegian Krone\n22. NZD - New Zealand Dollar\n23. PHP - Philippine Peso\n24. PLN - Polish Złoty\n25. RON - Romanian Leu\n26. SEK - Swedish Krona\n27. SGD - Singapore Dollar\n28. THB - Thai Baht\n29. TRY - Turkish Lira\n30. USD - United States Dollar\n31. ZAR - South African Rand\n\nIf you want to convert USD to any of these currencies, you can select the one you are interested in."
|
||||
|
||||
|
||||
Observability Console
|
||||
---------------------
|
||||
|
||||
Run ``planoai obs`` in a second terminal for a live, in-memory view of LLM traffic: per-request tokens, cached/cache-creation/reasoning tokens, TTFT, latency, cost (when DO Gradient pricing is available), session grouping, and route distribution.
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ planoai obs
|
||||
# In another terminal, start the proxy — with no config, planoai synthesizes
|
||||
# a pass-through config for all known providers and auto-wires OTel export
|
||||
# to localhost:4317 so the console receives spans automatically.
|
||||
$ planoai up
|
||||
|
||||
With no API keys set, every provider runs in pass-through mode — supply the ``Authorization`` header yourself on each request. For example, using DigitalOcean Gradient:
|
||||
|
||||
.. code-block:: console
|
||||
|
||||
$ curl localhost:12000/v1/chat/completions \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "Authorization: Bearer $DO_API_KEY" \
|
||||
-d '{"model":"do/router:software-engineering",
|
||||
"messages":[{"role":"user","content":"write code to print prime numbers in python"}],
|
||||
"stream":false}'
|
||||
|
||||
When you do export ``OPENAI_API_KEY`` / ``ANTHROPIC_API_KEY`` / ``DO_API_KEY`` / etc. before ``planoai up``, Plano picks them up automatically and clients no longer need to send ``Authorization``.
|
||||
|
||||
If you already use your own ``plano_config.yaml``, add this block so spans flow to the console:
|
||||
|
||||
.. code-block:: yaml
|
||||
|
||||
tracing:
|
||||
random_sampling: 100
|
||||
opentracing_grpc_endpoint: http://localhost:4317
|
||||
|
||||
Press ``Ctrl-C`` in the obs terminal to exit. Data lives in memory only — nothing is persisted to disk.
|
||||
|
||||
Next Steps
|
||||
==========
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue