mirror of
https://github.com/Routstr/routstr-core.git
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109 lines
3.4 KiB
Python
109 lines
3.4 KiB
Python
"""Local handling of Anthropic ``/v1/messages/count_tokens`` for upstreams
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that do not natively expose the endpoint.
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Most non-Anthropic upstreams (OpenAI-compat, Gemini OpenAI-compat,
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OpenRouter chat-completions, generic providers) return 400/404 when asked
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to ``POST /messages/count_tokens``. Claude Code and other Anthropic SDK
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clients call this endpoint before each turn to size context windows and
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trigger compaction, so a failure breaks the whole chat.
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We answer locally. ``litellm.token_counter`` understands the Anthropic
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message shape and the per-model tokenizers, so we prefer it. If it raises
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(unknown model, encoding lookup failure, ...), we fall back to the
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project's own ``estimate_tokens`` heuristic, which is always defined and
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never raises.
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"""
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from __future__ import annotations
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import json
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from typing import Any
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import litellm
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from fastapi.responses import Response
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from ..core import get_logger
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from ..payment.helpers import estimate_tokens
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from ..payment.models import Model
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logger = get_logger(__name__)
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def _parse_request_body(request_body: bytes | None) -> dict[str, Any]:
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if not request_body:
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return {}
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try:
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parsed = json.loads(request_body)
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except (ValueError, TypeError):
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return {}
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return parsed if isinstance(parsed, dict) else {}
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def _count_with_litellm(model: str, body: dict[str, Any]) -> int:
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messages = body.get("messages")
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if not isinstance(messages, list):
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messages = []
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system = body.get("system")
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if isinstance(system, str) and system:
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messages = [{"role": "system", "content": system}, *messages]
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elif isinstance(system, list):
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text = "".join(
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block.get("text", "")
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for block in system
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if isinstance(block, dict) and block.get("type") == "text"
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)
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if text:
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messages = [{"role": "system", "content": text}, *messages]
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tools = body.get("tools") if isinstance(body.get("tools"), list) else None
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return int(
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litellm.token_counter(
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model=model,
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messages=messages,
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tools=tools,
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)
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)
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def count_tokens_locally(
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request_body: bytes | None,
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model_obj: Model | None,
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) -> Response:
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"""Return an Anthropic-compatible count_tokens response without
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touching the upstream. Always returns 200; never raises."""
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body = _parse_request_body(request_body)
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model_name = ""
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if model_obj is not None:
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model_name = model_obj.forwarded_model_id or model_obj.id or ""
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if not model_name:
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body_model = body.get("model")
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if isinstance(body_model, str):
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model_name = body_model
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input_tokens: int
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try:
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input_tokens = _count_with_litellm(model_name, body)
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except Exception as exc:
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messages = body.get("messages")
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fallback_messages = messages if isinstance(messages, list) else []
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input_tokens = estimate_tokens(fallback_messages)
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logger.debug(
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"litellm token_counter failed; using local estimator",
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extra={
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"model": model_name,
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"error": str(exc),
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"error_type": type(exc).__name__,
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"estimated_tokens": input_tokens,
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},
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)
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payload = {"input_tokens": max(0, int(input_tokens))}
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return Response(
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content=json.dumps(payload).encode(),
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status_code=200,
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media_type="application/json",
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)
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