projects/ui/serena-new/src/serena/analytics.py

from __future__ import annotations

import logging
import threading
from abc import ABC, abstractmethod
from collections import defaultdict
from copy import copy
from dataclasses import asdict, dataclass
from enum import Enum

from anthropic.types import MessageParam, MessageTokensCount
from dotenv import load_dotenv

log = logging.getLogger(__name__)


class TokenCountEstimator(ABC):
    @abstractmethod
    def estimate_token_count(self, text: str) -> int:
        """
        Estimate the number of tokens in the given text.
        This is an abstract method that should be implemented by subclasses.
        """


class TiktokenCountEstimator(TokenCountEstimator):
    """
    Approximate token count using tiktoken.
    """

    def __init__(self, model_name: str = "gpt-4o"):
        """
        The tokenizer will be downloaded on the first initialization, which may take some time.

        :param model_name: see `tiktoken.model` to see available models.
        """
        import tiktoken

        log.info(f"Loading tiktoken encoding for model {model_name}, this may take a while on the first run.")
        self._encoding = tiktoken.encoding_for_model(model_name)

    def estimate_token_count(self, text: str) -> int:
        return len(self._encoding.encode(text))


class AnthropicTokenCount(TokenCountEstimator):
    """
    The exact count using the Anthropic API.
    Counting is free, but has a rate limit and will require an API key,
    (typically, set through an env variable).
    See https://docs.anthropic.com/en/docs/build-with-claude/token-counting
    """

    def __init__(self, model_name: str = "claude-sonnet-4-20250514", api_key: str | None = None):
        import anthropic

        self._model_name = model_name
        if api_key is None:
            load_dotenv()
        self._anthropic_client = anthropic.Anthropic(api_key=api_key)

    def _send_count_tokens_request(self, text: str) -> MessageTokensCount:
        return self._anthropic_client.messages.count_tokens(
            model=self._model_name,
            messages=[MessageParam(role="user", content=text)],
        )

    def estimate_token_count(self, text: str) -> int:
        return self._send_count_tokens_request(text).input_tokens


_registered_token_estimator_instances_cache: dict[RegisteredTokenCountEstimator, TokenCountEstimator] = {}


class RegisteredTokenCountEstimator(Enum):
    TIKTOKEN_GPT4O = "TIKTOKEN_GPT4O"
    ANTHROPIC_CLAUDE_SONNET_4 = "ANTHROPIC_CLAUDE_SONNET_4"

    @classmethod
    def get_valid_names(cls) -> list[str]:
        """
        Get a list of all registered token count estimator names.
        """
        return [estimator.name for estimator in cls]

    def _create_estimator(self) -> TokenCountEstimator:
        match self:
            case RegisteredTokenCountEstimator.TIKTOKEN_GPT4O:
                return TiktokenCountEstimator(model_name="gpt-4o")
            case RegisteredTokenCountEstimator.ANTHROPIC_CLAUDE_SONNET_4:
                return AnthropicTokenCount(model_name="claude-sonnet-4-20250514")
            case _:
                raise ValueError(f"Unknown token count estimator: {self.value}")

    def load_estimator(self) -> TokenCountEstimator:
        estimator_instance = _registered_token_estimator_instances_cache.get(self)
        if estimator_instance is None:
            estimator_instance = self._create_estimator()
            _registered_token_estimator_instances_cache[self] = estimator_instance
        return estimator_instance


class ToolUsageStats:
    """
    A class to record and manage tool usage statistics.
    """

    def __init__(self, token_count_estimator: RegisteredTokenCountEstimator = RegisteredTokenCountEstimator.TIKTOKEN_GPT4O):
        self._token_count_estimator = token_count_estimator.load_estimator()
        self._token_estimator_name = token_count_estimator.value
        self._tool_stats: dict[str, ToolUsageStats.Entry] = defaultdict(ToolUsageStats.Entry)
        self._tool_stats_lock = threading.Lock()

    @property
    def token_estimator_name(self) -> str:
        """
        Get the name of the registered token count estimator used.
        """
        return self._token_estimator_name

    @dataclass(kw_only=True)
    class Entry:
        num_times_called: int = 0
        input_tokens: int = 0
        output_tokens: int = 0

        def update_on_call(self, input_tokens: int, output_tokens: int) -> None:
            """
            Update the entry with the number of tokens used for a single call.
            """
            self.num_times_called += 1
            self.input_tokens += input_tokens
            self.output_tokens += output_tokens

    def _estimate_token_count(self, text: str) -> int:
        return self._token_count_estimator.estimate_token_count(text)

    def get_stats(self, tool_name: str) -> ToolUsageStats.Entry:
        """
        Get (a copy of) the current usage statistics for a specific tool.
        """
        with self._tool_stats_lock:
            return copy(self._tool_stats[tool_name])

    def record_tool_usage(self, tool_name: str, input_str: str, output_str: str) -> None:
        input_tokens = self._estimate_token_count(input_str)
        output_tokens = self._estimate_token_count(output_str)
        with self._tool_stats_lock:
            entry = self._tool_stats[tool_name]
            entry.update_on_call(input_tokens, output_tokens)

    def get_tool_stats_dict(self) -> dict[str, dict[str, int]]:
        with self._tool_stats_lock:
            return {name: asdict(entry) for name, entry in self._tool_stats.items()}

    def clear(self) -> None:
        with self._tool_stats_lock:
            self._tool_stats.clear()