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https://github.com/Alishahryar1/free-claude-code.git
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25c7123e33
Phase 7 - Optimization handlers: - Create api/optimization_handlers.py with try_prefix_detection, try_quota_mock, try_title_skip, try_suggestion_skip, try_filepath_mock - Add try_optimizations() that runs handlers in order - Refactor routes.create_message to use try_optimizations() - Update test_routes_optimizations patch targets Phase 8 - Request utils split: - Create api/detection.py: is_quota_check_request, is_title_generation_request, is_prefix_detection_request, is_suggestion_mode_request, is_filepath_extraction_request - Create api/command_utils.py: extract_command_prefix, extract_filepaths_from_command - Slim request_utils.py to get_token_count + re-exports for backward compat Co-authored-by: Ali Khokhar <alishahryar2@gmail.com>
93 lines
3 KiB
Python
93 lines
3 KiB
Python
"""Request utility functions for API route handlers.
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Contains token counting and re-exports detection/command utilities.
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"""
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import json
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import logging
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from typing import List, Optional, Union
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import tiktoken
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from .models.anthropic import MessagesRequest
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from .detection import (
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is_quota_check_request,
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is_title_generation_request,
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is_prefix_detection_request,
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is_suggestion_mode_request,
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is_filepath_extraction_request,
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)
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from .command_utils import extract_command_prefix, extract_filepaths_from_command
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logger = logging.getLogger(__name__)
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ENCODER = tiktoken.get_encoding("cl100k_base")
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__all__ = [
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"is_quota_check_request",
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"is_title_generation_request",
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"is_prefix_detection_request",
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"is_suggestion_mode_request",
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"is_filepath_extraction_request",
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"extract_command_prefix",
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"extract_filepaths_from_command",
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"get_token_count",
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]
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def get_token_count(
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messages: List,
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system: Optional[Union[str, List]] = None,
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tools: Optional[List] = None,
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) -> int:
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"""Estimate token count for a request.
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Uses tiktoken cl100k_base encoding to estimate token usage.
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Includes system prompt, messages, tools, and per-message overhead.
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"""
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total_tokens = 0
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if system:
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if isinstance(system, str):
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total_tokens += len(ENCODER.encode(system))
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elif isinstance(system, list):
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for block in system:
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if hasattr(block, "text"):
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total_tokens += len(ENCODER.encode(block.text))
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for msg in messages:
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if isinstance(msg.content, str):
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total_tokens += len(ENCODER.encode(msg.content))
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elif isinstance(msg.content, list):
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for block in msg.content:
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b_type = getattr(block, "type", None)
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if b_type == "text":
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total_tokens += len(ENCODER.encode(getattr(block, "text", "")))
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elif b_type == "thinking":
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total_tokens += len(ENCODER.encode(getattr(block, "thinking", "")))
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elif b_type == "tool_use":
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name = getattr(block, "name", "")
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inp = getattr(block, "input", {})
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total_tokens += len(ENCODER.encode(name))
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total_tokens += len(ENCODER.encode(json.dumps(inp)))
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total_tokens += 10
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elif b_type == "tool_result":
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content = getattr(block, "content", "")
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if isinstance(content, str):
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total_tokens += len(ENCODER.encode(content))
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else:
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total_tokens += len(ENCODER.encode(json.dumps(content)))
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total_tokens += 5
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if tools:
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for tool in tools:
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tool_str = (
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tool.name + (tool.description or "") + json.dumps(tool.input_schema)
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)
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total_tokens += len(ENCODER.encode(tool_str))
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total_tokens += len(messages) * 3
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if tools:
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total_tokens += len(tools) * 5
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return max(1, total_tokens)
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