71 lines
2.0 KiB
Python
71 lines
2.0 KiB
Python
"""Anthropic prompt caching (system_and_3 strategy).
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Reduces input token costs by ~75% on multi-turn conversations by caching
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the conversation prefix. Uses 4 cache_control breakpoints (Anthropic max):
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1. System prompt (stable across all turns)
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2-4. Last 3 non-system messages (rolling window)
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Pure functions -- no class state, no AIAgent dependency.
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"""
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import copy
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from typing import Any, Dict, List
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def _apply_cache_marker(msg: dict, cache_marker: dict) -> None:
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"""Add cache_control to a single message, handling all format variations."""
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role = msg.get("role", "")
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content = msg.get("content")
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if role == "tool":
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msg["cache_control"] = cache_marker
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return
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if content is None or content == "":
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msg["cache_control"] = cache_marker
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return
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if isinstance(content, str):
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msg["content"] = [
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{"type": "text", "text": content, "cache_control": cache_marker}
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]
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return
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if isinstance(content, list) and content:
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last = content[-1]
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if isinstance(last, dict):
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last["cache_control"] = cache_marker
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def apply_anthropic_cache_control(
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api_messages: List[Dict[str, Any]],
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cache_ttl: str = "5m",
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) -> List[Dict[str, Any]]:
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"""Apply system_and_3 caching strategy to messages for Anthropic models.
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Places up to 4 cache_control breakpoints: system prompt + last 3 non-system messages.
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Returns:
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Deep copy of messages with cache_control breakpoints injected.
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"""
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messages = copy.deepcopy(api_messages)
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if not messages:
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return messages
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marker = {"type": "ephemeral"}
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if cache_ttl == "1h":
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marker["ttl"] = "1h"
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breakpoints_used = 0
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if messages[0].get("role") == "system":
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_apply_cache_marker(messages[0], marker)
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breakpoints_used += 1
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remaining = 4 - breakpoints_used
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non_sys = [i for i in range(len(messages)) if messages[i].get("role") != "system"]
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for idx in non_sys[-remaining:]:
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_apply_cache_marker(messages[idx], marker)
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return messages
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