7a4188dbda
* add compaction settings to ADE, add get default prompt for updated mode route * update patch to auto set prompt on mode change, related ade changes * reset api and update test * feat: add compaction configuration translation keys for fr and cn Add ADE/CompactionConfiguration translation keys to fr.json and cn.json to match the new keys added in en.json. Co-authored-by: Christina Tong <christinatong01@users.noreply.github.com> 🤖 Generated with [Letta Code](https://letta.com) Co-Authored-By: Letta <noreply@letta.com> * type/translation/etc fixes * fix typing * update model selector path w/ change from main * import mode from sdk --------- Co-authored-by: letta-code <248085862+letta-code@users.noreply.github.com> Co-authored-by: Letta <noreply@letta.com>
76 lines
3.3 KiB
Python
76 lines
3.3 KiB
Python
from typing import Literal
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from pydantic import BaseModel, Field
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from letta.prompts.summarizer_prompt import ALL_PROMPT, SELF_ALL_PROMPT, SELF_SLIDING_PROMPT, SLIDING_PROMPT
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from letta.schemas.enums import ProviderType
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from letta.schemas.model import ModelSettingsUnion
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from letta.settings import summarizer_settings
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def get_default_summarizer_model(provider_type: ProviderType) -> str | None:
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"""Get default model for summarization for given provider type."""
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summarizer_defaults = {
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ProviderType.anthropic: "anthropic/claude-haiku-4-5",
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ProviderType.openai: "openai/gpt-5-mini",
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ProviderType.google_ai: "google_ai/gemini-2.5-flash",
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}
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return summarizer_defaults.get(provider_type)
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def get_default_prompt_for_mode(mode: Literal["all", "sliding_window", "self_compact_all", "self_compact_sliding_window"]) -> str:
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"""Get the default prompt for a given compaction mode.
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Also used in /summarize endpoint if mode is changed and prompt is not explicitly set."""
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if mode == "self_compact_sliding_window":
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return SELF_SLIDING_PROMPT
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elif mode == "self_compact_all":
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return SELF_ALL_PROMPT
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elif mode == "sliding_window":
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return SLIDING_PROMPT
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else: # all
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return ALL_PROMPT
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class CompactionSettings(BaseModel):
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"""Configuration for conversation compaction / summarization.
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Per-model settings (temperature,
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max tokens, etc.) are derived from the default configuration for that handle.
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"""
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# Summarizer model handle (provider/model-name).
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# If None, uses lightweight provider-specific defaults (e.g., haiku for Anthropic, gpt-5-mini for OpenAI).
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model: str | None = Field(
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default=None,
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description="Model handle to use for sliding_window/all summarization (format: provider/model-name). If None, uses lightweight provider-specific defaults.",
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)
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# Optional provider-specific model settings for the summarizer model
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model_settings: ModelSettingsUnion | None = Field(
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default=None,
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description="Optional model settings used to override defaults for the summarizer model.",
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)
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prompt: str | None = Field(default=None, description="The prompt to use for summarization. If None, uses mode-specific default.")
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prompt_acknowledgement: bool = Field(
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default=False, description="Whether to include an acknowledgement post-prompt (helps prevent non-summary outputs)."
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)
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clip_chars: int | None = Field(
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default=50000, description="The maximum length of the summary in characters. If none, no clipping is performed."
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)
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mode: Literal["all", "sliding_window", "self_compact_all", "self_compact_sliding_window"] = Field(
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default="sliding_window", description="The type of summarization technique use."
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)
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sliding_window_percentage: float = Field(
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default_factory=lambda: summarizer_settings.partial_evict_summarizer_percentage,
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description="The percentage of the context window to keep post-summarization (only used in sliding window modes).",
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)
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# Called upon agent creation and if mode is changed in summarize endpoint request
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def set_mode_specific_prompt(self):
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"""Set mode-specific default prompt if none provided."""
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if self.prompt is None:
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self.prompt = get_default_prompt_for_mode(self.mode)
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return self
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