4f2833873c
* mental models * DRAFT: refactor entity observations * fix db patch * agentic * agentic * reflect agent * new style * more * fix ci * fix * fix
114 lines
5.6 KiB
Python
114 lines
5.6 KiB
Python
"""
|
|
Pydantic models for the reflect agent.
|
|
"""
|
|
|
|
from typing import Any, Literal
|
|
|
|
from pydantic import BaseModel, Field
|
|
|
|
|
|
class MentalModelObservation(BaseModel):
|
|
"""An observation within a mental model with its supporting memories."""
|
|
|
|
title: str = Field(description="Observation header (can be empty for intro)")
|
|
text: str = Field(description="Observation content - no headers, use lists/tables/bold")
|
|
memory_ids: list[str] = Field(default_factory=list, description="Memory IDs supporting this observation")
|
|
|
|
|
|
class MentalModelInput(BaseModel):
|
|
"""Input for the learn tool to create a mental model placeholder.
|
|
|
|
The agent only specifies name and description - the actual content/observations
|
|
are generated during refresh, similar to pinned models.
|
|
"""
|
|
|
|
name: str = Field(description="Human-readable name for the mental model")
|
|
description: str = Field(description="What to track - used as prompt for content generation during refresh")
|
|
entity_id: str | None = Field(default=None, description="Optional link to existing entity ID")
|
|
|
|
|
|
class AnswerSection(BaseModel):
|
|
"""A section of the answer with its supporting evidence (DEPRECATED)."""
|
|
|
|
title: str = Field(description="Section header/title")
|
|
text: str = Field(description="Section content")
|
|
memory_ids: list[str] = Field(default_factory=list, description="Memory IDs supporting this section")
|
|
model_ids: list[str] = Field(default_factory=list, description="Mental model IDs supporting this section")
|
|
|
|
|
|
class ReflectAction(BaseModel):
|
|
"""Single action the reflect agent can take."""
|
|
|
|
tool: Literal["list_mental_models", "get_mental_model", "recall", "learn", "expand", "done"] = Field(
|
|
description="Tool to invoke: list_mental_models, get_mental_model, recall, learn, expand, or done"
|
|
)
|
|
# Tool-specific parameters
|
|
model_id: str | None = Field(default=None, description="Mental model ID for get_mental_model")
|
|
query: str | None = Field(default=None, description="Search query for recall")
|
|
max_tokens: int | None = Field(default=None, description="Max tokens for recall results (default 2048)")
|
|
mental_model: MentalModelInput | None = Field(default=None, description="Mental model to create/update for learn")
|
|
memory_ids: list[str] | None = Field(default=None, description="Memory unit IDs for expand (batched)")
|
|
depth: Literal["chunk", "document"] | None = Field(default=None, description="Expansion depth for expand")
|
|
sections: list[AnswerSection] | None = Field(default=None, description="DEPRECATED: Use answer field instead")
|
|
observations: list[MentalModelObservation] | None = Field(
|
|
default=None, description="Observations for done action (when output_mode=observations)"
|
|
)
|
|
# Plain text answer fields (for output_mode=answer)
|
|
answer: str | None = Field(default=None, description="Plain text answer for done action (no markdown)")
|
|
answer_memory_ids: list[str] | None = Field(
|
|
default=None, description="Memory IDs supporting the answer", alias="memory_ids"
|
|
)
|
|
answer_model_ids: list[str] | None = Field(
|
|
default=None, description="Mental model IDs supporting the answer", alias="model_ids"
|
|
)
|
|
reasoning: str | None = Field(default=None, description="Brief reasoning for this action")
|
|
|
|
|
|
class ReflectActionBatch(BaseModel):
|
|
"""Batch of actions for parallel execution."""
|
|
|
|
actions: list[ReflectAction] = Field(description="List of actions to execute in parallel")
|
|
|
|
|
|
class ToolCall(BaseModel):
|
|
"""A single tool call made during reflect."""
|
|
|
|
tool: str = Field(description="Tool name: lookup, recall, learn, expand")
|
|
input: dict = Field(description="Tool input parameters")
|
|
output: dict = Field(description="Tool output/result")
|
|
duration_ms: int = Field(description="Execution time in milliseconds")
|
|
iteration: int = Field(default=0, description="Iteration number (1-based) when this tool was called")
|
|
|
|
|
|
class LLMCall(BaseModel):
|
|
"""A single LLM call made during reflect."""
|
|
|
|
scope: str = Field(description="Call scope: agent_1, agent_2, final, etc.")
|
|
duration_ms: int = Field(description="Execution time in milliseconds")
|
|
|
|
|
|
class Observation(BaseModel):
|
|
"""A single observation with supporting memories."""
|
|
|
|
title: str = Field(description="Observation title/header")
|
|
text: str = Field(description="Observation content")
|
|
memory_ids: list[str] = Field(default_factory=list, description="Memory IDs supporting this observation")
|
|
|
|
|
|
class ReflectAgentResult(BaseModel):
|
|
"""Result from the reflect agent."""
|
|
|
|
text: str = Field(description="Final answer text")
|
|
observations: list[Observation] = Field(
|
|
default_factory=list, description="Structured observations (when output_mode=observations)"
|
|
)
|
|
structured_output: dict[str, Any] | None = Field(
|
|
default=None, description="Structured output parsed according to provided response_schema"
|
|
)
|
|
iterations: int = Field(default=0, description="Number of iterations taken")
|
|
tools_called: int = Field(default=0, description="Total number of tool calls made")
|
|
mental_models_created: list[str] = Field(default_factory=list, description="IDs of mental models created/updated")
|
|
tool_trace: list[ToolCall] = Field(default_factory=list, description="Trace of all tool calls made")
|
|
llm_trace: list[LLMCall] = Field(default_factory=list, description="Trace of all LLM calls made")
|
|
used_memory_ids: list[str] = Field(default_factory=list, description="Validated memory IDs actually used in answer")
|
|
used_model_ids: list[str] = Field(default_factory=list, description="Validated model IDs actually used in answer")
|