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https://github.com/himanshudongre/smriti.git
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73f26e721c
Tasks in checkpoints evolve from flat strings to objects with optional intent_hint (implement/review/investigate/docs/test), blocked_by (dependency label), and status (open/done). Agents reading the state brief can now self-select complementary work by matching task intents against active claim intent_types — no founder routing needed. Backward-compatible: old string tasks normalize at render time. No schema migration. JSONB handles both shapes. Skill pack v1.7 teaches the autonomous selection reflex.
160 lines
5 KiB
Python
160 lines
5 KiB
Python
"""API request and response schemas using Pydantic."""
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import uuid
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from datetime import datetime
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from typing import Any, Optional, Union
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from pydantic import BaseModel, Field, field_validator
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from app.domain.enums import SessionStatus, TargetTool
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# ── Request Schemas ──────────────────────────────────────────────────────────
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class SessionCreateRequest(BaseModel):
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"""Request body for creating a new session."""
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raw_transcript: str = Field(..., min_length=1, description="The raw transcript text")
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title: str | None = Field(None, max_length=255, description="Optional session title")
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source_tool: str | None = Field(None, description="Source AI tool")
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class ContextPackCreateRequest(BaseModel):
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"""Request body for generating a context pack."""
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target_tool: TargetTool = Field(..., description="Target tool for continuation pack")
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# ── Response Schemas ─────────────────────────────────────────────────────────
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class SessionResponse(BaseModel):
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"""Response for a session."""
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id: uuid.UUID
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title: str | None
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source_tool: str | None
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status: SessionStatus
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raw_transcript: str
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created_at: datetime
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model_config = {"from_attributes": True}
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class DecisionResponse(BaseModel):
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description: str
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context: str = ""
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class TaskResponse(BaseModel):
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description: str
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status: str = "pending"
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class OpenQuestionResponse(BaseModel):
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question: str
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context: str = ""
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class EntityResponse(BaseModel):
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name: str
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type: str
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context: str = ""
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class CodeSnippetResponse(BaseModel):
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language: str
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code: str
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description: str = ""
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class ArtifactsResponse(BaseModel):
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"""Response for extracted artifacts."""
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summary: str
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decisions: list[DecisionResponse]
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tasks: list[TaskResponse]
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open_questions: list[OpenQuestionResponse]
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entities: list[EntityResponse]
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code_snippets: list[CodeSnippetResponse]
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class ContextPackResponse(BaseModel):
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"""Response for a generated context pack."""
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id: uuid.UUID
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session_id: uuid.UUID
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target_tool: str
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content: str
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format: str
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created_at: datetime
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model_config = {"from_attributes": True}
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class ErrorResponse(BaseModel):
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"""Standard error response."""
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error: str
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detail: str | None = None
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class CheckpointDraftRequest(BaseModel):
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session_id: uuid.UUID
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num_turns: int = Field(15, ge=1, le=100)
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mounted_checkpoint_id: Optional[str] = Field(
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None,
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description="If set, draft only uses turns after history_base_seq (mirrors send_message isolation)."
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)
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history_base_seq: Optional[int] = Field(
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None,
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description="Sequence boundary from mount event. Only turns with sequence_number > this value are included."
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)
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class CheckpointDraftResponse(BaseModel):
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title: str = ""
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objective: str = ""
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summary: str = ""
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decisions: list[str] = Field(default_factory=list)
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assumptions: list[str] = Field(default_factory=list)
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tasks: list[Union[str, dict[str, Any]]] = Field(default_factory=list)
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open_questions: list[str] = Field(default_factory=list)
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entities: list[str] = Field(default_factory=list)
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class ReviewIssue(BaseModel):
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type: str # contradiction, hidden_assumption, resolved_question, unused_entity
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description: str
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class CheckpointReviewResponse(BaseModel):
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checkpoint_id: uuid.UUID
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issues: list[ReviewIssue] = Field(default_factory=list)
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suggestions: list[str] = Field(default_factory=list)
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class CheckpointExtractRequest(BaseModel):
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"""Request body for the freeform-markdown checkpoint extractor.
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Takes a freeform markdown document and asks the background LLM to
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extract Smriti checkpoint schema fields from it. Stateless — no
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session or checkpoint ID required.
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"""
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content: str = Field(..., description="Freeform markdown document to extract checkpoint fields from")
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use_mock: bool = Field(False, description="Force MockAdapter even when a real provider is configured (for tests and dry-run flows)")
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@field_validator("content")
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@classmethod
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def validate_content(cls, v: str) -> str:
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stripped = v.strip()
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if not stripped:
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raise ValueError("content must not be empty")
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if len(stripped) > 200_000:
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raise ValueError("content exceeds 200000 character limit")
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return v
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class CheckpointExtractResponse(BaseModel):
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title: str = ""
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objective: str = ""
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summary: str = ""
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decisions: list[str] = Field(default_factory=list)
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assumptions: list[str] = Field(default_factory=list)
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tasks: list[Union[str, dict[str, Any]]] = Field(default_factory=list)
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open_questions: list[str] = Field(default_factory=list)
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entities: list[str] = Field(default_factory=list)
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artifacts: list[dict] = Field(default_factory=list)
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