CIAgent
88a1dab810
---ci--- phase: 7 milestone: v0.2 status: complete requirements: covered: [REQ-2-001, REQ-2-002, REQ-2-003, REQ-2-004, REQ-2-005, REQ-2-006, REQ-2-007, REQ-2-008, REQ-2-009, REQ-2-010, REQ-2-011, REQ-2-012] partial: [] ---/ci--- Milestone v0.2 (ai-tutor-architecture) merged to main. Escalation record (audit remediation, durable): P1 executor delegation failed twice (empty subagent results, zero files created); auto-resolved at full autonomy to inline execution with identical plan fidelity (commit 3271373, reflog-only after phase branch squash-delete).
78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
"""BaseAgent ABC — the contract all six tutor agents implement (D-018).
|
|
|
|
Subclasses set `name`, override `system_prompt()`, and rarely `stream_reply()`.
|
|
The default pipeline: build_messages() → provider.stream_chat()/chat().
|
|
"""
|
|
|
|
from abc import ABC, abstractmethod
|
|
from collections.abc import AsyncIterator
|
|
|
|
from pydantic import BaseModel
|
|
|
|
from ..config import Settings
|
|
from ..corpus.learner_context import LearnerContext
|
|
from ..llm.base import LLMProvider
|
|
from ..llm.types import Message
|
|
from .structured import structured_completion
|
|
|
|
|
|
class BaseAgent(ABC):
|
|
"""A tutor agent: system prompt + message assembly + provider delegation."""
|
|
|
|
name: str = "base"
|
|
|
|
def __init__(self, provider: LLMProvider, settings: Settings) -> None:
|
|
self.provider = provider
|
|
self.settings = settings
|
|
|
|
@abstractmethod
|
|
def system_prompt(self, learner_context: LearnerContext | None = None) -> str:
|
|
"""Return the agent's system prompt, learner-context-aware."""
|
|
|
|
def build_messages(
|
|
self,
|
|
history: list[Message] | None = None,
|
|
user_input: str = "",
|
|
learner_context: LearnerContext | None = None,
|
|
) -> list[Message]:
|
|
"""Compose the full message list: system prompt + history + user turn."""
|
|
messages: list[Message] = [
|
|
Message(role="system", content=self.system_prompt(learner_context))
|
|
]
|
|
for m in history or []:
|
|
messages.append(m)
|
|
if user_input:
|
|
messages.append(Message(role="user", content=user_input))
|
|
return messages
|
|
|
|
async def stream_reply(
|
|
self,
|
|
history: list[Message] | None = None,
|
|
user_input: str = "",
|
|
learner_context: LearnerContext | None = None,
|
|
response_format: dict | None = None,
|
|
) -> AsyncIterator[str]:
|
|
"""Stream incremental content deltas for a conversational reply."""
|
|
messages = self.build_messages(history, user_input, learner_context)
|
|
async for token in self.provider.stream_chat(
|
|
messages, model=self.settings.model, response_format=response_format
|
|
):
|
|
yield token
|
|
|
|
async def structured_reply(
|
|
self,
|
|
history: list[Message] | None = None,
|
|
user_input: str = "",
|
|
learner_context: LearnerContext | None = None,
|
|
schema: type[BaseModel] | None = None,
|
|
schema_hint: str = "",
|
|
) -> BaseModel:
|
|
"""Non-streaming completion parsed into a pydantic model (D-020 defense)."""
|
|
if schema is None:
|
|
raise ValueError("structured_reply requires a schema")
|
|
messages = self.build_messages(history, user_input, learner_context)
|
|
return await structured_completion(
|
|
self.provider, messages, model=self.settings.model,
|
|
schema=schema, schema_hint=schema_hint,
|
|
)
|