dongarandClaude Opus 5.5
f32ccf9881
Shared parts, not a shared scheduler (agreed): nisse-review will have its own small scheduler; a common one waits until two exist to compare. - nisse_core.loop.driver: the driver run, moved; the agent names its waiting tools per run (nisse-dev's are WAITING_TOOLS), and execution goes to any Executor (ToolOutcome moves with it). - nisse_core.loop.tools: the tool helper and the tools any agent offers (notes, memory, memory_done, ask_expert); nisse_core.loop.expert. - nisse_core.knowledge.handlers: Knowledge, the vault and memory as control tools, the index and prompt text, out of LoopAgent. - nisse_core.config: file reading, references, people, tuning sections of the agent's own, LLM numbers, the vault. - New core tests: a driver run with a non-workspace executor and the agent's own waiting set; Knowledge alone; the shared config pieces. Docs: the Development Plan marks M7.2 done; Architecture Part L and the nisse-core README name the new parts. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01J5Utyfzw7gF7UhjRDEtjTk
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
"""Configuration any agent builds on: references resolved from the
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environment, people lists, tuning sections of the agent's own, the LLM's
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numbers, and the vault; every problem collected, none raised one by one."""
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from dataclasses import dataclass, field
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from pathlib import Path
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from nisse_core.config import (
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flatten,
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llm_numbers,
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parse_tuning,
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parse_vault,
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people,
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resolve,
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)
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@dataclass(frozen=True)
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class Review:
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max_rounds: int = 3
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wait_s: float = 60.0
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@dataclass(frozen=True)
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class Tuning:
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review: Review = field(default_factory=Review)
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def test_an_agents_own_tuning_sections() -> None:
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errors: list[str] = []
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tuning = parse_tuning({"review": {"max_rounds": 2}}, Tuning, errors)
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assert tuning == Tuning(Review(max_rounds=2)) and errors == []
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assert parse_tuning(None, Tuning, errors) == Tuning()
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parse_tuning(
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{"review": {"max_rounds": 0, "x": 1}, "old": {}, "new": {}},
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Tuning,
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errors,
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retired={"old": "use review"},
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)
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assert errors == [
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"tuning.review.max_rounds: must be a positive number",
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"tuning.review.x: unknown setting",
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"tuning.old: retired — use review",
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"tuning.new: unknown section",
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]
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def test_references_and_people() -> None:
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errors: list[str] = []
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env = {"TOKEN": "s3cret"}
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assert resolve("${TOKEN}", "gitea.token", env, errors) == "s3cret"
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resolve("${NOPE}", "llm.token", env, errors)
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assert errors == ["llm.token: references ${NOPE}, which is unset or empty"]
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assert people("don, ,ann,") == {"don", "ann"}
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assert flatten({"llm": {"base_url": "u"}, "tuning": {"a": {}}}) == {
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"llm.base_url": "u",
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"tuning": {"a": {}},
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}
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def test_llm_numbers_and_the_vault(tmp_path: Path) -> None:
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errors: list[str] = []
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assert llm_numbers({"llm.max_output_tokens": 100}, errors)[0] == 100
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llm_numbers({"llm.request_timeout_s": -1}, errors)
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assert errors == ["llm.request_timeout_s: must be a positive number of seconds"]
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vault = parse_vault({"root": str(tmp_path), "folders": {"notes": "ro"}}, [])
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assert vault is not None and vault.folders == {"notes": "ro"}
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bad: list[str] = []
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assert parse_vault({"root": "/v", "folders": {"../x": "rw"}}, bad) is None
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assert bad == ["vault.folders.../x: a folder inside the vault"]
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