core: Registry, Skills, Router, Executor, Verifier, Runner mit Tests
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
Claude Opus 5.5
@@ -0,0 +1,3 @@
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"""knaster – Skill-Destillation von starken zu schwachen LLMs."""
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__version__ = "0.1.0"
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"""Token-Budget: Passt Prompt + Ausgabe in den effektiven Kontext eines Slots?"""
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from __future__ import annotations
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import math
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from collections.abc import Callable
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from dataclasses import dataclass
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MESSAGE_OVERHEAD = 64
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Tokenizer = Callable[[str], int]
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def estimate_tokens(text: str) -> int:
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"""Konservative Schätzung (eher zu hoch): ~3 Zeichen pro Token."""
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return math.ceil(len(text) / 3)
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@dataclass(frozen=True)
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class Budget:
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prompt_tokens: int
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output_tokens: int
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limit: int
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@property
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def required(self) -> int:
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return self.prompt_tokens + self.output_tokens + MESSAGE_OVERHEAD
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@property
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def fits(self) -> bool:
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return self.required <= self.limit
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def compute(system: str, user: str, max_output_tokens: int, limit: int,
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tokenizer: Tokenizer = estimate_tokens) -> Budget:
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return Budget(tokenizer(system) + tokenizer(user), max_output_tokens, limit)
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"""Kalibrierung des effektiven Kontexts aus beobachteten Pass-Rates nach Prompt-Länge."""
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from __future__ import annotations
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from collections.abc import Iterable
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DEFAULT_BUCKETS = (2048, 4096, 8192, 16384, 32768, 65536, 131072)
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def effective_context(
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observations: Iterable[tuple[int, bool]],
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*,
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target: float = 0.9,
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min_n: int = 20,
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buckets: tuple[int, ...] = DEFAULT_BUCKETS,
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) -> int | None:
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"""observations: (prompt_tokens, bestanden).
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Liefert die Obergrenze des größten Buckets, bis zu dem alle Buckets mit genug Daten
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die Ziel-Pass-Rate erreichen. Buckets ohne genug Daten unterbrechen die Kette nicht,
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zählen aber auch nicht als Nachweis. Ohne Nachweis: None.
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"""
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counts = {b: [0, 0] for b in buckets} # [n, passed]
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for tokens, passed in observations:
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for b in buckets:
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if tokens <= b:
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counts[b][0] += 1
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counts[b][1] += int(passed)
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break
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result = None
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for b in buckets:
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n, ok = counts[b]
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if n < min_n:
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continue
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if ok / n >= target:
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result = b
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else:
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break
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return result
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"""Fälle und Präferenzpaare: modellneutral speichern, deterministisch splitten, exportieren."""
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from __future__ import annotations
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import hashlib
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import json
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from collections.abc import Iterator
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from typing import Any
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from .store import Store
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EVAL_SHARE_PERCENT = 20
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def split_for(text: str) -> str:
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"""Fester Split pro Eingabe: gleiche Eingabe landet immer im selben Split."""
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bucket = int(hashlib.sha256(text.encode("utf-8")).hexdigest(), 16) % 100
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return "eval" if bucket < EVAL_SHARE_PERCENT else "train"
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def export_sft(store: Store, *, cluster: str, split: str = "train") -> Iterator[str]:
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"""JSONL für Supervised Finetuning: nur unabhängig verifizierte Fälle."""
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for case in reversed(store.list_cases(cluster=cluster, split=split, passed=True)):
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messages = list(case["messages"] or [])
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messages.append({"role": "assistant", "content": case["output"]})
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meta = {k: case[k] for k in ("id", "skill", "skill_version", "stage", "model", "origin")}
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yield json.dumps({"messages": messages, "meta": meta}, ensure_ascii=False)
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def export_preferences(store: Store, *, cluster: str, split: str = "train") -> Iterator[str]:
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for pair in store.list_preference_pairs(cluster=cluster):
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if pair["split"] != split:
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continue
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yield json.dumps(
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{"prompt": pair["prompt"], "chosen": pair["chosen"], "rejected": pair["rejected"]},
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ensure_ascii=False,
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)
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def pass_rate(rows: list[dict[str, Any]]) -> float | None:
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decided = [r for r in rows if r.get("passed") is not None]
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if not decided:
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return None
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return sum(1 for r in decided if r["passed"]) / len(decided)
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"""Stufe 3: Entscheidungen direkt auf Embeddings (Nearest Centroid mit Konfidenz)."""
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from __future__ import annotations
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import json
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import math
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from collections import defaultdict
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from pathlib import Path
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from .embeddings import Vector, cosine
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class EmbeddingClassifier:
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def __init__(self, centroids: dict[str, Vector], counts: dict[str, int] | None = None):
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if not centroids:
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raise ValueError("Classifier braucht mindestens eine Klasse")
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self.centroids = centroids
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self.counts = counts or {}
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@classmethod
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def train(cls, examples: list[tuple[Vector, str]]) -> EmbeddingClassifier:
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sums: dict[str, Vector] = {}
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counts: dict[str, int] = defaultdict(int)
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for vec, label in examples:
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if label not in sums:
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sums[label] = [0.0] * len(vec)
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sums[label] = [a + b for a, b in zip(sums[label], vec, strict=True)]
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counts[label] += 1
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centroids = {}
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for label, total in sums.items():
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norm = math.sqrt(sum(v * v for v in total)) or 1.0
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centroids[label] = [v / norm for v in total]
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return cls(centroids, dict(counts))
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def predict(self, vec: Vector) -> tuple[str, float]:
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"""Liefert (Label, Konfidenz). Konfidenz = Softmax-Abstand der besten zwei Klassen (0..1)."""
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scores = sorted(((cosine(vec, c), label) for label, c in self.centroids.items()), reverse=True)
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best_score, best_label = scores[0]
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if len(scores) == 1:
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return best_label, max(0.0, best_score)
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exps = [math.exp(s * 20) for s, _ in scores] # Temperatur 0.05
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return best_label, exps[0] / sum(exps)
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def save(self, path: str | Path) -> None:
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Path(path).write_text(json.dumps({"centroids": self.centroids, "counts": self.counts}))
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@classmethod
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def load(cls, path: str | Path) -> EmbeddingClassifier:
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data = json.loads(Path(path).read_text())
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return cls(data["centroids"], data.get("counts"))
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@@ -0,0 +1,136 @@
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"""Embedding- und Rerank-Clients sowie Skill-Index."""
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from __future__ import annotations
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import hashlib
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import math
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import re
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from collections.abc import Sequence
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from typing import Protocol
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import httpx
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Vector = list[float]
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class Embedder(Protocol):
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def embed(self, texts: Sequence[str]) -> list[Vector]: ...
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class Reranker(Protocol):
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def rerank(self, query: str, documents: Sequence[str]) -> list[float]: ...
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def cosine(a: Sequence[float], b: Sequence[float]) -> float:
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dot = sum(x * y for x, y in zip(a, b, strict=True))
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na = math.sqrt(sum(x * x for x in a))
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nb = math.sqrt(sum(y * y for y in b))
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if na == 0 or nb == 0:
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return 0.0
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return dot / (na * nb)
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class HashingEmbedder:
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"""Deterministischer Bag-of-Words-Embedder für Tests und als Notfall-Fallback ohne GPU/Netz."""
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_WORD = re.compile(r"\w+", re.UNICODE)
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def __init__(self, dim: int = 512):
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self.dim = dim
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def _vector(self, text: str) -> Vector:
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vec = [0.0] * self.dim
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for word in self._WORD.findall(text.lower()):
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h = int.from_bytes(hashlib.sha1(word.encode()).digest()[:4], "big")
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vec[h % self.dim] += 1.0 if (h >> 31) & 1 else -1.0
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norm = math.sqrt(sum(v * v for v in vec)) or 1.0
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return [v / norm for v in vec]
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def embed(self, texts: Sequence[str]) -> list[Vector]:
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return [self._vector(t) for t in texts]
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class OpenAIEmbedder:
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"""Embeddings über ein OpenAI-kompatibles API (LiteLLM → llama.cpp/TEI)."""
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def __init__(self, base_url: str, model: str, api_key: str = "", timeout: float = 30.0,
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client: httpx.Client | None = None):
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self.base_url = base_url.rstrip("/")
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self.model = model
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self.headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
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self.client = client or httpx.Client(timeout=timeout)
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def embed(self, texts: Sequence[str]) -> list[Vector]:
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resp = self.client.post(
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f"{self.base_url}/v1/embeddings",
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json={"model": self.model, "input": list(texts)},
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headers=self.headers,
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)
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resp.raise_for_status()
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data = sorted(resp.json()["data"], key=lambda d: d["index"])
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return [d["embedding"] for d in data]
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class LlamaCppReranker:
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"""Reranking über den llama.cpp-Server (`--reranking`, Endpoint /v1/rerank)."""
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def __init__(self, base_url: str, model: str = "rerank-default", timeout: float = 30.0,
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client: httpx.Client | None = None):
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self.base_url = base_url.rstrip("/")
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self.model = model
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self.client = client or httpx.Client(timeout=timeout)
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def rerank(self, query: str, documents: Sequence[str]) -> list[float]:
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resp = self.client.post(
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f"{self.base_url}/v1/rerank",
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json={"model": self.model, "query": query, "documents": list(documents)},
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)
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resp.raise_for_status()
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scores = [0.0] * len(documents)
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for item in resp.json()["results"]:
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scores[item["index"]] = float(item["relevance_score"])
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return scores
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class SkillIndex:
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"""Hält pro Skill die Vektoren von Beschreibung und Triggern (wenige hundert Skills → im RAM)."""
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def __init__(self, embedder: Embedder):
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self.embedder = embedder
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self._cache: dict[str, Vector] = {}
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self._entries: dict[str, list[Vector]] = {}
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def _embed_cached(self, texts: list[str]) -> list[Vector]:
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missing = [t for t in texts if t not in self._cache]
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if missing:
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for text, vec in zip(missing, self.embedder.embed(missing), strict=True):
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self._cache[text] = vec
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return [self._cache[t] for t in texts]
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def rebuild(self, skills: dict[str, list[str]]) -> None:
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"""skills: name -> Texte (Beschreibung + Trigger)."""
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all_texts = sorted({t for texts in skills.values() for t in texts})
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self._embed_cached(all_texts)
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self._entries = {name: [self._cache[t] for t in texts] for name, texts in skills.items()}
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def vector(self, name: str) -> Vector:
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return self._entries[name][0]
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def search(self, query_vec: Vector, k: int = 5, only: set[str] | None = None) -> list[tuple[str, float]]:
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scored = []
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for name, vecs in self._entries.items():
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if only is not None and name not in only:
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continue
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scored.append((name, max(cosine(query_vec, v) for v in vecs)))
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scored.sort(key=lambda x: (-x[1], x[0]))
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return scored[:k]
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def pairwise_similar(self, threshold: float) -> list[tuple[str, str, float]]:
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names = sorted(self._entries)
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pairs = []
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for i, a in enumerate(names):
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for b in names[i + 1:]:
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sim = cosine(self.vector(a), self.vector(b))
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if sim >= threshold:
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pairs.append((a, b, sim))
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return pairs
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@@ -0,0 +1,97 @@
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"""Ausführung einer RouteDecision auf Stufe 0–3 und Erkennung von Laufzeit-Signalen."""
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from __future__ import annotations
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from typing import Any
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from pydantic import BaseModel, Field
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from . import programs
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from .classifier import EmbeddingClassifier
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from .embeddings import Embedder
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from .llm import ChatClient, ContextOverflowError, LLMError
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from .problems import Signal, extract_json, is_degenerate
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from .router import RouteDecision, system_prompt
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from .skills import Skill, SkillLibrary
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class ExecutionResult(BaseModel):
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ok: bool
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output: str = ""
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signals: list[Signal] = Field(default_factory=list)
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messages: list[dict[str, str]] = Field(default_factory=list)
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finish_reason: str | None = None
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usage: dict[str, Any] = Field(default_factory=dict)
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error: str | None = None
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label: str | None = None
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confidence: float | None = None
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def build_messages(skill: Skill | None, decision: RouteDecision, text: str) -> list[dict[str, str]]:
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system = system_prompt(skill, decision.prompt_variant)
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messages = []
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if system:
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messages.append({"role": "system", "content": system})
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messages.append({"role": "user", "content": text})
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return messages
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class Executor:
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def __init__(self, library: SkillLibrary, chat: ChatClient, embedder: Embedder,
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classifiers: dict[str, EmbeddingClassifier] | None = None):
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self.library = library
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self.chat = chat
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self.embedder = embedder
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self.classifiers = classifiers or {}
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def execute(self, decision: RouteDecision, text: str) -> ExecutionResult:
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if decision.status != "routed":
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return ExecutionResult(ok=False, error=f"Entscheidung nicht ausführbar: {decision.status}")
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skill = self.library.get(decision.skill) if decision.skill else None
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if decision.stage == 3:
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return self._classify(skill, text)
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if decision.stage == 2:
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return self._program(skill, text)
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return self._llm(skill, decision, text)
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def _classify(self, skill: Skill | None, text: str) -> ExecutionResult:
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assert skill and skill.meta.classifier
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clf = self.classifiers.get(skill.meta.classifier)
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if clf is None:
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return ExecutionResult(ok=False, signals=[Signal.PROGRAM_ERROR],
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error=f"Classifier {skill.meta.classifier} nicht geladen")
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label, confidence = clf.predict(self.embedder.embed([text])[0])
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if confidence < skill.meta.min_confidence:
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return ExecutionResult(ok=False, output=label, label=label, confidence=confidence,
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signals=[Signal.CLASSIFIER_LOW_CONFIDENCE])
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return ExecutionResult(ok=True, output=label, label=label, confidence=confidence)
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def _program(self, skill: Skill | None, text: str) -> ExecutionResult:
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assert skill and skill.meta.program
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try:
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output = programs.get(skill.meta.program)(text)
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except Exception as exc: # noqa: BLE001 – Programmfehler sind ein Signal, kein Absturz
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return ExecutionResult(ok=False, signals=[Signal.PROGRAM_ERROR], error=f"{type(exc).__name__}: {exc}")
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return ExecutionResult(ok=True, output=output)
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def _llm(self, skill: Skill | None, decision: RouteDecision, text: str) -> ExecutionResult:
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messages = build_messages(skill, decision, text)
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try:
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res = self.chat.chat(decision.model or "", messages, max_tokens=decision.max_output_tokens)
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except ContextOverflowError as exc:
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return ExecutionResult(ok=False, messages=messages, signals=[Signal.CONTEXT_OVERFLOW], error=str(exc))
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except LLMError as exc:
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return ExecutionResult(ok=False, messages=messages, signals=[Signal.ENDPOINT_ERROR], error=str(exc))
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signals: list[Signal] = []
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if res.finish_reason == "length":
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signals.append(Signal.OUTPUT_TRUNCATED)
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if is_degenerate(res.content):
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signals.append(Signal.DEGENERATE_OUTPUT)
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if skill is not None and skill.meta.output.format == "json":
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try:
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extract_json(res.content)
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except ValueError:
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signals.append(Signal.INVALID_FORMAT)
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return ExecutionResult(ok=not signals, output=res.content, signals=signals, messages=messages,
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finish_reason=res.finish_reason, usage=res.usage)
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@@ -0,0 +1,71 @@
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"""Chat-Client für das OpenAI-kompatible API von LiteLLM."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Protocol
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import httpx
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from .problems import is_context_overflow_message
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class LLMError(Exception):
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"""Basisklasse; `infra=True` bedeutet: Endpunkt/Netz, nicht Modellqualität."""
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infra = True
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class EndpointError(LLMError):
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pass
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class ContextOverflowError(LLMError):
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pass
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@dataclass
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class ChatResult:
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content: str
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finish_reason: str | None = None
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usage: dict[str, Any] = field(default_factory=dict)
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model: str | None = None
|
||||
|
||||
|
||||
class ChatClient(Protocol):
|
||||
def chat(self, model: str, messages: list[dict[str, str]], *, max_tokens: int | None = None,
|
||||
temperature: float | None = None) -> ChatResult: ...
|
||||
|
||||
|
||||
class OpenAIChatClient:
|
||||
def __init__(self, base_url: str, api_key: str = "", timeout: float = 120.0,
|
||||
client: httpx.Client | None = None):
|
||||
self.base_url = base_url.rstrip("/")
|
||||
self.headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
|
||||
self.client = client or httpx.Client(timeout=timeout)
|
||||
|
||||
def chat(self, model: str, messages: list[dict[str, str]], *, max_tokens: int | None = None,
|
||||
temperature: float | None = None) -> ChatResult:
|
||||
body: dict[str, Any] = {"model": model, "messages": messages}
|
||||
if max_tokens is not None:
|
||||
body["max_tokens"] = max_tokens
|
||||
if temperature is not None:
|
||||
body["temperature"] = temperature
|
||||
try:
|
||||
resp = self.client.post(f"{self.base_url}/v1/chat/completions", json=body, headers=self.headers)
|
||||
except httpx.HTTPError as exc:
|
||||
raise EndpointError(f"{type(exc).__name__}: {exc}") from exc
|
||||
if resp.status_code == 400 and is_context_overflow_message(resp.text):
|
||||
raise ContextOverflowError(resp.text[:500])
|
||||
if resp.status_code == 429 or resp.status_code >= 500:
|
||||
raise EndpointError(f"HTTP {resp.status_code}: {resp.text[:300]}")
|
||||
if resp.status_code >= 400:
|
||||
raise EndpointError(f"HTTP {resp.status_code}: {resp.text[:300]}")
|
||||
data = resp.json()
|
||||
choice = data["choices"][0]
|
||||
return ChatResult(
|
||||
content=choice["message"].get("content") or "",
|
||||
finish_reason=choice.get("finish_reason"),
|
||||
usage=data.get("usage") or {},
|
||||
model=data.get("model"),
|
||||
)
|
||||
@@ -0,0 +1,71 @@
|
||||
"""Signale (Fehlerklassen) und ihre Erkennung."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
import re
|
||||
from enum import StrEnum
|
||||
|
||||
|
||||
class Signal(StrEnum):
|
||||
NO_SKILL = "no_skill"
|
||||
SKILL_CONFLICT = "skill_conflict"
|
||||
SLOT_UNAVAILABLE = "slot_unavailable"
|
||||
CONTEXT_EXCEEDED = "context_exceeded" # präventiv vom Router erkannt
|
||||
COMPACT_PROMPT = "compact_prompt" # Ausweichen auf kompakte Skill-Variante
|
||||
LARGER_SLOT = "larger_slot" # Ausweichen auf Slot mit mehr Kontext
|
||||
NEEDS_SPLIT = "needs_split"
|
||||
OUTPUT_TRUNCATED = "output_truncated"
|
||||
DEGENERATE_OUTPUT = "degenerate_output"
|
||||
INVALID_FORMAT = "invalid_format"
|
||||
CONTEXT_OVERFLOW = "context_overflow" # Engine meldet Overflow
|
||||
ENDPOINT_ERROR = "endpoint_error"
|
||||
VERIFIER_FAILED = "verifier_failed"
|
||||
CLASSIFIER_LOW_CONFIDENCE = "classifier_low_confidence"
|
||||
PROGRAM_ERROR = "program_error"
|
||||
MISROUTE_SUSPECTED = "misroute_suspected"
|
||||
|
||||
|
||||
INFRA_SIGNALS = {Signal.ENDPOINT_ERROR, Signal.CONTEXT_OVERFLOW}
|
||||
QUALITY_SIGNALS = {
|
||||
Signal.VERIFIER_FAILED,
|
||||
Signal.INVALID_FORMAT,
|
||||
Signal.DEGENERATE_OUTPUT,
|
||||
Signal.OUTPUT_TRUNCATED,
|
||||
Signal.CLASSIFIER_LOW_CONFIDENCE,
|
||||
Signal.PROGRAM_ERROR,
|
||||
}
|
||||
|
||||
_OVERFLOW_PATTERNS = re.compile(
|
||||
r"context length|maximum context|too many tokens|context window|prompt is too long", re.IGNORECASE
|
||||
)
|
||||
_WORD = re.compile(r"\w+", re.UNICODE)
|
||||
|
||||
|
||||
def is_context_overflow_message(text: str) -> bool:
|
||||
return bool(_OVERFLOW_PATTERNS.search(text or ""))
|
||||
|
||||
|
||||
def repetition_ratio(text: str, n: int = 4) -> float:
|
||||
"""Anteil der n-Gramme, die Wiederholungen sind (0 = keine)."""
|
||||
words = _WORD.findall(text.lower())
|
||||
if len(words) < n:
|
||||
return 0.0
|
||||
grams = [tuple(words[i:i + n]) for i in range(len(words) - n + 1)]
|
||||
return 1.0 - len(set(grams)) / len(grams)
|
||||
|
||||
|
||||
def is_degenerate(text: str, min_words: int = 40, max_ratio: float = 0.5) -> bool:
|
||||
return len(_WORD.findall(text)) >= min_words and repetition_ratio(text) > max_ratio
|
||||
|
||||
|
||||
def extract_json(text: str) -> object:
|
||||
"""Parst JSON, toleriert ```json-Codeblöcke. Wirft ValueError bei ungültigem JSON."""
|
||||
stripped = text.strip()
|
||||
fence = re.match(r"^```(?:json)?\s*\n(.*?)\n```\s*$", stripped, re.DOTALL)
|
||||
if fence:
|
||||
stripped = fence.group(1)
|
||||
try:
|
||||
return json.loads(stripped)
|
||||
except json.JSONDecodeError as exc:
|
||||
raise ValueError(str(exc)) from exc
|
||||
@@ -0,0 +1,33 @@
|
||||
"""Stufe 2: registrierte, geprüfte Programme (vom starken LLM erzeugt, vom Menschen freigegeben)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from collections.abc import Callable
|
||||
|
||||
Program = Callable[[str], str]
|
||||
|
||||
_REGISTRY: dict[str, Program] = {}
|
||||
|
||||
|
||||
def register(name: str) -> Callable[[Program], Program]:
|
||||
def deco(fn: Program) -> Program:
|
||||
if name in _REGISTRY:
|
||||
raise ValueError(f"Programm {name} bereits registriert")
|
||||
_REGISTRY[name] = fn
|
||||
return fn
|
||||
|
||||
return deco
|
||||
|
||||
|
||||
def get(name: str) -> Program:
|
||||
return _REGISTRY[name]
|
||||
|
||||
|
||||
def names() -> list[str]:
|
||||
return sorted(_REGISTRY)
|
||||
|
||||
|
||||
@register("echo-upper")
|
||||
def _echo_upper(text: str) -> str:
|
||||
"""Beispielprogramm für Tests."""
|
||||
return text.upper()
|
||||
@@ -0,0 +1,172 @@
|
||||
"""Registry: deklarative Slots/Adapter (YAML) plus Laufzeitzustand (Store)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from pathlib import Path
|
||||
from typing import Any, Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from .store import Store
|
||||
from .util import load_yaml
|
||||
|
||||
SlotStatus = Literal["ok", "degraded", "unavailable"]
|
||||
|
||||
|
||||
class SlotSpec(BaseModel):
|
||||
name: str
|
||||
tier: int
|
||||
device: Literal["gpu", "cpu", "external"] = "gpu"
|
||||
engine: str = "vllm"
|
||||
node: str | None = None
|
||||
litellm_model: str
|
||||
base_model: str
|
||||
max_context: int
|
||||
effective_context: int | None = None
|
||||
supports_lora: bool = False
|
||||
fallbacks: list[str] = Field(default_factory=list)
|
||||
kind: Literal["chat", "embedding", "rerank"] = "chat"
|
||||
|
||||
|
||||
class AdapterSpec(BaseModel):
|
||||
name: str
|
||||
base_model: str
|
||||
cluster: str
|
||||
version: int = 1
|
||||
status: Literal["active", "candidate", "retired"] = "active"
|
||||
|
||||
|
||||
class Slot(BaseModel):
|
||||
"""Effektive Sicht: Spezifikation + Laufzeitzustand."""
|
||||
|
||||
spec: SlotSpec
|
||||
status: SlotStatus = "ok"
|
||||
reason: str | None = None
|
||||
set_by: str | None = None
|
||||
calibrated_context: int | None = None
|
||||
base_model_override: str | None = None
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
return self.spec.name
|
||||
|
||||
@property
|
||||
def base_model(self) -> str:
|
||||
return self.base_model_override or self.spec.base_model
|
||||
|
||||
@property
|
||||
def effective_context(self) -> int:
|
||||
for value in (self.calibrated_context, self.spec.effective_context):
|
||||
if value:
|
||||
return min(value, self.spec.max_context)
|
||||
return self.spec.max_context
|
||||
|
||||
@property
|
||||
def available(self) -> bool:
|
||||
return self.status == "ok"
|
||||
|
||||
|
||||
class Registry:
|
||||
def __init__(self, slots: dict[str, SlotSpec], adapters: list[AdapterSpec], store: Store):
|
||||
self._slots = slots
|
||||
self._adapters = {a.name: a for a in adapters}
|
||||
self.store = store
|
||||
|
||||
@classmethod
|
||||
def from_file(cls, path: str | Path, store: Store) -> Registry:
|
||||
data = load_yaml(path)
|
||||
return cls.from_dict(data, store)
|
||||
|
||||
@classmethod
|
||||
def from_dict(cls, data: dict[str, Any], store: Store) -> Registry:
|
||||
slots = {name: SlotSpec(name=name, **spec) for name, spec in (data.get("slots") or {}).items()}
|
||||
adapters = [AdapterSpec(**a) for a in data.get("adapters") or []]
|
||||
for spec in slots.values():
|
||||
for fb in spec.fallbacks:
|
||||
if fb not in slots:
|
||||
raise ValueError(f"Slot {spec.name}: unbekannter Fallback {fb}")
|
||||
return cls(slots, adapters, store)
|
||||
|
||||
# --- Slots ------------------------------------------------------------------------------
|
||||
def slot_names(self) -> list[str]:
|
||||
return list(self._slots)
|
||||
|
||||
def has_slot(self, name: str) -> bool:
|
||||
return name in self._slots
|
||||
|
||||
def slot(self, name: str) -> Slot:
|
||||
spec = self._slots[name]
|
||||
state = self.store.get_slot_state(name) or {}
|
||||
return Slot(
|
||||
spec=spec,
|
||||
status=state.get("status") or "ok",
|
||||
reason=state.get("reason"),
|
||||
set_by=state.get("set_by"),
|
||||
calibrated_context=state.get("effective_context"),
|
||||
base_model_override=state.get("base_model"),
|
||||
)
|
||||
|
||||
def slots(self) -> list[Slot]:
|
||||
return [self.slot(n) for n in self._slots]
|
||||
|
||||
def strong_slot(self) -> Slot:
|
||||
chat = [s for s in self.slots() if s.spec.kind == "chat" and s.spec.tier == 0]
|
||||
if not chat:
|
||||
raise LookupError("Kein Slot mit tier 0 konfiguriert")
|
||||
available = [s for s in chat if s.available]
|
||||
return (available or chat)[0]
|
||||
|
||||
def set_status(self, name: str, status: SlotStatus, reason: str = "", set_by: str = "human") -> None:
|
||||
if name not in self._slots:
|
||||
raise KeyError(name)
|
||||
self.store.set_slot_state(name, status=status, reason=reason, set_by=set_by)
|
||||
|
||||
def set_calibrated_context(self, name: str, tokens: int | None) -> None:
|
||||
self.store.set_slot_state(name, effective_context=tokens)
|
||||
|
||||
def set_base_model(self, name: str, base_model: str | None) -> None:
|
||||
self.store.set_slot_state(name, base_model=base_model)
|
||||
|
||||
# --- Adapter ----------------------------------------------------------------------------
|
||||
def adapters(self) -> list[AdapterSpec]:
|
||||
merged = {n: a.model_copy() for n, a in self._adapters.items()}
|
||||
for row in self.store.all_adapter_states():
|
||||
if row["name"] in merged:
|
||||
if row.get("status"):
|
||||
merged[row["name"]].status = row["status"]
|
||||
else:
|
||||
merged[row["name"]] = AdapterSpec(
|
||||
name=row["name"],
|
||||
base_model=row["base_model"],
|
||||
cluster=row["cluster"],
|
||||
version=row.get("version") or 1,
|
||||
status=row.get("status") or "candidate",
|
||||
)
|
||||
return list(merged.values())
|
||||
|
||||
def active_adapter(self, base_model: str, cluster: str) -> AdapterSpec | None:
|
||||
found = [
|
||||
a for a in self.adapters()
|
||||
if a.base_model == base_model and a.cluster == cluster and a.status == "active"
|
||||
]
|
||||
return max(found, key=lambda a: a.version) if found else None
|
||||
|
||||
def set_adapter(self, adapter: AdapterSpec) -> None:
|
||||
self.store.set_adapter_state(
|
||||
adapter.name,
|
||||
base_model=adapter.base_model,
|
||||
cluster=adapter.cluster,
|
||||
version=adapter.version,
|
||||
status=adapter.status,
|
||||
)
|
||||
|
||||
def export(self) -> dict[str, Any]:
|
||||
"""Effektiver Stand als YAML-fähiges Dict (für Rückführung nach Git)."""
|
||||
slots = {}
|
||||
for s in self.slots():
|
||||
data = s.spec.model_dump(exclude={"name"})
|
||||
data["base_model"] = s.base_model
|
||||
if s.calibrated_context:
|
||||
data["effective_context"] = s.calibrated_context
|
||||
slots[s.name] = data
|
||||
return {"slots": slots, "adapters": [a.model_dump() for a in self.adapters()]}
|
||||
@@ -0,0 +1,276 @@
|
||||
"""Router: Anfrage → Skill → Stufe → Slot/Modell (+Adapter), inkl. Token-Budget und Eskalation."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Literal
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from . import budget as budget_mod
|
||||
from .embeddings import Embedder, Reranker, SkillIndex
|
||||
from .problems import INFRA_SIGNALS, Signal
|
||||
from .registry import Registry, Slot
|
||||
from .skills import Skill, SkillLibrary
|
||||
|
||||
PromptVariant = Literal["full", "compact", "none"]
|
||||
|
||||
FORMAT_HINT_JSON = "Antworte ausschließlich mit gültigem JSON ohne weiteren Text."
|
||||
|
||||
|
||||
class RouteDecision(BaseModel):
|
||||
status: Literal["routed", "failed", "too_large"] = "routed"
|
||||
stage: int = 0
|
||||
slot: str | None = None
|
||||
model: str | None = None
|
||||
skill: str | None = None
|
||||
skill_version: int | None = None
|
||||
adapter: str | None = None
|
||||
prompt_variant: PromptVariant = "none"
|
||||
similarity: float | None = None
|
||||
estimated_tokens: int | None = None
|
||||
max_output_tokens: int | None = None
|
||||
attempt: int = 0
|
||||
tried_slots: list[str] = Field(default_factory=list)
|
||||
reasons: list[str] = Field(default_factory=list)
|
||||
signals: list[Signal] = Field(default_factory=list)
|
||||
|
||||
|
||||
@dataclass
|
||||
class RouterConfig:
|
||||
top_k: int = 5
|
||||
conflict_margin: float = 0.02
|
||||
max_escalations: int = 3
|
||||
default_max_output_tokens: int = 1024
|
||||
|
||||
|
||||
def system_prompt(skill: Skill | None, variant: PromptVariant) -> str:
|
||||
parts: list[str] = []
|
||||
if skill is not None and variant != "none":
|
||||
parts.append(skill.prompt(variant))
|
||||
if skill is not None and skill.meta.output.format == "json":
|
||||
parts.append(FORMAT_HINT_JSON)
|
||||
return "\n\n".join(parts)
|
||||
|
||||
|
||||
class Router:
|
||||
def __init__(
|
||||
self,
|
||||
registry: Registry,
|
||||
library: SkillLibrary,
|
||||
embedder: Embedder,
|
||||
reranker: Reranker | None = None,
|
||||
tokenizer: budget_mod.Tokenizer = budget_mod.estimate_tokens,
|
||||
config: RouterConfig | None = None,
|
||||
):
|
||||
self.registry = registry
|
||||
self.library = library
|
||||
self.embedder = embedder
|
||||
self.reranker = reranker
|
||||
self.tokenizer = tokenizer
|
||||
self.config = config or RouterConfig()
|
||||
self.index = SkillIndex(embedder)
|
||||
self.refresh()
|
||||
|
||||
def refresh(self) -> None:
|
||||
"""Index neu aufbauen (nach Skill-Änderungen)."""
|
||||
self.index.rebuild({s.name: s.index_texts() for s in self.library.all()})
|
||||
|
||||
# --- Routing ----------------------------------------------------------------------------
|
||||
def route(self, text: str, hint_skill: str | None = None) -> RouteDecision:
|
||||
reasons: list[str] = []
|
||||
signals: list[Signal] = []
|
||||
routable = {s.name for s in self.library.routable()}
|
||||
|
||||
skill: Skill | None = None
|
||||
similarity: float | None = None
|
||||
if hint_skill:
|
||||
if hint_skill not in routable:
|
||||
raise KeyError(f"Skill {hint_skill} unbekannt oder nicht routbar")
|
||||
skill = self.library.get(hint_skill)
|
||||
reasons.append(f"Skill per Hinweis: {hint_skill}")
|
||||
else:
|
||||
skill, similarity = self._match(text, routable, reasons, signals)
|
||||
|
||||
if skill is None:
|
||||
return self._plan_strong(text, None, reasons, signals, similarity=similarity)
|
||||
|
||||
meta = skill.meta
|
||||
if meta.status == "demoted" or meta.stage == 0:
|
||||
reasons.append(f"Skill {skill.name} ist auf Stufe 0 ({meta.status})")
|
||||
return self._plan_strong(text, skill, reasons, signals, similarity=similarity)
|
||||
|
||||
if meta.execution == "classifier" and meta.stage >= 3 and meta.classifier:
|
||||
return self._decision(stage=3, skill=skill, similarity=similarity, reasons=reasons,
|
||||
signals=signals)
|
||||
if meta.execution == "program" and meta.stage >= 2 and meta.program:
|
||||
return self._decision(stage=2, skill=skill, similarity=similarity, reasons=reasons,
|
||||
signals=signals)
|
||||
return self._plan_weak(text, skill, reasons, signals, similarity=similarity)
|
||||
|
||||
def _match(self, text: str, routable: set[str], reasons: list[str],
|
||||
signals: list[Signal]) -> tuple[Skill | None, float | None]:
|
||||
if not routable:
|
||||
reasons.append("Keine routbaren Skills")
|
||||
signals.append(Signal.NO_SKILL)
|
||||
return None, None
|
||||
qvec = self.embedder.embed([text])[0]
|
||||
hits = self.index.search(qvec, k=self.config.top_k, only=routable)
|
||||
if not hits:
|
||||
signals.append(Signal.NO_SKILL)
|
||||
return None, None
|
||||
similarity_of = dict(hits)
|
||||
|
||||
ordered = hits
|
||||
if self.reranker is not None and len(hits) > 1:
|
||||
docs = [self.library.get(name).description for name, _ in hits]
|
||||
scores = self.reranker.rerank(text, docs)
|
||||
ordered = sorted(zip([h[0] for h in hits], scores, strict=True), key=lambda x: -x[1])
|
||||
reasons.append("Reranking aktiv")
|
||||
|
||||
best_name = ordered[0][0]
|
||||
best_sim = similarity_of[best_name]
|
||||
skill = self.library.get(best_name)
|
||||
if best_sim < skill.meta.threshold:
|
||||
reasons.append(f"Beste Ähnlichkeit {best_sim:.3f} < Schwelle {skill.meta.threshold} ({best_name})")
|
||||
signals.append(Signal.NO_SKILL)
|
||||
return None, best_sim
|
||||
|
||||
if len(hits) > 1:
|
||||
second_name, second_sim = hits[1] if hits[0][0] == best_name else hits[0]
|
||||
if abs(best_sim - second_sim) <= self.config.conflict_margin:
|
||||
signals.append(Signal.SKILL_CONFLICT)
|
||||
reasons.append(f"Konflikt {best_name} ({best_sim:.3f}) vs. {second_name} ({second_sim:.3f})")
|
||||
if self.reranker is None:
|
||||
return None, best_sim
|
||||
reasons.append(f"Skill {best_name} (Ähnlichkeit {best_sim:.3f})")
|
||||
return skill, best_sim
|
||||
|
||||
# --- Planung ----------------------------------------------------------------------------
|
||||
def _decision(self, **kw) -> RouteDecision:
|
||||
skill: Skill | None = kw.pop("skill", None)
|
||||
if skill is not None:
|
||||
kw.setdefault("skill", skill.name)
|
||||
kw.setdefault("skill_version", skill.meta.version)
|
||||
kw.setdefault("max_output_tokens", skill.meta.max_output_tokens)
|
||||
return RouteDecision(**kw)
|
||||
|
||||
def _fit(self, text: str, skill: Skill | None, slots: list[Slot]
|
||||
) -> tuple[Slot, PromptVariant, budget_mod.Budget] | None:
|
||||
max_out = skill.meta.max_output_tokens if skill else self.config.default_max_output_tokens
|
||||
variants: list[PromptVariant] = ["full", "compact"] if skill else ["none"]
|
||||
for slot in slots:
|
||||
for variant in variants:
|
||||
b = budget_mod.compute(system_prompt(skill, variant), text, max_out,
|
||||
slot.effective_context, self.tokenizer)
|
||||
if b.fits:
|
||||
return slot, variant, b
|
||||
return None
|
||||
|
||||
def _weak_slots(self, skill: Skill, exclude: set[str]) -> tuple[list[Slot], bool]:
|
||||
names = [skill.meta.slot, *self._fallbacks(skill.meta.slot)]
|
||||
slots = [self.registry.slot(n) for n in names if self.registry.has_slot(n) and n not in exclude]
|
||||
primary_ok = bool(slots) and slots[0].name == skill.meta.slot and slots[0].available
|
||||
return [s for s in slots if s.available], primary_ok
|
||||
|
||||
def _fallbacks(self, name: str) -> list[str]:
|
||||
if not self.registry.has_slot(name):
|
||||
return []
|
||||
return list(self.registry.slot(name).spec.fallbacks)
|
||||
|
||||
def _plan_weak(self, text: str, skill: Skill, reasons: list[str], signals: list[Signal], *,
|
||||
similarity: float | None, attempt: int = 0, tried: list[str] | None = None
|
||||
) -> RouteDecision:
|
||||
tried = list(tried or [])
|
||||
slots, primary_ok = self._weak_slots(skill, set(tried))
|
||||
if not primary_ok and skill.meta.slot not in tried:
|
||||
signals.append(Signal.SLOT_UNAVAILABLE)
|
||||
reasons.append(f"Slot {skill.meta.slot} nicht verfügbar")
|
||||
if not slots:
|
||||
reasons.append("Kein verfügbarer Slot für Stufe 1")
|
||||
return self._plan_strong(text, skill, reasons, signals, similarity=similarity,
|
||||
attempt=attempt, tried=tried)
|
||||
fit = self._fit(text, skill, slots)
|
||||
if fit is None:
|
||||
signals.append(Signal.CONTEXT_EXCEEDED)
|
||||
reasons.append("Token-Budget passt in keinen Stufe-1-Slot")
|
||||
return self._plan_strong(text, skill, reasons, signals, similarity=similarity,
|
||||
attempt=attempt, tried=tried)
|
||||
slot, variant, b = fit
|
||||
if variant == "compact":
|
||||
signals.append(Signal.COMPACT_PROMPT)
|
||||
if slot.name != slots[0].name:
|
||||
signals.append(Signal.LARGER_SLOT)
|
||||
reasons.append(f"Ausweichen auf {slot.name} wegen Kontextlänge")
|
||||
|
||||
model, adapter = slot.spec.litellm_model, None
|
||||
if slot.spec.supports_lora and skill.meta.adapter == "auto":
|
||||
found = self.registry.active_adapter(slot.base_model, skill.cluster)
|
||||
if found:
|
||||
model, adapter = found.name, found.name
|
||||
reasons.append(f"Adapter {found.name}")
|
||||
return self._decision(stage=1, slot=slot.name, model=model, adapter=adapter, skill=skill,
|
||||
prompt_variant=variant, similarity=similarity,
|
||||
estimated_tokens=b.prompt_tokens, attempt=attempt,
|
||||
tried_slots=tried + [slot.name], reasons=reasons, signals=signals)
|
||||
|
||||
def _plan_strong(self, text: str, skill: Skill | None, reasons: list[str], signals: list[Signal], *,
|
||||
similarity: float | None = None, attempt: int = 0, tried: list[str] | None = None
|
||||
) -> RouteDecision:
|
||||
tried = list(tried or [])
|
||||
strong = [s for s in self.registry.slots()
|
||||
if s.spec.kind == "chat" and s.spec.tier == 0 and s.name not in tried]
|
||||
available = [s for s in strong if s.available] or strong
|
||||
if not available:
|
||||
reasons.append("Kein Stufe-0-Slot mehr übrig")
|
||||
return self._decision(status="failed", stage=0, skill=skill, similarity=similarity,
|
||||
attempt=attempt, tried_slots=tried, reasons=reasons, signals=signals)
|
||||
fit = self._fit(text, skill, available)
|
||||
if fit is None and skill is not None:
|
||||
# notfalls ohne Skill-Text
|
||||
fit = self._fit(text, None, available)
|
||||
if fit is None:
|
||||
signals.append(Signal.NEEDS_SPLIT)
|
||||
reasons.append("Eingabe passt in keinen Slot – Aufgabe muss geteilt werden")
|
||||
return self._decision(status="too_large", stage=0, skill=skill, similarity=similarity,
|
||||
attempt=attempt, tried_slots=tried, reasons=reasons, signals=signals)
|
||||
slot, variant, b = fit
|
||||
return self._decision(stage=0, slot=slot.name, model=slot.spec.litellm_model, skill=skill,
|
||||
prompt_variant=variant, similarity=similarity,
|
||||
estimated_tokens=b.prompt_tokens, attempt=attempt,
|
||||
tried_slots=tried + [slot.name], reasons=reasons, signals=signals)
|
||||
|
||||
# --- Eskalation -------------------------------------------------------------------------
|
||||
def escalate(self, decision: RouteDecision, signal: Signal, text: str) -> RouteDecision:
|
||||
attempt = decision.attempt + 1
|
||||
reasons = [f"Eskalation {attempt} wegen {signal.value} (Stufe {decision.stage})"]
|
||||
signals: list[Signal] = [signal]
|
||||
skill = self.library.get(decision.skill) if decision.skill and self.library.has(decision.skill) else None
|
||||
|
||||
if attempt > self.config.max_escalations:
|
||||
reasons.append("Maximale Eskalationen erreicht")
|
||||
return self._decision(status="failed", stage=decision.stage, skill=skill, attempt=attempt,
|
||||
tried_slots=decision.tried_slots, reasons=reasons, signals=signals)
|
||||
|
||||
infra = signal in INFRA_SIGNALS
|
||||
if decision.stage == 0:
|
||||
if infra:
|
||||
return self._plan_strong(text, skill, reasons, signals, similarity=decision.similarity,
|
||||
attempt=attempt, tried=decision.tried_slots)
|
||||
reasons.append("Stufe 0 bereits erreicht")
|
||||
return self._decision(status="failed", stage=0, skill=skill, attempt=attempt,
|
||||
tried_slots=decision.tried_slots, reasons=reasons, signals=signals)
|
||||
|
||||
if decision.stage == 1 and skill is not None:
|
||||
if infra:
|
||||
return self._plan_weak(text, skill, reasons, signals, similarity=decision.similarity,
|
||||
attempt=attempt, tried=decision.tried_slots)
|
||||
return self._plan_strong(text, skill, reasons, signals, similarity=decision.similarity,
|
||||
attempt=attempt)
|
||||
|
||||
# Stufe 2/3 → Stufe 1 (falls erlaubt) oder 0
|
||||
if skill is not None and skill.meta.llm_fallback:
|
||||
return self._plan_weak(text, skill, reasons, signals, similarity=decision.similarity,
|
||||
attempt=attempt)
|
||||
return self._plan_strong(text, skill, reasons, signals, similarity=decision.similarity,
|
||||
attempt=attempt)
|
||||
@@ -0,0 +1,157 @@
|
||||
"""Die komplette Schleife: Route → Ausführung → Verifier → Eskalation → Fall/Präferenzpaar."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from .cases import split_for
|
||||
from .executor import ExecutionResult, Executor
|
||||
from .problems import Signal
|
||||
from .router import RouteDecision, Router
|
||||
from .skills import SkillLibrary
|
||||
from .store import Store, new_id
|
||||
from .verifier import Verifier, VerifyResult
|
||||
|
||||
PREVENTIVE_EVENT_SIGNALS = {
|
||||
Signal.CONTEXT_EXCEEDED, Signal.COMPACT_PROMPT, Signal.LARGER_SLOT, Signal.NEEDS_SPLIT,
|
||||
Signal.SKILL_CONFLICT, Signal.SLOT_UNAVAILABLE,
|
||||
}
|
||||
|
||||
|
||||
class Attempt(BaseModel):
|
||||
decision: RouteDecision
|
||||
execution: ExecutionResult | None = None
|
||||
verification: VerifyResult | None = None
|
||||
|
||||
|
||||
class RunResult(BaseModel):
|
||||
run_id: str
|
||||
status: str # ok | failed | too_large
|
||||
output: str | None = None
|
||||
stage: int | None = None
|
||||
skill: str | None = None
|
||||
attempts: list[Attempt] = Field(default_factory=list)
|
||||
|
||||
@property
|
||||
def escalations(self) -> int:
|
||||
return max(0, len(self.attempts) - 1)
|
||||
|
||||
|
||||
class Runner:
|
||||
def __init__(self, router: Router, executor: Executor, verifier: Verifier, library: SkillLibrary,
|
||||
store: Store, metrics: Any | None = None):
|
||||
self.router = router
|
||||
self.executor = executor
|
||||
self.verifier = verifier
|
||||
self.library = library
|
||||
self.store = store
|
||||
self.metrics = metrics
|
||||
|
||||
def run(self, text: str, hint_skill: str | None = None) -> RunResult:
|
||||
run_id = new_id()
|
||||
decision = self.router.route(text, hint_skill=hint_skill)
|
||||
attempts: list[Attempt] = []
|
||||
failed_weak: list[tuple[Attempt, ExecutionResult]] = []
|
||||
|
||||
while True:
|
||||
self._events(decision)
|
||||
attempt = Attempt(decision=decision)
|
||||
attempts.append(attempt)
|
||||
if decision.status != "routed":
|
||||
return self._finish(run_id, decision.status, attempts, None)
|
||||
|
||||
execution = self.executor.execute(decision, text)
|
||||
attempt.execution = execution
|
||||
skill = self.library.get(decision.skill) if decision.skill else None
|
||||
|
||||
signal: Signal | None = None
|
||||
if execution.signals:
|
||||
signal = execution.signals[0]
|
||||
for s in execution.signals:
|
||||
self.store.add_event(s.value, subject=decision.slot or decision.skill)
|
||||
else:
|
||||
verification = self.verifier.verify(skill, text, execution.output)
|
||||
attempt.verification = verification
|
||||
if not verification.passed:
|
||||
signal = Signal.VERIFIER_FAILED
|
||||
if decision.similarity is not None and decision.stage >= 1:
|
||||
self.store.add_event(Signal.MISROUTE_SUSPECTED.value, subject=decision.skill)
|
||||
|
||||
infra = signal in {Signal.ENDPOINT_ERROR, Signal.CONTEXT_OVERFLOW}
|
||||
if not infra:
|
||||
self._record_case(run_id, text, decision, attempt, skill)
|
||||
|
||||
if signal is None:
|
||||
self._record_pairs(run_id, text, attempt, failed_weak, skill)
|
||||
return self._finish(run_id, "ok", attempts, attempt)
|
||||
if decision.stage == 1 and not infra and execution.output:
|
||||
failed_weak.append((attempt, execution))
|
||||
decision = self.router.escalate(decision, signal, text)
|
||||
|
||||
# --- Hilfen -----------------------------------------------------------------------------
|
||||
def _events(self, decision: RouteDecision) -> None:
|
||||
for s in decision.signals:
|
||||
if s in PREVENTIVE_EVENT_SIGNALS:
|
||||
self.store.add_event(s.value, subject=decision.slot or decision.skill)
|
||||
|
||||
def _record_case(self, run_id: str, text: str, decision: RouteDecision, attempt: Attempt,
|
||||
skill: Any) -> None:
|
||||
execution = attempt.execution
|
||||
assert execution is not None
|
||||
verification = attempt.verification
|
||||
if verification is None:
|
||||
passed: bool | None = False
|
||||
elif verification.inconclusive:
|
||||
passed = None # nicht unabhängig geprüft → nicht ins Training
|
||||
else:
|
||||
passed = verification.passed
|
||||
self.store.add_case(
|
||||
run_id=run_id,
|
||||
input=text,
|
||||
skill=decision.skill,
|
||||
skill_version=decision.skill_version,
|
||||
cluster=skill.cluster if skill else None,
|
||||
stage=decision.stage,
|
||||
slot=decision.slot,
|
||||
model=decision.model,
|
||||
adapter=decision.adapter,
|
||||
messages=execution.messages or [{"role": "user", "content": text}],
|
||||
output=execution.output,
|
||||
passed=passed,
|
||||
signals=[s.value for s in execution.signals],
|
||||
prompt_tokens=decision.estimated_tokens,
|
||||
split=split_for(text),
|
||||
origin="route",
|
||||
)
|
||||
|
||||
def _record_pairs(self, run_id: str, text: str, winner: Attempt,
|
||||
failed: list[tuple[Attempt, ExecutionResult]], skill: Any) -> None:
|
||||
if skill is None or not failed or winner.verification is None or winner.verification.inconclusive:
|
||||
return
|
||||
for _loser, execution in failed:
|
||||
# Prompt im Format der schwachen Stufe: genau das soll das schwache Modell lernen
|
||||
prompt = execution.messages or [{"role": "user", "content": text}]
|
||||
self.store.add_preference_pair(
|
||||
run_id=run_id,
|
||||
skill=skill.name,
|
||||
cluster=skill.cluster,
|
||||
prompt=prompt,
|
||||
chosen=winner.execution.output if winner.execution else "",
|
||||
rejected=execution.output,
|
||||
split=split_for(text),
|
||||
)
|
||||
|
||||
def _finish(self, run_id: str, status: str, attempts: list[Attempt], last: Attempt | None) -> RunResult:
|
||||
result = RunResult(
|
||||
run_id=run_id,
|
||||
status=status,
|
||||
output=last.execution.output if last and last.execution else None,
|
||||
stage=last.decision.stage if last else attempts[-1].decision.stage,
|
||||
skill=attempts[0].decision.skill,
|
||||
attempts=attempts,
|
||||
)
|
||||
if self.metrics is not None:
|
||||
self.metrics.observe_run(result)
|
||||
return result
|
||||
@@ -0,0 +1,64 @@
|
||||
"""Konfiguration aus Umgebungsvariablen."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
|
||||
REPO_ROOT = Path(__file__).resolve().parents[3]
|
||||
|
||||
|
||||
def _bool(value: str | None, default: bool) -> bool:
|
||||
if value is None:
|
||||
return default
|
||||
return value.strip().lower() in {"1", "true", "yes", "ja", "on"}
|
||||
|
||||
|
||||
@dataclass
|
||||
class Settings:
|
||||
api_token: str = ""
|
||||
db_url: str = "sqlite:///data/knaster.db"
|
||||
site_dir: Path = field(default_factory=lambda: REPO_ROOT / "sites" / "knaster")
|
||||
skills_dir: Path = field(default_factory=lambda: REPO_ROOT / "skills")
|
||||
config_dir: Path = field(default_factory=lambda: REPO_ROOT / "config")
|
||||
dry_run: bool = True
|
||||
litellm_base_url: str = "http://litellm:4000"
|
||||
litellm_api_key: str = ""
|
||||
embed_model: str = "embed-default"
|
||||
rerank_url: str = ""
|
||||
rerank_model: str = "rerank-default"
|
||||
portainer_host: str = ""
|
||||
portainer_key: str = ""
|
||||
proxmox_url: str = ""
|
||||
proxmox_token_id: str = ""
|
||||
proxmox_token_secret: str = ""
|
||||
notify_webhook_url: str = ""
|
||||
job_webhook_url: str = ""
|
||||
request_timeout: float = 120.0
|
||||
|
||||
@classmethod
|
||||
def from_env(cls) -> Settings:
|
||||
env = os.environ
|
||||
base = cls()
|
||||
return cls(
|
||||
api_token=env.get("KNASTER_API_TOKEN", base.api_token),
|
||||
db_url=env.get("KNASTER_DB_URL", base.db_url),
|
||||
site_dir=Path(env.get("KNASTER_SITE_DIR", base.site_dir)),
|
||||
skills_dir=Path(env.get("KNASTER_SKILLS_DIR", base.skills_dir)),
|
||||
config_dir=Path(env.get("KNASTER_CONFIG_DIR", base.config_dir)),
|
||||
dry_run=_bool(env.get("KNASTER_DRY_RUN"), True),
|
||||
litellm_base_url=env.get("LITELLM_BASE_URL", base.litellm_base_url),
|
||||
litellm_api_key=env.get("LITELLM_API_KEY", base.litellm_api_key),
|
||||
embed_model=env.get("KNASTER_EMBED_MODEL", base.embed_model),
|
||||
rerank_url=env.get("RERANK_URL", base.rerank_url),
|
||||
rerank_model=env.get("KNASTER_RERANK_MODEL", base.rerank_model),
|
||||
portainer_host=env.get("PORTAINER_HOST", ""),
|
||||
portainer_key=env.get("PORTAINER_KEY", ""),
|
||||
proxmox_url=env.get("PROXMOX_URL", ""),
|
||||
proxmox_token_id=env.get("PROXMOX_TOKEN_ID", ""),
|
||||
proxmox_token_secret=env.get("PROXMOX_TOKEN_SECRET", ""),
|
||||
notify_webhook_url=env.get("NOTIFY_WEBHOOK_URL", ""),
|
||||
job_webhook_url=env.get("KNASTER_JOB_WEBHOOK_URL", ""),
|
||||
request_timeout=float(env.get("KNASTER_REQUEST_TIMEOUT", base.request_timeout)),
|
||||
)
|
||||
@@ -0,0 +1,161 @@
|
||||
"""SKILL.md-Bibliothek (agentskills-/Hermes-kompatibel, Zusatzfelder unter metadata.knaster)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from pathlib import Path
|
||||
from typing import Any, Literal
|
||||
|
||||
import yaml
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from .store import Store
|
||||
|
||||
_FRONTMATTER = re.compile(r"^---\s*\n(.*?)\n---\s*\n?(.*)$", re.DOTALL)
|
||||
_OPTIONAL_HEADING = re.compile(r"^##\s+Beispiele\b", re.IGNORECASE)
|
||||
|
||||
SkillStatus = Literal["draft", "active", "demoted", "retired"]
|
||||
|
||||
|
||||
class OutputSpec(BaseModel):
|
||||
format: Literal["text", "json"] = "text"
|
||||
schema_: dict[str, Any] | None = Field(default=None, alias="schema")
|
||||
|
||||
model_config = {"populate_by_name": True}
|
||||
|
||||
|
||||
class KnasterMeta(BaseModel):
|
||||
version: int = 1
|
||||
status: SkillStatus = "draft"
|
||||
stage: int = Field(default=0, ge=0, le=3)
|
||||
execution: Literal["llm", "program", "classifier"] = "llm"
|
||||
llm_fallback: bool = True
|
||||
slot: str = "tier1-general"
|
||||
cluster: str | None = None
|
||||
adapter: Literal["auto", "none"] = "auto"
|
||||
threshold: float = 0.80
|
||||
triggers: list[str] = Field(default_factory=list)
|
||||
output: OutputSpec = Field(default_factory=OutputSpec)
|
||||
verifier: list[dict[str, Any]] = Field(default_factory=list)
|
||||
max_output_tokens: int = 1024
|
||||
program: str | None = None
|
||||
classifier: str | None = None
|
||||
min_confidence: float = 0.6
|
||||
|
||||
|
||||
class Skill(BaseModel):
|
||||
name: str
|
||||
description: str
|
||||
body: str
|
||||
meta: KnasterMeta
|
||||
path: str | None = None
|
||||
|
||||
@property
|
||||
def cluster(self) -> str:
|
||||
return self.meta.cluster or self.name
|
||||
|
||||
@property
|
||||
def routable(self) -> bool:
|
||||
return self.meta.status in {"active", "demoted"}
|
||||
|
||||
def prompt(self, variant: Literal["full", "compact"]) -> str:
|
||||
if variant == "full":
|
||||
return self.body.strip()
|
||||
return compact_body(self.body)
|
||||
|
||||
def index_texts(self) -> list[str]:
|
||||
return [self.description, *self.meta.triggers]
|
||||
|
||||
|
||||
def compact_body(body: str) -> str:
|
||||
"""Entfernt '## Beispiele…'-Abschnitte bis zur nächsten Überschrift gleicher/höherer Ebene."""
|
||||
out: list[str] = []
|
||||
skipping = False
|
||||
for line in body.splitlines():
|
||||
if _OPTIONAL_HEADING.match(line):
|
||||
skipping = True
|
||||
continue
|
||||
if skipping and re.match(r"^#{1,2}\s", line):
|
||||
skipping = False
|
||||
if not skipping:
|
||||
out.append(line)
|
||||
return "\n".join(out).strip()
|
||||
|
||||
|
||||
def parse_skill(text: str, path: str | None = None) -> Skill:
|
||||
match = _FRONTMATTER.match(text)
|
||||
if not match:
|
||||
raise ValueError(f"{path or 'Skill'}: Frontmatter fehlt")
|
||||
front = yaml.safe_load(match.group(1)) or {}
|
||||
for key in ("name", "description"):
|
||||
if not front.get(key):
|
||||
raise ValueError(f"{path or 'Skill'}: Feld '{key}' fehlt")
|
||||
knaster = ((front.get("metadata") or {}).get("knaster")) or {}
|
||||
return Skill(
|
||||
name=str(front["name"]),
|
||||
description=str(front["description"]).strip(),
|
||||
body=match.group(2),
|
||||
meta=KnasterMeta(**knaster),
|
||||
path=path,
|
||||
)
|
||||
|
||||
|
||||
class SkillLibrary:
|
||||
"""Lädt Skills aus einem Verzeichnis (<dir>/<name>/SKILL.md) und überlagert Laufzeitzustand."""
|
||||
|
||||
def __init__(self, skills: list[Skill], store: Store | None = None):
|
||||
self._skills = {s.name: s for s in skills}
|
||||
if len(self._skills) != len(skills):
|
||||
raise ValueError("Doppelte Skill-Namen")
|
||||
self.store = store
|
||||
|
||||
@classmethod
|
||||
def from_dir(cls, directory: str | Path, store: Store | None = None) -> SkillLibrary:
|
||||
skills = []
|
||||
for file in sorted(Path(directory).glob("*/SKILL.md")):
|
||||
skills.append(parse_skill(file.read_text(encoding="utf-8"), str(file)))
|
||||
return cls(skills, store)
|
||||
|
||||
def _states(self) -> dict[str, dict[str, Any]]:
|
||||
if not self.store:
|
||||
return {}
|
||||
return {row["name"]: row for row in self.store.all_skill_states()}
|
||||
|
||||
@staticmethod
|
||||
def _apply_state(skill: Skill, states: dict[str, dict[str, Any]]) -> Skill:
|
||||
row = states.get(skill.name)
|
||||
if not row:
|
||||
return skill
|
||||
meta = skill.meta.model_copy()
|
||||
if row.get("status"):
|
||||
meta.status = row["status"]
|
||||
if row.get("stage") is not None:
|
||||
meta.stage = row["stage"]
|
||||
return skill.model_copy(update={"meta": meta})
|
||||
|
||||
def get(self, name: str) -> Skill:
|
||||
return self._apply_state(self._skills[name], self._states())
|
||||
|
||||
def has(self, name: str) -> bool:
|
||||
return name in self._skills
|
||||
|
||||
def all(self) -> list[Skill]:
|
||||
states = self._states()
|
||||
return [self._apply_state(s, states) for s in self._skills.values()]
|
||||
|
||||
def routable(self) -> list[Skill]:
|
||||
return [s for s in self.all() if s.routable]
|
||||
|
||||
def set_state(self, name: str, *, status: str | None = None, stage: int | None = None,
|
||||
reason: str = "") -> None:
|
||||
if name not in self._skills:
|
||||
raise KeyError(name)
|
||||
if not self.store:
|
||||
raise RuntimeError("SkillLibrary ohne Store kann keinen Zustand speichern")
|
||||
current = self.get(name)
|
||||
self.store.set_skill_state(
|
||||
name,
|
||||
status=status or current.meta.status,
|
||||
stage=current.meta.stage if stage is None else stage,
|
||||
reason=reason,
|
||||
)
|
||||
@@ -0,0 +1,348 @@
|
||||
"""Persistenz (SQLAlchemy Core). SQLite für Tests/kleine Setups, Postgres im Betrieb."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
from typing import Any
|
||||
|
||||
from sqlalchemy import (
|
||||
JSON,
|
||||
Boolean,
|
||||
Column,
|
||||
Float,
|
||||
Integer,
|
||||
MetaData,
|
||||
String,
|
||||
Table,
|
||||
Text,
|
||||
create_engine,
|
||||
delete,
|
||||
func,
|
||||
insert,
|
||||
select,
|
||||
update,
|
||||
)
|
||||
from sqlalchemy.engine import Engine
|
||||
from sqlalchemy.pool import StaticPool
|
||||
|
||||
from .util import now
|
||||
|
||||
metadata = MetaData()
|
||||
|
||||
cases = Table(
|
||||
"cases",
|
||||
metadata,
|
||||
Column("id", String(36), primary_key=True),
|
||||
Column("ts", Float, nullable=False, index=True),
|
||||
Column("run_id", String(36), index=True),
|
||||
Column("input", Text, nullable=False),
|
||||
Column("skill", String(200), index=True),
|
||||
Column("skill_version", Integer),
|
||||
Column("cluster", String(200), index=True),
|
||||
Column("stage", Integer, nullable=False),
|
||||
Column("slot", String(100)),
|
||||
Column("model", String(200)),
|
||||
Column("adapter", String(200)),
|
||||
Column("messages", JSON),
|
||||
Column("output", Text),
|
||||
Column("passed", Boolean),
|
||||
Column("signals", JSON),
|
||||
Column("prompt_tokens", Integer),
|
||||
Column("split", String(10), nullable=False),
|
||||
Column("origin", String(20), nullable=False),
|
||||
)
|
||||
|
||||
preference_pairs = Table(
|
||||
"preference_pairs",
|
||||
metadata,
|
||||
Column("id", String(36), primary_key=True),
|
||||
Column("ts", Float, nullable=False),
|
||||
Column("run_id", String(36)),
|
||||
Column("skill", String(200), index=True),
|
||||
Column("cluster", String(200)),
|
||||
Column("prompt", JSON),
|
||||
Column("chosen", Text),
|
||||
Column("rejected", Text),
|
||||
Column("split", String(10)),
|
||||
)
|
||||
|
||||
proposals = Table(
|
||||
"proposals",
|
||||
metadata,
|
||||
Column("seq", Integer, primary_key=True, autoincrement=True),
|
||||
Column("kind", String(50), nullable=False),
|
||||
Column("params", JSON),
|
||||
Column("summary", Text),
|
||||
Column("reason", Text),
|
||||
Column("risk", String(10)),
|
||||
Column("state", String(20), nullable=False, index=True),
|
||||
Column("source", String(20)),
|
||||
Column("dedup_key", String(300), index=True),
|
||||
Column("created_at", Float),
|
||||
Column("decided_at", Float),
|
||||
Column("decided_by", String(100)),
|
||||
Column("result", JSON),
|
||||
)
|
||||
|
||||
slot_state = Table(
|
||||
"slot_state",
|
||||
metadata,
|
||||
Column("slot", String(100), primary_key=True),
|
||||
Column("status", String(20)),
|
||||
Column("effective_context", Integer),
|
||||
Column("base_model", String(200)),
|
||||
Column("reason", Text),
|
||||
Column("set_by", String(50)),
|
||||
Column("updated_at", Float),
|
||||
)
|
||||
|
||||
adapter_state = Table(
|
||||
"adapter_state",
|
||||
metadata,
|
||||
Column("name", String(200), primary_key=True),
|
||||
Column("base_model", String(200)),
|
||||
Column("cluster", String(200)),
|
||||
Column("version", Integer),
|
||||
Column("status", String(20)),
|
||||
Column("updated_at", Float),
|
||||
)
|
||||
|
||||
skill_state = Table(
|
||||
"skill_state",
|
||||
metadata,
|
||||
Column("name", String(200), primary_key=True),
|
||||
Column("status", String(20)),
|
||||
Column("stage", Integer),
|
||||
Column("reason", Text),
|
||||
Column("updated_at", Float),
|
||||
)
|
||||
|
||||
candidates = Table(
|
||||
"candidates",
|
||||
metadata,
|
||||
Column("id", String(36), primary_key=True),
|
||||
Column("kind", String(30)),
|
||||
Column("slot", String(100)),
|
||||
Column("target", JSON),
|
||||
Column("baseline", JSON),
|
||||
Column("state", String(30), index=True),
|
||||
Column("metrics", JSON),
|
||||
Column("history", JSON),
|
||||
Column("snapshot", JSON),
|
||||
Column("created_at", Float),
|
||||
Column("updated_at", Float),
|
||||
)
|
||||
|
||||
healer_state = Table(
|
||||
"healer_state",
|
||||
metadata,
|
||||
Column("key", String(300), primary_key=True),
|
||||
Column("data", JSON),
|
||||
Column("updated_at", Float),
|
||||
)
|
||||
|
||||
events = Table(
|
||||
"events",
|
||||
metadata,
|
||||
Column("id", Integer, primary_key=True, autoincrement=True),
|
||||
Column("ts", Float, nullable=False, index=True),
|
||||
Column("kind", String(50), nullable=False, index=True),
|
||||
Column("subject", String(200), index=True),
|
||||
Column("data", JSON),
|
||||
)
|
||||
|
||||
|
||||
def new_id() -> str:
|
||||
return str(uuid.uuid4())
|
||||
|
||||
|
||||
class Store:
|
||||
"""Dünne Repository-Schicht. Jede Methode öffnet ihre eigene Transaktion."""
|
||||
|
||||
def __init__(self, url: str):
|
||||
kwargs: dict[str, Any] = {}
|
||||
if url.startswith("sqlite"):
|
||||
kwargs["connect_args"] = {"check_same_thread": False}
|
||||
if url in {"sqlite://", "sqlite:///:memory:"}:
|
||||
kwargs["poolclass"] = StaticPool
|
||||
self.engine: Engine = create_engine(url, future=True, **kwargs)
|
||||
metadata.create_all(self.engine)
|
||||
|
||||
# --- Fälle -----------------------------------------------------------------------------
|
||||
def add_case(self, **row: Any) -> str:
|
||||
row.setdefault("id", new_id())
|
||||
row.setdefault("ts", now())
|
||||
with self.engine.begin() as conn:
|
||||
conn.execute(insert(cases).values(**row))
|
||||
return row["id"]
|
||||
|
||||
def list_cases(
|
||||
self,
|
||||
*,
|
||||
skill: str | None = None,
|
||||
cluster: str | None = None,
|
||||
min_stage: int | None = None,
|
||||
stage: int | None = None,
|
||||
split: str | None = None,
|
||||
passed: bool | None = None,
|
||||
since: float | None = None,
|
||||
limit: int | None = None,
|
||||
) -> list[dict[str, Any]]:
|
||||
q = select(cases).order_by(cases.c.ts.desc())
|
||||
if skill is not None:
|
||||
q = q.where(cases.c.skill == skill)
|
||||
if cluster is not None:
|
||||
q = q.where(cases.c.cluster == cluster)
|
||||
if min_stage is not None:
|
||||
q = q.where(cases.c.stage >= min_stage)
|
||||
if stage is not None:
|
||||
q = q.where(cases.c.stage == stage)
|
||||
if split is not None:
|
||||
q = q.where(cases.c.split == split)
|
||||
if passed is not None:
|
||||
q = q.where(cases.c.passed == passed)
|
||||
if since is not None:
|
||||
q = q.where(cases.c.ts >= since)
|
||||
if limit is not None:
|
||||
q = q.limit(limit)
|
||||
with self.engine.connect() as conn:
|
||||
return [dict(r._mapping) for r in conn.execute(q)]
|
||||
|
||||
def add_preference_pair(self, **row: Any) -> str:
|
||||
row.setdefault("id", new_id())
|
||||
row.setdefault("ts", now())
|
||||
with self.engine.begin() as conn:
|
||||
conn.execute(insert(preference_pairs).values(**row))
|
||||
return row["id"]
|
||||
|
||||
def list_preference_pairs(self, *, cluster: str | None = None) -> list[dict[str, Any]]:
|
||||
q = select(preference_pairs).order_by(preference_pairs.c.ts)
|
||||
if cluster is not None:
|
||||
q = q.where(preference_pairs.c.cluster == cluster)
|
||||
with self.engine.connect() as conn:
|
||||
return [dict(r._mapping) for r in conn.execute(q)]
|
||||
|
||||
# --- Ereignisse ------------------------------------------------------------------------
|
||||
def add_event(self, kind: str, subject: str | None = None, data: dict | None = None) -> None:
|
||||
with self.engine.begin() as conn:
|
||||
conn.execute(insert(events).values(ts=now(), kind=kind, subject=subject, data=data or {}))
|
||||
|
||||
def count_events(self, kind: str, *, subject: str | None = None, since: float | None = None) -> int:
|
||||
q = select(func.count()).select_from(events).where(events.c.kind == kind)
|
||||
if subject is not None:
|
||||
q = q.where(events.c.subject == subject)
|
||||
if since is not None:
|
||||
q = q.where(events.c.ts >= since)
|
||||
with self.engine.connect() as conn:
|
||||
return int(conn.execute(q).scalar_one())
|
||||
|
||||
def event_subjects(self, kind: str, *, since: float | None = None) -> dict[str, int]:
|
||||
q = select(events.c.subject, func.count()).where(events.c.kind == kind).group_by(events.c.subject)
|
||||
if since is not None:
|
||||
q = q.where(events.c.ts >= since)
|
||||
with self.engine.connect() as conn:
|
||||
return {r[0]: int(r[1]) for r in conn.execute(q) if r[0] is not None}
|
||||
|
||||
# --- generische Zustandstabellen -----------------------------------------------------
|
||||
def _upsert(self, table: Table, key_col: str, key: str, values: dict[str, Any]) -> None:
|
||||
values = {**values, "updated_at": now()}
|
||||
with self.engine.begin() as conn:
|
||||
exists = conn.execute(select(table).where(table.c[key_col] == key)).first()
|
||||
if exists:
|
||||
conn.execute(update(table).where(table.c[key_col] == key).values(**values))
|
||||
else:
|
||||
conn.execute(insert(table).values(**{key_col: key, **values}))
|
||||
|
||||
def _get(self, table: Table, key_col: str, key: str) -> dict[str, Any] | None:
|
||||
with self.engine.connect() as conn:
|
||||
row = conn.execute(select(table).where(table.c[key_col] == key)).first()
|
||||
return dict(row._mapping) if row else None
|
||||
|
||||
def _all(self, table: Table) -> list[dict[str, Any]]:
|
||||
with self.engine.connect() as conn:
|
||||
return [dict(r._mapping) for r in conn.execute(select(table))]
|
||||
|
||||
def set_slot_state(self, slot: str, **values: Any) -> None:
|
||||
self._upsert(slot_state, "slot", slot, values)
|
||||
|
||||
def get_slot_state(self, slot: str) -> dict[str, Any] | None:
|
||||
return self._get(slot_state, "slot", slot)
|
||||
|
||||
def all_slot_states(self) -> list[dict[str, Any]]:
|
||||
return self._all(slot_state)
|
||||
|
||||
def set_adapter_state(self, name: str, **values: Any) -> None:
|
||||
self._upsert(adapter_state, "name", name, values)
|
||||
|
||||
def all_adapter_states(self) -> list[dict[str, Any]]:
|
||||
return self._all(adapter_state)
|
||||
|
||||
def set_skill_state(self, name: str, **values: Any) -> None:
|
||||
self._upsert(skill_state, "name", name, values)
|
||||
|
||||
def all_skill_states(self) -> list[dict[str, Any]]:
|
||||
return self._all(skill_state)
|
||||
|
||||
# --- Vorschläge ------------------------------------------------------------------------
|
||||
def add_proposal(self, **row: Any) -> int:
|
||||
row.setdefault("created_at", now())
|
||||
row.setdefault("state", "open")
|
||||
with self.engine.begin() as conn:
|
||||
res = conn.execute(insert(proposals).values(**row))
|
||||
return int(res.inserted_primary_key[0])
|
||||
|
||||
def get_proposal(self, seq: int) -> dict[str, Any] | None:
|
||||
return self._get(proposals, "seq", seq) # type: ignore[arg-type]
|
||||
|
||||
def update_proposal(self, seq: int, **values: Any) -> None:
|
||||
with self.engine.begin() as conn:
|
||||
conn.execute(update(proposals).where(proposals.c.seq == seq).values(**values))
|
||||
|
||||
def list_proposals(self, *, state: str | None = None) -> list[dict[str, Any]]:
|
||||
q = select(proposals).order_by(proposals.c.seq)
|
||||
if state is not None:
|
||||
q = q.where(proposals.c.state == state)
|
||||
with self.engine.connect() as conn:
|
||||
return [dict(r._mapping) for r in conn.execute(q)]
|
||||
|
||||
def open_proposal_with_key(self, dedup_key: str) -> dict[str, Any] | None:
|
||||
q = select(proposals).where(proposals.c.dedup_key == dedup_key, proposals.c.state == "open")
|
||||
with self.engine.connect() as conn:
|
||||
row = conn.execute(q).first()
|
||||
return dict(row._mapping) if row else None
|
||||
|
||||
# --- Kandidaten ------------------------------------------------------------------------
|
||||
def add_candidate(self, **row: Any) -> str:
|
||||
row.setdefault("id", new_id())
|
||||
row.setdefault("created_at", now())
|
||||
row.setdefault("updated_at", row["created_at"])
|
||||
with self.engine.begin() as conn:
|
||||
conn.execute(insert(candidates).values(**row))
|
||||
return row["id"]
|
||||
|
||||
def get_candidate(self, cid: str) -> dict[str, Any] | None:
|
||||
return self._get(candidates, "id", cid)
|
||||
|
||||
def update_candidate(self, cid: str, **values: Any) -> None:
|
||||
values["updated_at"] = now()
|
||||
with self.engine.begin() as conn:
|
||||
conn.execute(update(candidates).where(candidates.c.id == cid).values(**values))
|
||||
|
||||
def list_candidates(self, *, state: str | None = None) -> list[dict[str, Any]]:
|
||||
q = select(candidates).order_by(candidates.c.created_at)
|
||||
if state is not None:
|
||||
q = q.where(candidates.c.state == state)
|
||||
with self.engine.connect() as conn:
|
||||
return [dict(r._mapping) for r in conn.execute(q)]
|
||||
|
||||
# --- Healer-Zustand --------------------------------------------------------------------
|
||||
def get_healer_state(self, key: str) -> dict[str, Any] | None:
|
||||
row = self._get(healer_state, "key", key)
|
||||
return row["data"] if row else None
|
||||
|
||||
def set_healer_state(self, key: str, data: dict[str, Any]) -> None:
|
||||
self._upsert(healer_state, "key", key, {"data": data})
|
||||
|
||||
def delete_healer_state(self, key: str) -> None:
|
||||
with self.engine.begin() as conn:
|
||||
conn.execute(delete(healer_state).where(healer_state.c.key == key))
|
||||
@@ -0,0 +1,56 @@
|
||||
"""Kleine Hilfsfunktionen ohne Fachlogik."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import yaml
|
||||
|
||||
_DURATION = re.compile(r"^\s*(\d+(?:\.\d+)?)\s*([smhd]?)\s*$")
|
||||
_FACTORS = {"": 1, "s": 1, "m": 60, "h": 3600, "d": 86400}
|
||||
_PLACEHOLDER = re.compile(r"\{([a-zA-Z0-9_]+)\}")
|
||||
|
||||
|
||||
def now() -> float:
|
||||
return time.time()
|
||||
|
||||
|
||||
def load_yaml(path: str | Path) -> Any:
|
||||
with open(path, encoding="utf-8") as fh:
|
||||
return yaml.safe_load(fh) or {}
|
||||
|
||||
|
||||
def parse_duration(value: str | int | float | None) -> float:
|
||||
"""'5m' -> 300.0; Zahlen gelten als Sekunden."""
|
||||
if value is None:
|
||||
return 0.0
|
||||
if isinstance(value, int | float):
|
||||
return float(value)
|
||||
match = _DURATION.match(value)
|
||||
if not match:
|
||||
raise ValueError(f"Ungültige Dauer: {value!r}")
|
||||
return float(match.group(1)) * _FACTORS[match.group(2)]
|
||||
|
||||
|
||||
class MissingLabel(KeyError):
|
||||
pass
|
||||
|
||||
|
||||
def fill_template(value: Any, labels: dict[str, str]) -> Any:
|
||||
"""Ersetzt {label} rekursiv; fehlende Labels lösen MissingLabel aus."""
|
||||
if isinstance(value, str):
|
||||
def repl(m: re.Match[str]) -> str:
|
||||
key = m.group(1)
|
||||
if key not in labels:
|
||||
raise MissingLabel(key)
|
||||
return str(labels[key])
|
||||
|
||||
return _PLACEHOLDER.sub(repl, value)
|
||||
if isinstance(value, dict):
|
||||
return {k: fill_template(v, labels) for k, v in value.items()}
|
||||
if isinstance(value, list):
|
||||
return [fill_template(v, labels) for v in value]
|
||||
return value
|
||||
@@ -0,0 +1,116 @@
|
||||
"""Verifier: deterministische Prüfungen + optionaler LLM-Judge."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import re
|
||||
from typing import Any
|
||||
|
||||
import jsonschema
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
from .llm import ChatClient, LLMError
|
||||
from .problems import extract_json
|
||||
from .registry import Registry
|
||||
from .skills import Skill
|
||||
|
||||
JUDGE_SYSTEM = (
|
||||
"Du bist ein strenger Prüfer. Bewerte, ob die Antwort die Aufgabe gemäß den Kriterien erfüllt. "
|
||||
"Antworte mit genau einer Zeile: 'PASS' oder 'FAIL: <kurzer Grund>'."
|
||||
)
|
||||
|
||||
|
||||
class CheckResult(BaseModel):
|
||||
type: str
|
||||
passed: bool | None # None = nicht entscheidbar (z. B. Judge nicht erreichbar)
|
||||
detail: str = ""
|
||||
|
||||
|
||||
class VerifyResult(BaseModel):
|
||||
passed: bool
|
||||
inconclusive: bool = False
|
||||
checks: list[CheckResult] = Field(default_factory=list)
|
||||
|
||||
@property
|
||||
def failed_checks(self) -> list[CheckResult]:
|
||||
return [c for c in self.checks if c.passed is False]
|
||||
|
||||
|
||||
class Verifier:
|
||||
def __init__(self, registry: Registry | None = None, chat: ChatClient | None = None,
|
||||
default_judge_slot: str = "verifier-small"):
|
||||
self.registry = registry
|
||||
self.chat = chat
|
||||
self.default_judge_slot = default_judge_slot
|
||||
|
||||
def verify(self, skill: Skill | None, text: str, output: str) -> VerifyResult:
|
||||
if skill is None:
|
||||
return VerifyResult(passed=True, checks=[])
|
||||
checks: list[CheckResult] = []
|
||||
specs = list(skill.meta.verifier)
|
||||
schema = skill.meta.output.schema_
|
||||
if skill.meta.output.format == "json":
|
||||
checks.append(self._json(output, schema))
|
||||
specs = [s for s in specs if s.get("type") != "json_schema"]
|
||||
for spec in specs:
|
||||
checks.append(self._run(spec, skill, text, output))
|
||||
conclusive = [c for c in checks if c.passed is not None]
|
||||
return VerifyResult(
|
||||
passed=all(c.passed for c in conclusive),
|
||||
inconclusive=len(conclusive) != len(checks),
|
||||
checks=checks,
|
||||
)
|
||||
|
||||
def _run(self, spec: dict[str, Any], skill: Skill, text: str, output: str) -> CheckResult:
|
||||
kind = spec.get("type")
|
||||
if kind == "json_schema":
|
||||
return self._json(output, skill.meta.output.schema_)
|
||||
if kind == "regex":
|
||||
ok = re.search(spec["pattern"], output, re.MULTILINE) is not None
|
||||
return CheckResult(type=kind, passed=ok, detail="" if ok else f"Muster fehlt: {spec['pattern']}")
|
||||
if kind == "contains":
|
||||
ok = spec["value"] in output
|
||||
return CheckResult(type=kind, passed=ok, detail="" if ok else f"fehlt: {spec['value']}")
|
||||
if kind == "not_contains":
|
||||
ok = spec["value"] not in output
|
||||
return CheckResult(type=kind, passed=ok, detail="" if ok else f"enthält: {spec['value']}")
|
||||
if kind == "max_chars":
|
||||
ok = len(output) <= int(spec["value"])
|
||||
return CheckResult(type=kind, passed=ok, detail=f"{len(output)} Zeichen")
|
||||
if kind == "judge":
|
||||
return self._judge(spec, text, output)
|
||||
return CheckResult(type=str(kind), passed=False, detail="Unbekannter Prüftyp")
|
||||
|
||||
@staticmethod
|
||||
def _json(output: str, schema: dict[str, Any] | None) -> CheckResult:
|
||||
try:
|
||||
data = extract_json(output)
|
||||
except ValueError as exc:
|
||||
return CheckResult(type="json", passed=False, detail=f"kein gültiges JSON: {exc}")
|
||||
if schema:
|
||||
try:
|
||||
jsonschema.validate(data, schema)
|
||||
except jsonschema.ValidationError as exc:
|
||||
return CheckResult(type="json_schema", passed=False, detail=exc.message)
|
||||
return CheckResult(type="json_schema" if schema else "json", passed=True)
|
||||
|
||||
def _judge(self, spec: dict[str, Any], text: str, output: str) -> CheckResult:
|
||||
if self.chat is None or self.registry is None:
|
||||
return CheckResult(type="judge", passed=None, detail="kein Judge konfiguriert")
|
||||
slot_name = spec.get("slot", self.default_judge_slot)
|
||||
slot = self.registry.slot(slot_name)
|
||||
if not slot.available:
|
||||
return CheckResult(type="judge", passed=None, detail=f"Judge-Slot {slot_name} nicht verfügbar")
|
||||
user = f"Aufgabe:\n{text}\n\nKriterien:\n{spec.get('rubric', 'Korrekt und vollständig.')}\n\nAntwort:\n{output}"
|
||||
try:
|
||||
res = self.chat.chat(slot.spec.litellm_model,
|
||||
[{"role": "system", "content": JUDGE_SYSTEM}, {"role": "user", "content": user}],
|
||||
max_tokens=60, temperature=0)
|
||||
except LLMError as exc:
|
||||
return CheckResult(type="judge", passed=None, detail=f"Judge-Fehler: {exc}")
|
||||
verdict = res.content.strip()
|
||||
first = verdict.split(maxsplit=1)[0].upper().rstrip(":") if verdict else ""
|
||||
if first == "PASS":
|
||||
return CheckResult(type="judge", passed=True)
|
||||
if first == "FAIL":
|
||||
return CheckResult(type="judge", passed=False, detail=verdict[:200])
|
||||
return CheckResult(type="judge", passed=None, detail=f"Unklare Judge-Antwort: {verdict[:100]}")
|
||||
Reference in New Issue
Block a user