File size: 2,860 Bytes
66485a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | """Transparent baseline pipeline for the generated AI project."""
from __future__ import annotations
import json
import math
import re
from pathlib import Path
def tokenize(value: str) -> list[str]:
return re.findall(r"[a-z0-9]+", value.lower())
class Pipeline:
def __init__(self, model: dict):
self.model = model
@classmethod
def from_file(cls, path: str | Path) -> "Pipeline":
return cls(json.loads(Path(path).read_text(encoding="utf-8")))
def classify(self, text: str) -> tuple[str, float]:
tokens = tokenize(text)
scores = {
label: sum(weights.get(token, 0) for token in tokens)
for label, weights in self.model["prototypes"].items()
}
ranked = sorted(scores.items(), key=lambda item: (-item[1], item[0]))
label, best = ranked[0]
total = sum(max(score, 0) for _, score in ranked) or 1
return label, best / total
def search(self, query: str, limit: int = 3) -> list[dict]:
query_tokens = set(tokenize(query))
ranked = []
for document in self.model["documents"]:
document_tokens = set(tokenize(document["text"]))
lexical = sum(
self.model["idf"].get(token, 1.0)
for token in query_tokens & document_tokens
)
ranked.append({**document, "score": round(lexical, 6)})
return sorted(ranked, key=lambda item: (-item["score"], item["id"]))[:limit]
def graph_evidence(self, text: str) -> list[dict]:
tokens = set(tokenize(text))
matches = []
for subject, relation, target in self.model.get("graph_edges", []):
edge_tokens = set(tokenize(f"{subject} {relation} {target}"))
overlap = len(tokens & edge_tokens)
if overlap:
matches.append(
{
"subject": subject,
"relation": relation,
"target": target,
"overlap": overlap,
}
)
return sorted(matches, key=lambda item: -item["overlap"])
def run(self, text: str) -> dict:
label, confidence = self.classify(text)
evidence = self.search(text)
result = {
"prediction": label,
"confidence": round(confidence, 4),
"requires_review": confidence < self.model["confidence_threshold"],
"evidence": evidence,
}
if self.model["mode"] == "graph":
result["graph_evidence"] = self.graph_evidence(text)
if self.model["mode"] == "agent":
result["proposed_tool"] = label
result["approval_required"] = label in {
"request-approval",
"request-human-help",
}
return result
|