File size: 1,952 Bytes
198bd6d | 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 | from __future__ import annotations
import json
from pathlib import Path
from typing import Any
from bayesian_langgraph import BayesianIndex, BayesianRAGGraph
from evaluation.rag_benchmark import CLEAN_CASES, CLEAN_DOCS, CONFLICT_CASES, CONFLICT_DOCS
def _case_ok(out: dict[str, Any], case: dict[str, Any]) -> bool:
if case["abstain"]:
return bool(out.get("abstained")) and not out.get("citations")
return (
not out.get("abstained")
and case["doc"] in out.get("citations", [])
and str(case["gold"]).lower() in str(out.get("answer", "")).lower()
)
def _run_suite(docs, cases) -> dict[str, Any]:
rag = BayesianRAGGraph(BayesianIndex(docs), k=4)
rows = []
for case in cases:
out = rag.invoke(case["q"])
rows.append({
"id": case["id"],
"ok": _case_ok(out, case),
"answer": out["answer"],
"citations": out["citations"],
"abstained": out["abstained"],
"top_doc": out["hits"][0]["id"] if out["hits"] else None,
})
return {
"n_cases": len(rows),
"passed": sum(r["ok"] for r in rows),
"accuracy": sum(r["ok"] for r in rows) / len(rows),
"rows": rows,
}
def run_framework_benchmark(output_path: str | Path | None = None) -> dict[str, Any]:
rag = BayesianRAGGraph(BayesianIndex(CLEAN_DOCS), k=4)
graph = rag.graph.get_graph()
result = {
"framework": "langgraph",
"stategraph_nodes": sorted(graph.nodes.keys()),
"stategraph_edges": sorted([e.source, e.target] for e in graph.edges),
"clean": _run_suite(CLEAN_DOCS, CLEAN_CASES),
"conflict": _run_suite(CONFLICT_DOCS, CONFLICT_CASES),
}
if output_path is not None:
Path(output_path).write_text(json.dumps(result, indent=2), encoding="utf-8")
return result
if __name__ == "__main__":
print(json.dumps(run_framework_benchmark(), indent=2))
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