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8058e7e | 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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 | import argparse
import asyncio
import json
import sys
import time
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from datasets import Dataset
from langchain_groq import ChatGroq
from ragas import RunConfig, evaluate
from ragas.embeddings import LangchainEmbeddingsWrapper
from ragas.llms import LangchainLLMWrapper
from ragas.metrics import (
answer_relevancy,
context_precision,
context_recall,
faithfulness,
)
from app.core.config import settings
from app.db.sqlite import init_db
from app.rag.embeddings import get_embedding_model
from app.rag.graph import ClaimsRAGGraph
from app.rag.ingestion import DocumentIngestionService
from app.rag.qdrant_store import QdrantVectorStore
def load_jsonl(path: Path) -> list[dict[str, Any]]:
rows = []
for line in path.read_text(encoding="utf-8").splitlines():
if line.strip():
rows.append(json.loads(line))
return rows
def default_reference(case: dict[str, Any]) -> str:
if case.get("reference"):
return str(case["reference"])
decision = case["expected_decision"]
return (
f"Decision: {decision}. The answer should use the retrieved insurance claim "
"guidance to explain the coverage triage, identify missing evidence, and "
"recommend the next action without inventing unsupported policy terms."
)
def build_eval_rows(dataset_path: Path, user_id: str, limit: int | None) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
init_db()
QdrantVectorStore().ensure_collections()
DocumentIngestionService().ingest_pdf_directory()
graph = ClaimsRAGGraph()
cases = load_jsonl(dataset_path)
if limit:
cases = cases[:limit]
ragas_rows = []
run_rows = []
for case in cases:
started = time.perf_counter()
state = graph.run(case["query"], user_id=user_id, use_cache=False)
latency_ms = round((time.perf_counter() - started) * 1000, 2)
sources = state.get("reranked_sources") or state.get("sources", [])
contexts = [str(source.get("text", "")) for source in sources if source.get("text")]
ragas_rows.append(
{
"user_input": case["query"],
"response": state.get("answer", ""),
"retrieved_contexts": contexts,
"reference": default_reference(case),
}
)
run_rows.append(
{
"id": case["id"],
"expected_decision": case["expected_decision"],
"sources": len(contexts),
"latency_ms": latency_ms,
}
)
return ragas_rows, run_rows
async def run_ragas(dataset_path: Path, user_id: str, limit: int | None) -> dict[str, Any]:
if not settings.groq_api_key:
raise RuntimeError("GROQ_API_KEY is required for RAGAS LLM-judge metrics.")
ragas_rows, run_rows = build_eval_rows(dataset_path, user_id, limit)
dataset = Dataset.from_list(ragas_rows)
judge_llm = ChatGroq(
model=settings.groq_model,
temperature=0,
max_retries=2,
api_key=settings.groq_api_key,
)
ragas_llm = LangchainLLMWrapper(judge_llm)
ragas_embeddings = LangchainEmbeddingsWrapper(get_embedding_model().model)
answer_relevancy.strictness = 1
metrics = [faithfulness, answer_relevancy, context_precision, context_recall]
result = evaluate(
dataset=dataset,
metrics=metrics,
llm=ragas_llm,
embeddings=ragas_embeddings,
run_config=RunConfig(timeout=180, max_workers=2, max_retries=2),
)
scores = result.to_pandas().to_dict(orient="records")
rows = []
for run_row, score_row in zip(run_rows, scores, strict=False):
rows.append({**run_row, "ragas": score_row})
metric_names = ["faithfulness", "answer_relevancy", "context_precision", "context_recall"]
summary = {
"total": len(rows),
"metrics": {},
"results": rows,
}
for metric in metric_names:
values = [
float(row["ragas"][metric])
for row in rows
if row["ragas"].get(metric) is not None and str(row["ragas"][metric]).lower() != "nan"
]
summary["metrics"][metric] = round(sum(values) / len(values), 3) if values else None
return summary
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--dataset", default="data/eval/golden_claim_scenarios.jsonl")
parser.add_argument("--user-id", default="ragas_eval_user")
parser.add_argument("--limit", type=int, default=None)
parser.add_argument("--output", default=None)
args = parser.parse_args()
summary = asyncio.run(run_ragas(Path(args.dataset), args.user_id, args.limit))
text = json.dumps(summary, indent=2)
print(text)
if args.output:
Path(args.output).write_text(text + "\n", encoding="utf-8")
if __name__ == "__main__":
main()
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