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196a48f | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | #!/usr/bin/env python3
"""Benchmark ResearchMind RAG retrieval and optional full chat latency."""
from __future__ import annotations
import argparse
import json
import statistics
import sys
import time
from pathlib import Path
from researchmind.config import get_config
from researchmind.embeddings import embed_texts
from researchmind.ingest import IngestPipeline
from researchmind.retrieve import retrieve
def _load_sessions() -> list[tuple[str, str]]:
store = IngestPipeline().store
return [(s.id, s.topic or "Untitled") for s in store.list_sessions()]
def benchmark_retrieve(
question: str,
*,
session_id: str,
runs: int,
) -> dict[str, object]:
cfg = get_config()
store = IngestPipeline().store
chunks_in_scope = store.get_chunks_with_embeddings(session_id=session_id or None)
timings: list[float] = []
retrieved = 0
for _ in range(runs):
started = time.perf_counter()
chunks = retrieve(question, store, config=cfg, session_id=session_id or None)
timings.append((time.perf_counter() - started) * 1000)
retrieved = len(chunks)
warm = timings[1:] if len(timings) > 1 else timings
embed_started = time.perf_counter()
embed_texts(["warmup query"], model_name=cfg.embed_model)
embed_warm_ms = (time.perf_counter() - embed_started) * 1000
return {
"question": question,
"session_id": session_id,
"chunks_in_scope": len(chunks_in_scope),
"retrieved_chunks": retrieved,
"top_k": cfg.top_k,
"max_context_chunks": cfg.max_context_chunks,
"embed_model": cfg.embed_model,
"embedder_warm_ms": round(embed_warm_ms, 1),
"retrieve_ms_cold": round(timings[0], 1) if timings else 0.0,
"retrieve_ms_mean": round(statistics.mean(warm), 1),
"retrieve_ms_stdev": round(statistics.stdev(warm), 1) if len(warm) > 1 else 0.0,
"retrieve_ms_min": round(min(warm), 1),
"retrieve_ms_max": round(max(warm), 1),
}
def benchmark_chat(
question: str,
*,
session_id: str,
model_key: str | None,
) -> dict[str, object]:
from agent.runner import AgentRunner
from gradio_space.model_loading import ensure_model_loaded, get_active_model_key
from inference.factory import get_backend
key = model_key or get_active_model_key()
load_err = ensure_model_loaded(key)
if load_err:
return {"error": load_err, "model": key}
backend = get_backend(key)
runner = AgentRunner()
started = time.perf_counter()
result = runner.run_researchmind_chat(
question=question,
session_id=session_id,
model_key=key,
backend=backend,
doc_ids=None,
)
total_ms = (time.perf_counter() - started) * 1000
trace = json.loads(Path(result.trace_path).read_text(encoding="utf-8"))
steps = [
{
"name": step.get("name"),
"label": step.get("label"),
"duration_ms": step.get("duration_ms"),
}
for step in trace.get("steps", [])
if step.get("type") == "step"
]
return {
"model": key,
"question": question,
"session_id": session_id,
"total_ms": round(total_ms, 1),
"citations": len(result.citations),
"answer_preview": result.answer[:240],
"steps": steps,
"trace_path": result.trace_path,
}
def main() -> int:
parser = argparse.ArgumentParser(description="Benchmark ResearchMind RAG chat")
parser.add_argument(
"--question",
default="how we can finetune model",
help="Question to benchmark",
)
parser.add_argument("--session-id", default="", help="Research session id")
parser.add_argument("--runs", type=int, default=5, help="Retrieve benchmark repetitions")
parser.add_argument(
"--full-chat",
action="store_true",
help="Also run one full RAG chat (loads local LLM)",
)
parser.add_argument("--model-key", default="", help="Override ACTIVE_MODEL preset")
args = parser.parse_args()
sessions = _load_sessions()
session_id = args.session_id.strip()
if not session_id:
session_id = sessions[0][0] if sessions else ""
if not session_id:
print("No indexed session found. Ingest sources first.")
return 1
retrieve_report = benchmark_retrieve(
args.question,
session_id=session_id,
runs=max(1, args.runs),
)
print(json.dumps({"retrieve": retrieve_report}, indent=2))
if args.full_chat:
chat_report = benchmark_chat(
args.question,
session_id=session_id,
model_key=args.model_key or None,
)
print(json.dumps({"chat": chat_report}, indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())
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