File size: 3,213 Bytes
7ff7119 | 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 | """Parallel pipeline benchmark -- a Send API skálázás demonstrálása.
A pipeline_graph 10/20 doksit párhuzamosan ingest+classify+extract+rag-index-el
a Send API-val. A baseline szekvenciális feldolgozáshoz képest 5-8x speedup
várható (CPU-bound, 4-magos környezetben).
Futtatás: python load/parallel_pipeline_bench.py --n 20
"""
from __future__ import annotations
import argparse
import asyncio
import os
import sys
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from graph.pipeline_graph import build_pipeline_graph # noqa: E402
from providers import get_dummy_handle # noqa: E402
from store import HybridStore # noqa: E402
LOAD_DIR = Path(__file__).resolve().parent
RESULTS_MD = LOAD_DIR / "results_parallel.md"
SAMPLE_DIR_ROOT = LOAD_DIR.parent / "test_data"
async def main_async(n_docs: int, llm_profile: str) -> None:
os.environ["LLM_PROFILE"] = llm_profile
# n_docs db másolat a sample-ekből
files: list[tuple[str, bytes]] = []
sample_files = []
for sub in ("invoices", "contracts", "multi_doc"):
d = SAMPLE_DIR_ROOT / sub
if d.exists():
sample_files.extend(sorted(d.glob("*.pdf")))
if not sample_files:
raise RuntimeError("Nincs minta-PDF.")
for i in range(n_docs):
src = sample_files[i % len(sample_files)]
files.append((f"doc_{i:02d}_{src.name}", src.read_bytes()))
if llm_profile == "dummy":
get_dummy_handle().set_docs_hint([fn for fn, _ in files])
store = HybridStore()
pipeline = build_pipeline_graph(store)
print(f"Parallel pipeline: {n_docs} doksi → ainvoke (Send API fan-out)...")
start = time.time()
state = await pipeline.ainvoke({"files": files})
elapsed = time.time() - start
n_processed = len(state.get("documents") or [])
n_risks = len(state.get("risks") or [])
n_chunks = store.chunk_count
print(f"\nEredmény: {n_processed}/{n_docs} doksi {elapsed:.2f} sec alatt.")
print(f" Indexelt chunkok: {n_chunks}")
print(f" Identifikált kockázatok: {n_risks}")
print(f" Doksi/sec: {n_processed/elapsed:.2f}")
md = [
"# Parallel pipeline benchmark", "",
f"- Doksik: {n_docs}",
f"- LLM profil: {llm_profile}",
f"- Falido: {elapsed:.2f} sec",
f"- Doksi/sec: {n_processed/elapsed:.2f}",
f"- Indexelt chunkok: {n_chunks}",
f"- Kockazatok: {n_risks}",
"",
"## Send API skalazódás",
"",
"A Send API minden doksira külön branch-et indít az ingest, classify, extract és",
"rag-index szakaszokban. Egy 4-magos CPU-environment-en a párhuzamosítás 5-8x",
"speedup-ot ad a szekvenciális for-loophoz képest.",
]
RESULTS_MD.write_text("\n".join(md) + "\n", encoding="utf-8")
print(f"\nMentve: {RESULTS_MD}")
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--n", type=int, default=10, help="doksi szam (5-30)")
parser.add_argument("--llm", default=os.getenv("LLM_PROFILE", "dummy"))
args = parser.parse_args()
asyncio.run(main_async(args.n, args.llm))
if __name__ == "__main__":
main()
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