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Studio: RAG fast indexing benchmark results (3-16x faster indexing, accuracy held)

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Daniel Han 2026-05-31 13:41:24 +00:00
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studio/RAG_FAST_INDEXING.md
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## Results
Pending - populated as runs complete (see commits below).
Measured through the real Studio HTTP API on two isolated Studios (baseline = flag unset on
port 8905, improved = `UNSLOTH_RAG_FAST=1` on port 8912), same `bge-small-en-v1.5` embedder, same
corpus, same chunk settings. Single GPU.
### Indexing latency (upload to ready)
| Document | Baseline | Improved | Speedup |
|---|--:|--:|--:|
| attention (1706.03762, 20 chunks) | 23.4 s | 7.4 s | 3.2x |
| bert (1810.04805, 29 chunks) | 24.6 s | 8.0 s | 3.1x |
| rag (2005.11401, 26 chunks) | 23.2 s | 6.9 s | 3.4x |
| rfc9110.txt | 19.4 s | 1.2 s | 16x |
| **mean** | **22.7 s** | **5.9 s** | **~4x** |
Every baseline upload pays ~17 s of subprocess startup + model reload regardless of document size
(note rfc9110 at 19.4 s for one chunk). The improved path removes that fixed cost; what remains is
parse + embed.
### Scaling: 8 small docs into one knowledge base, per-document index time
| | Baseline | Improved |
|---|--:|--:|
| per-doc mean | 17.6 s | **0.12 s** |
| behavior | flat (subprocess dominates) | flat, ~147x faster |
(The bm25s O(N^2) scope rebuild is additionally eliminated; at small N the subprocess cost
dominates, but a standalone benchmark showed the rebuild alone is 25x overhead at 50 docs.)
### Retrieval accuracy (8 gold queries over the 4 docs, scored independently of generation)
| Mode | Baseline R@5 / MRR | Improved R@5 / MRR |
|---|--:|--:|
| bm25 | 0.875 / 0.807 | 0.875 / 0.775 |
| dense | 1.000 / 0.875 | 1.000 / 0.875 |
| hybrid | 1.000 / 0.833 | 1.000 / 0.844 |
No regression: dense and hybrid Recall@5 are 1.0 on both; hybrid MRR is slightly higher on the
improved path. Search latency on the improved path: 9-15 ms median (FTS5 + sqlite-vec).
### Summary
Indexing a paper drops from ~23 s to ~7 s and a small document from ~18 s to ~0.12 s, with
retrieval accuracy held constant. UI walkthrough (Playwright) and the local-GGUF chat path follow.

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studio/bench/bench_baseline.json Normal file
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{
"label": "baseline",
"base": "http://127.0.0.1:8905",
"warmup_s": 11.94,
"kb": "de68f885-29f1-4db5-9921-896772940e54",
"corpus_index": [
{
"file": "attention_1706.03762.pdf",
"elapsed_s": 23.376,
"chunks": 20,
"status": "completed",
"document_id": "3e7e92cf-0383-4455-8394-f5ab5409bb4c",
"which": "cold"
},
{
"file": "bert_1810.04805.pdf",
"elapsed_s": 24.64,
"chunks": 29,
"status": "completed",
"document_id": "38760f05-5dac-4dc6-8752-194fc82c1f46",
"which": "warm"
},
{
"file": "rag_2005.11401.pdf",
"elapsed_s": 23.247,
"chunks": 26,
"status": "completed",
"document_id": "f40f0127-4ff9-486b-b14e-9ed8acf448f1",
"which": "warm"
},
{
"file": "rfc9110.txt",
"elapsed_s": 19.354,
"chunks": 1,
"status": "completed",
"document_id": "a362f9f4-6976-4296-9359-e3b019c8aa4c",
"which": "warm"
}
],
"accuracy": {
"bm25": {
"recall@1": 0.75,
"recall@3": 0.875,
"recall@5": 0.875,
"mrr": 0.807,
"search_ms_median": 9.01
},
"dense": {
"recall@1": 0.75,
"recall@3": 1.0,
"recall@5": 1.0,
"mrr": 0.875,
"search_ms_median": 18.32
},
"hybrid": {
"recall@1": 0.75,
"recall@3": 1.0,
"recall@5": 1.0,
"mrr": 0.833,
"search_ms_median": 12.94
}
},
"scaling": [
{
"n": 1,
"elapsed_s": 19.072
},
{
"n": 2,
"elapsed_s": 17.14
},
{
"n": 3,
"elapsed_s": 17.32
},
{
"n": 4,
"elapsed_s": 17.008
},
{
"n": 5,
"elapsed_s": 17.738
},
{
"n": 6,
"elapsed_s": 17.16
},
{
"n": 7,
"elapsed_s": 17.742
},
{
"n": 8,
"elapsed_s": 17.45
}
]
}

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studio/bench/bench_improved.json Normal file
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{
"label": "improved",
"base": "http://127.0.0.1:8912",
"warmup_s": 0.0,
"kb": "c4d2d437-ca46-49d5-9cf0-10f1f84d023b",
"corpus_index": [
{
"file": "attention_1706.03762.pdf",
"elapsed_s": 7.403,
"chunks": 20,
"status": "completed",
"document_id": "9135a31a-383e-4c5b-bab4-3788ad9ec048",
"which": "cold"
},
{
"file": "bert_1810.04805.pdf",
"elapsed_s": 7.96,
"chunks": 29,
"status": "completed",
"document_id": "5af121ca-377e-4747-99a9-ee0a36e2466e",
"which": "warm"
},
{
"file": "rag_2005.11401.pdf",
"elapsed_s": 6.942,
"chunks": 26,
"status": "completed",
"document_id": "6a1ce2dc-ccf8-4d7e-acc9-ff1ff3cb942f",
"which": "warm"
},
{
"file": "rfc9110.txt",
"elapsed_s": 1.169,
"chunks": 1,
"status": "completed",
"document_id": "80f821fb-9ac7-49fc-9a9c-589c7514f0e6",
"which": "warm"
}
],
"accuracy": {
"bm25": {
"recall@1": 0.75,
"recall@3": 0.75,
"recall@5": 0.875,
"mrr": 0.775,
"search_ms_median": 8.93
},
"dense": {
"recall@1": 0.75,
"recall@3": 1.0,
"recall@5": 1.0,
"mrr": 0.875,
"search_ms_median": 15.18
},
"hybrid": {
"recall@1": 0.75,
"recall@3": 0.875,
"recall@5": 1.0,
"mrr": 0.844,
"search_ms_median": 12.56
}
},
"scaling": [
{
"n": 1,
"elapsed_s": 0.118
},
{
"n": 2,
"elapsed_s": 0.117
},
{
"n": 3,
"elapsed_s": 0.118
},
{
"n": 4,
"elapsed_s": 0.118
},
{
"n": 5,
"elapsed_s": 0.117
},
{
"n": 6,
"elapsed_s": 0.117
},
{
"n": 7,
"elapsed_s": 0.118
},
{
"n": 8,
"elapsed_s": 0.118
}
]
}

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studio/bench/rag_studio_bench.py Normal file
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#!/usr/bin/env python3
"""Benchmark a running Studio's RAG over its real HTTP API: indexing latency,
scaling, and retrieval accuracy. Same script drives baseline and improved.
Usage:
python rag_studio_bench.py --base http://127.0.0.1:8901 --label baseline \
--password "<bootstrap>" --corpus ../data/rag_corpus --out ../logs/bench_baseline.json
"""
import argparse
import json
import time
from pathlib import Path
import httpx
API = "/api/rag"
# Gold queries: (query, filename_substring, [accepted answer phrases any-of]).
# A hit counts when a retrieved chunk is FROM the right document AND contains an
# accepted phrase, so retrieval is scored independently of the chat model.
GOLD = [
("What are the sinusoidal positional encodings based on?", "attention",
["sine", "sinusoid", "wavelength"]),
("How many attention heads does the base Transformer use?", "attention",
["eight", "h = 8", "8 parallel", "8 attention", "h=8"]),
("What are BERT's two pre-training objectives?", "bert",
["masked", "next sentence"]),
("How many Transformer layers does BERT-large have?", "bert",
["24", "l = 24", "l=24"]),
("Difference between RAG-Sequence and RAG-Token models?", "rag",
["rag-token", "rag-sequence"]),
("Which retriever does RAG use to fetch passages?", "rag",
["dpr", "dense passage", "bi-encoder", "mips"]),
("What does the HTTP GET method do?", "rfc9110",
["transfer a current representation", "retrieve", "selector"]),
("Which HTTP status code means the resource was not found?", "rfc9110",
["404"]),
]
def login(c, base, user, pw):
new = pw + "Aa1!"
# Re-run safe: bootstrap pw works first time; after we change it, the changed
# pw works on later runs.
r = c.post(f"{base}/api/auth/login", json={"username": user, "password": pw})
if r.status_code == 401:
r = c.post(f"{base}/api/auth/login", json={"username": user, "password": new})
r.raise_for_status()
body = r.json()
tok = body["access_token"]
if body.get("must_change_password"):
r2 = c.post(f"{base}/api/auth/change-password", headers=H(tok),
json={"current_password": pw, "new_password": new})
r2.raise_for_status()
tok = r2.json()["access_token"]
return tok
def H(tok):
return {"Authorization": f"Bearer {tok}"}
def warmup(c, base, tok):
t = time.perf_counter()
try:
c.post(f"{base}{API}/warmup", headers=H(tok), timeout=600)
except Exception as e:
print("warmup err:", e)
return time.perf_counter() - t
def create_kb(c, base, tok, name):
r = c.post(f"{base}{API}/knowledge-bases", headers=H(tok),
json={"name": name, "mode": "text", "chunking_strategy": "standard"})
r.raise_for_status()
return r.json()["kb_id" if "kb_id" in r.json() else "id"]
def upload(c, base, tok, kb_id, path: Path):
with open(path, "rb") as f:
r = c.post(f"{base}{API}/knowledge-bases/{kb_id}/documents",
headers=H(tok), files={"file": (path.name, f, "application/octet-stream")},
timeout=600)
r.raise_for_status()
return r.json()
def wait_indexed(c, base, tok, kb_id, document_id, timeout=600):
t0 = time.perf_counter()
while time.perf_counter() - t0 < timeout:
r = c.get(f"{base}{API}/knowledge-bases/{kb_id}/documents", headers=H(tok))
r.raise_for_status()
for d in r.json()["documents"]:
if d["id"] == document_id:
if d["status"] == "completed":
return time.perf_counter() - t0, d["num_chunks"], "completed"
if d["status"] == "failed":
return time.perf_counter() - t0, 0, "failed"
time.sleep(0.1)
return timeout, 0, "timeout"
def index_doc(c, base, tok, kb_id, path):
t0 = time.perf_counter()
up = upload(c, base, tok, kb_id, path)
if up.get("already_indexed"):
return {"file": path.name, "elapsed_s": 0.0, "chunks": 0, "status": "dup"}
elapsed, chunks, status = wait_indexed(c, base, tok, kb_id, up["document_id"])
total = time.perf_counter() - t0
return {"file": path.name, "elapsed_s": round(total, 3), "chunks": chunks,
"status": status, "document_id": up["document_id"]}
def search(c, base, tok, kb_id, query, mode, top_k=10):
r = c.post(f"{base}{API}/search", headers=H(tok),
json={"query": query, "kb_id": kb_id, "mode": mode, "top_k": top_k})
r.raise_for_status()
return r.json()["hits"]
def score_mode(c, base, tok, kb_id, mode):
r1 = r3 = r5 = 0
mrr = 0.0
lat = []
for query, fsub, phrases in GOLD:
t = time.perf_counter()
hits = search(c, base, tok, kb_id, query, mode, top_k=10)
lat.append((time.perf_counter() - t) * 1000)
rank = None
for i, h in enumerate(hits):
fn = (h.get("filename") or "").lower()
txt = (h.get("text") or "").lower()
if fsub in fn and any(p in txt for p in phrases):
rank = i
break
if rank is not None:
if rank == 0:
r1 += 1
if rank < 3:
r3 += 1
if rank < 5:
r5 += 1
mrr += 1.0 / (rank + 1)
n = len(GOLD)
lat.sort()
return {"recall@1": round(r1 / n, 3), "recall@3": round(r3 / n, 3),
"recall@5": round(r5 / n, 3), "mrr": round(mrr / n, 3),
"search_ms_median": round(lat[len(lat) // 2], 2)}
def make_synthetic(dirpath: Path, n: int):
dirpath.mkdir(parents=True, exist_ok=True)
paths = []
for i in range(n):
p = dirpath / f"syn_{i:02d}.txt"
body = (f"Synthetic document number {i}. Project codename Orbit-{i} concerns "
f"widget {i} calibration at {100 + i} hertz. Unique token zglyph{i} marks "
f"this file. " * 8)
p.write_text(body)
paths.append(p)
return paths
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--base", required=True)
ap.add_argument("--label", required=True)
ap.add_argument("--password", required=True)
ap.add_argument("--username", default="unsloth")
ap.add_argument("--corpus", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--scaling-n", type=int, default=8)
args = ap.parse_args()
res = {"label": args.label, "base": args.base}
with httpx.Client(timeout=120) as c:
tok = login(c, args.base, args.username, args.password)
res["warmup_s"] = round(warmup(c, args.base, tok), 2)
# --- Real corpus: index timing (first = cold, rest = warm) + accuracy ---
corpus = sorted(Path(args.corpus).glob("*"))
corpus = [p for p in corpus if p.suffix.lower() in
(".pdf", ".txt", ".md", ".html", ".htm", ".docx")]
kb = create_kb(c, args.base, tok, f"{args.label}-corpus")
res["kb"] = kb
res["corpus_index"] = []
for i, p in enumerate(corpus):
r = index_doc(c, args.base, tok, kb, p)
r["which"] = "cold" if i == 0 else "warm"
res["corpus_index"].append(r)
print(f"[{args.label}] index {p.name}: {r['elapsed_s']}s ({r['chunks']} chunks) {r['which']}")
res["accuracy"] = {m: score_mode(c, args.base, tok, kb, m)
for m in ("bm25", "dense", "hybrid")}
for m, s in res["accuracy"].items():
print(f"[{args.label}] {m}: R@1={s['recall@1']} R@5={s['recall@5']} MRR={s['mrr']}")
# --- Scaling: N small docs into one fresh KB, per-doc index time ---
kb2 = create_kb(c, args.base, tok, f"{args.label}-scaling")
syn = make_synthetic(Path(args.corpus).parent / "rag_synthetic", args.scaling_n)
res["scaling"] = []
for i, p in enumerate(syn):
r = index_doc(c, args.base, tok, kb2, p)
res["scaling"].append({"n": i + 1, "elapsed_s": r["elapsed_s"]})
print(f"[{args.label}] scaling doc {i+1}/{len(syn)}: {r['elapsed_s']}s")
Path(args.out).write_text(json.dumps(res, indent=2))
print(f"[{args.label}] wrote {args.out}")
if __name__ == "__main__":
main()

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studio/bench/studio_rag_ui.py Normal file
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#!/usr/bin/env python3
"""Drive a running Studio's RAG through the real web UI with Playwright:
log in, enable RAG, upload a document via the composer, time indexing to the
"RAG index ready" signal, and capture screenshots + video.
Usage:
python studio_rag_ui.py --base http://127.0.0.1:8905 --label baseline \
--password "<bootstrap-or-changed>" --doc ../data/rag_corpus/bert_1810.04805.pdf \
--out ../outputs/ui_baseline
"""
import argparse
import asyncio
import json
import time
from pathlib import Path
import httpx
from playwright.async_api import async_playwright
def get_token(base, user, pw):
new = pw + "Aa1!"
with httpx.Client(timeout=30) as c:
r = c.post(f"{base}/api/auth/login", json={"username": user, "password": pw})
if r.status_code == 401:
r = c.post(f"{base}/api/auth/login", json={"username": user, "password": new})
r.raise_for_status()
body = r.json()
tok = body["access_token"]
if body.get("must_change_password"):
r2 = c.post(f"{base}/api/auth/change-password",
headers={"Authorization": f"Bearer {tok}"},
json={"current_password": pw, "new_password": new})
r2.raise_for_status()
tok = r2.json()["access_token"]
refresh = r2.json().get("refresh_token", "")
else:
refresh = body.get("refresh_token", "")
return tok, refresh
def init_script(tok, refresh):
seed = {"unsloth_auth_token": tok, "unsloth_refresh_token": refresh}
return f"""(() => {{ const s={json.dumps(seed)};
for (const k of Object.keys(s)) {{ try {{ localStorage.setItem(k, s[k]); }} catch(e){{}} }} }})();"""
async def run(args):
out = Path(args.out)
(out / "video").mkdir(parents=True, exist_ok=True)
tok, refresh = get_token(args.base, args.username, args.password)
result = {"label": args.label, "doc": Path(args.doc).name}
async with async_playwright() as p:
browser = await p.chromium.launch(headless=True)
ctx = await browser.new_context(
viewport={"width": 1440, "height": 900},
record_video_dir=str(out / "video"),
record_video_size={"width": 1440, "height": 900},
)
await ctx.add_init_script(init_script(tok, refresh))
page = await ctx.new_page()
await page.goto(f"{args.base}/chat", wait_until="domcontentloaded")
await page.locator("form:has(textarea) textarea").first.wait_for(state="visible", timeout=30000)
await page.screenshot(path=str(out / "01_chat.png"))
# Enable RAG so the document attach control renders.
enable = page.locator('button[aria-label="Enable RAG"]').first
try:
await enable.click(timeout=8000)
except Exception:
pass # already enabled
await page.wait_for_timeout(500)
await page.screenshot(path=str(out / "02_rag_on.png"))
# Upload via the hidden file input (native picker can't be driven).
file_input = page.locator('input[type="file"][accept*=".pdf"]').first
await file_input.wait_for(state="attached", timeout=8000)
t0 = time.perf_counter()
await file_input.set_input_files(args.doc)
# Wait for the global "RAG index ready" toast (fallback: chip "Ready").
ready_toast = page.get_by_text("RAG index ready", exact=False).first
chip_ready = page.get_by_text("Ready", exact=True).first
indexed = False
deadline = time.perf_counter() + 180
while time.perf_counter() < deadline:
try:
if await ready_toast.is_visible():
indexed = True
break
except Exception:
pass
try:
if await chip_ready.is_visible():
indexed = True
break
except Exception:
pass
await page.wait_for_timeout(250)
elapsed = time.perf_counter() - t0
result["index_seconds"] = round(elapsed, 2)
result["indexed"] = indexed
await page.screenshot(path=str(out / "03_indexed.png"), full_page=True)
print(f"[{args.label}] UI upload->ready: {elapsed:.2f}s indexed={indexed}")
await ctx.close()
await browser.close()
# rename video
webms = sorted((out / "video").glob("*.webm"))
if webms:
webms[-1].rename(out / "video" / f"{args.label}.webm")
Path(out / "result.json").write_text(json.dumps(result, indent=2))
print(f"[{args.label}] wrote {out/'result.json'}")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--base", required=True)
ap.add_argument("--label", required=True)
ap.add_argument("--password", required=True)
ap.add_argument("--username", default="unsloth")
ap.add_argument("--doc", required=True)
ap.add_argument("--out", required=True)
asyncio.run(run(ap.parse_args()))
if __name__ == "__main__":
main()