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修复快照完整性：raw/llm_wiki 由 gitlink 转为普通目录（.git 备份为 .git.bak），新增 .gitignore 排除 __pycache__/pyc 与子仓库元数据

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AAsige
2026-08-07 04:42:28 +08:00
parent ee302f615c
commit c6ec760e71
443 changed files with 147453 additions and 142 deletions
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raw/llm_wiki/scripts/debug_ollama_tokens.py
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#!/usr/bin/env python3
"""
debug_ollama_tokens.py — reproduce LLM Wiki's wiki-generation request against a
raw Ollama endpoint so we can see exactly why generation reports "too many
tokens".
Why this exists
---------------
The app's Step-2 "Generate wiki pages" call (src/lib/ingest.ts) sends, to the
OpenAI-compatible /v1/chat/completions endpoint:
{ model, stream:true, temperature:0.1, reasoning_effort:"none",
max_tokens: computeIngestGenerationMaxTokens(maxContextSize),
messages:[ {system: big generation prompt}, {user: analysis + source} ] }
`maxContextSize` is measured in CHARACTERS (default 204_800). The crucial
mismatch: the OpenAI-compat endpoint has NO num_ctx control, so Ollama serves
with whatever num_ctx the model was loaded at (default, NOT the model's full
262k). When prompt_tokens + max_tokens overflow that window, Ollama complains.
This script lets you:
* see the model's loaded context window (/api/show, /api/ps),
* fire the exact app-shaped request at a chosen prompt size + max_tokens,
* sweep prompt sizes to find the failure threshold,
* compare the OpenAI-compat path (no num_ctx) against the native /api/chat
path WITH options.num_ctx, to confirm num_ctx is the real lever.
Pure stdlib — no pip install. Run: python3 scripts/debug_ollama_tokens.py --help
"""
from __future__ import annotations
import argparse, json, sys, time, urllib.request, urllib.error
# ── The app's actual generation max_tokens ladder (src/lib/ingest.ts:45-48,
# 1687-1693). maxContextSize is in CHARACTERS. ────────────────────────────
def app_generation_max_tokens(max_context_chars: int) -> int:
if max_context_chars >= 512_000: return 32_768
if max_context_chars >= 256_000: return 24_576
if max_context_chars >= 128_000: return 16_384
return 8_192
def http_json(url: str, payload: dict, timeout: float) -> tuple[int, dict | str]:
data = json.dumps(payload).encode()
req = urllib.request.Request(url, data=data, headers={"Content-Type": "application/json"})
try:
with urllib.request.urlopen(req, timeout=timeout) as r:
raw = r.read().decode()
try:
return r.status, json.loads(raw)
except json.JSONDecodeError:
return r.status, raw
except urllib.error.HTTPError as e:
return e.code, e.read().decode()
except Exception as e: # noqa: BLE001 — we want every failure mode visible
return -1, f"{type(e).__name__}: {e}"
def make_prompt(chars: int) -> str:
"""Filler roughly `chars` long (~5 chars/token, so tokens ≈ chars/5)."""
return "word " * max(1, chars // 5)
def show_model(base: str, model: str, timeout: float) -> None:
code, body = http_json(f"{base}/api/show", {"model": model}, timeout)
print(f"── /api/show ({model}) ──")
if isinstance(body, dict):
print(" parameters (Modelfile defaults):")
for line in str(body.get("parameters", "(none)")).splitlines():
print(f" {line}")
mi = body.get("model_info", {})
ctx = next((v for k, v in mi.items() if k.endswith("context_length")), "?")
print(f" model max context_length: {ctx}")
else:
print(f" HTTP {code}: {body}")
code, ps = http_json(f"{base}/api/ps", {}, timeout)
if isinstance(ps, dict):
for m in ps.get("models", []):
if m.get("name", "").startswith(model.split(":")[0]):
print(f" LOADED num_ctx (context_length in /api/ps): {m.get('context_length','?')}")
print()
def call_openai(base: str, model: str, prompt: str, max_tokens: int, timeout: float) -> dict:
"""Exactly what the app sends (OpenAI-compat, no num_ctx possible)."""
payload = {
"model": model, "stream": False, "temperature": 0.1,
"reasoning_effort": "none", "max_tokens": max_tokens,
"messages": [
{"role": "system", "content": "You generate wiki FILE blocks. Reply briefly."},
{"role": "user", "content": prompt},
],
}
t0 = time.time()
code, body = http_json(f"{base}/v1/chat/completions", payload, timeout)
dt = time.time() - t0
out = {"path": "openai", "http": code, "secs": round(dt, 1), "max_tokens": max_tokens}
if isinstance(body, dict):
u = body.get("usage", {})
out.update(prompt_tokens=u.get("prompt_tokens"), completion_tokens=u.get("completion_tokens"),
finish=body.get("choices", [{}])[0].get("finish_reason"))
else:
out["error"] = str(body)[:500]
return out
def call_native(base: str, model: str, prompt: str, max_tokens: int, num_ctx: int | None, timeout: float) -> dict:
"""Native /api/chat — lets us set options.num_ctx, which /v1 cannot."""
options = {"temperature": 0.1, "num_predict": max_tokens}
if num_ctx is not None:
options["num_ctx"] = num_ctx
payload = {
"model": model, "stream": False, "think": False, "options": options,
"messages": [
{"role": "system", "content": "You generate wiki FILE blocks. Reply briefly."},
{"role": "user", "content": prompt},
],
}
t0 = time.time()
code, body = http_json(f"{base}/api/chat", payload, timeout)
dt = time.time() - t0
out = {"path": "native", "http": code, "secs": round(dt, 1), "max_tokens": max_tokens, "num_ctx": num_ctx}
if isinstance(body, dict):
out.update(prompt_eval_count=body.get("prompt_eval_count"), eval_count=body.get("eval_count"),
done_reason=body.get("done_reason"), error=body.get("error"))
else:
out["error"] = str(body)[:500]
return out
def main() -> int:
ap = argparse.ArgumentParser(description="Debug Ollama 'too many tokens' for LLM Wiki generation.")
ap.add_argument("--base", default="http://localhost:11434", help="Ollama base URL")
ap.add_argument("--model", default="gemma4:12b")
ap.add_argument("--timeout", type=float, default=180.0)
ap.add_argument("--prompt-chars", type=int, default=60_000,
help="approx prompt size in characters (tokens ~= chars/5)")
ap.add_argument("--prompt-file", help="use this file's contents as the user prompt instead of filler")
ap.add_argument("--max-tokens", type=int, default=None,
help="override; default = app's ladder for --max-context-chars")
ap.add_argument("--max-context-chars", type=int, default=204_800,
help="the app's maxContextSize (chars); picks max_tokens via the app ladder")
ap.add_argument("--num-ctx", type=int, default=None,
help="native path only: num_ctx to allocate (the lever /v1 lacks)")
ap.add_argument("--native", action="store_true", help="use native /api/chat instead of /v1")
ap.add_argument("--sweep", action="store_true",
help="sweep prompt sizes (2k,20k,60k,120k,200k chars) at the app's max_tokens")
args = ap.parse_args()
max_tokens = args.max_tokens if args.max_tokens is not None else app_generation_max_tokens(args.max_context_chars)
print(f"App ladder: maxContextSize={args.max_context_chars} chars -> max_tokens={max_tokens}\n")
show_model(args.base, args.model, args.timeout)
prompt = open(args.prompt_file, encoding="utf-8").read() if args.prompt_file else None
if args.sweep:
print("── sweep (each row is one generation; watch where http!=200 / error appears) ──")
for pc in [2_000, 20_000, 60_000, 120_000, 200_000]:
p = prompt or make_prompt(pc)
r = (call_native(args.base, args.model, p, max_tokens, args.num_ctx, args.timeout)
if args.native else call_openai(args.base, args.model, p, max_tokens, args.timeout))
print(f" prompt~{pc:>7}c {json.dumps(r)}")
return 0
p = prompt or make_prompt(args.prompt_chars)
r = (call_native(args.base, args.model, p, max_tokens, args.num_ctx, args.timeout)
if args.native else call_openai(args.base, args.model, p, max_tokens, args.timeout))
print("── single request ──")
print(json.dumps(r, indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())