Spaces:
Sleeping
Sleeping
File size: 7,356 Bytes
90645a4 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | """Calculate total token counts over data/ folder.
Reports three tiers:
1. Raw — full OpenAlex JSON as-is on disk
2. Optimised — only the fields the RAG pipeline actually reads
(id, title, abstract, authorships, topics, keywords,
referenced_works, cited_by_count, publication_year, doi,
best_oa_location.pdf_url)
3. LLM context — what actually reaches the embedding model and the LLM:
plain-text "Title: …\\n\\nAbstract: …" per paper
"""
import json
import sys
from pathlib import Path
# data/ lives at the project root, two levels up from utils/
DATA_DIR = Path(__file__).parents[1] / "data"
# Approximate token ratio (1 token ≈ 4 chars for English text — GPT/Llama heuristic)
CHARS_PER_TOKEN = 4
# Fields the pipeline actually uses
_KEEP_KEYS = {
"id", "title", "display_name", "abstract_inverted_index",
"publication_year", "cited_by_count",
"authorships", "topics", "keywords", "referenced_works",
"primary_topic", "doi", "type",
}
def reconstruct_abstract(inv_index: dict) -> str:
"""Rebuild plain-text abstract from OpenAlex inverted index."""
if not inv_index:
return ""
pairs = []
for word, positions in inv_index.items():
for pos in positions:
pairs.append((pos, word))
pairs.sort()
return " ".join(w for _, w in pairs)
def slim_authorships(authorships: list) -> list:
"""Keep only author name + position (drop institutions, affiliations, etc.)."""
return [
{
"name": a.get("author", {}).get("display_name", ""),
"position": a.get("author_position", ""),
}
for a in (authorships or [])
]
def optimise_paper(raw: dict) -> dict:
"""Strip a raw OpenAlex JSON to only pipeline-relevant fields."""
optimised = {k: raw[k] for k in _KEEP_KEYS if k in raw}
# Replace inverted index with plain text abstract
if "abstract_inverted_index" in optimised:
optimised["abstract"] = reconstruct_abstract(optimised.pop("abstract_inverted_index"))
# Slim authorships
if "authorships" in optimised:
optimised["authorships"] = slim_authorships(optimised["authorships"])
# Slim topics to just display_name
if "topics" in optimised:
optimised["topics"] = [t.get("display_name", "") for t in (optimised["topics"] or [])]
# Slim keywords
if "keywords" in optimised:
optimised["keywords"] = [k.get("display_name", "") for k in (optimised["keywords"] or [])]
# Slim referenced_works to just IDs
if "referenced_works" in optimised:
optimised["referenced_works"] = [
r.rsplit("/", 1)[-1] if "/" in r else r
for r in (optimised["referenced_works"] or [])
]
return optimised
def count_tokens(text: str) -> dict:
chars = len(text)
words = len(text.split())
tokens_est = chars // CHARS_PER_TOKEN
return {"chars": chars, "words": words, "tokens_est": tokens_est}
def main():
if not DATA_DIR.exists():
print(f"ERROR: {DATA_DIR} not found. Expected: {DATA_DIR.resolve()}")
sys.exit(1)
files = sorted(DATA_DIR.glob("*.json"))
total = len(files)
print(f"Found {total} JSON files in {DATA_DIR.resolve()}\n")
raw_total = {"chars": 0, "words": 0, "tokens_est": 0, "bytes": 0}
opt_total = {"chars": 0, "words": 0, "tokens_est": 0, "bytes": 0}
llm_total = {"chars": 0, "words": 0, "tokens_est": 0, "bytes": 0}
for f in files:
raw_text = f.read_text(encoding="utf-8")
raw_total["bytes"] += len(raw_text.encode("utf-8"))
stats = count_tokens(raw_text)
raw_total["chars"] += stats["chars"]
raw_total["words"] += stats["words"]
raw_total["tokens_est"] += stats["tokens_est"]
try:
data = json.loads(raw_text)
# Tier 2: optimised JSON (all pipeline-read fields)
optimised = optimise_paper(data)
opt_text = json.dumps(optimised, ensure_ascii=False)
opt_total["bytes"] += len(opt_text.encode("utf-8"))
opt_stats = count_tokens(opt_text)
opt_total["chars"] += opt_stats["chars"]
opt_total["words"] += opt_stats["words"]
opt_total["tokens_est"] += opt_stats["tokens_est"]
# Tier 3: LLM context — the exact text sent to the embedding model and LLM
# Mirrors indexer.py _doc_text() and setup.py embed text construction
title = data.get("title") or ""
abstract = reconstruct_abstract(data.get("abstract_inverted_index") or {})
llm_text = f"Title: {title}\n\nAbstract: {abstract}"
llm_total["bytes"] += len(llm_text.encode("utf-8"))
llm_stats = count_tokens(llm_text)
llm_total["chars"] += llm_stats["chars"]
llm_total["words"] += llm_stats["words"]
llm_total["tokens_est"] += llm_stats["tokens_est"]
except json.JSONDecodeError:
pass
def fmt(n):
return f"{n:,}"
print("=" * 60)
print(" TIER 1 — RAW (full OpenAlex JSON as-is on disk)")
print("=" * 60)
print(f" Files: {fmt(total)}")
print(f" Total bytes: {fmt(raw_total['bytes'])} ({raw_total['bytes'] / 1e6:.1f} MB)")
print(f" Total chars: {fmt(raw_total['chars'])}")
print(f" Total words: {fmt(raw_total['words'])}")
print(f" Est. tokens: {fmt(raw_total['tokens_est'])} (~{raw_total['tokens_est'] / 1e6:.2f}M)")
print()
print("=" * 60)
print(" TIER 2 — OPTIMISED (pipeline-relevant fields only)")
print(" Fields: id, title, abstract, authorships, topics,")
print(" keywords, referenced_works, doi, year,")
print(" cited_by_count, best_oa_location.pdf_url")
print("=" * 60)
print(f" Files: {fmt(total)}")
print(f" Total bytes: {fmt(opt_total['bytes'])} ({opt_total['bytes'] / 1e6:.1f} MB)")
print(f" Total chars: {fmt(opt_total['chars'])}")
print(f" Total words: {fmt(opt_total['words'])}")
print(f" Est. tokens: {fmt(opt_total['tokens_est'])} (~{opt_total['tokens_est'] / 1e6:.2f}M)")
print()
print("=" * 60)
print(" TIER 3 — LLM CONTEXT (what reaches the embedding model + LLM)")
print(' Format: "Title: …\\n\\nAbstract: …" per paper')
print("=" * 60)
print(f" Files: {fmt(total)}")
print(f" Total bytes: {fmt(llm_total['bytes'])} ({llm_total['bytes'] / 1e6:.1f} MB)")
print(f" Total chars: {fmt(llm_total['chars'])}")
print(f" Total words: {fmt(llm_total['words'])}")
print(f" Est. tokens: {fmt(llm_total['tokens_est'])} (~{llm_total['tokens_est'] / 1e6:.2f}M)")
print()
r2_pct = (1 - opt_total["bytes"] / raw_total["bytes"]) * 100 if raw_total["bytes"] else 0
r3_pct = (1 - llm_total["bytes"] / raw_total["bytes"]) * 100 if raw_total["bytes"] else 0
print(f" Tier 1 → Tier 2 reduction: {r2_pct:.1f}% "
f"({fmt(raw_total['tokens_est'] - opt_total['tokens_est'])} tokens saved)")
print(f" Tier 1 → Tier 3 reduction: {r3_pct:.1f}% "
f"({fmt(raw_total['tokens_est'] - llm_total['tokens_est'])} tokens saved)")
if __name__ == "__main__":
main()
|