File size: 8,834 Bytes
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Chunk quality-filtered documents into:
1. A pre-training corpus (data/pretrain/corpus_pretrain.txt)
- Concatenated source code with document separators
- Used for base LM pre-training
2. Retrieval chunks (data/chunks/chunks.jsonl)
- Semantically meaningful code units (functions, classes, structs)
- Used by the search agent and for SFT trace generation
Input: data/quality/documents_quality.jsonl
Output: data/pretrain/corpus_pretrain.txt
data/chunks/chunks.jsonl
"""
import json
import os
import re
PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
DATA_DIR = os.path.join(PROJECT_DIR, "data")
QUALITY_DOCS_PATH = os.path.join(DATA_DIR, "quality", "documents_quality.jsonl")
PRETRAIN_DIR = os.path.join(DATA_DIR, "pretrain")
CORPUS_PRETRAIN_PATH = os.path.join(PRETRAIN_DIR, "corpus_pretrain.txt")
CHUNKS_DIR = os.path.join(DATA_DIR, "chunks")
CHUNKS_PATH = os.path.join(CHUNKS_DIR, "chunks.jsonl")
# ─── Chunk start patterns per language ───────────────────────────────────────
CHUNK_STARTS = {
"python": re.compile(r"^(def |class |@|async def )", re.M),
"javascript": re.compile(r"^(function |const |let |class |export |async function|interface |type \w+ =)", re.M),
"typescript": re.compile(r"^(function |const |let |class |export |async function|interface |type \w+ =|enum )", re.M),
"rust": re.compile(r"^(fn |pub fn |impl |struct |enum |trait |mod |macro_rules!|pub struct|pub enum|pub trait)", re.M),
"go": re.compile(r"^(func |type \w+ struct|type \w+ interface)", re.M),
"c": re.compile(r"^(static |void |int |char |size_t |typedef |struct \w+ \{|#define|#if|#ifndef|ngx_)", re.M),
"cpp": re.compile(r"^(static |void |int |char |size_t |template |class \w+|namespace |struct \w+|#define|#if|#ifndef)", re.M),
"java": re.compile(r"^(public |private |protected |class \w+|interface |enum )", re.M),
"csharp": re.compile(r"^(public |private |protected |internal |class \w+|interface |enum )", re.M),
"ruby": re.compile(r"^(def |class |module )", re.M),
}
NAME_PATTERNS = [
(re.compile(r"def (\w+)"), "function"),
(re.compile(r"class (\w+)"), "class"),
(re.compile(r"fn (\w+)"), "function"),
(re.compile(r"pub fn (\w+)"), "function"),
(re.compile(r"struct (\w+)"), "struct"),
(re.compile(r"enum (\w+)"), "enum"),
(re.compile(r"trait (\w+)"), "trait"),
(re.compile(r"impl (\w+)"), "impl"),
(re.compile(r"func (\w+)"), "function"),
(re.compile(r"type (\w+) struct"), "struct"),
(re.compile(r"type (\w+) interface"), "interface"),
(re.compile(r"function (\w+)"), "function"),
(re.compile(r"async function (\w+)"), "function"),
(re.compile(r"typedef struct (\w+)"), "typedef"),
(re.compile(r"#define (\w+)"), "macro"),
(re.compile(r"interface (\w+)"), "interface"),
(re.compile(r"module (\w+)"), "module"),
]
def extract_name(code: str) -> tuple[str, str]:
for pat, typ in NAME_PATTERNS:
m = pat.search(code)
if m:
return m.group(1), typ
return "unknown", "block"
def chunk_document(doc: dict) -> list[dict]:
"""Split a document into chunks based on language-specific patterns."""
content = doc["content"]
lang = doc["language"]
filepath = doc["filepath"]
repo = doc["repo"]
lines = content.split("\n")
n_lines = len(lines)
start_pat = CHUNK_STARTS.get(lang)
if start_pat is None:
name, typ = extract_name(content)
return [{
"id": f"{repo}:{filepath}:0",
"language": lang,
"name": name,
"type": typ,
"code": content,
"filepath": filepath,
"repo": repo,
"start_line": 1,
"end_line": n_lines,
}]
starts = [(m.start(), m.group()) for m in start_pat.finditer(content)]
if not starts:
name, typ = extract_name(content)
return [{
"id": f"{repo}:{filepath}:0",
"language": lang,
"name": name,
"type": typ,
"code": content,
"filepath": filepath,
"repo": repo,
"start_line": 1,
"end_line": n_lines,
}]
if starts[0][0] > 0:
starts.insert(0, (0, ""))
chunks = []
for i, (start_pos, _) in enumerate(starts):
end_pos = starts[i + 1][0] if i + 1 < len(starts) else len(content)
chunk_code = content[start_pos:end_pos].strip()
if len(chunk_code) < 40:
continue
# Split huge chunks on blank lines
if len(chunk_code) > 6000:
sub_parts = re.split(r"\n\n+", chunk_code)
for j, part in enumerate(sub_parts):
part = part.strip()
if len(part) < 40:
continue
name, typ = extract_name(part)
start_line = content[:start_pos].count("\n") + 1 + sum(p.count("\n") + 2 for p in sub_parts[:j])
chunks.append({
"id": f"{repo}:{filepath}:{i}_{j}",
"language": lang,
"name": name,
"type": typ,
"code": part,
"filepath": filepath,
"repo": repo,
"start_line": start_line,
"end_line": start_line + part.count("\n"),
})
continue
name, typ = extract_name(chunk_code)
start_line = content[:start_pos].count("\n") + 1
chunks.append({
"id": f"{repo}:{filepath}:{i}",
"language": lang,
"name": name,
"type": typ,
"code": chunk_code,
"filepath": filepath,
"repo": repo,
"start_line": start_line,
"end_line": start_line + chunk_code.count("\n"),
})
return chunks
def main():
os.makedirs(PRETRAIN_DIR, exist_ok=True)
os.makedirs(CHUNKS_DIR, exist_ok=True)
print(f"Loading quality documents from {QUALITY_DOCS_PATH}...")
docs = []
with open(QUALITY_DOCS_PATH, "r", encoding="utf-8") as f:
for line in f:
docs.append(json.loads(line))
print(f" Loaded {len(docs):,} documents")
print(f" Total size: {sum(len(d['content']) for d in docs) / 1e6:.1f} MB")
# ─── Build pre-training corpus ─────────────────────────────────────────
print("\nBuilding pre-training corpus...")
corpus_size = 0
with open(CORPUS_PRETRAIN_PATH, "w", encoding="utf-8") as f:
for doc in docs:
f.write(doc["content"].strip())
f.write("\n\n\n") # document separator
corpus_size += len(doc["content"])
print(f" Pre-training corpus: {corpus_size / 1e6:.1f} MB -> {CORPUS_PRETRAIN_PATH}")
# ─── Build retrieval chunks ────────────────────────────────────────────
print("\nChunking documents...")
all_chunks = []
lang_counts = {}
type_counts = {}
for doc in docs:
chunks = chunk_document(doc)
for chunk in chunks:
all_chunks.append(chunk)
lang_counts[chunk["language"]] = lang_counts.get(chunk["language"], 0) + 1
type_counts[chunk["type"]] = type_counts.get(chunk["type"], 0) + 1
print(f"\nTotal chunks: {len(all_chunks):,}")
print(f" By language: {lang_counts}")
print(f" By type: {type_counts}")
sizes = [len(c["code"]) for c in all_chunks]
if sizes:
sizes.sort()
print(f" Chunk size: min={sizes[0]}, median={sizes[len(sizes)//2]}, "
f"max={sizes[-1]}, mean={sum(sizes)//len(sizes)}")
with open(CHUNKS_PATH, "w", encoding="utf-8") as f:
for chunk in all_chunks:
f.write(json.dumps(chunk, ensure_ascii=False) + "\n")
print(f"\nChunks written to {CHUNKS_PATH}")
# Stats
stats = {
"input_docs": len(docs),
"pretrain_corpus_mb": corpus_size / 1e6,
"total_chunks": len(all_chunks),
"language_distribution": lang_counts,
"type_distribution": type_counts,
}
stats_path = os.path.join(CHUNKS_DIR, "chunk_stats.json")
with open(stats_path, "w") as f:
json.dump(stats, f, indent=2)
print(f"Stats written to {stats_path}")
if __name__ == "__main__":
main()
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