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985f3ee | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | import os
import sys
import json
import hashlib
from pathlib import Path
from typing import Dict, List
from dotenv import load_dotenv
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import (
MarkdownHeaderTextSplitter,
RecursiveCharacterTextSplitter,
)
from langchain_core.documents import Document
load_dotenv()
BASE_DIR = Path(__file__).resolve().parent.parent
DATA_DIR = BASE_DIR / "data"
DB_DIR = BASE_DIR / "db"
STATE_FILE = DB_DIR / "ingestion_state.json"
# Folders to parse for the Vector DB
INGEST_DIRS = ["github", "raw", "linkedin", "static"]
def get_file_hash(filepath: Path) -> str:
with open(filepath, "r", encoding="utf-8") as f:
return hashlib.md5(f.read().encode("utf-8")).hexdigest()
def load_state() -> Dict[str, Dict]:
if not STATE_FILE.exists():
return {}
try:
with open(STATE_FILE, "r", encoding="utf-8") as f:
raw_state = json.load(f)
if isinstance(raw_state, dict) and "files" in raw_state and isinstance(raw_state["files"], dict):
normalized = {}
for rel_path, entry in raw_state["files"].items():
if isinstance(entry, dict):
normalized[rel_path] = {
"hash": entry.get("hash", ""),
"chunk_ids": entry.get("chunk_ids", []),
}
return normalized
normalized = {}
if isinstance(raw_state, dict):
for rel_path, value in raw_state.items():
if isinstance(value, str):
normalized[rel_path] = {
"hash": value,
"chunk_ids": [],
}
elif isinstance(value, dict):
normalized[rel_path] = {
"hash": value.get("hash", ""),
"chunk_ids": value.get("chunk_ids", []),
}
return normalized
except Exception as e:
print(f"[LOAD STATE ERROR] {e}")
return {}
def save_state(state: Dict[str, Dict]) -> None:
DB_DIR.mkdir(parents=True, exist_ok=True)
with open(STATE_FILE, "w", encoding="utf-8") as f:
json.dump({"files": state}, f, indent=2)
def run_ingestion(force: bool = False):
print("=" * 60)
print("ArunCore Vector Knowledge Ingestion Pipeline (ChromaDB)")
print("=" * 60)
state = load_state()
current_files: List[Path] = []
for folder in INGEST_DIRS:
target_dir = DATA_DIR / folder
if target_dir.exists():
for p in target_dir.rglob("*.md"):
current_files.append(p)
if not current_files:
print("[WARNING] No markdown files found in data/ directories.")
return
openai_key = os.getenv("OPENAI_API_KEY")
if not openai_key:
print("[ERROR] OPENAI_API_KEY environment variable is missing.")
sys.exit(1)
embeddings = OpenAIEmbeddings(
model="text-embedding-3-small",
api_key=openai_key,
)
vector_store = Chroma(
persist_directory=str(DB_DIR),
embedding_function=embeddings,
)
headers_to_split_on = [
("#", "Header 1"),
("##", "Header 2"),
("###", "Header 3"),
]
markdown_splitter = MarkdownHeaderTextSplitter(
headers_to_split_on=headers_to_split_on,
strip_headers=False,
)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=150,
)
processed_count = 0
for file_path in current_files:
rel_path = str(file_path.relative_to(BASE_DIR))
current_hash = get_file_hash(file_path)
stored_hash = state.get(rel_path, {}).get("hash", "")
old_chunk_ids = state.get(rel_path, {}).get("chunk_ids", [])
if not force and stored_hash == current_hash:
print(f"[SKIP] {rel_path} (unchanged)")
continue
print(f"[INGESTING] {rel_path}...")
if old_chunk_ids:
try:
vector_store.delete(ids=old_chunk_ids)
except Exception as e:
print(f" └─ Warning deleting old chunk IDs: {e}")
with open(file_path, "r", encoding="utf-8") as f:
content = f.read()
md_header_splits = markdown_splitter.split_text(content)
splits = text_splitter.split_documents(md_header_splits)
documents_to_add: List[Document] = []
new_chunk_ids: List[str] = []
folder_category = rel_path.split(os.sep)[1] if len(rel_path.split(os.sep)) > 1 else "general"
for idx, split in enumerate(splits):
chunk_id = f"{folder_category}_{file_path.stem}_chunk_{idx}_{current_hash[:6]}"
new_chunk_ids.append(chunk_id)
meta = split.metadata or {}
meta["source"] = rel_path
meta["category"] = folder_category
meta["chunk_id"] = chunk_id
doc = Document(
page_content=split.page_content,
metadata=meta,
)
documents_to_add.append(doc)
if documents_to_add:
vector_store.add_documents(documents_to_add, ids=new_chunk_ids)
state[rel_path] = {
"hash": current_hash,
"chunk_ids": new_chunk_ids,
}
processed_count += 1
save_state(state)
print("=" * 60)
print(f"[SUCCESS] Ingestion completed. Processed {processed_count} updated files into ChromaDB.")
print("=" * 60)
if __name__ == "__main__":
force_run = "--force" in sys.argv
run_ingestion(force=force_run)
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