File size: 5,414 Bytes
bdb1957 | 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 | import os
import hashlib
import shutil
import pandas as pd
from langchain_community.document_loaders import UnstructuredFileLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.schema import Document
# -------------------------------
# Configuration
# -------------------------------
BASE_DIR = "resources/data"
CHROMA_DIR = "chroma_db"
# Known departments
DEPARTMENTS = ["engineering", "finance", "general", "hr", "marketing"]
embedding_model = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# Markdown/CSV-aware splitter — respects heading boundaries
md_splitter = RecursiveCharacterTextSplitter(
chunk_size=500,
chunk_overlap=50,
separators=["\n## ", "\n### ", "\n\n", "\n", " "]
)
# -------------------------------
# Deduplication
# -------------------------------
seen_hashes = set()
def get_hash(text: str) -> str:
return hashlib.md5(text.strip().encode()).hexdigest()
def deduplicate(docs: list) -> list:
unique = []
for doc in docs:
h = get_hash(doc.page_content)
if h not in seen_hashes:
seen_hashes.add(h)
unique.append(doc)
return unique
# -------------------------------
# Main ingestion loop
# -------------------------------
all_split_docs = []
for department in DEPARTMENTS:
dept_path = os.path.join(BASE_DIR, department)
if not os.path.isdir(dept_path):
print(f"⚠️ Folder not found, skipping: {dept_path}")
continue
print(f"\n🔍 Processing: {department}")
dept_docs = []
for file in sorted(os.listdir(dept_path)):
file_path = os.path.join(dept_path, file)
file_ext = os.path.splitext(file)[-1].lower()
# -------------------------------
# Handle CSV files
# -------------------------------
if file_ext == ".csv":
print(f" 📄 Loading CSV for embedding: {file}")
try:
df = pd.read_csv(file_path)
for _, row in df.iterrows():
# Convert each row into a text document
text = "\n".join([f"{col}: {row[col]}" for col in df.columns])
doc = Document(
page_content=text,
metadata={
"source": file,
"file_type": ".csv",
"role": department.lower(),
"category": department.lower()
}
)
dept_docs.append(doc)
print(f" ✅ Loaded {len(df)} rows from {file}")
except Exception as e:
print(f" ❌ Failed to load CSV {file}: {e}")
continue
# -------------------------------
# Handle Markdown files
# -------------------------------
if file_ext != ".md":
print(f" ⏭️ Skipping unsupported file type: {file}")
continue
try:
try:
loader = UnstructuredFileLoader(file_path)
docs = loader.load()
except Exception:
loader = TextLoader(file_path, encoding="utf-8")
docs = loader.load()
for doc in docs:
doc.metadata["source"] = file
doc.metadata["file_type"] = ".md"
doc.metadata["role"] = department.lower()
doc.metadata["category"] = department.lower()
dept_docs.extend(docs)
print(f" 📄 Loaded: {file} ({len(docs)} doc(s))")
except Exception as e:
print(f" ❌ Failed to load {file}: {e}")
if not dept_docs:
print(f" ⚠️ No documents loaded for: {department}")
continue
# -------------------------------
# Split large documents
# -------------------------------
split_docs = md_splitter.split_documents(dept_docs)
# -------------------------------
# Deduplicate
# -------------------------------
split_docs = deduplicate(split_docs)
all_split_docs.extend(split_docs)
print(f" ✅ {len(split_docs)} unique chunks stored for: {department}")
# -------------------------------
# Build Chroma DB
# -------------------------------
if not all_split_docs:
print("\n❌ No documents to embed. Check your resources/data folders.")
exit(1)
print(f"\n⚙️ Building Chroma DB with {len(all_split_docs)} total chunks...")
shutil.rmtree(CHROMA_DIR, ignore_errors=True)
db = Chroma.from_documents(
documents=all_split_docs,
embedding=embedding_model,
persist_directory=CHROMA_DIR,
collection_name="company_docs"
)
# -------------------------------
# Validation Summary
# -------------------------------
stored = db._collection.get()
roles_found = sorted({m.get("role", "?") for m in stored["metadatas"]})
sources_found = sorted({m.get("source", "?") for m in stored["metadatas"]})
print(f"\n🎉 Embedding complete!")
print(f" Total chunks : {len(stored['ids'])}")
print(f" Roles indexed : {roles_found}")
print(f" Files indexed : {sources_found}")
print(f"\n📋 Sample metadata (first 3):")
for meta in stored["metadatas"][:3]:
print(f" {meta}") |