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ca1fc7c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 | #!/usr/bin/env python3
"""
Dynamic RAG Database Updater
Processes new PDFs and updates the vector database in real-time
"""
import os
import json
import numpy as np
from pathlib import Path
from typing import List, Dict
import pickle
from datetime import datetime
# PDF processing
from pdf2image import convert_from_path
# OCR (CPU optimized)
from paddleocr import PaddleOCR
# Embeddings
from sentence_transformers import SentenceTransformer
# FAISS (CPU)
import faiss
class DynamicRAGUpdater:
"""
Handles dynamic updates to RAG database:
1. Upload PDF
2. OCR extraction (PaddleOCR CPU)
3. Generate embeddings (BiomedBERT)
4. Update FAISS index
5. Update metadata
"""
def __init__(
self,
vector_db_path: str,
embedding_model: str = "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
upload_dir: str = "uploaded_reports"
):
self.vector_db_path = Path(vector_db_path)
self.upload_dir = Path(upload_dir)
self.upload_dir.mkdir(exist_ok=True)
self.ocr_dir = self.upload_dir / "ocr_text"
self.embeddings_dir = self.upload_dir / "embeddings"
self.ocr_dir.mkdir(exist_ok=True)
self.embeddings_dir.mkdir(exist_ok=True)
# PaddleOCR (explicit CPU mode)
self.ocr = PaddleOCR(
use_angle_cls=True,
lang="en",
cpu_threads=4,
enable_mkldnn=True
)
# BiomedBERT only
self.embedding_model = SentenceTransformer(
embedding_model,
device="cpu"
)
self.embedding_dim = self.embedding_model.get_sentence_embedding_dimension()
self.load_database()
def load_database(self):
index_file = self.vector_db_path / "faiss.index"
metadata_file = self.vector_db_path / "metadata.pkl"
self.faiss_index = faiss.read_index(str(index_file))
with open(metadata_file, "rb") as f:
data = pickle.load(f)
self.chunks = data["chunks"]
self.chunk_id_to_idx = data["chunk_id_to_idx"]
def save_database(self):
faiss.write_index(self.faiss_index, str(self.vector_db_path / "faiss.index"))
with open(self.vector_db_path / "metadata.pkl", "wb") as f:
pickle.dump(
{
"chunks": self.chunks,
"chunk_id_to_idx": self.chunk_id_to_idx,
"embedding_dim": self.embedding_dim,
"model": "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext"
},
f
)
def extract_text_from_pdf(self, pdf_path: str) -> str:
images = convert_from_path(pdf_path, dpi=300)
full_text = []
for page_num, image in enumerate(images, 1):
image_np = np.array(image)
ocr_result = self.ocr.ocr(image_np, cls=True)
page_text = []
if ocr_result and ocr_result[0]:
for line in ocr_result[0]:
page_text.append(line[1][0])
full_text.append(
f"\n{'='*50}\nPAGE {page_num}\n{'='*50}\n" +
"\n".join(page_text)
)
return "\n".join(full_text)
def chunk_text(self, text: str, chunk_size: int = 512) -> List[str]:
sentences = text.split(". ")
chunks = []
current = []
length = 0
for s in sentences:
s = s.strip()
if not s:
continue
s = s + ". "
if length + len(s) > chunk_size and current:
chunks.append("".join(current))
current = [s]
length = len(s)
else:
current.append(s)
length += len(s)
if current:
chunks.append("".join(current))
return chunks
def generate_embeddings(self, chunks: List[str]) -> np.ndarray:
return self.embedding_model.encode(
chunks,
batch_size=32,
convert_to_numpy=True,
show_progress_bar=True
)
def add_to_database(
self,
embeddings: np.ndarray,
chunks: List[str],
filename: str
) -> int:
start_idx = self.faiss_index.ntotal
self.faiss_index.add(embeddings.astype("float32"))
for i, text in enumerate(chunks):
meta = {
"chunk_id": start_idx + i,
"text": text,
"filename": filename,
"upload_date": datetime.now().isoformat(),
"source": "user_upload"
}
self.chunks.append(meta)
self.chunk_id_to_idx[f"{filename}_{i}"] = start_idx + i
return len(embeddings)
def process_and_add_pdf(self, pdf_path: str) -> Dict:
start = datetime.now()
filename = Path(pdf_path).stem
text = self.extract_text_from_pdf(pdf_path)
(self.ocr_dir / f"{filename}.txt").write_text(text, encoding="utf-8")
chunks = self.chunk_text(text)
embeddings = self.generate_embeddings(chunks)
np.save(self.embeddings_dir / f"{filename}_embeddings.npy", embeddings)
vectors_added = self.add_to_database(embeddings, chunks, filename)
self.save_database()
return {
"filename": filename,
"text_length": len(text),
"num_chunks": len(chunks),
"vectors_added": vectors_added,
"total_vectors": self.faiss_index.ntotal,
"processing_time_seconds": (datetime.now() - start).total_seconds(),
"timestamp": datetime.now().isoformat()
}
def main():
vector_db_path = "/usr/users/3d_dimension_est/selva_sur/RAG/output/biomedbert_vector_db"
updater = DynamicRAGUpdater(
vector_db_path=vector_db_path,
embedding_model="microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext",
upload_dir="uploaded_reports"
)
test_pdf = "path/to/new_report.pdf"
if Path(test_pdf).exists():
stats = updater.process_and_add_pdf(test_pdf)
print(json.dumps(stats, indent=2))
else:
print("Test PDF not found. Update the path in main().")
if __name__ == "__main__":
main()
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