Spaces:
Sleeping
Sleeping
File size: 5,984 Bytes
fe2458a e2ea72c fe2458a |
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 |
from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel
import torch
import torch.nn.functional as F
import logging
import sys
from pinecone_text.sparse import SpladeEncoder
import re
logger = logging.getLogger(__name__)
logging.basicConfig(
level=logging.getLevelName("INFO"),
handlers=[logging.StreamHandler(sys.stdout)],
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
logging.info('Logging module started')
def get_session():
return True
def is_database_online(session: bool = Depends(get_session)):
return session
app = FastAPI()
# app.add_api_route("/healthz", health([is_database_online]))
class EmbeddingModels:
def __init__(self, device="cuda" if torch.cuda.is_available() else "cpu"):
self.device = device
logging.info(f'Using Device {self.device}')
self.sparse_model = SpladeEncoder(device=self.device)
def preprocessing_patent_data(self,text):
# Removing Common tags in patent
pattern0 = r'\b(SUBSTITUTE SHEET RULE 2 SUMMARY OF THE INVENTION|BRIEF DESCRIPTION OF PREFERRED EMBODIMENTS|BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES|BEST MODE FOR CARRYING OUT THE INVENTION|BACKGROUND AND SUMMARY OF THE INVENTION|FIELD AND BACKGROUND OF THE INVENTION|BACKGROUND OF THE PRESENT INVENTION|FIELD AND BACKGROUND OF INVENTION|STAND DER TECHNIK- BACKGROUND ART|BRIEF DESCRIPTION OF THE DRAWINGS|DESCRIPTION OF THE RELATED ART|BRIEF SUMMARY OF THE INVENTION|UTILITY MODEL CLAIMS A CONTENT|DESCRIPTION OF BACKGROUND ART|BRIEF DESCRIPTION OF DRAWINGS|BACKGROUND OF THE INVENTION|BACKGROUND TO THE INVENTION|TÉCNICA ANTERIOR- PRIOR ART|DISCLOSURE OF THE INVENTION|BRIEF SUMMARY OF INVENTION|BACKGROUND OF RELATED ART|SUMMARY OF THE DISCLOSURE|SUMMARY OF THE INVENTIONS|SUMMARY OF THE INVENTION|OBJECTS OF THE INVENTION|THE CONTENT OF INVENTION|DISCLOSURE OF INVENTION|Disclosure of Invention|Complete Specification|RELATED BACKGROUND ART|BACKGROUND INFORMATION|BACKGROUND TECHNOLOGY|DETAILED DESCRIPTION|SUMMARY OF INVENTION|DETAILED DESCRIPTION|PROBLEM TO BE SOLVED|EFFECT OF INVENTION|WHAT IS CLAIMED IS|What is claimed is|What is Claim is|SUBSTITUTE SHEET|SELECTED DRAWING|BACK GROUND ART|BACKGROUND ART|Background Art|JPO&INPIT|CONSTITUTION|DEFINITIONS|Related Art|BACKGROUND|JPO&INPIT|JPO&NCIPI|COPYRIGHT|SOLUTION|SUMMARY)\b'
text = re.sub(pattern0, '[SEP]', text, flags=re.IGNORECASE)
text = ' '.join(text.split())
# Removing all tags between Heading to /Heading and id=
regex = r'<\s*heading[^>]*>(.*?)<\s*/\s*heading>|<[^<]+>|id=\"p-\d+\"|:'
result = re.sub(regex, '[SEP]', text, flags=re.IGNORECASE)
# find_formula_names from pat text to exclude it from below logic regex
chemical_list = []
pattern1 = r'\b((?:(?:H|He|Li|Be|B|C|N|O|F|Ne|Na|Mg|Al|Si|P|S|Cl|Ar|K|Ca|Sc|Ti|V|Cr|Mn|Fe|Co|Ni|Cu|Zn|Ga|Ge|As|Se|Br|Kr|Rb|Sr|Y|Zr|Nb|Mo|Tc|Ru|Rh|Pd|Ag|Cd|In|Sn|Sb|Te|I|Xe|Cs|Ba|La|Hf|Ta|W|Re|Os|Ir|Pt|Au|Hg|Tl|Pb|Bi|Po|At|Rn|Fr|Ra|Ac|Rf|Db|Sg|Bh|Hs|Mt|Ds|Rg|Cn|Nh|Fl|Mc|Lv|Ts|Og|Ce|Pr|Nd|Pm|Sm|Eu|Gd|Tb|Dy|Ho|Er|Tm|Yb|Lu|Th|Pa|U|Np|Pu|Am|Cm|Bk|Cf|Es|Fm|Md|No|Lr)\d*)+)\b'
formula_names = re.findall(pattern1, result)
for formula in formula_names:
if len(formula)>=2:
chemical_list.append(formula)
# print("chemical_list:", chemical_list)
# Remove numbers and alphanum inside brackets excluding chemical forms
pattern2 = r"\((?![A-Za-z]+\))[\w\d\s,-]+\)|\([A-Za-z]\)"
def keep_strings(text):
matched = text.group(0)
if any(item in matched for item in chemical_list):
return matched
return ' '
cleaned_text = re.sub(pattern2, keep_strings, result)
cleaned_text = ' '.join(cleaned_text.split())
cleaned_text= re.sub("(\[SEP\]+\s*)+", ' ', cleaned_text, flags=re.IGNORECASE)
# below new logic to remove chemical compounds (eg.chemical- polymerizable compounds)
p_text2=re.sub('[\—\-\═\=]', ' ', cleaned_text)
pattern1 = r'\b((?:(?:H|He|Li|Be|B|C|N|O|F|Ne|Na|Mg|Al|Si|P|S|Cl|Ar|K|Ca|Sc|Ti|V|Cr|Mn|Fe|Co|Ni|Cu|Zn|Ga|Ge|As|Se|Br|Kr|Rb|Sr|Y|Zr|Nb|Mo|Tc|Ru|Rh|Pd|Ag|Cd|In|Sn|Sb|Te|I|Xe|Cs|Ba|La|Hf|Ta|W|Re|Os|Ir|Pt|Au|Hg|Tl|Pb|Bi|Po|At|Rn|Fr|Ra|Ac|Rf|Db|Sg|Bh|Hs|Mt|Ds|Rg|Cn|Nh|Fl|Mc|Lv|Ts|Og|Ce|Pr|Nd|Pm|Sm|Eu|Gd|Tb|Dy|Ho|Er|Tm|Yb|Lu|Th|Pa|U|Np|Pu|Am|Cm|Bk|Cf|Es|Fm|Md|No|Lr)\d*)+)\b'
cleaned_text = re.sub(pattern1, "", p_text2)
cleaned_text = re.sub(' ,+|, +', ' ', cleaned_text)
cleaned_text = re.sub(' +', ' ', cleaned_text)
cleaned_text = re.sub('\.+', '.', cleaned_text)
cleaned_text = re.sub('[0-9] [0-9] +', ' ', cleaned_text)
cleaned_text = re.sub('( )', ' ', cleaned_text)
cleaned_text=cleaned_text.strip()
return cleaned_text
def get_single_sparse_text_embedding(self, df_chunk):
df_chunk = self.preprocessing_patent_data(df_chunk)
txt_sp = self.sparse_model.encode_documents(df_chunk)
# tensor = torch.tensor(txt_sp['values'])
# normalized_tensor = F.normalize(tensor, p=2.0, dim=0, eps=1e-12)
# values = normalized_tensor.tolist()
# # Update the sparse_vector with normalized values
# normalized_sparse_vector = {
# 'indices': txt_sp['indices'],
# 'values': values
# }
# return normalized_sparse_vector
return txt_sp
model = EmbeddingModels()
class TextInput(BaseModel):
text: str
@app.post("/sparse/")
async def embed_text(item: TextInput):
try:
logging.info(f'Received text for embedding: {item.text}')
embeddings = model.get_single_sparse_text_embedding(item.text)
logging.info('Embedding process completed')
return embeddings
except Exception as e:
logging.error(f'Error during embedding process: {e}')
raise HTTPException(status_code=500, detail=str(e))
|