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import os
from langchain.embeddings import HuggingFaceEmbeddings
from pymilvus import Collection
from PyPDF2 import PdfReader
import pandas as pd
import docx
# Extract text from document
def extract_text(file):
if file.type == "application/pdf":
pdf = PdfReader(file)
return " ".join([page.extract_text() for page in pdf.pages])
elif file.type == "application/vnd.openxmlformats-officedocument.wordprocessingml.document":
doc = docx.Document(file)
return " ".join([p.text for p in doc.paragraphs])
elif file.type == "text/plain":
return file.read().decode("utf-8")
elif file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet":
df = pd.read_excel(file)
return df.to_string()
# Process and store document
def process_document(file, collection_name):
text = extract_text(file)
embeddings = HuggingFaceEmbeddings().embed_text(text)
# Store embeddings in Milvus
collection = Collection(collection_name)
collection.insert([embeddings])
# import os
# import hashlib
# import io
# import pandas as pd
# from PyPDF2 import PdfReader
# from docx import Document
# from langchain_huggingface import HuggingFaceEmbeddings
# from pymilvus import connections, FieldSchema, CollectionSchema, DataType, Collection
# import json
# class FileHandler:
# def __init__(self,api_token,logger):
# self.logger = logger
# self.logger.info("Initializing FileHandler...")
# # Initialize the embedding model using Hugging Face
# self.embeddings = HuggingFaceEmbeddings(
# model_name="sentence-transformers/all-MiniLM-L6-v2",
# model_kwargs={"token": api_token},
# )
# def handle_file_upload(self, file, document_name, document_description):
# try:
# content = file.read()
# file_hash = hashlib.md5(content).hexdigest()
# collection_name = f"collection_{file_hash}"
# # Check if the collection exists
# if connections._fetch_handler().has_collection(collection_name):
# self.logger.info(f"Collection '{collection_name}' already exists.")
# return {"message": "File already processed."}
# # Process file based on type
# if file.name.endswith(".pdf"):
# texts, metadatas = self.load_and_split_pdf(file)
# elif file.name.endswith(".docx"):
# texts, metadatas = self.load_and_split_docx(file)
# elif file.name.endswith(".txt"):
# texts, metadatas = self.load_and_split_txt(content)
# elif file.name.endswith(".xlsx"):
# texts, metadatas = self.load_and_split_table(content)
# elif file.name.endswith(".csv"):
# texts, metadatas = self.load_and_split_csv(content)
# else:
# self.logger.info("Unsupported file format.")
# raise ValueError("Unsupported file format.")
# if not texts:
# return {"message": "No text extracted from the file. Check the file content."}
# # self._store_vectors(collection_name, texts, metadatas)
# filename = file.name
# filelen = len(content)
# self._store_vectors(collection_name, texts, metadatas, document_name, document_description,filename,filelen)
# self.logger.info(f"File processed successfully. Collection name: {collection_name}")
# return {"message": "File processed successfully."}
# except Exception as e:
# self.logger.error(f"Error processing file: {str(e)}")
# return {"message": f"Error processing file: {str(e)}"}
# def _store_vectors(self, collection_name, texts, metadatas, document_name, document_description,file_name,file_len):
# fields = [
# FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=True),
# FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=384),
# FieldSchema(name="file_name_hash", dtype=DataType.INT64), # Hash of file name
# FieldSchema(name="document_name_hash", dtype=DataType.INT64), # Hash of document name
# FieldSchema(name="document_description_hash", dtype=DataType.INT64), # Hash of document description
# FieldSchema(name="file_meta_hash", dtype=DataType.INT64),
# FieldSchema(name="file_size", dtype=DataType.INT64),
# ]
# schema = CollectionSchema(fields, description="Document embeddings with metadata")
# collection = Collection(name=collection_name, schema=schema)
# # Generate embeddings
# embeddings = [self.embeddings.embed_query(text) for text in texts]
# # Convert metadata to hashed values
# file_name_hash = int(hashlib.md5(file_name.encode('utf-8')).hexdigest(), 16) % (10 ** 12)
# document_name_hash = int(hashlib.md5((document_name or "Unknown Document").encode('utf-8')).hexdigest(), 16) % (
# 10 ** 12)
# document_description_hash = int(
# hashlib.md5((document_description or "No Description Provided").encode('utf-8')).hexdigest(), 16) % (
# 10 ** 12)
# # Convert metadata list to JSON string and hash it
# metadata_string = json.dumps(metadatas, ensure_ascii=False)
# file_meta_hash = int(hashlib.md5(metadata_string.encode('utf-8')).hexdigest(), 16) % (10 ** 12)
# # Prepare data for insertion
# data = [
# embeddings,
# [file_name_hash] * len(embeddings),
# [document_name_hash] * len(embeddings),
# [document_description_hash] * len(embeddings),
# [file_meta_hash] * len(embeddings),
# [file_len or 0] * len(embeddings),
# ]
# # Insert data into collection
# collection.insert(data)
# collection.load()
# def load_and_split_pdf(self, file):
# reader = PdfReader(file)
# texts = []
# metadatas = []
# for page_num, page in enumerate(reader.pages):
# text = page.extract_text()
# if text:
# texts.append(text)
# metadatas.append({"page_number": page_num + 1})
# return texts, metadatas
# def load_and_split_docx(self, file):
# doc = Document(file)
# texts = []
# metadatas = []
# for para_num, paragraph in enumerate(doc.paragraphs):
# if paragraph.text:
# texts.append(paragraph.text)
# metadatas.append({"paragraph_number": para_num + 1})
# return texts, metadatas
# def load_and_split_txt(self, content):
# text = content.decode("utf-8")
# lines = text.split('\n')
# texts = [line for line in lines if line.strip()]
# metadatas = [{}] * len(texts)
# return texts, metadatas
# def load_and_split_table(self, content):
# excel_data = pd.read_excel(io.BytesIO(content), sheet_name=None)
# texts = []
# metadatas = []
# for sheet_name, df in excel_data.items():
# df = df.dropna(how='all', axis=0).dropna(how='all', axis=1)
# df = df.fillna('N/A')
# for _, row in df.iterrows():
# row_dict = row.to_dict()
# # Combine key-value pairs into a string
# row_text = ', '.join([f"{key}: {value}" for key, value in row_dict.items()])
# texts.append(row_text)
# metadatas.append({"sheet_name": sheet_name})
# return texts, metadatas
# def load_and_split_csv(self, content):
# csv_data = pd.read_csv(io.StringIO(content.decode('utf-8')))
# texts = []
# metadatas = []
# csv_data = csv_data.dropna(how='all', axis=0).dropna(how='all', axis=1)
# csv_data = csv_data.fillna('N/A')
# for _, row in csv_data.iterrows():
# row_dict = row.to_dict()
# row_text = ', '.join([f"{key}: {value}" for key, value in row_dict.items()])
# texts.append(row_text)
# metadatas.append({"row_index": _})
# return texts, metadatas
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