admin_utils.py

from pypdf import PdfReader
from langchain_huggingface import HuggingFaceEndpoint
from langchain.text_splitter import RecursiveCharacterTextSplitter
#from langchain.embeddings import OpenAIEmbeddings     #This import has been replaced by the below one :)
from langchain_community.embeddings import OpenAIEmbeddings
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
#from langchain.llms import OpenAI #This import has been replaced by the below one :)
from langchain_openai import OpenAI
#Pinecone team has been making a lot of changes to there code and here is how it should be used going forward :)
from pinecone import Pinecone as PineconeClient
#from langchain.vectorstores import Pinecone     #This import has been replaced by the below one :)
from langchain_community.vectorstores import Pinecone
import pandas as pd
from sklearn.model_selection import train_test_split




#**********Functions to help you load documents to PINECONE************

#Read PDF data
def read_pdf_data(pdf_file):
    pdf_page = PdfReader(pdf_file)
    text = ""
    for page in pdf_page.pages:
        text += page.extract_text()
    return text

#Split data into chunks
def split_data(text):
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=50)
    docs = text_splitter.split_text(text)
    docs_chunks =text_splitter.create_documents(docs)
    return docs_chunks

#Create embeddings instance
def create_embeddings_load_data():
    #embeddings = OpenAIEmbeddings()
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    return embeddings

#Function to push data to Pinecone
def push_to_pinecone(pinecone_apikey,pinecone_environment,pinecone_index_name,embeddings,docs):

    PineconeClient(
    api_key=pinecone_apikey,
    environment=pinecone_environment
    )

    index_name = pinecone_index_name
    index = Pinecone.from_documents(docs, embeddings, index_name=index_name)
    return index

def read_data(data):
    df = pd.read_csv(data,delimiter=',', header=None)  
    return df

#Create embeddings instance
def get_embeddings():
    embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    return embeddings

#Generating embeddings for our input dataset
def create_embeddings(df,embeddings):
    df[2] = df[0].apply(lambda x: embeddings.embed_query(x))
    return df

#Splitting the data into train & test
def split_train_test__data(df_sample):
    # Split into training and testing sets
    sentences_train, sentences_test, labels_train, labels_test = train_test_split(
    list(df_sample[2]), list(df_sample[1]), test_size=0.25, random_state=0)
    print(len(sentences_train))
    return sentences_train, sentences_test, labels_train, labels_test

#Get the accuracy score on test data
def get_score(svm_classifier,sentences_test,labels_test):
    score = svm_classifier.score(sentences_test, labels_test)
    return score