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| #Allows you to use Streamlit, a framework for building interactive web applications. | |
| #It provides functions for creating UIs, displaying data, and handling user inputs. | |
| import streamlit as st | |
| #This module provides a way to interact with the operating system, such as accessing environment variables, working with files | |
| #and directories, executing shell commands, etc | |
| import os | |
| #Helps us generate embeddings | |
| #An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. | |
| #Small distances suggest high relatedness and large distances suggest low relatedness. | |
| #As Langchain team has been working aggresively on improving the tool, we can see a lot of changes happening every weeek, | |
| #As a part of it, the below import has been depreciated | |
| #from langchain.embeddings import OpenAIEmbeddings | |
| #New import from langchain, which replaces the above | |
| from langchain_openai import OpenAIEmbeddings | |
| #FAISS is an open-source library developed by Facebook AI Research for efficient similarity search and clustering of large-scale datasets, particularly with high-dimensional vectors. | |
| #It provides optimized indexing structures and algorithms for tasks like nearest neighbor search and recommendation systems. | |
| #As Langchain team has been working aggresively on improving the tool, we can see a lot of changes happening every weeek, | |
| #As a part of it, the below import has been depreciated | |
| #from langchain.vectorstores import FAISS | |
| #New import from langchain, which replaces the above | |
| from langchain_community.vectorstores import FAISS | |
| #load_dotenv() is a function that loads variables from a .env file into environment variables in a Python script. | |
| #It allows you to store sensitive information or configuration settings separate from your code | |
| #and access them within your application. | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| #By using st.set_page_config(), you can customize the appearance of your Streamlit application's web page | |
| st.set_page_config(page_title="Educate Kids", page_icon=":robot:") | |
| st.header("Hey, Ask me something & I will give out similar things") | |
| #Initialize the OpenAIEmbeddings object | |
| embeddings = OpenAIEmbeddings() | |
| #The below snippet helps us to import CSV file data for our tasks | |
| from langchain.document_loaders.csv_loader import CSVLoader | |
| loader = CSVLoader(file_path='myData.csv', csv_args={ | |
| 'delimiter': ',', | |
| 'quotechar': '"', | |
| 'fieldnames': ['Words'] | |
| }) | |
| #Assigning the data inside the csv to our variable here... | |
| data = loader.load() | |
| #Display the data | |
| print(data) | |
| db = FAISS.from_documents(data, embeddings) | |
| #Function to receive input from user and store it in a variable | |
| def get_text(): | |
| input_text = st.text_input("You: ", key= input) | |
| return input_text | |
| user_input=get_text() | |
| submit = st.button('Find similar Things') | |
| if submit: | |
| #If the button is clicked, the below snippet will fetch us the similar text | |
| docs = db.similarity_search(user_input) | |
| print(docs) | |
| st.subheader("Top Matches:") | |
| st.text(docs[0]) | |
| st.text(docs[1].page_content) | |