LLM Course – Build a Semantic Book Recommender (Python, OpenAI, LangChain, Gradio)

Introduction

In this tutorial, you'll learn how to build a semantic book recommendation system using Python, OpenAI, and LangChain. By transforming book descriptions into mathematical representations, you'll enable precise content-based matching. By the end of this guide, you'll have created a functional recommendation engine that helps readers discover their next favorite book.

Step 1: Getting and Preparing Text Data

• Collect Data: Start by downloading a suitable dataset for your book recommendations. For example, you can use the 7K books dataset available on Kaggle.
• Load Data: Use Pandas to load the dataset into your Python environment.

  import pandas as pd
  df = pd.read_csv('path_to_your_dataset.csv')
  

• Initial Exploration: Check the structure of the data to understand the columns available, such as title, author, description, and categories.

Step 2: Starting a New PyCharm Project

• Create Project: Open PyCharm and create a new project for your book recommender.
• Set Up Environment: Ensure you have the necessary libraries installed, such as Pandas, LangChain, Gradio, and OpenAI.

  pip install pandas langchain gradio openai
  

Step 3: Data Cleaning

• Identify Missing Data: Check for any missing values in your dataset.

  df.isnull().sum()
  

• Remove Short Descriptions: Filter out any book descriptions that are too short to be useful.

  df = df[df['description'].str.len() > 20]
  

• Final Cleaning Steps: Ensure the data is consistent and free from duplicates.

Step 4: Introduction to LLMs and Vector Search

• Understanding LLMs: Familiarize yourself with large language models, which will help you transform textual descriptions into vector representations.
• Vector Search: Learn about vector indexing techniques that allow for efficient searching through embeddings.

Step 5: Using LangChain

• Set Up LangChain: Start using LangChain to facilitate natural language processing tasks.
  • Create a text splitter to break down the book descriptions into manageable chunks.

  from langchain.text_splitter import CharacterTextSplitter
  text_splitter = CharacterTextSplitter()
  texts = text_splitter.split_text(df['description'].tolist())
  

Step 6: Building the Vector Database

• Create Vectors: Use the OpenAI API to convert the book descriptions into vectors.

  import openai
  
  openai.api_key = 'your_api_key'
  vectors = [openai.Embedding.create(input=text)['data'][0]['embedding'] for text in texts]
  

• Store Vectors: Save these vectors in a database or a suitable structure for later retrieval.

Step 7: Getting Book Recommendations Using Vector Search

• Search Implementation: Implement a function that takes a user query, converts it into a vector, and retrieves the nearest book descriptions based on cosine similarity.
• Example Code:

  from sklearn.metrics.pairwise import cosine_similarity
  
  def recommend_books(user_input_vector):
      similarities = cosine_similarity([user_input_vector], vectors)
      recommended_indices = similarities.argsort()[0][-5:][::-1]
      return df.iloc[recommended_indices]
  

Step 8: Zero-Shot Text Classification

• Find LLMs for Classification: Explore models on Hugging Face for zero-shot classification.
• Implementation: Use the selected model to classify book descriptions into predefined categories.

Step 9: Sentiment Analysis

• Extract Emotions: Use a fine-tuned LLM to analyze sentiments in book descriptions.
• Emotion Extraction Code:

  emotion_model = 'your_fine_tuned_model'
  emotions = [openai.Completion.create(model=emotion_model, prompt=description) for description in df['description']]
  

Step 10: Building a Gradio Dashboard

• Set Up Gradio: Create a user interface for your book recommendation system that allows users to input queries and receive recommendations.

  import gradio as gr
  
  def recommend(input_text):
      input_vector = openai.Embedding.create(input=input_text)['data'][0]['embedding']
      return recommend_books(input_vector)
  
  gr.Interface(fn=recommend, inputs="text", outputs="dataframe").launch()
  

Conclusion

You've built a semantic book recommender using Python, OpenAI, and LangChain. This tutorial covered data preparation, LLM usage, sentiment analysis, and the creation of a user-friendly interface using Gradio. For further exploration, consider enhancing your model with additional datasets or improving the user interface. Happy coding!