How to Create a Basic Search Engine Using Python

  • You can easily build your own simple search engine in Python using language models.
  • You just need to use the txtai Python package and pandas to load the dataset and get the search results.
  • The embeddings of the titles are used to create the vector store and you can search for specific items using a search field.
  • By embedding the titles into the vector store, you can get top results for your search query.
  • The process is not too complex and it’s a great way to learn something new in Python.


In this video, we will learn how to build our own simple search engine using language models in Python. We will not be building a search engine for the internet like Google. Instead, we will be learning how to build a search engine for our own set of products or blog posts.

Key Takeaways

Tooltxt ai

Using Python to Build a Simple Search Engine

So, to begin with, we’ll install the necessary packages using pip. We will also need to import pandas and import the dataset from Amazon. This dataset will be used to train our simple search engine.

How to Install Required Packages

PackageInstallation Command
txt aipip install txt

Loading and Preparing the Dataset

Once the necessary packages are installed, we will load the dataset from Amazon into a pandas dataframe and explore the structure of the dataset.

Exploring the Data Set

titleProduct Titles
contentProduct Information

Embedding the Titles into a Vector Store

We will use the txt ai package to embed the titles of the products into a vector store. This will enable us to search for similar items based on the embedded titles.

Embedding Process

  • Install the hugging face Transformer model "sentence-transformers-mini-LM"
  • Use the embedding model to embed the titles into vectors

Creating a Streamlit Application

We will use the Streamlit package to create a user interface that allows users to input a search query and retrieve relevant results from the embedded titles.

User Interface Elements

  • Text Input for Search Query
  • Button to Trigger Search
  • Display of Search Results

Use Case with Different Data Sets

We will modify our process to work with a different dataset containing blog posts. This will demonstrate the versatility of our simple search engine in Python.

Loading and Embedding a Different Dataset

We will load a dataset of blog posts, embed the content, and modify the Streamlit interface to display and search the blog post titles.


In this tutorial, we learned how to build a simple search engine in Python using language models and the txt ai package. We demonstrated the process of embedding titles into a vector store and creating a user interface with Streamlit. We also explored the flexibility of our search engine by working with different datasets.


  1. What other packages can be used for embedding titles?

    • Different models such as "paraphrase-mpnet-base-v2" can also be used for embedding the titles.
  2. Can the search engine be integrated with other applications?

    • Yes, the search engine results can be integrated with different applications to provide relevant information to users.

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