How to Build Your Own Search Engine with Python: A Step-by-Step Guide

Have you ever wondered how Google or Bing can find relevant information from billions of web pages in a matter of seconds? The answer is semantic search, a technique that uses natural language processing and machine learning to understand the meaning of queries and documents, and rank them accordingly.

In this article, you will learn how to build your own semantic search engine using Python and some open source tools. You will be able to search your own collection of documents (also known as a corpus) using natural language queries, and get the best matches based on meaning rather than keywords.

What is semantic search?

Semantic search is the task of retrieving documents from a corpus in response to a query asked in natural language. Powered by the latest Transformer language models, semantic search allows you to access the best matches from your document collection within seconds, and on the basis of meaning rather than keyword matches.

As well as being helpful in its own right, semantic search also forms the basis for many complex tasks, like question answering or text summarization.

Like all Transformer-based language models, the models used in semantic search encode text (both the documents and the query) as high-dimensional vectors or embeddings. We can then use similarity measures like cosine similarity to understand how close in meaning two vectors (and their associated texts) are. Texts that are similar in meaning are closer to each other, while unrelated texts are more distant.

While illegible to humans, the vector-based representation works very well for computers to represent meaning. The superiority of semantic search over a keyword-based approach becomes clear if we look at an example. Think of the difference between the queries “why can’t I commit changes” — a perennial problem for the novice Git user — and “why can’t I commit to changes” — a problem for the indecisive. The preposition “to” entirely changes the meaning of the query, which is impossible to detect with simple keyword matching. A semantic language model (like the one used by Google) will embed the two queries in disparate locations of the vector space. Semantic search is great for disentangling subtleties like this.

Why use Python for semantic search?

Over the last decade or so, Python has become the principal language for machine learning (ML) and natural language processing (NLP). In this article, we will show you how to set up a semantic search engine in Python, placing it on top of your document collection of choice, with our open source Haystack framework.

Thanks to Haystack’s modular setup and the availability of high-quality pre-trained language models, you’ll be able to set up your own semantic search system in less than twenty minutes.

How to build a semantic search engine in Python?

To build a semantic search engine in Python, you will need to follow these steps:

Install Haystack and its dependencies
Prepare your document collection
Initialize a document store
Index your documents
Initialize a retriever
Initialize a pipeline
Run queries and get results

We will explain each step in detail below.

What is Haystack?

Haystack is an open source NLP framework that enables you to use Transformer models and LLMs (GPT-4, ChatGPT and alike) in your applications. Haystack offers production-ready tools to quickly build complex question answering, semantic search, text generation applications, and more.

Haystack is based on a modular pipeline architecture that allows you to combine different components such as document stores, retrievers, readers, rankers, generators, and summarizers into flexible and scalable pipelines. You can also use any Transformer model from Hugging Face’s Model Hub, experiment with different vector databases, and fine-tune your models with Haystack’s domain adaptation modules.

Haystack also provides a REST API and a web-based annotation tool to make it easy to deploy and improve your NLP applications.

How to install Haystack and its dependencies?

To install Haystack and its dependencies, you will need to have Python 3.6 or higher and pip installed on your system. You can then run the following command in your terminal:

pip install farm-haystack

This will install Haystack along with its core dependencies such as Farm, Transformers, Elasticsearch, and FAISS. You can also install additional dependencies for specific features such as image processing, PDF conversion, or web scraping by adding extra flags to the pip command. For example:

pip install farm-haystack[img2text]

This will install Haystack with the additional dependency of Tesseract OCR for image to text conversion. You can find the full list of extra dependencies and their flags in the installation guide.

How to prepare your document collection?

Before you can use Haystack to perform semantic search, you need to have a document collection that you want to search. A document collection is a set of text files that contain the information you want to retrieve. For example, you can have a document collection of news articles, product reviews, scientific papers, legal documents, or any other type of text content.

You can store your document collection in various formats and locations, such as local files, cloud storage, databases, or web pages. Haystack supports different types of document stores, such as Elasticsearch, OpenSearch, SQL, or In-Memory. You can also use different file converters and web scrapers to extract text from various sources and formats.

To prepare your document collection for semantic search, you need to follow these steps:

Choose a document store that suits your needs and preferences. You can find more information about the supported document stores and their pros and cons here.
Convert your documents into plain text if they are not already in that format. You can use Haystack’s file converters to handle different file types such as PDF, DOCX, CSV, or HTML. You can find more information about the supported file converters and how to use them here.
Optionally, preprocess your documents to improve their quality and readability. You can use Haystack’s preprocessors to perform tasks such as cleaning, splitting, deduplication, or normalization of your documents. You can find more information about the supported preprocessors and how to use them here.
Index your documents into the document store using Haystack’s indexing functions. This will create a searchable index of your documents that can be accessed by the retriever component of your semantic search pipeline. You can find more information about how to index your documents here.

How to initialize a retriever?

A retriever is a component that generates embeddings for the query and the documents, and performs semantic search to retrieve the most relevant documents for a given query. Haystack supports different types of retrievers, such as dense retrievers, sparse retrievers, or hybrid retrievers. You can find more information about the supported retrievers and their pros and cons here.

To initialize a retriever, you need to follow these steps:

Choose a retriever type that suits your needs and preferences. For example, you can use a dense retriever that uses Transformer models to generate embeddings, or a sparse retriever that uses TF-IDF or BM25 to generate embeddings.
Choose a pre-trained model that supports your retriever type and your document language. You can use any model from Hugging Face’s Model Hub, or use one of the pre-trained models provided by Haystack or Sentence Transformers. You can find more information about how to choose a model here.
Initialize the retriever with the chosen model and the document store. You can use Haystack’s Retriever class to create an instance of the retriever with the model name and the document store object as arguments. You can find more information about how to initialize a retriever here.

Here is an example of how to initialize a dense retriever with a pre-trained model from Sentence Transformers and an Elasticsearch document store:

from haystack.retriever.dense import DensePassageRetriever
from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
# create an Elasticsearch document store
document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\", index=\"document\")
# choose a pre-trained model from Sentence Transformers
model_name = \"sentence-transformers/msmarco-distilbert-base-v4\"
# initialize a dense retriever
retriever = DensePassageRetriever(document_store=document_store, query_embedding_model=model_name, passage_embedding_model=model_name)

How to initialize a pipeline?

A pipeline is a component that connects different nodes (such as retrievers, readers, generators, or summarizers) into a sequential workflow. Haystack supports different types of pipelines, such as DocumentSearchPipeline, QuestionAnsweringPipeline, TextGenerationPipeline, or TextSummarizationPipeline. You can find more information about the supported pipelines and their use cases here.

To initialize a pipeline, you need to follow these steps:

Choose a pipeline type that suits your needs and preferences. For example, you can use a DocumentSearchPipeline for semantic search, a QuestionAnsweringPipeline for question answering, or a TextGenerationPipeline for text generation.
Choose the nodes that you want to include in your pipeline. You can use any of the nodes provided by Haystack, or create your own custom nodes. You can find more information about how to create custom nodes here.
Initialize the pipeline with the chosen nodes and their names. You can use Haystack’s Pipeline class to create an instance of the pipeline with a dictionary of node names and node objects as arguments. You can also specify the order of the nodes and their connections using the set_params method. You can find more information about how to initialize a pipeline here.

Here is an example of how to initialize a DocumentSearchPipeline with a dense retriever and an Elasticsearch document store:

from haystack.pipelines import DocumentSearchPipeline
from haystack.retriever.dense import DensePassageRetriever
from haystack.document_store.elasticsearch import ElasticsearchDocumentStore
# create an Elasticsearch document store
document_store = ElasticsearchDocumentStore(host=\"localhost\", username=\"\", password=\"\", index=\"document\")
# choose a pre-trained model from Sentence Transformers
model_name = \"sentence-transformers/msmarco-distilbert-base-v4\"
# initialize a dense retriever
retriever = DensePassageRetriever(document_store=document_store, query_embedding_model=model_name, passage_embedding_model=model_name)
# initialize a document search pipeline with the retriever
pipeline = DocumentSearchPipeline(retriever=retriever)

Conclusion

In this article, we have shown you how to build your own semantic search engine in Python using Haystack, an open source NLP framework. We have explained the main concepts and components of semantic search, such as document collection, document store, retriever, reader, and pipeline. We have also demonstrated how to prepare your document collection, initialize your document store, index your documents, initialize your retriever, and initialize your pipeline. Finally, we have given you some examples of how to run queries and get results using your semantic search engine.

We hope you have enjoyed this article and learned something new. If you want to learn more about Haystack and its features, you can visit the official website or the GitHub repository. You can also join the Haystack community to ask questions, share feedback, or contribute to the project.

Happy searching!

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