Enhance User Engagement: Philosophy Quote Generator Implementation with Astra DB

A revolution in computing has been triggered by the field of generative AI (GenAI), which offers a variety of tools, concepts, and paradigms. Vector search has been a fundamental tool in GenAI among these.

We’ll demonstrate the capabilities of vector search in this three-part series by building a vector store from the ground up and applying it to two common tasks: text creation from examples and semantic search. We’ll concentrate on developing a semantic search engine to find quotations from well-known thinkers. We’ll later use this engine to build fresh, plausible epistolary snippets to show how these parts work together to form a complete GenAI application.

Python will be the foundation of our trip, while DataStax Astra DB will act as the vector-search backend. Although the ideas are universal, our solution makes use of this database’s special architecture in several specific ways.

There are two versions of the reference application that we will examine:

1. Direct communication with the database through database drivers.
2. Making use of the “CassIO” module, which abstracts away database-specific details and offers a more Pythonic interface, to streamline the procedure.

We will point out the differences between these methods and provide extensive sources for more research. The only prerequisites for both solutions are an OpenAI API key and a free-tier Astra DB account, which can be found as notebooks in the OpenAI Cookbook repository.

Below is a summary of the three sections:

1. Current Post: We’ll discuss the importance of vector search and give a quick rundown of how it operates.
2. Part 2: To find quotes from well-known philosophers, we’ll build a search engine using vector embeddings.
Part 3: In this section, we’ll demonstrate how our search engine may be used to create a generator of fresh philosophical quotations, which is the central component of a comprehensive GenAI program.

Vector Search: Why Use It?

Large language models (LLMs) produce text for a variety of purposes, such as answering customer questions, providing summaries, or making contextual recommendations, in typical GenAI applications. Deploying LLMs directly out of the box, however, could not have the necessary subject expertise. A popular workaround for this is retrieval-augmented generation (RAG), which avoids the need for expensive model fine-tuning.

According to the RAG paradigm, a search first returns textual pieces that are pertinent to the task (such as relevant documentation snippets for client queries). These briefs are then put into the LLM, instructing it to formulate responses based on the data that is provided. RAG is a key technique for improving LLM capabilities and is essential to increasing their usefulness.

We start this procedure by addressing the problem of finding “relevant” data. Keyword-based searches were sufficient in the past, but vector search has become the better option. Let’s examine its operation and reasons for success.

Vector search is based on two fundamental ideas:

1. Calculating an “embedding vector”—a fixed-length series of numbers that represents the meaning of a specific text passage.
2. Measuring the semantic similarity of texts by applying vector distance criteria found in mathematics.

Essentially, vector search locates related phrases in this vector space by mapping phrases to associated vectors. By treating natural language processing (NLP) as a geometric problem, this method simplifies the effort.

Let’s say you are creating a service that searches a large text corpus for phrases similar to what a user has entered. Queries are made more efficient by pre-calculating vector embeddings for every phrase and keeping them with texts in an appropriate database. Determine the vector of a given query sentence and search the database for rows containing the closest vectors.

Vector databases are defined as databases that have certain features. Vector-oriented capabilities are currently available in many databases due to the explosion of GenAI, and new databases are specifically designed to meet this demand. One notable feature of Astra DB, which is based on Apache Cassandra®, is its extensive support for vector search workloads.

Filtering based on metadata becomes important when implementing an application that uses vector search. For example, it’s crucial to filter results in an e-commerce setting using attributes like “style = casual” or “special_offer = True” in addition to semantic similarities. We’ll look at two ways to allow vector-search filtering while building the vector store in the database, as well as some real-world uses for it.

What Comes Next?

We will take a closer look at the actual implementation in the upcoming section of our series, where we will build a vector storage and create a search engine on top of it.