In the growing world of books, you may find it difficult to discover the right title that piques your interest. However, with advancements in artificial intelligence technologies, the task has become easier than ever. In this article, we will explore how to use the popular “Milvus” platform with the “OpenAI” interface to create text representations (embeddings) to describe books and how to use these representations to search for related books in a dataset containing over a million titles and descriptions. Stay tuned to learn how these powerful tools can help you find the perfect book that suits your taste.
Getting Started with Milvus and OpenAI
This experience begins with familiarizing yourself with two important technologies in the field of big data and information analysis: Milvus and OpenAI. Milvus is a database dedicated to storing and retrieving unstructured data, while OpenAI provides services to generate embeddings, which are mathematical representations used to analyze and understand textual data. By integrating these two systems, the effectiveness of data searching can be enhanced through the use of text-based embeddings, contributing to high accuracy in information retrieval. In this article, we illustrate how to use OpenAI to create embeddings for book descriptions, and then use Milvus to search for related books based on those descriptions.
Setting Up the Environment and Required Software
To start using Milvus and OpenAI, you must first install the necessary libraries. The mentioned libraries, such as openai, pymilvus, datasets, and tqdm, play vital roles in facilitating the connection between the two systems and retrieving data. After installing the libraries, ensure that the Milvus service is up and running. This is achieved by running the Docker Compose file, which is a key link for starting a group of containers that make up the working environment for Milvus. It is useful to use tools like progress bars, represented in the tqdm library, to provide visual feedback to the user about the progress of ongoing operations, especially when dealing with large amounts of data like that found in the books dataset exceeding one million pairs of titles and descriptions.
Setting Up the Connection with Milvus
After ensuring that the Milvus service is running, you need to configure database variables such as HOST, PORT, and COLLECTION_NAME. These variables assist in establishing the correct connection with the database. At this stage, a collection within Milvus named “book_search” is created, and the required dimensions for the embeddings are specified, such as using 1536 dimensions as a standard. It is verified whether the collection already exists, and if it does, it is deleted to create a new one. By using the pymilvus libraries, we can define the structure of the collection, including the data types related to titles, book descriptions, and numerical representations.
Using the Hugging Face Dataset
To obtain book data, the Hugging Face dataset is used, which contains over a million books. This collection has patterns of titles and descriptions that are analyzed. The loading and handling of the data are accomplished using the datasets library, which provides an easy interface for loading and processing data. After loading the data, the description and title data are inserted into Milvus after being converted into embeddings via the OpenAI interface. This enriches the database with information and makes it easier for users to search for related books based on descriptions. It is also important to consider the data size when inserting it into the process, as adding data in batches is preferred to facilitate data management.
Executing Queries on Milvus
After entering the data, queries can be conducted to search for information about books. This requires designing a search function that takes a specific description and then searches for correlations in the database. Using OpenAI, the queried descriptions are converted into embeddings, which are used to query Milvus. When performing the search, results are displayed alongside the entered description, providing easy-to-understand information about related book titles. This method makes it easier for users to find books that match their interests. In case of an error, such as the required collection not existing, this provides the user with valuable information about the error and how to correct it.
Link
Source: https://cookbook.openai.com/examples/vector_databases/milvus/getting_started_with_milvus_and_openai
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