In the age of information and big data, it has become essential to use advanced tools for searching and analyzing information. In this article, we will explore how to utilize the technologies “Zilliz” and “OpenAI” to generate and analyze book descriptions. We will generate embedding models from book descriptions using OpenAI services, and then use these models in “Zilliz” to find related books. With a dataset containing over a million titles and book descriptions, we will demonstrate how these technologies can enhance the experience of searching for suitable books. Stay tuned to explore the steps for implementing this process and how AI can facilitate the search for knowledge.
Introduction to Zilliz and OpenAI
Machine learning and artificial intelligence technologies are among the most significant technological advancements of our modern era. AI-powered tools like OpenAI have gained immense popularity in various fields, including natural language processing. Cloud solutions such as Zilliz have the capability to manage databases with high speed and efficiency. In this context, it demonstrates how to use OpenAI to create textual representations (embeddings) for book descriptions and how Zilliz can help in searching for related books using these embeddings. By providing a separate model for data collection, we can process information quickly and smoothly.
We start with the OpenAI library, which is used to communicate with the text embedding service. The pymilvus library is also used to connect to the Zilliz database. This project uses a dataset sourced from HuggingFace, consisting of over a million pairs of book titles and descriptions. The goal is to create a simplified environment that helps users find books related to specific topics based on the input descriptions.
Setting Up the Work Environment
To begin working efficiently, we must first set up the appropriate environment. This requires installing necessary libraries such as openai, pymilvus, and datasets to ensure the different tools work together seamlessly. After testing, make sure you have the latest version of each library and all other requirements are met. These libraries are installed by entering specific commands such as:
“`bash
pip install openai pymilvus datasets tqdm
“`
Then, review the instructions for configuring Zilliz and ensure you are familiar with how to set up your database, from choosing the collection name to setting indexing parameters. The Zilliz setup process includes running some code, including connecting to the database and creating a collection that includes the required fields such as title, description, and embedding.
Data Input into Zilliz
After setting up the work environment, the next step is to import data into Zilliz. It is important first to upload the book dataset, which contains thousands of descriptions. In this example, we are using a dataset related to books from the HuggingFace library. We upload the dataset directly and use the training section of it. More than a million entries can be utilized to achieve accurate and supportive search results.
After importing the data, the next phase involves using the dedicated function to create embeddings. Text embedding technology is used to provide a numerical representation for each description. The application makes a request to OpenAI and saves the results in Zilliz. It is preferable to process the input in batches, which facilitates rapid input in cases involving large amounts of data. For example, the function can be set to convert descriptions into embeddings and then input them into Zilliz in batches of 1000 descriptions at a time.
Querying the Database for Accurate Results
Now that the data has been successfully inputted, we can begin querying the database to search for related books. The user is allowed to enter a phrase or a list of phrases for searching. One of the important features is that the results show the title and the description associated with the book along with clear scoring points. Through a simple user interface, anyone searching for books can find suitable options based on the entered descriptions.
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A query is made, and the library developer identifies the similarities between the embeddings and the data available in the database. The results are returned, illustrating their order based on how close they are to the desired description. For example, if the user requests a book related to “a book about a dog,” the system will search the embeddings and provide suggestions related to books that include similar topics, which is very useful for users looking to discover new books with similar interests.
These integrated functions significantly enhance the search experience and allow readers to find books they may not know about, but that reflect their interests. By using Zilliz with OpenAI, a powerful interface is provided to interact with the data and to offer detailed and straightforward information to the user.
Source link: https://cookbook.openai.com/examples/vector_databases/zilliz/getting_started_with_zilliz_and_openai
Artificial intelligence was used ezycontent
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