In the era of big data, the importance of databases capable of handling vast amounts of data efficiently and quickly stands out. Redis is one of these leading solutions, characterized by being an open-source database that offers speed and efficiency. In this article, we will discuss how to use Redis as a database system for data vectors by integrating it with OpenAI’s text display and data retrieval technologies. We will showcase how to implement effective indexing and querying operations using the RediSearch module, outlining the practical steps needed to create a database capable of processing and retrieving data represented as lists. Let us delve into the world of Redis and explore the vast possibilities it offers to developers and data experts.
What is Redis and How to Use it as a Vector Database?
Redis is an open-source database that relies on a key-value storage model and has been used by developers for many years in caching, message management, and database applications. Redis’s popularity is primarily due to its exceptional speed and the existence of a wide ecosystem of client libraries. However, thanks to Redis Modules extensions, Redis functionality can be expanded to support new data types and commands such as RedisJSON, RedisTimeSeries, RedisBloom, and RediSearch.
RediSearch, one of the Redis libraries, allows you to create secondary indexes on data that can be searched using full-text search and vector search. This feature is particularly interesting when dealing with data that requires quick and precise searching. With RediSearch, developers can define indexes on their Redis data and leverage the RediSearch APIs for search purposes.
For example, a developer wishing to build a recommendation system can use Redis as a database with the help of RediSearch. By storing vector representations of products or texts, they can perform similar queries to find which items bear the most similarity to an item, providing accurate and quick recommendations for users.
Setting Up and Deploying Redis with RediSearch
There are several ways to deploy Redis, including using a Docker container or utilizing cloud Redis services like Redis Cloud, which offers full database management. In local environments, using a Redis Stack container via Docker is the quickest option that requires minimal setup effort. Redis Stack provides a suite of modular components that can be used together to create a multi-model database.
To set up Redis, you can use Docker by typing the command $ docker-compose up -d, which will directly start a Redis Stack container. Once started, you can use the RedisInsight graphical interface to manage your Redis database. Installing necessary libraries, such as Redis-Py, is an important step to enable communication with the database through Python, allowing developers to perform the required operations easily.
During the setup of the communication means with the OpenAI API, it is crucial to verify the presence of an API key and set it up. This step assists the developer in using OpenAI to generate vector representations that can be used later for text or embedded data search purposes. This preparation serves as the beginning of gearing up the entire working environment.
Creating a Search Index in Redis and Loading Documents
After setting up the database, the next step is to create a search index using RediSearch. This requires defining index criteria such as the index name and the dimensional metrics used. Developers can use RediSearch to specify how data will be treated during search operations. For instance, a cosine distance metric can be employed to deliver precise search results.
To make the data ready for searching, developers must load documents into the index. This can be done using Redis data types such as the HASH type. This method provides a flexible structure for organizing data within Redis. Through custom functions, documents can be converted into vectors that are easily uploaded to Redis. These steps enhance the search capability through a set of documents, making them available for efficient querying.
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loading documents, it is essential to ensure that the vectors are created correctly and converted into a suitable format for use in Redis. For example, lists of vectors should be converted to bytes using numpy. After storing these documents, developers can see the number of documents uploaded, ensuring that the search system is functioning as planned.
Performing Vector Search Queries using OpenAI
Once developers have an index created and documented in Redis, they can start performing advanced search queries. By using OpenAI’s API, textual queries can be transformed into vectors, facilitating the rapid discovery of relevant data. Using OpenAI enables the integration of artificial intelligence into the search process, providing accurate and relevant results.
When designing this functionality, the developer creates a vector from the used query. Then, a RediSearch query is prepared to search for documents that match those vectors. This technique is ideal for various content types, such as online articles or educational materials, giving users the ability to search for specific topics accurately.
As a practical example, when performing a query related to “modern art in Europe,” the system can retrieve more than just textual information; it can also provide links and content related to similar fields. The results obtained can include informational sections about modern art, creating a rich and immersive search experience for the user.
Hybrid Search using Redis
In recent years, the need for advanced search technologies has become urgent, especially with the increasing volume and diversity of data. Hybrid search technology is one of the promising technologies that combines full-text search and dimensional search, allowing users to search for the most accurate and rapid results. By leveraging the RediSearch library, the search for images and text can be enhanced in an integrated way. An example of this is using hybrid queries to search for articles referring to famous battles in Scottish history, with a focus on results that contain the word “Scottish” in the title. This type of query significantly improves the quality of retrieved data.
Setting up hybrid search requires using a function to create the appropriate fields, where textual search can be merged with dimensional search. Specifically, the function is used to create a hybrid field and define search criteria based on texts and titles continuously, which helps in filtering results more accurately. For example, when searching for articles related to the art of “Leonardo da Vinci,” focus is placed on texts that include this name, increasing the precision of the selected results.
By combining different techniques such as full-text search and dimensional queries, this system provides users with customized and comprehensive search results from advanced data, making understanding culture and history easier. For instance, if there is research on the arts, this system can be used to retrieve a rich art collection related to Leonardo da Vinci, highlighting historical periods and contemporary artists.
HNSW Index and Improving Search Performance
When dealing with large datasets, adopting efficient and rapid indexing techniques becomes essential. The HNSW (Hierarchical Navigable Small World) technique is one of the most prominent modern indexing techniques that offers faster performance than traditional indexes. HNSW is used to build indexes based on complex graphs, significantly enhancing query performance.
This index requires some time and resources to build, but it ensures high performance when retrieving data, especially with large datasets. Using HNSW, approximate queries can be executed more quickly, facilitating access to important information. For example, in the case of searching for “modern art in Europe,” the user is likely to retrieve results more quickly and perform better compared to traditional indexes like FLAT.
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The comparison between the HNSW index and the FLAT index shows that HNSW provides significantly faster response times. For example, a query using HNSW can take less than half the time it takes to use FLAT, reflecting HNSW’s ability to efficiently handle repeated and complex queries. These results are crucial for any application that relies on data analysis and searching through vast amounts of information, increasing companies’ desire to adopt this technology to enhance their search strategies.
Query Results and Timings
A key part of analyzing index performance is measuring the response time for queries. In this context, practical tests show that using HNSW significantly enhances search performance compared to the traditional index. Multiple queries were executed using both indexes, and the results were clear. For instance, querying using the FLAT index took 0.263 seconds, while querying using HNSW took 0.129 seconds.
These numbers provide a clear indication of HNSW’s effectiveness, as the difference between the two time intervals reflects the substantial improvements that can be achieved through advanced indexing techniques. Obtaining fast and accurate results is the primary need for businesses today, making investment in technologies like HNSW critically important.
Moreover, the ability to handle multiple queries and deliver results instantaneously indicates that this technology can effectively support large and complex applications. Providing accurate results more quickly aligns with modern user expectations, especially in the field of search and analysis.
Source link: https://cookbook.openai.com/examples/vector_databases/redis/getting-started-with-redis-and-openai
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