The 2024 Playbook: Top Use Cases for Vector Search
An exploration of vector search technologies and their most popular use cases.
Read the entire series
- Image-based Trademark Similarity Search System: A Smarter Solution to IP Protection
- HM-ANN Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory
- How to Make Your Wardrobe Sustainable with Vector Similarity Search
- Proximity Graph-based Approximate Nearest Neighbor Search
- How to Make Online Shopping More Intelligent with Image Similarity Search?
- An Intelligent Similarity Search System for Graphical Designers
- How to Best Fit Filtering into Vector Similarity Search?
- Building an Intelligent Video Deduplication System Powered by Vector Similarity Search
- Powering Semantic Similarity Search in Computer Vision with State of the Art Embeddings
- Supercharged Semantic Similarity Search in Production
- Accelerating Similarity Search on Really Big Data with Vector Indexing (Part II)
- Understanding Neural Network Embeddings
- Making Machine Learning More Accessible for Application Developers
- Building Interactive AI Chatbots with Vector Databases
- The 2024 Playbook: Top Use Cases for Vector Search
- Leveraging Vector Databases for Enhanced Competitive Intelligence
- Revolutionizing IoT Analytics and Device Data with Vector Databases
- Everything You Need to Know About Recommendation Systems and Using Them with Vector Database Technology
- Building Scalable AI with Vector Databases: A 2024 Strategy
- Enhancing App Functionality: Optimizing Search with Vector Databases
- Applying Vector Databases in Finance for Risk and Fraud Analysis
- Enhancing Customer Experience with Vector Databases: A Strategic Approach
- Transforming PDFs into Insights: Vectorizing and Ingesting with Zilliz Cloud Pipelines
- Safeguarding Data: Security and Privacy in Vector Database Systems
- Integrating Vector Databases with Existing IT Infrastructure
- Transforming Healthcare: The Role of Vector Databases in Patient Care
- Creating Personalized User Experiences through Vector Databases
- The Role of Vector Databases in Predictive Analytics
- Unlocking Content Discovery Potential with Vector Databases
- Leveraging Vector Databases for Next-Level E-Commerce Personalization
- Mastering Text Similarity Search with Vectors in Zilliz Cloud
- Enhancing Customer Experience with Vector Databases: A Strategic Approach
Search and information retrieval have long been fundamental in navigating the vast expanse of digital information. Early search engines relied on simple keyword matching, often frustrating users with irrelevant results. However, in the late 1990s, Google's PageRank algorithm revolutionized search by considering keywords and the authority and relevance of web pages.
Since then, search has evolved and improved through semantic analysis, machine learning, and natural language processing. Now, vector search promises to unlock the secrets of complex, high-dimensional data like never before.
What is Vector Search?
Vector search diverges from conventional methods by encoding data points as vectors in a multi-dimensional space. These vectors encapsulate the semantic meaning within text, images, videos, or any other unstructured data. This empowers users to uncover relevant information, even when search queries lack specificity.
When a user query is received, vector search uses different methods, such as Cosine Similarity or Euclidean distance, to find and retrieve the closest or most similar vectors. Due to the vast amount of data, searching and computing vector similarity can be overwhelming. Therefore, vector indexing is crucial for organizing and retrieving relevant vectors efficiently. It also speeds up search operations by structuring vectors for quick retrieval based on similarity measures.
Different methods for vector indexing include:
Flat indexing: This technique stores each vector "as is" without modifications. While offering perfect accuracy, its main drawback lies in its potential slowness, particularly with large datasets. Flat indexing calculates the similarity between the query vector and every other vector in the index, returning the Top-K most similar vectors in the dataset.
Inverted File Index (IVF): IVF partitions the vector space into smaller subspaces called cells, each with a centroid representing the average vector of that region. Vectors in the database are then allocated to nearby centroids, forming clusters. During the lookup process, the query vector calculates distances to each centroid first, restricting comparisons to vectors belonging to the selected centroids.
Locality-Sensitive Hashing (LSH): The basic idea behind LSH is to hash data points so that similar points are mapped to the same or nearby hash buckets with high probability. By doing so, it becomes possible to quickly identify approximate nearest neighbors (ANN) by searching only within the hash buckets that are likely to contain them.
Cluster based (product quantization): Quantization is a technique for reducing the total size of the database by reducing the overall precision of the vectors. Compared to dimensionality reduction (PCA, LDA, etc), which attempts to reduce the length of the vectors.:
Graph-based indexing (HNSW, CAGRA): Graph-based indexing algorithms are the most popular way to index vectors.
Vector Search Technologies Available in the Market
Various technologies are available for vector searching. In 2017, Meta open-sourced FAISS, significantly reducing the costs and barriers associated with vector searching. In 2019, Zilliz introduced Milvus, a purpose-built open-source vector database leading the way in the industry. Since then, many other vector database companies have emerged. The trend of vector databases took off in 2022 with the entry of many traditional search products such as Elasticsearch and Redis and the widespread use of LLMs like ChatGPT.
Vector Search Technologies
What are their differences now that there are so many vector search products? I roughly categorize them into the following types:
Vector search libraries. They are collections of algorithms without basic database functionalities like insert, delete, update, query, data persistence, and scalability. FAISS is a primary example.
Lightweight vector databases. They are built on vector search libraries, making them lightweight in deployment but with poor scalability and performance. Chroma is one such example.
Vector search plugins. These are vector search add-ons that rely on traditional databases. However, their architecture is for conventional workloads, which can negatively impact their performance and scalability. Elasticsearch and Pgvector are primary examples.
Purpose-built vector databases. These databases are purpose-built for vector searching and offer significant advantages over other vector-searching technologies. For example, dedicated vector databases provide more user-friendly features such as distributed computing and storage, disaster recovery, and data persistence. Milvus is a primary example.
Vector Search in Action: Key Applications
Vector search has significantly enhanced various applications where information retrieval and ranking are critical, in addition to revolutionizing search engines. Here are some key areas where vector search excels:
E-commerce
Product Discovery: Vector search enables users to find items similar to their interests, even without precise articulation. This approach enhances user experience and boosts purchase likelihood.
Recommendation Engines: Powered by vector search, recommendation engines compare user preferences and item embeddings to suggest products similar to those the user has interacted with, driving higher conversion rates.
Content Management
Categorization and Retrieval: Vector search aids in categorizing and retrieving content based on semantic similarity rather than just keywords, improving content organization and search result accuracy.
Content Similarity Analysis: Leveraging vector search, content similarity analysis identifies duplicates, near-duplicates, and related content, refining content management strategies.
Customer Support
Intelligent Chatbots: Vector search empowers intelligent chatbots to understand and respond to user queries effectively by retrieving relevant information from knowledge bases or past interactions. This enhances chatbot accuracy and prevents Large Language Models like OpenAI’s GPT-3.5 or Facebook’s LLAMA2 from generating erroneous information.
Helpdesk Solutions: Helpdesk solutions utilize vector search to analyze historical ticket data and customer interactions, suggesting relevant solutions or escalating tickets to appropriate agents for faster issue resolution and improved customer satisfaction.
Healthcare
Medical Image Analysis: In medical imaging, vector search can potentially aid in diagnosing diseases by comparing features extracted from images to a database of known cases, facilitating faster and more accurate diagnoses.
Drug Discovery: Vector search accelerates drug discovery by identifying compounds with similar molecular structures or biological activity, expediting the search for potential candidates for new drugs.
Enhancing Vector Search: Best Practices and Key Considerations
Implementing efficient vector search is crucial for extracting insights from data and improving user experience. Here are some key considerations and best practices to ensure the effectiveness of your vector search implementation:
Selecting the Right Infrastructure: The foundation of a successful vector search system lies in carefully evaluating benchmarking results to select the appropriate infrastructure. Consider options like Milvus or Zilliz Cloud (the managed version of Milvus) for your vector database, and leverage cloud services such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform for scalable computing power.
Dynamic Updating for Real-Time Relevance: In dynamic environments where data constantly changes, implement mechanisms for seamlessly updating and re-indexing vectors. This approach ensures that your vector search system remains relevant and accurate over time, reflecting the latest insights.
Query optimization: Storing metadata at the time of vector creation can optimize the query search process by filtering out user queries, leading to improved relevant vector search performance.
Looking Ahead
The future of vector search appears promising as more advanced encoding algorithms emerge, enhancing the quality and relevance of search results.
Improved encoding and retrieval will spawn applications like multi-modal search, which utilize text, image, audio, and video inputs, necessitating sophisticated vector indexing methods capable of handling mixed media formats efficiently.
Additionally, the advent of generative AI has further emphasized the importance of vector search in improving the response of Large Language Models.
High-performance vector databases supporting real-time analytics will become increasingly vital for industries requiring instantaneous decision-making, such as autonomous vehicles.
Conclusion
Ultimately vector search has improved our ability to search and retrieve information from complex unstructured datasets. Whether it's enhancing search engines or powering various applications across industries, vector search has improved our understanding of data, unlike before. As we continue to improve encoding and retrieval techniques, the future of vector search looks bright, and it is set to transform how we navigate and harness data in the digital age.
- What is Vector Search?
- Vector Search Technologies Available in the Market
- Vector Search in Action: Key Applications
- Enhancing Vector Search: Best Practices and Key Considerations
- Looking Ahead
- Conclusion
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