Introduction:
Vector databases have gained significant popularity in recent times, as indicated by the influx of startups entering the space and the increasing investment from investors. This surge can be attributed to the rise of large language models (LLMs) and the generative AI (GenAI) movement, which have created a conducive environment for vector database technologies to thrive.
The Limitations of Traditional Relational Databases:
Traditional relational databases like Postgres or MySQL are well-suited for structured data that can be organized neatly in rows and columns. However, they fall short when it comes to handling unstructured data such as images, videos, emails, and social media posts. These types of data do not adhere to predefined data models, making it challenging to store and process them effectively.
The Power of Vector Databases:
Vector databases, on the other hand, offer a solution for storing and processing unstructured data. They operate by converting various forms of data, including text, documents, and images, into numerical representations known as vector embeddings. These representations capture the meaning and relationships between different data points. This spatial organization of data makes it easier to retrieve semantically similar information, making vector databases ideal for machine learning applications.
Enhancing AI Capabilities:
Vector search plays a crucial role in enhancing the capabilities of large language models like OpenAI’s GPT-4. By analyzing previous conversations and understanding their context, AI chatbots can better engage in meaningful conversations. Additionally, vector search enables real-time applications such as content recommendations in social networks or e-commerce platforms. It can quickly identify and retrieve similar items based on user searches.
Mitigating “Hallucinations” in AI Applications:
Vector search also helps address the issue of “hallucinations” in LLM applications. These hallucinations occur when AI models generate information that was not part of the original training dataset. By providing additional information through vector similarity search, vector databases can help reduce these hallucinations and improve the accuracy of AI applications.
The Rise of Vector Database Startups:
Several vector database startups have recently raised significant funding, underscoring the growing demand for this technology. Qdrant, a vector search startup, secured $28 million in funding and emerged as one of the top 10 fastest-growing commercial open-source startups in 2021. Other startups, such as Vespa, Weaviate, Pinecone, and Chroma, collectively raised $200 million for their vector offerings. These investments highlight the confidence investors have in the potential of vector databases.
Expansion into Different Domains:
The adoption of vector databases is not limited to the tech industry. Index Ventures led a $9.5 million seed round into Superlinked, a platform that converts complex data into vector embeddings. Y Combinator’s Winter ’24 cohort included Lantern, a startup offering a hosted vector search engine for Postgres. This expansion into different domains demonstrates the versatility and applicability of vector databases across various industries.
The Story of Marqo:
Marqo, a vector database platform founded by former Amazon engineers Tom Hamer and Jesse N. Clark, offers a comprehensive set of vector tools for data generation, storage, and retrieval. Their experience at Amazon exposed them to the need for semantic and flexible searching across different modalities like text and images. Marqo aims to provide an all-in-one solution through a single API, eliminating the reliance on third-party tools.
Vector Databases in Enterprise Search:
While vector databases are gaining momentum, they are not a one-size-fits-all solution for enterprise search scenarios. Specialized databases designed for specific use cases offer superior performance and user experience compared to general-purpose databases. However, established database providers like Elastic, Redis, OpenSearch, Cassandra, Oracle, and MongoDB are incorporating vector database search capabilities into their offerings. Cloud service providers like Azure, AWS, and Cloudflare are also joining the trend. This trend parallels what happened with the adoption of JSON in the past, where dedicated document databases emerged alongside relational databases with added JSON support.
The Future of Vector Databases:
Industry experts believe that vector databases will follow a similar trajectory as document databases did with the rise of JSON. While dedicated vector search databases will cater to complex and large-scale AI applications, existing databases with vector search functionality will suffice for applications requiring limited AI capabilities. Native solutions like Qdrant aim to provide specialized, high-performance vector search capabilities from the ground up, rather than as an afterthought added to existing databases.
Conclusion:
The rise of vector databases has been driven by the increasing demand for efficient processing and retrieval of unstructured data. These specialized databases offer unique capabilities in storing and organizing data through vector embeddings. Their applications range from enhancing AI chatbots to real-time content recommendations. While startups like Qdrant and Marqo have secured significant funding, established database providers and cloud service providers are also incorporating vector database search functionalities into their offerings. As the field evolves, the future of vector databases lies in providing specialized solutions for complex AI applications while also catering to the needs of existing database users.