Vector databases offer an efficient & effective approach to store and retrieve extensive amounts of vector data for Large Language Models (LLMs) to use. We’ll five leading vector databases that are transforming machine learning and similarity search.
Before we dive into vector databases, let’s take a step back and walk through what LLMs are in order to better understand why vector databases are so important.
What is an LLM?
Large language models, often referred to as LLMs, are cutting-edge AI models that have been extensively trained on vast amounts of text data. These models employ advanced techniques such as deep learning and transformer architectures to comprehend and generate human-like language.
By analyzing patterns, grammar, and context, LLMs can generate coherent and contextually relevant responses, making them invaluable tools for a wide range of NLP tasks.
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Disadvantages of LLMs
While LLMs offer tremendous potential, they are only as good as the data that they’ve been trained on. This can lead to some interesting results such as inconsistent, unreliable, and downright hallucinatory responses.
How to effectively use LLMs?
For the reasons above, it’s important that LLMs are augmented with new data. LLMs such as ChatGPT only have training data up until September 2021. It does not know anything about the world beyond that date, which means that it will need new data in order to remain relevant.
This is where vector databases come into play. Vector bases allow businesses to take LLMs, add their own data on top of them, and apply them to use cases that drive new ways of acting on that data.
What is a vector database?
Vector databases or vector stores are a type of database that stores high-dimensional vectors. Vectors are mathematical representations of features or attributes of the data. Each type of vector has a certain amount of dimensions which can range from tens to thousands depending on the complexity and granularity of the data.
Vector databases support many different formats and can either upload raw data or embed data such as ML models, word embedding, and feature abstractions as well.
They are really good at identifying similar data and can be leveraged in a number of different ways depending on the data and who it can best serve.
Why are vector databases important?
Vector databases are able to store data in ways that are much more relevant and conducive to the way LLMs “think” and are far more flexible than a classic SQL database.
The main advantages of vector databases are that it allows for:
- Fast and accurate similarity search
- Retrieval of data based on their vector distance
If you take two different points, the closer the point the more similar they are. So instead of using a relational database that runs a query to find an exact match, you can use a vector database to find the most similar or relevant data based on their semantic or contextual meaning.
Use cases for vector databases
As we mentioned previously, vector databases allow for quick search and retrieval of data based on vector distance and similarity. So where could this apply? Some basic use cases are:
- Image similarity based on attributes
- Doc similarity based on meaning & context
- Product similarity based on attributes
For the sake of an example, let’s imagine that you have a data warehouse that’s continuously updated and needs to be useful for customer service representatives. Without meaning and context, this information would sit just fine in a relational database.
However, let’s say you want to personalize responses (add meaning and context) using customer data. Now that’s something a vector database can help with. Vector databases can help draw the connections between user-agnostic data and personal data so that an LLM can generate a response to a customer’s question.
Why do you need a vector database for an LLM?
There are a couple of reasons why vector databases are important for LLMs and especially important for businesses looking to integrate LLMs to deliver more value to customers.
LLMs can be quite bad at producing factual data. In fact, they don’t have factual consistency and can provide contradicting and irrelevant information. In some cases, it can even “hallucinate”.
To overcome this limitation, you have to augment new data to the large language model.
The other limitation occurs when you use ChatGPT and try to pick up a conversation from the previous day. Vector databases are what allow LLMs to conduct long-term memory retrieval and create seamless chat experiences over time.
So to recap, vector databases help LLMs:
- Produce factual data
- Provide relevant and consistent information
- Have long-term memory retrieval
Vector Databases
Pinecone
Pinecone is a cloud-based managed vector database specifically designed to simplify the development and deployment of large-scale machine learning applications for businesses and organizations. It sets itself apart by utilizing closed-source code, distinguishing it from other popular vector databases.
Built on the efficient similarity search library Faiss, Pinecone is tailored to machine learning applications, offering impressive speed, scalability, and support for various machine learning algorithms.
What truly sets Pinecone apart is its emphasis on developer-friendliness. With an intuitive and straightforward interface, it allows developers to focus on application building rather than grappling with underlying infrastructure complexities.
Pinecone excels in supporting high-dimensional vector databases, enabling diverse use cases like similarity search, recommendation systems, personalization, and semantic search. It also offers the advantageous capability of single-stage filtering. In addition, its real-time data analysis capabilities make it an excellent choice for threat detection and monitoring in the cybersecurity industry.
Furthermore, Pinecone boasts seamless integrations with multiple systems and applications, including Google Cloud Platform, Amazon Web Services (AWS), OpenAI (including GPT-3, GPT-3.5, GPT-4, and ChatGPT Plus), Elasticsearch, Haystack, and more, expanding its compatibility and versatility.
Weviate
Weaviate stands out as an open-source vector database solution that can be deployed as either a self-hosted or fully managed option. It empowers organizations with a robust toolset for efficient data handling and management, offering exceptional performance, scalability, and user-friendliness.
Whether utilized in a managed setup or self-hosted environment, Weaviate showcases versatile functionality capable of accommodating various data types and applications.
One powerful feature of Weaviate is its ability to store both vectors and objects, making it a suitable choice for applications that integrate multiple search techniques, such as vector search and keyword-based search.
Weaviate finds common usage across various scenarios, including similarity search, semantic search, data classification within ERP systems, e-commerce search, powering recommendation engines, image search, anomaly detection, automated data harmonization, and cybersecurity threat analysis. Its wide range of applications reflects its flexibility and adaptability to diverse use cases.
Chroma
Chroma serves as an open-source vector database designed to empower developers and organizations of all sizes with the necessary tools for constructing large language model (LLM) applications. It provides developers with a highly scalable and efficient solution, enabling seamless storage, search, and retrieval of high-dimensional vectors.
The popularity of Chroma stems from its remarkable flexibility. It offers the flexibility to deploy either in the cloud or as an on-premise solution, catering to diverse infrastructure preferences.
Chroma boasts support for multiple data types and formats, making it well-suited for a wide range of applications. Notably, it excels in handling audio data, making it an optimal choice for audio-based search engines, music recommendations, and other audio-related use cases.
Qdrant
Qdrant is a powerful vector similarity search engine and database. With its production-ready service and user-friendly API, it offers seamless storage, search, and management of points, which are vectors accompanied by additional payload information.
Qdrant is specifically designed to excel in extended filtering support, making it invaluable for various applications such as neural network or semantic-based matching, faceted search, and more.
Milvus
Milvus has emerged as a new open-source vector database, garnering popularity within the realms of data science and machine learning. A notable strength of Milvus lies in its robust support for vector indexing and querying, leveraging cutting-edge algorithms to accelerate the search process. This translates to swift retrieval of similar vectors, even when dealing with extensive datasets.
Another contributing factor to its popularity is Milvus’ seamless integration capabilities with other widely used frameworks such as PyTorch and TensorFlow. This allows for smooth incorporation into existing machine learning workflows, enhancing its versatility and adoption potential.
Milvus finds diverse applications across multiple industries. In the e-commerce sector, it proves valuable in recommendation systems, facilitating personalized product suggestions based on user preferences. Within image and video analysis, Milvus excels in tasks like object recognition, image similarity search, and content-based image retrieval. Additionally, it finds common use in natural language processing applications, powering document clustering, semantic search, and question-answering systems.
Faiss
Faiss is a highly effective tool for indexing and searching vast collections of high-dimensional vectors. It excels in similarity search, clustering, and memory optimization, ensuring efficient storage and retrieval of vectors, even with numerous dimensions.
It includes algorithms capable of searching within vector sets of any size, even those that may exceed RAM capacity. The library also provides supporting code for evaluation and parameter tuning. Faiss is primarily developed by Meta’s Fundamental AI Research group.
Image recognition is one of the prominent applications of Faiss. It empowers the creation of large-scale image search engines capable of indexing and searching millions or billions of images.
Additionally, Faiss can be utilized to build semantic search systems, enabling swift retrieval of similar documents or paragraphs from vast text repositories.
Vespa
Vespa is a search engine and vector database that offers comprehensive functionality. It encompasses vector search (approximate nearest neighbors), lexical search, and the ability to search within structured data, all within a single query.
It incorporates integrated machine-learned model inference, enabling real-time AI-powered insights and analysis of your data.
Some interesting use cases that Vespa can address are conversational AI, semi-structured navigation, and question-answering.
pgvector
pgvector is an open-source extension for PostgreSQL. It empowers users to store and query vector embeddings seamlessly within their database environment. Built on the foundation of the widely recognized Faiss library, pgvector is known for its efficient similarity search capabilities for dense vectors, and ensures optimal performance.
One of the notable advantages of pgvector is its user-friendly nature, making it incredibly easy to use. With a simple installation process that requires just a single command, users can quickly incorporate pgvector into their PostgreSQL setup, enhancing the vector storage and querying capabilities of their database.
Vald
Vald is a distributed and highly scalable search engine specifically designed for fast and approximate nearest neighbor dense vector search. Its architecture is built upon the Cloud-Native principles, ensuring optimal performance in distributed environments.
Utilizing the NGT algorithm, which is known for its speed, Vald excels in efficiently searching for neighbors.
Vald offers automatic vector indexing and index backup, bolstering data integrity and resilience. With its horizontal scaling capabilities, Vald is capable of handling searches across billions of feature vectors, making it an ideal solution for large-scale datasets.
Elasticsearch
Elasticsearch serves as a powerful distributed search and analytics engine, offering robust support for diverse data types. Among these data types, Elasticsearch accommodates vector fields, enabling the storage of dense numeric value vectors.
Starting from version 7.10, Elasticsearch introduced specialized data structures to efficiently index vectors, facilitating rapid k-nearest neighbors (kNN) retrieval through the kNN search API. Furthermore, with the release of version 8.0, Elasticsearch expanded its capabilities to include native support for natural language processing (NLP) utilizing vector fields.
OpenSearch
OpenSearch, a community-led and open-source project, emerged as a fork of Elasticsearch and Kibana in response to the license change in early 2021. OpenSearch encompasses a powerful vector database functionality, enabling the storage, indexing, and retrieval of vectors along with associated metadata.
Leveraging k-nearest neighbors (k-NN) indexes, OpenSearch empowers users to conduct efficient vector similarity searches.
How to choose the best vector database
Choosing the right vector database is a decision that can significantly impact the efficiency and effectiveness of your applications.
Consider the following attributes when evaluating a vector database.
- Scalability: Choose vector databases that possess the ability to handle large volumes of high-dimensional data efficiently. They offer scalability to accommodate your expanding data requirements.
- Performance: Speed and efficiency are critical. The vector databases featured in this list have exceptional performance in data retrieval, search operations, and various vector-related tasks.
- Flexibility: The databases included in this article support diverse data types and formats, making them adaptable to a wide range of use cases. They can effectively handle both structured and unstructured data and are compatible with multiple machine-learning models.
- Ease of Use: User-friendliness and manageability are key attributes of these databases. They are designed for straightforward installation and setup, provide intuitive APIs, and offer comprehensive documentation and support resources.
- Reliability: Each vector database highlighted here has a proven track record of reliability and robustness, instilling confidence in their performance and durability.
At the end of the day, the right vector database for you depends on your specific needs and business goals. It’s helpful to evaluate the current options to see how well they align with what you’re trying to accomplish. Also check to see if similar companies are leveraging the same databases, if at all.
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Vector databases – final thoughts
Vector databases are a critical part of the AI stack. They solve the stateless problem of LLMs and increase the accuracy, relevance, and consistency of the information they provide to end-users.
The vector database landscape is being transformed by top contenders such as Chroma, Pinecone, Weaviate, Milvus, and Faiss. Each of these databases brings unique strengths to the table, revolutionizing data indexing and similarity search.
Chroma stands out for its exceptional capabilities in constructing large-scale language model applications and catering to audio-based use cases. Meanwhile, Pinecone offers organizations a straightforward and intuitive way for developing and deploying machine learning applications.
If flexibility is a priority, Weaviate is a good choice, as it provides a versatile vector database suitable for a broad spectrum of applications. Faiss, on the other hand, has gained recognition for its high-performance similarity search capabilities.
Milvus is rapidly gaining popularity due to its ability to scale indexing and querying operations effectively.
The vector database landscape is constantly evolving, and there may be more specialized databases on the horizon, pushing the boundaries of data analysis and similarity search. For now, we hope this curated list serves as a valuable shortlist for considering vector databases for your project.
However, just getting a vector database alone won’t solve your LLM integration challenges. If you’re looking to build an AI or ChatGPT integration but lack the resources to do so, we’d be happy to help!
Contact us today, and we’ll connect with you straight away to learn more about how we can help you deliver more value with OpenAI, ChatGPT, or any other LLM.
