Optimizing data retrieval: vector databases for large language models

A language model doesn't understand the meanings of the words input by humans, as it is a computational system. It can only comprehend numerical representations of words known as vectors. Using these vectors, the model identifies similarities between words, emphasizing significant information about each word. In this article, we will dive deeper into the use of vectors and vector databases in large language models (LLMs), exploring what they are and why they are crucial for machine learning and AI in general.

What are vector databases?

Vectors and embeddings in ML

To grasp the idea of vector databases, let's first understand what vectors are in machine learning (ML) models. Vectors are fundamental building blocks in ML. They represent data in ways that make it easier for models to process, analyze, and derive insights. Vectors are a foundational concept in linear algebra. They are used in data science to represent input features in machine learning models and, in some cases, the target variable.

Vectors, in the context of machine learning, are numerical representations of data. They are made up of components, which are ordinary numbers. So, a vector can be considered a list of numbers, where each represents some data feature. In ML and AI, data points like words, images, or audio clips are often represented as vectors through a process called embedding. The idea is to encode complex characteristics of the data into these vectors.

When an ML model processes data, it transforms that data into a vector embedding—a dense array of numbers of a fixed size that encodes the data's essential features, also called vector representations of data. 

The process of converting data (often text data, but it can also be images, sounds, etc.) into a set of numbers or vector embeddings machine processes is called vectorization or embedding. Such an embedding transforms high-dimensional data into vectors in a lower-dimensional space. These vectors are key to understanding the patterns, similarities, and relationships between different data points.

When we mention dimension, we refer to the number of components or features that make up a data point, which is often huge, encompassing hundreds or thousands of components, and a vector may have many dimensions. 

In high-dimensional vector data, each "dimension" corresponds to a feature or attribute that captures some represented data characteristic. In other words, the "dimensionality" refers to the number of elements in the list of numbers that is a vector. When vectors are highly dimensional, in other words, they have many components, it means they can store more information about the data.

For example, a vector embedding of a word using a large language model could have 768 or more dimensions. Each dimension encodes some aspect of the word’s meaning, context, or relationships with other words. In computer vision, a vector representing an image might have thousands of dimensions, with each dimension capturing information about different visual features, textures, or patterns. 

In the context of vector embeddings, the terms vectors and embeddings are often used interchangeably because they both refer to the same concept: a numerical representation of detailed, high-dimensional data where each data point is represented as a more abstract data in the form of a vector in a lower-dimensional space.

However, a vector is also a general mathematical concept referring to an ordered list or array of numbers. Vectors can represent many kinds of data in different fields, not just machine learning. In general, vectors represent points in a space where each number in the vector corresponds to a coordinate or feature of that point in a specific dimension.

In ML, an embedding is a specific type of vector. Such embeddings are representations of data that map high-dimensional inputs into a lower-dimensional space. The term "embedding" usually implies that the vector is used to capture the essential information or relationships from raw data.

In natural language processing (NLP), a word like "plant" might be represented as a 300-dimensional vector. This vector is an embedding that captures relationships between "plant" and other words, like "flower" or "garden," by placing them near each other in the vector space. So, while every embedding is a vector, not every vector is necessarily an embedding. Embeddings are vectors with the specific goal of representing complex data in ML tasks.

Vector database

A vector database is a set of data stored in mathematical form. It stores information in the form of vectors (vector embeddings). Vector databases simplify machine learning models by remembering how previous input data was used. As a result, they enable ML models to be used for sophisticated semantic search, data retrieval, recommendation, and text generation. Data can be identified based on similarity scores rather than exact matches. In this way, a computer model can understand the data according to context.

Vector databases are specialized data management systems designed to store, retrieve, and process vector representations of data, also called vector embeddings. Unlike traditional databases optimized for structured, tabular data in rows and columns of numbers or strings, vector databases are built to manage high-dimensional, continuous data efficiently. They work differently: they store high-dimensional data as vectors, where each data point is represented by a numeric array.

With the advent of vector databases, a new form of search known as vector search has emerged. At their core, vector databases are optimized for similarity search—the process of finding the closest vectors to a given query vector. This search type retrieves objects based on their similarity in vector space rather than relying on exact matches or simple keyword matching. These operations, though mathematically intensive, are critical for modern AI applications.

In traditional search systems, results are retrieved based on the presence of exact keywords or phrases. For example, if you search for "best coffee machine," the system will return documents containing those specific words. In contrast, vector search retrieves items based on the semantic similarity of their vector embeddings. If two items have similar meanings or are conceptually related, their vectors will be close in the vector space, allowing the system to retrieve relevant results even if they don’t match the query word-for-word.

Importance of embeddings

So, vector embeddings are dense vectors or long lists of numbers generated by ML models. Such vector data allows for the representation of complex, often unstructured data in a way that machines can process. It captures the essence or key features of the data while reducing its complexity.

Complex data often exist in high-dimensional spaces. For example, a text document might be represented as a vast collection of words with thousands of dimensions (one for each unique word). Embeddings compress this high-dimensional data into lower-dimensional vectors, typically with hundreds of dimensions, without losing essential features. Working with embeddings allows for faster computations and more efficient models because the size of the data is significantly reduced. It becomes easier to handle large datasets. 

Vectors are important in machine learning because they enable us to compare data mathematically. Thanks to the data being represented as vector embeddings, we can use mathematical operations like calculating distances or similarities between points. This is the core of how ML models make predictions or classify data.

Thanks to embeddings, vector databases excel at calculating similarity between objects by measuring distances in vector space. This enables efficient similarity searches, where semantically similar items are retrieved even if they don’t share the same keywords.

How vector databases empower large language models

Large language models (LLMs) generate dense vector embeddings that capture semantic information, and vector databases provide the infrastructure to efficiently store, search, and retrieve these embeddings. Here's how vector databases support and enhance the functionality of LLMs.

Semantic search and retrieval

As we already mentioned before, traditional databases rely on exact keyword matching. Vector databases, on the other hand, leverage the embeddings generated by LLMs to enable semantic search. In this search type, queries are transformed into vectors and matched with stored vectors based on similarity, allowing for more relevant information retrieval in LLMs.

Storing and accessing embeddings

LLMs generate vector embeddings for text inputs. These embeddings are high-dimensional, dense numerical representations that capture the semantic essence of the text. Vector databases are optimized to store millions or billions of these embeddings. This storage method allows for efficient retrieval and management of this massive data. This infrastructure is critical for scaling LLM applications, as embeddings must be stored in a way that allows for fast access and search.

The impact of vector databases on LLMs in conversational AI

LLMs are a basis for conversational AI. Their success, particularly in applications like customer support chatbots or virtual assistants, hinges on meeting these three key criteria:

Generate human-like language and reasoning

LLMs already excel at this by leveraging massive training datasets to generate coherent, human-like text based on user inputs. This allows the AI to simulate understanding and provide natural language responses that feel like real conversations.

Remember the conversation history

Holding a meaningful conversation requires the ability to remember what was said earlier. However, general-purpose LLMs are stateless, meaning they do not have built-in memory between turns in a conversation. This is where vector databases can help by storing embeddings of conversation history and enabling the AI to retrieve relevant parts of prior exchanges.

Access to factual information beyond general knowledge

LLMs are trained on a vast corpus of general information but can fall short when asked domain-specific or up-to-date questions. They might generate plausible but incorrect answers — a phenomenon known as "hallucination." To avoid this, LLMs need a reliable mechanism to query external factual databases, particularly for specialized knowledge or the latest information.

How Vector Databases Enhance LLMs

Giving LLMs “State”

LLMs are stateless, meaning once an LLM is trained, its knowledge becomes static or "frozen." While fine-tuning can adjust or add to the LLM’s knowledge, this process is time-consuming and makes the model "frozen" again after each update. This limitation poses challenges when maintaining long-term context in a conversation or keeping the model updated with the latest information. 

Vector databases can act as an external dynamic memory for LLMs. Instead of fine-tuning the entire model, enterprises can store data as vector embeddings in the database. These vectors represent past interactions, knowledge, or any other relevant information that can be continuously updated. In that way, vector databases provide a state for LLMs.

Instead of having a fixed knowledge base embedded within the LLM, you can store knowledge like text or interactions as vector embeddings in the vector database. These embeddings are high-dimensional representations of the data, and they can be easily added, updated, or removed.

Serving as an external knowledge base

LLMs often produce hallucinations. The factual accuracy of such outputs is questionable, especially when the query concerns niche, domain-specific topics, or recent events. Without access to relevant, up-to-date information, LLMs may generate plausible but incorrect responses.

In such cases, vector databases come to the rescue. Through vector database integration, LLMs can perform retrieval-augmented generation (RAG). This approach allows the LLM to query the vector search engine for factual information in real time. The system retrieves domain-specific or updated data and passes it into the LLM’s context window, significantly improving factual accuracy and reducing hallucinations.

As new information becomes available, whether it’s new company policies, updated FAQs, or recent financial data, vector databases can instantly store this knowledge. When the LLM is queried, it can retrieve the latest and most relevant data from the database, enhancing the flexibility and accuracy of responses without requiring the LLM itself to be retrained.

Considerations when choosing a vector database for LLMs

The proper vector database can significantly improve the LLM's ability to manage large-scale, high-dimensional data, retrieve relevant information efficiently, and ensure scalability and reliability. The most important considerations when selecting a vector database for LLMs are below.

Handling large datasets

The vector database should scale efficiently to handle millions or billions of vectors without a drop in performance. This is because LLMs often interact with massive datasets, such as knowledge bases, customer interactions, and unstructured data like documents or images.

Horizontal scaling in vector databases refers to distributing the data and workload across multiple machines or nodes. A vector database that supports horizontal scaling can spread the storage and search tasks across several servers to manage increasing amounts of data and query volume more efficiently. When the load increases and the AI application grows, you can simply add more nodes to handle the extra data and traffic.

Vertical scaling involves increasing the capacity of a single machine, for example, by adding more memory, CPU, or storage. This approach helps improve performance by enabling a single machine to process more data or handle more tasks simultaneously without distributing the workload across multiple servers.

While vertical scaling can help up to a certain point, it eventually faces limits in hardware capabilities as it has a natural limit. Eventually, you reach the physical limits of the machine, as there is only so much CPU, RAM, or storage that can be added to a single server.

Security and compliance

Since vector databases often store high-dimensional data representations or embeddings used in machine learning and AI applications, securing these databases is vital to prevent data breaches and unauthorized access and ensure compliance with legal and regulatory frameworks. Security in a vector database means protecting the data and ensuring the secure functioning of the database system itself.

Security approaches like encryption ensure that data is secure both when stored in the database, also referred to as "at rest," and when transmitted between users and services or "in transit."

Controlling access to the vector database is also crucial for preventing unauthorized users from interacting with the system. Authentication ensures that only authorized users and systems can access the database using passwords, API keys, or Multi-Factor Authentication (MFA).

Secure backups ensure that data is regularly backed up to prevent loss in case of failure or breach. Backups should also be encrypted to prevent unauthorized access. Monitoring detects unusual activity, such as large numbers of queries from an unauthorized source or suspicious changes to the database.

Vector databases also need to align with various industry standards and best practices for security. It means the database operates within legal and ethical boundaries, providing confidence to users and organizations relying on the system.

The goal of all security measures in vector databases is to ensure that sensitive data remains protected, the database can recover from failures or breaches, and all interactions with the database are authorized and traceable.

Top vector database solutions: comparison of leading vector databases and their specific strengths for LLMs

Best Practices for building LLM apps with vector databases

Choosing the right LLM and vector database

Data scientists evaluate various LLMs based on the application's specific needs to select the suitable model. They consider model performance, size, inference time, and compatibility with the task, such as semantic search, content generation, or dialogue management. Further, they choose the most suitable vector database based on criteria like speed, scalability, and ease of integration.

Preprocessing and vector embeddings creation

The ML specialists preprocess the input data through cleaning, tokenizing, etc., and use pre-trained or fine-tuned models to generate high-quality vector embeddings that capture the semantic meaning of the data.

Implement similarity search with efficient indexing

These embeddings are then stored in the vector database to create an index. The database will use this index to retrieve the closest matching vectors during queries quickly. Data scientists implement efficient indexing algorithms, such as FAISS or HNSW, to ensure the system performs fast, accurate similarity searches even as the dataset scales.

Integrate the vector database and the language model

The database retrieves the most semantically similar responses based on the distance between the user's query and the stored vector embeddings. Once the closest matching vectors (responses) are retrieved, the language model generates a coherent and contextually accurate reply to the user. This can involve paraphrasing or expanding on the retrieved text.

ML specialists leverage retrieval-augmented generation (RAG) to enhance the LLM's accuracy. They configure the system to retrieve relevant, contextually appropriate information from the vector database and feed it into the LLM during real-time interactions.

Future trends in vector databases for LLMs

Key hardware advancements and innovations in retrieval models drive the future of vector databases for large language models (LLMs). As GPUs become more cost-effective and ARM-based CPUs gain traction, vector databases are poised to see enhanced performance and scalability. Advanced storage solutions also contribute to maintaining low-latency operations even with large datasets.

Emerging technologies like hybrid search models, combining keyword and vector retrieval techniques, will improve search accuracy and efficiency. Additionally, multimodal and advanced machine learning models refine vector embeddings, allowing more efficient use of computational resources.

As vector databases evolve, they will continue integrating these hardware and software innovations to become better and faster. Their growth and advancements are essential to maintaining the relevance and effectiveness of LLMs in the dynamic field of artificial intelligence-based solutions.