Vector Databases: Hyper‑dimensional Embeddings, LLMs and RAG Systems

Purpose of Vector Databases

Vector databases are specialized systems designed to store and search high‑dimensional numerical vectors called embeddings. These databases index compact, learned representations of text, images, audio and other unstructured data so that similarity search is efficient. Instead of indexing raw documents, a vector database stores each document's embedding -- a vector of floating‑point numbers produced by a machine‑learning model. When a user issues a query, the database compares the query's embedding with stored vectors to find semantically similar items. Microsoft notes that a vector database is "designed to store and manage vector embeddings"; these embeddings represent data points in a high‑dimensional space where each dimension encodes a feature of the data[1]. Dev.to's 2025 guide explains that vector databases store and retrieve high‑dimensional numerical representations known as embeddings and that modern embedding models produce vectors with 384--1536 dimensions[2]. By organizing data in this high‑dimensional space, vector databases enable rapid similarity search---finding semantically related documents, images or audio clips in milliseconds[3].

Common AI tasks that need vector databases include:

• Semantic search and question answering. In semantic search, the database returns documents whose embeddings are close to the query embedding rather than those that share exact keywords[4].
• Recommendation systems. Vectors encode user preferences or product characteristics, enabling nearest‑neighbour search to surface similar items[5].
• Image, audio and multi‑modal retrieval. Embeddings generated by convolutional or transformer models allow vector databases to locate similar images or audio clips[6][7].
• Anomaly detection and fraud monitoring. SingleStore describes how vector databases can store high‑dimensional behaviour vectors and perform fast similarity checks to detect anomalies[8].

These capabilities make vector databases essential infrastructure for production AI systems, powering applications that would be infeasible with traditional data stores.

Why Traditional Databases Fall Short

Relational databases excel at exact matches and structured queries. They employ B‑tree or hash indexes that quickly find rows based on equality or range conditions. However, they are not designed to efficiently search high‑dimensional vectors. Dev.to's guide explains that in a relational system like PostgreSQL, a query such as "find documents similar to this concept" is prohibitively slow because comparing a 1536‑dimensional vector against millions of records requires expensive brute‑force computations[9]. The same article notes that B‑tree indexes do not support similarity search in vector spaces[10].

Vector databases address this limitation by using specialized indexing algorithms such as Hierarchical Navigable Small World (HNSW) graphs, Inverted File (IVF) indexes and Product Quantization (PQ). These algorithms perform approximate nearest‑neighbour (ANN) searches, trading exactness for speed. HNSW builds a multi‑layer graph where each vector is a node connected to its nearest neighbours; queries traverse the graph to converge quickly on similar vectors[11]. IVF clusters the space using algorithms like k‑means and searches only within promising clusters[12]. PQ compresses vectors into codebooks that fit more vectors in memory at the cost of reduced precision[13]. These structures allow vector databases to return results within milliseconds even on collections containing hundreds of millions of vectors[11], whereas brute‑force scanning in a traditional DB would take minutes[14].

Another difference is data representation. Relational tables store structured rows, whereas vector databases represent data as high‑dimensional vectors. F22 Labs notes that vector databases employ "specialized systems for managing high‑dimensional vectors" and highlight differences in query types, indexing methods and scalability compared to relational databases[15]. Because of these differences, vector databases complement rather than replace traditional databases. For small collections (< 1 million vectors) the pgvector extension for PostgreSQL may suffice, but dedicated vector stores deliver 10--100× faster queries at larger scales[16].

Hyper‑dimensional Space and How a Vector Fits In

What is a High‑dimensional Vector?

In mathematics, a vector is an array of numbers that describes both direction and magnitude. To specify a point in 2D or 3D space we need two or three coordinates. Microsoft Learn explains that high‑dimensional vectors extend this concept: each dimension represents a different feature of the data[17]. A single embedding may have hundreds or thousands of dimensions; for example, text embeddings generated by large language models (LLMs) often contain 768 or 1536 numbers[2]. High‑dimensional vectors can thus capture fine‑grained semantics---such as tone, context and topic---by assigning different aspects of the data to different dimensions[17].

Distance and Closeness

Distances in high‑dimensional space are used to quantify similarity. Two vectors that represent similar items will be close together, while dissimilar items lie farther apart. The Azure documentation notes that the distance between embeddings correlates with semantic similarity[18]. Cosine similarity measures the angle between vectors and is widely used for text embeddings; Euclidean (L2) distance and dot product are also common[19].

Due to the curse of dimensionality, the volume of the space grows exponentially with the number of dimensions, making naive nearest‑neighbour search inefficient. SingleStore remarks that high‑dimensional data often has sparsity and overfitting challenges[20]. Specialized indexes like HNSW or IVF mitigate these issues by pruning the search space[21].

Vectors vs. Embeddings

While all embeddings are vectors, not all vectors are embeddings. TigerData explains that a vector is merely a list of numbers, whereas an embedding uses vectors to represent data points in a structured and meaningful way within continuous space[22]. Embeddings capture semantic relationships by mapping similar items close together. For instance, word embeddings place "king" and "queen" near each other in vector space because they share related semantic attributes[23].

What Are Embeddings?

Embeddings are dense numerical representations of complex data. The Meilisearch article describes them as vectors of floating‑point numbers that map text, images or audio into a space where proximity reflects conceptual similarity[24]. They are generated by machine‑learning models that learn patterns in data. Models such as BERT, GloVe and Word2Vec process text to produce continuous embeddings by training on large corpora[25]. For images, convolutional neural networks produce embeddings based on visual features[26]. Embeddings capture context and meaning, enabling systems to perform semantic search, recommendations, clustering and anomaly detection[27].

Embeddings have several benefits:

• Enhanced semantic retrieval. They enable search engines and RAG systems to return relevant results even when queries use different wording[4].
• Uncover hidden patterns. By mapping data to a lower‑dimensional space while preserving relationships, embeddings reveal patterns that might not be obvious in the original high‑dimensional format[28].
• Unified representation of multi‑modal data. Text, images, audio and other modalities can be represented in the same vector space, facilitating cross‑modal retrieval and analysis[29].

However, embeddings also bring challenges. Their quality depends on the training data; semantic drift can occur as language or user behaviour changes[30]. Generating and storing embeddings at scale demands significant computational resources[31].

How LLMs Create Embeddings for Vector Databases

Large language models generate embeddings by passing text through tokenization and transformer layers. The Instaclustr guide notes that LLMs transform text into numerical vectors that capture semantic meaning and contextual relationships[32]. The process begins by splitting text into tokens (words or subwords), mapping each token into a high‑dimensional space, and using neural networks to produce dense vector representations[33]. Modern LLMs like OpenAI's GPT‑4 or Hugging Face's sentence transformers compute embeddings of size 768--3072. These embeddings can then be stored in a vector database for later retrieval.

To build an embedding pipeline:

1. Pre‑process and chunk data. Break documents into passages or chunks that fit within the embedding model's context window. Clean and normalize text to improve embedding quality[34].
2. Generate embeddings. Use an LLM's embedding API or open‑source model to convert each chunk into a vector. Ensure the same model is used for both indexing and querying to maintain comparability.
3. Store embeddings in a vector database. Index vectors using HNSW, IVF or PQ structures for fast similarity search[21]. Attach metadata (e.g., document ID, source) to each vector for later retrieval.

Using Embeddings as a Source of Truth for LLMs

Embedding‑based retrieval forms the backbone of Retrieval‑Augmented Generation (RAG). When an LLM receives a query, the system converts the query into an embedding and retrieves the most similar vectors from the vector database. The retrieved documents (context) are fed back into the model to ground its response. Instaclustr notes that vector similarity measures like cosine similarity rank stored vectors based on relevance, enabling LLMs to access contextually relevant information quickly[35]. This method ensures that answers are grounded in actual data rather than being generated solely from the model's training distribution[36].

During retrieval, the system often fetches more candidates than needed and uses a re‑ranking model (e.g., a cross‑encoder) to pick the top passages. Metadata filters (e.g., date, author or category) can be applied either before or after the vector search for refined results[37]. The selected passages are appended to the user's query as context in the prompt. LLMs then produce answers that cite or summarize this context instead of inventing new information.

What Are RAG Systems?

Retrieval‑Augmented Generation (RAG) is an AI architecture that couples a generative model with an information‑retrieval component. Wikipedia defines RAG as a technique that enables large language models to retrieve and incorporate new information. LLMs do not respond until they have referred to a specified set of documents, supplementing their pre‑existing training data[38]. This allows them to use domain‑specific and updated knowledge not available during training[39]. RAG reduces hallucinations by grounding responses in external sources and reduces the need to retrain models[40].

The RAG pipeline has four stages[41]:

1. Indexing. Convert data (text, images, graphs) into embeddings and store them in a vector database[42].
2. Retrieval. Given a user query, generate its embedding and retrieve the most relevant documents using similarity search[43].
3. Augmentation. Inject the retrieved content into the prompt sent to the LLM, ensuring the model prioritizes the supplied information[44].
4. Generation. The LLM synthesizes an answer using both the augmented context and its internal knowledge[45].

RAG systems are widely used in customer support, knowledge bases, code assistants and enterprise search to provide accurate, up‑to‑date answers while minimizing hallucinations.

System Prompt Examples for RAG

When integrating a vector database with an LLM, prompt engineering plays an important role in keeping the model grounded. A system prompt defines the rules the model must follow. To ensure a RAG system uses only the retrieved context, the system prompt should explicitly instruct the model not to rely on external knowledge. Below are example prompts:

Example 1 -- Strict grounding

System prompt: "You are a specialized technical assistant. You must answer using only the information provided in the context below, which comes from our trusted vector database. Do not consult any other sources, and avoid making up facts. If the context does not contain enough information to answer, respond with 'I don't know based on the provided context.'"

This prompt sets clear rules: the assistant must limit its knowledge to the retrieved passages and admit lack of context when necessary.

Example 2 -- Summarize and cite

System prompt: "You are an AI assistant that summarizes technical documents stored in a vector database. When given context passages and a question, reference only the provided context. Summarize the relevant sections and include citations (e.g., [1], [2]) pointing to the context items. Do not use information from your training unless it appears in the context."

By requesting citations, this prompt encourages transparency. It restricts the LLM to rely exclusively on the context and discourages hallucinations.

Example 3 -- Follow domain guidelines

System prompt: "You are an enterprise assistant for internal documentation. Always base your answers strictly on the supplied context from our vector store. If the user asks about anything outside the scope of the context, politely explain that the knowledge is unavailable. Never access or suggest information from the public internet or from your own knowledge."

Such prompts help enforce domain boundaries and support compliance requirements.

Pros of Vector Databases

Vector databases offer several advantages for AI applications:

Advantage	Evidence
Efficient similarity search	Vector databases support approximate nearest‑neighbour algorithms (HNSW, IVF, PQ) that deliver sub‑millisecond similarity searches over millions of vectors[11]. They can handle billions of vectors with real‑time performance, enabling RAG applications to respond within 100 ms[46].
Scalability and distributed architectures	Dedicated vector stores scale horizontally and manage memory for high‑dimensional data; they achieve 10--100× faster queries on datasets larger than 10 million vectors compared with integrated solutions[16].
Multi‑modal support	Embeddings allow different data types—text, images, audio, graphs—to be stored and searched in a unified vector space[29]. This enables cross‑modal retrieval and makes vector databases suitable for recommendation systems, image search and multi‑modal AI.
Real‑time AI applications	RAG and semantic search require sub‑100 ms latencies. Vector databases use in‑memory indexes and SIMD‑accelerated distance calculations to meet these demands[46].
Better relevance and personalization	Embedding proximity captures semantic meaning. Vector search returns results that match user intent even when queries use different phrasing[4]. This improves recommendation quality and reduces bounce rates in e‑commerce or content platforms.
Unified storage of embeddings and metadata	Integrated solutions like pgvector store embeddings alongside relational data, eliminating data duplication and facilitating ACID transactions[47].

Cons and Challenges

Despite their benefits, vector databases have limitations:

Challenge	Evidence
Scalability and storage overhead	As datasets grow, managing billions of high‑dimensional embeddings becomes complex. Meilisearch notes that scalability issues arise because points become equidistant, making retrieval inefficient (the curse of dimensionality)[48]. Vector indexes such as HNSW require significant memory since the entire graph resides in RAM[11].
Semantic drift and data staleness	Embeddings trained on specific datasets can lose relevance as language or user behaviour changes. Meilisearch highlights semantic drift, where relationships captured by embeddings no longer align with real‑world usage[49]. Models must be retrained regularly, incurring computational cost[50].
Computational cost	Generating embeddings and performing real‑time vector search requires powerful GPUs or TPUs. Real‑time applications like self‑driving cars must process embeddings at high speeds, demanding expensive hardware[31].
Approximation trade‑offs	ANN indexes trade accuracy for speed. IVF and PQ compress vectors and search only parts of the space[51]; this may miss some relevant results. Systems must tune parameters (e.g., ef_search, nprobe) to balance latency and recall[52].
Complexity and operational overhead	Running dedicated vector databases requires new tooling and expertise. SingleStore and others note that maintaining indexes, scaling clusters and tuning parameters add operational complexity[53].
Security risks and prompt injection	RAG systems are vulnerable to prompt injections and system‑prompt leakage, which can cause LLMs to access unintended sources or reveal sensitive information. Without proper content filtering and access control, malicious inputs can override system prompts[54].

Alternatives and Hybrid Approaches

Vector databases are powerful, but they are not the only solution for AI retrieval. Alternatives include:

• Integrated vector search in relational/NoSQL databases. Extensions like pgvector turn PostgreSQL into a capable vector database for smaller datasets. This approach simplifies deployment by keeping embeddings and structured data in one store[55].

• Hybrid search (vector + keyword). Some systems combine semantic vector search with traditional keyword or BM‑25 search to handle complex queries. Dev.to notes that hybrid indexes can pre‑filter by metadata then apply vector search[37].

• Graph databases and Knowledge Graphs. HybridRAG combines vector databases with graph databases to leverage both semantic similarity and explicit relationships. Memgraph explains that graph databases excel at reasoning over relationships, while vector databases find semantically similar entities; combining them allows RAG systems to provide context and reasoning[56]. This approach has been applied to biomedical knowledge bases and other complex domains[57].

• In‑memory libraries (FAISS, HNSWlib). For smaller deployments or research projects, open‑source libraries allow developers to perform vector search without running a separate database. They provide HNSW and IVF indexes in memory, but lack persistence and distributed features.

• Cache‑augmented generation (CAG). An emerging alternative, CAG uses caching of previous responses to reduce retrieval latency and compute cost. Meilisearch notes that some applications leverage caching and approximate caches instead of full vector search[58]. However, caches may miss new information and require careful invalidation policies.

• Hybrid file‑based or search‑engine solutions. Systems like ElasticSearch or OpenSearch integrate vector search into existing search engines, supporting hybrid retrieval with ranking and filtering.

Each alternative comes with trade‑offs. Integrated solutions simplify operations but may not scale; graph‑based approaches provide reasoning but add complexity; and in‑memory libraries lack persistence. Selecting the right option depends on data volume, latency requirements, and the complexity of the use case.

Conclusion

Vector databases are an essential component of modern AI systems. They enable efficient storage and retrieval of high‑dimensional embeddings, powering semantic search, recommendations, anomaly detection and retrieval‑augmented generation. Specialized indexes such as HNSW and IVF allow them to handle millions or billions of vectors, delivering millisecond‑level latencies that traditional databases cannot achieve. When combined with LLMs, vector databases let applications provide grounded, context‑aware responses by retrieving relevant information and feeding it back into the generation process.

Yet vector databases are not a panacea. They face challenges including scalability, semantic drift, computational cost, approximate search trade‑offs, operational complexity and security vulnerabilities. Alternatives such as integrated vector search in relational databases, hybrid vector‑keyword search, graph databases and in‑memory libraries offer options that suit different workloads and constraints. As AI applications evolve, hybrid approaches like HybridRAG that combine vector embeddings with knowledge graphs will likely become more prevalent. For developers, data scientists and AI engineers, understanding the strengths and limitations of vector databases is crucial for building robust, scalable and trustworthy AI systems.

[1] Integrated vector database - Azure Cosmos DB | Microsoft Learn

https://learn.microsoft.com/en-us/azure/cosmos-db/vector-database

[2] [4] [9] [10] [11] [12] [13] [14] [16] [19] [21] [37] [46] [47] [51] [52] [55] Vector Databases Guide: RAG Applications 2025 - DEV Community

https://dev.to/klement_gunndu_e16216829c/vector-databases-guide-rag-applications-2025-55oj

[3] [6] [24] [25] [26] [27] [30] [31] [48] [49] [50] [58] What are vector embeddings? A complete guide [2025]

https://www.meilisearch.com/blog/what-are-vector-embeddings

[5] [15] [29] Vector Databases: A Beginner's Guide - F22 Labs

https://www.f22labs.com/blogs/vector-databases-a-beginners-guide/

[7] [22] [28] A Beginner's Guide to Vector Embeddings | TigerData

https://www.tigerdata.com/blog/a-beginners-guide-to-vector-embeddings

[8] [20] [23] [53] The Power of Vector Databases in Anomaly Detection | SingleStoreDB for Vectors

https://www.singlestore.com/blog/the-power-of-vector-databases-in-anomaly-detection/

[17] Understanding Vector Databases | Microsoft Learn

https://learn.microsoft.com/en-us/data-engineering/playbook/solutions/vector-database/

[18] High-dimensional vector embeddings - Azure Cosmos DB | Microsoft Learn

https://learn.microsoft.com/en-us/azure/cosmos-db/gen-ai/vector-embeddings

[32] [33] [34] [35] [36] Vector databases and LLMs: Better together

https://www.instaclustr.com/education/open-source-ai/vector-databases-and-llms-better-together/

[38] [39] [40] [41] [42] [43] [44] [45] [54] Retrieval-augmented generation - Wikipedia

https://en.wikipedia.org/wiki/Retrieval-augmented_generation

[56] [57] HybridRAG and Why Combine Vector Embeddings with Knowledge Graphs for RAG?

https://memgraph.com/blog/why-hybridrag