Vector databases store contextual representaion of data ( knowm as vectors ) in n-dimensional space. The vector representation allows the ability to preserve contextual information and also a huge scale of diversity in the data. Thus Vector databases store very accurate representation of thr data and can be used to accurately identify semantic similarity ( similarity search ) through it's fast andscalable ANN algorithm.
LLMs are generative, meaning that they produce meaningful, coherent text in a sequential manner based on a user prompt and are very good as conversational agents. However, when using LLMs to answer a human’s questions, they often produce irrelevant or factually incorrect results.
- An LLM often hallucinates, i.e., it fabricates information, such as making up stuff that don’t exist
- LLMs learn/memorize a compressed version of their training data – some information is always “lost” in a model’s internal representation of the data
- An LLM cannot know facts that occurred after its training was completed
Vector databases help address these problems, by functioning as the underlying storage layer that can be efficiently queried by an LLM to retrieve facts. Unlike traditional databases, vector DBs specialize in natively representing data as vectors. As a result, we can now build applications with an LLM sitting on top of a vector storage layer that contains recent, up-to-date, factual data (well past the LLM’s training date) and use them to “ground” the model, alleviating the hallucination problem.Because of their amenability to operating in embedding space, vector databases are proving to be very useful for semantic or similarity-based search for multiple forms of data
In many cases, traditional keyword search can yield more relevant results and increased user satisfaction4. It’s largely to do with the fact that ranking based on metrics like cosine similarity causes results that have a higher similarity score to appear above partial matches that may contain specific input keywords, reducing their relevance to the end user. However, pure keyword search also has its own limitations – in case the user enters a term that is semantically similar to the stored data (but is not exact), potentially useful and relevant results are not returned. As a result of this trade-off, real-world use cases for search & retrieval demand a combination of keyword and vector searches, of which vector databases form a key component (because they house the embeddings, enabling semantic similarity search and are able to scale to very large datasets).