Efficient Data Caching with Vector Datastore for LLMs

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Introduction

In the ever-evolving landscape of artificial intelligence, Large Language Models (LLMs) have taken center stage, transforming the way we interact with and understand vast amounts of textual data. With the proliferation of these advanced models, the need for efficient data management and retrieval has become paramount. Enter Vector Datastore, a game-changer in the realm of data caching for LLMs. This article explores how Vector Datastore's innovative approach, based on vector representations and similarity search, empowers LLMs to swiftly access and process data, revolutionizing their performance and capabilities.

How does Vector datastore enable data cache for LLMs?

With every online source you scan, you will come across terms like chatbots, LLMs, or GPT. Most people are speaking about the large language models, and we can see every new language model gets released every week.

Before seeing how vector databases enable data caches for large language models, one must learn about them and their importance to the language models.

Vector databases: What are they?

Getting an idea of vector embeddings is essential to know about vector databases. It is a data representation that consists of semantic information, helping the artificial intelligence system better understand all the datasets. At the same time, it helps to maintain long-term memory. The most critical element is understanding and remembering, especially if you want to learn anything new.

AI models usually generate embeddings. Every large language model consists of a variety of features. Due to this reason, it becomes difficult to manage their representations. With the help of embeddings, one could represent the various dimensions of the data. Therefore, the artificial intelligence models would understand the patterns, relationships, and hidden structures.

In this scenario, the vector embeddings that use the traditional scalar-based databases could be challenging. It cannot keep up or handle the scale and complexity of various data. The complexities that often come with vector embeddings would require a specialized database. It is the reason why one would need vector databases.

With the help of vector databases, one could get optimized storage and query capabilities of any unique structure presented by vector embeddings. As a result, the user would get high performance along with easy search capabilities, data retrieval, and scalability only by comparing the similarities and values of findings between one another.

Though vector databases are difficult to implement, until now, various tech giants companies are not only developing them but also making them manageable. Since they are expensive to implement, one must ensure proper calibration to receive high performance.

How it works?

Taking the simple example of a link with a large language model like chat GPT, we know it consists of a large volume of data and content. At the same time, the user can only proceed with the chat GPT application.

Being the user, one has to improve your queries in the application. Once you complete this step, the query gets inserted into the embedding model. It initiates the process of vector embeddings based on the content that requires indexing. After completing this process, the vector embeddings move into the vector databases. It usually occurs regarding the content that wants to be used for embedding.

As a result, you will receive the outcome produced by vector databases. Therefore, the system sends it back to the user as a result.

As a user continues making different queries, it goes through the same embedding model that helps create embeddings. It helps in processing the database query for similar—vector embeddings.

Let us know the whole process in detail.

A vector database incorporates diverse algorithms dedicated to facilitating Approximate Nearest Neighbor (ANN) searches. These algorithms encompass techniques such as hashing, graph-based search, and quantization, which are intricately combined into a structured pipeline for retrieving neighboring vectors concerning a queried input.

The outcomes of this search operation are contingent upon the proximity or approximation of the retrieved vectors to the original query. Hence, the pivotal factors under consideration are accuracy and speed. A trade-off exists between the query output's speed and the results' precision; a slower output corresponds to a more accurate outcome.

The process of querying a vector database unfolds in three fundamental stages:

 1. Indexing

A diverse array of algorithms comes into play upon the ingress of the vector embedding into the vector database. These algorithms serve the purpose of mapping the vector embedding onto specific data structures, thus optimizing the search process. This preparatory step significantly enhances the speed and efficiency of subsequent searches.

 2. Querying

The vector database systematically compares the queried vector and the previously indexed vectors. This comparison entails the application of a similarity metric, a crucial determinant in identifying the nearest neighbor amidst the indexed vectors. The precision and efficacy of this phase are paramount to the overall accuracy of the search results.

 3. Post Processing

Upon pinpointing the nearest neighbor, the vector database initiates a post-processing stage. The specifics of this stage may vary based on the particular vector database in use. Post-processing activities may involve refining the final output of the query, ensuring it aligns seamlessly with the user's requirements.

Additionally, the vector database might undertake the task of re-ranking the nearest neighbors, a strategic move to enhance the database's future search capabilities. This meticulous post-processing step guarantees that the delivered results are accurate and optimized for subsequent reference, thereby elevating the overall utility of the vector database in complex search scenarios.

Implementing vector data stores in LLM

 Let us consider an example to understand how a vector data store can be installed or implemented in a large language model. Before we can start with the implementation, one has to install the vector datastore library.

 pip install vectordatastore

 Assuming you have the data set containing the text snippets, you will get the following in code format. 

# Sample dataset
dataset = {
    "1": "Text snippet 1",
    "2": "Text snippet 2",
    # ... more data points ...
}
 
# Initialize Vector Datastore
from vectordatastore import VectorDatastore
vector_datastore = VectorDatastore()
 
# Index data into Vector Datastore
for key, text in dataset.items():
    vector_datastore.index(key, text)
 
# Query Vector Datastore from LLM
query = "Query text snippet"
similar_texts = vector_datastore.query(query)
 
# Process similar_texts in the LLM
# ...

In this example, one can see that the vector data store efficiently indexes the datasets while utilizing vector representations. When the large language model requires retrieving data similar to the query text, it often uses a vector datastore to obtain the relevant snippets quickly.

Process of enabling data caches in LLMs

Vector Datastore enables efficient data caching for Large Language Models (LLMs) through its unique approach to handling data. Traditional caching mechanisms store data based on keys, and retrieving data involves matching these keys. However, LLMs often work with complex and high-dimensional data, such as text embeddings, which are not easily indexed or retrieved using traditional key-value pairs. Vector Datastore addresses this challenge by leveraging vector representations of data points.

Process of how Vector Datastore enables data cache for LLMs

 1. Vector Representation:

Vector Datastore stores data points in vectorized form. Each data point, whether a text snippet or any other type of information, is transformed into a high-dimensional numerical vector. This vectorization process captures the semantic meaning and relationships between data points.

 2. Similarity Search:

 Instead of relying on exact matches of keys, Vector Datastore performs similarity searches based on vector representations. When an LLM needs specific data, it translates its query into a vector representation using the same method employed during data storage. This query vector is then compared against the stored vectors using similarity metrics like cosine similarity or Euclidean distance.

 3. Efficient Retrieval:

 By organizing data as vectors and employing similarity searches, Vector Datastore can quickly identify the most similar vectors to the query vector. This efficient retrieval mechanism allows LLMs to access relevant data points without scanning the entire dataset, significantly reducing the time required for data retrieval.

 4. Adaptive Indexing:

 Vector Datastore dynamically adjusts its indexing strategy based on the data and queries it receives. As the dataset grows or the query patterns change, Vector Datastore adapts its indexing structures to maintain optimal search efficiency. This adaptability ensures the cache remains efficient even as the data and query patterns evolve.

 5. Scalability:

 Vector Datastore is designed to handle large-scale datasets commonly encountered in LLM applications. Its architecture allows horizontal scaling, efficiently distributing the workload across multiple nodes or servers. This scalability ensures that Vector Datastore can accommodate the vast amount of data processed by LLMs without compromising performance.

Vector Datastore's ability to work with vectorized data and perform similarity searches based on vector representations enables it to serve as an efficient data cache for Large Language Models. By avoiding the limitations of traditional key-based caching mechanisms, Vector Datastore significantly enhances the speed and responsiveness of LLMs, making it a valuable tool in natural language processing.

Conclusion

The development of LLM is one of the crucial technological advancements of our time. Not only does it have the potential to revolutionize various aspects of our lives, but at the same time, it is imperative on our part to utilize them ethically and responsibly to retrieve all its benefits.

Author Bio

Karthik Narayanan Venkatesh (aka Kaptain), founder of WisdomSchema, has multifaceted experience in the data analytics arena. He has been associated with the data analytics domain since the early 2000s, with a ringside view of transformations in this industry. He has led teams that architected and built scalable data platform solutions across the technology spectrum.

As a niche consulting provider, he bridged the gap between business and technology and drove BI adoption through innovative approaches in an agnostic manner. He is a sought-after speaker who has presented many lectures on SAP, Analytics, Snowflake, AWS, and GCP technologies.