How Kyvos is Transforming Conversational Analytics with RAG and Vector Embeddings

Large language models are continually redefining what is achievable in natural language processing. These models can understand natural language with context, generate human-quality text and can perform a wide variety of language-related tasks in fields like customer service, content creation and research. The release of AI-powered content-creating tools such as Chat GPT, Gemini and more has democratized the use of AI with its out-of-the-box accessibility. Users don’t need ML expertise to interact with these tools, as the latter can engage in human-like conversations by comprehending words, sentences and paragraphs to answer queries or write code.

Typically, LLMs are trained on a wide array of generic data to provide an adequate learning base. Despite their impressive capabilities, LLMs are prone to factual errors due to their lack of enterprise-specific knowledge and outdated information. This can lead to difficulties in understanding the underlying context and nuances, often leading to hallucinations and biased outputs.

For instance, suppose a user asks an LLM to create an SQL query to retrieve total sales for each product category from the sales database. In this case, if the LLM is trained on a generic dataset and not on an enterprise’s knowledge base or its SQL queries and database schemas, then it might generate an inaccurate response. It might assume that the sales_data table has columns named product_category and sales_amount, but in reality, the column names are different, such as item_category and sales_price.

This is why LLMs need context. Techniques like RAG and vector search can provide this context by helping LLMs understand the enterprise’s knowledge base. This understanding enables LLMs to generate accurate SQL queries.

What are Vector Embeddings?

A vector embedding is a distinctive type of vector that represents complex entities in machine learning models and natural language processing (NLP). It captures the semantic meaning or relationship between entities by representing high-dimensional data, such as words, sentences, or even images, in a lower-dimensional space. There are several different types of embeddings such as word, sentence, document and image embeddings.

Let’s take an example of word embeddings, the words “happy” and “joyful,” have similar meanings so they will be positioned close to each other in vector space. However, the embeddings of these words would be further apart from the word “angry” because the meaning and context are different. This transformation of data objects into vector embeddings is performed by an embedding model that uses ML techniques to extract meaningful patterns and relationships within data.

How to Use Vector Embeddings for Semantic Search and Clustering?

While vector embeddings have diverse applications, their most common uses lie in the realms of semantic search, clustering and classification.

• Semantic Search is a data searching technique that uses AI/ML and NLP to comprehend the meaning behind the query. The word semantic refers to the similarity in meaning between words and phrases. It goes beyond keyword matching and employs vector embeddings to capture the semantic meaning of the query and match it to relevant content.

  For example, a search engine powered by vector embeddings would be able to measure the semantic similarity between phrases like “I need a dog sitter” and “Pet care service available.” This allows for more accurate search results, even when the exact keywords don’t match.

  Another example of semantic search powered by vector embeddings is recommendation systems. Vector embeddings can help pinpoint products that are contextually similar to user picks. All enterprises need to do is convert user preferences and item characteristics into vector embeddings and then use semantic search technique to identify close matches between user preferences vector and the items vector. If the vectors are closely aligned, it indicates that the user is likely to appreciate similar items.

• Clustering is a type of unsupervised learning technique that groups unlabeled data points into distinct clusters based on their similarity. Vector embeddings can be used to create clusters of similar items. train clustering algorithms to group similar items that are in close proximity in vector space. For instance, if an organization wants to decipher user sentiment for different products from their reviews, they will first have to vectorize all the reviews, create embeddings and capture the sentiment expressed in them. These embeddings will be given to a clustering algorithm to group similar reviews. All the positive sentiment reviews will form one cluster and negative sentiment reviews will form another cluster. Organizations can use these clusters for many purposes, such as analyzing reviews and identifying areas of product improvement.

How do Kyvos Use Vector Embeddings to Generate Accurate Responses?

LLMs are trained on massive datasets, but they lack contextual understanding. This is where Kyvos’ Gen AI-powered semantic layer comes into the picture. Kyvos employs vector embeddings to represent semantic models and utilizes RAG and semantic search techniques to identify relevant columns within semantic models in response to user queries. RAG is an AI framework that makes LLM responses accurate by querying external data sources, such as an enterprise data repository.

Now let’s see how Kyvos does it?

First, Kyvos creates embeddings of all the documents in the knowledge base by leveraging metadata (descriptions and tags) that are available in a semantic model and its fields (dimensions, attributes and measures). These embeddings are stored in a vector database, a specialized database that stores unstructured data in the form of embeddings.

When a user asks a question, Kyvos, with the help of an embedding model, creates an embedding of the question. Once the embedding for the user’s query is created, Kyvos uses RAG to perform a semantic search to compare this query vector embedding for semantic similarity with the vectors of Kyvos’ semantic model metadata stored in the vector database. A vector database can provide a list of semantic models in decreasing order of a semantic search affinity. The model at the top of the list has the highest probability of serving the answer. Once the relevant semantic model is identified, Kyvos uses semantic search to identify the most relevant dimensions and metrics within the model that can help address the user’s query. These identified metrics and dimensions become the context for LLM. The LLM then combines the user’s query embedding with the retrieved information from the knowledge base embedding and generates a SQL query. The process includes an autocorrection layer that ensures that the generated SQL query aligns with the data structure of semantic model. Kyvos’ lightning-fast query processing capabilities ensure that the generated SQL query is executed in sub-seconds and delivers accurate results with unparalleled speed.

Kyvos, with the help of vector embeddings and RAG, is revolutionizing the way LLMs generate accurate responses. It enables organizations to achieve context-aware, conversational data exploration, ensuring a seamless journey from data to insights. With Kyvos, enterprises have access to fast trusted data to improve the accuracy of their AI applications.

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