Vector Knowledge AI Agents

What are Vector Knowledge AI Agents?

In the ever-evolving world of artificial intelligence, Vector Knowledge AI Agents stand out as a groundbreaking innovation.

At their core, these agents are designed to understand, process, and retrieve information in a manner that's more aligned with how humans think and perceive information. Instead of relying on traditional, linear data processing methods, these agents use vectors — mathematical representations of data — to encapsulate knowledge. This approach allows a more nuanced understanding of context, relationships, and semantics.

In simpler terms, Vector Knowledge AI Agents can be considered advanced search engines with a deeper grasp of content, capable of drawing connections and insights previously out of reach for conventional search & knowledge retrieval models. As we delve further into embeddings and vector-based searches, we'll uncover these agents' transformative potential for various industries and applications.

How does embedding-based search work?

Embedding-based search is a paradigm shift from traditional keyword-based search methods. Instead of merely matching words or phrases, this approach delves deeper into the essence of the content. But how does it achieve this?

At the heart of embedding-based search lies the concept of embeddings. These are dense vector representations of data, be it words, sentences, or entire documents. Embedding models (e.g., ada-002, FlagEmbedding, SetFit, S-BERT, USE, etc.) transform data into these vectors in a high-dimensional space.

The beauty of these vectors is that they capture the semantic meaning and relationships between data points. For instance, words with similar meanings or contexts are positioned closer together in this vector space.

When a search query is made, it's also converted into a vector. The search then becomes a matter of finding the most similar vectors in the database to the query vector. This is typically done using cosine similarity or other distance metrics.

The result? Instead of getting matches based on exact wordings, users receive results based on their query's deeper, semantic meaning. This ensures more relevant, context-aware results, bridging the gap between human intent and machine understanding.

What else is needed for the system to be complete?

While embeddings provide a powerful means to capture the essence of data, managing and retrieving these vectors efficiently is crucial for a practical, real-world system. This is where indexes and databases come into play.

Indexes like FAISS (Facebook AI Similarity Search): FAISS is a library specifically designed for efficient similarity search and clustering of dense vectors. It allows for rapid searches among large collections of vectors, ensuring that the system can retrieve relevant results in real time, even with massive datasets.

Vector Databases can store those vectors and additional metadata. Such DBs support different indexing engines for efficient vector retrieval. Vector databases themself implement a lot of additional optimizations like random projection, quantization, LSH, HNSW, etc.

There are many great vector DBs out there for different purposes and different usage mechanics.

One such DB is Milvus — an open-source vector database that supports similarity search and analytics. Milvus is optimized for both performance and scalability. It can handle billions of vectors and supports various machine-learning models, making it a popular choice for embedding-based systems.

Another one worth mentioning is Pinecone — a managed vector database service. Pinecone simplifies deploying and scaling vector search applications. With its easy-to-use interface and robust backend, developers can focus on building applications without worrying about the intricacies of vector management.

Finally, you need an actual Language Model for final response generation. Once you retrieve the most relevant vectors, you would want to feed their corresponding text into the prompt to generate a reply to the user's query. In the same way, there is a variety of API-based LLMs (e.g., GPTs, Claude, etc.) and open-sourced ones (e.g., LLaMa 2, Stable Beluga, etc.)

What are the use cases for such a system? What are some success examples?

The amount of potential use cases is countless. Here are just a couple of diverse examples:

• Customer Support and Chatbots
  For example, a company could deploy a chatbot that uses embedding-based search to understand customer queries more deeply. When a user asks a question, the system retrieves the most relevant information from its database. Then it uses an LLM like GPT or LLaMa to generate a coherent, helpful response.

• Content Recommendation Systems
  Streaming platforms can utilize embedding-based search to understand the essence of movies, shows, or songs. When a user watches a particular content piece, the system can recommend others with similar vector representations, ensuring more personalized suggestions.

• Research and Knowledge Retrieval
  Academic researchers can input complex queries into a database of scholarly articles. The system, understanding the semantic depth of the question, can pull out the most relevant papers and even generate summaries using LLMs.

• E-commerce and Product Search
  An online shopping platform can employ embedding-based search to match product listings with user queries better. If a user searches for "vintage leather boots," the system can understand the nuances of "vintage" and "leather" and provide more accurate product matches.

• Medical Diagnostics and Patient Interaction
  In a telehealth application, patients can describe their symptoms. Using embedding-based search, the system can retrieve similar cases or relevant medical literature and then use an LLM to guide the patient with potential next steps or questions for their doctor.

• FAQ bot for any Website
  You can feed all the text you have on your website into a similar system and create an FAQ bot that can answer any question relevant to the information on your website.

• Conversational Agent Memories
  Embedding-based techniques could be easily used as long-term memory storage & retrieval for conversational agents. Imagine storing pieces of memories extracted from the conversation and dynamically embedded and stored for further usage. Such memories would be a unique set of vectors for each user.

A lot of companies are currently adopting this. One of the most notable use cases is Morgan Stanely using such a system in combination with GPT-4 for their internal Knowledge AI Agent.

At DRL, we've developed a framework that efficiently bootstraps a minimalistic Vector Knowledge AI Agent deployment with all the necessary components and APIs for fast testing and integration at scale.

At the same time, we usually focus on more nuanced search enrichment logic, data cleaning, and in some cases, open-source LLM fine-tuning to reach the optimal result for your use case.

Conclusions

Integrating embedding-based search with advanced language models marks a significant leap in AI's ability to understand and interact with data. These systems offer unparalleled depth and precision in their responses, moving beyond broad results to deliver nuanced, contextually relevant information. As technology progresses, this synergy promises to redefine our expectations of machine interaction, setting the stage for a future where the lines between human intent and machine output become increasingly blurred.