GenAI Dev Stack, LLMOps & Vector Databases!

The Significance of GenAI Dev Stack!

The image depicts a stack of technologies used in the GenAI Dev Stack, which is likely a development stack for a Generative Artificial Intelligence (AI) application. The stack is structured in layers, each representing a different aspect of the application's architecture:

? UI Layer: At the top, represented by "React", indicating that React (a JavaScript library for building user interfaces) is used for creating the user interface of the application.

? Service/Chain Layer: Below the UI layer, there's "LangChain", which is?an open-source framework that helps developers create applications using large language models (LLMs).

? Model Layer: The next layer includes OpenAI's CatGPT and HuggingFace. Hugging Face is a machine learning and data science platform that helps users build, train, and deploy machine learning models.

? Storage Layer: The "SingleStore" in the following layer suggests that a database technology that unifies multiple data storage capabilities (like transactions, vector embeddings and real-time analytics) is used for data storage (vector database).

? Infrastructure Layer: At the bottom of the stack, "AWS", "Azure", and "Google Cloud" indicate that the application's infrastructure is cloud-based, possibly utilizing services from all three major cloud providers for hosting, computing, and other infrastructure needs.

The significance of vector databases stems from their ability to handle complex, high-dimensional data while offering efficient querying and retrieval mechanisms. One such database that always comes to my mind is SingleStore database.

Sign up for a free SingleStore database cloud account and avail $600 worth of free resources now: https://lnkd.in/gCAbwtTC

Here is my article that describes vector databases in details: https://lnkd.in/gpWJajGN

The Landscape of Vector Databases!

Let's look at five approaches for persisting and retrieving vector data

? Pure vector databases?like Pinecone

? Full text search databases?like ElasticSearch

? Vector libraries?like Faiss, Annoy and Hnswlib

? Vector-capable?NoSQL databases?like MongoDB, Cosmos DB and Cassandra

? Vector-capable?SQL databases?like SingleStoreDB or PostgreSQL

In pure vector databases, data is organized and indexed based on the vector representation of objects or data points.

These vectors can be numerical representations of various types of data including images, text documents, audio files or any other form of structured or unstructured data.

SingleStoreDB: A Robust, Full-Context Vector Database:SingleStoreDB provides a simpler, more powerful approach to handling vector data.?It allows you to store and query vector data alongside traditional structured data, providing a unified platform for various types of queries and analysis. As a distributed SQL database, SingleStoreDB is also highly performant,?highly available and can scale out to adapt to growing data sets.

SingleStore has supported over a dozen vector functions since 2017! These include dot_product for cosine similarity, Euclidean distance, vector normalization and various vector arithmetic functions. SingleStore customers deploy vectors in production use cases.

LLMOps

Not just MLOps but even LLMOps is starting to become a THING!

But, what the heck is LLMOps anyways? Its a workflow for developing and deploying large language models (LLMs) into production.

The manual deployment process can be replaced with an automated continuous delivery pipeline which takes care of testing your LLMs and application thoroughly.

Developers may only be needed to finally approve the deployment process but not to execute a whole list of manual steps. Instead of regular, tedious checking of charts and values, you could configure a monitoring platform to send you a notification (it could be Slack, e-mail or other tools you use) whenever, e.g., a metric drops below a certain threshold or an application becomes unresponsive for a certain amount of time.

Take a look at an example of such a setup presented in the image. In such a setup there are only two manual actions where a human-in-the-loop is necessary while the rest happens without human intervention:

? a Data Scientist?merges a change?in the training code or parameters to a codebase. This triggers a bunch of quality checks (e.g. linting, tests) and starts a training job. During training, we log all the model metrics and metadata automatically. A successful run produces model artifacts saved in a model registry, ready to be picked up for inference.

? in the second part, the team validates model metrics and decides to deploy the model they have just trained. A Machine Learning Engineer?approves a model?in the registry and this action automatically triggers a validation procedure which, if successful, delivers a working deployment into an inference environment.

Know more in this original article: https://lnkd.in/gPspKjbE

Vector Databases

When a user query is initiated, various types of raw data such as images, documents, videos, and audio, which can be either unstructured or structured, are first processed through an embedding model. This model, often a complex neural network, translates the data into high-dimensional numerical vectors, effectively encoding the data's characteristics into a mathematical form known as vector embeddings.

These embeddings, which are arrays of numerical values, are then stored in a specialized type of database called a vector database, such as SingleStore. When retrieval is required, the vector database performs operations such as similarity searches to find and retrieve the vectors most similar to the query, efficiently handling complex queries and delivering relevant results to the user.

This entire process enables the rapid and accurate management of vast and varied data types in applications that require high-speed search and retrieval functions.Harness the robust vector database capabilities of SingleStore DB, tailored to seamlessly serve AI-driven applications, chatbots, image recognition systems, and more.

With SingleStoreDB at your disposal, the necessity for maintaining a dedicated vector database for your vector-intensive workloads becomes obsolete.

Diverging from conventional vector database approaches, SingleStore DB takes a novel approach by housing vector data within relational tables alongside diverse data types. This innovative amalgamation empowers you to effortlessly access comprehensive metadata and additional attributes pertaining to your vector data, all while leveraging the extensive querying prowess of SQL.

Sign up for a free SingleStore database cloud account and avail $600 worth of free resources now: https://lnkd.in/gCAbwtTC

Here is my article that describes vector databases in details: https://www.singlestore.com/blog/a-complete-guide-to-vector-databases/

Developers & DevOps engineers who like to automate their DevOps workflows can try Kubiya. Kubiya is an intelligent DevOps assistant that helps developers automate their developer workflows in minutes. It is like a ChatGPT for DevOps professionals :)

Also, don't forget to signup to SingleStore to claim your $600 worth free cloud resources.

Like to build LLM apps that can see hear & speak? Check out.

Also, checkout similar demo examples at SingleStore Spaces.