How to Build AI-driven Knowledge Assistants with a Vector Store, LLMs, and RAG Pipelines
CrateDB is the enterprise database for time series, documents and vectors. It combines the simplicity of SQL, and the performance of NoSQL, providing instant insights into these different types of data. It is enabled for AI and is used for a large variety of use cases, including Real-time Analytics, AI/ML, chatbots, IoT, digital twins, log analysis, cyber security, application monitoring, and database consolidation.
This white paper explores how CrateDB provides a scalable platform to build Generative AI applications that cover the requirements of modern applications, such as AI-driven knowledge assistants. CrateDB is not just handling vectors, but also provides in a single storage engine a unique combination of all the data types needed for end-to-end applications, including RAG pipelines.
What you will learn
- What the key concepts of Generative AI are and what common challenges are
- How to provide custom context and private data in Generative AI
- What the architecture of knowledge assistants looks like
- How to implement a vector store with CrateDB
- How TGW Logistics Group is using CrateDB to develop their Digital Knowledge Assistants
FAQ
In the context of Generative AI, multimodal vector embeddings are getting more popular. No matter the kind of source data—text, images, audio, or video—an embedding algorithm of your choice is used to translate the given data into a vector representation. This vector comprises numerous values, the length of which can vary based on the algorithm used. These vectors, along with chunks of the source data, are then stored in a vector store. The process of integrating this vector data with CrateDB is straightforward, thanks to its native SQL interface. Download the white paper to learn more.
Vector databases are ideal for tasks such as similarity search, natural language processing, and computer vision. They provide a structured way to comprehend intricate patterns within large volumes of data. The process of integrating this vector data with CrateDB is straightforward, thanks to its native SQL interface. Learn more by downloading the white paper.
Retrieval Augmented Generation (RAG) pipelines are pivotal in the realm of generative AI. They are essentially a two-phase process: data preparation and data retrieval.
- Phase 1: Data Preparation
During the data preparation phase, raw data such as text, audio, etc., is extracted and divided into smaller chunks. These chunks are then translated into embeddings and stored in a vector database. It is important to store the chunks and their metadata together with the embeddings in order to reference back to the actual source of information in the retrieval phase. - Phase 2: Data Retrieval
The retrieval phase is initiated by a user prompt or question. An embedding of this prompt is created and used to search for the most similar pieces of content in the vector database. The relevant data extracted from the source data is used as context, along with the original question, for the Large Language Model (LLM) to generate a response.
Interested in learning more? Download the white paper to see a graphical representation of the structure of a RAG Pipeline and to explore how CrateDB provides the flexibility needed for storing and querying both vector and contextual data.
- Are context-aware: connect a language model to sources of context such as
prompt instructions. - Can Reason: rely on a language model to reason (defining ways to answer based on provided context, defining actions to take, etc.)