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AI chatbots are applications or interfaces that engage in human-like conversations using natural language understanding (NLU), natural language processing (NLP), and machine learning (ML). These chatbots use conversational AI to communicate with users, though not all chatbots employ this technology. Databases such as CrateDB can enhance AI-powered chatbots by providing robust vector store support and real-time data analysis, enabling them to deliver intelligent and personalized responses.
AI chatbot needs a database to store chat information and relevant user metadata. This database is the chatbot's memory, organizing data to enable quick and accurate responses. AI applications benefit from flexible data modeling, so a database that supports various data structures is advantageous. Examples of databases suitable for AI-powered chatbots include CrateDB, MongoDB, MySQL, and Postgres. CrateDB offers support for multiple data formats within a single database and even within a single table, thanks to its dynamic data schema.
AI chatbots gather data from a variety of sources, including publicly available data, databases, websites, APIs, and structured knowledge bases. They also rely on real-time information and updates to provide accurate responses. CrateDB’s dynamic schema supports multiple data formats from various data sources seamlessly, including structured, unstructured, semi-structured, and binary data.
RAG Pipelines, short for Retrieval Augmented Generation Pipelines, are a crucial component of generative AI, that combines the vast knowledge of large language models (LLMs) with the specific context of your private data.
A RAG Pipeline works by breaking down your data (text, PDFs, images, etc.) into smaller chunks, creating a unique "fingerprint" for each chunk called an embedding, and storing these embeddings in a database. When you ask a question, the system identifies the most relevant chunks based on your query and feeds this information to the LLM, ensuring accurate and context-aware answers. They operate through a streamlined process involving data preparation, data retrieval, and response generation.
- 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.
While this is a simplified representation of the process, the real-world implementation involves more intricate steps. Questions such as how to properly chunk and extract information from sources like PDF files or documentation and how to define and measure relevance for re-ranking results are part of broader considerations.
A database is crucial for storing and efficiently retrieving the embeddings and associated data chunks. It acts as the “memory” of your knowledge assistant, enabling lightning-fast access to relevant information when a user asks a question.
Without a database, searching for relevant information would be incredibly slow and inefficient, hindering the responsiveness and usefulness of your knowledge assistant.
As AI adoption continues to grow, the need for databases that can adapt to complex data landscapes becomes paramount. Leveraging a multi-model database capable of managing both structured, semi-structured, and unstructured data, is an ideal fit to serve as the foundation for data modelling and application development in AI/ML scenarios. It is an enabler of complex, contextual-rich, and real-time intelligent applications.