> Markdown version of [/videos/782-building-real-time-ai-ml-agents-with-distributed-data-using-apache-cassandra-and-astra-db?t=561](https://www.wearedevelopers.com/videos/782-building-real-time-ai-ml-agents-with-distributed-data-using-apache-cassandra-and-astra-db?t=561). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Building Real-Time AI/ML Agents with Distributed Data using Apache Cassandra and Astra DB Standard LLMs fall short on real-time context. Stop overpaying for redundant model calls. Build lightning-fast AI agents using RAG, Astra DB, and semantic caching. - **Speakers:** Dieter Flick - **Event:** World Congress 2023 - **Published:** October 23, 2023 - **Duration:** 28:20 - **URL:** https://www.wearedevelopers.com/videos/782-building-real-time-ai-ml-agents-with-distributed-data-using-apache-cassandra-and-astra-db ## Summary Building generative AI agents that provide accurate, context-aware responses requires looking beyond basic large language models. The retrieval-augmented generation (RAG) pattern addresses the limitations of standard model training cycles by passing proprietary, real-time context directly into the prompt. Utilizing Apache Cassandra and its managed cloud counterpart, Astra DB, developers can ingest and query vector embeddings alongside classical data through flexible API endpoints like GraphQL and REST, ensuring that AI responses are grounded in an organization's most current information. A robust RAG architecture starts with chunking internal data—such as product catalogs, travel security guidelines, or user behavior histories—and converting it into numeric embeddings. These vectors capture the semantic characteristics of the data and are stored in a vector database. When a user issues a query, the application translates the question into its own vector and performs a similarity search. The resulting top-matched documents, combined with conversational memory via frameworks like LangChain, dynamically build a comprehensive prompt for the agent. Effectively structuring prompts is critical for optimal AI performance; prompts should establish a specific persona, incorporate user profiles, and append recent chat histories alongside retrieved documents. Furthermore, storing previous user prompts and their corresponding outputs directly within the vector database acts as an intelligent semantic cache. This architectural choice eliminates redundant calls to external language models, drastically reducing infrastructure costs and shrinking latency for commonly asked questions. **Keywords:** retrieval augmented generation, apache cassandra, astra db, vector databases, semantic similarity search, llm prompt engineering, rag architecture, data embeddings, ai agent memory, langchain integration, openai api, real-time ai pipelines, graphql data ingestion, vector cache cost reduction, llm conversational history ## Chapters 1. **Introduction to building real-time generative agents** (00:03) — An overview of the session goals and DataStax's real-time data cloud offerings. 1. **Interacting with Astra DB using GraphQL APIs** (03:25) — Creating database schemas and ingesting records through the cloud platform API gateway. 1. **Enabling context with retrieval augmented generation** (05:54) — How retrieval-augmented generation supplies proprietary enterprise data to foundational language models. 1. **Architectural components of a generative AI agent** (09:21) — Coordinating language models, prompt engineering, and semantic databases enables reliable chatbot processing. 1. **Processing text for semantic vector search operations** (13:34) — Splitting content into manageable chunks allows the generation of embeddings for similarity matching. 1. **Constructing prompts and executing chatbot data queries** (18:42) — Combining user history, behavioral context, and semantic similarity produces rich queries for language models. 1. **Evaluating development tiers and answering audience questions** (25:43) — Exploring free database tiers and addressing inquiries about cloud regions and data privacy. ## Related Moments - [Scaling semantic search with Astra DB and Apache Cassandra](https://www.wearedevelopers.com/videos/966-accelerating-genai-development-harnessing-astra-db-vector-store-and-langflow-for-llm-powered-apps) (from "Accelerating GenAI Development: Harnessing Astra DB Vector Store and Langflow for LLM-Powered Apps") - [Expanding AI capabilities using retrieval-augmented generation](https://www.wearedevelopers.com/videos/950-supercharge-your-cloud-native-applications-with-generative-ai) (from "Supercharge your cloud-native applications with Generative AI") - [Architectural patterns for developing robust generative AI applications](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) (from "Building AI Applications with LangChain and Node.js") - [Understanding basic retrieval-augmented generation architectures in chatbots](https://www.wearedevelopers.com/videos/1130-chatbots-are-going-to-destroy-infrastructures-and-your-cloud-bills) (from "Chatbots are going to destroy infrastructures and your cloud bills") - [Introducing conversational queries with AI data agents](https://www.wearedevelopers.com/videos/1547-data-analytics-with-microsoft-fabric-end-to-end-use-case-with-data-agents) (from "Data Analytics with Microsoft Fabric: End-to-End Use Case with Data Agents") - [Providing context for retrieval augmented generation and agents](https://www.wearedevelopers.com/videos/1612-how-to-decipher-user-uncertainty-with-genai-and-vector-search) (from "How to Decipher User Uncertainty with GenAI and Vector Search") ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Introducing Redis Agent Memory Server](https://www.wearedevelopers.com/magazine/699-introducing-redis-agent-memory-server) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [WWC24 Talk - Scott Hanselman - AI: Superhero or Supervillain?](https://www.wearedevelopers.com/magazine/469-wwc24-talk-scott-hanselman-ai-superhero-or-supervillain) ## Related Jobs - [AI Software Engineer (Germany)](https://www.wearedevelopers.com/jobs/48317-ai-software-engineer-germany) at **Sunhat** - [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/319507-principal-engineer-ai-search-vector-infrastructure) at **Redis** - [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/353953-principal-engineer-ai-search-vector-infrastructure) at **Redis** - [Senior AI Agent Software Engineer (Go, Python) (m/f/x)](https://www.wearedevelopers.com/jobs/48277-senior-ai-agent-software-engineer-go-python-m-f-x) at **Dynatrace** - [AI & Machine Learning Engineer (all genders)](https://www.wearedevelopers.com/jobs/48217-ai-machine-learning-engineer-all-genders) at **msg** - [Principal Engineer - AI Search & Vector Infrastructure](https://www.wearedevelopers.com/jobs/ext/381484-principal-engineer-ai-search-vector-infrastructure) at **Redis**