We explore how Apache NiFi can be used to integrate open source LLMs to implement scalable and efficient RAG pipelines. He shows how any kind of data including semistructured, structured and unstructured data from a variety of sources and types can be processed, queried, and used to feed large language models for smart, contextually aware answers. Look for his example utilizing Cortex AI, LLAMA, Apache NiFi, Apache Iceberg, Snowflake, open source tools, libraries, and Notebooks.

Tim Spann is the Senior SolutionsEngineer - Financial Services working withSnowflake, Cortex AI, Apache NiFi, Apache Iceberg, Python, Apache Polaris, Streamlit, Cloud Data, Vectors, Data, Generative AI, ML, Hugging Face, Apache Kafka, Apache Flink, Pytorch, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming.

Previously, he was a Principal Developer Advocate at Zilliz/Milvus, Principal Developer Advocate at Cloudera, Developer Advocate at StreamNative, Principal Field Engineer at Cloudera, a Senior Solutions Architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark.

Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.