Big Data

Harness the Power of Pinecone with Cloudera’s New Applied Machine Learning Prototype


Elevate your AI applications with our latest applied ML prototype

At Cloudera, we continuously strive to empower organizations to unlock the full potential of their data, catalyzing innovation and driving actionable insights. And so we are thrilled to introduce our latest  applied ML prototype (AMP)a large language model (LLM) chatbot customized with website data using Meta’s Llama2 LLM and Pinecone’s vector database

Innovation in architecture

In order to leverage their own unique data in the deployment of an LLM’s (or other generative model), organizations must coordinate pipelines to continuously feed the system fresh data to be used for model refinement and augmentation.   

This AMP is built on the foundation of one of our previous AMPs, with the additional enhancement of enabling customers to create a knowledge base from data on their own website using Cloudera DataFlow (CDF) and then augment questions to the chatbot from that same knowledge base in Pinecone. DataFlow helps our customers quickly assemble pre-built components to build data pipelines that can capture, process, and distribute any data, anywhere in real time. The entire pipeline for this AMP is available in a configurable ReadyFlow template that features a new connector to the Pinecone vector database to further accelerate deployment of LLM applications with updatable context. The connector makes it easy to update the LLM context by loading, chunking, generating embeddings, and inserting them into the Pinecone database as soon as new data is available. 

Fig 1. High-level overview of real-time data ingest with Cloudera DataFlow to Pinecone vector database.

Navigating the challenge of “hallucinations”

Our newest AMP is engineered to address a prevalent challenge in the deployment of generative AI solutions: “hallucinations.” The AMP demonstrates how organizations can create a dynamic knowledge base from website data, enhancing the chatbot’s ability to deliver context-rich, accurate responses. Its architecture, known as retrieval-augmented generation (RAG), is key in reducing hallucinated responses, enhancing the reliability and utility of LLM applications, making user experience more  meaningful and valuable.

Fig 2. An overview of the RAG architecture with a vector database used to minimize hallucinations in the chatbot application.

The Pinecone advantage

Pinecone’s vector database emerges as a pivotal asset, acting as the long-term memory for AI, essential for imbuing interactions with context and accuracy. The use of Pinecone’s technology with Cloudera creates an ecosystem that facilitates the creation and deployment of robust, scalable, real-time AI applications fueled by an organization’s unique high-value data. Managing the data that represents organizational knowledge is easy for any developer and does not require exhaustive cycles of data science work.

Utilizing Pinecone for vector data storage over an in-house open-source vector store can be a prudent choice for organizations. Pinecone alleviates the operational burden of managing and scaling a vector database, allowing teams to focus more on deriving insights from data. It offers a highly optimized environment for similarity search and personalization, with a dedicated team ensuring continual service enhancement. Conversely, self-managed solutions may demand significant time and resources to maintain and optimize, making Pinecone a more efficient and reliable choice.

Embrace the new capabilities

Our new LLM chatbot AMP, enhanced by Pinecone’s vector database and real-time embedding ingestion, is a testament to our dedication to pushing the boundaries in applied machine learning. It embodies our commitment to providing refined, innovative, and practical solutions that meet the evolving demands and challenges in the field of AI and machine learning.  We invite you to explore the improved functionalities of this latest AMP