Cloudera, Inc., a data company for enterprise artificial intelligence (AI), and Pinecone, a vector database company providing long-term memory for AI, announced a strategic partnership that integrates Pinecone’s AI vector database expertise into Cloudera’s open data platform, aimed to help organizations use AI to streamline operations and improve customer experiences.

Pinecone is optimized to store AI representations of data (vector embeddings) and search through them by semantic similarity. This capability is necessary for adding context to queries against applications that use Large Language Models (LLMs) to reduce erroneous outputs and helps search and Generative AI applications deliver more accurate and relevant responses.

Pinecone’s vector database will also be integrated into Cloudera Data Platform (CDP), and includes the release of a new Applied ML Prototype (AMP) that will allow developers to more quickly create and augment new knowledge bases from data on their own website, as well as pre-built connectors that will enable customers to quickly set up ingest pipelines in AI applications.

Customers can use this same architecture to set up or improve support chatbots or internal support search systems to reduce operational costs and improve customer experience by decreasing human case-handling efforts and faster resolution times.

https://www.cloudera.com ■ https://www.pinecone.io

DataStax announced a new integration with LangChain, the popular orchestration framework for developing applications with large language models (LLMs). The integration makes it easy to add Astra DB – the real-time database for developers building production Gen AI applications – or Apache Cassandra, as a new vector source in the LangChain framework. 

As companies implement retrieval augmented generation (RAG) – the process of providing context from outside data sources to deliver more accurate LLM query responses – into their generative AI applications, they require a vector store that provides real-time updates with zero latency on critical production workloads.

Generative AI applications built with RAG stacks require a vector-enabled database and an orchestration framework like LangChain to provide memory or context to LLMs for accurate and relevant answers. Developers use LangChain as an AI-first toolkit to connect their application to different data sources.

The integration lets developers leverage the Astra DB vector database for their LLM, AI assistant, and real-time generative AI projects through the LangChain plugin architecture for vector stores. Together, Astra DB and LangChain help developers to take advantage of framework features like vector similarity search, semantic caching, term-based search, LLM-response caching, and data injection from Astra DB (or Cassandra) into prompt templates. 

https://www.datastax.com/blog/llamaindex-and-astra-db-building-petabyte-scale-genai-apps-just-got-easier