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Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Use the Pinecone Plugin for Claude Code to develop AI Applications Faster Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone Build more knowledgeable AI applications with new LLMs and greater control in Pinecone Assistant #NYTECHWEEK 2025 Retrieval-Augmented Generation (RAG) Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database | Pinecone Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale | Pinecone Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone | Pinecone Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness Vector databases aren't just for large-scale enterprise AI Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval | Pinecone Fast and Effective Early Termination for Simple Ranking Functions | Pinecone Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone | Pinecone Using Pinecone asynchronously with FastAPI A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings | Pinecone Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone Tool up: Pinecone’s first MCP servers are here Add context to your agent with Pinecone Assistant MCP remote server E2Rank: Efficient and Effective Layer-wise Reranking | Pinecone ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring | Pinecone Efficient Constant-Space Multi-Vector Retrieval | Pinecone How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses | Pinecone Pinecone Named to Fast Company's Annual List of the World's Most Innovative Companies of 2025 Launch Week: Pinecone for agents, search, recommendations, and more Optimizing Pinecone for agents (and more) Retrieval Inference for scale and performance How 1up Turns Sales Reps Into Product Experts with Pinecone | Pinecone Don’t be dense: Launching sparse indexes in Pinecone Unlock High-Precision Keyword Search with pinecone-sparse-english-v0 Evolving Pinecone's architecture to meet the demands of Knowledgeable AI Pinpoint references faster with citation highlights in Pinecone Assistant Bringing the leading vector database to your cloud Getting started with llama-text-embed-v2 Natural Language Counterfactual Explanations for Graphs Using Large Language Models | Pinecone Easily build knowledgeable chat and agent-based applications in minutes with Pinecone Assistant, now generally available How to build an agentic, chat or RAG knowledge system using Pinecone Assistant Real-time RAG with Pinecone and Estuary Flow BigQuery to Pinecone in Real-Time with Estuary Flow Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference | Pinecone Accelerate prototyping and development with Pinecone Local First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Strengthening security and increasing control with CMEK and API key roles Introducing Pinecone Rerank V0 Introducing cascading retrieval: Unifying dense and sparse with reranking From Idea to Action: How Pinecone Assistant Meaningfully Accelerates AI Business Building AI apps on Azure with Pinecone just got a lot easier Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone Four features of the Assistant API you aren't using - but should Deploying Pinecone with Infrastructure as Code (IaC) Streamlining CI/CD with Pinecone Local September 2024 Product Update Results of the Big ANN: NeurIPS'23 competition | Pinecone Introducing import from object storage for more efficient data transfer to Pinecone serverless Simplify, enhance, and evaluate RAG development with Pinecone Assistant, now in public preview Vectors and Graphs: Better Together August 2024 Product Update Pinecone Helps Deep Talk Deliver World-Class AI Assistants with Lower Engineering Overhead | Pinecone Assembled Delivers Better, Faster AI- Driven Support with Pinecone | Pinecone Llama 3.1 Agent using LangGraph and Ollama Build knowledgeable AI with Pinecone serverless, now generally available on Microsoft Azure Pinecone serverless is now generally available on Google Cloud, adding knowledge to AI assistants and other applications Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI Bridging Dense and Sparse Maximum Inner Product Search | Pinecone Refine Retrieval Quality with Pinecone Rerank Introducing reranking to Pinecone Inference to simplify building accurate AI July 2024 Product Update Connect to Pinecone within your platform to enable a seamless AI development experience Introducing Pinecone API Versioning RAG Brag with Inkeep Co-Founder Nick Gomez LangGraph and Research Agents Introducing Pinecone Inference to streamline your AI workflow
Simplify Stream Processing For GenAI Applications With Confluent Cloud for Apache Flink®
Anne Colbeck · 2024-03-19 · via Pinecone

Pinecone is thrilled to be partnered with Confluent today as they announce the general availability of the industry’s only cloud-native, serverless Apache Flink® service. Available directly within Confluent’s data streaming platform alongside a cloud-native service for Apache Kafka®, the new Flink offering is now ready for use on AWS, Azure, and Google Cloud. Integrated with Pinecone serverless, Confluent provides a simple solution for accessing and processing data streams from across the entire business to build a real-time, contextual, and trustworthy knowledge base to fuel AI applications.

Real-time GenAI applications require real-time data processing

Successfully deploying GenAI use cases will require retrieval augmented generation, or “RAG”, pipelines that provide relevant, real-time data streams sourced from every corner of the business. However, preparing pipelines of this sort is no easy task—especially when accounting for an ever-increasing amount of diverse data sources spanning across both legacy and modern data environments.

Ensuring applications have access to real-time pipelines with processed, prepared data will often require allocation of valuable engineering resources to manage open source tooling in-house rather than focusing on business-impacting innovation. Alternatively, securely processing data streams in multiple downstream systems (or across multiple distributed systems) is complex and inhibits data (re)usability, requiring redundant and expensive processing.

Without a reliable, cost-effective means of processing and preparing real-time data streams required by downstream tools, the benefits of GenAI will stay out of reach for most.

Easily build high-quality, reusable data streams with the industry’s only cloud-native, serverless Flink service

Apache Flink® is a unified stream and batch processing framework that has been a top-five Apache project for many years. Flink has a strong, diverse contributor community backed by companies like Alibaba and Apple. It powers stream processing platforms at many companies, including digital natives like Uber, Netflix, and Linkedin, as well as successful enterprises like ING, Goldman Sachs, and Comcast.

Fully integrated with Apache Kafka® on Confluent Cloud, Confluent’s new Flink service allows businesses to:

  • Effortlessly filter, join, and enrich your Confluent data streams with Flink, the de facto standard for stream processing
  • Enable high-performance and efficient stream processing at any scale, without the complexities of infrastructure management
  • Experience Kafka and Flink as a unified platform, with fully integrated monitoring, security, and governance

By leveraging Kafka and Flink as a unified platform, teams can connect to data sources across any environment, clean and enrich data streams on the fly, and deliver them in real-time to Pinecone. This ensures that their GenAI apps have the most up-to-date view of their business.

Confluent’s fully managed Flink service is now generally available across all three major cloud service providers, providing customers with a true multicloud solution and the flexibility to seamlessly deploy stream processing workloads everywhere their data and applications reside. Backed by a 99.99% uptime SLA, Confluent ensures reliable stream processing with support and services from the leading Kafka and Flink experts.

Together, Pinecone and Confluent enable simple development of GenAI applications

Pinecone became the most popular choice for developers building GenAI applications. The Pinecone and Confluent integration empowers users to quickly tap into a continuously enriched real-time knowledge base, so they can quickly scale and build AI applications using trusted data streams:

Pinecone and Confluent enable simple development of GenAI applications

Pinecone and Confluent enable simple development of GenAI applications

When asked how the two technologies help our end customer, Pinecone's Vice President of Business Development said, “Pinecone’s recent Serverless offering changed the game for developers who want to create remarkably better GenAI applications at scale. Confluent’s new Apache Flink Service takes it a step further by unifying and curating trustworthy data streams. Our joint solution presents a reliable and cost-effective way for developers to build and deploy knowledgeable AI.”

Getting Started

If you are new to Pinecone, see why over 5,000 customers are running Pinecone in pilots or production applications and try us for free today.

Learn more about Confluent’s integration with Pinecone serverless and be sure to check out the Quick Start Guide to configure your integration. Not yet a Confluent customer? Start your free trial of Confluent Cloud today. New signups receive $400 to spend during their first 30 days—no credit card required.