惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

T
The Blog of Author Tim Ferriss
WordPress大学
WordPress大学
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
小众软件
小众软件
博客园_首页
Blog — PlanetScale
Blog — PlanetScale
B
Blog RSS Feed
Martin Fowler
Martin Fowler
M
MIT News - Artificial intelligence
博客园 - 三生石上(FineUI控件)
博客园 - 【当耐特】
N
News | PayPal Newsroom
K
Kaspersky official blog
大猫的无限游戏
大猫的无限游戏
人人都是产品经理
人人都是产品经理
N
Netflix TechBlog - Medium
B
Blog
Recorded Future
Recorded Future
U
Unit 42
J
Java Code Geeks
Security Latest
Security Latest
H
Hackread – Cybersecurity News, Data Breaches, AI and More
V
Vulnerabilities – Threatpost
Cisco Talos Blog
Cisco Talos Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Scott Helme
Scott Helme
Apple Machine Learning Research
Apple Machine Learning Research
aimingoo的专栏
aimingoo的专栏
T
Threatpost
Last Week in AI
Last Week in AI
Know Your Adversary
Know Your Adversary
Project Zero
Project Zero
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
Cloudbric
Cloudbric
AWS News Blog
AWS News Blog
NISL@THU
NISL@THU
有赞技术团队
有赞技术团队
博客园 - 叶小钗
N
News and Events Feed by Topic
V
V2EX
T
Troy Hunt's Blog
月光博客
月光博客
博客园 - Franky
P
Proofpoint News Feed
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog
C
Cisco Blogs
The Cloudflare Blog
T
Tor Project blog
Google Online Security Blog
Google Online Security Blog

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
Explore the power of Pinecone with public collections
Gibbs Cullen · 2022-09-16 · via Pinecone

Note: Public collections are no longer supported as part of collections. Visit our documentation to learn more.

Last month, we announced a new feature in public preview: collections. Collections allow users to save vectors and metadata from an index as a snapshot, and create new indexes from any collection.

Today we are excited to announce the addition of public collections to help users quickly run a sample index pre-loaded with data and experience the power of the Pinecone vector database.

What are public collections?

For users to run a query in Pinecone, they need to upload data to an index. This takes time. Public collections make it easier to explore Pinecone by providing public data from real-world data sources that can be used to create an index in one click.

Pinecone users can now create an index from pre-loaded vector embeddings in one of three example collections. Each collection features data from Pinecone partners:

  • Glue SSTB collection from OpenAI
  • Text REtrieval Conference (TREC) question classification collection from Cohere
  • Stanford Question Answering Dataset (SQuAD) collection from Stanford

These collections contain real-world data, load in less than a minute, and have matching guides to get started:

How do they work?

The collections are available under Public Collections within the Pinecone console. You can create an index from the example collections and use the guides to get started including code snippets in Python showing how to use the particular index.

To create an index from a public collection, follow these steps:

  1. Open the Pinecone console.
  2. Click the name of the project in which you want to create the index.
  3. In the left menu, click Public Collections.
  4. Find the public collection from which you want to create an index. Next to that public collection, click Create Index.
  5. When index creation is complete, a message appears stating that the index is created and that vectors are successfully upserted. The Click to View button will take you to the new index.

Get started today

If you don’t have an embedding model or ready-to-use data to start testing Pinecone, then public collections can help. All Pinecone users will have access to three example collections — Glue SSTB, TREC question classification, and SQuaD — starting today. We will add more public collections over time.

To learn more about public collections, check out the guides or try them for yourself in the console.