A curated list of awesome Pinecone resources, libraries, tools and applications
Feel free to improve this list by contributing!
- Quickstart - Guide on how to set up and use Pinecone Database
- Sample Notebooks - Colab notebook walkthroughs
- Sample Code - Sample applications
- Architecture - Explanation of Pinecone architecture
- Release Notes
- What is a Vector Database?
- What is RAG?
- What is Context Engineering?
- How to Build a RAG Chatbot Without Coding
- Teaching Your AI to Read: A Guide to Scraping, RAG, and Smart Data Insights
- Building an AI-Powered Discord Bot with Railway and Pinecone
- Full RAG Chatbot in Slack
- Populate Pinecone from a website using n8n
- 5 Ways to Automate Pinecone with Zapier
- AI Assistants with Document Retrieval (RAG) Using Pinecone
- Getting Started with Pinecone - Walkthrough of Quickstart notebook
- Semantic Search and Reranking with Cohere and Pinecone - How to apply reranking to search pipelines
- Why RAG Remains Essential for Modern AI - Learn why RAG remains the backbone of agentic applications
- Build Contextual Retrieval with Anthropic and Pinecone
- Multi-Modal Semantic Search Starter Kit
- RAG Upload Pipeline
- Hybrid RAG: Mastering Context By Combining GraphRAG and VectorRAG - Combining GraphRAG and VectorRAG using LangGraph, neo4j, and Pinecone
- Pinecone Assistant API Overview
- Pinecone RAG Tutorial
- Databases in Higher Dimensions - Jack Pertschuk, Principal Engineer at Pinecone on Talking Serverless
- AI, Ambition, and Innovation - Edo Liberty on Inside the ICE House
- Efficient Constant-Space Multi-Vector Retrieval
- Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval
- Python SDK - The Pinecone Python SDK is distributed on PyPI using the package name
pinecone - NodeJS SDK
- Java SDK
- Golang SDK
- .NET SDK
- Rust SDK
- context-window - TypeScript RAG toolkit for ingesting documents, creating indexed collections with Pinecone, and querying them with OpenAI
- Pinecone Embedding Atlas: Interactive Vector Visualization - Explore embeddings directly from a Jupyter notebook
- Pinecone Assistant MCP
- Pinecone Developer MCP
- Infinite Context MCP - MCP server that stores conversations and retrieves relevant context using Pinecone, reducing prompt bloat across chats and AI models
- Kafka-To-Pinecone - A data streaming pipeline to consume real-time messages from kafka topic, generate embeddings using OpenAI and upsert vectors into Pinecone index. To support AI agents and RAG applications that continuously require fresh data.
- Chonkie - Chunking library integration with Pinecone
- Pinecone Assistant Demo - RAG system using the Pinecone Assistant API to upload, index, and query PDF documents
- Autonomous RAG Agent - Event-driven agent that routes queries between a Pinecone vector store and local file system using LangChain and Gemini