Lilly Tech Systems
Learn Pinecone
Master the leading managed vector database. Learn to store embeddings, perform similarity search, build RAG pipelines, and deploy production AI applications — all without managing infrastructure.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is Pinecone? Vector databases explained, similarity search concepts, and use cases for AI applications.
2. Setup & Configuration
Create an account, install the Python SDK, create your first index, and choose serverless vs pod-based.
3. Indexing Vectors
Upsert vectors, batch operations, metadata, namespaces, and managing large-scale vector datasets.
4. Querying & Search
Similarity search, metadata filtering, top-k results, hybrid search, and query optimization.
5. RAG Integration
Build a full RAG pipeline with Pinecone, LangChain, and OpenAI. Chunking, embedding, and retrieval strategies.
6. Best Practices
Cost optimization, index sizing, performance tuning, monitoring, and production deployment patterns.
What You'll Learn
By the end of this course, you'll be able to:
Store Vectors
Upsert embeddings with metadata into Pinecone indexes, organized by namespaces for multi-tenant applications.
Similarity Search
Query vectors with filtering, retrieve the most relevant results, and optimize search performance at scale.
Build RAG Pipelines
Combine Pinecone with LLMs to build retrieval-augmented generation systems that answer questions from your data.
Deploy to Production
Optimize costs, monitor performance, and scale your vector search infrastructure for production workloads.