⚙️Engineering
🗄️
Vector Databases
Learn to store and query embeddings efficiently for semantic search and RAG
Prerequisites
🌱
Beginner
BeginnerVector database basics
What to Learn
- •What are embeddings and why vector DBs?
- •Similarity metrics (cosine, euclidean, dot product)
- •Getting started with Pinecone/Chroma/Weaviate
- •Indexing and querying vectors
- •Integration with LLM applications
Resources
- 📚Pinecone learning center
- 📚Chroma documentation
- 📚LangChain vector store guides
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Intermediate
IntermediateProduction vector systems
What to Learn
- •ANN algorithms (HNSW, IVF, PQ)
- •Index tuning and optimization
- •Hybrid search implementation
- •Filtering and metadata queries
- •Scaling and sharding strategies
Resources
- 📚FAISS documentation
- 📚Milvus architecture guide
- 📚Vector DB comparison benchmarks
🌳
Advanced
AdvancedAdvanced vector infrastructure
What to Learn
- •Building custom vector indexes
- •Real-time indexing systems
- •Multi-tenancy patterns
- •Cost optimization strategies
- •Research on new index structures
Resources
- 📚ANN-Benchmarks analysis
- 📚Vector DB papers
- 📚Building vector databases from scratch