Artificial Intelligence

Vector Databases: Qdrant, Pinecone, ChromaDB & More

Learn every major vector database — ChromaDB for local prototyping, Pinecone for managed production, Qdrant for advanced filtering, plus Weaviate, Milvus, and pgvector. One course, all the options.

Level: IntermediateArtificial Intelligence

What You'll Learn

Vector embeddings and similarity search fundamentals
ChromaDB: local dev, collections, metadata filtering
Pinecone: managed cloud, namespaces, hybrid search
Qdrant: payload filtering, on-disk indexing, self-hosted
Weaviate: schema, modules, and GraphQL queries
Milvus: distributed, GPU acceleration, high-scale
pgvector: vector search inside PostgreSQL
Choosing the right vector DB for your use case

What's Included

🧭AI Tutor🔒

Interactive learning path with AI-generated lessons read aloud by your personal tutor

🧪Practice Lab🔒

Step-by-step lab instructions with AI guidance — run in your own IDE or sandbox.

🎙️Mock Interviews🔒

AI interviewer asks tailored questions, gives real-time feedback and a score report

📝Evaluation🔒

MCQ questions (theory, practical, code) to test your knowledge and earn a badge

⚔️Challenge AI🔒

Compete head-to-head against AI on real production problems — judged on correctness, performance, scalability, code quality, and edge cases

Course Syllabus

Module 1: Vector Search Fundamentals

  • •What are embeddings and why they matter
  • •Cosine similarity, dot product, Euclidean distance
  • •Dense vs sparse vs hybrid vectors
  • •ANN algorithms: HNSW, IVF, ScaNN
  • •Metadata filtering alongside vector search
  • •Chunking strategies that affect retrieval quality

Module 2: ChromaDB

  • •ChromaDB architecture: in-memory vs persistent
  • •Creating collections and adding documents
  • •Embedding functions: OpenAI, sentence-transformers
  • •Querying with metadata filters and where clauses
  • •Updating and deleting documents
  • •ChromaDB in a RAG pipeline (local dev)

Module 3: Pinecone

  • •Pinecone indexes: pod-based vs serverless
  • •Upserting vectors with metadata
  • •Namespaces for multi-tenant isolation
  • •Hybrid search: dense + sparse (BM25)
  • •Filtering with metadata at query time
  • •Pinecone with LangChain and LlamaIndex
  • •Production: batching, rate limits, monitoring

Module 4: Qdrant

  • •Qdrant collections and vector config
  • •Payload schema and indexed fields
  • •Advanced filtering: must, should, must-not
  • •Named vectors and multi-vector collections
  • •Scalar quantisation for memory efficiency
  • •Self-hosting Qdrant with Docker and Kubernetes
  • •Qdrant cloud vs self-managed trade-offs

Module 5: Weaviate, Milvus & pgvector

  • •Weaviate schema: classes, properties, modules
  • •Weaviate vectorizers: text2vec-openai, cohere
  • •GraphQL queries and hybrid search in Weaviate
  • •Milvus architecture: coordinator, data nodes, proxies
  • •Milvus collections, partitions, and indexes
  • •pgvector: enabling vector search in PostgreSQL
  • •pgvector index types: ivfflat vs hnsw
  • •Querying pgvector alongside relational data

Module 6: Choosing & Operating in Production

  • •Decision matrix: latency, scale, cost, filtering needs
  • •Embedding model selection and dimensionality trade-offs
  • •Index tuning: ef_construction, M, nprobe
  • •Re-ranking after vector retrieval
  • •Monitoring recall and search quality over time
  • •Data freshness: incremental upserts vs full re-index
  • •Security: API keys, VPC, encryption at rest
  • •Capstone: build a multi-source knowledge base with Qdrant

You're all set

Everything in this course is unlocked.

Continue Learning →
✓AI Tutor
✓Practice Lab
✓Mock Interviews
✓Evaluation
✓Challenge AI