Module 09
Vector Databases & RAG Pipelines
Build retrieval systems that transform documents into embeddings, store them in vector databases, and serve them to LLM applications.
7 lessons · 8 videos · 1h 54m- 09.01
Embeddings and Vector Search
Explain embedding vectors, similarity metrics, nearest neighbor search, recall, precision, and ranking in retrieval systems.
- 09.02
Document Ingestion and Chunking
Build a document ingestion step that extracts text, chunks documents, attaches metadata, and prepares records for embedding.
- 09.03
Embedding Pipeline Design
Design an embedding pipeline with batching, retry handling, model versioning, metadata capture, and incremental updates.
- 09.04
Pinecone Managed Vector Search
Create a Pinecone index, upsert embeddings with metadata, run similarity queries, and filter search results.
- 09.05
pgvector in Postgres
Store embeddings in Postgres with pgvector and execute vector similarity queries with metadata constraints.
- 09.06
Milvus and Open-Source Vector Search
Compare Milvus collection design, indexing options, and deployment tradeoffs against managed vector databases.
- 09.07
RAG Evaluation and Retrieval Quality
Evaluate a RAG retriever using recall at k, mean reciprocal rank, groundedness checks, and qualitative failure analysis.