Search Engineering Tutorials
Master vector search modalities, reranking, and retrieval quality.
| Tutorial | Objective | Stack | Time | Level |
|---|---|---|---|---|
| Semantic Search Intro | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
| Hybrid Search with FastEmbed | Combine dense and sparse search. | FastAPI | 20m | Beginner |
| Collaborative Filtering | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
| Multivector Document Retrieval | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
| Retrieval Quality Evaluation | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
| Hybrid Search with Reranking | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
| Semantic Search for Code | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
| Multivectors and Late Interaction | Effective use of multivector representations. | Python | 30m | Intermediate |
| Static Embeddings | Evaluate the utility of static embeddings. | Python | 20m | Intermediate |
