--- title: Search Engineering weight: 18 is_empty: false aliases: - how-to - tutorials partition: qdrant --- ### Search Engineering Tutorials *Master vector search modalities, reranking, and retrieval quality.* | Tutorial | Objective | Stack | Time | Level | | :--- | :--- | :--- | :--- | :--- | | [Semantic Search Intro](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner | | [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search. | FastAPI | 20m | Beginner | | [Collaborative Filtering](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate | | [Multivector Document Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate | | [Retrieval Quality Evaluation](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate | | [Hybrid Search with Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate | | [Semantic Search for Code](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate | | [Multivectors and Late Interaction](/documentation/advanced-tutorials/using-multivector-representations/) | Effective use of multivector representations. | Python | 30m | Intermediate | | [Static Embeddings](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the utility of static embeddings. | Python | 20m | Intermediate |