---
title: Search Engineering
weight: 32
is_empty: true
hideInSidebar: true
aliases:
- how-to
- tutorials
partition: qdrant
---
# Search Engineering Tutorials
*Master vector search modalities, reranking, and retrieval quality.*
| Tutorial | Objective | Stack | Time | Level |
| :--- | :--- | :--- | :--- | :--- |
| [Hybrid Search with FastEmbed](/documentation/tutorials-search-engineering/hybrid-search-fastembed/) | Combine dense and sparse search for startups. | FastAPI | 20m | Beginner |
| [Neural Search Service](/documentation/tutorials-search-engineering/neural-search/) | Deploy a search service for company descriptions. | FastAPI | 30m | Beginner |
| [Movie Recommendations](/documentation/tutorials-search-engineering/collaborative-filtering/) | Collaborative filtering using sparse embeddings. | Python | 45m | Intermediate |
| [Advanced PDF Retrieval](/documentation/tutorials-search-engineering/pdf-retrieval-at-scale/) | PDF RAG using ColPali and embedding pooling. | Python | 30m | Intermediate |
| [Retrieval Quality Benchmarking](/documentation/tutorials-search-engineering/retrieval-quality/) | Measure quality and tune HNSW parameters. | Python | 30m | Intermediate |
| [Multivector Reranking](/documentation/search-precision/reranking-semantic-search/) | Use multivector representations for better ranking. | Python | 30m | Intermediate |
| [Hybrid Search Reranking](/documentation/tutorials-search-engineering/reranking-hybrid-search/) | Implement late interaction and sparse reranking. | Python | 40m | Intermediate |
| [Semantic Code Search](/documentation/tutorials-search-engineering/code-search/) | Navigate codebases using vector similarity. | Python | 45m | Intermediate |
| [Static Embeddings Analysis](/documentation/tutorials-search-engineering/static-embeddings/) | Evaluate the renaissance of static embeddings. | Python | 20m | Intermediate |