Tutorials

These tutorials demonstrate different ways you can build vector search into your applications.

TutorialDescriptionStack
Configure Optimal UseConfigure Qdrant collections for best resource use.Qdrant
Separate PartitionsServe vectors for many independent users.Qdrant
Bulk Upload VectorsUpload a large scale dataset.Qdrant
Create Dataset SnapshotsTurn a dataset into a snapshot by exporting it from a collection.Qdrant
Semantic Search for BeginnersCreate a simple search engine locally in minutes.Qdrant
Simple Neural SearchBuild and deploy a neural search that browses startup data.Qdrant, BERT, FastAPI
Aleph Alpha SearchBuild a multimodal search that combines text and image data.Qdrant, Aleph Alpha
Mighty Semantic SearchBuild a simple semantic search with an on-demand NLP service.Qdrant, Mighty
Asynchronous APICommunicate with Qdrant server asynchronously with Python SDK.Qdrant, Python
Multitenancy with LlamaIndexHandle data coming from multiple users in LlamaIndex.Qdrant, Python, LlamaIndex
HuggingFace datasetsLoad a Hugging Face dataset to QdrantQdrant, Python, datasets
Measure retrieval qualityMeasure and fine-tune the retrieval qualityQdrant, Python, datasets
Use semantic search to navigate your codebaseImplement semantic search application for code search taskQdrant, Python, sentence-transformers, Jina
Implement custom connector for Cohere RAGBring data stored in Qdrant to Cohere RAGQdrant, Cohere, FastAPI
TroubleshootingSolutions to common errors and fixesQdrant