Tutorials

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

Tutorial	Description	Stack
Configure Optimal Use	Configure Qdrant collections for best resource use.	Qdrant
Separate Partitions	Serve vectors for many independent users.	Qdrant
Bulk Upload Vectors	Upload a large scale dataset.	Qdrant
Create Dataset Snapshots	Turn a dataset into a snapshot by exporting it from a collection.	Qdrant
Semantic Search for Beginners	Create a simple search engine locally in minutes.	Qdrant
Simple Neural Search	Build and deploy a neural search that browses startup data.	Qdrant, BERT, FastAPI
Aleph Alpha Search	Build a multimodal search that combines text and image data.	Qdrant, Aleph Alpha
Mighty Semantic Search	Build a simple semantic search with an on-demand NLP service.	Qdrant, Mighty
Asynchronous API	Communicate with Qdrant server asynchronously with Python SDK.	Qdrant, Python
Multitenancy with LlamaIndex	Handle data coming from multiple users in LlamaIndex.	Qdrant, Python, LlamaIndex
HuggingFace datasets	Load a Hugging Face dataset to Qdrant	Qdrant, Python, datasets
Measure retrieval quality	Measure and fine-tune the retrieval quality	Qdrant, Python, datasets
Use semantic search to navigate your codebase	Implement semantic search application for code search task	Qdrant, Python, sentence-transformers, Jina
Personalize learning experience with RAG	Speak to the course materials as if you were speaking to the teacher	Qdrant, Red Hat OpenShift, Haystack, Hugging Face, Mistral, Hybrid Cloud
Troubleshooting	Solutions to common errors and fixes	Qdrant