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 |
Implement custom connector for Cohere RAG | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
Troubleshooting | Solutions to common errors and fixes | Qdrant |