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Kacper Łukawski c137af4dc6 Add a tutorial on building Cohere RAG connector (#761)
* Add a tutorial on building Cohere RAG connector

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2024-03-29 10:32:34 +01:00

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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