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Merge pull request #178 from superlinked/ClaireSuperlinked-patch-3
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ClaireSuperlinked authored Jan 30, 2024
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Expand Up @@ -43,13 +43,15 @@ Here are some examples from the community, more coming soon!

Subscribe to be updated when new ones come out & check the blog section.

- [Personalized Search](https://hub.superlinked.com/personalized-search-harnessing-the-power-of-vector-embeddings)
- [Recommender Systems](https://hub.superlinked.com/a-recommender-system-collaborative-filtering-with-sparse-metadata)
- [Retrieval Augmented Generation](https://hub.superlinked.com/retrieval-augmented-generation)
- [Enhancing RAG With A Multi-Agent System](https://hub.superlinked.com/enhancing-rag-with-a-multi-agent-system)
- [Vector Embeddings In The Browser](https://hub.superlinked.com/vector-embeddings-in-the-browser)
- [Answering Questions with Knowledge Embeddings](https://hub.superlinked.com/answering-questions-with-knowledge-graph-embeddings)
- [Improving RAG performance with Knowledge Graphs](use_cases/knowledge_graphs.md)
- [01/25 - Improving RAG performance with Knowledge Graphs](use_cases/knowledge_graphs.md): Adding knowledge graph embeddings as contextual data to improve the performance of RAG.
- [01/18 Representation Learning on Graph Structured Data](https://hub.superlinked.com/representation-learning-on-graph-structured-data): Understanding how combining KGEs and semantic embeddings can improve understanding of your solution.
- [01/11 - VDB Feature Matrix](https://vdbs.superlinked.com/): Find the right Vector Database (VDB) for your use case.
- [01/04 - Answering Questions with Knowledge Embeddings](https://hub.superlinked.com/answering-questions-with-knowledge-graph-embeddings): An introduction to knowledge graph embeddings and comparing the performance with LLMs.
- [12/15 - Vector Embeddings In The Browser](https://hub.superlinked.com/vector-embeddings-in-the-browser): Creating an LLM powered application in browser with React.
- [12/08 - Enhancing RAG With A Multi-Agent System](https://hub.superlinked.com/enhancing-rag-with-a-multi-agent-system): Using agents to improve the performance of your RAG with a multi-agent system to improve relevance, latency, and coherence.
- [12/01 - Personalized Search](https://hub.superlinked.com/personalized-search-harnessing-the-power-of-vector-embeddings): How to use vector embeddings to create personalised search recommendations with user vectors.
- [11/26 - Recommender Systems](https://hub.superlinked.com/a-recommender-system-collaborative-filtering-with-sparse-metadata): Building a recommender system using vector embeddings when you have sparce metadata.
- [11/26 - Retrieval Augmented Generation](https://hub.superlinked.com/retrieval-augmented-generation): The basics of RAG, what it is and how to implement.

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