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Adaptive In-Context Learning 🤝 Vespa - part one
Adaptive In-Context Learning (ICL) with Vespa to retrieve context-sensitive examples
Unlocking Ecommerce Growth: The Power of AI in Personalization and Recommendation
Leveraging personalization and recommendation engines in e-commerce enhances customer experience and drives business growth.
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Improving retrieval with LLM-as-a-judge
How to create your own reusable retrieval evaluation dataset for your data and use it to assess your retrieval system's effectiveness
Vespa Newsletter, May 2024
Advances in Vespa features and performance include improved vector search performance, fuzzy search with prefix match, RAG, and new Pyvespa and embedding features.
Matryoshka 🤝 Binary vectors: Slash vector search costs with Vespa
Announcing Matryoshka (dimension flexibility) and binary quantization in Vespa and how these features slashes costs.
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Vespa Newsletter, April 2024
Advances in Vespa features and performance include a new SPLADE embedder, float16 support for ONNX models, new Cohere guides, and support for using ColBERT with long texts.
Farfetch: Scaling recommendations with Vespa
E-commerce platform Farfetch explains how they use Vespa to scale their online recommendation system
Marqo chooses Vespa
Vector search experts Marqo choose Vespa as their vector database after benchmarking against Milvus, OpenSearch, Weaviate, Redis, and Qdrant.
Migrating to the Vespa Search Engine
In this post, I will detail the journey at Stanby of how we have addressed the challenges faced by our existing search system through migrating to Vespa.
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Perspectives on R in RAG
In this blog post, I share perspectives on the R in RAG.
Photo by Phil Botha on Unsplash
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