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Achieving AI at Scale Without Breaking the Bank in Financial Services
When implementing AI solutions in Financial Services, selecting a platform that maximizes efficiency, scalability, and performance is critical.
Photo by David Travis on Unsplash
PDF Retrieval with Vision Language Models
Connecting the ColPali model with Vespa for complex document format retrieval.
Photo by Pau Sayrol on Unsplash
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.
Photo by Conny Schneider on Unsplash
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.
Photo by Scott Graham on Unsplash
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.
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