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Perspectives on R in RAG
In this blog post, I share perspectives on the R in RAG.
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Scaling vector search using Cohere binary embeddings and Vespa
Three comprehensive guides to using the Cohere Embed v3 binary embeddings with Vespa.
The Singapore Government Pair Search
The Singaporean government deploys state of the art semantic search
The Singaporean government leverages Vespa to do semantic search in every word ever said in Parliament
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Announcing Vespa Long-Context ColBERT
Announcing long-context ColBERT, giving it larger context for scoring and simplifying long-document RAG applications.
Embedding flexibility in Vespa
Why did Vespa score "Exceptional" on Embedding Flexibility in GigaOm's report on Vector Databases?
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Vespa Newsletter, February 2024
Advances in Vespa features and performance include the YQL IN operator, new streaming search features, and embed using parameter substitution.
When you're using vectors you're doing search
Combining scale and quality takes more than vector similarity search
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Announcing the Vespa ColBERT embedder
Announcing the native Vespa ColBERT embedder in Vespa, enabling explainable semantic search using token-level vector representations
GigaOm Sonar for Vector Databases Positions Vespa as a Leader
Although we're more than a vector database, we're happy to be recognized as a leader in this category
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Exploring the potential of OpenAI Matryoshka 🪆 embeddings with Vespa
Using the "shortening" properties of OpenAI v3 embedding models to greatly reduce latency/cost while retaining near-exact quality
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Announcing IN query operator
The new IN operator is a shorthand for multiple OR conditions, enabling writing more concise queries with better performance
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