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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.
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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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Redefining Hybrid Search Possibilities with Vespa - part one
This is the first blog post in a series on hybrid search. This first post focuses on efficient hybrid retrieval and representational approaches in IR
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Announcing Vespa Cloud Enclave: Bring your own cloud
Deploy your Vespa Cloud applications inside AWS accounts or GCP projects you own, while still managed and operated by Vespa.ai.
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Vespa Newsletter, December 2023
Advances in Vespa features and performance include rank score normalization in the global ranking phase, improved feeding using PyVespa, token authentication, and query match debugging!
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Turbocharge RAG with LangChain and Vespa Streaming Mode for Sharded Data
A hands-on guide to connect LangChain with Vespa streaming mode to build cost-efficient RAG applications over naturally sharded data.
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