Check out our blog comparing SingleStore vector search performance to Milvus, pgvector. https://lnkd.in/gfS82kmd We're on par with Milvus and way faster than pgvector, and we're getting better fast -- 2x to 4x perf gains or more in near term. And our index build times are much better.
Eric Hanson’s Post
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Learn how to write blazing fast Latest N Rows queries by leveraging the Hydrolix catalog. You'll get a peek under the hook at what makes the Hydrolix catalog unique and learn how to structure Latest N Rows queries for maximum partition pruning and efficiency. https://lnkd.in/g7FkM4Cj
Latest N Rows Optimized: Crafting Efficiency with the Hydrolix Catalog - Hydrolix
https://hydrolix.io
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A simple feature-based time series classifier using Kolmogorov–Arnold Networks 👉 https://lnkd.in/drgJwjtC #machinelearning
GitHub - MSD-IRIMAS/Simple-KAN-4-Time-Series: A simple feature-based time series classifier using Kolmogorov–Arnold Networks
github.com
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Graph-based metadata filtering for improving vector search in RAG applications. Optimizing vector retrieval with advanced graph-based metadata techniques using LangChain and Neo4j https://lnkd.in/dqYTEm-W
Graph-based metadata filtering for improving vector search in RAG applications
blog.langchain.dev
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Vector Search Optimization via KMeans, Voronoi Cells and Inverted File Index (aka “Cell-Probing”) https://lnkd.in/dj7pTyTd
Vector Search Optimization via KMeans, Voronoi Cells and Inverted File Index (aka "Cell-Probing") - Azure SQL Devs’ Corner
https://devblogs.microsoft.com/azure-sql
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You can build an article recommendation system with just the Jina Reranker API—no pipeline, no embeddings, no vector search, only reranking. Find out how in 20 lines of code. https://lnkd.in/eYwe_kxZ
How to Build Article Recommendations with Jina Reranker API Only
jina.ai
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Interesting read with significance of purpose built vector indexes.
Great Algorithms Are Not Enough | Pinecone
pinecone.io
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These latest Terragrunt features are all about improving quality of life. - `terragrunt graph` is a complement to `terragrunt run-all`. Now you can let Terragrunt figure out the dependencies of each of your terragrunt.hcl files and run your plans, applies, or other commands in exactly the right order, with parallelism. - Structured logging allows you to output Terragrunt logs as JSON, which is useful for tools like Splunk to get structured data on every single thing that happens, giving you more visibility into what Terragrunt is doing. - We've also added telemetry! Not to send to Gruntwork but for you to optionally send your own telemetry to tools like Prometheus. And...we've got even more on the way! Exciting times in Terragrunt land.
Co-founder of Gruntwork. Author of "Fundamentals of DevOps and Software Delivery," "Terraform: Up & Running," and "Hello, Startup."
Hot on the heels of 'terragrunt catalog' and 'terragrunt scaffold', which we announced on Monday, we have 3 additional new Terragrunt features to share with you today: 1. Terragrunt graph: Run a command against the graph of dependencies. 2. Structured logging: Output all logs in JSON format. 3. Telemetry: Output traces & metrics in OpenTelemetry format. https://lnkd.in/gtgKZqEw
New Terragrunt features: graph, structured logging, telemetry
blog.gruntwork.io
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Building a baseline for a RAG pipeline is not usually difficult, but enhancing it to make it suitable for production and ensuring the quality of your responses is almost always hard. Choosing the right tools and parameters for RAG can itself be challenging when there is an abundance of options available. For evaluating, visualizing, and analyzing RAG, several open source libraries can be really helpful: ✔ Ragas for synthetic test data generation and evaluation ✔ Phoenix for tracing, visualization, and cluster analysis ✔ LlamaIndex for building RAG pipelines The just-published Phoenix + Ragas guest post by Shahul ES, Xander Song and Mikyo King covers how to use all of the above, leveraging a new integration between Phoenix and Ragas! Dive in: https://lnkd.in/gwMMcH9N
Evaluating and Analyzing Your RAG Pipeline with Ragas and Phoenix
arize.com
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Vector indexes increase the efficiency when performing vector searches using the VectorDistance system function. Vectors searches will have significantly lower latency, higher throughput, and less RU consumption when leveraging a vector index. https://lnkd.in/dTeh5CT7
Overview of indexing - Azure Cosmos DB
learn.microsoft.com
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Vector indexes increase the efficiency when performing vector searches using the VectorDistance system function. Vectors searches will have significantly lower latency, higher throughput, and less RU consumption when leveraging a vector index. https://lnkd.in/dTeh5CT7
Overview of indexing - Azure Cosmos DB
learn.microsoft.com
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