#vectorsearch — Public Fediverse posts

📋 #AWS has made vector search in #DynamoDB generally available: embeddings live in the same table as operational data #vectorsearch #RAG #AI #DevOps
🧵👇

⚡ Native similarity search with single-digit millisecond latency at 99%+ recall, designed to scale to trillions of vectors

📋 #AWS has made vector search in #DynamoDB generally available: embeddings live in the same table as operational data #vectorsearch #RAG #AI #DevOps
🧵👇

⚡ Native similarity search with single-digit millisecond latency at 99%+ recall, designed to scale to trillions of vectors

📋 #AWS has made vector search in #DynamoDB generally available: embeddings live in the same table as operational data #vectorsearch #RAG #AI #DevOps
🧵👇

⚡ Native similarity search with single-digit millisecond latency at 99%+ recall, designed to scale to trillions of vectors

📋 #AWS has made vector search in #DynamoDB generally available: embeddings live in the same table as operational data #vectorsearch #RAG #AI #DevOps
🧵👇

⚡ Native similarity search with single-digit millisecond latency at 99%+ recall, designed to scale to trillions of vectors

📋 #AWS has made vector search in #DynamoDB generally available: embeddings live in the same table as operational data #vectorsearch #RAG #AI #DevOps
🧵👇

⚡ Native similarity search with single-digit millisecond latency at 99%+ recall, designed to scale to trillions of vectors

🚀 OpenSearch 3.8 is officially here!

Packed with key upgrades for #AI, #vectorsearch, & #observability:
⚡ Up to 4.16x faster vector ingestion (Base64 encoding) & 2.1x faster radial search
🤖 MCP support extended across all agent types + gRPC ML prediction streaming
📊 New visual PPL builder, SQL support in Discover logs, & in-editor query linting
🧠 LLM-as-a-Judge relevance evaluations for any LLM provider

📖 Read more: https://opensearch.org/blog/whats-new-in-opensearch-3-8/
#OpenSearch #opensource #OpenSearchAmbassador

🚀 OpenSearch 3.8 is officially here!

Packed with key upgrades for #AI, #vectorsearch, & #observability:
⚡ Up to 4.16x faster vector ingestion (Base64 encoding) & 2.1x faster radial search
🤖 MCP support extended across all agent types + gRPC ML prediction streaming
📊 New visual PPL builder, SQL support in Discover logs, & in-editor query linting
🧠 LLM-as-a-Judge relevance evaluations for any LLM provider

📖 Read more: https://opensearch.org/blog/whats-new-in-opensearch-3-8/
#OpenSearch #opensource #OpenSearchAmbassador

🚀 OpenSearch 3.8 is officially here!

Packed with key upgrades for #AI, #vectorsearch, & #observability:
⚡ Up to 4.16x faster vector ingestion (Base64 encoding) & 2.1x faster radial search
🤖 MCP support extended across all agent types + gRPC ML prediction streaming
📊 New visual PPL builder, SQL support in Discover logs, & in-editor query linting
🧠 LLM-as-a-Judge relevance evaluations for any LLM provider

📖 Read more: https://opensearch.org/blog/whats-new-in-opensearch-3-8/
#OpenSearch #opensource #OpenSearchAmbassador

🚀 OpenSearch 3.8 is officially here!

Packed with key upgrades for #AI, #vectorsearch, & #observability:
⚡ Up to 4.16x faster vector ingestion (Base64 encoding) & 2.1x faster radial search
🤖 MCP support extended across all agent types + gRPC ML prediction streaming
📊 New visual PPL builder, SQL support in Discover logs, & in-editor query linting
🧠 LLM-as-a-Judge relevance evaluations for any LLM provider

📖 Read more: https://opensearch.org/blog/whats-new-in-opensearch-3-8/
#OpenSearch #opensource #OpenSearchAmbassador

🚀 OpenSearch 3.8 is officially here!

Packed with key upgrades for #AI, #vectorsearch, & #observability:
⚡ Up to 4.16x faster vector ingestion (Base64 encoding) & 2.1x faster radial search
🤖 MCP support extended across all agent types + gRPC ML prediction streaming
📊 New visual PPL builder, SQL support in Discover logs, & in-editor query linting
🧠 LLM-as-a-Judge relevance evaluations for any LLM provider

📖 Read more: https://opensearch.org/blog/whats-new-in-opensearch-3-8/
#OpenSearch #opensource #OpenSearchAmbassador

This Microsoft article explains how Azure HorizonDB can support AI-powered retrieval directly inside PostgreSQL by combining operational data, vector search, keyword search, filters, indexing, and relevance ranking in one platform.
#PostgreSQL #AzureHorizonDB #MicrosoftAzure #ArtificialIntelligence #GenerativeAI #RAG #VectorSearch #DatabaseArchitecture #DataEngineering
https://techcommunity.microsoft.com/blog/adforpostgresql/ai-powered-retrieval-in-postgresql-with-azure-horizondb/4540160?wt.mc_id=DP-MVP-4015656

This Microsoft article explains how Azure HorizonDB can support AI-powered retrieval directly inside PostgreSQL by combining operational data, vector search, keyword search, filters, indexing, and relevance ranking in one platform.
#PostgreSQL #AzureHorizonDB #MicrosoftAzure #ArtificialIntelligence #GenerativeAI #RAG #VectorSearch #DatabaseArchitecture #DataEngineering
https://techcommunity.microsoft.com/blog/adforpostgresql/ai-powered-retrieval-in-postgresql-with-azure-horizondb/4540160?wt.mc_id=DP-MVP-4015656

This Microsoft article explains how Azure HorizonDB can support AI-powered retrieval directly inside PostgreSQL by combining operational data, vector search, keyword search, filters, indexing, and relevance ranking in one platform.
#PostgreSQL #AzureHorizonDB #MicrosoftAzure #ArtificialIntelligence #GenerativeAI #RAG #VectorSearch #DatabaseArchitecture #DataEngineering
https://techcommunity.microsoft.com/blog/adforpostgresql/ai-powered-retrieval-in-postgresql-with-azure-horizondb/4540160?wt.mc_id=DP-MVP-4015656

This Microsoft article explains how Azure HorizonDB can support AI-powered retrieval directly inside PostgreSQL by combining operational data, vector search, keyword search, filters, indexing, and relevance ranking in one platform.
#PostgreSQL #AzureHorizonDB #MicrosoftAzure #ArtificialIntelligence #GenerativeAI #RAG #VectorSearch #DatabaseArchitecture #DataEngineering
https://techcommunity.microsoft.com/blog/adforpostgresql/ai-powered-retrieval-in-postgresql-with-azure-horizondb/4540160?wt.mc_id=DP-MVP-4015656

This Microsoft article explains how Azure HorizonDB can support AI-powered retrieval directly inside PostgreSQL by combining operational data, vector search, keyword search, filters, indexing, and relevance ranking in one platform.
#PostgreSQL #AzureHorizonDB #MicrosoftAzure #ArtificialIntelligence #GenerativeAI #RAG #VectorSearch #DatabaseArchitecture #DataEngineering
https://techcommunity.microsoft.com/blog/adforpostgresql/ai-powered-retrieval-in-postgresql-with-azure-horizondb/4540160?wt.mc_id=DP-MVP-4015656

Watch Now: https://zurl.co/9qezZ

What Are Vector Search Data Actions in Salesforce Data Cloud? Complete Guide

#Salesforce #SalesforceDataCloud #VectorSearch #DataActions #Agentforce #SalesforceAI #GenerativeAI #SemanticSearch #DataCloud #SalesforceDeveloper #CRM #ArtificialIntelligence #AI #SalesforceTutorial #SalesforceTraining #DataEngineering #VectorDatabase #RetrievalAugmentedGeneration #RAG #PeopleWooSkills

Watch Now: https://zurl.co/9qezZ

What Are Vector Search Data Actions in Salesforce Data Cloud? Complete Guide

#Salesforce #SalesforceDataCloud #VectorSearch #DataActions #Agentforce #SalesforceAI #GenerativeAI #SemanticSearch #DataCloud #SalesforceDeveloper #CRM #ArtificialIntelligence #AI #SalesforceTutorial #SalesforceTraining #DataEngineering #VectorDatabase #RetrievalAugmentedGeneration #RAG #PeopleWooSkills

Watch Now: https://zurl.co/9qezZ

What Are Vector Search Data Actions in Salesforce Data Cloud? Complete Guide

#Salesforce #SalesforceDataCloud #VectorSearch #DataActions #Agentforce #SalesforceAI #GenerativeAI #SemanticSearch #DataCloud #SalesforceDeveloper #CRM #ArtificialIntelligence #AI #SalesforceTutorial #SalesforceTraining #DataEngineering #VectorDatabase #RetrievalAugmentedGeneration #RAG #PeopleWooSkills

Watch Now: https://zurl.co/9qezZ

What Are Vector Search Data Actions in Salesforce Data Cloud? Complete Guide

#Salesforce #SalesforceDataCloud #VectorSearch #DataActions #Agentforce #SalesforceAI #GenerativeAI #SemanticSearch #DataCloud #SalesforceDeveloper #CRM #ArtificialIntelligence #AI #SalesforceTutorial #SalesforceTraining #DataEngineering #VectorDatabase #RetrievalAugmentedGeneration #RAG #PeopleWooSkills

Watch Now: https://zurl.co/9qezZ

What Are Vector Search Data Actions in Salesforce Data Cloud? Complete Guide

#Salesforce #SalesforceDataCloud #VectorSearch #DataActions #Agentforce #SalesforceAI #GenerativeAI #SemanticSearch #DataCloud #SalesforceDeveloper #CRM #ArtificialIntelligence #AI #SalesforceTutorial #SalesforceTraining #DataEngineering #VectorDatabase #RetrievalAugmentedGeneration #RAG #PeopleWooSkills

watch now: https://zurl.co/G9kWD

Overview of Data Action Types in Salesforce Data Cloud | Standard vs Real-Time vs Vector Search Explained

#Salesforce #SalesforceDataCloud #DataCloud #DataActions #StandardDataActions #RealTimeData #VectorSearch #Customer360 #CRM #SalesforceAdmin #SalesforceDeveloper #DataIntegration #CustomerDataPlatform #CDP #Agentforce #MarketingCloud #SalesforceTraining #TechTutorial #AI #PeopleWoo

watch now: https://zurl.co/G9kWD

Overview of Data Action Types in Salesforce Data Cloud | Standard vs Real-Time vs Vector Search Explained

#Salesforce #SalesforceDataCloud #DataCloud #DataActions #StandardDataActions #RealTimeData #VectorSearch #Customer360 #CRM #SalesforceAdmin #SalesforceDeveloper #DataIntegration #CustomerDataPlatform #CDP #Agentforce #MarketingCloud #SalesforceTraining #TechTutorial #AI #PeopleWoo

watch now: https://zurl.co/G9kWD

Overview of Data Action Types in Salesforce Data Cloud | Standard vs Real-Time vs Vector Search Explained

#Salesforce #SalesforceDataCloud #DataCloud #DataActions #StandardDataActions #RealTimeData #VectorSearch #Customer360 #CRM #SalesforceAdmin #SalesforceDeveloper #DataIntegration #CustomerDataPlatform #CDP #Agentforce #MarketingCloud #SalesforceTraining #TechTutorial #AI #PeopleWoo

watch now: https://zurl.co/G9kWD

Overview of Data Action Types in Salesforce Data Cloud | Standard vs Real-Time vs Vector Search Explained

#Salesforce #SalesforceDataCloud #DataCloud #DataActions #StandardDataActions #RealTimeData #VectorSearch #Customer360 #CRM #SalesforceAdmin #SalesforceDeveloper #DataIntegration #CustomerDataPlatform #CDP #Agentforce #MarketingCloud #SalesforceTraining #TechTutorial #AI #PeopleWoo

watch now: https://zurl.co/G9kWD

Overview of Data Action Types in Salesforce Data Cloud | Standard vs Real-Time vs Vector Search Explained

#Salesforce #SalesforceDataCloud #DataCloud #DataActions #StandardDataActions #RealTimeData #VectorSearch #Customer360 #CRM #SalesforceAdmin #SalesforceDeveloper #DataIntegration #CustomerDataPlatform #CDP #Agentforce #MarketingCloud #SalesforceTraining #TechTutorial #AI #PeopleWoo

Why are #GenAI systems still slow—despite #Vector DBs, caches & scaling? Because architecture is fragmented. Gerald K. breaks down a #JavaNative alternative: #VectorSearch, state & persistence in one model. Understand the trade-offs before scaling further: https://javapro.io/2026/04/08/high-performance-vector-search-grids-with-java/

Why are #GenAI systems still slow—despite #Vector DBs, caches & scaling? Because architecture is fragmented. Gerald K. breaks down a #JavaNative alternative: #VectorSearch, state & persistence in one model. Understand the trade-offs before scaling further: https://javapro.io/2026/04/08/high-performance-vector-search-grids-with-java/

AI isn't just chatbots. It's also helping users find the right document, product, answer or recommendation.

Vector search is one of the foundational building blocks behind modern AI applications.

Learn how to build it yourself with JavaScript.

Vector Search with JavaScript
by Ben Greenberg
#javascript #vectorsearch #AI
https://pragprog.com/titles/bgvector/vector-search-with-javascript/

AI isn't just chatbots. It's also helping users find the right document, product, answer or recommendation.

Vector search is one of the foundational building blocks behind modern AI applications.

Learn how to build it yourself with JavaScript.

Vector Search with JavaScript
by Ben Greenberg
#javascript #vectorsearch #AI
https://pragprog.com/titles/bgvector/vector-search-with-javascript/

AI isn't just chatbots. It's also helping users find the right document, product, answer or recommendation.

Vector search is one of the foundational building blocks behind modern AI applications.

Learn how to build it yourself with JavaScript.

Vector Search with JavaScript
by Ben Greenberg
#javascript #vectorsearch #AI
https://pragprog.com/titles/bgvector/vector-search-with-javascript/

AI isn't just chatbots. It's also helping users find the right document, product, answer or recommendation.

Vector search is one of the foundational building blocks behind modern AI applications.

Learn how to build it yourself with JavaScript.

Vector Search with JavaScript
by Ben Greenberg
#javascript #vectorsearch #AI
https://pragprog.com/titles/bgvector/vector-search-with-javascript/

AI isn't just chatbots. It's also helping users find the right document, product, answer or recommendation.

Vector search is one of the foundational building blocks behind modern AI applications.

Learn how to build it yourself with JavaScript.

Vector Search with JavaScript
by Ben Greenberg
#javascript #vectorsearch #AI
https://pragprog.com/titles/bgvector/vector-search-with-javascript/

Production incident and the first question is: have we seen this before?

I built a Quarkus service that turns Java incidents into vectors with deterministic feature hashing — no embedding model, no LLM. Store them in Qdrant, search by failure shape, filter by service and environment.

The vectors are inspectable and repeatable. The scores are explainable from the input.

New tutorial on The Main Thread:

https://www.the-main-thread.com/p/incident-vector-search

#Quarkus #Java #Qdrant #VectorSearch #IncidentManagement

Production incident and the first question is: have we seen this before?

I built a Quarkus service that turns Java incidents into vectors with deterministic feature hashing — no embedding model, no LLM. Store them in Qdrant, search by failure shape, filter by service and environment.

The vectors are inspectable and repeatable. The scores are explainable from the input.

New tutorial on The Main Thread:

https://www.the-main-thread.com/p/incident-vector-search

#Quarkus #Java #Qdrant #VectorSearch #IncidentManagement

Production incident and the first question is: have we seen this before?

I built a Quarkus service that turns Java incidents into vectors with deterministic feature hashing — no embedding model, no LLM. Store them in Qdrant, search by failure shape, filter by service and environment.

The vectors are inspectable and repeatable. The scores are explainable from the input.

New tutorial on The Main Thread:

https://www.the-main-thread.com/p/incident-vector-search

#Quarkus #Java #Qdrant #VectorSearch #IncidentManagement

Production incident and the first question is: have we seen this before?

I built a Quarkus service that turns Java incidents into vectors with deterministic feature hashing — no embedding model, no LLM. Store them in Qdrant, search by failure shape, filter by service and environment.

The vectors are inspectable and repeatable. The scores are explainable from the input.

New tutorial on The Main Thread:

https://www.the-main-thread.com/p/incident-vector-search

#Quarkus #Java #Qdrant #VectorSearch #IncidentManagement

Production incident and the first question is: have we seen this before?

I built a Quarkus service that turns Java incidents into vectors with deterministic feature hashing — no embedding model, no LLM. Store them in Qdrant, search by failure shape, filter by service and environment.

The vectors are inspectable and repeatable. The scores are explainable from the input.

New tutorial on The Main Thread:

https://www.the-main-thread.com/p/incident-vector-search

#Quarkus #Java #Qdrant #VectorSearch #IncidentManagement

https://winbuzzer.com/2026/06/30/google-positions-spanner-as-one-database-for-ai-agents-xcxwbn/

Google Cloud is positioning its Spanner SQL database service as a multi-model database for AI agents, combining graph, vector, analytics and hybrid deployments.

#AI #SpannerOmni #SpannerGraph #VectorSearch #Google #GoogleCloudPlatform #AIAgents #AgenticAI

https://winbuzzer.com/2026/06/30/google-positions-spanner-as-one-database-for-ai-agents-xcxwbn/

Google Cloud is positioning its Spanner SQL database service as a multi-model database for AI agents, combining graph, vector, analytics and hybrid deployments.

#AI #SpannerOmni #SpannerGraph #VectorSearch #Google #GoogleCloudPlatform #AIAgents #AgenticAI

https://winbuzzer.com/2026/06/30/google-positions-spanner-as-one-database-for-ai-agents-xcxwbn/

Google Cloud is positioning its Spanner SQL database service as a multi-model database for AI agents, combining graph, vector, analytics and hybrid deployments.

#AI #SpannerOmni #SpannerGraph #VectorSearch #Google #GoogleCloudPlatform #AIAgents #AgenticAI

https://winbuzzer.com/2026/06/30/google-positions-spanner-as-one-database-for-ai-agents-xcxwbn/

Google Cloud is positioning its Spanner SQL database service as a multi-model database for AI agents, combining graph, vector, analytics and hybrid deployments.

#AI #SpannerOmni #SpannerGraph #VectorSearch #Google #GoogleCloudPlatform #AIAgents #AgenticAI

https://winbuzzer.com/2026/06/30/google-positions-spanner-as-one-database-for-ai-agents-xcxwbn/

Google Cloud is positioning its Spanner SQL database service as a multi-model database for AI agents, combining graph, vector, analytics and hybrid deployments.

#AI #SpannerOmni #SpannerGraph #VectorSearch #Google #GoogleCloudPlatform #AIAgents #AgenticAI

oracle-ai-ready-data Skill で Oracle Database のスキーマを AI Ready Data 評価してみてみた
https://qiita.com/shirok/items/3b9ccefff059ff1ab40d?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #AI #oraclecloud #rag #VectorSearch #AIAgent

oracle-ai-ready-data Skill で Oracle Database のスキーマを AI Ready Data 評価してみてみた
https://qiita.com/shirok/items/3b9ccefff059ff1ab40d?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #AI #oraclecloud #rag #VectorSearch #AIAgent

Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

https://manticoresearch.com/blog/manticore-search-27-1-5/

#HackerNews #ManticoreSearch #VectorSearch #Auth #Sharding #TechUpdate

Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

https://manticoresearch.com/blog/manticore-search-27-1-5/

#HackerNews #ManticoreSearch #VectorSearch #Auth #Sharding #TechUpdate

Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

https://manticoresearch.com/blog/manticore-search-27-1-5/

#HackerNews #ManticoreSearch #VectorSearch #Auth #Sharding #TechUpdate

Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

https://manticoresearch.com/blog/manticore-search-27-1-5/

#HackerNews #ManticoreSearch #VectorSearch #Auth #Sharding #TechUpdate

Manticore Search 27.1.5: Auth, sharding, conversational and faster vector search

https://manticoresearch.com/blog/manticore-search-27-1-5/

#HackerNews #ManticoreSearch #VectorSearch #Auth #Sharding #TechUpdate

We solved vector search without a single server call. The hierarchical filesystem has been the dominant paradigm for digital file organization since the advent of Multics in the 1960s. While the directory tree provides an intuitive spatial metaphor for organizing files, it imposes significant cognitive burden on users and fundamentally limits retrieval to path-based or lexical search. This document presents a comprehensive zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

We solved vector search without a single server call. The hierarchical filesystem has been the dominant paradigm for digital file organization since the advent of Multics in the 1960s. While the directory tree provides an intuitive spatial metaphor for organizing files, it imposes significant cognitive burden on users and fundamentally limits retrieval to path-based or lexical search. This document presents a comprehensive zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

We solved vector search without a single server call. The hierarchical filesystem has been the dominant paradigm for digital file organization since the advent of Multics in the 1960s. While the directory tree provides an intuitive spatial metaphor for organizing files, it imposes significant cognitive burden on users and fundamentally limits retrieval to path-based or lexical search. This document presents a comprehensive zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

We solved vector search without a single server call. The hierarchical filesystem has been the dominant paradigm for digital file organization since the advent of Multics in the 1960s. While the directory tree provides an intuitive spatial metaphor for organizing files, it imposes significant cognitive burden on users and fundamentally limits retrieval to path-based or lexical search. This document presents a comprehensive zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

We solved vector search without a single server call. The hierarchical filesystem has been the dominant paradigm for digital file organization since the advent of Multics in the 1960s. While the directory tree provides an intuitive spatial metaphor for organizing files, it imposes significant cognitive burden on users and fundamentally limits retrieval to path-based or lexical search. This document presents a comprehensive zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

The cloud was never necessary for vector search. The deployment of large language models (LLMs) on consumer hardware represents a critical enabling technology for sovereign, privacy-preserving AI applications. The ability to run state-of-the-art neural models on personal devices without cloud dependency transforms the relationship between users and their data, eliminating the privacy risks, latency penalties, and recurring zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

The cloud was never necessary for vector search. The deployment of large language models (LLMs) on consumer hardware represents a critical enabling technology for sovereign, privacy-preserving AI applications. The ability to run state-of-the-art neural models on personal devices without cloud dependency transforms the relationship between users and their data, eliminating the privacy risks, latency penalties, and recurring zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

The cloud was never necessary for vector search. The deployment of large language models (LLMs) on consumer hardware represents a critical enabling technology for sovereign, privacy-preserving AI applications. The ability to run state-of-the-art neural models on personal devices without cloud dependency transforms the relationship between users and their data, eliminating the privacy risks, latency penalties, and recurring zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

The cloud was never necessary for vector search. The deployment of large language models (LLMs) on consumer hardware represents a critical enabling technology for sovereign, privacy-preserving AI applications. The ability to run state-of-the-art neural models on personal devices without cloud dependency transforms the relationship between users and their data, eliminating the privacy risks, latency penalties, and recurring zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

The cloud was never necessary for vector search. The deployment of large language models (LLMs) on consumer hardware represents a critical enabling technology for sovereign, privacy-preserving AI applications. The ability to run state-of-the-art neural models on personal devices without cloud dependency transforms the relationship between users and their data, eliminating the privacy risks, latency penalties, and recurring zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research

Most vector search companies are data companies. We built an alternative. Vector search represents a paradigm shift in information retrieval, moving beyond keyword matching to semantic understanding through dense embedding representations. This document presents a comprehensive examination of semantic vector search as the foundational technology powering the Kamelot file system. We trace the evolution from TF-IDF through m zenodo.org/search?q=anticloud #vectorsearch

#Anticloud #Research