#vectordatabase — Public Fediverse posts

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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

I'm now experimenting with Open WebUI and local bge-m3 (embeddings), bge-reranker and Gemma-4-26b (instruct). I'm slowly learning how to integrate and test RAG systems. It's not so easy. If the instruct model is too eager to please it's not so clear if it uses the provided sources at all. I test with a rather "outdated" instruct model, llama-3.1-9b. It doesn't have enough stored information to "decode" more than a re-hash of the prompt.

My current test set includes Nancy Leveson "Engineering a Safer World", Robert Rosen "Essays on Life Itself" and Enrico Martino "Intuitionistic Proof Versus Classical Truth". There are lots of structural similarities between "Systems Safety" and Rosennean (M,R)-systems. The connection to Brouwer is more subtle.

My takeaway on RAG: people who just consume such solutions will have a hard time spotting shortcomings.

Edit: after a lot of tuning my homelab RAG system is now useful.
#homelab #RAG #vectordatabase

I'm now experimenting with Open WebUI and local bge-m3 (embeddings), bge-reranker and Gemma-4-26b (instruct). I'm slowly learning how to integrate and test RAG systems. It's not so easy. If the instruct model is too eager to please it's not so clear if it uses the provided sources at all. I test with a rather "outdated" instruct model, llama-3.1-9b. It doesn't have enough stored information to "decode" more than a re-hash of the prompt.

My current test set includes Nancy Leveson "Engineering a Safer World", Robert Rosen "Essays on Life Itself" and Enrico Martino "Intuitionistic Proof Versus Classical Truth". There are lots of structural similarities between "Systems Safety" and Rosennean (M,R)-systems. The connection to Brouwer is more subtle.

My takeaway on RAG: people who just consume such solutions will have a hard time spotting shortcomings.

Edit: after a lot of tuning my homelab RAG system is now useful.
#homelab #RAG #vectordatabase

I'm now experimenting with Open WebUI and local bge-m3 (embeddings), bge-reranker and Gemma-4-26b (instruct). I'm slowly learning how to integrate and test RAG systems. It's not so easy. If the instruct model is too eager to please it's not so clear if it uses the provided sources at all. I test with a rather "outdated" instruct model, llama-3.1-9b. It doesn't have enough stored information to "decode" more than a re-hash of the prompt.

My current test set includes Nancy Leveson "Engineering a Safer World", Robert Rosen "Essays on Life Itself" and Enrico Martino "Intuitionistic Proof Versus Classical Truth". There are lots of structural similarities between "Systems Safety" and Rosennean (M,R)-systems. The connection to Brouwer is more subtle.

My takeaway on RAG: people who just consume such solutions will have a hard time spotting shortcomings.

Edit: after a lot of tuning my homelab RAG system is now useful.
#homelab #RAG #vectordatabase

I'm now experimenting with Open WebUI and local bge-m3 (embeddings), bge-reranker and Gemma-4-26b (instruct). I'm slowly learning how to integrate and test RAG systems. It's not so easy. If the instruct model is too eager to please it's not so clear if it uses the provided sources at all. I test with a rather "outdated" instruct model, llama-3.1-9b. It doesn't have enough stored information to "decode" more than a re-hash of the prompt.

My current test set includes Nancy Leveson "Engineering a Safer World", Robert Rosen "Essays on Life Itself" and Enrico Martino "Intuitionistic Proof Versus Classical Truth". There are lots of structural similarities between "Systems Safety" and Rosennean (M,R)-systems. The connection to Brouwer is more subtle.

My takeaway on RAG: people who just consume such solutions will have a hard time spotting shortcomings.

Edit: after a lot of tuning my homelab RAG system is now useful.
#homelab #RAG #vectordatabase

I'm now experimenting with Open WebUI and local bge-m3 (embeddings), bge-reranker and Gemma-4-26b (instruct). I'm slowly learning how to integrate and test RAG systems. It's not so easy. If the instruct model is too eager to please it's not so clear if it uses the provided sources at all. I test with a rather "outdated" instruct model, llama-3.1-9b. It doesn't have enough stored information to "decode" more than a re-hash of the prompt.

My current test set includes Nancy Leveson "Engineering a Safer World", Robert Rosen "Essays on Life Itself" and Enrico Martino "Intuitionistic Proof Versus Classical Truth". There are lots of structural similarities between "Systems Safety" and Rosennean (M,R)-systems. The connection to Brouwer is more subtle.

My takeaway on RAG: people who just consume such solutions will have a hard time spotting shortcomings.

Edit: after a lot of tuning my homelab RAG system is now useful.
#homelab #RAG #vectordatabase

🔗 Building RAG in Laravel: Four Ingestion Bugs That Silently Wreck Retrieval
https://mujahidabbas.dev/blog/building-rag-laravel-pgvector/
#php #laravel #ai #rag #vectordatabase

🔗 Building RAG in Laravel: Four Ingestion Bugs That Silently Wreck Retrieval
https://mujahidabbas.dev/blog/building-rag-laravel-pgvector/
#php #laravel #ai #rag #vectordatabase

🔗 Building RAG in Laravel: Four Ingestion Bugs That Silently Wreck Retrieval
https://mujahidabbas.dev/blog/building-rag-laravel-pgvector/
#php #laravel #ai #rag #vectordatabase

🔗 Building RAG in Laravel: Four Ingestion Bugs That Silently Wreck Retrieval
https://mujahidabbas.dev/blog/building-rag-laravel-pgvector/
#php #laravel #ai #rag #vectordatabase

🔗 Building RAG in Laravel: Four Ingestion Bugs That Silently Wreck Retrieval
https://mujahidabbas.dev/blog/building-rag-laravel-pgvector/
#php #laravel #ai #rag #vectordatabase

How many times do you need to shuffle a card deck to make it truly random?

How much uranium does one need for a nuclear bomb?

How does autocomplete work?

The answer are #MarkovChains

How a feud in Russia led to modern prediction algorithms.

https://www.youtube.com/watch?v=KZeIEiBrT_w

https://piped.video/watch?v=KZeIEiBrT_w

https://inv.nadeko.net/watch?v=KZeIEiBrT_w

#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo

How many times do you need to shuffle a card deck to make it truly random?

How much uranium does one need for a nuclear bomb?

How does autocomplete work?

The answer are #MarkovChains

How a feud in Russia led to modern prediction algorithms.

https://www.youtube.com/watch?v=KZeIEiBrT_w

https://piped.video/watch?v=KZeIEiBrT_w

https://inv.nadeko.net/watch?v=KZeIEiBrT_w

#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo

How many times do you need to shuffle a card deck to make it truly random?

How much uranium does one need for a nuclear bomb?

How does autocomplete work?

The answer are #MarkovChains

How a feud in Russia led to modern prediction algorithms.

https://www.youtube.com/watch?v=KZeIEiBrT_w

https://piped.video/watch?v=KZeIEiBrT_w

https://inv.nadeko.net/watch?v=KZeIEiBrT_w

#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo

How many times do you need to shuffle a card deck to make it truly random?

How much uranium does one need for a nuclear bomb?

How does autocomplete work?

The answer are #MarkovChains

How a feud in Russia led to modern prediction algorithms.

https://www.youtube.com/watch?v=KZeIEiBrT_w

https://piped.video/watch?v=KZeIEiBrT_w

https://inv.nadeko.net/watch?v=KZeIEiBrT_w

#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo

How many times do you need to shuffle a card deck to make it truly random?

How much uranium does one need for a nuclear bomb?

How does autocomplete work?

The answer are #MarkovChains

How a feud in Russia led to modern prediction algorithms.

https://www.youtube.com/watch?v=KZeIEiBrT_w

https://piped.video/watch?v=KZeIEiBrT_w

https://inv.nadeko.net/watch?v=KZeIEiBrT_w

#Mathematics #science #computerScience #LLMs #AI #artificialIntelligence #vectorDatabase #RAG #WordEmbeddings #MachineLearning #DeepLearning #ManhattanProject #Veritasium #Yahoo

Zilliz - Provides a managed vector database service.

Cossmology Profile: https://dub.sh/HxFCcoG

Key People: Charles Xie, James Luan

#VectorDatabase #OpenSource #OSS #COSS

Databases for #AI: Should you use a vector #database? 🤔

This article compares #opensource projects competing to handle modern #AI workloads, including #machinelearning and #LLMs. Discover which databases best meet today’s AI challenges: https://lpi.org/636x

(Disclaimer: This post contains an AI-generated image.)

#AndyOram #AI #vectordatabase #machinelearning #LLMs #SQL #opensource #hybridsearch #generativeAI #MariaDB #MongoDB #Milvus #Qdrant #Weaviate #Vespa #ChromaDB #LanceDB

Databases for #AI: Should you use a vector #database? 🤔

This article compares #opensource projects competing to handle modern #AI workloads, including #machinelearning and #LLMs. Discover which databases best meet today’s AI challenges: https://lpi.org/636x

(Disclaimer: This post contains an AI-generated image.)

#AndyOram #AI #vectordatabase #machinelearning #LLMs #SQL #opensource #hybridsearch #generativeAI #MariaDB #MongoDB #Milvus #Qdrant #Weaviate #Vespa #ChromaDB #LanceDB

Databases for #AI: Should you use a vector #database? 🤔

This article compares #opensource projects competing to handle modern #AI workloads, including #machinelearning and #LLMs. Discover which databases best meet today’s AI challenges: https://lpi.org/636x

(Disclaimer: This post contains an AI-generated image.)

#AndyOram #AI #vectordatabase #machinelearning #LLMs #SQL #opensource #hybridsearch #generativeAI #MariaDB #MongoDB #Milvus #Qdrant #Weaviate #Vespa #ChromaDB #LanceDB

Databases for #AI: Should you use a vector #database? 🤔

This article compares #opensource projects competing to handle modern #AI workloads, including #machinelearning and #LLMs. Discover which databases best meet today’s AI challenges: https://lpi.org/636x

(Disclaimer: This post contains an AI-generated image.)

#AndyOram #AI #vectordatabase #machinelearning #LLMs #SQL #opensource #hybridsearch #generativeAI #MariaDB #MongoDB #Milvus #Qdrant #Weaviate #Vespa #ChromaDB #LanceDB

Databases for #AI: Should you use a vector #database? 🤔

This article compares #opensource projects competing to handle modern #AI workloads, including #machinelearning and #LLMs. Discover which databases best meet today’s AI challenges: https://lpi.org/636x

(Disclaimer: This post contains an AI-generated image.)

#AndyOram #AI #vectordatabase #machinelearning #LLMs #SQL #opensource #hybridsearch #generativeAI #MariaDB #MongoDB #Milvus #Qdrant #Weaviate #Vespa #ChromaDB #LanceDB

🔗 Chat with your documents: a practical guide to RAG using the Laravel AI SDK
https://tighten.com/insights/chat-with-your-documents-a-practical-guide-to-rag-using-the-new-laravel-ai-sdk/
#php #laravel #ai #rag #vectordatabase

🔗 Chat with your documents: a practical guide to RAG using the Laravel AI SDK
https://tighten.com/insights/chat-with-your-documents-a-practical-guide-to-rag-using-the-new-laravel-ai-sdk/
#php #laravel #ai #rag #vectordatabase

🔗 Chat with your documents: a practical guide to RAG using the Laravel AI SDK
https://tighten.com/insights/chat-with-your-documents-a-practical-guide-to-rag-using-the-new-laravel-ai-sdk/
#php #laravel #ai #rag #vectordatabase

🔗 Chat with your documents: a practical guide to RAG using the Laravel AI SDK
https://tighten.com/insights/chat-with-your-documents-a-practical-guide-to-rag-using-the-new-laravel-ai-sdk/
#php #laravel #ai #rag #vectordatabase

🔗 Chat with your documents: a practical guide to RAG using the Laravel AI SDK
https://tighten.com/insights/chat-with-your-documents-a-practical-guide-to-rag-using-the-new-laravel-ai-sdk/
#php #laravel #ai #rag #vectordatabase

Your RAG’s Secret Backdoor: Leaking Data Through Vector Databases
This article exposes a vulnerability in Retrieval-Augmented Generation (RAG) systems, where misconfigured vector databases can lead to sensitive data leakage. By improperly securing these databases, attackers can gain access to internal documents such as HR policies and top-secret product roadmaps. The RAG system works by storing document chunks as embeddings in a special-purpose vector database and querying it to provide context for the LLM. The focus on securing the LLM while neglecting the vector database leaves it vulnerable to data exfiltration. The attacker can exploit weak access controls and clever retrieval attacks to gain access to sensitive data. Key lesson: Secure vector databases to prevent data breaches caused by RAG system vulnerabilities. #BugBounty #ArtificialIntelligence #DataLeak #Infosec #VectorDatabase

https://infosecwriteups.com/your-rags-secret-backdoor-leaking-data-through-vector-databases-c33755a5abbe?source=rss------bug_bounty-5

Your RAG’s Secret Backdoor: Leaking Data Through Vector Databases
This article exposes a vulnerability in Retrieval-Augmented Generation (RAG) systems, where misconfigured vector databases can lead to sensitive data leakage. By improperly securing these databases, attackers can gain access to internal documents such as HR policies and top-secret product roadmaps. The RAG system works by storing document chunks as embeddings in a special-purpose vector database and querying it to provide context for the LLM. The focus on securing the LLM while neglecting the vector database leaves it vulnerable to data exfiltration. The attacker can exploit weak access controls and clever retrieval attacks to gain access to sensitive data. Key lesson: Secure vector databases to prevent data breaches caused by RAG system vulnerabilities. #BugBounty #ArtificialIntelligence #DataLeak #Infosec #VectorDatabase

https://infosecwriteups.com/your-rags-secret-backdoor-leaking-data-through-vector-databases-c33755a5abbe?source=rss------bug_bounty-5

Your RAG’s Secret Backdoor: Leaking Data Through Vector Databases
This article exposes a vulnerability in Retrieval-Augmented Generation (RAG) systems, where misconfigured vector databases can lead to sensitive data leakage. By improperly securing these databases, attackers can gain access to internal documents such as HR policies and top-secret product roadmaps. The RAG system works by storing document chunks as embeddings in a special-purpose vector database and querying it to provide context for the LLM. The focus on securing the LLM while neglecting the vector database leaves it vulnerable to data exfiltration. The attacker can exploit weak access controls and clever retrieval attacks to gain access to sensitive data. Key lesson: Secure vector databases to prevent data breaches caused by RAG system vulnerabilities. #BugBounty #ArtificialIntelligence #DataLeak #Infosec #VectorDatabase

https://infosecwriteups.com/your-rags-secret-backdoor-leaking-data-through-vector-databases-c33755a5abbe?source=rss------bug_bounty-5

Your RAG’s Secret Backdoor: Leaking Data Through Vector Databases
This article exposes a vulnerability in Retrieval-Augmented Generation (RAG) systems, where misconfigured vector databases can lead to sensitive data leakage. By improperly securing these databases, attackers can gain access to internal documents such as HR policies and top-secret product roadmaps. The RAG system works by storing document chunks as embeddings in a special-purpose vector database and querying it to provide context for the LLM. The focus on securing the LLM while neglecting the vector database leaves it vulnerable to data exfiltration. The attacker can exploit weak access controls and clever retrieval attacks to gain access to sensitive data. Key lesson: Secure vector databases to prevent data breaches caused by RAG system vulnerabilities. #BugBounty #ArtificialIntelligence #DataLeak #Infosec #VectorDatabase

https://infosecwriteups.com/your-rags-secret-backdoor-leaking-data-through-vector-databases-c33755a5abbe?source=rss------bug_bounty-5

Your RAG’s Secret Backdoor: Leaking Data Through Vector Databases
This article exposes a vulnerability in Retrieval-Augmented Generation (RAG) systems, where misconfigured vector databases can lead to sensitive data leakage. By improperly securing these databases, attackers can gain access to internal documents such as HR policies and top-secret product roadmaps. The RAG system works by storing document chunks as embeddings in a special-purpose vector database and querying it to provide context for the LLM. The focus on securing the LLM while neglecting the vector database leaves it vulnerable to data exfiltration. The attacker can exploit weak access controls and clever retrieval attacks to gain access to sensitive data. Key lesson: Secure vector databases to prevent data breaches caused by RAG system vulnerabilities. #BugBounty #ArtificialIntelligence #DataLeak #Infosec #VectorDatabase

https://infosecwriteups.com/your-rags-secret-backdoor-leaking-data-through-vector-databases-c33755a5abbe?source=rss------bug_bounty-5

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

via @dotnet : Vector Data in .NET – Building Blocks for AI Part 2

https://ift.tt/VtJUvye
#VectorData #NET #AI #BuildingBlocks #SemanticSearch #RAG #Embedding #Embeddings #VectorDatabase #Qdrant #Redis #CosmosDB #SQLServer #PostgreSQL #SQLite #InMemory #VectorSto…

Did you know? Our pgedge-vectorizer tool (on GitHub: https://github.com/pgEdge/pgedge-vectorizer) automatically chunks text content and generates vector embeddings with the help of background workers.

OpenAI, Voyage AI, and Ollama are supported as embedding providers, and a simple SQL interface allows you to enable vectorization on any table. (There’s even built-in views and functions for monitoring queue status.)

#github #opensource #semanticsearch #vector #vectordatabase #openai #ollama #voyageai

Did you know? Our pgedge-vectorizer tool (on GitHub: https://github.com/pgEdge/pgedge-vectorizer) automatically chunks text content and generates vector embeddings with the help of background workers.

OpenAI, Voyage AI, and Ollama are supported as embedding providers, and a simple SQL interface allows you to enable vectorization on any table. (There’s even built-in views and functions for monitoring queue status.)

#github #opensource #semanticsearch #vector #vectordatabase #openai #ollama #voyageai

Did you know? Our pgedge-vectorizer tool (on GitHub: https://github.com/pgEdge/pgedge-vectorizer) automatically chunks text content and generates vector embeddings with the help of background workers.

OpenAI, Voyage AI, and Ollama are supported as embedding providers, and a simple SQL interface allows you to enable vectorization on any table. (There’s even built-in views and functions for monitoring queue status.)

#github #opensource #semanticsearch #vector #vectordatabase #openai #ollama #voyageai

Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
https://www.glukhov.org/rag/
#AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
https://www.glukhov.org/rag/
#AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
https://www.glukhov.org/rag/
#AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
https://www.glukhov.org/rag/
#AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

Retrieval-Augmented Generation (RAG) Tutorial: Architecture, Implementation, and Production Guide:
https://www.glukhov.org/rag/
#AI #LLM #RAG #Embeddings #Reranking #VectorDatabase

PostgreSQL with DiskANN indexing now beats Pinecone by 28x on latency at 75% lower cost. AdwaitX analyzes how OpenAI scaled to 800M users and why developers consolidate AI workloads. Technical breakdown #AdwaitX #PostgreSQL #VectorDatabase #AI

https://www.adwaitx.com/postgresql-ai-applications-vector-database/

If you're located near Illinois, Shaun Thomas will be presenting on "The New Postgres AI Ecosystem" at the Illinois Prairie PostgreSQL User Group this February 18th at 5:30 PM CST. 🐘

Come by the DRW and say hi: https://www.meetup.com/illinois-prairie-postgresql-user-group/events/312929674/

#postgresql #postgres #ai #vectordatabase #pgvector #vectorization #aidev #illinois #chicago

If you're located near Illinois, Shaun Thomas will be presenting on "The New Postgres AI Ecosystem" at the Illinois Prairie PostgreSQL User Group this February 18th at 5:30 PM CST. 🐘

Come by the DRW and say hi: https://www.meetup.com/illinois-prairie-postgresql-user-group/events/312929674/

#postgresql #postgres #ai #vectordatabase #pgvector #vectorization #aidev #illinois #chicago

If you're located near Illinois, Shaun Thomas will be presenting on "The New Postgres AI Ecosystem" at the Illinois Prairie PostgreSQL User Group this February 18th at 5:30 PM CST. 🐘

Come by the DRW and say hi: https://www.meetup.com/illinois-prairie-postgresql-user-group/events/312929674/

#postgresql #postgres #ai #vectordatabase #pgvector #vectorization #aidev #illinois #chicago

If you're located near Illinois, Shaun Thomas will be presenting on "The New Postgres AI Ecosystem" at the Illinois Prairie PostgreSQL User Group this February 18th at 5:30 PM CST. 🐘

Come by the DRW and say hi: https://www.meetup.com/illinois-prairie-postgresql-user-group/events/312929674/

#postgresql #postgres #ai #vectordatabase #pgvector #vectorization #aidev #illinois #chicago

pgedge-vectorizer: #Postgres extension that automatically vectorizes document contents and keeps vector embeddings current when the underlying content changes.

Unlike other solutions, no external services or third party pipelines are required. It's also 100% open source under the #PostgreSQL license. ✨

Check it out on GitHub: 👉 https://github.com/pgEdge/pgedge-vectorizer

#programming #vector #vectordatabase #vectorsearch #vectordb #ai #llm #aiengineering #aidev #dba

pgedge-vectorizer: #Postgres extension that automatically vectorizes document contents and keeps vector embeddings current when the underlying content changes.

Unlike other solutions, no external services or third party pipelines are required. It's also 100% open source under the #PostgreSQL license. ✨

Check it out on GitHub: 👉 https://github.com/pgEdge/pgedge-vectorizer

#programming #vector #vectordatabase #vectorsearch #vectordb #ai #llm #aiengineering #aidev #dba

pgedge-vectorizer: #Postgres extension that automatically vectorizes document contents and keeps vector embeddings current when the underlying content changes.

Unlike other solutions, no external services or third party pipelines are required. It's also 100% open source under the #PostgreSQL license. ✨

Check it out on GitHub: 👉 https://github.com/pgEdge/pgedge-vectorizer

#programming #vector #vectordatabase #vectorsearch #vectordb #ai #llm #aiengineering #aidev #dba

pgedge-vectorizer: #Postgres extension that automatically vectorizes document contents and keeps vector embeddings current when the underlying content changes.

Unlike other solutions, no external services or third party pipelines are required. It's also 100% open source under the #PostgreSQL license. ✨

Check it out on GitHub: 👉 https://github.com/pgEdge/pgedge-vectorizer

#programming #vector #vectordatabase #vectorsearch #vectordb #ai #llm #aiengineering #aidev #dba

Trợ lý AI như ChatGPT thường quên lịch sử sau mỗi phiên làm việc, khiến người dùng phải mô tả lại lỗi nhiều lần. Bài viết đề xuất giải pháp: thêm lớp "bộ nhớ liên tục" dùng vector storage giữa CLI và AI, tự động lưu/lấy giải pháp cho các lỗi lặp lại. Braves este các thách thức như vệ sinh dữ liệu, định dạng fix lệnh chuẩn và bảo mật. Thử nghiệm CLI Python dùng DeepSeek V3 cho thấy giảm chi phí token về 0 cho sự cố đã giải quyết.

#AI #CLI #DevTools #VectorDatabase #Programming
#TríTuệNhânTạo #L

SurgeDB: Cơ sở dữ liệu vector nhúng, hiệu năng cao, chạy nhẹ trên thiết bị biên, laptop hay VPS nhỏ. Viết bằng Rust, không phụ thuộc ngoài, hỗ trợ SIMD, HNSW, lọc metadata và bền vững với WAL. Chỉ tốn ~39MB RAM cho 100k vectors (768-dim), độ trễ tìm kiếm 0.64ms. Khác biệt với SQLite-vec và LanceDB ở kiến trúc hybrid in-memory + nén mạnh (SQ8/Binary). Đang tìm cộng sự phát triển WASM, tối ưu SIMD, binding Python/Node. Mã nguồn mở MIT. #SurgeDB #VectorDatabase #Rust #EdgeAI #HNSW #SQLite #AI #Mach