explore | Grilled Cheese

Haystack sits at 93/100 heat with +30 pts in the last 24h. Open-source RAG and semantic search pipelines, production-ready. Building document Q&A or conversational AI? This framework is earning its score. hookflow.ai #RAG #OpenSource

"I Tested Amazon S3 Vectors' New Pre-Filtering Against Exact Ground Truth" by Ajay kumar

#amazon-s3-vectors #vector-search #rag #generative-ai #aws-community-builders

El #RAG está pasando de recuperar «chunks parecidos» a entender la estructura del documento y razonar sobre ella.

Vector RAG = recuperar por similitud
Vectorless RAG = navegar por estructura
#AgenticRAG = razonar sobre esa estructura con autonomía

Más trazable y más fiable.

Adding a RAG pipeline to GenePattern Copilot actually degraded performance, making the chatbot's answers worse than the baseline frontier model. #RAG

社内の業務マニュアルを探すのに時間をかけていたり、同僚に聞いて迷惑かけていませんか？
社内に安全に立てたAIにPDFやWebを登録すれば、AIアバターが必要な情報を神速で提示します♪
頼れる相棒の詳細はプロフノリンクから！ #AIアシスタント #RAG

New arXiv paper evaluates LLM retrieval robustness in RAG setups, asking whether retrieval always helps, if more documents improve results, and whether document order matters, using a 1,891-sample benchmark across five datasets.

#LLMs #RAG #AIResearch
https://arxiv.org/abs/2505.21870

"Beyond the Prompt: How RAG Gives LLMs Access to Your Own Data on AWS" by Sarah Enaile

#rag #llm #aws-builders

🤖 How do you know if a RAG system is giving reliable answers?

🔍 RAG Evaluation measures retrieval quality, context relevance, faithfulness, answer correctness & hallucination risks.

Read the article: www.linkedin.com/pulse/what-r...

#RAG #RAGEvaluation #GenerativeAI #AI #LLM

I’ve been building a Python-based multi-agent framework with LangGraph, RAG, FastAPI, MCP & tool-based workflows. I’d really appreciate feedback from developers working on AI agents: what would you improve or add?
github.com/himanshukuma...

#AI #AIAgents #LangGraph #RAG

The best embedding model isn’t necessarily the one that performs best on a public benchmark.

It’s the one that retrieves the right answers from your own data.

That’s why I built Truvec:

→ Bring your RAG data
→ Benchmark embedding models
→ Compare retrieval quality
→ Find what actually works

#RAG

How do you turn a local RAG app in C# into an AI Agent?
In Part 1, I use Microsoft Agent Framework to expose Qdrant search as a tool and let the model decide when retrieval is needed.
www.ottorinobruni.com/from-local-r...
#dotnet #csharp #aiagent #rag #ollama #qdrant

Un millón de #Embeddings de 1.024 dimensiones en float32: ~4 GB. Casi nunca hace falta tanto.

Matryoshka a 256 + binaria: ~32 MB, 128 veces menos. Y con rescoring recuperas buena parte de la calidad.

Mide contra el original: el ahorro que vale es el que no se nota en tu #RAG.

"Building a RAG Pipeline on AWS with Amazon Bedrock, pgvector, and FastAPI" by Vighnesh Sankpal

#amazon-bedrock #amazon-bedrock-agents #vector-database #amazon-s3-vectors #rag

"RAG Using AWS: Connecting AI With Your Own Data" by Jatin Adlak

#rag #aws-codebuild #agentic-ai #ai #aws-builders

【ざっくり理解】ベクトルDBは新しい技術なのか？ ─ 1975年からRAGまで、古いものと新しいものを分けて見る

#ベクトル検索 #べクトルDB #rag #LLM #歴史