https://www.europesays.com/2323559/ Kaune atšaukiamas britų žvaigždės Rag’n’Bone Man koncertas #Kaunas #koncertas #Lietuva #lithuania #naujienos #Rag’n’BoneMan

https://www.europesays.com/2323559/ Kaune atšaukiamas britų žvaigždės Rag’n’Bone Man koncertas #Kaunas #koncertas #Lietuva #lithuania #naujienos #Rag’n’BoneMan
Update. This new study compares two #AI tools (#LLMs with #RAG) on the task of predicting the citation impact of scholarly articles.
https://aclanthology.org/2025.sdp-1.11/
"Il RAG: la chiave per il successo aziendale nel nuovo mondo digitale. Scopri come può trasformare la tua impresa! #RAG #InnovazioneAziendale"
Using the example of a retrieval augmented generation (RAG) pipeline developed at the University of Victoria Libraries, Unlocking Web Archives Using RAG investigates the potential and challenges of integrating large language models with #RAG to transform access to web archives, with a focus on infrastructure, data quality, and ethical AI integration. Watch the CNI video at: https://youtu.be/cs3iXyV4kLs
LLMs don’t know your PDF.
They don’t know your company wiki either. Or your research papers.
What they can do with RAG is look through your documents in the background and answer using what they find.
But how does that actually work? Here’s the basic idea behind RAG: Chunking: The document is split into small, overlapping parts so the LLM can handle them. This keeps structure and context.
Embeddings & Search: Each part is turned into a vector (a numerical representation of meaning). Your question is also turned into a vector, and the system compares them to find the best matches.
Retriever + LLM: The top matches are sent to the LLM, which uses them to generate an answer based on that context.
Want to really understand how RAG, vector search & chunking work?
Then stop reading theory and build your own chatbot.
This guide shows you how to create a local PDF chatbot using:
LangChain
FAISS (vector DB)
Mistral via Ollama
Python & Streamlit
Step-by-step, from environment setup to deployment. Ideal for learning how Retrieval-Augmented Generation works in practice.
Comment “WANT” if you need the friends link to the article, as you don’t have paid Medium.
Using #Gemini and long context for indexing rich documents (PDF, HTML... containing images & diagrams) for your #RAG pipelines
https://glaforge.dev/posts/2025/07/14/advanced-rag-using-gemini-and-long-context-for-indexing-rich-documents/
Build an AI-Powered Document Assistant with Quarkus and LangChain4j
From Docs to Insightful Answers in Milliseconds
https://myfear.substack.com/p/quarkus-langchain4j-ai-document-assistant
#Java #LangChain4j #Quarkus #RAG #pgVector
Build an AI-powered document assistant with Quarkus and LangChain4j
Cloud-native AI for enterprise Java: RAG, embeddings, and native compilation
https://developer.ibm.com/tutorials/build-ai-assistant-quarkus-langchain/
#Java #Quarkus #LangChain4j #RAG #IBMDeveloper
Any company making outrageous claims about #ai or their use of should be labeled as an #aislopshop or #slopshop for short.
Like calling a dishonest lawyer a #shyster or a newspaper with no credibility, a #rag.
Hello World! #introduction
Work in cybersec for 25+ years. Big OSS proponent.
Latest projects:
VectorSmuggle is acomprehensive proof-of-concept demonstrating vector-based data exfiltration techniques in AI/ML environments. This project illustrates potential risks in RAG systems and provides tools and concepts for defensive analysis.
https://github.com/jaschadub/VectorSmuggle
SchemaPin protocol for cryptographically signing and verifying AI agent tool schemas to prevent supply-chain attacks (aka MCP Rug Pulls).
https://github.com/ThirdKeyAI/SchemaPin
Wir freuen uns ein weiteres der vier geförderten Projekte der zweiten Runde unseres #Forschungsstudienprogramms am Leibniz-Institut für Europäische Geschichte bekanntzugeben!
Rainer Simon (@aboutgeo) und Michela Vignoli für ihr Projekt „Digital Camerarius RAG: Multimodal Information Retrieval Prototype for CH and DH“.
Digital Camerarius: https://furman-editions-in-progress.github.io/camerarius/
Herzlichen Glückwunsch! Wir freuen uns auf die innovativen Erkenntnisse, die dieses Projekt hervorbringen wird
Why enterprise RAG systems fail: Google study introduces ‘sufficient context’ solution https://buff.ly/q6Esck6
"Interestingly, while RAG generally improves overall performance, additional context can also reduce a model’s ability to abstain from answering when it doesn’t have sufficient information. "
Why enterprise RAG systems fail: Google study introduces ‘sufficient context’ solution https://venturebeat.com/ai/why-enterprise-rag-systems-fail-google-study-introduces-sufficient-context-solution/ #AI #RAG #hallucination
https://www.europesays.com/2092537/ NetApp partners with NVIDIA to boost AI data storage in Australia #AIAdoption #AIAgents(AgenticAI) #ArtificialIntelligenceAI #australia #CloudStorage #DataGovernance #DataInfrastructure #DataSecurity #EnterpriseStorage #GenerativeAI(GenAI) #NetApp #Nvidia #RAG #RetrievalAugmentedGeneration(RAG)
Spent some time and had fun building a #Django documentation #RAG chatbot today. It answers questions by retrieving context from Django docs using embeddings. Currently using OpenAI/pgvector just to get some foundational knowledge, but I'd like to switch to entirely local and open-source embedding models (like sentence-transformers) and sqlite-vss for the vector search.
Link to the tutorial: https://docs.giskard.ai/en/stable/reference/notebooks/RAGET_Banking_Supervision.html
Build your own local RAG with Ramalama and granite model https://medium.com/@nicolabertoli92/build-your-own-local-rag-with-ramalama-and-granite-model-d5d89e612114
#Ramalama #aiml #rag
Neo4j treibt mit GraphRAG, Vektor-Indizes & Agentic RAG die #KI-Entwicklung voran. Ob #LangChain, #LlamaIndex, #SpringAI oder #VertexAI – das neue Python-Paket und das Model Context Protocol (MCP) verknüpfen Graphdaten nahtlos mit #LLM-Anwendungen.
#Neo4j #GenAI #RAG #GraphQL #Cypher #VectorSearch #AgenticAI
https://www.bigdata-insider.de/leistungssprung-bei-graph-datenbanken-mit-ki-integration-cloud-skalierung-und-terabyte-graphen-a-2307ed20cfaf562a1a0094b712b5be95/