One new contender in the Vector search space: Amazon S3 Vectors.
By using S3 as the storage layer, it provides a cost-effective solution for an AI embedding store.
#aws #ai #rag
https://aws.amazon.com/fr/blogs/aws/introducing-amazon-s3-vectors-first-cloud-storage-with-native-vector-support-at-scale/
Amazon's S3 Vectors are now in preview, providing cost-effective storage for embeddings at the cost of latency. Ideal for reducing expenses on rarely accessed data in RAG applications where speed isn't critical.
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/
Local Chatbot RAG with FreeBSD Knowledge
https://hackacad.net/post/2025-07-12-local-chatbot-rag-with-freebsd-knowledge/
Deep Dive into Three AI Academic Search Tools https://katinamagazine.org/content/article/reviews/2025/deep-dive-into-three-ai-academic-search-tools #AI #libraries #search #RAG
Implement RAG With PGVector, LangChain4j and Ollama
#ai #java #langchain4j #ollama #pgvector #rag
https://mydeveloperplanet.com/2025/01/22/implement-rag-with-pgvector-langchain4j-and-ollama/
Kicking off in about 45 min at 5pm AZ time (MST/PDT)! I’ll be giving a talk on “The Future of Information Retrieval: A Deep Dive into RAG” with the .NET Virtual User Group and others. If you’re curious about how retrieval-augmented generation is reshaping search, swing by: https://www.meetup.com/dotnet-virtual-user-group/events/308838500
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
Infos aus dem Kreisarchiv Reutlingen zu einem KI-gestützten Chatbot-Prototypen zur Verbesserung des Zugangs zur Regionalgeschichte:
#OffeneArchive #Reutlingen #Kreisarchiv #RAG (tk)
https://www.kultur-machen.de/Digitales-Kreisarchiv/GeRT
CopilotKit é um ‘framework’ open‑source em TypeScript para criar copilotos de IA em minutos. Oferece:
• Abstrações genéricas para LLMs e adaptadores para OpenAI, Anthropic, Azure, etc.
• Pipeline de RAG com gestão automático de contexto.
• Suporte a CoAgents paralelos e orquestração de fluxos conversacionais.
• Módulos de UI generativa e hooks para React/Vue.
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
OpenSearch’s new MCP standard lets LLMs like Claude securely access + act on your data — no brittle glue code.
Dynamic tool discovery
Built-in auth + security
Unified JSON interface
Build smarter AI assistants + RAG apps → https://opensearch.org/blog/introducing-mcp-in-opensearch/
#ContextEngineering - Unlocking #AgenticAI’s True Potential
> Today's #LLMs are far more complex with context size of millions of tokens and the ability for calling external systems, tools, and even #agentic orchestration with multi-agent #AI systems. #Context has therefore evolved beyond the prompt to include System Prompt, User Input/Prompt, Memory, Retrieved Information (#RAG etc.), information on tools (#MCP), responses from tools, and structured output format
https://deepgains.substack.com/p/context-engineering-unlocking-agentic
AI 評估常見問題 (與解答) – Hamel 的部落格
➤ 解開 AI 評估的疑難雜症
✤ https://hamel.dev/blog/posts/evals-faq/
這篇文章整理了作者在 AI 評估課程中,來自 700 多位工程師和產品經理的常見問題。文章涵蓋了 RAG 的應用、模型選擇、評估工具、評估指標以及錯誤分析的重要性等議題,並強調了針對特定應用場景建立客製化評估工具的重要性。作者建議以二元評估(通過/失敗)取代傳統的 1-5 分評分,並強調理解失敗模式和有效上下文獲取是提升 LLM 應用效能的關鍵。
+ 內容非常實用,讓我對 AI 評估有了更清晰的認識。客製化評估工具的建議尤其有價值。
+ 文章深入淺出,解釋了 RAG 的本質,並指出了許多常見的誤解。對於正在開發 AI 應用的工程師來說,是一篇必讀的文章。
#人工智慧 #評估 #LLM #RAG
https://www.europesays.com/de/238217/ RAG 2024 mit Erfolg von fast einer Milliarde Euro – SR.de #Bilanz #Deutschland #Entertainment #Germany #Music #Musik #PK #RAG #Saarland #Unterhaltung
"As frontier model context windows continue to grow, with many supporting up to 1 million tokens, I see many excited discussions about how long context windows will unlock the agents of our dreams. After all, with a large enough window, you can simply throw everything into a prompt you might need – tools, documents, instructions, and more – and let the model take care of the rest.
Long contexts kneecapped RAG enthusiasm (no need to find the best doc when you can fit it all in the prompt!), enabled MCP hype (connect to every tool and models can do any job!), and fueled enthusiasm for agents.
But in reality, longer contexts do not generate better responses. Overloading your context can cause your agents and applications to fail in suprising ways. Contexts can become poisoned, distracting, confusing, or conflicting. This is especially problematic for agents, which rely on context to gather information, synthesize findings, and coordinate actions.
Let’s run through the ways contexts can get out of hand, then review methods to mitigate or entirely avoid context fails."
https://www.dbreunig.com/2025/06/22/how-contexts-fail-and-how-to-fix-them.html
Gerade hat der Paketbode geklingelt und das druckfrische Buch vom @rheinwerkverlag gebracht. Ich bin schon gespannt auf den Inhalt und freue mich es dann selbst praktisch auszuprobieren.