Build a real RAG API with .NET 8, Semantic Kernel, Phi-3, and Qdrant!
Enrich LLMs with real product data
Local ONNX inference with Phi-3
Full API endpoints ready!
Dive into the architecture 
https://elguerre.com/2025/04/22/%f0%9f%9a%80-building-a-rag-api-with-net-semantic-kernel-phi-3-and-qdrant-enrich-your-e-commerce-experience/
Very interesting paper.
TL;DR: how to have insanely huge context for your LLM
Morphik – Open-source RAG that understands PDF images, runs locally
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
What if your database understood you, not just your SQL?
Just released: My tutorial on querying PostgreSQL using Vanna.ai's RAG technology—where natural language meets data engineering.
This clip from my LinkedIn Learning course shows how to turn "show me last month's sales trends" into perfect PostgreSQL queries.
Future or present? See for yourself: https://link.illustris.org/qryvannapg
#PostgreSQL #RAG #VannaAI #DataScience
ARCeR builds a full cyber range from plain‑language prompts! Slash setup time, cut costs & level‑up blue-team skills. 
Is agentic RAG the future of cybersecurity training?
Learn more: https://zerodaily.me/blog/2025-04-21-ai-revolutionizes-cybersecurity-training
Oracle Autonomous Database で In-database 埋め込みモデルを使ってベクトルを生成する
https://qiita.com/yuji-arakawa/items/e43e0ad9f0f033fd55ab?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
Claudeにばかり気を取られてた…エンベディングモデル側の最大トークン数
https://qiita.com/infra365/items/10d1b553283daacc6f1b?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
Citegeist is a free tool that helps you find articles related to a topic you are interested in. You need to seed it by entering the abstract of a relevant article or by uploading a relevant article. Citegeist not only finds related articles, but it generates a short report with info from the related articles also!
Citegeist works by using RAG info retrieval on the arxiv corpus of research papers.
#research #science #2ndOrderSearch
#Citegeist #arxivTools #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/
I finally cobbled all my thoughts about retrieval-augmented generation, or RAG, together in one place. So many people claimed it was so easy, but it's not.
This post is a high level overview of my lessons learned and some suggestions for you if you go down this path. 
Understand RAG at Easter? 
Why not use the time to learn something new — and build your own local PDF chatbot?
Learn how chunking, embeddings and vector search work in practice - with LangChain, FAISS, Ollama and Mistral running entirely on your machine (no API key required).
Perfect for beginners - here's the full guide & GitHub repo 
step-by-step guide: https://bit.ly/3EfOHB9 GitHub Repo: https://bit.ly/3EtqYgK
RAGで使用するHTMLの前処理を検証（マークダウン形式→チャンク分割）
https://qiita.com/infra365/items/bff82bc8414c90c09edc?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items
If you're trying to figure out how the heck vector searches work (for retrieval-augmented generation, or RAG), this set of slides will get you up to speed incredibly quickly.
https://pamelafox.github.io/my-py-talks/pgvector-python/
Thanks to @pamelafox for putting this together. 
Preview 2 of the .NET AI Template Now Available
https://devblogs.microsoft.com/dotnet/announcing-dotnet-ai-template-preview2/
I used AI2 Scholar QA to ask about knowledge conflicts in RAG query applications. It was illuminating to learn about some of the limitations of RAG applications and LLMs.
https://ai2-scholar-qa.allen.ai/query/ddae7a82-e708-492b-8baf-de732a74123f
From #FreeCodeCamp: "How to Build a Local #RAG App with #Ollama and #ChromaDB in the R Programming Language"
https://www.freecodecamp.org/news/build-a-local-rag-app-with-ollama-and-chromadb-in-r/
Want to improve your RAG (Retrieval-Augmented Generation) pipeline?
Our latest blog breaks down four retrieval techniques to boost accuracy, relevance and performance in your GenAI systems.
Read more: https://ter.li/fcr52o
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