# Retrieval-Augmented Generation Published articles for Retrieval-Augmented Generation. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs DevFeed: [Presentation: From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs]() Original publisher: [Read original article]() Author: Cassie Shum Published: 2026-09-12T11:00:00Z Content type: tutorial Language: en Sources: [InfoQ]() Topics: [Graphs](), [Architecture & Design](), [Retrieval Augmented Generation (RAG)](), [Code]() Tags: [agentic-ai](), [agentic-ai-architecture](), [agents](), [ai](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [development](), [engineering](), [infoq](), [knowledge-graph](), [knowledge-graphs-agentic-systems-patterns](), [large-language-models](), [ml-data-engineering](), [presentation](), [production](), [qcon-ai-boston-2026](), [qcon-software-development-conference](), [rag](), [reasoning](), [retrieval-augmented-generation](), [transcripts]() ### AI overview A presentation on using knowledge graphs as a foundation for production-ready agentic AI systems. It covers architectural patterns for context bundling, decision provenance, code as truth, and agent visibility, along with a graph-based engineering harness for feedback loops, token optimization, and reliability. ### Source excerpt Cassie Shum discusses why knowledge graphs serve as a critical foundation for agentic systems. Moving beyond basic RAG, she explains 4 practical architectural patterns: context bundling, decision provenance, code as truth, and agent visibility. She demonstrates an engineering harness built on a knowledge graph to streamline feedback loops, optimize token usage, and maintain system reliability. By Cassie Shum ## How Embedding Models Affect Retrieval-Augmented Generation Quality DevFeed: [How Embedding Models Affect Retrieval-Augmented Generation Quality]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-09-02T15:31:12Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Embeddings](), [Retrieval Augmented Generation (RAG)](), [Language models](), [Artificial Intelligence]() Tags: [embedding](), [embeddings](), [model](), [rag](), [retrieval-augmented-generation]() ### AI overview This article explains how embedding models support retrieval-augmented generation by translating words into vectors and controlling the search for relevant information before an answer is generated. It discusses why poor retrieval can produce incorrect answers even when the language model is capable, and outlines considerations for evaluating and selecting embedding models. ### Source excerpt In this article, we're going to look at how this embedding model works in an RAG setup and what makes it such a critical part of the system. ## When LLM judges agree, should we believe them? DevFeed: [When LLM judges agree, should we believe them?]() Original publisher: [Read original article]() Author: Krishna Balasubramanian; Sasha Podkopaev Published: 2026-08-26T17:10:40Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Large Language Model](), [Retrieval-Augmented Generation](), [Ising](), [benchmark overfitting machine learning](), [Network](), [AI, ML & Data Engineering]() Tags: [conversational-ai](), [evaluation](), [ising](), [large-language-models-llms](), [llm](), [machine-learning](), [metrics](), [retrieval](), [retrieval-augmented-generation]() ### AI overview The article examines whether agreement among LLM judges is trustworthy when their outputs may be correlated. It presents a dependence-aware aggregation method based on Ising models that discounts shared blind spots and outperforms historical-accuracy-weighted majority voting on three tasks. ### Source excerpt Discounting the opinions of LLM judges with highly correlated outputs ensures that panels of judges reflect a true diversity of perspectives. ## GraphRAG: How AI Answers Questions Hidden Across Many Documents DevFeed: [GraphRAG: How AI Answers Questions Hidden Across Many Documents]() Original publisher: [Read original article]() Author: ByteByteGo Published: 2026-08-19T15:31:18Z Content type: tutorial Language: en Sources: [ByteByteGo]() Topics: [Graphs](), [Retrieval-Augmented Generation](), [Artificial Intelligence]() Tags: [graphs](), [knowledge-graphs](), [rag](), [retrieval-augmented-generation](), [search]() ### AI overview This tutorial explains how GraphRAG addresses questions whose answers emerge from patterns across an entire document collection, contrasting it with standard RAG, which is designed to retrieve information from specific documents. ### Source excerpt GraphRAG was designed to handle the second kind of questions, and we are going to learn more about it in this article. ## How we selected the next vector database at Booking.com DevFeed: [How we selected the next vector database at Booking.com]() Original publisher: [Read original article]() Author: Başak Tuğçe Eskili Published: 2026-08-11T10:31:50Z Content type: article Language: en Sources: [Booking.com Data Science]() Topics: [Database](), [Retrieval Augmented Generation (RAG)](), [Embeddings](), [Back end](), [opensearch](), [AI search]() Tags: [ai](), [database](), [embeddings](), [featured](), [genai](), [hybrid-search](), [retrieval-augmented-generation](), [retrieval-augmented-generation-rag](), [semantic](), [vector-database]() ### AI overview Booking.com explains why selecting a vector database became an infrastructure decision as embeddings and vector search expanded across its machine learning and GenAI systems. The article describes diverse functional and operational requirements, including hybrid search, multi-vector support, capacity, request rates, metadata filtering, and concurrency, and introduces OpenSearch as the initial choice. ### Source excerpt This work was done in collaboration with Klaus Schaefers. Over the past few years, embeddings and vector search have become an important capability in many of our machine learning and GenAI systems at Booking.com. We initially started with a handful of use cases and experiments, and later this capability has grown into shared infrastructure that powers similarity search, semantic filtering, and retrieval-augmented generation (RAG). We used to treat vector search as a backend implementation detail, but today it directly drives the user experience. The real win isn't only speed but also the context. Expanding the variety of domain data we can retrieve efficiently gives our system the depth of context it needs to deliver accurate, and personalized experiences across the platform. This makes selecting the underlying vector database an infrastructure decision similar to choosing a primary datastore or message queue. It has to be predictable and scalable. As more teams started using our vector store, we began seeing highly diverse functional and operational requirements across different use cases. Some teams needed advanced capabilities like hybrid search or multi-vector support, while others demanded larger vector capacities and higher RPS metrics. These architectural needs ultimately brought us to a point where we needed to reassess whether our current setup could support this next phase of growth. Context: how embeddings fit into our stack Embeddings are vectors: fixed-length arrays of numbers produced by a model to represent an item (text, image, etc.). Each vector can be seen as a point in a high-dimensional space, where distance (or similarity) between points approximates semantic relatedness. By searching for the nearest vectors to a query vector, we retrieve items that are semantically "similar". This simple mechanism enables a wide range of use cases for us due its ability to do semantic similarity search. RAG-based use cases are the most well known examples. Ano ## Amazon DynamoDB now supports real-time vector search at any scale DevFeed: [Amazon DynamoDB now supports real-time vector search at any scale]() Original publisher: [Read original article]() Author: Esra Kayabali Published: 2026-08-05T14:45:10Z Content type: release Language: en Sources: [AWS News Blog]() Topics: [AI search](), [AI, ML & Data Engineering](), [Retrieval Augmented Generation (RAG)]() Tags: [amazon-bedrock](), [amazon-dynamodb](), [announcements](), [api](), [artificial-intelligence](), [database](), [embeddings](), [generative-ai](), [latency](), [launch](), [news](), [openai](), [real-time](), [retrieval-augmented-generation](), [scale](), [search](), [serverless](), [vector]() ### AI overview Amazon DynamoDB has added generally available native vector search, allowing embeddings and operational data to be stored and queried together. The feature provides similarity-ranked results with configurable dimensions, distance functions, and filters, without a separate vector database or synchronization pipeline. ### Source excerpt DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall. It is designed for any scale, even trillions of vectors and requires zero infrastructure management. ## How avatarin built a 24/7 retail agent with GPT-Realtime DevFeed: [How avatarin built a 24/7 retail agent with GPT-Realtime]() Original publisher: [Read original article]() Published: 2026-07-30T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [AI Bots](), [Retrieval Augmented Generation (RAG)]() Tags: [agent](), [ai](), [ai-agent](), [api](), [customer](), [gpt](), [low-latency](), [openai](), [retail](), [retrieval](), [retrieval-augmented-generation](), [speech](), [voice]() ### AI overview avatarin built a 24/7 multilingual retail shopping agent for Yamada Denki using OpenAI's GPT-Realtime. The agent supports voice conversations and product recommendations; a two-week public campaign reported about 30,000 users and 92% positive survey responses. ### Source excerpt avatarin uses OpenAI's GPT-Realtime to give Yamada Denki shoppers 24/7 multilingual support. In two weeks, 30,000 people used the agent and 92% of survey responses were positive. ## Doximity Ask DevFeed: [Doximity Ask]() Original publisher: [Read original article]() Author: Doximity Published: 2026-06-23T09:30:00Z Content type: article Language: en Sources: [Doximity]() Topics: [Artificial Intelligence](), [Retrieval-Augmented Generation](), [Hallucination detection]() Tags: [agentic](), [ai](), [grounding](), [hallucinations](), [retrieval-augmented-generation](), [review](), [verification]() ### AI overview The article explains how Doximity Ask approaches reliability in clinical AI. It describes a HIPAA-compliant clinical reference platform that combines retrieval-augmented generation, validated tools, a reasoning model, citation-grounded responses, automated verification, physician-authored evaluation, and production monitoring. It argues that reliability is a system property spanning retrieval, ranking, reasoning, tool use, verification, and evaluation. ### Source excerpt Medical AI has attracted confident claims: perfect scores, no hallucinations, a system that gracefully declines whenever the evidence runs out. But are these dimensions the right way to think about a tool that physicians use to make real-world decisions? For clinical AI, reliability is not just a question of whether an answer is correct. It is also a question of whether a physician can understand where the answer came from, evaluate the supporting evidence, and identify situations where uncertainty remains. How Doximity Ask Answers Clinical Questions Few domains face stakes as high as medical AI, where system performance can directly influence patient outcomes, clinical workflows, and healthcare professionals' decisions. Clinical reference requires traceable grounding, numerically exact doses and scores, and explicit handling of situations in which the available evidence is insufficient to support an answer. At the same time, healthcare providers often work under significant time constraints and have exceptionally high expectations for the performance and reliability of AI systems. Doximity Ask is a HIPAA compliant composite clinical reference platform for question answering at the point of care. It combines retrieval-augmented generation over a physician-governed evidence library, multi-turn agentic tool use through a fixed registry of validated calculators and structured lookups, and a post-trained reasoning model that orchestrates retrieval, tool calls, and citation-grounded generation. Automated verifiers, physician-authored rubrics, and in-product physician review form one evaluation loop that grades releases, defines training rewards, and monitors production. Reliability Is A System Property Over 150 health systems have purchased the Doximity Clinical AI Suite including 8 of the top 20 hospitals in the country, therefore reliability is paramount. The reliability of a clinical AI system depends on more than the capabilities of its underlying language model. Pro ## Razorpay Oncall Agent: From 30-Minute Investigations to 90-Second AI Analysis DevFeed: [Razorpay Oncall Agent: From 30-Minute Investigations to 90-Second AI Analysis]() Original publisher: [Read original article]() Author: Anuj Gupta Published: 2026-04-29T06:56:11Z Content type: article Language: en Sources: [Razorpay Engineering - Medium]() Topics: [Artificial Intelligence](), [Langgraph](), [incident](), [SRE](), [observability](), [Large Language Model](), [Retrieval-Augmented Generation]() Tags: [agent](), [ai](), [incident](), [langgraph](), [logs](), [metrics](), [observability](), [retrieval-augmented-generation](), [sre]() ### AI overview Razorpay describes building a multi-agent AI system to automate production incident investigations. The Oncall Agent uses LangGraph, an LLM, alerting tools, and two retrieval-augmented generation systems containing architecture, dependency, and diagnostic runbook context. ### Source excerpt Our on-call engineers were spending 30 minutes investigating every production alert. Here's what happened when we automated it. At 3 AM, alerts don't care about your sleep schedule. When our payment infrastructure threw an error last month, our on-call engineer spent 32 minutes jumping between six different monitoring systems before understanding what was broken. One tool for metrics. Another for logs. Third tool for pod health. And multiple more for infrastructure, deployment history and database health. By the time they identified the root cause (a bad deployment), payment failures had already impacted customers for nearly 40 minutes. This wasn't their fault. They followed our runbook perfectly. The problem was that no single system could tell them "here's what's wrong and why." They had to manually connect dots across disconnected observability tools. That's when we asked ourselves: what if AI could do this investigation for us? The Metric Nobody Optimizes For The SRE world talks endlessly about Mean Time to Detect (how fast you catch problems) and Mean Time to Resolve (how fast you fix them). But there's a critical phase hiding between them: Mean Time to Investigate. MTTI is the gap from "we know it's broken" to "we know what to fix." At Razorpay, this phase was consuming 20-40 minutes per incident. With 15-20 incidents weekly, that's 6-8 hours of engineering time spent doing repetitive investigative work. Worse, the quality was inconsistent. Senior engineers knew exactly which systems to check for payment alerts. Junior engineers sometimes checked irrelevant dashboards or missed critical correlations. The investigation depended entirely on who was on-call that night. What We Built (And Why It Works) Razorpay Oncall Agent is a multi-agent AI system that automates incident investigation. The architecture is built on LangGraph, a framework for creating stateful workflows with conditional logic, and uses LLM as the reasoning engine. Here's how the components work t ## Agent Development with CockroachDB using the LangChain Framework DevFeed: [Agent Development with CockroachDB using the LangChain Framework]() Original publisher: [Read original article]() Author: David Bressler Published: 2026-02-26T00:00:00Z Content type: release Language: en Sources: [Cockroach Labs]() Topics: [CockroachDB](), [LangChain](), [agentic AI applications](), [Orchestration](), [Python](), [Databases](), [Retrieval-Augmented Generation]() Tags: [agentic-ai-applications](), [cockroachdb](), [langchain](), [orchestration](), [pgvector](), [postgresql](), [python](), [retrieval-augmented-generation]() ### AI overview CockroachDB describes its integration with LangChain for building production-oriented agentic AI applications in Python. The integration supports CockroachDB as a vector source and helps connect language models with CockroachDB data for retrieval and other multi-step application workflows. ### Source excerpt Recently we launched an integration with LangChain, the most popular orchestration framework for developing applications with large language models, to simplify development of production-ready agentic AI applications with CockroachDB. This integration provides out-of-the-box support for CockroachDB as a vector source for any LangChain user using LangChain Python. ## Building AI Search on Heroku DevFeed: [Building AI Search on Heroku]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2026-01-29T17:50:08Z Content type: tutorial Language: en Sources: [Heroku]() Topics: [Heroku](), [AI search](), [Retrieval-Augmented Generation](), [Claude]() Tags: [ai](), [ai-search](), [building](), [engineering](), [heroku](), [heroku-ai](), [managed-inference-and-agents](), [news](), [pgvector](), [rag](), [retrieval](), [retrieval-augmented-generation](), [search](), [vector-search]() ### AI overview This article presents a Heroku reference architecture for production-grade enterprise AI search using two-stage retrieval. It combines vector search with semantic reranking so documents are scored for whether they answer the query, then uses Claude to generate an answer. ### Source excerpt If you've built a RAG (Retrieval Augmented Generation) system, you've probably hit this wall: your vector search returns 20 documents that are semantically similar to the query, but half of them don't actually answer it. A user asks "how do I handle authentication errors?" and gets back documentation about authentication, errors, and error handling in [...] The post Building AI Search on Heroku appeared first on Heroku. ## Optimize Search Precision with Reranking on Heroku AI DevFeed: [Optimize Search Precision with Reranking on Heroku AI]() Original publisher: [Read original article]() Author: Anush DSouza Published: 2026-01-15T16:00:48Z Content type: release Language: en Sources: [Heroku]() Topics: [Heroku](), [Inference](), [Retrieval-Augmented Generation](), [model-deployment](), [amazon](), [cohere]() Tags: [ai](), [api](), [availability](), [heroku](), [heroku-ai](), [inference](), [managed-inference-and-agents](), [news](), [rag](), [retrieval-augmented-generation]() ### AI overview Heroku announces general availability of reranking models in Heroku Managed Inference and Agents, with support for Cohere Rerank 3.5 and Amazon Rerank 1.0. The article explains how semantic reranking can improve RAG pipelines by filtering documents for query relevance and reducing noise and token costs. ### Source excerpt Today, we are announcing the general availability of reranking models on Heroku Managed Inference and Agents, featuring support for Cohere Rerank 3.5 and Amazon Rerank 1.0. Semantic reranking models score documents based on their relevance to a specific query. Unlike keyword search or vector similarity, rerank models understand nuanced semantic relationships to identify the most [...] The post Optimize Search Precision with Reranking on Heroku AI appeared first on Heroku. ## Building Arc: An AI Messenger Powered by Firebase, Flutter, and Vertex AI DevFeed: [Building Arc: An AI Messenger Powered by Firebase, Flutter, and Vertex AI]() Original publisher: [Read original article]() Author: TAKAYUKI MIYANO Published: 2025-10-27T07:49:55Z Content type: article Language: en Sources: [Firebase Developers - Medium]() Topics: [App](), [Architecture & Design](), [Firebase](), [Flutter](), [Cloud Run](), [Cloud Functions](), [Retrieval Augmented Generation (RAG)](), [Messaging](), [Microservices](), [Security](), [Resilience](), [networking]() Tags: [architecture](), [cloud-functions](), [cloud-run](), [firebase](), [flutter](), [gemini](), [messenger](), [microservices](), [resilience](), [retrieval-augmented-generation](), [security](), [vertex-ai]() ### AI overview This article describes the architecture of Arc, a private messaging app built with Flutter and Firebase. It covers ephemeral messaging, mesh networking for resilience, Cloud Run microservices, Cloud Functions for event-driven processing, and a RAG architecture using Vertex AI Search and Gemini models. It also discusses security, privacy, and GenAI companions. ### Source excerpt Introduction: The Vision of Arc Our journey at Arc began with a singular vision: to redefine private communication in the digital age. We envisioned a world of ephemeral, intimate conversations -- a space where privacy isn't an afterthought, but the very foundation. This philosophy gave birth to Arc, a next-generation messaging app where every message, even in group chats, is designed to disappear over time. Our vision goes beyond just ephemerality. We also saw a critical need to build resilience, especially in regions with unstable internet infrastructure and in a world where disasters are a constant threat. This led us to develop our mesh networking technology, a potential lifeline when traditional communication fails. Our mission is to transform our app into a vital piece of social infrastructure that protects people's safety and gives them peace of mind. However, a vision of this scale demands an architecture that can compete on a global level. For us, true "optimization" is a comprehensive concept built on four pillars: Stability, Speed, Lightweight Design, and High Security. In this article, we'll explore the architectural decisions that shaped Arc. We'll detail how we integrated Flutter as our client, used Firebase's managed services as our foundation, and combined them with Cloud Run for scalable microservices, Cloud Functions for event-driven processing, and an advanced RAG (Retrieval-Augmented Generation) architecture using Vertex AI Search for grounding and Gemini models. This is the story of how we built a platform that is not only secure and high-performance but also intelligent and ready for future evolution. The Challenge: Going Beyond a Standard Messenger To make Arc a reality, we faced numerous technical hurdles that went beyond conventional architectures. Our goal wasn't just to build another messaging app; it was to create a new conversational experience and a resilient communication platform. Our app also includes GenAI companions (AI Characters) ## RAG Demystified: From Math to Self-Hosted Code DevFeed: [RAG Demystified: From Math to Self-Hosted Code]() Original publisher: [Read original article]() Author: RisingStack Engineering Published: 2025-09-18T03:02:08Z Content type: tutorial Language: en Sources: [RisingStack]() Topics: [Retrieval Augmented Generation (RAG)](), [Large Language Model](), [foundation-models](), [Self-hosted](), [Artificial Intelligence]() Tags: [ai](), [code](), [large-language-model](), [open-source](), [rag](), [retrieval-augmented-generation](), [self-hosted](), [use-cases]() ### AI overview This introductory article explains Retrieval-Augmented Generation (RAG), foundation models, and why adding specialized or up-to-date context can extend large language model capabilities. It introduces use cases such as support chatbots and tailored summaries and indicates that later chapters will cover RAG-related code, including self-hosted open-source models. ### Source excerpt In today's AI hype you cannot miss the term "RAG," which stands for Retrieval Augmented Generation. In plain English, it stands for customizing large language model reasoning with your own context and knowledge. I searched a lot of resources and AI-generated content for this fairly simple technique to be explained well. I'm still looking for [...] The post RAG Demystified: From Math to Self-Hosted Code appeared first on RisingStack Engineering. ## How often do LLMs visit llms.txt? DevFeed: [How often do LLMs visit llms.txt?]() Original publisher: [Read original article]() Author: Tiffany Chen Published: 2025-06-27T00:00:00Z Content type: article Language: en Sources: [Mintlify Blog]() Topics: [Large Language Model](), [Markdown](), [Data analysis](), [Crawler](), [Retrieval-Augmented Generation]() Tags: [ai-trends](), [claude](), [data-analysis](), [google](), [llms](), [markdown](), [pages](), [retrieval](), [retrieval-augmented-generation]() ### AI overview The article presents a Profound data analysis of how language models access llms.txt and llms-full.txt. The supplied text says both files receive AI traffic, with a strong preference for llms-full.txt and ChatGPT accounting for most visits. It attributes this preference to models embedding full content instead of relying on retrieval-augmented generation, while noting that the analysis covers 25 companies. ### Source excerpt Last month, we explored signals for the emerging standard of llms.txt, which is a Markdown file that makes websites easier for LLMs to index. ## Codestral Embed DevFeed: [Codestral Embed]() Original publisher: [Read original article]() Published: 2025-05-28T12:00:00Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [Embeddings](), [Retrieval-Augmented Generation](), [AI-assisted coding](), [code-completion](), [AI search](), [coding]() Tags: [code-completion](), [code-search](), [coding](), [cost](), [developer](), [embeddings](), [performance](), [retrieval-augmented-generation]() ### AI overview Mistral releases Codestral Embed, an embedding model specialized for code retrieval. The article presents benchmark results, configurable embedding dimensions and precisions, and trade-offs between retrieval quality and storage cost. It highlights applications including retrieval-augmented generation, semantic code search, similarity search, duplicate detection, clustering, and code analytics. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Filtering in Vector Search with Metadata and RAG Pipelines DevFeed: [Filtering in Vector Search with Metadata and RAG Pipelines]() Original publisher: [Read original article]() Author: Volodymyr Pavlyshyn Published: 2024-11-11T00:00:00Z Content type: tutorial Language: en Sources: [Turso Blog]() Topics: [AI search](), [Language models](), [AI Bots]() Tags: [agents](), [ai](), [ai-agents](), [database](), [embeddings](), [large-language-models-llms](), [libsql](), [rag](), [retrieval](), [retrieval-augmented-generation](), [search](), [sqlite](), [vector]() ### AI overview The article explains how to combine vector search with structured metadata filters in RAG pipelines. It describes libSQL's vector storage and indexing, along with pre-filtering, post-filtering, and CTE-based query optimization. ### Source excerpt How Kin combines libSQL vector search with metadata filters to build RAG pipelines: pre-filtering, post-filtering, and CTE-based query patterns explained. ## red\_mad\_robot meetup on GenAI, RAG platforms, and Go microservice automation DevFeed: [red\_mad\_robot meetup on GenAI, RAG platforms, and Go microservice automation]() Original publisher: [Read original article]() Author: redmadrobot (red\_mad\_robot) Published: 2024-10-28T15:15:15Z Content type: news Language: ru Sources: [Redmadrobot EN](), [Redmadrobot RU]() Topics: [Back end](), [Generative AI](), [Retrieval-Augmented Generation](), [Go Language]() Tags: [ai](), [backend](), [backend-206c103d1080](), [dev](), [genai](), [go](), [golang](), [rag](), [red-mad-robot](), [retrieval](), [retrieval-augmented-generation](), [tag-61cd5a476b1d](), [tag-622612a5798e](), [tag-86b843454893](), [tag-e076f05a03bb](), [tag-e95ee8b94dca]() ### AI overview red_mad_robot announces a backend meetup covering GenAI systems, RAG platforms, AI-based knowledge management, enterprise AI services, and automated Go microservice development. The event offers in-person and online participation. ### Source excerpt Привет мастерам backend-разработки! Если вам интересны прогрессивные подходы в создании AI-решений и автоматизации микросервисной архитектуры, ждём вас 7 ноября в 19:00 в московском офисе red_mad_robot (но можно и онлайн). Читать далее ## Firebase Data Connect: now in public preview! DevFeed: [Firebase Data Connect: now in public preview!]() Original publisher: [Read original article]() Author: Andrea Wu Published: 2024-10-09T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firebase](), [PostgreSQL](), [GraphQL](), [SDK](), [Node.js](), [Retrieval Augmented Generation (RAG)](), [Artificial Intelligence]() Tags: [admin-sdk](), [data](), [firebase](), [graphql](), [launch](), [node-js](), [postgresql](), [preview](), [retrieval-augmented-generation](), [sdk](), [sql-connect](), [updates](), [vertex-ai]() ### AI overview Firebase Data Connect is now available in Public Preview. The service uses a Cloud SQL Postgres database, GraphQL-based schemas, generated SDKs, schema migrations, semantic vector search, and Vertex AI integration for retrieval-augmented generation workflows. ### Source excerpt News, tutorials, and updates from the Firebase team. ## Our Transformers Code Agent beats the GAIA benchmark 🏅 DevFeed: [Our Transformers Code Agent beats the GAIA benchmark 🏅]() Original publisher: [Read original article]() Author: Aymeric Roucher; Sergei Petrov Published: 2024-07-01T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [gaia](), [Benchmark](), [Transformers](), [smolagents](), [Large Language Model](), [Graphs](), [Retrieval-Augmented Generation]() Tags: [agent](), [agents](), [artificial-intelligence](), [benchmark](), [code](), [community](), [gaia](), [leaderboard](), [multimodal](), [nlp](), [research](), [retrieval-augmented-generation](), [smolagents](), [tool](), [tools](), [transformers]() ### AI overview The article presents a Transformers Code Agent that achieved top performance on the GAIA benchmark. It explains how LLM-based agents use tools and dynamically change their execution graph, and notes that the framework has since been upgraded to the standalone smolagents library. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## SQLite Retrieval Augmented Generation and Vector Search DevFeed: [SQLite Retrieval Augmented Generation and Vector Search]() Original publisher: [Read original article]() Author: Pekka Enberg Published: 2024-06-21T00:00:00Z Content type: tutorial Language: en Sources: [Turso Blog]() Topics: [Retrieval Augmented Generation (RAG)](), [AI search](), [Language models]() Tags: [embeddings](), [llms](), [retrieval-augmented-generation](), [sqlite](), [tutorial](), [vector]() ### AI overview An introduction to using retrieval-augmented generation with SQLite, embeddings, and vector search to provide language models with current, specific external information. ### Source excerpt Learn how to use retrieval augmented generation to improve vector search results with SQLite.