524 stories
Ask an AI a simple question about a supplier, and it can hand back a confident, well-formatted answer that's completely made up, a vendor that was never signed, a contract term that doesn't exist. That's not a quirky bug. In a regulated business, it's a compliance red flag.
Support macros vs AI training data: a real refund macro shows exactly what belongs in an AI knowledge base and what breaks it.
We introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model.
Level: Advanced Time to complete: 25 minutes Components/Packages Used: agent-pack-haystack ( create_advanced_rag_agent, create_deep_research_agent), Agent, InMemoryDocumentStore Prerequisites: Haystack 3.1 or later, an OpenAI API key, and a Tavily API key (free tier available) Goal: After…
Level: Advanced Time to complete: 20 minutes Components Used: InMemoryDocumentStore, SentenceTransformersDocumentEmbedder, SentenceTransformersTextEmbedder, InMemoryEmbeddingRetriever, ChatPromptBuilder, OpenAIChatGenerator, Agent Prerequisites: You must have an OpenAI API Key and be familiar with…
Your AI starts from zero every session. OM2 is the organizational memory that learns continuously across your tools, so it stops re-reading and starts thinking.
SereneDB has built a VectorStore integration package for LangChain, pairing flexible AI orchestration with the analytical power of a full-featured database.
See which YouTube transcript and data API to use: Google v3, Social Fetch, or Apify. Coverage, pricing, and a free caption tool.
Coding agents fail on context, not capability. A practical guide to context engineering for a real repository: the four layers, a token budget you can reason about, how to diagnose which layer is failing, and how to keep it from going stale.
One package gives Mastra agents a single database for conversations, workflow state, observability and vectors, plus managed memory that extracts and semantically recalls facts.
We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent.
A team runs an AI agent on what looks like a clean dataset and asks a simple question: what's the projected Q4 revenue for the East Coast division? The agent returns a number, confident and completely wrong. The real culprit usually isn't the model.
You've felt it. You tell Claude your product roadmap, then jump into ChatGPT to draft a launch email and realize it has no idea what you're talking about. The knowledge you painstakingly built in one tool is completely absent in another.
Your team lives in Slack. Your answers live in a warehouse, a CRM, and a dozen other SaaS tools that don't talk to each other. So people toggle tabs, copy-paste numbers into a channel, and hope the figure they just posted is still current by the time someone reads it.
Introduction You know that feeling of dread when someone asks a question you know is answered in that doc you wrote six months ago? The doc that’s now riddled with stale screenshots and outdated numbers. Traditional wikis rot.
Hello, and welcome back to the Cognitive Revolution!Today my guest is Pete Johnson, Field CTO of AI at MongoDB.We start with a brief history of database technology, going back to the 1970 paper that gave us the relational model and, with it, SQL — unpacking how the relative
MongoDB Field CTO Pete Johnson traces the company's shift from lexical to vector and hybrid search, new $rankFusion and $rerank query stages, and the Voyage AI acquisition's contextualized chunking and embedding techniques.
PDFs full of charts and tables are notoriously hard to put through a traditional RAG pipeline. In this post, we show how late-interaction multi-vector retrieval lets you search PDFs by what the page looks like: no OCR, no chunking, no text extraction.
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