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The Model Context Protocol (MCP) is the connective tissue of the modern AI stack. If you are building AI agents or production RAG systems, understanding how MCP works with Haystack is one of the most direct ways to make your applications composable, reusable, and reachable from the tools your users…
Every new generation of Large Language Models arrives with a bigger context window - and the temptation to use it fully. If the model can read a million tokens, why not feed it everything?
Embeddings are the backbone of modern AI applications, from semantic search and recommendation systems to Retrieval-Augmented Generation (RAG). However, most embedding models operate in a single modality, typically focusing only on textual data.
When you think of Lufthansa, you might picture planes, airports, or global travel, but Lufthansa Industry Solutions (LHIND) is making an impact in a different way: as a full-service IT company delivering digital solutions for clients both inside and outside the Lufthansa Group.
When a leading company like TELUS Agriculture & Consumer Goods (TAC), with a strong presence in agriculture and consumer goods, turns to AI to streamline complex processes, it’s worth taking a closer look.
Welcome to this step-by-step tutorial where we’ll build a simple Retrieval-Augmented Generation (RAG) pipeline using Haystack and OPEA. We’ll fetch the newest Hacker News posts, feed them to a lightweight LLM endpoint (OPEAGenerator), and generate concise one-sentence summaries (based on this…
Haystack is an AI orchestration framework that enables developers to effortlessly build custom AI pipelines using a modular, building-block approach. However, when it’s time to take those pipelines from your development environment to production, you’re often left with a tough decision: write…
In retrieval-augmented generation (RAG) applications, the quality of the retrieved documents plays a critical role in delivering accurate and meaningful responses. But what happens when embedding similarity is not enough to get an accurate ordering of the reference documents?
The latest release from DeepSeek confirms an essential truth about AI: there won’t be one model or provider to rule them all. As the field evolves, it’s evident that diverse models bring unique strengths, making a model-agnostic approach vital for developers and organizations alike.
In this article, we’ll explore how to build an agentic Retrieval Augmented Generation (RAG) pipeline on deepset Studio, the AI application prototyping tool for developers.
We’re thrilled to announce an exciting new addition to the Haystack ecosystem: deepset Studio! After countless requests from our community for a visual editor to create AI workflows and invaluable feedback during the beta phase, we’re officially launching deepset Studio, a powerful tool for…
In artificial intelligence, multimodal agents are becoming increasingly popular for their ability to understand and integrate multiple types of input, such as text and images.
In our previous article, we explored how to build and deploy two key AI pipelines in a retrieval-augmented generation (RAG) application using Haystack with NVIDIA NIM: Indexing pipeline: Prepares data by preprocessing, chunking, and embedding PDF files, finally storing them in a vector database.
This is part one of the Advanced Use Cases series: 1️⃣ Extract Metadata from Queries to Improve Retrieval 2️⃣ Query Expansion 3️⃣ Query Decomposition 4️⃣ Automated Metadata Enrichment Sometimes a single question is multiple questions in disguise.
For most RAG applications, where we first have to retrieve the most relevant context, we end up having to split up documents first, and index those smaller splits of documents.
This is part one of the Advanced Use Cases series: 1️⃣ Extract Metadata from Queries to Improve Retrieval 2️⃣ Query Expansion 3️⃣ Query Decomposition 4️⃣ Automated Metadata Enrichment The quality of RAG (retrieval augmented generation) highly depends on the quality of the first step in the process:…
One of the main and critical components of a retrieval augmented generation (RAG) pipeline is the embedding process, which forms the foundation for efficient information retrieval by transforming raw text into machine-readable vector representations.
It’s 2024 and yet, most models today are still primarily geared towards English speaking markets. Today, deepset and Mixedbread are jointly announcing our latest contribution towards changing that landscape: A new open-source German/English embedding model - deepset-mxbai-embed-de-large-v1.
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