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retrieval augmented generation

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  • Announcing Studio: Your Development Environment for Haystack

    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…

    Haystack Blog3 min read

  • Building a Multimodal Nutrition Agent

    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.

    Haystack Blog9 min read

  • Design Haystack AI Applications Visually in deepset Studio with NVIDIA NIM

    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.

    Haystack Blog10 min read

  • Advanced RAG: Query Decomposition & Reasoning

    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.

    Haystack Blog8 min read

  • Improving Retrieval with Auto-Merging

    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.

    Haystack Blog9 min read

  • Advanced RAG: Query Expansion

    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:…

    Haystack Blog7 min read

  • CPU-Optimized Embedding Models with fastRAG and Haystack

    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.

    Haystack Blog9 min read

  • Mixedbread 🤝 deepset: Announcing our New German/English Embedding Model

    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.

    Haystack Blog2 min read

  • Extrinsic Hallucinations in LLMs

    Hallucination in large language models usually refers to the model generating unfaithful, fabricated, inconsistent, or nonsensical content. As a term, hallucination has been somewhat generalized to cases when the model makes mistakes.

    Lil'Log (Lilian Weng)25 min read

  • Benchmarking Haystack Pipelines for Optimal Performance

    In this article, we will show you how to use Haystack to evaluate the performance of a RAG pipeline. Note that the code in this article is meant to be illustrative and may not run as is; if you want to run the code, please refer to the python script.

    Haystack Blog23 min read

  • RAG Evaluation with Prometheus 2

    When building real-world applications based on Language Models (such as RAG), evaluation plays an important role. Recently, evaluating generated answers using powerful proprietary Language Models (such as GPT-4) has become popular and correlates well with human judgment, but it comes with its own…

    Haystack Blog11 min read

  • Building RAG Applications with NVIDIA NIM and Haystack on K8s

    Retrieval-augmented generation (RAG) systems combine generative AI with information retrieval for contextualized answer generation. Building reliable and performant RAG applications at scale is challenging.

    Haystack Blog17 min read

  • Monitor and trace your Haystack pipelines with Langfuse

    Getting your LLM application into production is a huge milestone, but that’s only the beginning. It’s critical to monitor how your pipeline is performing in the real world so you can keep improving performance and cost, and proactively address any issues that might arise.

    Haystack Blog9 min read

  • Advanced Retrieval: Extract Metadata from Queries to Improve Retrieval

    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 In Retrieval-Augmented Generation (RAG) applications, the retrieval step, which provides relevant context to your…

    Haystack Blog8 min read

  • Command R+

    This post is an update on what I’ve been up to since I joined Cohere. I’ve had fun contributing to the launch of Command R and R+, the latest Cohere models. I’ll discuss more details once the tech report is out; in the meantime I’ll share what I’m most excited about.

    NLP News (Sebastian Ruder)7 min read

  • Mamba Explained

    Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.

    The Gradient21 min read

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