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Agentic RAG architectures can have various levels of complexity. In the simplest form, a single-agent RAG architecture is a simple router. However, you can also add multiple agents into a multi-agent RAG architecture. This section discusses the two fundamental RAG architectures. Single-Agent RAG (Router): In its simplest form, agentic RAG is a router. This means you have at least two external knowledge sources, and the agent decides which one to retrieve additional context from. However, the external knowledge sources don't have to be limited to (vector) databases. You can retrieve further information from tools as well. For example, you can conduct a web search, or you could use an API to retrieve additional information from Slack channels or your email accounts. Multi-agent RAG Systems: As you can guess, the single-agent system also has its limitations because it's limited to only one agent with reasoning, retrieval, and answer generation in one. Therefore, it is beneficial to chain multiple agents into a multi-agent RAG application. Know more about agentic RAG architectures: https://lnkd.in/dUqS3cxS Here's how we finally got our agentic RAG to work right: https://lnkd.in/ehA7tWKn You know, now you can create production-ready RAG pipelines in minutes with Vectorize. Try yourself creating a RAG pipeline for free: https://vectorize.io/

What are the different RAG approaches you know? At its core,?RAG integrates retrieval mechanisms directly into the generation pipeline of large language models.?This enables systems to transcend purely generative capabilities and ground their outputs in external, reliable, and often dynamic data sources. For engineering leaders revisiting hands-on work or exploring the latest in AI, RAG offers a unique combination of foundational concepts and real-world practicality. Here is an amazing article by Gaurav Nigam, where he shares 16 distinct RAG types, detailing their?key features,?benefits,?enterprise suitability, and?implementation strategies. Read the complete article: https://lnkd.in/gw7c7P6s

Single-Agent vs. Multi-Agent Systems! Agentic systems are broadly classified into single-agent and multi-agent systems, each tailored to different problem-solving paradigms and application domains. Single-Agent Systems: A single-agent system consists of an autonomous entity that operates independently, without interacting with other agents. It focuses on executing predefined tasks efficiently within its domain. These systems are ideal for environments where collaboration or competition with other agents is unnecessary. Example: A personal AI assistant that manages a user’s schedule, responds to queries, and sets reminders operates as a single-agent system. It processes tasks in isolation without requiring coordination with other agents. Multi-Agent Systems (MAS): A multi-agent system (MAS) comprises multiple autonomous agents that interact, collaborate, or compete within a shared environment. These agents communicate, coordinate, and negotiate to achieve individual or collective objectives. MAS are particularly effective in dynamic, distributed, and complex problem spaces where task delegation enhances efficiency and adaptability. Example: In swarm robotics, multiple robots work together in search-and-rescue missions, efficiently covering vast areas, sharing information, and adapting to environmental challenges in real time. Their collective intelligence enables them to achieve goals beyond the capability of a single agent. By leveraging the strengths of each approach, organizations can design intelligent systems that balance autonomy, cooperation, and efficiency based on the specific requirements of their applications. Here you can find more about designing agentic AI systems: https://lnkd.in/duVUgbpB

How To Make RAG Models Perform Like A Pro? This video by Chris explains how Vectorize can help improve the performance of RAG models. The video demonstrates how to use Vectorize to evaluate different vectorization strategies by uploading documents, configuring vector indexes with various embedding models and chunking strategies, and generating synthetic questions to test retrieval quality. The platform then identifies a winning strategy based on relevancy, recall, and normalised discounted cumulative gain. Additionally, the video highlights the RAG sandbox feature, which allows users to interactively test and fine-tune their vector indexes with different prompts and LLM settings before deploying them. Know more in this step-by-step video: https://lnkd.in/gwEgjzTX

The agentic design pattern is introduced as a solution for making LLMs more autonomous. Instead of just giving the model one prompt and expecting a final answer, an agent-like approach involves prompting the LLM multiple times, step by step. Each step refines the task, with the model improving its output iteratively. Agentic Design Patterns emphasize the transformative potential of agentic workflows in making AI models, particularly?large language models (LLMs), more autonomous and efficient. Know more about designing agentic AI systems: https://lnkd.in/duVUgbpB

Parsing PDFs and documents in RAG is essential for extracting meaningful information that enhances retrieval and generation accuracy. Many valuable insights are locked in unstructured formats, requiring efficient text extraction and structuring. Proper parsing ensures that text, tables, and metadata are accurately captured, improving searchability and relevance. Chunking techniques help break large documents into retrievable units, optimizing context retrieval for language models. Additionally, handling scanned documents with OCR ensures no information is lost. Effective parsing bridges the gap between raw data and intelligent retrieval, making AI-powered systems more reliable for answering domain-specific queries. Here is the best way to parse PDF in RAG (2025): https://lnkd.in/eGze49Zi

In RAG, "chunking" refers to?the process of dividing large documents into smaller, manageable segments called "chunks," which allows the AI model to more efficiently search and retrieve relevant information by focusing on smaller, contextually cohesive pieces of text instead of processing the entire document at once;?essentially, it breaks down complex information into easily digestible units for better understanding and response generation. How chunking works in RAG: - Dividing text: Documents are split into chunks based on various strategies, including splitting at paragraph breaks, sentence boundaries, or even using semantic analysis to identify logical topic shifts within the text. - Embedding creation: Each chunk is then converted into a vector embedding, which represents the semantic meaning of the text within that chunk. - Query comparison: When a user asks a question, the query is also transformed into a vector embedding and compared to the embeddings of all the chunks in the document database. - Relevance ranking: The system identifies the chunks with the highest similarity to the query, meaning they are most likely to contain the relevant information. - Response generation: The retrieved chunks are then used by the language model to generate a comprehensive response to the query, drawing from the most relevant information across the selected chunks. Know more about chunking strategies: https://lnkd.in/eDEQqMuR See how to chunk and Vectorize text in 5 minutes: https://lnkd.in/ejnG6VEP

Do you need a database solution for your AI app? Here are 10 vector databases that are revolutionizing machine learning and similarity search. In today’s highly digital world, we generate tons of data daily?—?over 3.5 quintillion bytes, to be?more precise. To make sense of all this data and glean meaningful insights from it, we need a way to efficiently search and analyze vast amounts of information. Whether it’s finding similar images, recommending products, or understanding complex patterns in high-dimensional data, the importance of advanced database systems cannot be understated. This is where vector databases shine. They provide?an effective and efficient solution?for storing and retrieving vector data quickly and accurately. In this article, we’ll explore the world of vector databases and look at the 10 best contenders revolutionizing machine learning and similarity search. In addition, we’ll tackle open source vector databases in particular. https://lnkd.in/gF6bchDM

RAG systems integrate a retrieval mechanism with a generation model to enhance the quality and relevance of generated content. By retrieving documents from a data repository and embedding them into the context of a user query, RAG ensures that responses are grounded in relevant, factual information. However, the success of a RAG system heavily depends on how well the retrieval and generation components work together. This is where RAG evaluation becomes essential. Evaluating different retrieval and generation strategies before building a RAG pipeline helps you avoid common pitfalls such as hallucinations (where the system generates incorrect or irrelevant information). RAG evaluation is critical in identifying the best-performing strategy that will yield the most accurate, contextually relevant results for your data. Why is RAG Evaluation Critical? Building a RAG pipeline without prior evaluation can lead to poor performance, resulting in unreliable outputs, wasted resources, and a system that doesn’t meet business goals. Here’s why RAG evaluation should be the first step in optimizing your RAG system: https://lnkd.in/g_4_jGjd Image credits: Chandan Durgia

Designing Agentic AI Systems: Data Retrieval and Agentic RAG! Modern vector databases and semantic search techniques have paved the way for unstructured data retrieval at scale. These systems convert text into high-dimensional vector embeddings, allowing for similarity-based lookups. A user or agent query is also converted into a vector, and the closest vectors in the database (i.e., the most contextually similar text chunks) are retrieved. Large documents are split into smaller “chunks,” each separately indexed. This allows your retriever to retrieve only the relevant chunk(s), rather than stuffing the entire document into the prompt. Many vector DBs let you attach metadata (e.g., author, date, document type) to each chunk. This metadata can guide downstream logic—for example, retrieving only the latest product manuals or knowledge base articles relevant to the user’s issue. In high-traffic scenarios or where data quality is critical (e.g., legal or medical use cases), you can apply additional filters and ranking criteria after the semantic search to ensure only top-quality or domain-approved content is returned. Know more about designing Agentic AI Systems: https://lnkd.in/gaMgJmEY