Vectorize offers tools for turning unstructured data into optimized vector search indexes for retrieval augmented generation, allowing the integration of various data sources into AI applications.

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Jamie Ferguson

Web Architect and Digital Accessibility Advocate

Scott Davis

Co-founder at vectorize.io. Transforming unstructured data into vectors to power AI RAG apps.

Chris Bartholomew

Founder of Vectorize.io 🚀 | Sharing Insights on LLMs, RAG, and AI Agents. | Follow me for tips on building production-ready Gen AI systems using…

Chris Latimer

This is the third #multimodal #RAG pattern discussed in our article👇 Pattern 3: Text embeddings with a raw media pointer stored as metadata. If you want to avoid direct object storage in your vector database, you can instead host your images or other media in object storage. This gives you an inexpensive way of scaling your media independently of your vector database. For situations where your users are globally distributed and you need to serve images or other media in your application, a CDN can be another good option. When you submit your prompt to the LLM, you can then include the URL of the relevant media. If your LLM is producing a result such as HTML or Markdown, it can include image tags or other embedded media in its response. Know all the discussed patterns in our article: https://lnkd.in/gUTD7CpS

This is the second #multimodal #RAG pattern discussed in our article👇 Multimodal embeddings and media storage in your vector database. With this we can use a multimodal embedding model to generate a vector representation of our non-text data directly. For example, models like Amazon Titan will take JPG or PNG images and generate embeddings in the same semantic vector space as text. Depending on your database, storing media like images in your database may or may not be a recommended pattern. Relational databases which support binary data types such as BLOBs can suitable for image storage in simple use cases. As your requirements scale to include high volumes of multimodal data, the prospect of scaling your persistence engine to support file storage is likely a poor tradeoff. Likewise, as your requirements require scaling the number of users, fetching images can become a bottleneck. In these situations, you may want to consider our next pattern. The other patterns are discussed in our article here: https://lnkd.in/gUTD7CpS

Real-time AI just got easier: Vectorize now integrates with SingleStore! ⚡️ By combining Vectorize’s flexible RAG pipelines with SingleStore’s high-performance vector search and real-time processing, building intelligent data pipelines that adapt as your data changes is now simpler than ever. Whether you're pulling data from cloud storage, web crawlers, or specialized platforms, Vectorize’s source connectors handle everything from extraction to building vector search indexes. Learn more about how this integration can help you build faster, smarter AI applications: https://lnkd.in/gqt6bvJ4 #AI #Vectorize #SingleStore #RAG

Building a Contextual Retrieval System for Improving #RAG Accuracy. To enhance AI models for specific tasks, they require domain-specific knowledge. For instance, customer support chatbots need business-related information, while legal bots rely on historical case data. Developers commonly use Retrieval-Augmented Generation (RAG) to fetch relevant knowledge from a database and improve AI responses. However, traditional RAG approaches often miss context during retrieval, leading to failures. In this post, we introduce "Contextual Retrieval," a method using Contextual Embeddings to improve retrieval accuracy, cutting failures with reranking. The challenge with traditional RAG lies in how documents are split into smaller chunks for efficient retrieval, sometimes losing important context. For instance, consider an academic database where you're asked, "What was Dr. Smith's primary research focus in 2021?" If a retrieved chunk states, "The research emphasized AI," it might lack clarity without specifying Dr. Smith or the exact year, making it hard to pinpoint the answer. This issue can reduce the accuracy and utility of retrieval results in such knowledge-heavy domains. Contextual Retrieval solves this problem by prepending chunk-specific explanatory context to each chunk before embedding (“Contextual Embeddings”). We will generate contextual text for each chunk. Know more about Contextual Retrieval in RAG: https://lnkd.in/dTbjssrp

#FunctionCalling with #LLMs is like giving your AI the power to connect with the world. By integrating your LLM with external tools such as user-defined functions or APIs, you can build applications that solve real-world problems. In this blog post, we’ll check how to integrate Llama 3.1 with external tools like Milvus and APIs to build powerful, context-aware applications. Introduction to Function Calling: LLMs like GPT-4, Mistral Nemo, and Llama 3.1 can now detect when they need to call a function and then output JSON with arguments to call that function. This makes your AI applications more versatile and powerful. Functional calling enables developers to create: - LLM-powered solutions for extracting and tagging data (e.g., extracting people’s names from a Wikipedia article) - applications that can help convert natural language to API calls or valid database queries - conversational knowledge retrieval engines that interact with a knowledge base The tools - Ollama: Brings the power of LLMs to your laptop, simplifying local operation. - Milvus: Our go-to vector database for efficient data storage and retrieval. - Llama 3.1–8B: The upgraded version of the 8B model is multilingual and has a significantly longer context length of 128K, and can leverage tool use. Know more in this complete article: https://lnkd.in/d2vNF6ts

5 Chunking Strategies For #RAG ! 1) Fixed-size chunking: The most intuitive and straightforward way to generate chunks is by splitting the text into uniform segments based on a pre-defined number of characters, words, or tokens. 2) Semantic chunking: The idea is simple. Segment the document based on meaningful units like sentences, paragraphs, or thematic sections. Next, create embeddings for each segment. 3) Recursive chunking: This is also simple. First, chunk based on inherent separators like paragraphs, or sections. Next, split each chunk into smaller chunks if the size exceeds a pre-defined chunk size limit. If, however, the chunk fits the chunk-size limit, no further splitting is done. 4) Document structure-based chunking: This is another intuitive approach. It utilizes the inherent structure of documents, like headings, sections, or paragraphs, to define chunk boundaries. This way, it maintains structural integrity by aligning with the document’s logical sections. 5) LLM-based chunking: Since every approach has upsides and downsides, why not use the LLM to create chunks? The LLM can be prompted to generate semantically isolated and meaningful chunks. Quite evidently, this method will ensure high semantic accuracy since the LLM can understand context and meaning beyond simple heuristics (used in the above four approaches). Know more: https://lnkd.in/diHHmd98 Now, try building RAG pipelines in minutes using Vectorize: https://vectorize.io

The multimodal #RAG design patterns you all should know. Just like any software development project, the more we can leverage established design patterns, the faster we can deliver high quality software. Multimodal AI applications are no different. Let’s start by looking at the simplest pattern we can use. Pattern 1: Embedding text descriptions of non-text data types: For cases where you don’t require direct multimodal retrieval in your application, you can often rely on text-to-image or a multimodal LLM to describe the contents of your multimodal data. Once you have the textual representation, you can load it into your vector database just like you would pure text data. Standard text embedding models will generate vector representations, and you can use kNN queries to find the most similar context using cosine similarity or another similarity metric. Know more about the other multimodal RAG patterns: https://lnkd.in/gUTD7CpS

Building Fault-Tolerant #RAG Pipelines: Strategies for Dealing with API Failures. Retrieval Augmented Generation (RAG) pipelines are one of many key elements in the AI realm. What they do is transform unstructured data into vector indexes that are searchable. If you peel back one of the layers, you’ll see that these pipelines rely on external data sources and APIs. As such, those RAG pipelines may face potential API failures that can result in poor performance and reliability. For this reason, we will be discussing how to build a fault-tolerant RAG pipeline. This way, if APIs fail, the pipelines themselves can still remain strong. Let’s begin with what you need to know. Know more in the article: https://lnkd.in/gMKdhSH8

Types of multimodal #RAG systems. Let's talk about the systems that need information about multimodal data. Depending on the type of system you’re building, you may or may not need direct access to multimodal data types like images. For instance, let’s look at this multimodal RAG system focused on real estate listings. Instead of having real estate agents spend time writing descriptions of a property and manually entering data attributes about the listing, the system would look at a set of photos of a property and generate the information needed for a listing. In this case, the developer had built a search index of real estate listings. They were generating a text description of images using a model on Google Vertex. They were then retrieving similar listings that the agency had published from their vector database. They passed the listing information, along with the images, to a multimodal LLM to generate a listing based on the images and past listings. In several other projects, we were working with images that were created by scanning paper documents. In these cases, we needed to use OCR to extract the useful text from the image, but no longer needed the raw image beyond that. Know more about the other multimodal RAG patterns in this article: https://lnkd.in/gUTD7CpS

Vectorize now integrates with Elastic search vector database! Elasticsearch’s vector database is built for fast, real-time search and retrieval of vector data. Vectorize automates the creation and management of vector indexes, and keeps those indexes up-to-date so your large language model (LLM) provides accurate results. The combination of Elasticsearch and Vectorize gives you optimal performance without the burden of manual data preparation, and reduces the amount of time needed to build your AI applications. With this integration, you can automate vector index creation and management, ensuring your AI applications deliver accurate, up-to-date results faster than ever. Know more & get started with Elastic cloud and Vectorize: https://lnkd.in/g-VUquUM

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