Weaviate

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Weaviate is an open-source, AI-native vector database designed to scale and improve AI application development.

Weaviate Image

Weaviate Image

How Weaviate can help you:

Facilitates the creation of intuitive and reliable AI-powered applications.
Enriches datasets with content generated by Large Language Models (LLMs), reducing manual data cleaning efforts.
Combines keyword and vector search with Machine Learning (ML) models for faster, more relevant, and contextual search results.
Improves the accuracy and reliability of generative AI applications using proprietary data.

Why choose Weaviate: Key features

Open-source platform with strong community support.
Specialized in hybrid search, combining the best of vector and keyword search.
Includes built-in modules for popular ML models and frameworks for ease of use.
Scalable multi-tenant architecture supports confident, efficient scaling.
Flexible deployment options to meet business needs, including as an open-source platform, a managed service, or within a VPC.

Who should choose Weaviate:

Developers of all levels looking to build production-ready AI applications.
Teams seeking to enrich and clean their datasets effectively.
Organizations needing scalable, efficient search capabilities across unstructured data.
Businesses aiming for secure, explainable, and reliable AI application deployments.

About Weaviate

Website

https://weaviate.io

Release Date

March 2024

Pricing

Contact for Pricing

Related fields

Categories

Large language model, Recommendation system, Engineering, Natural language processing

Related News

Multimodal Search in AI Apps: PostgreSQL pgvector - DZone

Large language models (LLMs) have significantly evolved beyond generating text responses to text prompts. These models are now trained to possess advanced capabilities like interpreting images and providing detailed descriptions from visual inputs. This gives users an even greater search capacity. In this article, I'll demonstrate how to build an application with multimodal search functionality. Users of this application can upload an image or provide text input that allows them to search a database of Indian recipes. The application is built to work with multiple LLM providers, allowing users to choose between OpenAI, or a model running locally with Ollama. Text embeddings are then stored and queried in PostgreSQL using pgvector. To check out the full source code, with instructions for building and running this application, visit the sample app on GitHub. A full walkthrough of the application and its architecture is also available on YouTube: Before diving into the code, let's outline the role that each component plays in building a multimodal search application. Now that we have an understanding of the application architecture and foundational components, let's put the pieces together! This project provides utility functions to generate embeddings from a provider of your choice. Let's walk through the steps required to generate and store text embeddings. The cuisines.csv file holding the original dataset is read and stored in a Pandas DataFrame to allow for manipulation. The description of each recipe is passed to the function to populate new column embeddings in the DataFrame. This data is then written to a new output.csv file, containing embeddings for similarity search. With a PostgreSQL instance storing vector embeddings for recipe descriptions, we're ready to run the application and execute queries. Let's connect the application to the database to begin executing queries on the recipe description embeddings. The search endpoint accepts both text and an image via a multipart form. While this API is pretty straightforward, there are two helper functions of interest: and . Let's look at how these work in more detail. The function takes an image file path and sends a base64 encoding to the user's preferred LLM. For simplicity, the app currently supports models running locally in Ollama, or those available via OpenAI. This base64 image representation is accompanied by a custom prompt, telling the LLM to act as an expert in Indian cuisine in order to accurately describe the uploaded image. When working with LLMs, clear prompt construction is crucial to yield the desired results. A short description of the image is returned from the function, which can then be passed to the function to generate a vector representation to store in the database. The function relies on a different class of models in the AI ecosystem, which generate a vector embedding from text. These models are also readily available via Ollama and OpenAI, returning 768 and 1536 dimensions respectively. By generating an embedding of each image description returned from the LLM (as well as optionally providing additional text via the form input), the API endpoint can query using cosine distance in pgvector to provide accurate results. By connecting the UI and searching via an image and short text description, the application can leverage pgvector to execute a similarity search on the dataset. Let's explore how we can leverage distributed SQL to make our applications even more scalable and resilient. Here are some key reasons that AI applications using pgvector benefit from distributed PostgreSQL databases: YugabyteDB, a distributed SQL database built on PostgreSQL, is feature and runtime-compatible with Postgres. It allows you to reuse the libraries, drivers, tools, and frameworks created for the standard version of Postgres. YugabyteDB has pgvector compatibility and provides all of the functionality found in native PostgreSQL. This makes it ideal for those looking to level up their AI applications. Using the latest multimodal models in the AI ecosystem makes adding image search to applications a breeze. This simple, but powerful application shows just how easily Postgres-backed applications can support the latest and greatest AI functionality.

DZone

Wed, 31 Jul, 2:03 PM UTC

Hyperspace is building custom instances to accelerate database searches | TechCrunch

The growth in the demand for generative AI apps has led to a need for larger and larger databases to store the associated data (e.g. model training data). These databases tend to be resource-intensive from a hardware perspective and depending on the algorithms used to orchestrate them, they can be high-latency. Often, companies are forced to make trade-offs between database cost, performance and accuracy. But it doesn't have to be this way, says Ohad Levi, the CEO and co-founder of Hyperspace. Hyperspace is building custom cloud instances to accelerate two specific database tasks: lexical searches and vector searches. Lexical searches are a type of keyword-based search that look for exact matches in a database, whereas vector searches consider the semantic meaning and context of the search query. Levi claims that Hyperspace's instances, which leverage a combination of FPGAs and GPUs, can deliver up to 10 times faster searches than traditional, non-accelerated databases. "Our product helps companies dealing with large-scale data retrieval, particularly in AI and generative AI applications," Levi told TechCrunch. "Unstructured data is outpacing traditional search capabilities. Data retrieval solutions must meet lexical and vector search datasets to meet current market demands." Prior to launching Hyperspace, Levi was an optimization engineer at Intel and then a product marketing lead at HP. He says he became frustrated with the limitations of legacy search solutions working for Big Tech, which led him to partner with ex-Intel design consultant Max Nigiri to found Hyperspace. Hyperspace doesn't sell its instances. Instead, it sells access to managed database software running in those instances (hosted on AWS for now). Hyperspace's databases can handle various types of structured and unstructured data, including videos, images and text, and are priced according to size and query volume. "Hyperspace is a cloud-native managed database that works as a software-as-a-service model, priced per usage," Levi explained. "Our team is able to design customized AI infrastructure solutions to help enterprises solve their search challenges." Hyperspace's performance gains are impressive if true; Levi says that the company's instances also deliver 5x higher throughput at a 50% lower cost than a typical database. (Those are average results; Levi declined to offer up a direct comparison with a competitor.) But can Hyperspace convince companies to use a newcomer database platform when there are so many incumbents -- like Azure, AWS and Google Cloud -- to choose from? Levi says yes, and he claims that Hyperspace is already seeing some early customer traction. The Tel Aviv-based firm has inked deals with enterprises in the fraud prevention and e-commerce spaces, including Forter, Nsure and Renovai, and tripled its annual recurring revenue and total contract volume over the last year. Hyperspace also recently closed a $9.5 million seed funding round led by Mizmaa with participation from JVP and toDay Ventures. Levi says that the money will be put toward scaling up Hyperspace's database offering to "thousands" of instances and launching a free, entry-level plan. "Hyperspace has an entire pipeline of new innovative products that will drive the search market forward and support the needs of our enterprise and small- and medium-sized clients," Levi said. "We're not seeing any headwinds. Every generative AI system is a search system, and search is becoming harder than before. The need for better AI infrastructure is growing daily, and with more data, the need for better search applications is becoming more apparent."

TechCrunch

Tue, 6 Aug, 2:05 PM UTC

MariaDB Vector Edition: Designed for AI - DZone

As a solutions architect with over two decades of experience in relational database systems, I recently started exploring MariaDB's new Vector Edition to see if it could address some of the AI data challenges we're facing. A quick look seemed pretty convincing, especially with how it could bring AI magic right into a regular database setup. However, I wanted to test it with a simple use case to see how it performs in practice. In this article, I will share my hands-on experience and observations about MariaDB's vector capabilities by running a simple use case. Specifically, I will be loading sample customer reviews into MariaDB and performing fast similarity searches to find related reviews. Now, create a table and load it with sample customer reviews that include sentiment scores and embeddings for each review. To generate text embeddings, I am using , which lets you use pre-trained models. To be specific, I decided to go with a model called paraphrase-MiniLM-L6-v2 that takes our customer reviews and maps them into a 384-dimensional space. Now, let's leverage MariaDB's vector capabilities to find similar reviews. This is more like asking "What other customers said similar to this review?". In the below example, I am going to find the top 2 reviews that are similar to a customer review that says "I am super satisfied!". To do this, I am using one of the vector functions () available in the latest release. Overall, I am impressed! MariaDB's Vector Edition is going to simplify certain AI-driven architectures. It bridges the gap between the traditional database world and the evolving demands of AI tools. In the coming months, I look forward to seeing how this technology matures and how the community adopts it in real-world applications.

DZone

Thu, 10 Oct, 8:08 PM UTC

Zilliz Unveils Major Upgrade to Cloud-Based Vector Database, Targeting Enterprise AI Efficiency

Zilliz, the company behind the open-source Milvus vector database, has announced significant updates to its Zilliz Cloud offering, aiming to reduce costs and complexity for enterprise AI deployments while improving performance.

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Wed, 20 Nov, 12:11 AM UTC

Pinecone Enhances Vector Database with Inference Capabilities and Cascading Retrieval, Boosting AI Accuracy by up to 48%

Pinecone introduces innovative features to its vector database, including inference capabilities and cascading retrieval, aiming to improve AI application development and accuracy. The update combines dense and sparse vector retrieval with reranking technologies.

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Tue, 3 Dec, 12:02 AM UTC

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