Pinecone

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Pinecone is the vector database that supercharges AI applications with efficient and scalable vector search capabilities, enabling businesses to build and deploy knowledgeable AI applications faster and at significantly lower costs.

How Pinecone can help you:

Enhance search capabilities with low-latency vector search for diverse applications like recommendation systems, detection, and more.
Improve application relevance by combining vector search with metadata filters.
Foster real-time data updating for the freshest and most accurate results.
Optimize search results by integrating vector search with keyword boosting.

Why choose Pinecone: Key features

Serverless architecture eliminates the need to manage or scale the database infrastructure.
Start and scale seamlessly, from a few vector embeddings to billions, within seconds.
Up to 50x lower cost than traditional methods.
High recall rate and low query latency for efficient and effective search results.

Who should choose Pinecone:

Developers and data scientists looking to enhance their AI applications with advanced search capabilities.
Businesses aiming to deploy knowledgeable AI applications rapidly and cost-effectively.
Organizations that require scalable, efficient, and precise search functionalities within their digital products.

About Pinecone

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https://www.pinecone.io

Release Date

March 2024

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Enhancing Query Performance With AI - DZone

In today's data-driven world, conventional search approaches frequently struggle with intricate queries and delivering pertinent results. The advent of AI-powered vector search presents a game-changing query efficiency and precision advancement. Azure Cosmos DB for PostgreSQL emerges as a robust platform that embraces AI-driven vector search, empowering users to elevate their query capabilities significantly. Vector search is an advanced data retrieval method beyond traditional keyword-based search techniques. Every data type, including text, image, or audio, is converted into a vector. This captures the data's inherent qualities and context. Using AI models like BERT for text or convolutional neural networks for images, raw data is transformed into vectors that capture intrinsic properties and context. This method excels at handling unstructured data and delivers more accurate results by mapping similar data points close to each other in the vector space. Azure Cosmos DB for PostgreSQL is an ideal platform for implementing vector search due to its scalability, flexibility, and robust support for AI and machine learning integrations. It enables efficient and scalable data retrieval across various applications. Create an Azure Cosmos DB account. This process is straightforward and begins with logging in to the Azure Portal. Once logged in, follow these steps: Finish the configuration of the PostgreSQL database to set up your database and tables. This configuration will provide the foundation for storing vectors and performing efficient searches. Follow the steps below to prepare your PostgreSQL database for vector search implementation. In this example, we'll use a pre-trained BERT model for text data to convert it into vectors. Ensure you have installed Python and the necessary libraries. Create a virtual environment and install the required packages: To leverage the power of BERT for transforming text data into vectors, follow these steps: The above code snippet can convert text data into dense vector representations using the BERT model. These representations can then be stored and utilized for vector search in your PostgreSQL database. The next step is to store vectors in our PostgreSQL database. Storing vectors allows us to efficiently perform similarity searches and leverage the power of vector search for enhanced query performance. This process involves connecting to the PostgreSQL database, inserting the vectors along with the original text data, and ensuring the database is set up to handle and query these vector representations effectively. To store the encoded vectors in your PostgreSQL database, follow these steps Establish a connection to your PostgreSQL database. Create a table to store texts and their corresponding vectors. Convert text data into vectors and insert them into the PostgreSQL table. The next step is to efficiently perform vector-based searches. Follow these steps to connect to your PostgreSQL database and store the vectors. As mentioned in the code snippet below, define a function to calculate the cosine similarity between vectors. This will help determine the similarity between the query and stored vectors. Fetch vectors from the database and compute similarity: This function encodes the query text and retrieves stored vectors from the database. It also calculates cosine similarities and returns the top N most similar vectors. Follow the approaches below to enhance vector search performance in the PostgreSQL database. To index vectors, use appropriate indexing methods, such as GiST (Generalized Search Tree) or SP-GiST (Space-partitioned Generalized Search Tree). Batch processing reduces the overhead of inserting vectors one by one and improves overall performance. Data is inserted in batches to minimize database round trips. GPUs can significantly speed up the computation of vector transformations and similarity calculations. Use libraries such as PyTorch that support GPU acceleration. Caching frequently queried data reduces the need for repeated computations and database access. Use caching mechanisms like Redis or built-in PostgreSQL caching. For more efficient similarity search, especially with large datasets, consider using Facebook's Faiss library, which is optimized for vector similarity search. Use these strategies to enhance the performance and efficiency of your vector search operations in Azure Cosmos DB for PostgreSQL. Integrating AI models can significantly enhance query performance and achieve more accurate search results. This approach is especially beneficial for handling unstructured data and complex queries. With the steps and code snippets this blog provides, enterprises can implement these advanced search capabilities in their projects.

dzone.com

Mon, 29 Jul, 2:05 PM UTC

Vector database startup Weaviate debuts 'AI workbench' and flexible storage tiers - SiliconANGLE

Vector database startup Weaviate debuts 'AI workbench' and flexible storage tiers AI-native vector database startup Weaviate B.V. is expanding into artificial intelligence development with the launch of a new "workbench" that consists of various cloud-based applications and tools for developers, along with a flexible, tiered storage offering. The company said it was inspired to launch its own suite of tools by the vibrant community of developers that has come to rely on its open-source database to store billions of essential "vectors" for AI models. The new tools are meant to help developers accelerate AI-native application development, while its ready-made apps are like white-label versions of common AI use cases. Weaviate, which last hit the headlines in April 2023 when it raised $50 million in funding, is the creator of an open-source vector database that's geared to AI development. Its database caters to the demand for unstructured data, which is essential for powering generative AI models such as ChatGPT. It stores this unstructured information as vectors, which are mathematical structures that represent everything from documents to purchase logs, to images and audio files. By storing the data as vectors, it's much easier for AI models to understand and process it. The startup says its database groups the records it stores based on their similarity. If the database receives two sets of academic papers that discuss processors and memory chips, respectively, it can organize them by topic into two separate collections. According to the startup, its database groups records by similarity using an approach that draws on the field of geometry. Each file is represented as a point in space, with those representing similar files placed close to one another, and dissimilar data points placed farther apart. This capability is essential for many AI applications. For instance, an AI-based cybersecurity tools can check if a new file is malicious by comparing it to known malware types. It can also aid in search and data classification tasks. Weaviate says its new AI workbench, available now within the Weaviate Cloud Console, includes a Query tool for querying data in its cloud using a GraphQL interface, and a Collections tool for creating and managing collections without needing to write any code. There's also a new Explorer tool, which developers can use to search and validate object data through a graphical user interface. The first application is the Weaviate Recommender app, which developers can use to rapidly build and scale customized recommendation systems. It provides configurable endpoints for item-to-item, item-to-user and user-to-user recommendation scenarios, with support for image, text, audio and other multimodal data types, the company said. Alongside its workbench, Weaviate has created a dedicated "labs" division that's focused on testing "daring ideas" and transforming them into new products to be offered alongside its flagship vector database. The Weaviate Labs team is currently developing a new application that will enable developers to quickly deploy production-ready "generative feedback loops" that can help to boost the intelligence of AI agents. Weaviate's new tiered storage offering might be even more interesting for AI developers, enabling applications that leverage its database to be optimized for speed, cost and performance, the startup says. Using the service, developers can choose between "hot" storage for high performance read/write data access in real time, "warm" for more balanced access of data that's used less frequently, and "cold," which provides the most cost-effective long-term storage of data that's accessed infrequently. The startup believes this flexibility is important to developers. For instance, a low-latency AI app that's closely tied to revenue, such as a e-commerce search and recommendation engine, can tap Weviate's hot storage tier to ensure the best performance, while applications with higher latency tolerance, such as a chatbot, can leverage cold storage to scale more efficiently. The startup claims it's one of the first dedicated vector databases to offer this kind of flexibility. It says most of its rivals' offerings treat all data as "hot," providing rapid access at high prices. Weviate believes developers need greater flexibility, so they can scale their AI applications based on how often they're being used, and how much revenue they generate. Co-founder and Chief Executive Bob van Luijt said the AI-native stack needs to evolve so that organizations can build AI applications more rapidly and deploy them at lower costs. "Listening to our community, it's clear that to take the next step, developers need an AI-native framework with flexible storage tiers, modular GUI tools to interact with their data, and a pipeline of new concepts to spark their creativity," Van Luijt said.

SiliconANGLE

Tue, 30 Jul, 2:03 PM UTC

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.com

Wed, 31 Jul, 2:03 PM UTC

SingleStore Kai Support for MongoDB $vectorSearch - DZone

SingleStore is a high-performance distributed SQL database and through SingleStore Kai extends its compatibility to MongoDB's vector search capabilities. By supporting MongoDB's operator, SingleStore Kai provides efficient querying and indexing of vector data, such as embeddings used in ML and AI applications. Users can benefit from SingleStore's optimized data handling and Kai's vector search capabilities to perform similarity searches across large datasets. In this article, we'll see an example of how to use SingleStore Kai with vector data. A previous article showed the steps to create a free SingleStoreDB Cloud account. We'll use the following settings: From Deployments > Firewall, we'll temporarily allow access from anywhere. We'll download the notebook from GitHub (linked in the opening section). From the left navigation pane in the SingleStore cloud portal, we'll select DEVELOP > Data Studio. In the top right of the web page, we'll select New Notebook > Import From File. We'll use the wizard to locate and import the notebook we downloaded from GitHub. After checking that we are connected to our SingleStore workspace, we'll run the cells one by one. In the database, we'll store the Iris flower data set. We'll first download the Iris CSV file into a Pandas Dataframe and then convert it into two columns, as follows: and get the number of dimensions for the vector column: This avoids the need to provide a long connection string. And we'll retrieve a few rows to confirm the data have been stored: Finally, let's use some fictitious data values to make a prediction: In this article, we've seen how to use with SingleStore Kai. Comparing the results we obtained running similar queries in a previous article, we can see that the results are the same. For larger datasets, the results may differ due to the vector indexing.

dzone.com

Wed, 31 Jul, 6:03 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

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