SearchStax, a cloud search platform enabling web teams to deliver search in an easy and cost-effective way, announced the launch of a new program, SearchStax for Good, that provides web and mobile development teams a frictionless way to simplify the management of Apache Solr workloads in the cloud.
SearchStax for Good is designed specifically for non-profits to help eliminate both the infrastructure management problem, as well as to remove the high barrier to entry from a budgetary perspective. By offering an extended no-cost period of full-featured service, SearchStax for Good enables qualifying non-profits a way to get search infrastructure up and running immediately, without having to figure out how to re-allocate budget, or needing to first get budget approval.
Upon the initial launch of SearchStax for Good at DrupalCon Pittsburgh 2023, SearchStax for Good will offer non-profit organizations 6 months free of SearchStax Cloud Serverless, a solution that delivers fast, scalable, and cost-effective Solr, thereby giving web and product teams the ability to build quickly and scale automatically while optimizing resource utilization. After the initial six-month period ends, participating organizations can continue using the service at a 40% discounted rate.
From the Snowflake blog…
Search is fundamental to how businesses interact with data, and the search experience is evolving rapidly with new conversational paradigms emerging in the way we ask questions and retrieve information, enabled by generative AI. The ability for teams to discover precisely the right data point, data asset, or data insight is critical to maximizing the value of data.
That’s why Snowflake is acquiring Neeva, a search company founded to make search even more intelligent at scale. Neeva created a unique and transformative search experience that leverages generative AI and other innovations to allow users to query and discover data in new ways.
We plan to infuse and leverage these innovations across the Data Cloud to the benefit of our customers, partners and developers. Neeva allows us to tap into some of the most cutting-edge search technologies available to bring search and conversation in Snowflake to a new level.
As part of the acquisition, we are joined by some of the brightest minds working in search today. Neeva’s leadership and team members have been instrumental in the creation of numerous successful products like Google’s search advertising and YouTube monetization.
Elastic announced the launch of the Elasticsearch Relevance Engine (ESRE), with built-in vector search and transformer models, which is designed to bring AI innovation to proprietary enterprise data. ESRE enables companies create secure deployments to take advantage of all their proprietary structured and unstructured data.
Elastic has made investments in foundational AI capabilities to democratize AI and machine learning for developers with a Unified APIs for vector search, BM25f search and hybrid search, plus a transformer model small enough to fit on a laptop’s memory.
Using a relevance engine, like ESRE, allows companies to take advantage of all of their structured and unstructured data to build custom generative AI (GAI) apps, without having to worry about the size and cost of running large language models. The ability to “bring your own” transformer model and integrate with third-party transformer models allows organizations to create secure deployments that leverage GAI on their specific business data. With ESRE, the companies and community of users that have invested in Elastic solutions can advance AI initiatives right now without a lot of additional resources.
Docugami, a document engineering company that transforms how businesses create and execute critical business documents, announced an initial integration of LlamaIndex with Docugami, via the Llama Hub.
The LlamaIndex framework provides a flexible interface between a user’s information and Large Language Models (LLMs). Coupling LlamaIndex with Docugami’s ability to generate a Document XML Knowledge Graph representation of long-form Business Documents opens opportunities for LlamaIndex developers to build LLM applications that connect users to their own Business Documents, without being limited by document size or context window restrictions.
General purpose LLMs alone cannot deliver the accuracy needed for business, financial, legal, and scientific settings because they are trained on the public internet, which introduces a wide range of irrelevant and low-quality source materials. By contrast, Docugami is trained exclusively for business scenarios, for greater accuracy and reliability.
Systems aiming to understand the content of documents, such as retrieval and question-answering, will benefit from Docugami’s semantic Document XML Knowledge Graph Representation. Our unique approach to document chunking allows for better understanding and processing of your documents
AI-powered search provider Sinequa has announced domain-specific enhancements to its intelligent search platform for Scientific Search and Clinical Trial Data. Its search platform now utilizes new Neural Search and ChatGPT capabilities for faster, more effective discovery and decisions in drug development and clinical research. Sinequa will present these capabilities at the 2023 Bio-IT World Conference, May 16-18, at the Boston Convention and Exhibition Center, during conference sessions and at booth #803 in Auditorium Hall C.
Combining the capabilities of Sinequa Neural Search – multiple deep learning and large language models for natural language understanding (NLU) – with the latest ChatGPT models through Azure OpenAI Service, Sinequa enables accurate, fast, traceable semantic search, insight generation, and summarization. Users can query and converse with a secure corpus of data, including proprietary life science systems, enterprise collaboration systems, and external data sources, to answer complex and nuanced questions. Comprehensive search results with high relevance and the ability to generate concise summaries enhance R&D intelligence, optimize clinical trials, and streamline regulatory workflows.
Algolia launched Algolia NeuralSearch, their next-generation vector and keyword search in a single API. Algolia NeuralSearch understands natural language and delivers results in milliseconds. Algolia NeuralSearch uses Large Language Models (LLM) and goes further with Algolia’s Neural Hashing for hyper-scale, and constantly learns from user interactions.
Algolia NeuralSearch analyzes the relationships between words and concepts, generating vector representations that capture their meaning. Because vector-based understanding and retrieval is combined with Algolia’s full-text keyword engine, it works for exact matching too. Algolia NeuralSearch addresses the limitation in neural search to scale with their Neural Hashing, which compresses search vectors.
Algolia incorporates AI across three primary functions: query understanding, query retrieval, and ranking of results.
- Query understanding – Algolia’s advanced natural language understanding (NLU) and AI-driven vector search provide free-form natural language expression understanding and AI-powered query categorization that prepares and structures a query for analysis. Adaptive Learning based on user feedback fine-tunes intent understanding.
- Retrieval – The retrieval process merges the Neural Hashing results in parallel with keywords using the same index for easy retrieval and ranking.
- Ranking – The best results are pushed to the top by Algolia’s AI-powered Re-ranking, which takes into account the many signals attached to the search query.
Algolia, a AI Search and Discovery platform, evolved its pricing and packaging to be more developer-friendly with the introduction of two new developer-oriented plans: a “Build” plan that is free and a “Grow” plan that offers easy scalability at affordable prices. The new Build plan increases the number of free records that a developer can store in Algolia from 10,000 to now 1 million records. Additionally, Algolia cut the cost of search requests in its Grow plan by 50% and records by 60%.
Algolia’s “Build” pricing plan provides developers with free access to the entire set of capabilities in its AI-powered Search and Discovery platform. The company’s “Grow” plan, for when a developer is ready to scale their application, enables developers with more developer-friendly usage-based pricing for live production settings.
A designer, creator, or builder, whether they are a casual or fully committed software engineer, can access all the tools, documentation, sample code, educational content, and cross-platform integration capabilities needed to get started with managing their data, building a search front-end, configuring analytics for free. They will have access to a developer community of more than 5 million builders. Algolia pricing and packaging reflecting this change is immediately available.
Slang Labs, a Google-backed startup from Bengaluru, announced the launch of CONVA, a full-stack solution that provides smart and highly accurate multilingual voice search capabilities inside e-commerce apps. CONVA is available as a simple SDK (Software Development Kit) that can be integrated into existing e-commerce apps in less than 30 minutes without developers needing any knowledge of Automatic Speech Recognition (ASR), Natural language processing (NLP), Text-to-Speech (TTS) and other advanced voice tech stack concepts.
CONVA-powered voice search comprehends mixed-code (multiple languages in one sentence) utterances, enabling consumers to speak naturally in their own language in order to search for products and information inside e-commerce mobile and web apps – while allowing the brand to maintain its app backend in only one language i.e. English. For instance, when people use English and another vernacular language within the same sentence for searching for something, CONVA will understand both languages and provide a seamless search experience to the consumer.
Customers can search for products inside the applications using their typical colloquial terms for well-known products using voice search that is enabled by CONVA, and the apps will still be able to recognise the correct product being searched.