Best Semantic Search Software - Page 2

Improve your embedding metadata and embedding tokens with a user-friendly UI. Seamlessly apply advanced NLP cleansing techniques like TF-IDF, normalize, and enrich your embedding tokens, improving efficiency and accuracy in your LLM-related applications. Optimize the relevance of the content you get back from a vector database, intelligently splitting or merging the content based on its structure and adding void or hidden tokens, making chunks even more semantically coherent. Get full control over your data, effortlessly deploying Embedditor locally on your PC or in your dedicated enterprise cloud or on-premises environment. Applying Embedditor advanced cleansing techniques to filter out embedding irrelevant tokens like stop-words, punctuations, and low-relevant frequent words, you can save up to 40% on the cost of embedding and vector storage while getting better search results.

Copy and paste queries or type right into our search box and our AI technology takes care of the rest. Increases efficiency by 50-80% with accurate search results. JAQI® makes life easier by translating the way a buyer describes products into searchable terms. Your ecommerce customers or sales reps no longer need to do the "matching" to your catalog. Our search technology supports industrial materials with flexible customization options. Say goodbye to searching one line item at a time using inefficient drop-down menus or filters. Search 10, 20, or even 100+ line items simultaneously and add to your quote in seconds. JAQI API plug-in easily integrates into ecommerce applications such as websites & ERP systems to power fast and accurate search results for your customers. Our technology makes it faster & easier than ever before to find any product in any catalog in any format. JAQI powers your ecommerce website or ERP with AI search technology.

Combine semantic relevance and user feedback to reliably retrieve the optimal document chunks in your retrieval augmented generation system. Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate. Build a real-time personalized ecommerce product feed with user vectors constructed from SKU embeddings the user interacted with. Discover behavioral clusters of your customers using a vector index in your data warehouse. Describe and load your data, use spaces to construct your indices and run queries - all in-memory within a Python notebook.

Ducky is an AI search platform that lets teams add powerful search to their products in minutes. It handles the full AI search pipeline, eliminating the need to build and maintain complex infrastructure. The platform supports multimodal search across text, images, and PDFs with high accuracy. Automated chunking, ranking, and reranking ensure the most relevant results surface first. Advanced metadata filtering enables precise and flexible search experiences. Ducky improves automatically over time without manual training or tuning. It helps teams ship AI-powered features faster while reducing development and operational overhead.

Objective is a multimodal search API that works for you, not the other way around. Objective understands your data & your users, enabling natural and relevant results. Even when your data is inconsistent or incomplete. Objective understands human language, and ‘sees’ inside images. Your web & mobile app search can understand what users mean, and even relate that to the meaning it sees in images. Objective understands the relationships between huge text articles and the parts of content in each, letting you build context-rich text search experiences. Best-in-class search comes from layering all the best search techniques. It’s not about any single approach. It’s about a curated, tight top-to-bottom integration of all the best search & retrieval techniques in the world. Evaluate search results at scale. Anton is your evaluation copilot that can judge search results with near‑human precision, available in an on‑demand API.

Voyage AI provides best-in-class embedding models and rerankers designed to supercharge search and retrieval for unstructured data. Its technology powers high-quality Retrieval-Augmented Generation (RAG) by improving how relevant context is retrieved before responses are generated. Voyage AI offers general-purpose, domain-specific, and company-specific models to support a wide range of use cases. The models are optimized for accuracy, low latency, and reduced costs through shorter vector dimensions. With long-context support of up to 32K tokens, Voyage AI enables deeper understanding of complex documents. The platform is modular and integrates easily with any vector database or large language model. Voyage AI is trusted by industry leaders to deliver reliable, factual AI outputs at scale.

Natively store data for graph, document and search needs. Utilize feature-rich access with one query language. Map data natively to the database and access it with the best patterns for the job – traversals, joins, search, ranking, geospatial, aggregations – you name it. Polyglot persistence without the costs. Easily design, scale and adapt your architectures to changing needs and with much less effort. Combine the flexibility of JSON with semantic search and graph technology for next generation feature extraction even for large datasets.

Dgraph is an open source, low-latency, high throughput, native and distributed graph database. Designed to easily scale to meet the needs of small startups as well as large companies with massive amounts of data, DGraph can handle terabytes of structured data running on commodity hardware with low latency for real time user queries. It addresses business needs and uses cases involving diverse social and knowledge graphs, real-time recommendation engines, semantic search, pattern matching and fraud detection, serving relationship data, and serving web apps.

Document processing is complicated, but it doesn’t have to be. Introducing an intelligent document processing platform that understands what you’re trying to find, extract, categorize, and format. Infinia ML uses machine learning to quickly grasp content in context, understanding not just words and charts, but the relationships between them. Whether your goal is process automation, predictive insights, relationship understanding, or a semantic search engine, we can build it with our end-to-end machine learning capabilities. Use machine learning to make better business decisions. We customize your code to address your specific business challenge, surfacing untapped opportunities, revealing hidden insights, and generating accurate predictions to help you zero in on success. Our intelligent document processing solutions aren’t magic. They’re based on advanced technology and decades of applied experience.

Build a natural language interface for your data. NLP is at the core of modern enterprise data processing. We provide developers with the right tools to build production-ready NLP systems quickly and efficiently. Our open-source framework for scalable, API-driven NLP application architectures. We believe in sharing. Our software is open source. We value our community, and we make modern NLP easily accessible, practical, and scalable. Natural language processing (NLP) is a branch of AI that enables machines to process and interpret human language. In general, by implementing NLP, companies can leverage human language to interact with computers and data. Areas of NLP include semantic search, question answering (QA), conversational AI (chatbots), semantic search, text summarization, question generation, text generation, machine translation, text mining, speech recognition, to name a few use cases.

TopK is a serverless, cloud-native, document database built for powering search applications. It features native support for both vector search (vectors are simply another data type) and keyword search (BM25-style) in a single, unified system. With its powerful query expression language, TopK enables you to build reliable search applications (semantic search, RAG, multi-modal, you name it) without juggling multiple databases or services. Our unified retrieval engine will evolve to support document transformation (automatically generate embeddings), query understanding (parse metadata filters from user query), and adaptive ranking (provide more relevant results by sending “relevance feedback” back to TopK) under one unified roof.