# Vector Database

RAG Web UI is an intelligent dialogue system based on RAG technology that combines document retrieval with large language models to provide intelligent question-and-answer services based on knowledge bases for enterprises and individuals. The system employs a decoupled architecture, supporting smart management of various document formats (such as PDF, DOCX, Markdown, Text), including automatic chunking and vectorization. Its dialogue engine supports multi-turn dialogue and citation references, delivering accurate knowledge retrieval and generation services. The system also allows flexible switching between high-performance vector databases (such as ChromaDB and Qdrant), ensuring good scalability and performance optimization. As an open-source project, it offers developers a wealth of technical implementations and application scenarios, making it suitable for building enterprise-class knowledge management systems or intelligent customer service platforms.

Zilliz Cloud Serverless is a high-performance vector database service designed for GenAI applications, offering auto-scaling database capabilities where costs increase with business growth. The product utilizes a tiered storage system that combines DRAM, SSD, and object storage to automatically optimize data placement, ensuring quick access to active data while reducing costs for less frequently accessed information, all without manual management. With its cost-effectiveness, data portability, and auto-scaling capabilities, Zilliz Cloud Serverless provides a powerful solution for enterprises that need to handle large-scale vector data.

Lamatic.ai is a managed PaaS platform designed for building, testing, and deploying high-performance GenAI applications at the edge. It provides a low-code visual builder, VectorDB, and integrates applications and models. By incorporating various tools and technologies, it helps AI founders and builders quickly realize complex AI workflows. The platform's primary advantages include reduced back-and-forth communication among teams, automated workflows, increased deployment speeds, and lowered latency. Lamatic.ai is developed by a team of engineers and community members with deep understanding and extensive experience in GenAI application development. Its pricing model includes a monthly subscription service covering all available management integrations, vector databases, hosting, edge deployment, and SDK, alongside hourly billed professional services.

Vectorize is a platform focused on transforming unstructured data into optimized vector search indices, specifically designed for retrieval-augmented generation (RAG). By connecting various data sources such as content management systems, file systems, CRM, and collaboration tools, it helps users create productivity-enhancing assistant systems and innovative customer experiences. Key advantages of Vectorize include ease of use, rapid deployment, and high-accuracy search results, making it suitable for enterprises that need to handle large amounts of data and wish to quickly realize AI applications.

ChatPaper.ai is an online platform powered by AI technology, designed to help users interact with academic papers through natural language processing and vector database technology. Users can upload PDF files or provide online PDF links, and ChatPaper.ai will analyze and provide summaries, as well as answer questions, thereby improving research efficiency and understanding depth. This platform is particularly suited for researchers, students, and individuals with a strong interest in academic research, facilitating more efficient and interactive engagement with academic materials through a simplified chat interface.

This project, built with Next.js, leverages Upstash Vector to provide semantic search capabilities for Wikipedia. It features optimized loading of the custom Google font, Inter, to enable efficient search and retrieval of Wikipedia content.

Cohere Compass is a novel multifaceted embedding model designed to address the challenges of multi-faceted concept and relationship retrieval in enterprise data. It can pass data in JSON format to the embedding model, which is then stored in a vector database. This preserves both metadata and text while capturing the relationships between concepts in multifaceted data. Compared to traditional RAG workflows, Compass, through its SDK, parses emails and their attachments into a single JSON, generating an embedded output with multifaceted representations. This allows for accurate understanding and matching of different facets within a query during search.

360 ZhiNian is an AI product based on a large model, covering ten major capabilities and hundreds of sub-functional features. It boasts a hard power in large model development and leads the industry in core technologies. It fully integrates with the 360 Internet ecosystem, empowering ecological partners and opening up large model API capabilities to enable the intelligent transformation of a hundred industries and thousands of professions. The main features include ZhiNian Dialogue, image capabilities, vector database, AI Box plugins, chat interaction, and text generation. Suitable for enterprises, governments, cities, small and medium-sized enterprises, and consumers.

DopplerAI is a solution for adding memory and semantic search to LLMs. With a few API calls, you can enable conversational search and insights into your data. DopplerAI manages the infrastructure for your vector database, allowing you to focus on product delivery.

Pinecone is a fully managed, developer-friendly, and easily scalable vector database that enables searching for similar matches among billions of items with millisecond-level latency via API calls. It is the next generation of search technology, accessible with a single API call.