Deep Seek is a web-based AI agent designed to transform how users discover and interact with information. Users can upload various file types—PDFs, DOCXs—or supply URLs to websites and YouTube videos. The platform automatically indexes content and applies retrieval-augmented generation to pull in relevant passages during a conversation. As you chat, Deep Seek retrieves context from your curated knowledge base, then generates clear, targeted answers. This hybrid approach ensures fast, accurate responses while preserving the depth and nuance of the original sources.
Deep Seek Core Features
Document and website indexing
YouTube video search and summarization
Conversational Q&A over custom knowledge bases
Supports PDF, DOCX, and URLs
Retrieval-augmented generation for accurate answers
Deep Seek Pro & Cons
The Cons
Limited visible detailed information about features or pricing.
No direct access to app stores or extension links is provided.
The Pros
Provides a focused search tool for AI-related topics and advancements.
Helps users stay updated with the latest AI research and startup news.
Keylight AI leverages state-of-the-art artificial intelligence to transform document searches, enabling users to retrieve relevant information swiftly and accurately. This powerful tool integrates seamlessly across various formats, ensuring accessibility and ease of use. Its robust features cater to both individual users and organizations, empowering them to overcome the limitations of traditional search methods. Designed for efficiency, Keylight AI not only enhances productivity but also clears the path for better decision-making through optimized information discovery.
Multi-Agent-RAG provides a modular framework for constructing retrieval-augmented generation (RAG) applications by orchestrating multiple specialized AI agents. Developers configure individual agents: a retrieval agent connects to vector stores to fetch relevant documents; a reasoning agent performs chain-of-thought analysis; and a generation agent synthesizes final responses using large language models. The framework supports plugin extensions, configurable prompts, and comprehensive logging, enabling seamless integration with popular LLM APIs and vector databases to improve RAG accuracy, scalability, and development efficiency.