Browse all content tagged with RAG
The mcp-local-rag MCP Server enables privacy-respecting, local Retrieval-Augmented Generation (RAG) web search for LLMs. It allows AI assistants to access, embed, and extract up-to-date information from the web without external APIs, enhancing research, content creation, and question answering workflows.
Context Portal (ConPort) is a memory bank MCP server that empowers AI assistants and developer tools by managing structured project context, enabling Retrieval Augmented Generation (RAG) and context-aware coding assistance within IDEs.
Integrate FlowHunt with Pinecone vector databases using the Pinecone MCP Server. Enable semantic search, Retrieval-Augmented Generation (RAG), and efficient document management directly within your AI workflows.
The RAG Web Browser MCP Server equips AI assistants and LLMs with live web search and content extraction capabilities, enabling retrieval-augmented generation (RAG), summarization, and real-time research inside FlowHunt workflows.
The Agentset MCP Server is an open-source platform enabling Retrieval-Augmented Generation (RAG) with agentic capabilities, allowing AI assistants to connect with external data sources, APIs, and services for developing intelligent, document-based applications.
The Inkeep MCP Server connects AI assistants and developer tools to up-to-date product documentation managed in Inkeep, enabling direct, secure, and efficient retrieval of relevant content for RAG workflows, chatbots, and onboarding solutions.
The mcp-rag-local MCP Server empowers AI assistants with semantic memory, enabling storage and retrieval of text passages based on meaning, not just keywords. It uses Ollama for embeddings and ChromaDB for vector search, supporting advanced knowledge management and contextual recall in local workflows.
The Graphlit MCP Server connects FlowHunt and other MCP clients to a unified knowledge platform, enabling seamless ingestion, aggregation, and retrieval of documents, messages, emails, and media from platforms like Slack, Google Drive, GitHub, and more. It provides a RAG-ready knowledge base with tools for search, extraction, and content transformation, powering advanced AI workflows.
Vectara MCP Server is an open source bridge between AI assistants and Vectara's Trusted RAG platform, enabling secure, efficient Retrieval-Augmented Generation (RAG) and enterprise search for generative AI workflows in FlowHunt.
Cache Augmented Generation (CAG) is a novel approach to enhancing large language models (LLMs) by preloading knowledge as precomputed key-value caches, enabling low-latency, accurate, and efficient AI performance for static knowledge tasks.
Document grading in Retrieval-Augmented Generation (RAG) is the process of evaluating and ranking documents based on their relevance and quality in response to a query, ensuring that only the most pertinent and high-quality documents are used to generate accurate, context-aware responses.
Document reranking is the process of reordering retrieved documents based on their relevance to a user's query, refining search results to prioritize the most pertinent information. It is a key step in Retrieval-Augmented Generation (RAG) systems, often combined with query expansion to enhance both recall and precision in AI-powered search and chatbots.
FlowHunt's Document Retriever enhances AI accuracy by connecting generative models to your own up-to-date documents and URLs, ensuring reliable and relevant answers using Retrieval-Augmented Generation (RAG).
FlowHunt's GoogleSearch component enhances chatbot accuracy using Retrieval-Augmented Generation (RAG) to access up-to-date knowledge from Google. Control results with options like language, country, and query prefixes for precise and relevant outputs.
Knowledge Sources make teaching the AI according to your needs a breeze. Discover all the ways of linking knowledge with FlowHunt. Easily connect websites, documents, and videos to enhance your AI chatbot's performance.
LazyGraphRAG is an innovative approach to Retrieval-Augmented Generation (RAG), optimizing efficiency and reducing costs in AI-driven data retrieval by combining graph theory and NLP for dynamic, high-quality query results.
Boost AI accuracy with RIG! Learn how to create chatbots that fact-check responses using both custom and general data sources for reliable, source-backed answers.
Query Expansion is the process of enhancing a user’s original query by adding terms or context, improving document retrieval for more accurate and contextually relevant responses, especially in RAG (Retrieval-Augmented Generation) systems.
Question Answering with Retrieval-Augmented Generation (RAG) combines information retrieval and natural language generation to enhance large language models (LLMs) by supplementing responses with relevant, up-to-date data from external sources. This hybrid approach improves accuracy, relevance, and adaptability in dynamic fields.
Explore how OpenAI O1's advanced reasoning capabilities and reinforcement learning outperform GPT4o in RAG accuracy, with benchmarks and cost analysis.
Retrieval Augmented Generation (RAG) is an advanced AI framework that combines traditional information retrieval systems with generative large language models (LLMs), enabling AI to generate text that is more accurate, current, and contextually relevant by integrating external knowledge.
Discover what a retrieval pipeline is for chatbots, its components, use cases, and how Retrieval-Augmented Generation (RAG) and external data sources enable accurate, context-aware, and real-time responses.
Discover the key differences between Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG) in AI. Learn how RAG dynamically retrieves real-time information for adaptable, accurate responses, while CAG uses pre-cached data for fast, consistent outputs. Find out which approach suits your project's needs and explore practical use cases, strengths, and limitations.
