Browse all content tagged with LLM
Eunomia MCP Server is an extension of the Eunomia framework that orchestrates data governance policies—like PII detection and access control—across text streams in LLM-based applications, ensuring robust compliance and security in AI-driven environments.
The MongoDB MCP Server enables seamless integration between AI assistants and MongoDB databases, allowing for direct database management, query automation, and data retrieval through the standardized Model Context Protocol (MCP).
The Nile MCP Server bridges AI assistants with the Nile database platform, enabling seamless automation of database operations, credential management, SQL execution, and region handling via LLM-powered workflows in FlowHunt.
The Astra DB MCP Server bridges Large Language Models (LLMs) and Astra DB, enabling secure, automated data querying and management. It empowers AI-driven workflows to interact directly with Astra DB, unlocking productivity and seamless database automation.
DocsMCP is a Model Context Protocol (MCP) server that empowers Large Language Models (LLMs) with real-time access to both local and remote documentation sources, enhancing developer workflows and AI-powered assistance by enabling instant, context-aware documentation lookup.
The Linear MCP Server connects Linear’s project management platform with AI assistants and LLMs, empowering teams to automate issue management, search, updates, and collaboration directly through conversational interfaces.
The LlamaCloud MCP Server connects AI assistants to multiple managed indexes on LlamaCloud, enabling enterprise-scale document retrieval, search, and knowledge augmentation through a clean, tool-based Model Context Protocol interface.
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.
The nx-mcp MCP Server bridges Nx monorepo build tools with AI assistants and LLM workflows via the Model Context Protocol. Automate workspace management, run Nx commands, and empower intelligent project analysis in your Nx-based codebase.
The Serper MCP Server bridges AI assistants with Google Search via the Serper API, enabling real-time web, image, video, news, maps, reviews, shopping, and academic search capabilities directly within FlowHunt workflows.
Home Assistant MCP Server (hass-mcp) bridges AI assistants with your Home Assistant smart home, enabling LLMs to query, control, and summarize devices and automations via the Model Context Protocol.
The any-chat-completions-mcp MCP Server connects FlowHunt and other tools to any OpenAI SDK-compatible Chat Completion API. It enables seamless integration of multiple LLM providers—including OpenAI, Perplexity, Groq, xAI, and PyroPrompts—by relaying chat-based queries through a unified, simple interface.
The Browserbase MCP Server enables secure, cloud-based browser automation for AI and LLMs, allowing powerful web interaction, data extraction, UI testing, and autonomous browsing with seamless integration into FlowHunt workflows.
Chat MCP is a cross-platform desktop chat application that leverages the Model Context Protocol (MCP) to interface with various Large Language Models (LLMs). It serves as a unified, minimalistic interface for developers and researchers to test, interact with, and configure multiple LLM backends, making it ideal for prototyping and learning MCP.
The Couchbase MCP Server connects AI agents and LLMs directly to Couchbase clusters, enabling seamless natural language database operations, automated management, and interactive querying within developer workflows.
The Firecrawl MCP Server supercharges FlowHunt and AI assistants with advanced web scraping, deep research, and content discovery capabilities. Seamless integration enables real-time data extraction and automated research workflows directly within your development environment.
The Microsoft Fabric MCP Server enables seamless AI-driven interaction with Microsoft Fabric's data engineering and analytics ecosystem. It supports workspace management, PySpark notebook development, delta table schema retrieval, SQL execution, and advanced LLM-powered code generation and optimization.
The OpenAPI Schema MCP Server exposes OpenAPI specifications to Large Language Models, enabling API exploration, schema search, code generation, and security review by providing structured access to endpoints, parameters, and components.
The Patronus MCP Server streamlines LLM evaluation and experimentation for developers and researchers, providing automation, batch processing, and robust setup for AI system benchmarking within FlowHunt.
The QGIS MCP Server bridges QGIS Desktop with LLMs for AI-driven automation—enabling project, layer, and algorithm control, as well as Python code execution directly from conversational interfaces.
The YDB MCP Server connects AI assistants and LLMs with YDB databases, enabling natural language access, querying, and management of YDB instances. It empowers AI-driven workflows and streamlines database operations without manual SQL.
The Mesh Agent MCP Server connects AI assistants with external data sources, APIs, and services, bridging large language models (LLMs) with real-world information for seamless workflow integration. It enables tasks like database queries, file management, and API interactions within the Model Context Protocol (MCP) ecosystem.
Integrate the Vectorize MCP Server with FlowHunt to enable advanced vector retrieval, semantic search, and text extraction for powerful AI-driven workflows. Effortlessly connect AI agents to external vector databases for real-time, context-rich interactions and large-scale data management.
The AI Agent component in FlowHunt empowers your workflows with autonomous decision-making and tool-using capabilities. It leverages large language models and connects to various tools to solve tasks, follow goals, and provide intelligent responses. Ideal for building advanced automations and interactive AI solutions.
Unlock the power of custom language models with the Custom OpenAI LLM component in FlowHunt. Seamlessly integrate your own OpenAI-compatible models—including JinaChat, LocalAI, and Prem—by specifying API keys and endpoints. Fine-tune core settings like temperature and max tokens, and enable result caching for efficient, scalable AI workflows.
Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.
The Structured Output Generator component lets you create precise, structured data from any input prompt using your chosen LLM model. Define the exact data fields and output format you want, ensuring consistent and reliable responses for advanced AI workflows.
Agentic RAG (Agentic Retrieval-Augmented Generation) is an advanced AI framework that integrates intelligent agents into traditional RAG systems, enabling autonomous query analysis, strategic decision-making, and adaptive information retrieval for improved accuracy and efficiency.
Explore the thought processes of AI Agents in this comprehensive evaluation of GPT-4o. Discover how it performs across tasks like content generation, problem-solving, and creative writing, using advanced metrics and in-depth analysis. Uncover the future of adaptive reasoning and multimodal AI capabilities.
AI is revolutionizing entertainment, enhancing gaming, film, and music through dynamic interactions, personalization, and real-time content evolution. It powers adaptive games, intelligent NPCs, and personalized user experiences, reshaping storytelling and engagement.
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.
Learn more about Claude by Anthropic. Understand what it is used for, the different models offered, and its unique features.
Discover the costs associated with training and deploying Large Language Models (LLMs) like GPT-3 and GPT-4, including computational, energy, and hardware expenses, and explore strategies for managing and reducing these costs.
Learn to build an AI JavaScript game generator in FlowHunt using the Tool Calling Agent, Prompt node, and Anthropic LLM. Step-by-step guide based on flow diagram.
FlowHunt 2.4.1 introduces major new AI models including Claude, Grok, Llama, Mistral, DALL-E 3, and Stable Diffusion, expanding your options for experimentation, creativity, and automation in AI projects.
Learn more about the Grok model by xAI, an advanced AI chatbot led by Elon Musk. Discover its real-time data access, key features, benchmarks, use cases, and how it compares to other AI models.
Explore the advanced capabilities of Llama 3.3 70B Versatile 128k as an AI Agent. This in-depth review examines its reasoning, problem-solving, and creative skills through diverse real-world tasks.
Instruction tuning is a technique in AI that fine-tunes large language models (LLMs) on instruction-response pairs, enhancing their ability to follow human instructions and perform specific tasks.
LangChain is an open-source framework for developing applications powered by Large Language Models (LLMs), streamlining the integration of powerful LLMs like OpenAI’s GPT-3.5 and GPT-4 with external data sources for advanced NLP applications.
LangGraph is an advanced library for building stateful, multi-actor applications using Large Language Models (LLMs). Developed by LangChain Inc, it extends LangChain with cyclic computational abilities, enabling complex, agent-like behaviors and human-in-the-loop workflows.
FlowHunt supports dozens of AI models, including Claude models by Anthropic. Learn how to use Claude in your AI tools and chatbots with customizable settings for tailored responses.
FlowHunt supports dozens of AI models, including the revolutionary DeepSeek models. Here's how to use DeepSeek in your AI tools and chatbots.
FlowHunt supports dozens of AI models, including Google Gemini. Learn how to use Gemini in your AI tools and chatbots, switch between models, and control advanced settings like tokens and temperature.
FlowHunt supports dozens of text generation models, including Meta's Llama models. Learn how to integrate Llama into your AI tools and chatbots, customize settings like max tokens and temperature, and streamline AI-powered workflows.
FlowHunt supports dozens of AI text models, including models by Mistral. Here's how to use Mistral in your AI tools and chatbots.
FlowHunt supports dozens of text generation models, including models by OpenAI. Here's how to use ChatGPT in your AI tools and chatbots.
FlowHunt supports dozens of text generation models, including models by xAI. Here's how to use the xAI models in your AI tools and chatbots.
Discover how MIT researchers are advancing large language models (LLMs) with new insights into human beliefs, novel anomaly detection tools, and strategies for aligning AI models with user expectations across diverse sectors.
Learn how FlowHunt used one-shot prompting to teach LLMs to find and embed relevant YouTube videos in WordPress. This technique ensures perfect iframe embeds, saving time and enhancing blog content quality.
Perplexity AI is an advanced AI-powered search engine and conversational tool that leverages NLP and machine learning to deliver precise, contextual answers with citations. Ideal for research, learning, and professional use, it integrates multiple large language models and sources for accurate, real-time information retrieval.
In the realm of LLMs, a prompt is input text that guides the model’s output. Learn how effective prompts, including zero-, one-, few-shot, and chain-of-thought techniques, enhance response quality in AI language models.
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.
Reduce AI hallucinations and ensure accurate chatbot responses by using FlowHunt's Schedule feature. Discover the benefits, practical use cases, and step-by-step guide to setting up this powerful tool.
Text Generation with Large Language Models (LLMs) refers to the advanced use of machine learning models to produce human-like text from prompts. Explore how LLMs, powered by transformer architectures, are revolutionizing content creation, chatbots, translation, and more.
Learn how to build robust, production-ready AI agents with our comprehensive 12-factor methodology. Discover best practices for natural language processing, context management, and tool integration to create scalable AI systems that deliver real business value.
A token in the context of large language models (LLMs) is a sequence of characters that the model converts into numeric representations for efficient processing. Tokens are the basic units of text used by LLMs such as GPT-3 and ChatGPT to understand and generate language.
