SaSame Research
Free AI research/OCR + agent-economy discovery tools (120+ tools, no-auth), includes engage_sasame to commission SaSame to build MCP/Claude/RAG/agents, full…
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Free AI research/OCR + agent-economy discovery tools (120+ tools, no-auth), includes engage_sasame to commission SaSame to build MCP/Claude/RAG/agents, full…
Java AI application development framework (supports LLM-tool,skill; RAG; MCP; Agent-ReAct,Team-Agent). Compatible with java8 ~ java25. It can also be embedded…
RagWiser is a Retrieval Augmented Generation (RAG) system built with Spring Boot that enables users to upload PDF documents, process them, and ask questions…
Spring Boot AI is a sample application demonstrating how to build an AI agent using Spring AI, the Model Context Protocol (MCP), and Retrieval Augmented…
Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, 9 file formats, file watcher, and 12…
Launch a hosted Gemini File Search true multimodal RAG layer that answers from your PDFs, docs, screenshots, charts, diagrams, help center, FAQs, and images…
x402 micropayment API for AI agents. In-memory vector store with cosine similarity search. For RAG pipelines. Pay per call with USDC on Base.
AI-powered knowledge base for your Terraform modules. Index, search, compose, and audit - all from one place.
This repo is dedicated to learning and working with large language models (LLMs), prompt engineering, and modern GenAI tools such as LangChain, RAG, and vector…
🚀 All-in-one MCP server with AI search, RAG, and multi-service integrations (GitLab/Jira/Confluence/YouTube) for AI-enhanced development workflows
Enables AI to query and analyze local documents and Git repositories through a RAG system built with TypeScript, LlamaIndex, and Gemini embeddings.
The MCP-RAG Server is an implementation of the Model Context Protocol (MCP) designed to enhance AI assistants by providing them with real-time access to…
Code Rag with Graph - local only installation
MCP server for Voyage AI embeddings, reranking, and MongoDB Atlas Vector Search. Provides 11 tools for semantic search, document ingestion, cost estimation…
Find your files with natural language and ask questions.
Enables Claude to search and retrieve relevant documentation through Inkeep's RAG API, returning structured citation data for technical support and…
Enables AI systems to access and retrieve information from multiple vector store backends including HNSWLib and Weaviate, providing a unified interface for…
The repository SMMS creates an MCP server for instance-level semantic maps and provides a series of functional modules for 3D instance objects in semantic maps.
A Retrieval-Augmented Generation (RAG) server for document processing, vector storage, and intelligent Q&A, powered by the Model Context Protocol.
A server for Retrieval Augmented Generation (RAG), providing AI clients access to a private knowledge base built from user documents.
Agentic RAG over your own documents on an embedded LanceDB, no database server to run. Hybrid search with reranking, Docling parsing for PDFs and 40+ formats…
A managed Retrieval-Augmented Generation (RAG) server using MCP, integrated with knowledge bases and OpenSearch.
A lightweight Python server for Retrieval-Augmented Generation (RAG) using AWS Lambda. It retrieves knowledge from external data sources like arXiv and PubMed.
A RAG-based Q&A server using a vector store built from Gemini CLI documentation.