RAG

A deterministic Python parser for Logseq graphs that preserves the outliner's parent-child hierarchy when exporting Markdown for retrieval-augmented generation, alongside an interactive visualizer and an append-only writer.

• RAG

An experimental retrieval-augmented generation lab for studying why RAG pipelines fail under constrained context windows, modest hardware and multi-turn chat, treating document classification, chunking and retrieval strategy as tunable, comparable parameters.

• RAG

A self-hosted semantic code-search platform, invoked as cix, that indexes a codebase into a vector store for meaning-based search, combining hybrid BM25 and dense embeddings through an embedded llama.cpp server.

• Agent memory
• RAG

A retrieval-augmented generation framework built on the HippoRAG 2 architecture that turns a static knowledge graph into a query-adaptive navigation structure, reproduced with the prompts and dataset from its ACL 2026 paper.

• RAG
• Papers

A multi-layer caching library for AI agent pipelines that sits in front of LLM calls, embeddings and retrieval queries across frameworks such as LangChain, LangGraph, AutoGen, CrewAI and Agno, caching by semantic similarity as well as exact match.

• Agent frameworks
• RAG

A document intelligence engine that parses PDFs, DOCX, PPTX, XLSX and CSV into validated, AI-ready chunks for retrieval-augmented generation, with human review, PII redaction and a production FastAPI server.

• RAG

A PyTorch library that trains a linear adapter with triplet loss to align retrieval query embeddings with real user queries, improving RAG search without retraining or re-indexing the embedding model.

• RAG
• Models

A demonstration project that uses Spring AI with Ollama or OpenAI to chat with documents stored in a library, embedding them into a PostgreSQL vector database to answer questions from their content.

• RAG
• Tutorials

TreeHop is a lightweight, embedding-level query rewriter for multi-hop retrieval-augmented generation that updates and prunes query embeddings directly, instead of repeatedly calling an LLM to rewrite the query between retrieval hops.

• RAG

Markdown Vault MCP is a generic MCP server that exposes a folder of Markdown notes, such as an Obsidian vault, to an AI client with hybrid keyword and semantic search, frontmatter-aware filtering, and optional write tools for editing the vault directly.

• MCP servers
• RAG

gmlx is a local inference platform for running K-quant and IQ-quant GGUF models on Apple Silicon, serving them over OpenAI- and Anthropic-compatible APIs, with terminal or browser chat, voice input, a local RAG stack and LoRA fine-tuning.

• RAG
• Inference

MASB is a FastAPI application that orchestrates a multi-agent system to answer questions by combining live web search with a FAISS-backed retrieval-augmented knowledge base, streaming its answer back over server-sent events.

• Agent frameworks
• RAG

ArxivExplorer is a semantic search engine for arXiv papers that combines keyword and vector search, generates AI summaries and entity extraction for each paper, and tracks citations, without requiring a login.

• RAG
• Papers

This is an end-to-end retrieval-augmented generation system for Chinese recipe question answering, covering data cleaning, hybrid retrieval, multi-turn conversation state and structured answer generation, verified with real end-to-end tests against a live model rather than mocks.

• RAG

Dug applies semantic web and knowledge graph techniques to biomedical research metadata, letting a researcher search underused datasets such as dbGaP by concept rather than exact keyword, based on curated ontologies.

• RAG
• Data engineering

CodexA is a local semantic code intelligence CLI that indexes a codebase, builds embeddings and a BM25 index, and exposes structured search, retrieval-augmented answers and repository analysis to AI coding assistants over HTTP, CLI or MCP.

• MCP servers
• RAG

t0-1 is a research demonstration from the Alan Turing Institute's t0 initiative, applying retrieval-augmented reasoning with lean language models to a domain-specific knowledge base built from NHS condition pages.

• RAG

A deterministic multi-hop retrieval-augmented generation system that reasons over a plain, append-only dense index instead of a knowledge graph, so it stays current on data that changes daily without any re-indexing pass.

• RAG

A Claude Code plugin that compiles raw research material into an LLM-maintained Obsidian wiki, then answers questions from it at three depth levels and keeps the wiki healthy over time.

• Agent skills
• RAG

A terminal-based support-ticket triage agent that classifies incoming tickets across three domains, retrieves answers from a local documentation corpus, and escalates high-risk or ambiguous requests instead of guessing.

• RAG

An AWS-native framework that turns large multilingual document collections into knowledge graphs and answers multi-hop questions over them, unifying Microsoft's GraphRAG and LightRAG retrieval methods on Bedrock, Neptune and OpenSearch.

• RAG

A reinforcement-learning framework that trains an LLM to decide, turn by turn, when to reason, whether to retrieve from unstructured text or a knowledge graph, and when to answer, for adaptive hybrid retrieval-augmented generation.

• RAG

An ICML 2026 hierarchical tree-based RAG framework that builds a corpus-level abstract tree index for cross-document retrieval, answering complex queries with a multi-granular agentic retrieval pipeline.

• RAG

A PyTorch implementation, from an ACL paper, of knowledge-graph retrieval-augmented generation for LLM-based recommendation, evaluated on MovieLens and Amazon Book datasets.

• RAG
• Papers