RAG

A single-binary, Go implementation of a RAG workspace and autonomous agent framework, inspired by the Leann backend, that runs embeddings and inference locally through Ollama or llama.cpp with AST-aware code indexing.

• Agent frameworks
• RAG

A Convex component for semantic search over text chunks and embeddings, used to give an AI agent retrieval-augmented context, with per-user namespaces, custom filtering and importance weighting.

• RAG
• Databases

An MCP server that gives AI assistants semantic search over a local Qdrant vector database, with AST-aware codebase indexing, git history search, hybrid keyword and vector search, and support for several embedding providers.

• MCP servers
• RAG

A lightweight RAG provenance middleware that checks, without calling an LLM, whether every claim in a generated answer is actually grounded in a retrieved source, and flags claims backed only by outdated sources.

• RAG
• AI evals

Reference implementations from Vector Institute's RAG bootcamp that show common retrieval-augmented generation methodologies over web, document, SQL and cloud data sources using LangChain and LlamaIndex.

• RAG
• Tutorials

A memory filter that judges each candidate memory against a query with one calibrated yes-or-no question, so an AI assistant retrieves memories that are actually relevant even when they are not phrased like the request.

• Agent memory
• RAG

A browser-only demo that runs Google's EmbeddingGemma model through Transformers.js to rank a set of documents against a search query by semantic similarity, with no server and no data leaving the device.

• RAG
• Tutorials

An Agentic GraphRAG framework that separates the AI decision layer from the graph database, running the same ingestion, retrieval and evaluation pipeline across Neo4j, Memgraph, Apache AGE and Kuzu.

• RAG
• Databases

An open infrastructure layer for Togolese public data: a scraped corpus of laws, government records and press, a retrieval-augmented generation engine, and a REST API with JS/TS and Python client SDKs.

• RAG
• APIs

An open-source semantic search engine for retrieving information from PDF documents, built from ingestion of text, tables and figures, ElasticSearch-based retrieval with an optional neural reranker, and a question-answering model for extracting answers from the retrieved spans.

• RAG

A framework for evaluating semantic search across a developer's own datasets, comparing embedding models, BM25 and other backends with metrics such as Accuracy@k and NDCG@k.

• RAG
• AI evals

A tool that indexes a codebase's domain concepts, naming conventions and vocabulary so a coding agent can answer a conceptual question in one MCP call instead of dozens of individual searches.

• MCP servers
• RAG

A single-binary local search CLI for Japanese Markdown notes, combining full-text and optional semantic search, built mainly so an AI coding agent can search a developer's own note archive.

• RAG
• CLI tools

A local tool that indexes documents from Jira, Confluence or local files into a vector database and searches them with hybrid vector and keyword search, keeping all data on the user's own machine.

• MCP servers
• RAG

A provider-agnostic Node.js SDK for retrieval-augmented generation that has grown a second half: a context and memory layer that decides what a model sees on a given turn under a fixed token budget.

• Agent memory
• RAG

A set of retrieval-augmented-generation demos that run entirely offline on CPU with quantized open models such as DeepSeek, Llama, Qwen, Mistral and Gemma.

• RAG
• Tutorials

A Claude Code plugin that indexes a codebase and answers searches by meaning using local embeddings and BM25 keyword matching, instead of plain text grep.

• Agent skills
• RAG

A self-hosted MCP server that gives an AI agent perception beyond text search: local speech transcription, reading behind a login wall with the user's own credentials, seeing images and video frames, crossing languages, and remembering past findings.

• MCP servers
• RAG

A retrieval-augmented generation application for searching environmental, social and governance policy documents, inspecting the citations behind an answer, and producing traceable reports, running with no external infrastructure required.

• RAG

Open-source deep-research infrastructure for AI agents that searches 40 channels, including more than ten Chinese sources, and returns indexed, deduplicated multi-source results while staying decoupled from any specific LLM provider.

• MCP servers
• RAG

A retrieval-augmented generation framework for Swift, providing document loading, chunking, embeddings, vector stores and query synthesis so document search and question answering can be built natively for Linux, macOS and iOS.

• RAG
• Libraries

A reference implementation of an LLM agent that keeps a large number of tools in a searchable vector-store library rather than listing them all in every prompt, cutting cost and getting around tool-set limits on context or API size.

• Agent frameworks
• RAG

A Rust port of PixelRAG that searches documents and video by how a page or frame visually looks rather than only by its text, using screenshots of pages turned into visual embeddings and an approximate-nearest-neighbor index.

• RAG
• Databases

An open-source, local-first alternative to NotebookLM that lets a person chat with their own PDFs, images, audio, video, URLs and YouTube links using whichever LLM provider they choose, including fully local models.

• RAG