RAG

Research infrastructure for AI agents that performs real-time web search with claim-by-claim verification against source pages, returning cited answers with an evidence-based confidence score instead of a model's own self-assessment.

• RAG
• Browser automation

A standalone Python system that keeps a vector database automatically and incrementally synced with a set of source files, watching for changes and re-embedding only what changed.

• RAG
• Databases

A self-hosted, privacy-focused retrieval-augmented assistant, written in C++, that indexes a codebase locally and answers questions about it using local or cloud language models instead of a paid, cloud-only coding assistant subscription.

• Agent memory
• RAG

A local-first retrieval-augmented research assistant that helps scientists and students search, understand, and organize academic literature across sources such as Semantic Scholar, PubMed, and arXiv, exposed through an MCP server and REST API.

• MCP servers
• RAG

A Microsoft sample application and workshop that builds a retrieval-augmented ChatGPT-style assistant in Java using LangChain4j, Quarkus, and Qdrant as the vector database, deployable to Azure or run fully locally with Ollama.

• RAG
• Tutorials

An AWS sample application demonstrating serverless semantic search over images and live video using Amazon OpenSearch as a vector database and Amazon Bedrock's Titan multimodal embeddings model.

• RAG
• Tutorials

A personal knowledge base that stores markdown and YAML sources on disk and answers queries with ranked, cited context blocks, following a documented pattern for grounding AI assistants in a person's own notes.

• Agent memory
• RAG

A minimal, async-first Python framework for building LLM applications with only two hard dependencies, offering unified access to dozens of model providers, vector stores, and agent tools.

• Agent frameworks
• RAG

A local, GPU-accelerated code search toolkit for AI coding agents that combines grep-style, semantic, and graph-based retrieval to cut tool calls and cost compared with plain grep and file reads.

• Agent memory
• RAG

A semantic search engine over arXiv, the Stacks Project and other sources that finds mathematical theorems and the papers containing them, offered as a REST API and as an MCP tool for AI agents.

• MCP servers
• RAG

Three locally fine-tuned Russian-language models for a self-hosted RAG stack: a document splitter, a search-query expander and a retrieval embedder, distilled from a teacher model and quantized to GGUF for llama.cpp on AMD hardware.

• RAG
• Models

A pure-Rust command-line tool that splits PDFs into chunks for retrieval-augmented generation by analyzing layout, font size and boldness rather than a fixed token or character count.

• RAG

Forty self-contained tutorials on production retrieval-augmented generation techniques, from chunking strategies to agentic and graph-based retrieval, runnable on any OpenAI-compatible provider.

• RAG
• Tutorials

A TypeScript retrieval framework for AI agents that structures multi-source knowledge as resources, chunks, entities, facts and access-controlled evidence in a knowledge graph, instead of treating a corpus as a flat vector index.

• Agent memory
• RAG

A production-grade Python toolkit for retrieval-augmented generation that ingests PDFs, DOCX and XLSX into Qdrant with LLM-based metadata extraction, hybrid search and SHA256 deduplication.

• RAG

A local-first retrieval-augmented knowledge base for the Pi and OMP coding agents, combining semantic embeddings with BM25 keyword search and code-aware chunking over a codebase, docs, PDFs and notes.

• Agent memory
• RAG

A browser-based retrieval-augmented generation app that answers questions over YouTube videos, podcasts and PDFs, with every claim in the answer carrying a citation that deep-links back to the source, built with Next.js and Gemini.

• RAG

A collection of reference implementations of agentic retrieval-augmented generation, evaluation and orchestration workflows, built for Vector Institute's Agent Bootcamp using current Python agent frameworks.

• RAG
• Tutorials

A high-performance, in-memory vector database and search engine in Rust for semantic search and document indexing, exposed over binary RPC, REST, gRPC, GraphQL and MCP with native SDKs in five languages.

• RAG
• Databases

A Rust command-line tool that infers a typed schema from a directory of Markdown files, validates their frontmatter against it, and adds local hybrid vector and keyword search over the whole corpus.

• RAG
• Documentation tools

A retrieval-augmented generation system that represents facts involving more than two entities as hyperedges in a hypergraph, using a memory-guided beam retriever and a trained retriever to reason over them.

• RAG

A coding-agent plugin that turns a named research paper into a local, interactive site where every claim links back to the exact page and quote it comes from, distinguishing measured results from the authors' own interpretation.

• Agent skills
• RAG

An open-source Python framework for building ETL pipelines that feed Retrieval-Augmented Generation systems, with multi-tenancy, cloud-native deployment and job scheduling.

• RAG
• Data engineering

A QuickStart deployment that lets a team experiment with retrieval-augmented generation in a chat interface, uploading documents, tuning sampling parameters and switching between simple and agent-based RAG.

• RAG