RAG

An open-source, Python-first, API-driven Retrieval-Augmented Generation platform with FastAPI endpoints, LangGraph-native agents, a pluggable vector store, and support for several LLM providers.

• RAG

A demonstration project showing how to build hybrid search and retrieval-augmented generation with OpenAI embeddings stored in a Pinecone vector database and orchestrated with LangChain, including a PDF-loading pipeline.

• RAG
• Tutorials

A Microsoft accelerator that deploys a multi-modal RAG pipeline on Azure Kubernetes Service using ColPali, a vision-language model that embeds document pages as images to preserve layout, tables, and charts without OCR.

• RAG
• Containers

A companion HR policy question-answering example that reimplements the same guarded RAG workflow as an explicit LangGraph state graph, with visible per-node execution traces instead of a linear chain.

• RAG
• Tutorials

An HR policy question-answering example built with LangChain LCEL that classifies whether a question is in scope before retrieving from a local FAISS index, refusing to answer out-of-scope questions.

• RAG
• Tutorials

Research code for a knowledge-graph-based RAG method that retrieves relevant subgraphs from large external knowledge graphs to augment LLM prompts for the schema-matching and data-integration task.

• RAG
• Papers

A RAG application that turns any documentation URL into a searchable, chat-based knowledge base, scraping and indexing the site and answering questions with source-backed citations.

• RAG

An open-source, self-hosted knowledge backend that gives AI agents persistent, searchable memory over uploaded documents or existing storage, through an MCP server, REST API, or CLI, built on .NET.

• Agent memory
• RAG

A research framework, published at ICLR'26, that decomposes multi-hop questions into a query subgraph and traverses a graph of document chunks to retrieve reasoning-aligned context for RAG at a fraction of the LLM cost of prior methods.

• RAG
• Papers

An example project that builds a question-answering system over a collection of videos, extracting frames and transcripts, embedding them into a shared multimodal space, and answering questions with a vision-language model.

• RAG
• Tutorials

A Python library and toolkit for GraphRAG built entirely on plain PostgreSQL, combining vector search, full-text search, and knowledge-graph traversal in a single SQL query without Neo4j or a separate vector database.

• RAG
• Databases

A vector compression toolkit for AI workloads that uses TurboQuant codecs to shrink embeddings and KV-cache tensors roughly tenfold, with typed encode and decode contracts and measured byte accounting.

• RAG
• Inference

An open-source Apache Spark extension for large-scale document processing that combines OCR, LLMs, and vision-language models to extract structured data from PDFs and images at scale.

• RAG
• Data engineering

A retrieval-only question-answering system for documentation that makes zero LLM calls at query time, matching questions against pre-extracted answer blocks using local vector search instead of generation.

• RAG

A production-style Retrieval-Augmented Generation pipeline built from scratch without LangChain, combining FAISS and BM25 hybrid retrieval with cross-encoder reranking and a built-in evaluation harness.

• RAG
• AI evals

A small RAG application that answers questions about uploaded PDFs or text files by first searching the documents for relevant passages, then grounding an OpenAI GPT-4o answer in that retrieved text.

• RAG

A reference implementation of a permission-aware Agentic RAG system for enterprise document knowledge bases, enforcing folder-level access control through every step of retrieval, reranking, and citation.

• RAG
• Security

A lightweight Python framework for evaluating information-retrieval systems such as RAG and search pipelines, computing standard metrics like Hit Rate, Recall, NDCG, and MRR.

• RAG
• AI evals

Two side-by-side sample RAG (Retrieval-Augmented Generation) projects for Korea's eGovFrame standard framework, one built with Spring AI and Redis Stack and the other with LangChain4j and PostgreSQL/pgvector.

• RAG
• Tutorials

A zero-dependency TypeScript implementation of Google's TurboQuant vector quantization algorithm that runs in both Node.js and browsers, for compressing embeddings used in search, retrieval-augmented generation and LLM key-value caches.

• RAG
• Libraries

A hybrid semantic search engine, built during an internship, that combines dense embedding search with sparse keyword matching, cross-encoder reranking, and Redis caching, served through a FastAPI backend.

• RAG
• Databases

A short, guided Google Colab walkthrough for trying retrieval-augmented generation using NVIDIA's NIM cloud API, which offers free large language model inference on a T4 GPU runtime.

• RAG
• Tutorials

The reference Java and Spring implementation of the Open Ingestion Standard, a vendor-neutral enterprise data-integration platform that orchestrates connectors, embedding, and vector search output using modern Java concurrency features.

• RAG
• Data engineering

A semantic search engine for the OHDSI medical vocabulary, built as a Rust API and autocomplete service with a React frontend, also reachable as an MCP server for AI agents.

• MCP servers
• RAG