retrieval-augmented-generation

An AI developer that writes LangChain Expression Language (LCEL).

Edge compatible PDF.ai Chat with any PDF document You can ask questions, get summaries, find information, and more. Built with Pinecone, OpenAI, Langchain, Nextjs13, TypeScript, Clerk Auth, Drizzle ORM for edge runtime environment, Shadcn UI.

Privacy-first document intelligence engine — parse PDFs, DOCX, PPTX, XLSX & CSV into AI-ready chunks for RAG pipelines. Includes HITL review, 3-layer memory chat, and a production FastAPI server.

Production-ready RAG framework for Python — multi-tenant chatbots with streaming, tool calling, agent mode (LangGraph), vector search (FAISS), and persistent MongoDB memory. Built on LangChain.

Un framework in Italiano ed Inglese, che permette di chattare con i propri documenti in RAG, anche multimediali (audio, video, immagini e OCR). It is an Italian and English framework, which allows you to chat with your documents in RAG, including multimedia (audio, video, images and OCR).

A pure-Python structured memory benchmark for multi-agent LLM systems — context graph vs vector RAG vs raw history dump, five scenarios, 18 graded queries, zero API calls.

Use OpenAI, Redis, and streamlit to recommend hotels using Large Language Models

Experimental RAG playground for exploring retrieval quality, corpus construction, and filter-chain design. Features configurable ranking and filtering pipelines, visual document grounding, chat interfaces, web search, Open WebUI integration, and rich debugging insights. Ollama and vLLM, running in Dev Containers or natively on Linux and Windows.

A structured learning repo for retrieval-augmented generation, from foundations to production patterns.

Fine-tuning black-box OpenAI embedding models

A comprehensive, professional guide explaining the differences, strengths, and best practices of Retrieval-Augmented Generation (RAG) and Fine-Tuning for LLMs, including workflows, comparisons, decision frameworks, and real-world hybrid AI use cases.

The AI-Powered Healthcare Intelligence Network is an AI-driven system offering disease prediction, drug recommendations, heart disease risk assessment, and an AI medical chatbot. Using ML, NLP, and LLMs, it provides accurate diagnoses, insights, and recommendations, enhancing healthcare accessibility, efficiency, and decision-making .

Self-hosted AI knowledge base with hybrid semantic search (pgvector + FTS + RRF), MCP server, multi-provider LLM inference (Ollama, OpenAI, OpenRouter, llama.cpp), multimodal ingestion (vision, audio transcription, speaker diarization), and knowledge graph. Rust + PostgreSQL.

Retrieval Augmented Generation (RAG) Application to offer Q&A like experience on a long format text using OpenAI and ElasticSearch as Vector DB.

GraphRAG over any codebase — a Neo4j knowledge graph of code entities plus a 3072-dim vector index, bridged so one natural-language query walks structure and ranks by meaning. Python · Neo4j · OpenAI embeddings · Streamlit · Docker. Hybrid semantic + structural code search.

An open source framework for Retrieval-Augmented System (RAG) uses semantic search helps to retrieve the expected results and generate human readable conversational response with the help of LLM (Large Language Model).

A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.

Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS.

FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain (EMNLP 2025)

[ACL 2026] CoCoA: Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy