Projects sharing features with NeMo Retriever

Paperless is a self-hosted document management system designed to digitize, index, and archive paper documents. It functions as an optical character recognition system that converts scanned images and PDFs into a searchable digital library, providing a web-based interface for querying and retrieving documents from a database. The system features an automated file ingestion pipeline that monitors specific directories and email inboxes to process and import documents without manual uploading. To maintain a private archive, it includes on-disk encryption for sensitive files and the ability to or

PyMuPDF is a comprehensive PDF manipulation library and document analysis tool. It serves as a text extraction tool, OCR engine, and image converter, providing a programmatic interface to edit, merge, split, and optimize PDF and Office documents. The project distinguishes itself through high-performance capabilities, including the use of C-bindings for low-level manipulation and parallelized page processing to accelerate workloads. It provides specialized conversion paths, such as transforming PDF content into Markdown for retrieval-augmented generation and large language model pipelines. It

Unstructured is an enterprise-grade data orchestration engine designed to transform raw, unstructured files into structured, machine-readable formats. It functions as a comprehensive platform for document ingestion, partitioning, and enrichment, specifically engineered to prepare complex data for retrieval-augmented generation and agentic AI workflows. The platform distinguishes itself through its sophisticated document processing strategies, which combine rule-based extraction with vision-language models to handle diverse file layouts, tables, and images. It provides a modular architecture t

LlamaIndex is a comprehensive development framework designed to connect private or external data sources to large language models. It functions as a data-centric toolkit that enables the construction of retrieval-augmented generation systems, allowing developers to build applications that provide context-aware answers based on specific organizational information. The project distinguishes itself through a robust agentic orchestration engine that supports the creation of autonomous agents capable of multi-step reasoning, memory management, and complex tool execution. Beyond simple retrieval, i

Kotaemon is an orchestration framework designed for building modular, agentic workflows that integrate document processing, retrieval-augmented generation, and multi-step reasoning. It provides a comprehensive platform for developing document-based question answering systems, allowing users to chain language models, prompt templates, and external tools into complex, automated pipelines. The system distinguishes itself through a highly modular architecture that emphasizes component-based composition and schema-driven data exchange. It supports autonomous agents capable of decomposing complex q

SurfSense is a self-hosted platform designed for building retrieval-augmented generation pipelines and managing private knowledge bases. It functions as a containerized research stack that allows users to index diverse data sources and query them using language models, ensuring that all information retrieval is grounded in specific source citations. The platform distinguishes itself through its modular architecture, which supports the integration of custom tools and diverse language models via a unified abstraction layer. It facilitates secure, collaborative research environments by implement

Ragas is an evaluation framework designed to measure the performance of retrieval-augmented generation pipelines and autonomous agent workflows. It provides a comprehensive suite of tools for benchmarking system outputs, utilizing language models as automated judges to score performance against defined rubrics and reference data. By standardizing inputs, retrieved contexts, and generated responses into a unified schema, the project enables consistent analysis across complex AI applications. The framework distinguishes itself through its ability to generate synthetic test datasets from existin

LangBot is an orchestration platform designed for building, managing, and deploying AI agents. It functions as a comprehensive framework for integrating large language models with custom workflows, enabling developers to connect intelligent agents to various messaging platforms and external tools. The platform distinguishes itself through a modular, plugin-based architecture that allows for the extension of agent capabilities via custom tools and file parsers. It features a secure, sandbox-isolated runtime environment that executes untrusted code and plugin logic within resource-constrained c

This platform is an automated documentation and codebase analysis system designed to generate structured wikis, technical guides, and interactive diagrams from source code repositories. It functions as a retrieval-augmented generation framework that connects codebases to language models, enabling context-aware answers, deep research, and automated documentation updates through semantic vector search. The system distinguishes itself through a self-hosted, containerized architecture that supports both cloud-based and local AI model execution. It provides sophisticated model orchestration, allow

This project is an educational collection of Jupyter notebooks and guides focused on building applications with the LangChain framework. It serves as a practical resource for developers learning to implement prompt engineering, retrieval-augmented generation, and autonomous agent workflows to create intelligent, context-aware systems. The repository distinguishes itself by providing hands-on tutorials for connecting language models to private datasets and external tools. It covers the end-to-end process of designing structured input templates, orchestrating multi-step task sequences, and main

This project is a containerized development stack and application framework for building retrieval-augmented generation systems. It provides a dockerized AI sandbox that integrates local model runtimes, knowledge graphs, and vector stores to enable the creation of contextual chatbots. The stack is distinguished by its graph-based vector store, which combines structured knowledge graphs with vector indices for both semantic and structural data retrieval. It allows for local model hosting with CPU or GPU acceleration, enabling generative tasks without reliance on external cloud APIs. The frame

RAG-Anything is a retrieval-augmented generation framework designed to index diverse document formats and perform semantic search using local machine learning models. It functions as a local multimodal data processor, extracting and organizing information from various file types into a unified knowledge base to facilitate private document analysis. The system distinguishes itself through its high-throughput ingestion engine, which processes large batches of documents into searchable vector embeddings. By executing machine learning models directly on local hardware, the framework ensures that

Cognita is a retrieval augmented generation orchestration framework used to build pipelines that connect document stores and language models to provide grounded answers. It functions as a document ingestion pipeline and a vector database integrator, managing the process of loading, parsing, and indexing files into a searchable knowledge base. The system includes a language model gateway proxy that provides a unified API to interact with multiple different model providers. This routing layer decouples the application from specific vendors, allowing requests to be proxied through a provider-agn

This repository serves as a comprehensive library of architectural blueprints and code examples for integrating large language models into software applications. It functions as a developer learning resource, providing structured tutorials and implementation patterns that demonstrate how to build intelligent features using advanced prompting and data processing techniques. The collection distinguishes itself by focusing on complex reasoning and data-grounding workflows. It provides practical guidance on implementing retrieval-augmented generation pipelines, which connect language models to pr

LanceDB is a vector database and columnar data store designed to function as a versioned dataset manager and vector search engine. It serves as a high-performance backend for indexing and retrieving high-dimensional embeddings, providing the foundation for machine learning data pipelines. The system distinguishes itself through a combination of cloud-native object storage and immutable version tracking, allowing for data time-travel and reproducible AI experiments. It integrates hybrid search capabilities, merging dense vector similarity with BM25 full-text search and SQL-like scalar filters

ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

Letta is a framework for building, deploying, and managing autonomous AI agents that maintain persistent state across long-term interactions. It provides a comprehensive suite of primitives for defining agents with configurable personas, modular memory blocks, and tool-use capabilities, enabling them to retain user preferences and conversation history over extended sessions. The platform distinguishes itself through its advanced memory management and orchestration capabilities. It allows agents to autonomously update their own memory, perform retrieval-augmented generation, and coordinate com

This platform serves as a comprehensive environment for managing private language models, document knowledge bases, and automated agent workflows within secure local infrastructure. It functions as a document-aware workspace that enables users to ingest diverse file formats into searchable repositories, ensuring that all data processing and model inference remain within private, local environments to maintain data sovereignty. The system distinguishes itself through a modular agentic engine that allows for the definition of custom skills and external tool execution. By utilizing a multi-model

This project provides a unified server environment and gateway for hosting and executing open-source large language models on private infrastructure. It functions as a standardized interface that exposes locally deployed models through widely-adopted API protocols, allowing existing applications to interact with them without requiring code modifications. The platform distinguishes itself by acting as a compatibility layer that translates standard REST requests into model-specific execution calls. It supports advanced interaction patterns including real-time token streaming, function calling f

Podcastfy is an AI content-to-podcast generator that converts text, URLs, PDFs, images, and videos into conversational audio podcasts. It integrates with over 100 language models for transcript creation and multiple text-to-speech engines for audio output, with support for customizable dialogue style and optional local transcript generation for privacy. The project distinguishes itself through a flexible architecture that decouples job submission from result retrieval via asynchronous polling, normalizes heterogeneous inputs into uniform text, and routes content through pluggable LLM and TTS

AutoGluon is an automated machine learning framework and multimodal library designed to automate the end-to-end pipeline from data preprocessing to high-accuracy model training and validation. It functions as an automated model trainer for tabular, image, text, and time series data, as well as a tool for time series forecasting and foundation model finetuning. The project is distinguished by its ability to jointly process and fuse different data types, allowing for the construction of multimodal neural networks that integrate images, text, and structured tables. It supports zero-shot inferenc

Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang

AutoRAG is an automation layer and optimization tool for retrieval-augmented generation. It provides a framework for measuring pipeline performance through an evaluation system and an automated search strategy that identifies the most effective combinations of retrieval and generation modules. The system distinguishes itself through AutoML-style optimization, using hyperparameter grid searches and automated trials to find the highest performing architectural configuration for a specific dataset. It includes a specialized dataset generator that creates synthetic question-answer pairs and groun

Easy Local RAG is a system for building and operating private, offline retrieval-augmented generation pipelines. It enables users to perform semantic search, document querying, and conversational analysis on local data sources without transmitting sensitive information to external cloud providers. The project distinguishes itself by integrating specialized utilities for archiving personal email communications alongside standard document processing. By leveraging locally hosted language models and a local vector database, it maintains full control over data ingestion, indexing, and model infer

LightRAG is a graph-based retrieval framework designed to build retrieval-augmented generation pipelines. It structures unstructured text into knowledge graphs, enabling multi-hop reasoning and complex query synthesis across large document collections. By integrating dense vector embeddings with structured knowledge graphs, the system facilitates both similarity-based and relationship-aware information retrieval. The framework distinguishes itself through a dual-level retrieval strategy that combines low-level keyword matching with high-level semantic graph traversal to capture both specific

Cherry Studio is a cross-platform desktop application that serves as a centralized workspace for managing and interacting with multiple artificial intelligence models. It functions as a local-first orchestrator, prioritizing user privacy by storing all conversation history and knowledge bases directly on your device. By providing a unified interface for both cloud-based and local AI services, the platform simplifies API key management and allows for consistent model interaction across different operating systems. The application distinguishes itself through a robust retrieval-augmented genera