Search Results for "indexing" - Page 2
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Showing 538 open source projects for "indexing"
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ROMM
A beautiful, powerful, self-hosted rom manager and player
...Romm also supports widgets, customization options, and theme choices so users can tailor the visual experience to their preferences while maintaining performance and responsiveness. Privacy is a highlight, with local indexing and search functions that operate without sending data to external servers unless explicitly permitted. -
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Apache Hudi
Upserts, Deletes And Incremental Processing on Big Data
...Hudi reimagines slow old-school batch data processing with a powerful new incremental processing framework for low latency minute-level analytics. Hudi provides efficient upserts, by mapping a given hoodie key (record key + partition path) consistently to a file id, via an indexing mechanism. This mapping between record key and file group/file id, never changes once the first version of a record has been written to a file. In short, the mapped file group contains all versions of a group of records. -
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pgai
A suite of tools to develop RAG, semantic search, and other AI apps
pgai is a suite of PostgreSQL extensions developed by Timescale to empower developers in building AI applications directly within their databases. It integrates tools for vector storage, advanced indexing, and AI model interactions, facilitating the development of applications like semantic search and Retrieval-Augmented Generation (RAG) without leaving the SQL environment. -
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codebase-memory-mcp
High-performance code intelligence MCP server
...It ships as a single static binary for macOS, Linux, and Windows with no shared library dependencies. It also includes semantic search, BM25 search, graph visualization, auto-indexing, and cross-repository intelligence. -
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Bazarr
Bazarr is a companion application to Sonarr and Radarr
...Bazarr supports a wide array of subtitle providers around the world and can track download history, manage multiple languages, and perform post-download cleanup if needed. Because it doesn’t itself scan your disk libraries but instead relies on Sonarr or Radarr indexing, it fits cleanly into automated media stacks on NAS, servers. -
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RunMat
The Fast, Free, Modern MATLAB / Octave code runtime
...The project emphasizes modern performance, describing a V8-inspired runtime architecture with a lightweight installer for Linux, macOS, and Windows. It targets full language grammar and core semantics, aiming to support arrays, indexing idioms, multiple return values, and classdef constructs that many scientific users rely on. The distribution includes one-line install scripts and highlights forthcoming editor integrations (VS Code and IntelliJ plugins) to provide syntax highlighting, IntelliSense, and integrated debugging. Its website positions RunMat as a drop-in engine for current codebases, with an OSS development model inviting contributors to inspect the code or build from source. ... -
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Graph Node
Graph Node indexes data from blockchains such as Ethereum
The Graph is an indexing protocol for querying networks like Ethereum and IPFS. Anyone can build and publish open APIs, called subgraphs, making data easily accessible. Subgraphs can be composed into a global graph of all the world's public information. This data can be transformed, organized, and shared across applications for anyone to query with just a few keystrokes. -
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ELisp Tree-sitter
Tree-sitter bindings for Emacs Lisp
...It aims to be the foundation for a new breed of Emacs packages that understand code structurally. Faster, fine-grained code highlighting. More flexible code folding. Structural editing (like Paredit, or even better) for non-Lisp code. More informative indexing for imenu. The author of Tree-sitter articulated its merits a lot better in this Strange Loop talk. The minor mode tree-sitter-mode provides a buffer-local syntax tree, which is kept up-to-date with changes to the buffer’s text. Run M-x tree-sitter-hl-mode to replace the regex-based highlighting provided by font-lock-mode with tree-based syntax highlighting. -
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Pixeltable
Data Infrastructure providing an approach to multimodal AI workloads
Pixeltable is an open-source Python data infrastructure framework designed to support the development of multimodal AI applications. The system provides a declarative interface for managing the entire lifecycle of AI data pipelines, including storage, transformation, indexing, retrieval, and orchestration of datasets. Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a table-based abstraction. Developers define data transformations and AI operations using computed columns on tables, allowing pipelines to evolve incrementally as new data or models are added. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Zinc Search Engine
ZincSearch. A lightweight alternative to elasticsearch
Zinc is a search engine that does full text indexing. It is a lightweight alternative to Elasticsearch and runs using a fraction of the resources. It uses bluge as the underlying indexing library. It is very simple and easy to operate as opposed to Elasticsearch which requires a couple dozen knobs to understand and tune which you can get up and running in 2 minutes. You can index large amounts of text Zinc and search instantly. -
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Sage Chat
Chat with any codebase in under two minutes | Fully local
...Developers can ask natural language questions about a project, and the system responds with explanations supported by references to the relevant code, documentation, or external technical resources. The project aims to act as a contextual knowledge layer for software teams by combining language models with repository indexing and documentation retrieval. Sage can operate locally or connect to external AI services, depending on the configuration, providing flexibility for privacy-sensitive environments. -
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Apache Lucene
Apache Lucene open-source search software
...The project releases a core search library, named Lucene™ core, as well as PyLucene, a Python binding for Lucene. Lucene Core is a Java library providing powerful indexing and search features, as well as spellchecking, hit highlighting and advanced analysis/tokenization capabilities. The PyLucene sub-project provides Python bindings for Lucene Core. The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are defined by collaborative consensus-based processes, an open, pragmatic software license and a desire to create high-quality software that leads the way in its field. ... -
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MongoDB Rust Driver
The official MongoDB Rust Driver
...It makes extensive use of Rust’s ownership and type systems to ensure memory safety, thread safety, and predictable performance without garbage collection overhead. The driver supports common MongoDB features like CRUD operations, indexing, aggregations, change streams, and transactions, enabling full use of the database’s capabilities from Rust applications. Because it’s asynchronous by design, it works well with Rust async runtimes like Tokio and async-std, letting developers build highly concurrent networked services that efficiently use modern multicore hardware. ... -
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qBittorrent RuTracker plugin
qBittorrent search engine plugin for rutracker
qBittorrent RuTracker plugin is a lightweight search engine extension designed to integrate the RuTracker torrent index directly into the qBittorrent client, allowing users to search for torrents without leaving the application interface. The plugin follows qBittorrent’s official search plugin architecture and is implemented as a Python script that communicates with the RuTracker website to retrieve and display search results. By embedding this functionality into the client, it streamlines... -
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VideoRAG
"VideoRAG: Chat with Your Videos
VideoRAG is a retrieval-augmented generation (RAG) framework tailored for video content that enables AI systems to answer questions, summarize, and reason over long videos by combining visual embeddings with contextual search. The system works by first breaking video into clips, extracting visual and audio-textual features, and indexing them into embeddings, then using an LLM with a retriever to pull relevant segments on demand. When a user query is received, VideoRAG locates semantically relevant moments in the video using the embedding index, retrieves associated clips or transcripts, and feeds them to a generative model to produce accurate, grounded answers or summaries. ... -
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Pinot
Apache Pinot - A realtime distributed OLAP datastore
Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency. Pinot is designed to answer OLAP queries with low latency on immutable data and mutable data(Upsert Support) Pluggable indexing technologies - Sorted Index, Bitmap Index, Inverted Index, StarTree Index, Bloom Filter, Range Index, Text Search Index(Lucence/FST), Json Index, Geospatial Index. Near Realtime ingestion with Apache Kafka, Apache Pulsar, Kinesissupports JSON, Avro, ProtoBuf, Thrift formats. Apache Pinot supports dimension lookup joins currently. ... -
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RavenDB
ACID Document Database
A NoSQL document database designed for high-performance, real-time applications with built-in distributed capabilities. -
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Advanced + Agentic RAG Cookbooks
Advanced RAG cookbooks for building accurate LLM applications
...It provides ready-to-use notebooks, implementations, and explanations that help developers move from basic RAG setups to more sophisticated workflows. Athina AI’s RAG Cookbooks covers the full RAG pipeline, including indexing, retrieval, augmentation, and generation, while also addressing evaluation to measure accuracy and relevance. It includes multiple approaches such as hybrid search, contextual compression, and agent-based retrieval strategies, allowing users to experiment and compare methods. It is designed to reduce development time by offering practical examples and references to research papers, making it useful for both learning and production use. ... -
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HeavyDB
HeavyDB (formerly MapD/OmniSciDB)
...The system is built as a SQL-based relational columnar database engine that leverages modern hardware parallelism, including GPUs and multicore CPUs. Its architecture allows users to query datasets containing billions of rows in milliseconds without requiring traditional indexing, pre-aggregation, or sampling techniques. HeavyDB was originally developed as part of the OmniSci platform (formerly MapD) and is commonly used for large-scale analytics and geospatial data processing. The database compiles queries into optimized machine code that executes efficiently on GPU hardware, significantly accelerating analytical workloads. ... -
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ccls
C/C++/ObjC language server supporting cross references & hierarchies
C/C++/ObjC language server supporting cross-references, hierarchies, completion, and semantic highlighting. It has a global view of the code base and supports a lot of cross-reference features, see wiki/FAQ. It starts indexing the whole project (including subprojects if exist) parallelly when you open the first file, while the main thread can serve requests before the indexing is complete. Saving files will incrementally update the index. Hierarchies, call (caller/callee) hierarchy, inheritance (base/derived) hierarchy, member hierarchy. Symbol rename. ... -
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DocuBrowse
This does for Documents what repo-browser does for repos
DocuBrowse is a local AI-powered document search engine for personal or professional file collections. It indexes documents such as PDFs, Word files, spreadsheets, presentations, ebooks, HTML, text, and Markdown. The system combines SQLite FTS5 keyword search with local semantic embeddings for meaning-based retrieval. Users can click search results to generate an AI synopsis before opening the original file. It runs entirely on the user’s machine with Ollama models, so it does not require... -
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clip-retrieval
Easily compute clip embeddings and build a clip retrieval system
...The system is optimized for performance and scalability, capable of processing tens or even hundreds of millions of embeddings using GPU acceleration. It includes components for inference, indexing, filtering, and serving results through APIs, making it a complete pipeline for building production-ready retrieval systems. The framework also supports querying by image, text, or embedding, enabling flexible use cases such as reverse image search or multimodal content discovery. Additionally, it provides a simple frontend interface and backend services that can be deployed to expose search functionality to users. -
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Cognita
Open source RAG framework for building scalable modular AI apps
...It includes both a backend service and a frontend interface, enabling users to upload documents, experiment with configurations, and perform question-answering tasks interactively. Cognita supports incremental indexing, meaning it processes only new or updated data to reduce computational overhead and improve efficiency. -
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SemTools
Semantic search and document parsing tools for the command line
SemTools is an open-source command-line toolkit designed for document parsing, semantic indexing, and semantic search workflows. The project focuses on enabling developers and AI agents to process large document collections and extract meaningful semantic representations that can be searched efficiently. Built with Rust for performance and reliability, the toolchain provides fast processing of text and structured documents while maintaining low system overhead. -
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Pathway AI Pipelines
Ready-to-run cloud templates for RAG
...It supports numerous connectors including local files, Google Drive, SharePoint, Kafka, PostgreSQL, and real-time APIs, making it suitable for enterprise data environments. The templates include built-in indexing, vector search, hybrid search, and caching capabilities that remove the need to assemble separate infrastructure components. Developers can run the applications locally or deploy them to cloud platforms using Docker with minimal setup. Overall, llm-app functions as a practical accelerator for teams building real-time, production-ready AI knowledge systems.
