Search Results for "document indexing"
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Showing 77 open source projects for "document indexing"
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Nitrite Database
NoSQL embedded document store for Java
Nitrite is an embedded NoSQL database for Java applications, offering lightweight document storage with indexing and query capabilities. -
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Search-Index
A persistent, network resilient, full text search library
Search-Index is a lightweight and fast JavaScript-based search engine that enables full-text search indexing and retrieval for web applications. -
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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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fess
Open source enterprise search server for websites, files, and data
...It enables organizations to quickly deploy a scalable search environment without requiring deep knowledge of underlying search technologies. Fess is built on top of OpenSearch and offers an integrated solution for crawling, indexing, and searching documents from websites, file systems, and various data stores. Fess includes a built-in crawler that can collect content from sources such as databases, CSV files, and shared storage, making it suitable for centralized knowledge discovery. It supports indexing and searching across many document formats including office documents, PDFs, and compressed archives. ... -
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PoloDB
PoloDB is an embedded document database
PoloDB is an embedded document-oriented NoSQL database that provides MongoDB-like functionality in a lightweight package, ideal for local storage in applications. -
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WeKnora
LLM framework for document understanding and semantic retrieval
WeKnora is an open source framework developed for deep document understanding and semantic information retrieval using large language models. It focuses on analyzing complex and heterogeneous documents by combining multiple processing stages such as multimodal document parsing, vector indexing, and intelligent retrieval. It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. ... -
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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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bleve
A modern text indexing library for go
...Bleve includes general-purpose analyzers as well as pre-built text analyzers for the following languages, Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Persian, Portuguese, Romanian, Russian, Sorani, Spanish, Swedish, Thai, and Turkish. Support for aggregating facet information across search results. Supported facet types include Terms Facet, Numeric Range Facet, and Date Range Facet. By indexing your data with bleve you gain the ability to compose query types such as Term, Phrase, Match, Match Phrase, Prefix, Conjunction, Disjunction, Boolean, Numeric and Date Ranges, as well as Query String. Industry standard tf-idf scoring with query time boosting. Includes support for highlighting matching text within document fragments. -
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LightRAG
"LightRAG: Simple and Fast Retrieval-Augmented Generation"
LightRAG is a lightweight Retrieval-Augmented Generation (RAG) framework designed for efficient document retrieval and response generation. It is optimized for speed and lower resource consumption, making it ideal for real-time applications. -
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Kivik
Common interface to CouchDB or CouchDB-like databases for Go
Kivik is a Go client library for interacting with CouchDB and PouchDB databases, providing an abstraction layer for NoSQL document storage and retrieval. It simplifies database operations for Go developers. -
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ODMantic
Sync and Async ODM (Object Document Mapper) for MongoDB
Odmantic is an Object-Document Mapper (ODM) for MongoDB, designed for Python applications using Pydantic models, providing a seamless integration with type safety and validation. -
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Morphia
MongoDB object-document mapper in Java
MongoDB Object Document Mapping for the JVM. Bidirectional mapping to and from the database. Transparently map your Java entities to MongoDB documents and back. -
13
RAG API
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL
rag_api is an open-source REST API for building Retrieval-Augmented Generation (RAG) systems using LLMs like GPT. It lets users index documents, search semantically, and retrieve relevant content for use in generative AI workflows. Designed for rapid prototyping, it is ideal for chatbot development, document assistants, and knowledge-based LLM apps. -
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PixelRAG
The beginning of scalable pixel-native search
PixelRAG is a visual retrieval-augmented generation system that searches documents by how they look, not only by the text they contain. It renders web pages, PDFs, and images into screenshot tiles, then performs retrieval over those visual representations. This approach preserves layout, tables, charts, diagrams, infographics, and other visual structure that traditional HTML or text parsing can miss. The project includes tools for rendering, chunking, embedding, indexing, and serving visual... -
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Open Semantic Search
Open source semantic search and text analytics for large document sets
Open Semantic Search is an open source research and analytics platform designed for searching, analyzing, and exploring large collections of documents using semantic search technologies. It provides an integrated search server combined with a document processing pipeline that supports crawling, text extraction, and automated analysis of content from many different sources. Open Semantic Search includes an ETL framework that can ingest documents, process them through analysis steps, and... -
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Kernel Memory
Research project. A Memory solution for users, teams, and applications
Kernel Memory is an open-source reference architecture developed by Microsoft to help developers build memory systems for AI applications powered by large language models. The project focuses on enabling applications to store, index, and retrieve information so that AI systems can incorporate external knowledge when generating responses. It supports scenarios such as document ingestion, semantic search, and retrieval-augmented generation, allowing language models to answer questions using... -
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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... -
18
PageIndex
Document Index for Vectorless, Reasoning-based RAG
PageIndex is an innovative open-source framework that reimagines retrieval-augmented generation (RAG) by eliminating conventional vector similarity search and instead building hierarchical semantic indexes that mirror a document’s natural structure. Rather than chunking text and embedding it into a vector database, PageIndex constructs a tree-structured index — similar to a detailed, AI-enhanced table of contents — that a large language model can traverse to locate the most relevant sections... -
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OCRBase
MD/.JSON Document OCR and structured data extraction API
...The core output is designed for downstream automation, producing structured results like JSON according to user-defined schemas while also providing readable formats like Markdown for human review or indexing. It includes real-time job progress updates via WebSockets, which makes it easier to integrate into UIs, dashboards, or ingestion systems where users need feedback on long-running document processing. -
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All-in-RAG
Big Model Application Development Practice 1
...The repository provides a structured learning path that covers both theoretical foundations and practical implementation steps for RAG systems. It explains the full development pipeline required to create knowledge-aware AI assistants, including data preparation, document indexing, vector embedding generation, and retrieval strategies. The project also explores advanced topics such as hybrid retrieval methods, query optimization, and evaluation techniques for improving system accuracy. Alongside theoretical explanations, the repository includes hands-on exercises and example projects that demonstrate how to build production-ready RAG systems. ... -
21
shuyuan
Reading book source
...The name suggests “academy” or “study hall,” and the tool aims to help users ingest, organize, and manage reading content — possibly offering features like text parsing, annotation, metadata generation, translation, or storage for later reference. The repository is set up to support document ingestion, indexing, and maybe some AI-aided summarization or lookup functions, which helps users convert large text corpora into a structured, searchable knowledge base. For learners, researchers, or avid readers, Shuyuan offers a way to bridge from plain text files or eBooks into a manageable, interactive resource — one where notes, references, and reading progress can be tracked. ... -
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Pathway AI Pipelines
Ready-to-run cloud templates for RAG
Pathway AI Pipelines is a collection of ready-to-deploy AI pipeline templates designed to help developers rapidly build production-grade retrieval-augmented generation and enterprise search applications. The project provides end-to-end examples that connect live data sources to LLM workflows, enabling applications to stay synchronized with continuously changing information. It supports numerous connectors including local files, Google Drive, SharePoint, Kafka, PostgreSQL, and real-time APIs,... -
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MongoDB Rust Driver
The official MongoDB Rust Driver
...The crate also includes BSON encoding and decoding that maps cleanly to Rust types, so developers can work with rich document structures while retaining Rust’s performance guarantees. -
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ccls
C/C++/ObjC language server supporting cross references & hierarchies
Document symbols and approximate search of workspace symbol. Hover information. Diagnostics and code actions (clang FixIts). Semantic highlighting and preprocessor skipped regions. -
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gensim
Topic Modelling for Humans
Gensim is a Python library for topic modeling, document indexing, and similarity retrieval with large corpora. The target audience is the natural language processing (NLP) and information retrieval (IR) community.
