Search Results for "semantic search"
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Showing 195 open source projects for "semantic search"
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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. -
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Telegram Search
AI-powered Telegram chat backup and semantic search tool system
Telegram Search is a self-hosted tool designed to export, back up, and intelligently search Telegram chat histories using modern AI techniques. It addresses the limitations of Telegram’s native search by enabling accurate retrieval of messages across languages through advanced tokenization and semantic understanding. Telegram Search processes chat data into searchable formats, including vector embeddings, which allow users to perform fuzzy and meaning-based searches instead of relying solely on exact keyword matches. ... -
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rag-search
RAG Search API
rag-search is a lightweight Retrieval-Augmented Generation API service designed to provide structured semantic search and answer generation through a simple FastAPI backend. The project integrates web search, vector embeddings, and reranking logic to retrieve relevant context before passing it to a language model for response generation. It is built to be easily deployable, requiring only environment configuration and dependency installation to run a functional RAG service. ... -
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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. ... -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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QMD
mini cli search engine for your docs, knowledge bases, etc.
QMD is a powerful and lightweight command-line tool that acts as an on-device search engine for your personal knowledge base, allowing you to index and search files like Markdown notes, meeting transcripts, technical documentation, and other text collections without depending on cloud services. Designed to keep all search activity local, it combines classic full-text search techniques with modern semantic features such as vector similarity and hybrid ranking so that queries return not just literal matches but conceptually relevant results. ... -
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Memvid
Video-based AI memory library. Store millions of text chunks in MP4
Memvid encodes text chunks as QR codes within MP4 frames to build a portable “video memory” for AI systems. This innovative approach uses standard video containers and offers millisecond-level semantic search across large corpora with dramatically less storage than vector DBs. It's self-contained—no DB needed—and supports features like PDF indexing, chat integration, and cloud dashboards. -
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pgvector
Open-source vector similarity search for Postgres
pgvector is an open-source PostgreSQL extension that equips PostgreSQL databases with vector data storage, indexing, and similarity search capabilities—ideal for embeddings-based applications like semantic search and recommendations. You can add an index to use approximate nearest neighbor search, which trades some recall for speed. Unlike typical indexes, you will see different results for queries after adding an approximate index. An HNSW index creates a multilayer graph. ... -
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MCP Server Qdrant
An official Qdrant Model Context Protocol (MCP) server implementation
The Qdrant MCP Server is an official Model Context Protocol server that integrates with the Qdrant vector search engine. It acts as a semantic memory layer, allowing for the storage and retrieval of vector-based data, enhancing the capabilities of AI applications requiring semantic search functionalities. -
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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. -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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OceanBase seekdb
The AI-Native Search Database
seekdb is an AI-native search database from OceanBase that unifies vector, full-text, relational, JSON, and GIS data into a single query engine. The system is designed to support hybrid search workloads and in-database AI workflows without requiring multiple specialized databases. It enables developers to perform semantic search, keyword search, and structured SQL queries within the same platform, simplifying modern AI application stacks. seekdb also embeds AI capabilities directly in the database layer, including embedding generation, reranking, and LLM inference for end-to-end RAG pipelines. ... -
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Semantra
Multi-tool for semantic search
Semantra is an open-source semantic search tool designed to help users explore large collections of documents by meaning rather than simple keyword matching. The software analyzes text and PDF documents stored locally and creates embeddings that allow queries to retrieve results based on conceptual similarity. It is primarily intended for individuals who need to extract insights from large document collections, including researchers, journalists, students, and historians. ... -
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CocoIndex
ETL framework to index data for AI, such as RAG
CocoIndex is an open-source framework designed for building powerful, local-first semantic search systems. It lets users index and retrieve content based on meaning rather than keywords, making it ideal for modern AI-based search applications. CocoIndex leverages vector embeddings and integrates with various models and frameworks, including OpenAI and Hugging Face, to provide high-quality semantic understanding. It’s built for transparency, ease of use, and local control over your search data, distinguishing itself from closed, black-box systems. ... -
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grepai
Semantic Search & Call Graphs for AI Agents
grepai is a privacy-first, semantic code search CLI designed to replace traditional keyword-based search with meaning-aware queries, letting developers and code tools find relevant code by what it does rather than just text matches. It builds a semantic index of a project using vector embeddings, enabling natural language queries like “authentication logic” to return contextually relevant functions and modules even when naming differs dramatically, making code exploration far more intuitive. ... -
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Hands-On Large Language Models
Official code repo for the O'Reilly Book
...The repository is structured into chapters that align with the educational progression of the book — covering everything from foundational topics like tokens, embeddings, and transformer architecture to advanced techniques such as prompt engineering, semantic search, retrieval-augmented generation (RAG), multimodal LLMs, and fine-tuning. Each chapter contains executable Jupyter notebooks that are designed to be run in environments like Google Colab, making it easy for learners to experiment interactively with models, visualize attention patterns, implement classification and generation tasks. -
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yt-fts
Search all of YouTube from the command line
...The tool returns search results with timestamps and direct links to the exact moment in the video where the phrase occurs. In addition to traditional keyword search, the system supports experimental semantic search capabilities using embeddings from AI services and vector databases. This allows users to search videos by meaning rather than only exact keywords. -
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MindSearch
An LLM-based Multi-agent Framework of Web Search Engine
MindSearch is an AI-powered search engine based on large language models (LLMs) designed for deep semantic search and retrieval. It leverages InternLM's language model to understand complex queries and retrieve highly relevant answers from large datasets. -
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PaperAI
Semantic search and workflows for medical/scientific papers
PaperAI is an open-source framework for searching and analyzing scientific papers, particularly useful for researchers looking to extract insights from large-scale document collections. -
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SeaGOAT
local-first semantic code search engine
SeaGOAT is an open-source semantic code search engine designed to help developers explore and understand large codebases more efficiently. Instead of relying solely on traditional keyword search, it uses vector embeddings to represent the meaning of code and queries, allowing users to perform semantic searches that find relevant code even when the exact keywords are not present. -
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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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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
...It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients. -
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Eigenfocus
Self-Hosted - Project Management, Planning and Time Tracker
Eigenfocus is an AI-powered personal knowledge management system that uses embeddings and semantic search to help users organize and retrieve ideas across documents. Designed for researchers and creatives, it enables deep linking between notes and supports querying based on meaning rather than keywords. -
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txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). ... -
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mgrep
A calm, CLI-native way to semantically grep everything, like code
This project is a modern, semantic search tool that brings the simplicity of traditional command-line grep to the world of natural language and multimodal content, enabling users to search across codebases, documents, PDFs, and even images using meaning-aware queries. Built with a focus on calm CLI experiences, it lets you index and query your local files with semantic understanding, delivering results that are relevant to your intent rather than simple pattern matches, which is especially powerful in large or diverse projects. ... -
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VectorChord
Scalable, fast, and disk-friendly vector search in Postgres
...It supports efficient vector indexing and retrieval using ANN (approximate nearest neighbor) algorithms and is optimized for integration with LLM and AI applications. VectorChord is lightweight and can be embedded in a variety of environments for fast semantic search. -
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OpenViking
Context database designed specifically for AI Agents
...It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. The project is implemented with performance in mind, often leveraging optimized data structures that balance fast reads and writes with minimal resource consumption. Developers can integrate OpenViking into modern AI stacks to unify context storage across services, enabling consistent session history, personalized responses, and richer search experiences.
