Search Results for "find text"
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Showing 38 open source projects for "find text"
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Papermerge
Open Source Document Management System for Digital Archives
...OCR technology is vital part of Papermerge. It extracts text information from scanned documents, PDF, JPEG, TIFF files. -
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Mantic.sh
A structural code search engine for Al agents
Mantic.sh is a context-aware, structural code search engine designed specifically for use with AI coding agents and developers who need deep, semantically relevant search across large codebases. Unlike traditional text-based search tools that mainly match keywords, Mantic.sh understands code structure and meaning by combining syntactic heuristics with neural semantic reranking to produce results that reflect conceptual relevance, which helps find functions, definitions, and patterns that literal search might miss. It uses local embeddings and code graph awareness so that queries like “authentication flow” return not just superficially matching text but contextually related code across multiple repositories. ... -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an... -
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MTEB
MTEB: Massive Text Embedding Benchmark
...To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. -
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Translate-Subtitle-File
Subtitle Creation Assistant
Subtitle group machine translation assistant - [Function 1: Translate subtitle file] .srt .ass .vtt [Function 2: Voice to text] (Drag in video or audio to recognize subtitles) (The latest version v4.1.0 Update time 2021 2 May 23) 12 translation service providers can be configured, such as Google, Baidu, Tencent, Caiyun, IBM, Azure, Amazon, etc. (6 voice service providers can be configured: Alibaba Cloud, Xunfei, Tencent Cloud, IBM, Azure, Amazon ) Advantages: 1. You can use multiple service... -
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Open Notebook
An Open Source implementation of Notebook LM with more flexibility
Open Notebook is an open-source, privacy-focused alternative to Google’s Notebook LM that gives users full control over their research and AI workflows. Designed to be self-hosted, it ensures complete data sovereignty by keeping your content local or within your own infrastructure. The platform supports 16+ AI providers—including OpenAI, Anthropic, Ollama, Google, and LM Studio—allowing flexible model choice and cost optimization. Open Notebook enables users to organize and analyze... -
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Grounded-Segment-Anything
Marrying Grounding DINO with Segment Anything & Stable Diffusion
Grounded-Segment-Anything is a research-oriented project that combines powerful open-set object detection with pixel-level segmentation and subsequent creative workflows, effectively enabling detection, segmentation, and high-level vision tasks guided by free-form text prompts. The core idea behind the project is to pair Grounding DINO — a zero-shot object detector that can locate objects described by natural language — with Segment Anything Model (SAM), which can produce detailed masks for objects once they are localized. This fusion lets users provide arbitrary text descriptions (e.g., “a cat, a bicycle, or a coffee mug”), have the detection model find relevant bounding boxes, and then use SAM to generate precise segmentation masks that isolate each object in the scene. -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase mining. ... -
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Free LLM API resources
A list of free LLM inference resources accessible via API
...It collects a wide range of resources including hosted free-tier LLM APIs, documentation links, public model endpoints, open datasets useful for training or evaluation, tooling integrations, and examples showing how to interact with these services in real applications. This list helps developers, hobbyists, and researchers quickly find models they can use for prototyping, experimentation, or production proofs-of-concept without needing paid subscriptions, reducing friction for innovation. The repository typically categorizes offerings by provider, type of service (text, embeddings, vision), availability conditions (open without key, free tier with key), and usage examples to make discovery and adoption easier. -
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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Argilla
The open-source data curation platform for LLMs
Argilla is a production-ready framework for building and improving datasets for NLP projects. Deploy your own Argilla Server on Spaces with a few clicks. Use embeddings to find the most similar records with the UI. This feature uses vector search combined with traditional search (keyword and filter based). Argilla is free, open-source, and 100% compatible with major NLP libraries (Hugging Face transformers, spaCy, Stanford Stanza, Flair, etc.). In fact, you can use and combine your preferred... -
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files-to-prompt
Concatenate a directory full of files into a single prompt
...It includes rich filtering controls, letting you limit by extension, include or skip hidden files, and ignore paths that match glob patterns or .gitignore rules. The output format is flexible: you can emit plain text, Markdown with fenced code blocks, or a Claude-XML style format designed for structured multi-file prompts. It can read file paths from stdin (including NUL-separated paths), which makes it easy to combine with find, rg, or other shell tools. -
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txtai
Build AI-powered semantic search applications
...Machine-learning pipelines to run extractive question-answering, zero-shot labeling, transcription, translation, summarization and text extraction. Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes). Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai. -
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PandaWiki
AI-powered open source platform for building intelligent wiki bases
PandaWiki is an open source knowledge base system designed to help users build intelligent documentation platforms powered by large language models. It combines traditional wiki functionality with modern AI capabilities, allowing teams and individuals to create and manage product documentation, technical manuals, FAQs, and blog-style knowledge resources. PandaWiki provides tools for managing knowledge bases through an administrative interface while also generating public-facing wiki sites... -
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SeaGOAT
local-first semantic code search engine
...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. The tool runs locally on a developer’s machine and processes repositories using a combination of embedding models and conventional search utilities, enabling both semantic and text-based retrieval methods. By combining vector search with tools like ripgrep, SeaGOAT provides a hybrid approach that supports both natural language queries and precise keyword matching in source files. ... -
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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. In addition to semantic search, grepai offers call graph tracing so developers can understand which functions call or are called by others, aiding impact analysis and confident refactoring. ... -
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WrenAI
Open-source SQL AI Agent for Text-to-SQL. Make Text2SQL Easy
Wren AI is a SQL AI Agent for data teams to get results and insights faster by asking business questions without writing SQL, and it's open-source. Wren AI has implemented a semantic engine architecture to provide the LLM context of your business; you can easily establish a logical presentation layer on your data schema that helps LLM learn more about your business context. With Wren AI, you can process metadata, schema, terminology, data relationships, and the logic behind calculations and... -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
We are working on new way for visual programming. We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data,... -
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Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you... -
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ArXiv MCP Server
A Model Context Protocol server for searching and analyzing arXiv
arxiv-mcp-server bridges AI assistants and the arXiv repository through a clean MCP interface, enabling search, metadata retrieval, and content access without bespoke scraping. With simple tools like “search” and “fetch,” an agent can find papers, pull abstracts, and download PDFs for downstream summarization or analysis. The project includes packaging and CI to publish to PyPI, plus tests and linting for reliability. Issue threads show feature requests such as extracting embedded LaTeX and... -
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ML Ferret
Refer and Ground Anything Anywhere at Any Granularity
...The repo presents the vision-language pipeline, model assets, and paper resources that show how Ferret answers questions, follows instructions, and returns grounded outputs rather than just text. In practice, this enables tasks like “find that small red icon next to the chart and describe it” where both the linguistic reference and the visual region are ambiguous without fine spatial reasoning. -
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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. ... -
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AnyTXT Searcher
A Powerful Desktop Full-Text Search Engine, Just Like Local Google.
...You can quickly find any text in any file on your disk by Anytxt almost in 0.1 second. It works on Windows 11,10, 8, 7, Vista, XP, 2008, 2012, 2016,2022... AnyTXT Searcher supports the following file formats: Plain text (txt, cpp, py, html, etc.) Microsoft OneNote (one) Microsoft Word (doc, docx) Microsoft Excel (xls, xlsx) Microsoft PowerPoint (ppt, pptx) PDF WPS Office (wps, et, dps) EBook (epub, mobi, azw3, fb2 etc.) -
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refinery
Open-source choice to scale, assess and maintain natural language data
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact. You are one of the people we've built refinery for. refinery helps you to build better NLP models in a data-centric approach. Semi-automate your labeling, find low-quality subsets in your training data, and monitor your data in one place. refinery doesn't get rid of manual labeling, but it makes sure that your valuable time is spent well. Also, the makers... -
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MyBox
Easy Tools of PDF, Image, File, Network, Data, and Medias
javafx-desktop-apps pdf image ocr icc barcode color-palette text bytes markdown html archive compress digest video audio editor converter media https://github.com/Mararsh/MyBox Self-contain packages need not java env nor installation. Jar packages need Java 16 or higher. -
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General Knowledge Machine Project
Intellect Modeling Kit: assisting research, diagnostics, consulting
We humans are bound by intellectual abilities. All knowledge is far beyond power of any person. The only way to apply knowledge is to build machines able to present it human way but not limited by volume. Intellect Modeling Kit (IMK) is intended to build knowledge machines (KM) assisting experts on the steps of activity: * Observation; * Producing propositions based on knowledge; * Elimination of impossible propositions; * Selection and verification of the most appropriate...