Parsera is a lightweight Python library designed for scraping websites using LLMs. It offers simplicity and efficiency by minimizing token usage, enhancing speed, and reducing costs. Users can easily set up and run the tool to extract specific elements from web pages, generating JSON output with relevant data. Additionally, Parsera supports integration with various chat models, such as Azure, expanding its functionality and customization options for web scraping tasks.

SimpleAI is a self-hosted alternative to the not-so-open AI API, focused on replicating main endpoints for LLM such as text completion, chat, edits, and embeddings. It allows quick experimentation with different models, creating benchmarks, and handling specific use cases without relying on external services. Users can integrate and declare models through gRPC, query endpoints using Swagger UI or API, and resolve common issues like CORS with FastAPI middleware. The project is open for contributions and welcomes PRs, issues, documentation, and more.

LangServe helps developers deploy `LangChain` runnables and chains as a REST API. This library is integrated with FastAPI and uses pydantic for data validation. In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.

`npcsh` is a python-based command-line tool designed to integrate Large Language Models (LLMs) and Agents into one's daily workflow by making them available and easily configurable through the command line shell. It leverages the power of LLMs to understand natural language commands and questions, execute tasks, answer queries, and provide relevant information from local files and the web. Users can also build their own tools and call them like macros from the shell. `npcsh` allows users to take advantage of agents (i.e. NPCs) through a managed system, tailoring NPCs to specific tasks and workflows. The tool is extensible with Python, providing useful functions for interacting with LLMs, including explicit coverage for popular providers like ollama, anthropic, openai, gemini, deepseek, and openai-like providers. Users can set up a flask server to expose their NPC team for use as a backend service, run SQL models defined in their project, execute assembly lines, and verify the integrity of their NPC team's interrelations. Users can execute bash commands directly, use favorite command-line tools like VIM, Emacs, ipython, sqlite3, git, pipe the output of these commands to LLMs, or pass LLM results to bash commands.

ChatWeb is a tool that can crawl web pages, extract text from PDF, DOCX, TXT files, and generate an embedded summary. It can answer questions based on text content using chatAPI and embeddingAPI based on GPT3.5. The tool calculates similarity scores between text vectors to generate summaries, performs nearest neighbor searches, and designs prompts to answer user questions. It aims to extract relevant content from text and provide accurate search results based on keywords. ChatWeb supports various modes, languages, and settings, including temperature control and PostgreSQL integration.

Minja is a minimalistic C++ Jinja templating engine designed specifically for integration with C++ LLM projects, such as llama.cpp or gemma.cpp. It is not a general-purpose tool but focuses on providing a limited set of filters, tests, and language features tailored for chat templates. The library is header-only, requires C++17, and depends only on nlohmann::json. Minja aims to keep the codebase small, easy to understand, and offers decent performance compared to Python. Users should be cautious when using Minja due to potential security risks, and it is not intended for producing HTML or JavaScript output.

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

Ash AI is a tool that provides a Model Context Protocol (MCP) server for exposing tool definitions to an MCP client. It allows for the installation of dev and production MCP servers, and supports features like OAuth2 flow with AshAuthentication, tool data access, tool execution callbacks, prompt-backed actions, and vectorization strategies. Users can also generate a chat feature for their Ash & Phoenix application using `ash_oban` and `ash_postgres`, and specify LLM API keys for OpenAI. The tool is designed to help developers experiment with tools and actions, monitor tool execution, and expose actions as tool calls.

LangChain Extract is a simple web server that allows you to extract information from text and files using LLMs. It is built using FastAPI, LangChain, and Postgresql. The backend closely follows the extraction use-case documentation and provides a reference implementation of an app that helps to do extraction over data using LLMs. This repository is meant to be a starting point for building your own extraction application which may have slightly different requirements or use cases.

Phidata is a framework for building AI Assistants with memory, knowledge, and tools. It enables LLMs to have long-term conversations by storing chat history in a database, provides them with business context by storing information in a vector database, and enables them to take actions like pulling data from an API, sending emails, or querying a database. Memory and knowledge make LLMs smarter, while tools make them autonomous.

Python-tgpt is a Python package that enables seamless interaction with over 45 free LLM providers without requiring an API key. It also provides image generation capabilities. The name _python-tgpt_ draws inspiration from its parent project tgpt, which operates on Golang. Through this Python adaptation, users can effortlessly engage with a number of free LLMs available, fostering a smoother AI interaction experience.

Aire is a modern Laravel form builder with a focus on expressive and beautiful code. It allows easy configuration of form components using fluent method calls or Blade components. Aire supports customization through config files and custom views, data binding with Eloquent models or arrays, method spoofing, CSRF token injection, server-side and client-side validation, and translations. It is designed to run on Laravel 5.8.28 and higher, with support for PHP 7.1 and higher. Aire is actively maintained and under consideration for additional features like read-only plain text, cross-browser support for custom checkboxes and radio buttons, support for Choices.js or similar libraries, improved file input handling, and better support for content prepending or appending to inputs.

A chat interface using open source models, eg OpenAssistant or Llama. It is a SvelteKit app and it powers the HuggingChat app on hf.co/chat.

Perplexity is a module that utilizes emailnator to generate new accounts, providing users with 5 pro queries per account creation. It enables the creation of new Gmail accounts with emailnator, ensuring unlimited pro queries. The tool requires specific Python libraries for installation and offers both a web interface and an API for different usage scenarios. Users can interact with the tool to perform various tasks such as account creation, query searches, and utilizing different modes for research purposes. Perplexity also supports asynchronous operations and provides guidance on obtaining cookies for account usage and account generation from emailnator.

Dexter is a set of mature LLM tools used in production at Dexa, with a focus on real-world RAG (Retrieval Augmented Generation). It is a production-quality RAG that is extremely fast and minimal, and handles caching, throttling, and batching for ingesting large datasets. It also supports optional hybrid search with SPLADE embeddings, and is a minimal TS package with full typing that uses `fetch` everywhere and supports Node.js 18+, Deno, Cloudflare Workers, Vercel edge functions, etc. Dexter has full docs and includes examples for basic usage, caching, Redis caching, AI function, AI runner, and chatbot.

Open-DocLLM is an open-source project that addresses data extraction and processing challenges using OCR and LLM technologies. It consists of two main layers: OCR for reading document content and LLM for extracting specific content in a structured manner. The project offers a larger context window size compared to JP Morgan's DocLLM and integrates tools like Tesseract OCR and Mistral for efficient data analysis. Users can run the models on-premises using LLM studio or Ollama, and the project includes a FastAPI app for testing purposes.

Awesome AI is a repository that collects and shares resources in the fields of large language models (LLM), AI-assisted programming, AI drawing, and more. It explores the application and development of generative artificial intelligence. The repository provides information on various AI tools, models, and platforms, along with tutorials and web products related to AI technologies.

QMedia is an open-source multimedia AI content search engine designed specifically for content creators. It provides rich information extraction methods for text, image, and short video content. The tool integrates unstructured text, image, and short video information to build a multimodal RAG content Q&A system. Users can efficiently search for image/text and short video materials, analyze content, provide content sources, and generate customized search results based on user interests and needs. QMedia supports local deployment for offline content search and Q&A for private data. The tool offers features like content cards display, multimodal content RAG search, and pure local multimodal models deployment. Users can deploy different types of models locally, manage language models, feature embedding models, image models, and video models. QMedia aims to spark new ideas for content creation and share AI content creation concepts in an open-source manner.

This repository is part of Intelligent Document Processing with AWS AI Services workshop. It aims to automate the extraction of information from complex content in various document formats such as insurance claims, mortgages, healthcare claims, contracts, and legal contracts using AWS Machine Learning services like Amazon Textract and Amazon Comprehend. The repository provides hands-on labs to familiarize users with these AI services and build solutions to automate business processes that rely on manual inputs and intervention across different file types and formats.

ScrapeGraphAI is a web scraping Python library that utilizes LangChain, LLM, and direct graph logic to create scraping pipelines. Users can specify the information they want to extract, and the library will handle the extraction process. The tool is designed to simplify web scraping tasks by providing a streamlined and efficient approach to data extraction.

Parsera is a lightweight Python library designed for scraping websites using LLMs. It offers simplicity and efficiency by minimizing token usage, enhancing speed, and reducing costs. Users can easily set up and run the tool to extract specific elements from web pages, generating JSON output with relevant data. Additionally, Parsera supports integration with various chat models, such as Azure, expanding its functionality and customization options for web scraping tasks.

ScrapeGraphAI is a web scraping Python library that utilizes LangChain, LLM, and direct graph logic to create scraping pipelines. Users can specify the information they want to extract, and the library will handle the extraction process. This repository contains an official demo/trial for the ScrapeGraphAI library, showcasing its capabilities in web scraping tasks. The tool is designed to simplify the process of extracting data from websites by providing a user-friendly interface and powerful scraping functionalities.

You2Txt is a tool developed for the Vercel + Nvidia 2-hour hackathon that converts any YouTube video into a transcribed .txt file. The project won first place in the hackathon and is hosted at you2txt.com. Due to rate limiting issues with YouTube requests, it is recommended to run the tool locally. The project was created using Next.js, Tailwind, v0, and Claude, and can be built and accessed locally for development purposes.

Podwise is an AI knowledge management app designed specifically for podcast listeners. With the Podwise platform, you only need to follow your favorite podcasts, such as "Hardcore Hackers". When a program is released, Podwise will use AI to transcribe, extract, summarize, and analyze the podcast content, helping you to break down the hard-core podcast knowledge. At the same time, it is connected to platforms such as Notion, Obsidian, Logseq, and Readwise, embedded in your knowledge management workflow, and integrated with content from other channels including news, newsletters, and blogs, helping you to improve your second brain 🧠.

Extractor is an AI-powered data extraction library for Laravel that leverages OpenAI's capabilities to effortlessly extract structured data from various sources, including images, PDFs, and emails. It features a convenient wrapper around OpenAI Chat and Completion endpoints, supports multiple input formats, includes a flexible Field Extractor for arbitrary data extraction, and integrates with Textract for OCR functionality. Extractor utilizes JSON Mode from the latest GPT-3.5 and GPT-4 models, providing accurate and efficient data extraction.

ScrapeGraphAI is a Python library that uses Large Language Models (LLMs) and direct graph logic to create web scraping pipelines for websites, documents, and XML files. It allows users to extract specific information from web pages by providing a prompt describing the desired data. ScrapeGraphAI supports various LLMs, including Ollama, OpenAI, Gemini, and Docker, enabling users to choose the most suitable model for their needs. The library provides a user-friendly interface through its `SmartScraper` class, which simplifies the process of building and executing scraping pipelines. ScrapeGraphAI is open-source and available on GitHub, with extensive documentation and examples to guide users. It is particularly useful for researchers and data scientists who need to extract structured data from web pages for analysis and exploration.

Chaindesk is a no-code platform that allows users to easily set up a semantic search system for personal data without technical knowledge. It supports loading data from various sources such as raw text, web pages, files (Word, Excel, PowerPoint, PDF, Markdown, Plain Text), and upcoming support for web sites, Notion, and Airtable. The platform offers a user-friendly interface for managing datastores, querying data via a secure API endpoint, and auto-generating ChatGPT Plugins for each datastore. Chaindesk utilizes a Vector Database (Qdrant), Openai's text-embedding-ada-002 for embeddings, and has a chunk size of 1024 tokens. The technology stack includes Next.js, Joy UI, LangchainJS, PostgreSQL, Prisma, and Qdrant, inspired by the ChatGPT Retrieval Plugin.

Auto-News is an automatic news aggregator tool that utilizes Large Language Models (LLM) to pull information from various sources such as Tweets, RSS feeds, YouTube videos, web articles, Reddit, and journal notes. The tool aims to help users efficiently read and filter content based on personal interests, providing a unified reading experience and organizing information effectively. It features feed aggregation with summarization, transcript generation for videos and articles, noise reduction, task organization, and deep dive topic exploration. The tool supports multiple LLM backends, offers weekly top-k aggregations, and can be deployed on Linux/MacOS using docker-compose or Kubernetes.

SemanticFinder is a frontend-only live semantic search tool that calculates embeddings and cosine similarity client-side using transformers.js and SOTA embedding models from Huggingface. It allows users to search through large texts like books with pre-indexed examples, customize search parameters, and offers data privacy by keeping input text in the browser. The tool can be used for basic search tasks, analyzing texts for recurring themes, and has potential integrations with various applications like wikis, chat apps, and personal history search. It also provides options for building browser extensions and future ideas for further enhancements and integrations.

1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

Agently Daily News Collector is an open-source project showcasing a workflow powered by the Agent ly AI application development framework. It allows users to generate news collections on various topics by inputting the field topic. The AI agents automatically perform the necessary tasks to generate a high-quality news collection saved in a markdown file. Users can edit settings in the YAML file, install Python and required packages, input their topic idea, and wait for the news collection to be generated. The process involves tasks like outlining, searching, summarizing, and preparing column data. The project dependencies include Agently AI Development Framework, duckduckgo-search, BeautifulSoup4, and PyYAM.