asgeirtj/system_prompts_leaks
This repository provides a collection of extracted system prompts from various large language models (LLMs) including Anthropic's Claude, OpenAI's ChatGPT, Google's Gemini, and xAI's Grok, updated ...
Awesome AI Agents › Prompt Libraries
parthsarthi03/raptor
RAPTOR (Recursive Abstractive Processing for Tree-Organized Retrieval) is an innovative retrieval-augmented language model framework designed to enhance information retrieval from large text documents. It introduces a recursive tree structure to organize and process documents, enabling more efficient and context-aware retrieval compared to traditional language models. This approach addresses common limitations in handling extensive texts by breaking down documents into a hierarchical tree, allowing for recursive summarization and retrieval at multiple levels of granularity. The project provides an official implementation of RAPTOR, including tools to add documents, build the retrieval tree, and answer questions based on the indexed content. Users can easily set up RAPTOR by cloning the repository, installing dependencies, and configuring the system with an OpenAI API key. The framework supports adding text documents to the tree structure, querying the system with questions, and retrieving contextually relevant answers. RAPTOR is designed with extensibility in mind, allowing users to integrate custom models for summarization, question answering (QA), and embedding generation. This flexibility enables the use of various language models and embedding techniques tailored to specific needs. The repository includes base classes for these components, which users can extend to implement their own logic. The project also supports saving and loading the constructed tree, facilitating reuse and efficient management of indexed data. RAPTOR is open-source under the MIT License and encourages community contributions to improve and expand its capabilities. Overall, RAPTOR represents a significant advancement in retrieval-augmented language models by leveraging recursive, tree-organized processing to improve the efficiency and accuracy of information retrieval from large documents. It is suitable for applications requiring deep understanding and retrieval of information from complex, lengthy texts.
https://github.com/parthsarthi03/raptor
Also in Prompt Libraries
HKUDS/RAG-Anything
RAG-Anything is a next-generation all-in-one multimodal Retrieval-Augmented Generation system that processes and queries diverse document content including text, images, tables, and equations within a unified framework.
dzhng/deep-research
Open Deep Research is an AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models to generate comprehensive markdown reports.
dottxt-ai/outlines
Outlines is an open-source tool that enables structured text generation with large language models, ensuring predictable and schema-compliant outputs for various applications.
microsoft/RD-Agent
RD-Agent is an open-source R&D automation tool by Microsoft designed to automate and enhance industrial research and development processes focused on data and models using AI.
arcee-ai/mergekit
mergekit is a toolkit for efficiently merging pre-trained large language models using various algorithms, supporting CPU and GPU execution, and enabling creation of versatile merged models with maintained inference costs.
icip-cas/PPTAgent
PPTAgent is an innovative system that automatically generates and evaluates high-quality presentations from documents using a two-phase approach and a comprehensive multi-dimensional evaluation framework.
serenakeyitan/awesome-notebookLM-prompts
A curated collection of NotebookLM and Kael.im slide prompts designed to generate presentation-ready decks from various content types.