5 dépôts
Awesome GitHub RepositoriesLLM-Based Summarizations
Automated summarization of content extracted from URLs or PDFs using large language models.
Distinct from Content Summarization Tools: Distinct from Content Summarization Tools: specifically uses LLMs for summarization after web scraping or PDF conversion, not generic excerpt generation.
Explore 5 awesome GitHub repositories matching content management & publishing · LLM-Based Summarizations. Refine with filters or upvote what's useful.
Awesome LLM-Based Summarizations GitHub Repositories
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- pyspur-dev/pyspur
PySpur-Dev/pyspur
5,677Voir sur GitHubRoutes input through web scraping or PDF conversion, then summarizes extracted text using an LLM.
TypeScriptagentagentsai - campsite/campsite
campsite/campsite
4,829Voir sur GitHubCampsite est un outil de communication asynchrone et une suite d'espace de travail intégré conçue pour la coordination d'équipe et le partage de connaissances. Il fournit une interface unifiée qui combine des publications longues, de la documentation et le suivi de projets pour réduire le bruit associé au chat en temps réel. La plateforme est un environnement de collaboration alimenté par LLM qui utilise des modèles de langage de grande taille pour générer des résumés concis de fils de discussion étendus et de transcriptions de réunions. Elle se distingue en outre comme une passerelle d'automatisation par webhook, permettant la synchronisation des discussions internes avec des services et outils tiers externes. L'espace de travail couvre un large éventail de capacités de collaboration, notamment la visioconférence intégrée, l'organisation du travail en équipe et une base de connaissances consultable créée par le croisement des publications. Il inclut également des boucles de rétroaction asynchrones, le suivi de la résolution des conversations et un système de notification multicanal délivrant des mises à jour par e-mail et notification web. L'accès sécurisé est géré via un système d'authentification OAuth 2.0 utilisant les fournisseurs d'identité Google.
Processes long conversation threads through large language models to generate concise recaps.
TypeScript - madawei2699/mygptreader
madawei2699/myGPTReader
4,418Voir sur GitHubmyGPTReader is a suite of large language model applications including a chat interface, a document analysis tool, and a news aggregator. The system focuses on extracting information from digital files and web content to enable conversational analysis and automated content condensation. The project features a prompt template manager to structure conversation flows and increase response accuracy. It also includes a multilingual voice chat client that integrates speech-to-text and text-to-speech for real-time interactive tutoring and language practice. The platform covers broader capabilities i
Uses large language models to automatically summarize content extracted from URLs and PDFs.
Python - steipete/summarize
steipete/summarize
3,771Voir sur GitHubSummarize is a command line tool and multimodal content extractor designed to generate concise summaries from web pages, documents, and media files. It functions as an orchestrator that connects developer tools to various language model providers to process and condense information. The system provides specialized capabilities for audio and video processing, including transcription with speaker identification and the extraction of timestamped visual markers from video slides. It also includes a translation utility to convert generated summaries and extracted text into different target languag
Generates concise summaries from web pages, documents, and media files using large language models.
TypeScriptaiclisummarize - volcengine/openviking
volcengine/OpenViking
2,993Voir sur GitHubOpenViking is a multi-tenant context server and knowledge base administration system designed to provide AI agents with persistent long-term memory. It enables the indexing of diverse documents and codebases to support retrieval-augmented generation, allowing agents to recall past interactions, user preferences, and learned experiences across sessions. The project is distinguished by its use of a URI-based virtual filesystem to organize memories, resources, and skills. It implements a tiered context loading system that balances retrieval precision with token budgets by structuring data into a
Generates summaries for source code using AST skeleton extraction or LLM-based analysis.
Pythonagentagentic-ragai-agents
