9 repository-uri
Capabilities for supplying an entire code repository as a data source so the chatbot assists with programming tasks.
Distinct from Knowledge Retrieval Sources: Distinct from Knowledge Retrieval Sources: focuses on code repositories as a specific source type, not general document attachment.
Explore 9 awesome GitHub repositories matching artificial intelligence & ml · Codebase Knowledge Sources. Refine with filters or upvote what's useful.
QAnything is a retrieval-augmented generation application framework and self-hosted AI interface. It functions as a system that combines a vector database knowledge base, a document parsing service, and a hybrid search engine to generate answers based on private user data. The project features a modular pipeline architecture that allows users to independently replace components such as parsers, embedding models, and reranking engines. It supports local-first model deployment and offline operation to ensure data privacy, and includes a two-stage retrieval pipeline that merges dense vector embe
Displays knowledge fragments and source logs to verify the exact documents used to generate a response.
OpenChat is a conversational AI agent builder and customer service automation platform that uses large language models to power customer support chatbots across multiple channels. It provides tools for defining AI agent behavior, training on custom knowledge, managing actions, and controlling autopilot responses per channel. The platform enables deploying AI agents on web, phone, email, SMS, and WhatsApp, with a unified inbox for managing conversations across all channels. It includes CRM synchronization, automated workflows, contact segmentation, and analytics for tracking customer satisfact
Ingests code repositories to enable AI assistance with programming questions.
Casibase is an open-source platform that orchestrates multi-turn conversations with large language models and manages retrieval-augmented knowledge bases from a single interface. It provides a unified system for connecting to over 30 AI model providers, ingesting documents into vector embeddings for semantic search, and running autonomous agent loops that can drive a browser, search the web, execute commands, and integrate with external tools. The platform distinguishes itself by combining AI conversation management with infrastructure and application orchestration capabilities. It includes a
Shows knowledge fragments and web sources with relevance scores for answer verification.
GLM-4.5 is a multimodal large language model and advanced reasoning system. It functions as an AI coding assistant, an autonomous AI agent, and a multimodal content generator capable of processing and generating text, images, audio, and video within a single unified system. The project is distinguished by its deep reasoning capabilities, utilizing chain-of-thought processing to solve complex mathematical, logical, and technical problems. It features an agentic architecture that allows for autonomous task execution, long-horizon goal planning, and the ability to interact with external tools an
Answers technical questions about a project by integrating codebase knowledge with external data sources.
scrape-it este un scraper web și parser HTML pentru Node.js, conceput pentru a extrage date structurate de pe site-uri web și fișiere HTML. Funcționează ca un instrument de extracție a datelor web care preia informații specifice din elementele DOM și convertește conținutul web în câmpuri de date utilizabile. Instrumentul folosește selectori CSS pentru a viza puncte de date specifice și utilizează maparea datelor bazată pe schemă pentru a organiza textul web nestructurat într-un format consistent. Suportă transformarea personalizată a valorilor pentru a converti șirurile brute extrase în formate de date specifice. Sistemul oferă capabilități pentru extracția datelor web și maparea automată a conținutului. Poate analiza conținut HTML provenit din URL-uri, șiruri HTML brute sau stocare locală și se integrează cu browsere headless pentru a procesa conținutul din pagini web dinamice.
Maps CSS selectors to predefined output fields to ensure structured and consistent data extraction.
s2client-proto provides a set of structured data definitions and language-neutral communication protocols used to exchange information between an external client and the StarCraft II engine. It utilizes protocol buffer definitions to establish a binary serialization schema for these data exchanges. The project defines the underlying communication layer necessary for game engine automation, game state analysis, and the development of software agents for competitive gaming. The framework covers interface definition languages to ensure compatibility across programming languages and employs sche
Translates raw game state identifiers into human-readable names using predefined mapping files.
This project is an AI-powered development tool and IDE extension designed for codebase searching, automated code refactoring, and prompt context management. It functions as an LLM-driven code editor that enables users to rewrite code, scan projects, and track task completion using large language models. The system features a prompt context manager that automatically attaches relevant files and rule sets to requests to improve accuracy. It includes a codebase search tool that uses natural language prompts to locate specific logic and provide explanatory notes across a project. The tool covers
Uses the entire code repository as a data source to assist with programming tasks and logic understanding.
Adrenaline is an AI-powered codebase analysis tool and knowledge base designed for analyzing local software projects. It functions as a technical assistant that enables users to chat with their code, retrieve specific logic, and visualize system architectures using large language models. The project distinguishes itself by converting codebase analysis into visual system architectures and technical workflow diagrams. It utilizes retrieval augmented generation to ground responses in project-specific code snippets and documentation, while integrating real-time web search to supplement local know
Supplies the entire local code repository as a data source for AI-driven knowledge retrieval.
Entity-Relation-Extraction este un framework de machine learning conceput pentru a identifica entități și conexiunile lor logice în text nestructurat. Funcționează ca un pipeline care transformă documentele brute în grafuri de cunoștințe structurate, utilizând modele de deep learning și arhitecturi de tip transformer. Proiectul se distinge printr-o abordare bazată pe schemă, care mapează informațiile extrase către șabloane relaționale predefinite pentru a asigura consistența rezultatelor. Utilizează un proces în mai multe etape care combină clasificarea token-urilor prin etichetarea secvențială cu codificarea contextuală pentru a delimita limitele entităților și a clasifica relațiile dintre acestea. Toolkit-ul oferă componente pentru fine-tuning-ul modelelor de limbaj pre-antrenate și orchestrarea datelor prin grafuri computaționale. Include utilitare pentru evaluarea performanței modelului față de seturi de date de referință (ground truth) pentru a verifica acuratețea procesului de extracție.
Structures extracted data according to predefined relational templates to ensure output conforms to specific domain requirements.